This study aimed to design and validate a new risk scoring system to predict cardiovascular (CV) events in COVID-19 patients. The researchers conducted a retrospective study of 1000 COVID-19 patients divided into two groups. Ten independent risk factors for CV events were identified, including male gender, older age, chronic heart disease, low lymphocyte count, elevated blood urea nitrogen, and decreased kidney function. A risk scoring system was developed based on these factors and validated in the second patient group. CV events were found to be significantly associated with worse patient outcomes. The new risk score may help predict CV complications in COVID-19 infected individuals.

1. S1333
© 2021 Journal of Pharmacy and Bioallied Sciences | Published by Wolters Kluwer - Medknow
Introduction: The viral infection COVID‑19 is highly infectious and has claimed
many lives till date and is still continuing to consume lives. In the COVID‑19,
along with pulmonary symptoms, cardiovascular (CV) events were also recorded
that have known to significantly contribute to the mortality. In our study, we
designed and validated a new risk score that can predict CV events, and also
evaluated the effect of these complications on the prognosis in COVID‑19 patients.
Materials and Methods: A retrospective, multicenter, observational study was
done among 1000 laboratory‑confirmed COVID-19 patients between June 2020
and December 2020. All the data of the clinical and laboratory parameters were
collected. Patients were randomly divided into two groups for testing and validating
the hypothesis. The identification of the independent risk factors was done by the
logistic regression analysis method. Results: Of all the types of the clinical and
laboratory parameters, ten “independent risk factors” were identified associated
with CV events in Group A: male gender, older age, chronic heart disease, cough,
lymphocyte count <1.1 × 109
/L at admission, blood urea nitrogen >7 mmol/L at
admission, estimated glomerular filtration rate <90 ml/min/1.73 m2
at admission,
activated partial thromboplastin time >37 S, D‑dimer, and procalcitonin >0.5 mg/L.
In our study, we found that CV events were significantly related with inferior
prognosis (P < 0.001). Conclusions: A new risk scoring system was designed in
our study, which may be used as a predictive tool for CV complications among the
patients with COVID-19 infection.
Keywords: Cardiovascular events, COVID-19, scoring system
Cardiovascular Complications and its Impact on outcomes in
COVID‑19: An Original Research
Prashant Kumar, Kaousthubh Tiwari1
, Siva Kumar Pendyala2
, Ratnesh Kumar Jaiswal3
, Neelathil Lisa Chacko4
,
Ekta Srivastava5
, Rahul V. C. Tiwari6
Access this article online
Quick Response Code:
Website: www.jpbsonline.org
DOI: 10.4103/jpbs.jpbs_143_21
Address for correspondence: Dr. Rahul V. C. Tiwari,
Department of OMFS, Narsinbhai Patel Dental College and
Hospital, Sankalchand Patel University, Visnagar ‑ 384 315,
Gujarat, India.
E‑mail: drvct7388@gmail.com
with pulmonary symptoms, cardiovascular (CV) events
were also recorded that have known to significantly
contribute to the mortality.[4‑9]
There have been very few
Introduction
T he viral infection COVID‑19 is highly infectious
and has claimed many lives till date and is
still continuing to consume lives.[1]
It is assumed to
be affecting the respiratory system, and may lead
to the cascade of events and eventual death in few.
To date (January 2021), 10.6 million cases have been
recorded in India with 152,000 deaths and with 95 million
infected around the world.[1‑4]
In the COVID‑19, along
Department of Cardiology,
Rajendra Institute of
Medical Science, Ranchi,
Jharkhand, India, 1
General
Physician, Rollz India Waste
Management Pvt. Ltd,
Ghaziabad, Uttar Pradesh,
India, 2
Department of Oral
and Maxillofacial Surgery,
Faculty of Dentistry,
AIMST University, Kedah,
Malaysia, 3
Department
of Periodontics, RUHS
College of Dental Sciences,
Jaipur, Rajasthan, India,
4
Department of Periodontics,
SMBT Dental College
and Postgraduate Centre,
Sangamner, Maharashtra,
India, 5
Department of Pediatric
and Preventive Dentistry,
SMBT Dental College
and Hospital, Sangamner,
Maharashtra, 6
Department
of OMFS, Narsinbhai Patel
Dental College and Hospital,
Sankalchand Patel University,
Visnagar, Gujarat, India
Abstract
This is an open access article distributed under the terms of the Creative Commons At
tribution‑NonCommercial‑ShareAlike 4.0 License, which allows others to remix, tweak,
and build upon the work non‑commercially, as long as the author is credited and the new
creations are licensed under the identical terms.
For reprints contact: WKHLRPMedknow_reprints@wolterskluwer.com
How to cite this article: Kumar P, Tiwari K, Pendyala SK, Jaiswal RK,
Chacko NL, Srivastava E, et al. Cardiovascular complications and its
impact on outcomes in COVID‑19: An original research. J Pharm Bioall
Sci 2021;13:S1333-7.
Original Article
Submitted: 05‑Mar‑2021
Revised: 06-Apr-2021
Accepted: 06‑May‑2021
Published: 10-Nov-2021
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2. S1334 Journal of Pharmacy and Bioallied Sciences ¦ Volume 13 ¦ Supplement 2 ¦ August 2021
Kumar, et al.: Cardiovascular complication and COVID-19
Contd...
Table 1: Clinical variables among patients with/without cardiovascular events in Group A
Parameters Total (%) CV events present (n=123), n (%) CV events absent (n=377), n (%) P
Basic demographics
Age (years) 54 64.53 53.1 <0.001
≥60 333 (39.4) 76 (62.32) 175 (35.5) <0.001
Sex (male) 235 (47.11) 79 (63.91) 221 (44.3) <0.001
Vital signs on admission
Systolic pressure (mm hg) 127 128 127 0.5150
Respiratory rate (breath/min) 20 19 20 0.1132
Heart rate (beat/min) 85 85 86 0.7551
Temperature (°C) 36.7 36.7 36.7 0.0521
Diastolic pressure (mm hg) 81 81 80 0.2624
Symptoms and signs
Shortness of breath/dyspnea 22.51 28.3 21.4 0.0761
Cough 62.3 74.5 60 0.0020
Chest pain/distress 22.71 24.5 22.4 0.5943
Sore throat 8.3 9.1 8.3 0.7921
Arthralgia 1.4 3.32 1.2 0.1975
Diarrhea 11.1 9.0 11.6 0.40
Fever 64 74.5 64.6 0.0310
Nausea/vomiting 3.2 1.61 3.7 0.3641
Comorbidities
Diabetes mellitus 15 21.2 13 0.0361
Cerebrovascular disease 1.62 1.5 1.5 1.000
Hypertension 26.5 32.7 25.5 0.0961
CHD 7.0 16.3 5.8 <0.0010
Chronic liver disease 7.71 8.1 7.8 0.8201
Laboratory examinations
Triglyceride 1.24 1.32 1.24 0.161
Cholesterol 4.04 4.02 4.05 0.482
High‑density lipoprotein 1.03 1 1.03 0.145
Low‑density lipoprotein 2.44 2.38 2.45 0.19
Hemoglobin (g/L) 128 129.5 128 0.344
Hematocrit 0.39 0.39 0.38 0.395
Erythrocyte sedimentation rate 31 32 31 0.401
Platelet count (×109
/L) 201 184 207 0.005
≤100 41 11 30 0.02
ALT (U/L) 23.3 27 23 0.193
AST (U/L) 24 30.5 23.7 <0.001
≥35 174 42 132 <0.001
White blood cell count (×109
/L) 5.51 6.12 5.41 0.001
≥9.5 76.0 22.0 54.0 <0.001
Monocytes (×109
/L) 0.431 0.401 0.44 0.27
Lymphocytes (×109
/L) 1.21 0.910 1.25 <0.001
≤1.1 339 74 265 <0.001
Neutrophils count (×109
/L) 3.431 4.730 3.291 <0.001
≥6.3 109 34 75 (10.4) <0.001
≥7 116 38 78 (10.8) <0.001
Creatinine (μmol/L) 61 69 60 (49, 72) <0.001
≥106 28 11 17 (2.4) <0.001
Procalcitonin (μg/L) 0.05 0.112 0.51 <0.001
≥0.5 27 14 13 <0.001
eGFR‑(ml/min/1.730 m2
) 100.901 85.8 102.3 <0.001
≤90 79 25 54 <0.001
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3. S1335
Journal of Pharmacy and Bioallied Sciences ¦ Volume 13 ¦ Supplement 2 ¦ August 2021
Kumar, et al.: Cardiovascular complication and COVID-19
studies conducted to note the association between the
CV risk factors and outcome in COVID‑19 patients. In
our study, we designed and validated a new risk score
that can predict CV events, and also evaluated the effect
of these complications related to the outcome in these
patients.
Materials and Methods
We conducted a multicenter, retrospective, observational
study among 1000 COVID‑19 patients (who were
confirmed by reverse transcription–polymerase chain
reaction) between June 2020 and December 2020.
The data were collected from COVID care centers
and hospitals. Patients were randomly separated into
two groups: Group A (500) to formulate the risk
scoring and Group B (500) to validate the new scoring
system. The exclusion criteria were as follows: (1)
<18 years old, (2) pregnancy, and (3) recent/known
CV event. Demographics, vital signs, symptoms and
signs, comorbidities, and laboratory examination data
were collected. CV complications were deliberated
only when these were seen: (1) acute myocardial
infarction (AMI), (2) acute myocardial injury, (3) de
novo arrhythmia, (4) new or worsening HF, and (5)
deep vein thrombosis. Statistical investigation was done
using IBM SPSS Statistics for Windows, Version 23.0.
Armonk, NY: IBM Corp. IBM Corp. Released 2016.
Suitable tests were applied for comparison. P < 0.05
was measured to be significant statistically.
Results
In Group A, 145 (14.5%) patients had CV complications.
Nine patients had a new heart failure and 31 patients had
a new arrhythmia. Ninety‑nine patients among them had
acute myocardial injury and six progressed to AMI. Only
one patient had deep vein thrombosis. Male gender and the
higher age patients with complications were two variables
that were both significantly greater than those of patients
without complications (P < 0.0010). Significant variations
were seen in few variables among the patients with or
without CV complications such as cough (P = 0.0021),
chronic heart disease (CHD) (P < 0.0013), diabetes
mellitus (P = 0.0362), and fever (P = 0.0311). In
the patients with CV complications, higher aspartate
aminotransferase (P < 0.001), white blood cell (P =
0.001), and neutrophil (P < 0.001) were present, however,
lesser lymphocyte counts (P < 0.001), platelet (P =
0.005), and erythrocyte sedimentation rate (P < 0.001)
were seen in patients without CV complications [Table
1]. The variables that have P < 0.10 were considered
for the logistic regression model analysis. From our
observation, ten “independent risk factors” associated with
CV complications were identified: male, age ≥60 years,
CHD, lymphocyte count ≤1.1 × 109
/L at admission, cough,
blood urea nitrogen ≥7 mmol/L at admission, estimated
glomerular filtration rate (eGFR) ≤90 ml/min/1.73 m2
at
admission, activated partial thromboplastin time (APTT)
≥37 S, D‑dimer ≥0.5 mg/L, and procalcitonin ≥0.5 mg/L.
Hence, final risk scores altered from 0 to 23 for every
patient. The cutoff for predicting the CV complication was
given as 7.5 [Table 2]. Later, risk score validation was
done. In Group B, 17.5% of patients had CV complications.
Group A and Group B had similar risk scores. In Group B,
we noted that the optimal cutoff value was 7.5 [Table 3].
Discussion
Ten risk factors have been identified. We observed
Table 1: Contd...
Parameters Total (%) CV events present (n=123), n (%) CV events absent (n=377), n (%) P
APTT (s) 27.8 28.7 27.7 0.033
≥37 43 14 29 0.001
Glucose (mmol/L) 5.59 6.3 5.5 0.005
CRP (mg/L) 27.31 57.9 24.1 <0.001
D‑dimer (mg/L) 0.580 1.16 0.53 <0.001
≥0.5 366 82 284 <0.001
eGFR: Estimated glomerular filtration rate, APTT: Activated partial thromboplastin time, CRP: C‑reactive proteins, ALT: Alanine
aminotransferase, AST: Aspartate aminotransferase, CHD: Chronic heart disease
Table 2: Multivariate analysis of risk factors in
cardiovascular complications in Group A
The risk factors Multivariate,
OR (95% CI)
P Scores
Sex (male) 1.840 0.007 2
Age (years, ≥60) 2.01 1 0.002 2
Cough 1.861 0.010 2
CHD 2.30 0.011 2
Lymphocytes (×109
/L, ≤1.10) 1.601 0.0410 2
Blood urea nitrogen (mmol/L, ≥7.0) 2.142 0.0040 2
eGFR (ml/min/1.7 m2
, ≤90.0) 2.080 0.021 2
APTT (s, ≥37) 3.070 0.0060 3
D‑dimer (mg/L, ≥0.50) 2.121 0.0011 2
Procalcitonin (μg/L, ≥0.5) 3.580 0.0081 4
OR: Odds ratio, CI: Confidence interval, eGFR: Estimated
glomerular filtration rate, APTT: Activated partial thromboplastin
time, CHD: Chronic heart disease
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4. S1336 Journal of Pharmacy and Bioallied Sciences ¦ Volume 13 ¦ Supplement 2 ¦ August 2021
Kumar, et al.: Cardiovascular complication and COVID-19
that the total score increased from points 0 to 23.
The new scoring system is proportionally related to
the prognosis of COVID‑19 patients in relation to
the CV complications. Hence, early forecast of CV
complications is significant and fundamental. Our
study is similar to the study of Wei et al.[10]
where
procalcitonin, preexisting CV, age, sickness, and
eGFR were associated with AMI. It is accepted that
a risk score may help in thorough stratification than
a solitary indicator in COVID‑19. Along these lines,
we enumerated a new scoring system that includes
demographic attributes, manifestations, comorbidities,
and laboratory assessments. Study participants
were classified into two groups. Past examinations
have exhibited that in about 10% of patients with
COVID‑19, myocardial injury happened.[4,9]
In our
study among ~16% of the patients, the complex CV
events were noted. The CV events may be dependent
on many factors such as the characteristics of the
population and the disease severity. Male, prolonged
APTT, older age, CHD, and raised D‑dimer have
been broadly affirmed to be associated with CV
occasions in the risk score. Similar risk factors also
are attributed to pneumonia (community acquired).[11]
In our study, few independent risks are identified anew
which ought to be dealt with carefully. In the review
of Irwin, he stated that the cough may also lead to
CV complications.[12]
In any case, the effect of cough
on patients actually stays to be explained. Diminished
lymphocyte counts that may be caused due to systemic
inflammatory reaction and immunocompromised
are regular in COVID‑19 patients.[4,9]
There are few
studies, that demonstrates that in severe COVID‑19,
there are lower levels of T suppressor and helper
cells.[13]
Due to the impact on the lymphocytes in the
COVID, this may lead to various CV complications.[14]
The relation between the CV event and the blood urea
nitrogen (BUN), eGFR in COVID is imprecise. In any
case, our outcomes are reliable with a past investigation
of flu. In the study conducted by Nin et al., they
uncovered that in H1N1 viral pneumonia, patients
with acute kidney injury show more CV dysfunction
contrasted to those without.[15]
In the clinical setup for
the analysis of bacterial diseases, procalcitonin shows
a high precision. Inevitably, the bacterial infections are
commonly seen in those with the CV complications
as there is known immunosuppression. There are few
studies that demonstrate the association of procalcitonin
and cardiovascular morbidity and mortality.[16]
In the past predictive models for the COVID‑19 patients
for the ICU admissions, critical illness, and deaths,
similar risk factor models to our design have been
used. In the study by Gong et al., they developed a
“nomogram” wherein BUN and older age were related
with serious COVID‑19.[17]
As compared to the previous
predictive models, the area under curve in our risk
scores is lower than changed as of 0.80–0.90. Due to
the retrospective design plan, some basic data were not
noted that might have led to the jeopardized results in
the risk score’s discriminatory power. At that point, it is
conceivable that the endpoint of CV events was more
heterogeneous than disease itself. Furthermore, in the
included patients, the disease severity of different CV
events was not similar. To avoid any bias during the
study, the accepted definitions were used to identify the
disease in the patients and various doctors were assigned
to check the data.
From our study, the designated risk factors can be easily
obtained and evaluated. It may help clinicians settle on
ideal treatment choices for patients who are at danger
of CV complications, and assist scientists in future
with investigating the mechanism of the CV event in
COVID‑19.
There are a few limitations to our investigation. To
begin with, it was a retrospective examination that might
have had selection bias. The medications and therapies
prior to admission may have affect results. There was
no long‑term follow‑up. Impact of SARS‑CoV‑2 to CV
systems in the long run and related risk factors are to be
investigated. Larger population research is required to
verify our suppositions.
Conclusions
In our study, we designed and corroborated a new risk
score that is made of ten risk factors during admission:
Senior men, CHD, eGFR ≤90 ml/min/1.73 m2
,
D‑dimer ≥0.5 mg/L, lymphocyte count ≤1.1 × 109
/L,
cough, fever, procalcitonin ≥0.5 μg/L, BUN ≥7 mmol/L,
and APTT ≥37 s. A favorable predictive value for CV
Table 3: Validity of the novel risk score
Groups Patients with CV
complications/overall (%)
Area under the
curve (95% CI)
Optimal
cutoff value
The
sensitivity
The
specificity
Group A 14.4 0.7731 (0.723-0.822) 7.50 0.6560 0.7801
Group B 17.1 0.7560 (0.690-0.822) 5.51 0.8230 0.5731
Both 15.2 0.7660 (0.726-0.806) 7.51 0.6200 0.7850
CI: Confidence interval, CV: Cardiovascular
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5. S1337
Journal of Pharmacy and Bioallied Sciences ¦ Volume 13 ¦ Supplement 2 ¦ August 2021
Kumar, et al.: Cardiovascular complication and COVID-19
complications that can affect the outcome among the
COVID‑19 patients can be calculated from our risk
scores. However, further studies are required.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References
1. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel
coronavirus from patients with pneumonia in China, 2019.
N Engl J Med 2020;382:727‑33.
2. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al.
Early transmission dynamics in Wuhan, China, of
novel coronavirus‑infected pneumonia. N Engl J Med
2020;382:1199‑207.
3. COVID-19 Map – Johns Hopkins Coronavirus Resource
Center. Available from: https://coronavirus.jhu.edu/map.html
. [Last accessed on 2020 Jul 30].
4. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical
features of patients infected with 2019 novel coronavirus in
Wuhan, China. Lancet 2020;395:497‑506.
5. Driggin E, Madhavan MV, Bikdeli B, Chuich T, Laracy J,
Biondi‑Zoccai G, et al. Cardiovascular considerations for
patients, health care workers, and health systems during the
COVID‑19 pandemic. J Am Coll Cardiol 2020;75:2352‑71.
6. Wu Z, McGoogan JM. Characteristics of and important lessons
from the coronavirus disease 2019 (COVID-19) outbreak
in China: Summary of a report of 72314 cases from the
Chinese Center for Disease Control and Prevention. JAMA
2020;323:1239.
7. Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors
of mortality due to COVID‑19 based on an analysis of data
of 150 patients from Wuhan, China. Intensive Care Med
2020;46:846‑8.
8. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course
and risk factors for mortality of adult inpatients with COVID‑19
in Wuhan, China: A retrospective cohort study. Lancet
2020;395:1054‑62.
9. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical
characteristics of 138 hospitalized patients with 2019 novel
coronavirus‑infected pneumonia in Wuhan, China. JAMA
2020;323:1061‑9.
10. Wei JF, Huang FY, Xiong TY, Liu Q, Chen H, Wang H, et al.
Acute myocardial injury is common in patients with COVID‑19
and impairs their prognosis. Heart 2020;106:1154‑9.
11. Violi F, Cangemi R, Falcone M, Taliani G, Pieralli F,
Vannucchi V, et al. Cardiovascular complications and short-term
mortality risk in community – Acquired pneumonia. Clin Infect
Dis 2017;64:1486-93.
12. Irwin RS. Complications of cough: ACCP evidence-based
clinical practice guidelines. Chest 2006;129 Suppl 1:54S-8.
13. Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, et al.
Dysregulation of immune response in patients with coronavirus
2019 (COVID‑19) in Wuhan, China. Clin Infect Dis
2020;71:762‑8.
14. Nunez J, Minana G, Bodi V, Núñez E, Sanchis J, Husser O,
et al. Low lymphocyte count and cardiovascular diseases. Curr
Med Chem 2011;18:3226-33.
15. Nin N, Lorente JA, Soto L, Ríos F, Hurtado J, Arancibia F,
et al. Acute kidney injury in critically ill patients with 2009
influenza A (H1N1) viral pneumonia: An observational study.
Intensive Care Med 2011;37:768‑74.
16. Schiopu A, Hedblad B, Engström G, Struck J, Morgenthaler NG,
Melander O. Plasma procalcitonin and the risk of cardiovascular
events and death: A prospective population-based study. J Intern
Med 2012;272:484-91.
17. Gong J, Ou J, Qiu X, Jie Y, Chen Y, Yuan L, et al. A tool for
early prediction of severe coronavirus disease 2019 (COVID‑19):
A multicenter study using the risk nomogram in Wuhan and
Guangdong, China. Clin Infect Dis 2020;71:833‑40.
[Downloaded free from http://www.jpbsonline.org on Wednesday, November 10, 2021, IP: 49.204.225.73]