IJMSNR Full Volume-1 Issue-1

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Mrs. Jayanthi Sureshbabu,
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Associate Professor, Department of Epidemiology and Public Health,
College of Medicine and Health Sciences,
Sohar, National University of Science and Technology,
Sultanate of Oman.
Email ID: deputyeditor-in-chief@ijmsnr.com
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Amrita Institute of Medical Sciences, Ponekkara, Kochi, Kerala, India.
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Fairer world for a healthier and safer world
1
Senior Resident, Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India.
2
Assistant Professor, Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India.
3
Professor and HOD, Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India.
Introduction
The World Health Organization (WHO) marked the celebration of
world health day on the 7th
of April this year with the theme,
“building a fairer, healthier world for everyone”. [1] The theme
brings to the forefront some very pertinent issues especially in regard
to the current covid 19 pandemic situation the world is struggling
with. The current Covid 19 pandemic has magnified the stark
inequalities in our world. Some people are able to live healthier lives
and have better access to health services while others struggle to
make ends meet with little daily income, poor housing conditions and
loss or disrupted education, fewer employment opportunities,
experience greater gender inequality, and have little or no access to
safe environments, clean water and air, food security and health
services. [1]
Unequal world
The Covid 19 pandemic has hit the world hard, but has hit the poorer
countries, underserved communities and families and vulnerable
individuals the hardest. It has decimated the gains made in health
and economic development made so far and is pushing families and
communities into poverty and further socio-economic disadvantages
while increasing the number of premature deaths and avoidable
illnesses and hospitalizations. Globally, as of 3:24pm CEST, 29 May
2021, there have been 169,118,995 confirmed cases of COVID-19,
including 3,519,175 deaths, reported to World Health Organization
(WHO). [2]
The pandemic is estimated to have driven between 119 and 124
million more people into extreme poverty last year and there is
convincing evidence that it has widened gender gaps in employment,
with women exiting the labor force in greater numbers than men over
the past 12 months. [3] More than 1 billion people living in informal
This is an open access journal, and articles are distributed under the terms of the
Creative Commons Attribution-Non-Commercial-ShareAlike 4.0 International
License, which allows others to remix, tweak, and build upon the work
non-commercially, as long as appropriate credit is given and the new creations
are licensed under the identical terms.
How to cite this article: Priyanka Raj CK. Fairer world for a healthier
and safer world. Int J Med Sci and Nurs Res 2021;1(1):1–2.
Article Summary: Submitted: 02-August-2021 Revised: 30-August-2021 Accepted: 03-September-2021 Published:30-September-2021
International Journal of Medical Sciences and Nursing Research 2021;1(1):1-2 Page No: 1
settlements or slums are facing increased challenges in preventing
infection and transmission of the coronavirus [4]
Dangers ahead
Given the level of world globalization, this pandemic will continue
to remain a major threat to not just poorer countries but also the high-
income countries and the developed world, not just
epidemiologically but also economically and socio-politically. On
one hand globalization has led to the rapid spread of this pandemic
and on the other hand international policy measures to contain the
pandemic such as air travel restrictions, border closures,
enforcements of quarantine and limited mobility etc… have
disrupted international and local trade and commerce and have dealt
a severe blow to economies dependent on tourism, export of minerals
and oil and other commodities leading to rising unemployment, food
insecurity and extreme poverty. For the first time in 20 years, global
poverty levels are predicted to rise and hinder the progress towards
the Sustainable Development Goals. [5] This pandemic has given rise
to socio-economic tensions between countries and within countries.
This pandemic is not just a health emergency, but also a socio-
political and economic emergency with the potential to threaten
world peace and stability. The world economic forum in its global
risks report 2021 has stated that the global economy will be
threatened by the knock-on effects of the coronavirus crisis, while
geopolitical stability will be critically fragile over the next 5 to 10
years. [6] It is in this regard the WHO call for actions to eliminate
the health and social inequalities assume significant importance.
Way forward
Equity / health equity is defined as the absence of avoidable, unfair
or remediable differences (in health) among groups of people,
whether those groups are defined socially, economically,

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geographically or by other means [7]. Ensuring equity / health equity is
a fundamental human right and is central to achieving the sustainable
developmental goals (SDGs). The WHO campaign for 2021 for
building a fairer and healthier world, urges leaders to monitor and track
health inequities and its root causes, work together and hand in hand
with affected communities and individuals, and tackle inequalities and
to ensure that all people are able to access quality health services when
and where they need them. The social and health inequalities exposed
by COVID-19 have led to renewed interest by Member States in
WHO’s work on social determinants of health and the recent resolution
adopted by the world health assembly aims to strengthen action
globally and within countries on the social determinants of health; to
reduce health inequities by involving all sectors in taking concrete
action to improve living conditions and reduce social inequalities; and
improve monitoring of social determinants and health inequities. [8]
The WHO urges leaders to act beyond borders in ensuring an equitable
supply of vaccines, tests and treatments. Prioritizing health spending
and strengthening primary health care is vital to providing universal
access to quality health care and quality covid care and make the health
system resilient to future pandemics. The WHO recommends spending
an additional 1 % of GDP on primary health care and structuring social
protection schemes to mitigate the negative social impacts of Covid 19
pandemic. Building safer, healthier and inclusive neighborhoods and
ensuring the availability of timely and accurate data are key to removing
the barriers to an equitable and sustainable society. [3]
The focus should be now to stem the pandemic and rebuild and
restructure the health systems to make it fairer for everyone. Also, there
is need to reinforce trust between governments/organizations and
society during this crisis and to do that we need to guarantee social
accountability, transparency in the systems and provide safety nets for
the marginalized and underserved groups who have been greatly
affected by the pandemic. Confidence building measures to ensure
widespread community participation in covid control measures remains
vital.
References
1. World Health organization: World health day 2021 theme.
Available on https://www.who.int/campaigns/world-health-
day/2021 [Last Accessed on: 2021 March 8]
2. World health organization: corona virus (covid 19) dashboard,
Available on: https://covid19.who.int/ [Last Accessed on: 2021
March 14]
Priyanka Raj CK. Fairer world for a healthier and safer world
3. World health organization: World health day news release.
Available on: https://www.who.int/news/item/06-04-2021-
who-urges-countries-to-build-a-fairer-healthier-world-post-
covid-19 [Last Accessed on: 2021 March 25]
4. Goal 11 Make cities and human settlements inclusive, safe,
resilient and sustainable. In: United Nations Department of
Economic and Social Affairs Sustainable Development.
2020. Available on: https://sdgs.un.org/goals/goal11 [Last
Accessed on: 2021 April 18]
5. Profiles of the new poor due to the COVID-19 pandemic.
World Bank; 2020 Available on:
http://pubdocs.worldbank.org/en/767501596721696943/Prof
iles-of-the-new-poor-due-to-the-COVID-19-pandemic.pdf
[Last Accessed on: 2021 April 18]
6. World economic forum: The Davos agenda. Available on:
https://www.weforum.org/agenda/2021/01/these-are-the-
worlds-greatest-threats-2021 [Last Accessed on: 2021 April
25]
7. Health equity and its determinants in the Western Pacific
Region. Manila, Philippines, World Health Organization
Regional Office for the Western Pacific. 2019. Licence: CC
BY-NC-SA 3.0 IGO., Available on:
https://apps.who.int/iris/handle/10665/333944 [Last
Accessed on: 2021 April 20]
8. World Health organization: Update from the Seventy-fourth
World Health Assembly. Available on:
https://www.who.int/news/item/29-05-2021-update-from-
the-seventy-fourth-world-health-assembly-29-may-2021
[Last Accessed on: 2021 April 30]
Dr. C. K. Priyanka Raj
Deputy Editor-In-Chief, IJMSNR,
Associate Professor,
Department of Public Health and Epidemiology
National University of Science & Technology,
College of Medicine and Health Sciences,
Sohar, Al Batinah North, Sultanate of Oman.
Email ID: priyankaraj@nu.edu.om

Web Site
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Comparison of addition of either dexamethasone or dexmedetomidine
to caudal ropivacaine for Post-Operative analgesia after Paediatric
Circumcision: A Randomized Controlled Study
Smyrna Gnanasekaran1
, Mohana Rangam Thirupathi2
, Ashok Kulasekar3
1
Senior Resident, Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India. 2
Assistant Professor,
Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India. 3
Professor and HOD, Department of
Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India.
Background: Over recent years, there are lots of advancements in providing adequate postoperative analgesia for pediatric patients who are
undergoing infra-umbilical surgeries. Of which, the caudal block is a type of neuraxial block that is simple, easy to administer with more
reliability, thus providing a very effective pain–free period. This study aimed to compare the efficacy of Ropivacaine with dexmedetomidine
and dexamethasone in pediatric circumcision surgeries.
Materials and Methods: The prospective, randomized, double-blinded study included 60 children (30 children in each group, assigned by
computer-generated randomization code). In Group I: 0.25% Ropivacaine 0.5 ml/kg + Dexmedetomidine 1mcg/kg. Group II: 0.25%
Ropivacaine 0.5 ml/kg + Dexamethasone 0.1 mg/kg.
Results: FLACC score was used to assess the postoperative analgesia. The mean duration of postoperative analgesia was
478.04±61.22min in Dexmedetomidine group and 530.07±134.04 min in Dexamethasone group which was statistically significant.
The sedation score was better with Dexmedetomidine Group compared to Group Dexamethasone.
Conclusion: Our study proved that caudal administration of 0.25% Ropivacaine with Dexamethasone (0.1 mg/kg) resulted in a longer
duration (530.07 minutes) of action compared with 0.25% Ropivacaine with Dexmedetomidine (1 mcg/ kg) and the sedation was better with
Dexmedetomidine when compared to dexamethasone, without any other significant differences in the hemodynamic parameters and the
incidence of adverse events.
Keywords: Caudal, Analgesia, Postoperative, Ropivacaine, Dexamethasone, Dexmedetomidine
Introduction
The pain perception in children is a complex phenomenon which involves behavioral, psychological, physiological and developmental factors
[1]. Pediatric anesthesia has evolved over-time in making surgical procedures much safer, lesser anesthesia-induced neurotoxicity, and much
longer postoperative analgesia. In pediatric surgeries, the caudal epidural an anesthetic technique is one of the safe, reliable & easy to
administer technique. Therefore, it is used for postoperative analgesia in below-umbilical surgeries [2]. The most frequently used method to
further prolong its action is to add adjuvant drugs to the local anesthetic solution [3].
A highly potent and selective glucocorticoid is dexamethasone which has been used as an adjuvant to local anesthetics in different nerve
blocks. The variable effect includes onset, prolonged duration of analgesia, and motor block [4]. And Dexmedetomidine, highly selective
α2-adrenergic receptor (α2 -AR) agonist [5] is known to be associated with sedation and analgesia sparing effects, perioperative
sympatholysis, cardiovascular stabilizing effects, reduced delirium and agitation and maintenance of respiratory function. Ropivacaine is a
local anesthetic that is structurally similar to bupivacaine. It has less cardiovascular side effects, motor blockade and neurotoxicity. These
effects have made Ropivacaine a better option over Bupivacaine [6]. Hence in this randomized comparative study, we compared caudal
dexmedetomidine and dexamethasone with Ropivacaine for postoperative analgesia in pediatric circumcision.
How to cite this article: Gnanasekaran S, Thirupathi MR, Kulasekar A. Comparison of addition of either dexamethasone or
dexmedetomidine to caudal ropivacaine for Post-Operative analgesia after Paediatric Circumcision: A Randomized Controlled Study. Int J
Med Sci and Nurs Res 2021;1(1):3–7.
Corresponding Author: Dr. Mohana Rangam Thirupathi,
Chettinad Hospital And Research Institute,
Rajiv Gandhi Salai OMR, Kelampakkam, Chennai, Tamil Nadu.
Email ID: drtmr79@gmail.com
Abstract
Article Summary: Submitted: 05-July-2021 Revised: 25-August-2021 Accepted: 02-September-2021 Published: 30-September-2021

Materials and Methods:
Methodology: This randomized control study was conducted in the
Department of Anesthesiology & Critical Care, in a tertiary care Hospital
and Research Institute at Chennai, Tamil Nadu among children scheduled
for circumcision. The sample needed for this study was calculated based
on a previous observation made by Choudhary S et al., [7] The required
sample size for this study was calculated as 30 subjects for each of groups
at alpha error (α) = 0.05 and statistical power = 80%. All the 60 Children
was randomly divided into two groups (30 children in each group) using
a computer-generated randomization code. The inclusion criteria were
children aged 1-8 years, weighing 10-25 Kilograms and grade I – II based
on the American Society of Anesthesiologists (ASA) [8]. Children with
neurological disorder, local infection at the caudal site, history of allergy
to local anesthetics, Sacral or vertebral abnormalities, and bleeding
diathesis was excluded from the study. Patients in each group have
received caudal anesthesia as follows: All the 60 Children was randomly
divided into two groups (30 children in each group) using a computer-
generated randomization code. Group I received 0.25 % Ropivacaine 0.5
ml/ kg + Dexmedetomidine 1mcg/kg in normal saline (1ml) and Group II
received 0.25% Ropivacaine 0. 5 ml/kg + Dexamethasone 0.1mg/kg in
normal saline (1ml) with maximum volume of 25 ml (Armitage formula)
in both the groups.
The data was collected for this study was between November 2016 –
October 2018. The study was approved by the Institutional Human Ethics
Committee (64/IHEC/9-16) Informed written consent was obtained from
the parents of all study participants and only those who were willing to
sign the informed consent was included in this study. The risks and
benefits involved in the study and voluntary nature of participation was
explained to the participant's parents through participant information
sheet before obtaining written consent.
Procedure:
On the day of surgery, pre-medication in the form of Midazolam nasal
spray (0.3mg/kg) was administered and Glycopyrrolate injection (0.004
mg/kg) was administered if IV access was secured already. Standard
monitoring including electrocardiogram (ECG), non-invasive blood
pressure (NIBP) measurement, heart rate, pulse oximetry and
capnography was applied. All patients were induced with inhalational
agent sevoflurane (1-6%) with 50% nitrous oxide in oxygen. If the IV
access has not been established prior, it was established and secured under
aseptic precautions after induction. In the left lateral position, a caudal
block was performed using 22G or 24G 1½ hypodermic needle under
complete aseptic precaution. After confirmation and negative aspiration
for blood and cerebrospinal fluid, the study drug was given in the epidural
space. [9]
Postoperative sedation was assessed by using Ramsay sedation score and
Postoperative pain was assessed by using the FLACC score, the Motor
blockade was assessed by a Motor block scale. [10 – 12] To eliminate any
kind of bias in the study, the anesthesiologist performing the caudal block
was different from the person conducting the study and both were blinded
to the identity of the drug used (double-blinding). [13] The time of caudal
block was noted and the time of incision was 10 minutes after
administration of caudal block.
Statistical Analysis:
Data collected and complied by Microsoft Excel 2010 and was
analyzed by SPSS 20.0 version software. Descriptive statistics was
reported as mean and standard deviation for continuous variables and
frequency and proportions for categorical variable. An independent
t-test was used to find statistical significance between the groups.
[14] A two-sided p-value was taken as statistically significant.
Results:
The common presentation of the age group in our study participants
was between 1 – 3 years for both the groups which are about 66.7%
and 40% in Dexmedetomidine and dexamethasone group
respectively. The mean weight of the study participants was
14.31±3.88 kgs in the Dexmedetomidine group and 14.93±4.06 kgs
in the dexamethasone group as shown in Table–1a and Table–1b.
Table 1a: Demographic variables of study participants
Age- Group
Dexmedetomidine Dexamethasone
No. Percentage No. Percentage
1-3 years 20 66.7 12 40
3-5 years 3 10 9 30
>5 years 7 23.3 9 30
Mean ± SD 3.65 ± 2.19 4.27 ± 2.23
t-value -0. 879
p-value 0.380
Table: 1b Demographic variables of study participants
Variable
Dexmedetomidine
Mean ± SD
Dexamethasone
Mean ± SD
t-value p-value
Weight 14.31 ± 3.88 14.93 ± 4.06 -0. 601 0.550
The mean HR (Heart Rate) in Dexmedetomidine and
Dexamethasone. There was a significant (p<0.05) difference
between the two groups only at 0 minutes (after premedication).
Other timeline distribution shows no significant (p>0.05)
improvement in mean scores between the two groups as shown in
Table – 2.
Smyrna G et al., Dexamethasone vs Dexmedetomidine as an adjuvant in pediatric caudal block

Table: 2 Comparison of mean Heart Rate in Dexmedetomidine and
Dexamethasone: at Baseline, 0 min (after premedication), 15, 20, 60,
90, 120, 150 & 180 min.
Heart rate Dexmedetomidine Dexamethasone
p
value
Baseline 131.03 ± 20.03 123.23 ± 15.71 0.101
0 min 135.86± 15.63 126.90± 16.40 0.030
1 min 137.38± 16.05 135.57± 13.28 0.638
5 min 129.62 ± 15.41 127.20± 12.05 0.500
10 min 127.66 ± 14 93 123.53± 11.69 0.240
Intra OP 0 min 136.31 ± 12.96 133.23± 12.56 0.350
Intra OP 15 min 125.34 ± 13.33 123.20± 11.94 0.510
Intra OP 20 min 95.62 ± 56.06 82.10 ± 59.55 0.370
Post-op 1 hr 129.72 ± 12.03 128.13± 11.61 0.600
Post-op 2 hr 125.83± 12.64 123.07± 11.91 0.390
Post-op 3 hr 122.00± 12.98 117.97± 11.53 0.210
Post-op 4 hr 118.90± 13.21 114.80± 11.44 0.200
Bolded p – values< 0.05 Significant
Table 3: Comparison of FLACC score, duration of analgesia,
sedation score and motor block at various time interval in
Dexmedetomidine and Dexamethasone
Variables
Dexmedetomidine
Mean ± SD
Dexamethasone
Mean ± SD
t
value
p
value
FLACC score at
one hour
1.97 ± 1. 08 1.10 ± 0.75 3.561 0.010
Duration of
Analgesia
286. 90 ± 102.15 530. 07 ± 134.04 -7.817 0.001
Sedation score at
POP first hour
3.00 ± 0. 98 1.70 ± 1. 08 4.850 0.001
Sedation score at
POP second hour
0.80 ± 0.80 0.13 ± 0.34 4.160 0.001
Motor block at first
hour
0.17 ± 0. 37 0.10 ± 0.30 0.750 0.450
Bolded p – values< 0.05 Significant
The FLACC score was comparatively less in the dexamethasone group
and was statistically significant (p<0.05). The duration of analgesia was
more in the dexamethasone group and showed statistical significance
(p<0.05).
The sedation score of the study participants was significantly lower
in the dexamethasone group at postoperative 1hr and 2hr respectively.
The sedation score of the study participants was significantly lower
in the dexamethasone group at postoperative 1hr and 2hr respectively.
The motor block score in the study participants was similar in the
Dexmedetomidine group and the dexamethasone group as shown in
Table-3.
Discussion:
In our study, patients undergoing surgery in both groups was in
similar demographic profile. The common presentation of the age
group in our study participants was between 1 – 3 years for both the
groups. The mean weight of the study participants was 14.31±3.88
kgs in the Dexmedetomidine group and 14.93±4.06 kgs in the
dexamethasone group which was almost the same. Participants
receiving dexmedetomidine and dexamethasone was also compared
for the differences in their heart rate, systolic blood pressure, diastolic
blood pressure. In this study we noticed that there was a statistically
significant difference (p<0.05) in heart rate between the groups only
at 0 minutes that is after premedication with midazolam nasal spray.
[15] The heart rate in the intra-operative period of 0, 15, and 20
minutes in the dexmedetomidine group and dexamethasone group did
not show significant difference between them. Similarly, no
difference was noticed in the heart rate across both the groups in the
postoperative period with p>0.05.
The systolic blood pressure at various time frames was observed at
intra-operative and post-operative periods across the
dexmedetomidine group and dexamethasone group. Results showed
that there was a statistically significant (p<0.05) higher difference in
mean systolic blood pressure in the dexamethasone group at baseline,
intra-operative period 0 min & 15min, and post-operatively at 3hrs as
shown in Figure-1.
Figure: 1 Comparison of mean SBP in Dexmedetomidine and
Dexamethasone: at Baseline, After Premedication o min, 15, 30,
60, 90, 120, 150 & 180 min

Comparison of mean diastolic blood pressure in dexmedetomidine and
dexamethasone group showed at baseline, after premedication, at 0
min, 15, 20, 60, 90, 120, 150 and 180 mins was done as shown in
Figure-2.
Figure: 2 Comparison of mean DBP in Dexmedetomidine and
Dexamethasone: at Baseline, After Premedication o min, 15, 20, 60,
90, 120, 150 & 180 min
The statistically significant higher difference in mean scores among
dexamethasone group at baseline, intra-operative 0 min & 15 min, and
post-operatively at 1 hr and 3hrs respectively.
When pain scores (FLACC) was compared between two groups, it was
observed that in the dexamethasone group (1.10±0.75), the FLACC
score was found to be significantly less (p<0.001) as compared to
Dexmedetomidine (1.97±1.08). This infers that postoperative pain was
less in the dexamethasone group and was statistically significant. The
motor block score in the study participants was similar in the
Dexmedetomidine group and the dexamethasone group. Kim EM et al.
[16] in their study found that FLACC scores was almost comparable
between the groups and there was no difference seen in motor block
scores among the study participants which agreed with our study
findings.
The mean duration of analgesia in the dexamethasone group was
significantly more than the dexmedetomidine group that is 530.07 ±
134.04 minutes and 478.04±61.22 minutes (p < 0.0001), respectively.
Choudhary S et al., [7] revealed the same findings in his study with a
mean duration of analgesia in Group A as 248.4±54.1 minutes and
Group B as 478.05±104.57 minutes with p = 0.001 where Group 'A'
received 0.2% ropivacaine caudally and Group 'B' received a bolus of
0.2% ropivacaine with dexamethasone 0.1 mg/kg. Whereas many
studies like Isaac GA et al., [17] found that Caudal dexmedetomidine
1 µg/kg with 0. 25% of ropivacaine for a pediatric patient undergoing
infra-umbilical surgeries achieved postoperative pain relief up to 8
hours and the required dose of rescue analgesia was less with minimal
adverse effects. Also, Takrouri MS et al., [18] found that
dexmedetomidine, when compared with conventional sedatives and
opiates was found to be associated with both sedative and analgesic
sparing effects, minimal respiratory depression, reduced delirium and
agitation, and desirable cardiovascular effects. Similarly, Gurbet A et
al., [19] found that dexmedetomidine intra-operatively provides
effective postoperative analgesia, and reduces postoperative
morphine requirements without increasing the incidence of adverse
effects.
It was also found that the sedation score of the study participants was
significantly lower in the dexamethasone group at postoperative 1st
hr and 2nd hr (p<0.001). This shows that patients in the
dexmedetomidine group was more sedated than the dexamethasone
group. Similarly, a study conducted by Bharti N et al., [20] noticed
that patients receiving dexmedetomidine was more sedated as
compared to the other groups (p<0.01) which correlates with our
study.
Conclusion:
It is evident from our study that patients in the dexamethasone group
had less postoperative pain and the duration of analgesia was more
compared to that of dexmedetomidine. Hence dexamethasone is a
good adjuvant for post-op analgesia.
Limitations of this study:
1. This study has included only small number of patients. It needs
larger sample size to investigate the true effectiveness of adjuvants
added in caudal block.
2. Since few of the patients belonged to pre-verbal age group, the
assessment of pain was observer biased.
Acknowledgement: The authors thank the parents of the
participants, members of the Department of Anesthesia, Operation
Theatre Services and the Staff Nurses for co-operating throughout the
study period.
Authors Contributions: SG, MRT, AK: Conception and
design, Acquisition of Data. SG, MRT: Analysis and Interpretation
of data, all authors. SG, MRT, AK: Drafting the article, revising it for
Intellectual content, all authors; approval of final version of submitted
manuscript.
Here, SG-Smyrna Gnanasekaran, MRT-Mohana Rangam Thirupathi,
and AK-Ashok Kulasekar.
Source of funding: We didn’t get any types of financial
support from our parent institution and any other financial
organization.
Conflict of Interest: The authors declare no conflict of interest,
financial or otherwise.

International Journal of Medical Sciences and Nursing Research
References:
1. Morton NS. Pain assessment in children. Pediatric Anesthesia
1997;7(4):267-272. PMID: 9243682
2. Bajwa SJS, Kaur J, Bajwa SK, Bakshi G, Singh K, Panda A. Caudal
ropivacaine–clonidine: A better post-operative analgesic approach.
Indian J Anaesth 2010;54(3):226-230. PMID: 20885869
3. Sabbar S, Zamir, Khalid A, Khan FA. Caudal ketamine with
bupivacaine and bupivacaine alone for postoperative analgesia in
paediatric inguinoscrotal surgeries. Anaesthesia 2009;15(4):207-
210.
4. Cummings III KC, Napierkowski DE, Parra-Sanchez I, Kurz A,
Dalton JE, Brems JJ, et al. Effect of dexamethasone on the duration
of interscalene nerve blocks with ropivacaine or bupivacaine. British
Journal of Anaesthesia 2011;107(3):446-453. PMID: 21676892
5. Khan ZP, Ferguson CN, Jones RM. Alpha-2 and imidazoline
receptor agonistsTheir pharmacology and therapeutic role.
Anaesthesia 1999;54(2):146–165. PMID: 10215710
6. Khanna A, Saxena R, Dutta A, Ganguly N, Sood J. Comparison of
ropivacaine with and without fentanyl vs bupivacaine with fentanyl
for postoperative epidural analgesia in bilateral total knee
replacement surgery. Journal of Clinical Anesthesia 2017;37:7-13.
PMID: 28235533
7. Choudhary S, Dogra N, Dogra J, Jain P, Ola SK, Ratre B. Evaluation
of caudal dexamethasone with ropivacaine for post-operative
analgesia in paediatric herniotomies: A randomized controlled
study. Indian J Anaesth 2016;60(1):30. PMID: 26962252
8. Daabiss M. American Society of Anaesthesiologists physical status
classification. Indian J Anaesth 2011;55(2):111-115.
PMID: 21712864
9. Wiegele M, Marhofer P, Lönnqvist P-A. Caudal epidural blocks in
paediatric patients: a review and practical considerations. British
Journal of Anaesthesia 2019;122(4):509–517. PMID: 30857607
10. El Shamaa HA, Ibrahim M. A comparative study of the effect of
caudal dexmedetomidine versus morphine added to bupivacaine in
pediatric infra-umbilical surgery. Saudi J Anaesth 2014;8(2):155-
160. PMID: 24843324
11. Brasher C, Gafsous B, Dugue S, Thiollier A, Kinderf J, Nivoche Y,
et al. Postoperative Pain Management in Children and Infants: An
Update. Pediatr Drugs 2014;16(2):129–140. PMID: 24407716
12. Locatelli B, Ingelmo P, Sonzogni V, Zanella A, Gatti V, Spotti A, et
al. Randomized, double-blind, phase III, controlled trial comparing
levobupivacaine 0.25%, ropivacaine 0.25% and bupivacaine 0.25%
by the caudal route in children. Br J Anaesth 2005;94(3):366-371.
PMID: 15608043
13. Day SJ, Altman DG. Blinding in clinical trials and other studies.
BMJ 2000;321(7259):504. PMID: 10948038
14. Usman U. On Consistency and Limitation of independent t-test
Kolmogorov Smirnov Test and Mann Whitney U test. IOSR Journal
of Mathematics 2016;12(4):22-27. Corpus ID: 56151613 DOI:
http://doi.org.10.9790/5728-1204052227
15. Manoj M, Satyaprakash MVS, Swaminathan S, Kamaladevi RK.
Comparison of ease of administration of intranasal midazolam
spray and oral midazolam syrup by parents as premedication to
children undergoing elective surgery. J Anesth 2017;31(3):351-
357. PMID: 28271228
16. Kim EM, Lee JR, Koo BN, Im YJ, Oh HJ, Lee JH. Analgesic
efficacy of caudal dexamethasone combined with ropivacaine in
children undergoing orchiopexy. Br J Anaesth 2014;112(5):885-
891. PMID: 24491414
17. Isaac G A, Prabhavathi R, Reddy P N, Suresh J, A study to
evaluate efficacy and safety of dexmedetomidine (1 Âµg/Kg) as
an adjuvant to caudal ropivacaine (0.25%1 Ml/Kg) in paediatric
infraumbilical surgeries. Indian J Clin Anaesth 2017;4(4):453-
458.
18. Takrouri MS, Seraj MA, Channa AB, el-Dawlatly AA, Thallage
A, Riad W, et. al., Dexmedetomidine in intensive care unit: a
study of hemodynamic changes. Middle East J Anaesthesiol
2002;16(6):587-595. PMID: 12503262
19. Gurbet A, Basagan-Mogol E, Turker G, Ugun F, Kaya FN,
Ozcan B. Intraoperative infusion of dexmedetomidine reduces
perioperative analgesic requirements. Can J Anaesth
2006;53(7):646-652. PMID: 16803911
20. Bharti N, Praveen R, Bala I. A dose–response study of caudal
dexmedetomidine with ropivacaine in pediatric day care patients
undergoing lower abdominal and perineal surgeries: a
randomized controlled trial. Pediatric Anesthesia
2014;24(11):1158-1163. PMID: 25040840

Web Site
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Blood transfusion incidence in primary Total Knee Arthroplasty of
Unilateral vs Bilateral group with high prevalence of low
haemoglobin concentration: A Retrospective Observational Study
Jai Thilak Kailathuvalapil1
, Madhusudhan Tammanaiah2
, Nabeel Mohamed Therakka Parambil3
, Sujith Paliath
Shaju4
, Senthilvel Vasudevan5
1, 2, 3, 4
Department of Orthopaedics, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, Kerala, India. 5
Assistant
Professor of Statistics, Department of Pharmacy Practice, College of Pharmacy, King Saud Bin Abdulaziz University for Health Science, Riyadh,
Saudi Arabia.
Background: Blood transfusion is one of the major concerns following Total Knee Arthroplasty (TKA). We assessed the incidence rate of
blood transfusion (packed red cells) in our geographical group based on age, gender, preoperative haemoglobin(Hgb) and following both
unilateral and bilateral primary TKA done either in single stage or sequentially after a week.
Materials and Methods: This was a retrospective observational study which included 200 patients who underwent primary TKA unilateral,
bilateral done in single stage and sequential from June 2015 to May 2016. Two doses of parenteral Tranexamic acid and periarticular cocktail
injection given. Transfusion was indicated with postoperative Hgb below 8 g/dl associated with or without clinical signs of tissue
hypoperfusion.
Results: The study group was 200 patients with age group of 50-81 years, of which 154 (77%) were female and 46 (23%) were male and
had a mean preoperative Hgb level of 12.6 g/dl. 88 (44%) unilateral, 40 (20%) bilateral and 72 (36%) sequential TKA were performed and 7
(7.95%), 12 (30%) and 26 (36%) patients received blood transfusion respectively. Among the transfused 45 patients, 38 patients were
bilateral group, of which 30 (66.6%) patients had a preoperative Hgb levels of 10–12 g/dl, indicating high incidence of transfusion in bilateral
cases compared to unilateral and with preoperative Hgb levels of 10–12g/dl which was statistically significant with p-value <0.05.
Conclusion: In our study, age and gender were not the major factors for blood transfusion, but low preoperative Hgb levels and bilateral
single stage and sequential TKA showed significantly higher incidence of blood transfusion.
Keywords: total knee arthroplasty, blood transfusion, preoperative Hgb
Introduction
Total knee arthroplasty (TKA) is an elective procedure widely used for treating osteoarthritis of knee which is a disease of inflammatory and
degenerative nature that causes knee joint cartilage destruction leading to pain and variable deformities. It is one of the most common
procedures performed in the orthopedic department in recent years. The frequency of TKA has shown a growth projection of 601% between
2005 and 2030. [1] Like other major surgeries, there are several complications also noticed during and after TKA such as persistent knee
pain, stiffness, blood loss and thromboembolism. [2–4] The bleeding is mainly noticed after release of the tourniquet. [5] The amount of
blood loss is variable and sometimes it leads to scenarios where blood transfusion becomes inevitable. [6] Surgical blood loss and transfusion
is a concern for both patients and surgeons despite advances in blood conservation techniques. While the popularity of preoperative
autologous donation has declined for logistical reasons, erythropoietin (EPO) and perioperative autologous blood salvage strategies have
increased in popularity. [7] Still, homologous blood transfusion remains the gold-standard approach for increasing blood cell count in
anaemic patients in the perioperative period.
An incidence rates of 9-84% blood transfusion have been reported following TKA [8] and several factors were found associated with
increased risk of blood transfusion which includes patient related factors like gender, body mass index (BMI), preoperative haemoglobin
(Hgb) level, American Society of Anesthesiologists (ASA) score, and associated medical comorbidities and surgery related factors like
How to cite this article: Kailathuvalapil JT, Tammanaiah M, Parambil NMT, Shaju SP, Vasudevan S. Blood transfusion incidence in
primary Total Knee Arthroplasty of Unilateralvs Bilateral group with high prevalence of low haemoglobin concentration. Int J Med Sci and
Nurs Res 2021;1(1):8–11.
Corresponding Author: Dr. Madhusudhan Tammanaiah,
Consultant Orthopaedic Surgeon, Kamakshi Hospital,
Mysore, Karnataka, India.
Email ID: madhusudhan.doc@gmail.com Cell No: +918333873990
Abstract

operation time, technique, usage of tourniquet and amount of blood loss
during perioperative period. [9-12] Though blood transfusion is
lifesaving, it is associated with several complications such as hemolytic
reactions, transfusion-related lung injury (TRALI), transmission of
infectiouspathogens and overall high risk of morbidity and mortality. [13,
14] The aim of this study is to fill the knowledge gap on blood transfusion
(packed red cells) following TKA in a variant geographical and ethnic
group by estimating its incidence rate based on demographic parameters
such as age, gender, preoperative Hgb and following both unilateral and
bilateral TKA done either in single stage or sequentially.
This is a retrospective observational study which includes 200 patients
who underwent primary TKA - unilateral, bilateral single stage, bilateral
sequential (one week apart) from June 2015 to May 2016. Patients with
coagulation disorders, thrombocytopenia, disturbances of platelet
function, or other hematological diseases were excluded from the study.
Gender, age, preoperative Hgb, unilateral, bilateral(single stage and
sequential) were evaluated for their relationship to blood transfusion in
the perioperative period. All TKA were performed by a single senior
arthroplasty surgeon. All patients received intravenous tranexamic acid
1gm before incision and 4 hours after the surgery unless contraindicated.
Tourniquet was used at the time of exposure and cementation only.
Periarticular cocktail injection of 30 ml consisting of Bupivacaine,
Morphine, epinephrine, antibiotics diluted in normal saline given for all
patients. Drain was not used, and compression bandage was applied for
adequate tamponade. Hgb levels assessed in the post-anesthesia care unit
on postoperative day-1. The trigger for bloodtransfusion was Hgb ≤8 g/dl
with or without presence of symptoms of tissue hypoperfusion. [15] In
patients undergoing sequential TKA, subcutaneous Enoxaparin sodium
was given in the interimperiod between the two surgeries and stopped the
day prior. Aspirin orally was given in all patientsas DVT prophylaxis for
a period of 4 weeks.
Statistical analysis: All data were procured retrospectively from a
prospectively maintained electronic database (AHMS version 6.0.7) by
an independent investigator not involved in the surgery. Institutional
review board (IRB) approval was taken. Results were analyzed using
SPSS 20.0 version. Quantitative data were expressed as the mean ± SD.
Categorical data were analyzed with Chi-Square test / Fisher’s Exact test
wherever applicable. p-value less than 0.05 was considered as statistically
significant.
Results
221 patients underwent TKA from June 2015 to May 2016, of which 21
(9.5%) patients were excluded from the study (Revision TKA,
coagulation disorders). Of the 200 patients, 46 (23%) were males and
154 (77%) were females. 88 (44%), 40 (20%) and 72 (36%) patients
underwent unilateral, bilateral (single stage) and sequential TKA
respectively. Mean preoperative Hgb was 12.6 g/dl with overall
incidence of blood transfusion in 45 (22.5%) patients and mean
postoperative Hgb level with or without transfusion was 11.28 g/dl.
Age: To analyze the relationship between age and incidence of blood
transfusion, patients were divided into two age groups, 94 (47%) patients
with age less than 65 and 106 (53%) patients with age more than 65, of
which 20 (21%)
patients and 25 (23.5%) patients were transfused respectively. No
statistically significant relationship between age and blood
transfusion noted with p-value 0.696. Gender: Out of 154 females
and 46 males, 37 (24%) patients & 8 (17%) patients received blood
transfusion respectively. And the relationship between gender and
blood transfusion in TKA was not statistically significant with p-
value 0.231.
Unilateral / Bilateral / Sequential: Out of 200 TKA performed, 88
(44%) unilateral, 40 (20%) bilateral single stage and 72 (36%)
sequential TKA.Among them, 7 (7.95%), 12 (30%) and 26 (36%)
patients received blood transfusion respectively. Patients who
underwent bilateral single stage and sequential TKA received high
blood transfusion in comparison to unilateral TKA, which was
statistically highly significant with p-value <0.0001.
Relation to preoperative Hgb: On further evaluation, the need of
blood transfusion based on preoperative Hgb was classified under 10–
12g/dl and >12g/dl. 101 patients (50.5%) had preoperative Hgb level
of 10–12 g/dl and 99 patients (49.5%) >12 g/dl. 45 (22.5%) patients
were transfused and of which 30 (66.6%) patients had preoperative
Hgb levels of 10–12 g/dl. In unilateral and bilateral single stage TKA
the incidence of blood transfusion in patients with 10–12g/dl didn’t
show statistical significance with p-value 0.676 and 0.494 respectively
whereas in sequential TKA statistical significance noted with p-value
0.027. Overall transfusion rate in patients with preoperative Hgb
levels of 10–12g/dl was high and results were statistically significant
with p-value 0.019 (<0.05) as shown Figure-1.
Figure 1. Chart showing incidence of blood transfusion with
variablepreoperativeHgblevelsin unilateral/ bilateral/ sequential
TKA patients.
Discussion
Blood loss during total knee arthroplasty is variable. Several studies
didn’t consider "hidden" blood loss, including loss due to
extravasation into the tissues; residual blood in the joint; and loss due
to hemolysis, hematoma formation, or bleeding around the prosthesis
for assessment of actual blood loss during the surgery. The inability
to predict the need for transfusion in these patients has clinically and
economically important consequences. During unpredicted clinical
scenarios, patients often receive allogeneic blood which increases the
risk of allergic reactions, transmission of infectious agents, and
Tammanaiah M et al., Blood transfusion incidence in primary Total Knee Arthroplasty of Unilateral vs Bilateral group

immunomodulatory effects. Beirbaum et al [16] reported rates of 39% of
TKA patients received transfusion and, in our study, the mean incidence of
blood transfusion was 22.5%. There was no statistically significant
relationship in specific age groups or gender who were transfused in
unilateral or bilateral TKA. In one of the recent studies by Abdullah A. Al-
Turkiet al [17] conducted in a tertiary care centre, Riyadh, Saudi showed
high incidence of blood transfusion in older females with high BMI. In our
study BMI was not included.
Beirbaum et al [16] prospectively evaluated the need for autologous or
homologous blood transfusion in patients undergoing both Total hip
arthroplasty and Total Knee arthroplasty based on preoperative Hgb level
and found that patients with Hgb level less than 13gm/dl, particularly 10-
13 g/dl needed transfusion. Gerardo Alvarez-Uria et al [18] analyzed the
prevalence of anemia in our geographical area and noted a mean
haemoglobin concentration of 11.3g/dl. In our study group also >50% of
patients had preoperative Hgb levels of 10–12g/dl and in turn the need for
blood transfusion was high. David W. Fabi et al [19] retrospectively
analyzed the complications rate of unilateral TKA and bilateral
simultaneous TKA and found 4 times higher rate of single stage or
sequential compared to unilateral TKA, blood transfusion in bilateral
simultaneous TKA. In our studycohort also, blood transfusion ratewas high
in bilateral TKA done either single stage or sequential compared to
unilateral TKA.
Joshua B. Holt et al [20] conducted a prospective study in which 488
patients underwent primary TKA with a multimodal, multidisciplinary
approach to perioperative blood loss management and in turn reducing the
blood transfusion which included preoperative hemoglobin optimization,
minimization of perioperative blood loss and evidence-based transfusion
guidelines. This approach was also substantiated by Sara Moráis et al [21]
which also included femoral canal obturation; peri and intra-articular
cocktail injection; and two doses of parenteral tranexamic acid (TXA). In
our study group we also followed many of these protocols for reducing
blood loss.
Complications like blood borne disease, allergic reaction,
immunomodulatory reactions, post-operative infection, DVT post
transfusion were also observed in the study group. Nicholas B. Frisch et al
[22] reported DVT rate of 1.99% and deep surgical site infection (DSSI)
rates of statistically higher in the transfused patients. Bierbaum et al [16]
reported infection rates of 7% in transfused patients. But no such
complications noted in our transfused patients. Though DVT prophylaxis
was given in all patients, assessment of DVT in the form of ultrasound
doppler was done only in symptomatic patients. Hence actual incidence of
DVT or pulmonary embolism in asymptomatic patients not assessed.
Conclusion
In conclusion age and gender variability were not the major confounding
factors for blood transfusion but low preoperative Hgb levels in between
10–12g/dl found in above half of our geographic population had increased
incidence of blood transfusion. In comparison with unilateral vs bilateral,
bilateral TKA done either single stage or sequential showed a higher
incidence of blood transfusion and more significantly in the group with Hgb
10–12g/dl.
Acknowledgement: The authors thank the participants, members of the
Department of Orthopaedics and Anesthesia, Operation Theatre and
Nursing staff for co-operating throughout the study period.
Authors Contributions: JTK, MT: Conception and design.
JTK, MT, SPS: Acquisition of Data. SV, MT: Analysis and
Interpretation of data. All authors-JTK, MT, NMTP, SPS, and SV:
Drafting the article, revising it for Intellectual content. All authors
were checked and approved of the final version of the manuscript.
Here, JTK-Jai Thilak Kailathuvalapil, MT-Madhusudhan
Tammanaiah, NMTP-Nabeel Mohamed Therakka Parambil, SPS-
Sujith Paliath Shaju and SV-Senthilvel Vasudevan.
Source of funding: We didn’t get any types of financial
support from our parent institution and any other financial
organization.
Conflict of Interest: The authors declared no conflict of interest
References:
1. Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of
primary and revision hip and knee arthroplastyin the UnitedStates
from2005 to 2030. J BoneJoint Surg2007; 89(4):780-785.PMID:
17403800
2. Beswick AD, Wylde V, Gooberman-Hill R, Blom A, Dieppe P.
What proportion of patientsreport long-term pain after total hip or
knee replacement for osteoarthritis? A systematic review of
prospective studies in unselected patients. BMJ Open 2012;2(1):
e000435. PMID: 22357571
3. Januel JM, Chen G, Ruffieux C, Quan H, Douketis JD, Crowther
MA, et al. Symptomatic in-hospital deep vein thrombosis and
pulmonary embolism following hip and knee arthroplasty among
patients receiving recommended prophylaxis: a systematic
review. JAMA 2012; 307(3): 294-303. PMID: 22253396
4. Ritter MA, Harty LD, Davis KE, Meding JB, Berend ME.
Predicting range of motion after total knee arthroplasty.
Clustering, log-linear regression, and regression tree analysis. J
Bone Joint Surg Am 2003; 85(7): 1278-1285. PMID:12851353
5. Burke DW, O’Flynn H. Primary total knee arthroplasty. In:
Chapman MW, editor. Chapman’s orthopedicsurgery: 3rd
edition.
Lippincott Williams & Wilkins; Philadelphia: 2001:2870-2895.
6. Lotke PA, Faralli VJ, Orenstein EM, Ecker ML. Blood loss after
total knee replacement. Effects of tourniquet release and
continuous passive motion. J Bone Joint Surg Am. 1991;
73(7):1037-1040. PMID: 1874765
7. So-Osman C, Nelissen RGHH, Koopman-van Gemert AWMM,
Kluyver E, Pöll RG, Onstenk R et al. Patient blood management
in elective total hip and knee-replacement surgery (Part 1): A
randomized controlled trial on erythropoietin and blood salvage
as transfusion alternatives using a restrictive transfusion policy in
erythropoietin-eligible patients. Anesthesiology 2014;
120(4):839-851. PMID: 24424070
8. Barr PJ, Donnelly M, Cardwell C, Alam SS, Morris K, Parker M,
et. al. Driversof transfusion decision making and quality of the
evidence in orthopedic surgery: a systematic review of the
literature. Transfus Med Rev 2011;25(4):304–316. PMID:
21640550
9. Maxwell MJ, Wilson MJA. Complications of blood transfusion.
Continuing Education in Anaesthesia Critical Care & Pain 2006;
6(6):225-229. https://doi.org/10.1093/bjaceaccp/mkl053

10. Rawn J. The silent risks of blood transfusion. Curr Opin Anaesthesiol
2008;21(5):664-668. PMID: 18784496
11. Park JH, Rasouli MR, Mortazavi SM, Tokarski AT, Maltenfort MG,
Parvizi J. Predictors of perioperative blood loss in total joint
arthroplasty. J Bone Joint Surg Am 2013;95(19):1777-1783.
PMID: 24088970
12. Carling MS, Jeppsson A, Eriksson BI, Brisby H. Transfusions and
blood loss in total hip and knee arthroplasty: a prospective
observational study. J Orthop Surg Res 2015;10:48. PMID: 25889413
13. Prasad N, Padmanabhan V, Mullaji A. Blood loss in total knee
arthroplasty: an analysis of risk factors. Int Orthop 2007;31:39-44.
PMID: 16568327
14. Salido JA, Marín LA, Gómez LA, Zorrilla P, Martínez C.
Preoperative hemoglobin levels and the need for transfusion after
prosthetic hip and knee surgery: analysis of predictive factors. J Bone
Joint Surg Am 2002;84(2):216-220. PMID: 11861727
15. World Health Organization: Haemoglobin Concentrations for the
Diagnosis of Anaemia and Assessment of Severity, WHO, Geneva,
Switzerland, 2011. Available on:
http://www.who.int/vmnis/indicators/haemoglobin/en/ [Accessed on
15th
December 2020]
16. Bierbaum BE, Callaghan JJ, Galante JO, Rubash HE, Tooms RE,
Welch RB. An analysis of blood management in patients having a
total hip or knee arthroplasty. J Bone Joint Surg Am 1999;81(1):2-
10. PMID:9973048
17. Al-Turki AA, Al-Araifi AK, Badakhan BA, Al- Nazzawi MT,
Alghnam S, Al-Turki AS. Predictors of blood transfusion following
total knee replacement at a tertiary care center in Central Saudi Arabia.
Saudi MedJ 2017;38(6):598-603. PMID: 28578438
18. Gerardo Alvarez-Uria, Praveen K.Naik, Manoranjan Midde, Pradeep
S.Yalla and Raghavakalyan Pakam. Prevalence and Severity of
Anaemia Stratified by Age and Gender in Rural India. Anemia,
2014:176182. DOI: http://doi.org/10.1155/2014/176182
19. Fabi DW, Mohan V, Goldstein WM, Dunn JH, Murphy BP.Unilateral
vs Bilateral Total Knee Arthroplasty. Risk Factors Increasing
Morbidity. The Journal of Arthroplasty 2011;26(5):668-673. PMID:
20875943
20. Holt JB, Miller BJ, Callaghan JJ, Clark CR, Willenborg MD, Noiseux
NO. Minimizing Blood Transfusion in Total Hip and Knee
Arthroplasty through a Multimodal Approach. J Arthroplasty
2016;31(2):378–382. PMID: 26391927
21. Moráis S, Ortega-Andreu M, Rodríguez-Merchán EC, Padilla-
Eguiluz NG, Perez-Chrzanowska H, Figueredo-Zalve R, et al. Blood
transfusion after primary total knee arthroplasty can be significantly
minimized through a multimodal blood-loss prevention approach.
International Orthopaedics (SICOT) 2014 38(2):347–354. PMID:
24318318
22. Frisch NB, Wessell NM, Charters MA, Yu S, Jeffries JJ, Silverton CD.
Predictors and Complications of Blood Transfusion in Total Hip and
Knee Arthroplasty. J Arthroplasty 2014;29(9 Suppl):189–192. PMID:
25007727

Web Site
http://ijmsnr.com/
Impact of lockdown on sleep wake cycle and psychological wellbeing
in South Indian population: A Cross-Sectional and Descriptive Study
Sonali Devarajan1
, Samyuktha Mylsamy2
, Tamizhini Venkatachalam3
, Gobinath Veerasamy4
1, 2, 3
Third year BSC Psychology, PSGR Krishnammal College for Women, Peelamedu, Coimbatore, Tamil Nadu, India. 4
Department of Psychology, PSGR Krishnammal College for Women, Peelamedu, Coimbatore, Tamil Nadu, India.
Background: The COVID-19 pandemic has created a wide range of crises affecting many nations, resulting in adverse health consequences.
The implementation of the lock down upended the lifestyle of mostly all people and was associated with disturbed sleep. Our study is to
estimate the variation of the sleep-wake cycle during lockdown and after lock down among people aged 15-60 years and its impact on
Psychological wellbeing.
Materials and Methods: We have done a cross-sectional and descriptive study with a sample of 304 participants formed using convenience
sampling method by online google form. They were administered with The Munich Chronotype Questionnaire (MCTQ) and The Flourishing
scale. The responses were collected during and after lock down. The data obtained is subjected to descriptive analysis.
Results: In this study we have recruited and included 304 participants. Out of 304 participants, 151 (49.7%) were male and 153 (50.3%)
were female. Flourishing scale scores mean during lockdown was 28.83 ± 4.75 and after lockdown was 41.50 ± 4.42 and the mean value
was more in after lockdown period and a paired-t test showed statistically highly significant difference at p-value<0.01.
Conclusion: The variation in the sleep-wake cycle was more in adolescents than in other age groups and the Psychological wellbeing of
women was affected more than men in all age groups during lockdown.
Keywords: lockdown, sleep-wake cycle, psychological wellbeing, age difference, gender difference
Keywords:
Introduction
The World Health Organization (WHO) on 31 December 2019, was informed of a new case of pneumonia in Wuhan City, China. On 7
January 2020, a novel coronavirus was identified in China and was temporarily named “2019-nCoV ". Coronaviruses are a kind of dreadful
virus causing a wide range of illnesses that even leads to death. The first case of the Covid case in India was identified in Kerala. Seeing the
rapid negative effect of this virus, numerous countries have implemented curfews to safeguard the people. In Tamil Nadu, the first case was
confirmed in a resident from Kanchipuram in Chennai on 7 March 2020. On 23 March 2020, to prevent the spread of the virus, The
Government of Tamil Nadu announced a state-wide lockdown. There was an association between decreased sleep quality and increased
negative mood due to the outbreak of COVID-19. [1] COVID-19 pandemic resulted in home quarantine which had a detrimental effect on
sleep quality. [2] There was a reduction in night-time sleep and an increase in daytime napping due to shifts to a later bedtime and waking
time. [3] Sleep disturbances during the pandemic harmed the immune system function by affecting the regulation of immunological markers
and their cells. [4]
Increased sleep duration and decreased daytime functioning were observed even though there was longer sleep latency, worse sleep
efficiency, and massive sleep medication use during forced confinement. [5] There was a differential impact on the sleep wake cycle due to
excessive digital media exposure among Indians during the lockdown. [6] Hence is this study to estimate the variation in the sleep-wake
cycle during the lockdown and after lock down among people ofage group 15 to 60 and to compare the psychological wellbeing scores of
How to cite this article: Devarajan S, Mylsamy S, Venkatachalam T, Veerasamy G. Impact of lockdown on sleep wake cycle and
psychological wellbeing in South Indian population: A Cross-Sectional and Descriptive Study. Int J Med Sci and Nurs Res 2021;1(1):12-16.
Int J Sci and Med Res 2021;1(2):1–9
Corresponding Author: Dr. Gobinath Veerasamy,
Assistant Professor, Department of Psychology,
PSGR Krishnammal College for Women, Peelamedu, Coimbatore,
Tamil Nadu, India. Email ID: gobinath@psgrkcw.ac.in
Inrnational Journal of Medical Sciences and Nursing Research 2021;1(1):12-16 Page No: 12
Abstract

different groups of people during and after lockdown. The main
objectives of our present study was to find the variations in the sleep-
wake cycle between genders; to find the variations in the sleep-wake
cycle between adolescents, adults and middle Ages; to find the variations
in the sleep-wake cycle between males and females of different age
groups in workdays and free days; to find the difference in the
Psychological wellbeing scores of people of different age groups during
and after lockdown; and to compare the difference in the Psychological
wellbeing scores of males and females during and after lockdown.
Need for the study: There was much research, finding the association
of COVID-19 with major psychological distress and significant
symptoms of mental health illness. The sudden implementation of a
nationwide curfew by the government of India on 24 March 2020 had
put a barrier in the daily functioning of every individual. The lifestyle of
every human, right from kids to old age people was upended and the
focus was on social distancing, quarantine, and other health care
measures. They had no chance to avail themselves of much of the social
settings (educational institutions, working places, sacred sites, etc...)
which made them remain in their home. Some studies have concluded
that there is no uniform effect of the lock down on sleep quality. [7, 8]
Hence the need of our study is to specify the effect of lock down on the
sleep-wake cycle based on age differences and gender differences and its
impact on the Psychological wellbeing of the common population.
In our present cross-sectional and descriptive study, we have included
304 participants belonging to Coimbatore, South India including males
and females with an inclusion of aged between 15 and 60 years were
selected using convenience sampling method. Our study assessed the
same participants in 2 different time ranges. Those who were not willing
they were excluded from this study. The first response was collected
during May 2020 (during lockdown) and the second response was
collected during March 2021 (after the lockdown). In May 2020 an
informed consent was taken from the participants and the questionnaire
was administered through social media using google forms. First, the
participants were asked to fill up their socio-demographic details and
were asked to read the questions carefully before answering them. They
were also asked to answer the questions one by one as in the order in the
questionnaire.
Assessment tools and its descriptions:
(1). The Munich Chronotype Questionnaire (MCTQ) and
(2). Flourishing Scale
1. The Munich Chronotype Questionnaire (MCTQ): This
questionnaire was developed by Till Roenneberg and Martha Merrow at
Ludwig- Maximillian’s University (LMU). It is a self-rated scale to find
out the differences in the sleep wake pattern in work days and free days
for ages 6 to 65 years. It is a tool to collect information regarding sleep
time, sleep latency, and sleep inertia.
2. Flourishing Scale: The Flourishing Scale is a brief 8-item summary
measure of the respondent's self-perceived success in important areas
such as relationships, self-esteem, purpose, and optimism. [9] The
scale provides a single psychological well-being score. Once the
form was filled up, the responses of each individual was recorded.
The same procedure of administration was made in March 2021 with
the same participants as before. Both the responses were collected
and recorded. Data Management: Data were entered and complied
using Microsoft Excel 2010 [Microsoft Ltd., USA]. Data were
analyzed using SPSS 20.0 version [IBM Ltd., USA].
Statistical Analysis: The categorical variables were presented using
descriptive analysis like frequency and percentages. Measures of
central tendency like mean. Paired t-test was used to find the
difference between flourishing scale scores during and after
lockdown. p<0.05 was taken as statistically significant.
Ethical Consideration: This study was done with proper permission
and willingness from all study participants.
Results:
In our present study, we have recruited and incorporated 304
participants. Out of 304 participants, 151 (49.7%) were male and 153
(50.3%) were female. More or less equal no. of the participants in all
age-groups. Age group among gender classification as shown in
Table – 1.
Table - 1 Distribution of demographic data among gender
classification
Age groups Gender Classification No. of Responses
( %)
Adolescence
(15 to 18 years)
Males 50 (16.4)
Females 52 (17.1)
Adulthood
(19 to 40 years)
Males 50 (16.4)
Females 51 (16.8)
Middle age
(41 to 60 years)
Males 51 (16.8)
Females 50 (16.4)
The patterns of variations seen in the Sleep wake cycle of the
Participants in terms of (a). Time at which they get ready to sleep, (b).
Time at which they go to bed, (c). Time needed to fall asleep, (d).
Time at which they wake up, (e). The time taken to get out of the bed
after waking up as shown Table – 2.
Devarajan S et. al. Impact of lockdown on sleep wake cycle and psychological wellbeing

Table – 2 Variations in sleep - wake time among different
age-groups during and after lock down
The Flourishing scale score was very high in adult males and very low
score in adolescent females in during lockdown. The Flourishing scale
score was very high in adult males and low in adolescent males in after
lockdown as shown in Table – 3.
Table – 3 Flourishing scale scores – During Lockdown and
after lockdown
During Lockdown After Lockdown
Adolescent Males 24 Adolescent Males 36
Adolescent Females 22 Adolescent Females 45
Adult Males 34 Adult Males 47
Adult Females 30 Adult Females 44
Middle age Males 32 Middle age Males 38
Middle age females 31 Middle age females 39
The average Flourishing scale scores during and after lockdown
was 28.83 ± 4.75 and 41.50 ± 4.42. The mean value was more in
after lockdown period and a paired t-test showed statistically
significant difference with a critical value of -5.240 at p-value <0.01
as shown in Figure – 1.
Figure – 1 Distribution and comparison of average of
Flourishing scale scores between during and after
lockdown
Discussion:
This present cross-sectional and descriptive study was done with a
sample of 304 participants. They were administered with The
Munich Chronotype Questionnaire (MCTQ) and The Flourishing
scale. The responses were collected during and after lock down.
Middle-aged people get ready to sleep earlier, both during and after
lockdown.
Gender difference: On workdays, both during and after lockdown
Males get ready to sleep later. On free days, during the lockdown

both Males and Females get ready to sleep relatively at the same time
whereas, after lock down, Females get ready to sleep later. From
adolescence through early adulthood, sleep duration is
developmentally patterned. [11]
2. GOING TO BED
Age difference: During workdays there is a shift of bedtimes to late
hours [12] which supports our finding - During the lockdown, Early
adults go to bed later and adolescents go to bed earlier. After lock
down, adolescents go to bed later whereas Middle-aged people go to
bed-earlier.
Gender difference: On workdays, during lock down Males goes to
bed late whereas after lock down Females go to bed late. All age groups
show increased usage of digital media, especially males. On free days,
during lock down Females go to bed late whereas, after lock down,
Males go to bed late. In comparison to other circadian – types during
pandemic evening – types had an alarming increase in sleep and mental
health problems. [13]
3. TIME NEEDED TO FALL ASLEEP:
Age difference: Adolescents took more time to fall asleep, both during
and after lockdown. Middle-aged took less time to fall asleep, both
during and after lockdown. Changes in both the weekend bedtime and
wakeup time had detrimental effects on the brain which led to poor
school performance. [14]
Gender difference: On workdays, both during and after lock down
Males took more to fall asleep. On free days, both during and after
lockdown Females took more time to fall asleep. Certain adverse
childhood experiences such as physical, sexual, and emotional abuse
and neglect have a lasting impact on sleep quality in adulthood,
highlighting the need to mitigate their impact to prevent negative health
outcomes associated with poor sleep quality. [15]
4. WAKE UP TIME:
Age difference: Middle-aged people woke up early and early
adulthood woke up late, both during and after lockdown. Sleep profiles
are associated with cardio metabolic health in adults and children. The
overall good sleeper pattern is associated with more favorable cardio
metabolic health. Middle-aged woke up early. A study concludes a
sleep loss on free days (resulting from more overall sleep during
workdays in non-system relevant jobs on adulthood of well-educated
participants aged between 25 – 65 years and Adolescent woke up late,
both during and after lock down. [16]
Gender difference: On workdays, both during and after lockdown
Females woke up early. In free-days, both during and after lockdown
Females woke up early. Reports from 3,778 young adults (20.6±0.86
years) indicate a higher prevalence of poor sleep quality in females than
males (65.1% vs 49.8%). [17] On workdays, both during and after lock
down Males get up early. On free days, during lock down Males woke
up early but after lock down, Males and Females woke up at a relatively
similar time.
5. GETTING OUT OF BED:
Age difference: Adolescents get out of bed late, Middle-aged get out
of bed soon, both during and after lockdown. A finding suggests that
there is an increased risk of late -onset of dementia due to short sleep
duration in midlife. [18]
Gender difference: On workdays, during lock down males got out
of bed late, and after lockdown, Females got out of bed late. On free
days, both during and after lockdown, Males got out of bed late. As
women with Chronic Insomnia Disorder (CID) get older, they
increase time spent in bed to maintain the sleep time, but remain with
a resultant increase in the wake. [19]
6. PSYCHOLOGICAL WELL BEING:
Age difference: The Psychological wellbeing of adolescents seem to
be profoundly affected when compared to that of adults and middle
aged due to lock down. [20]
Gender difference: The Psychological wellbeing of women of
different age groups seem to be profoundly affected when compared
to that of men due to lock down. [20]
Conclusion:
Sleep wake problems were found to be present commonly during the
COVID-19 lock down. From our study, we could infer that there was
a variation of sleep-wake cycle among males than in females. The
variation of the sleep-wake cycle was more in adolescents, relatively
less in adults and much less in middle aged. The variation of the
sleep-wake cycle could be seen more during free days rather than on
working days. The Psychological wellbeing of individuals of different
age, Gender is found to be better after lockdown than during lockdown
Implications: Our study has a diverse group for assessment,
consisting of gender and age difference along with the variation in the
sleep-wake cycle during workdays and free days, which helps to
determine the severity of the physical and psychological problem for
a particular group of people which would help to improve the work-
life balance. It further helps in addressing the problems created by the
varied sleep wake cycle of students in adolescence in their academic
performance.
Limitations:
1. Our study comprises people belonging only to a particular part of
India – living in southern part of the country.
2. Our study comprises people belonging only to the age group of
adolescence, early adulthood, and middle adulthood.
3. This study was cross-sectional, conducted at a specific period
which comprises a single phase of lock down.
4. The present study estimates the variation of the sleep-wake cycle
among participants but does not infer any health effects.

Recommendations:
1. For much more precise results people from different temperatures,
zones be included in the study.
2. Future studies can include people from the age groups of childhood
and old age for better results.
3. Studies could be made longitudinal, for an extended period
comprising multiple phases of lock down.
4. Detrimental health effects due to variations in sleep wake patterns
could be inferred with the future study.
Acknowledgement: Authors are very much thankful to the
participants for their cooperation in the Covid-19 pandemic period. They
have acknowledged that the study is an original work and isn’t submitted
for publication in any other form of Literature other than this.
Authors Contribution: Conceptualization: GV, SD; Data
curation: SD, SM, TV; Formal analysis: GV; Investigation: SD, SM,
TV; Methodology: GV; Software: SD; Supervision: GV Writing –
original draft: SD, SM, TV, and GV; Writing – review & editing: GV.
Here, SD – Sonali Devarajan, SM – Samyuktha Mylsamy, TV –
Tamizhini Venkatachalam, GV – Gobinath Veerasamy.
Source of funding: The study wasn’t funded by any
source/institutions other than that of the authors.
Conflict of interest: Authors declared that they have no conflict of
interest in this study.
References:
1. Targa AD, Benítez ID, Moncusí-Moix A, Arguimbau M, de Batlle
J, Dalmases M, et. al. Decrease in sleep quality during COVID-19
outbreak. Sleep and Breathing 2021;25(2):1055-1061. DOI:
10.1007/s11325-020-02202-1.
2. Salehinejad MA, Majidinezhad M, Ghanavati E, Kouestanian S,
Vicario CM, Nitsche MA, et. al. Negative impact of COVID-19
pandemic on sleep quantitative parameters, quality, and circadian
alignment: Implications for health and psychological well-being.
EXCLI journal 2020;19:1297-1308. DOI: 10.17179/excli2020-
2831.
3. Gupta R, Grover S, Basu A, Krishnan V, Tripathi A, Subramanyam
A, et al. Changes in sleep pattern and sleep quality during COVID-
19 lockdown. Indian journal of psychiatry 2020;62(4):370-378.
DOI: 10.4103/pschiatry.IndianJPsychiatry_523_20.
4. Ono BHS, Souza JC. Sleep and immunity in times of COVID-19.
Revista da Associação Médica Brasileira 2020; 66:143-147. DOI:
10.1590/1806-9282.66.S2.143.
5. Alfonsi V, Gorgoni M, Scarpelli S, Zivi P, Sdoia S, Mari E, et. al.
COVID‐19 lockdown and poor sleep quality: Not the whole story.
Journal of Sleep Research 2021;e13368. DOI: 10.1111/jsr.13368.
6. Staller N, Randler C. Changes in sleep schedule and chronotype
due to COVID-19 restrictions and home office. Somnologie
2021;25(2):131-137. DOI: 10.1007/s11818-020-00277-2.
Research 2021;1(2):19-23 Page No: 19
7. Sinha M, Pande B, Sinha R. Impact of COVID-19 lockdown on
sleep-wake schedule and associated lifestyle related behavior: A
national survey. Journal of Public Health Research
2020;9(3):1826. DOI: 10.4081/jphr.2020.1826.
8. Kocevska D, Blanken TF, Van Someren EJ, Rösler L. Sleep
quality during the COVID-19 pandemic: not one size fits all. Sleep
medicine 2020; 76:86-88. DOI: 10.1016/j.sleep.2020.09.029.
9. Martin CA, Hiscock H, Rinehart N, Heussler HS, Hyde C, Fuller-
Tyszkiewicz M, et. al. Associations between sleep hygiene and
sleep problems in adolescents with ADHD: A cross sectional
study. Journal of attention disorders 2021;24(4):545-554. DOI:
10.1177/1087054718762513.
10. Maslowsky J, Ozer EJ. Developmental trends in sleep duration in
adolescence and young adulthood: evidence from a national
United States sample. Journal of Adolescent Health
2014;54(6):691-697. DOI: 10.1016/j.jadohealth.2013.10.201.
11. Florea C, Topalidis P, Hauser T, Angerer M, Kurapov A, Leon
CAB., et. al. Sleep during COVID-19 lockdown: A cross-cultural
study investigating job system relevance. Biochemical
Pharmacology 2021;114463. DOI:10.1016/j.bcp.2021.114463
12. Merikanto I, Partinen M, Kortesoja L, Benedict C, Chung F,
Cedernaes J, et. al. Evening-types show the highest increase of
sleep and mental health problems during the COVID-19
pandemic-Multinational study on 19,267 adults. Sleep, 2021;
DOI:10.1093/sleep/zsab216
13. Urrila AS, Artiges E, Massicotte J, Miranda R, Vulser H, Bézivin-
Frere P, et. al. Sleep habits, academic performance, and the
adolescent brain structure. Scientific reports 2017;7(1):1-9. DOI:
10.1038/srep41678
14. Chighaf Bakour, Jill Desch, Fahad Mansuri, Skai W Schwartz.
Sleep 2021;44(2):A132-A133. DOI: 10.1093/sleep/zsab072.330
15. Matricianni L, Pacquet C, Fraysee F, Grobler A, Wang Y, Baur L,
et. al. Sleep and cardiometabolic risk: a cluster analysis of
actigraphy-derived sleep profiles in adults and children. Sleep
2021;44(7):zsab014. DOI: 10.1093/sleep/zsab014.
16. Fatima, Y., Doi, S. A., Najman,J. M., & Al Mamun, A. Exploring
gender difference in sleep quality of young adults: findings from
a large population study. Clinical medicine & research 2016;4(3-
4):138-144. DOI:10.3121/cmr.2016.1338
17. Sabia S, Fayose A, Dumurgier J, van Hees VT, Paquet C,
Sommerlad A, et. al. Association of sleep duration in middle and
old age with incidence of dementia. Nature Communications
2021;12(1):1-10. DOI: s41467-021-22354-2
18. J J Hong, H Lee, I Yoon. 0845 The Difference in Sleep
Characteristics of Chronic Insomnia Disorder According to
Gender and Age. Sleep 43(Suppl_1):A322. DOI:
10.1093/sleep/zsaa056.841.
19. Lee Di Milia, Ana adan, Vincenzo Natale, Christoph Randler.
Reviewing the Psychometric Properties of Contemporary
Circadian Typology Measures, Chronobiology international
2013;30(10):1261-1271. DOI: 10.3109/07420528.2013.817415
20. Marelli S, Castelnuovo A, Somma A, Castronovo V, Mombelli S,
Bottoni D, et. al. Impact of COVID-19 lockdown on sleep quality
in university students and administration staff. Journal of
Neurology 2021;268(1):8-15.DOI:10.1007/s00415-020-10056-6.

Web Site
http://ijmsnr.com/
A study of modifiable and non-modifiable risk factors associated with
of diabetic nephropathy - A preliminary observational study in
Eastern Odisha, India
Suchanda Sahu1
, Manish Taywade2
, Sujata Devi3
, Saurav Nayak4
, Dipti Sudha M5
1
Associate Professor, Department of Biochemistry, 2
Assistant Professor, Department of Community Medicine and Family Medicine, 3
Assistant
Professor, Department of Medicine, 4
Second year Junior Resident, Department of Biochemistry, 5
First year Junior Resident, Department of
Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, India
1
Senior Resident, Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India. 2
Department of Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India. 3
Professor and HOD, Department of
Anesthesiology, Chettinad Hospital And Research Institute, Chennai, Tamilnadu, India.
Background: One of the commonest complications of poorly controlled Type 2 diabetes mellitus (T2DM) is Diabetic nephropathy (DN),
which occurs in 30-40% of DM cases. It is important to identify the high-risk group who are likely to develop DN with the modifiable and
non-modifiable risk factors. This study had the objectives to estimate and correlate the levels of the urine albumin creatinine ratio (UACR)
with age, anthropometric measures, glycaemic control markers, lipids and renal function. To estimate each variable as independent and
multivariate risk factors.
Materials and Methods: It was an observational and cross-sectional study conducted in a tertiary care centre in Eastern India. Totally, 221
consecutive ambulatory T2DM subjects were recruited after obtaining their written consent.
Results: The diabetics were classified as having diabetic nephropathy by the urine albumin creatinine ratio (ACR) of >30 mg/gm. 53.4% of
our study group had DN. There was a significant risk associated with PPBS with p=0.043 (<0.05), serum creatinine with p=0.032 (<0.05),
and urine albumin with p=0.0001 (<0.001). The multivariate regression analysis of all these variables there was a highly significant likelihood
ratio for predicting DN with p=0.0001 (<0.001) with a predictive value of 74.5% in females and 75% in males.
Conclusion: The additive factors contributed by the risk factors in prediction of DN will benefit the DM in prevention of DN.
Key words: diabetic nephropathy, risk factors, diabetic kidney disease, Asian Indian
Introduction
Diabetic nephropathy (DN) affects approximately 40% of the type 2 diabetes mellitus (T2DM) patients. [1] DN is diagnosed by the presence
of albumin in urine. They are classified as microalbuminuria and macroalbuminuria with urine albumin: creatinine ratio of 30 - 300 mg/gm
in the former and > 300 mg/gm in the latter. Microalbuminuria stage of renal involvement was termed as incipient nephropathy which may
already be present in T2DM at the time of diagnosis. [2] Progression of normo-albuminuria to micro and macroalbuminuria can occur silently
and faster with associated risk factors like dyslipidaemia, smoking habit, hypertension and poor glycaemic control. [3] In the South-Asian
population, there is an increased predisposition to DN irrespective of the central obesity, [4] hence the need to point causal factors to body fat
distribution initiating insulin resistance and inflammation. In routine management of diabetic patients, their blood and urine tests are done
annually to monitor the disease control and to screen for DN. Microalbuminuria is also associated with increased risk for cardiovascular
diseases and death. [1] Hence, it is imperative to adopt strategies for preventing the development of microalbuminuria and in delaying the
progression to advanced stages of DN. That can be achieved by good glycaemic control by maintaining glycated haemoglobin (HbA1C) at
7%, treating comorbidities like hypertension and dyslipidaemia.
Though microalbuminuria is the gold standard for screening and detection of DN, its determination in clinical laboratories are inconsistent
because of the immunoassay techniques used [5, 6]. In diabetics, the albumin in urine can be modified by non- enzymatic glycation and
How to cite this article: Sahu S, Taywade M, Devi S, Nayak S, Sudha DM. A study of modifiable and non-modifiable risk factors associated
with of diabetic nephropathy – A preliminary observational study in Eastern Odisha, India. Int J Med Sci and Nurs Res 2021;1(1):17-21
Corresponding Author: Dr. Suchanda Sahu,
Associate Professor, Department of Biochemistry, All India Institute of Medical
Sciences, Sijua, Dumduma (Post), Bhubaneswar, Odisha, India.
Email ID: biochem_suchanda@aiimsbhubaneswar.edu.in
Abstract
S

hydrolysis during its passage in the renal tubules. These modifications can
underestimate the albumin by the antibodies used for assay [7, 8], thereby
delay the detection of DN and its treatment. In this study we assessed the
correlation of ACR with non-modifiable risk factors like age and gender
of patient and with modifiable risk factors like body mass index (BMI),
waist hip ratio (WHR), atherogenic index (AI) calculated from fasting
blood lipid levels and HbA1C.
Material and Methods:
It was a cross-sectional comparative study on 221 ambulatory T2DM
subjects conducted in a tertiary care centre in Eastern India after the
approval of the Institutional Ethical Committee (IEC- T/IMF/18-19/32).
The cases were from our Non – Communicable Diseases (NCD) Out-
patients clinic (OPD), All India Institute of Medical Sciences,
Bhubaneswar, India, who attended for routine follow-up clinic during the
month of March 2020. Convenient sampling was done due to the COVID-
19 pandemic. Their clinical and anthropometric data were noted after
obtaining their written consent. 5ml of blood in fasting state and 10 ml of
midstream spot urine was collected in different vacutainers and urine vials
for estimation of the following: serum creatinine, glycated haemoglobin
fasting (FBS) and post prandial blood sugar (PPBS), urine creatinine and
albumin. The lipid profile included total cholesterol (TC), triglycerides
(TG), high density lipoprotein (HDL) and low-density lipoproteins
(LDL). All the estimations were done the same day using the Beckman
Coulter Chemistry Analyzer AU5800 (Beckman Coulter, Brea, USA).
The calculated parameters were body mass index (BMI), waist hip ratio
(WHR), atherogenic index of plasma (AIP) [9], urinary albumin:
creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR).
[10] The statistical analysis was done to compare the diabetics with and
without DN. The comparison was done by Mann Whitney U test.
The relative risk estimates were calculated and the correlation was
estimated for the independent variables by Spearman’s correlation as the
data were not normally distributed which was seen by the Kolmogorov-
Smirnov test. The comparisons between gender groups and those with and
without DN were by Kruskal Wallis Test and post hoc test by Tukey’s
test. The multivariate regression was estimated for the independent
variables. All these were done using SPSS 19.0 version [IBM, Armonk,
NY, USA]. p-value <0.05 was considered as statistically significant.
Results:
Clinical characteristics: There were 221 diabetic patients who consented
to participate in the study. There were 143 males and 78 females. Table-
1 shows the general clinical characteristics of the study participants.
Considering the anthropometric cut-off levels for Asian Indians, 72.4%
were overweight and obese; 94.4 % of males and 98.7 % females had high
WHR. 79.1% had poor glycaemic control as seen by HbA1C and 90.9%
had a high-risk dyslipidaemia as seen by the AIP. Though 53.4 % of our
study group had DN, a larger proportion of the total that is 72.8% had
decreased eGFR. The study participants were classified as having
diabetic nephropathy by the UACR.
Table 1: Distribution of general characteristics of the study
population (N=221)
Covariates
No. of
Patients
Percentage
Age (in years) < 60 172 77.8
≥ 60 49 22.2
Gender Male 143 64.7
Female 78 35.3
BMI <23 61 27.6
≥23 160 72.4
WHR M, <0.90 8 5.6
M ≥0.90 135 94.4
F < 0.80 1 1.3
F ≥0.80 77 98.7
HbA1C <7.0% 46 20.8
≥7.0% 175 79.1
AIP <0.24 20 9.1
≥0.24 201 90.9
ACR < 30 mg/gm 94 42.5
≥ 30 mg/gm 127 57.5
eGFR > 90 ml/min 60 27.2
< 90ml/min 161 72.8
DN Absent 103 46.6
Present 118 53.4
The comparison of the two groups by Mann Whitney U test. There
was a significant difference in PPBS (p=0.013), HbA1C (p=0.041)
and UACR (p<0.001). There was no significant difference in the
age, gender, BMI, WHR, FBS, lipid profile, AIP, serum creatinine
and eGFR between the groups as shown in Table-2.
Association of risk factors with DN:
The risk estimate calculated using the cut off values for each
variable relevant to our population and as per gender wherever
applicable. Though there was a 10–20% increased risk for age >60
years, male gender, BMI, WHR, FBS, HbA1C, AIP and LDL
among the serum lipids and a 20% decrease in HDL had increased
risk of DN, none were statistically significant as shown in Table-3.
This may be because of the consecutive convenient sampling of one
month evaluated as a preliminary study here. There was a
significant risk associated with PPBS (p=0.043), serum creatinine
(p=0.032), and urine albumin (p=0.0001).
Sahu S et al., A study of modifiable and non-modifiable risk factors associated with of diabetic nephropathy

Table 2: Differences Between Diabetic Nephropathy Group for
Various Risk Factors
Parameters
Diabetic Nephropathy
p
value
Absent
(n = 94)
Present
(n = 127)
Age 52 (43 – 60) 50 (43 – 58) 0.636
Sex
Male 62 (65.96%) 76 (59.84%)
0.353
Female 32 (34.04%) 51 (40.16%)
BMI (kg/m2
)
25.30
(23.46 – 27.69)
25.34
(22.22 – 28.06)
0.434
WHR 0.948 (0.91 – 0.99) 0.946 (0.91 – 0.98) 0.439
FBS (mg/dl) 141.5 (121 – 179) 152 (122 – 221) 0.187
PPBS (mg/dl) 208.5 (185 – 290) 245 (189 – 321) 0.013
HbA1C (%) 7.875 (7.08 – 8.8) 8.3 (7.2 – 10.3) 0.041
S. Creatinine
(mg/dl)
1 (0.8 – 1.1) 1 (0.8 – 1.2) 0.393
eGFR
(ml/min)
82.53
(66.67 – 94.31)
74.77
(64.94 – 91.08)
0.142
TC (mg/dl) 187 (161 – 210) 190 (159 – 227) 0.613
TG (mg/dl) 153 (119 – 204) 149 (108 – 204) 0.648
HDL (mg/dl) 44 (38 – 51) 46 (39 – 53) 0.160
LDL (mg/dl) 110 (91 – 135) 114 (92 – 136) 0.740
AIP 0.557 (0.43 – 0.69) 0.529 (0.37 – 0.69) 0.280
UACR
(mg/gm)
14.41
(6.53 – 22.23)
82.5
(46.95 – 190.43)
<0.001
Bolded p-value < 0.05 Statistically Significant
The Spearman’s correlation (Table 4) of ACR with FBS, PPBS, HbA1C
and u. albumin were positive with (p=0.028, <0.001, 0.001 and <0.001
respectively) on overall estimation of all cases (not shown here). But on
estimating the correlation in females, only PPBS correlated
significantly and in males there was significant positive correlation with
FBS, PPBS, HbA1C, serum creatinine and spot urine albumin was
negative with eGFR with p=0.028. The other individual independent
factors like age, BMI, WHR, serum lipids and AIP did not correlate
significantly with ACR in both sexes. On doing a multivariate logistic
regression analysis of all these variables [Table 5a and 5b] there was a
significant likelihood ratio for predicting DN (p=0.014 and 0.001 in
females and males respectively) with a substantial predictive value of
74.5% in females and 75% in males by Cox and Snell R square.
Discussion:
Diabetic nephropathy is a common complication of T2DM. It is usually
detected in late stages from where it rapidly progresses to end stage
renal disease (ESRD). Early detection, good glycaemic control and
nephro-protective treatment can prevent ESRD. [11] Apart from this it
is important to identify the subgroup among T2DM, likely to develop
DN considering the modifiable risk factors like body fat, serum
lipids, kidney function and glycaemic control. From our preliminary
it is evident that there is a risk associated with biomarkers of glycaemic
Table 3: Risk Factors (Modifiable & Non-Modifiable) for Diabetic
Nephropathy
Factors Parameters Classifications
Diabetic
Nephropathy Relative
Risk
P
Value
Absent Present
Non -
Modifiable
Age (in
years)
<60 74 108
1.218 0.284
≥60 20 19
Sex
Male 62 76
1.165 0.400
Female 32 51
Modifiable BMI
(kg/m2
)
<23 20 41
1.250 0.048
≥23 74 86
WHR
<0.9(M)/<0.85
(F
1 1
1.177 1.000
≥0.9(M)/
≥0.85(F)
93 126
FBS
(mg/dl)
<110 9 22
1.284 0.119
≥110 85 105
PPBS
(mg/dl)
<140 3 0
2.396 0.043
≥140 91 127
HbA1C (%)
<7 23 23
1.232 0.315
≥7 71 104
Serum
Creatinine
(mg/dl)
≤1.2(M)/
≤1.1(F)
87 105
1.877 0.032
>1.2(M)/>1.1(F) 7 22
eGFR
(ml/min)
<90 67 94
1.062 0.650
≥90 27 33
TC
(mg/dl)
<200 60 80
1.021 1.000
≥200 34 47
TG
(mg/dl)
<150 44 65
1.077 0.587
≥150 50 62
HDL
(mg/dl)
>35(M)/>39(F) 77 112
0.767 0.246
<35(M)/<39(F) 17 15
LDL
(mg/dl)
<100 36 42
1.138 0.477
≥100 58 85
AIP
≤0.24 6 14
1.245 0.343
>0.24 88 113
Urine
Albumin
(mg/L)
<30 70 33 3.341 0.0001
control (PPBS and HbA1C), more so in males as compared to females.
Similar results have been reported by authors in Asia [12] among Asians
in Europe [4], and from heterogenous populations from 20 cohorts. [13]
Though strict glycaemic control decreased the risk for DN b 40%, it
alone cannot prevent the initiation and progression of DN. [1] Majority
of our patients were overweight or obese and had higher WHR than
normal. Though BMI associated significantly as a risk factor for DN
with UACR, WHR didn’t. Neither of them was different in the two
groups. There was no difference in gender as a risk factor for DN.
However, studies among Asians have shown that women who have DN
rapidly progress to ESRD as compared to their male counterparts. [14,
15]

The baseline eGFR and kidney function are important factors in DN
risk and progression. [1] In our study though eGFR was not
significantly different in the two groups; with and without DN,
serum creatinine levels were a risk in both sexes and eGFR
negatively correlated with UACR in males as shown in Table-4. As
eGFR is closely related to age, baseline eGFR at the time of
diagnosis of T2DM and further monitoring to see the rate of decline
[16] is imperative for initiating treatment with antidiabetic agents
which will protect the kidneys also. [12]
In our study, the serum lipids were not statistically different in the
two groups, yet studies have shown that HDL and TG are
independent risk factors for cardiovascular disease, systolic blood
pressure (SBP) being the measure. [13] As we have not considered
the treatment naïve T2DM patients and we have not taken in account
the drug history of each participant, our statement for or against the
association of serum lipids in DN will not be exact.
As T2DM involves multiple organs and its complications can
coexist to varying degrees in individuals, multiple factors affect the
course of the disease. The multivariate regression analysis showed
significant predictive value of the risk factors considered in our
study. Studies on identification of risk factors have identified similar
factors and others like SBP, duration of disease, rate of decline in
eGFR, age and presence of diabetic retinopathy. [3, 12]
The strength of our study is that, though it is a preliminary study
conducted during the lockdown for pandemic, the conjoined effect
of the modifiable and non-modifiable risk factors showed substantial
predictive value. Our study is limited by the sample number and the
lack of drug history such as lipid lowering agents, antihypertensives,
insulin or oral hypoglycaemic agents.
Conclusion:
In conclusion, the findings of our study have implications in the
clinical scenario of diabetes. As the disease, T2DM is not only about
current glycaemic control, but involves constant clinical and lab
monitoring to evade complications. Patient education about disease
and empowering them with the knowledge and ability is more
important that medications alone. An overall change in diet,
physical activity, and other lifestyle modifications should benefit
each patient. Larger cohort studies are suggested to understand the
additive effects of risk factors.
Acknowledgment: We acknowledge the consent and
cooperation of our study participants and the help of other
department staff and colleagues.
Authors’ Contributions: SS, MT and SDM conceived the
study design and initial draft of the manuscript. SD, SN and DSM
collected clinical data. SS and SN analysed the data. All the authors
edited and approved of the final draft of the manuscript.
Here, SS - Suchanda Sahu, MT - Manish Taywade, SD - Sujata
Devi, SN - Saurav Nayak and, Dipti Sudha M - DSM
Table 4: Correlation of ACR with independent factors in the
diabetics grouped according to gender (N=221)
Parameters
Females Males
Spearman's rho p
value
Spearman's rho p
value
Age (years) -0.137 0.228 0.032 0.703
BMI (kg/m2
) 0.05 0.661 -0.095 0.262
WHR 0.008 0.947 -0.083 0.329
FBS (mg/dl) 0.085 0.455 0.176* 0.036
PPBS (mg/dl) 0.267* 0.017 0.213* 0.011
HbA1C (%) 0.161 0.156 0.248** 0.003
S. Creatinine
(mg/dl)
0.076 0.508 0.191* 0.022
eGFR (ml/min) 0.043 0.705 -0.185* 0.028
TC (mg/dl) 0.172 0.129 -0.005 0.956
TG (mg/dl) 0.060 0.601 -0.070 0.406
HDL (mg/dl) 0.111 0.332 0.030 0.724
LDL (mg/dl) 0.176 0.120 -0.011 0.901
AIP -0.015 0.895 -0.073 0.387
U. Albumin
(mg/L)
0.752** 0.001 0.658** 0.001
** Correlation is significant p<0.01; * Correlation is significant p<0.05
Table 5a: Multivariate Regression of the independent variables as
risk factors for DN
Model Fitting Information
Sex Model
Model Fitting
Criteria
Likelihood Ratio
Tests
-2 Log Likelihood
Chi-
Square
df Sig.
Female
Intercept
Only
107.981
Final 0 107.981 78 0.014
Male
Intercept
Only
196.741
Final 0 196.741 141 0.001
Table 5b: Predictive value of the multivariate analysis
Pseudo R-Square
Female Cox and Snell 0.745
Nagelkerke 1
McFadden 1
Male Cox and Snell 0.75
Nagelkerke 1
McFadden 1

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