1) Critically ill patients requesting admission to a busy medical ICU (MICU) had lower odds of acceptance when the MICU was at full capacity, despite availability in other ICUs.
2) For patients both accepted and denied MICU admission, longer boarding times in the emergency department post-consult were associated with higher risk of in-hospital mortality or persistent organ dysfunction.
3) The study highlights the impact of ICU bed availability and emergency department crowding on decisions to admit critically ill patients, and shows that increased boarding time is an independent risk factor for worse patient outcomes.
Determinants of Fall Risk and Injury in Hispanic Elderly Living in El Paso Community
Guillermina Solis, PhD, RN, F/GNP
Vanessa Guerrero, RN
Mano y Corazón Binational Conference of Multicultural Health Care Solutions, El Paso, Texas, September 27-28, 2013
Auditing Medication errors in hospitalised patients at Chiradzulu and QECH Ho...Samson Rangford Chilambe
A proposal for Pharmacy year undergraduate research study for Samson Chilambe and Frank Chadewa. The proposal was approved by the COMREC hence the study was conducted at a small scale level in . Should funding be there, it shall be conducted at larger scale.
Mobility is Medicine
Loretta Schoen Dillon, PT, DPT, MS
Director of Clinical Education and Clinical Associate Professor
UTEP Physical Therapy Program
Mano y Corazón Binational Conference of Multicultural Health Care Solutions, El Paso, Texas, September 27-28, 2013
How health analytics are changing the way we understand and manage healthcare. Presented by Professor Enrico Coiera, Faculty of Medicine at the University of NSW, Australia, at HINZ 2014, 11 November 2014, 10am, Plenary Room
Determinants of Fall Risk and Injury in Hispanic Elderly Living in El Paso Community
Guillermina Solis, PhD, RN, F/GNP
Vanessa Guerrero, RN
Mano y Corazón Binational Conference of Multicultural Health Care Solutions, El Paso, Texas, September 27-28, 2013
Auditing Medication errors in hospitalised patients at Chiradzulu and QECH Ho...Samson Rangford Chilambe
A proposal for Pharmacy year undergraduate research study for Samson Chilambe and Frank Chadewa. The proposal was approved by the COMREC hence the study was conducted at a small scale level in . Should funding be there, it shall be conducted at larger scale.
Mobility is Medicine
Loretta Schoen Dillon, PT, DPT, MS
Director of Clinical Education and Clinical Associate Professor
UTEP Physical Therapy Program
Mano y Corazón Binational Conference of Multicultural Health Care Solutions, El Paso, Texas, September 27-28, 2013
How health analytics are changing the way we understand and manage healthcare. Presented by Professor Enrico Coiera, Faculty of Medicine at the University of NSW, Australia, at HINZ 2014, 11 November 2014, 10am, Plenary Room
Physician age and outcomes in elderly patients in hospial in the US: observat...Akshay Mehta
It is an observational study Physicians age and outcomes of their treatment on elderly patients.
Datas are really very shocking and it tells more about the experience and technology.
The prognosis of unknown or unattended during hospital stay in
neuro-surgical department, JPNATC, AIIMS and the problems faced
during nursing care.
Anu Susan Mathew, Dr.Deepak Agrawal
BACKGROUND: The Delhi city alone witnessed 7,516(2009) road
traffic accidents and many were admitted to hospitals as unknown or
unattended.
AIMS: To assess the morbidity and mortality of unknown or
unattended patients and problems faced during nursing care.
MATERIALS AND METHODS: This is a retrospective analysis from
1st January 2010 to 31st December 2010 wherein all unknown
or unattended patients with head injury (GCS 1-15) admitted in
neurosurgery were included.The duration of hospital stay,admission
GCS and outcome were assessed and an attempt was also made
to analyse the problems faced by nursing personnel during their
hospital stay.
OBSERVATIONS: Total number of patients enrolled during the study
period was 111.105 patients were male and 6 were females.7%(7)
were below 18years and 93 % were more than 18 years of age.Of
these 95 were unknown and 16 were unattended. The average
hospital stay of unknown and unattended was 13(1-368) and 21(7-
120) days respectively.The mean GCS of unknown patients during
admission who discharged later was 9(3-15) and who expired later
was 6(3-15).The mean GCS of unknown patients during discharge
was 13(1-15). The mean GCS of unattended patients during
admission and discharge was 12(13-15) and 14(3-15) respectively.
Of the 95 unknown patients, 69 %( 66) became known during
hospital stay. Of the 66 who became known, 21 %( 14) shifted to
rehabilitation centre as unattended, 15 %( 10) expired on hospital
and 59 %( 39) discharged to home. Of the 95 unknown patients,
31% (29) remained unknown; out of which 66 % (19) expired on
hospital and 34 % (10) shifted to rehabilitation centre as unknown.
Of the 16 unattended patients, 25% went to home, 63% shifted to
rehabilitation homes and 12% expired. The most common problems
faced during nursing care were aspiration (2%), corneal ulceration
(4%), contractures (7%), UTI (7%), pressure sores (8%) and VAP (20%)
mainly because of long hospital stay.
CONCLUSION: Patients remaining unknown/unattended is a unique
problem as far as developing countries are concerned. Managing
these patients is difficult as they occupy hospital beds for longer
duration and require more nursing care with higher mortality and
morbidity. It remains surprising that in spite of advancements in the
field of mass communication almost 31 % of the unknown remain
unidentified.
Determine the Patients' Satisfaction Concerning In-hospital Information Progr...iosrjce
IOSR Journal of Nursing and health Science is ambitious to disseminate information and experience in education, practice and investigation between medicine, nursing and all the sciences involved in health care.
Nursing & Health Sciences focuses on the international exchange of knowledge in nursing and health sciences. The journal publishes peer-reviewed papers on original research, education and clinical practice.
By encouraging scholars from around the world to share their knowledge and expertise, the journal aims to provide the reader with a deeper understanding of the lived experience of nursing and health sciences and the opportunity to enrich their own area of practice
Physician age and outcomes in elderly patients in hospial in the US: observat...Akshay Mehta
It is an observational study Physicians age and outcomes of their treatment on elderly patients.
Datas are really very shocking and it tells more about the experience and technology.
The prognosis of unknown or unattended during hospital stay in
neuro-surgical department, JPNATC, AIIMS and the problems faced
during nursing care.
Anu Susan Mathew, Dr.Deepak Agrawal
BACKGROUND: The Delhi city alone witnessed 7,516(2009) road
traffic accidents and many were admitted to hospitals as unknown or
unattended.
AIMS: To assess the morbidity and mortality of unknown or
unattended patients and problems faced during nursing care.
MATERIALS AND METHODS: This is a retrospective analysis from
1st January 2010 to 31st December 2010 wherein all unknown
or unattended patients with head injury (GCS 1-15) admitted in
neurosurgery were included.The duration of hospital stay,admission
GCS and outcome were assessed and an attempt was also made
to analyse the problems faced by nursing personnel during their
hospital stay.
OBSERVATIONS: Total number of patients enrolled during the study
period was 111.105 patients were male and 6 were females.7%(7)
were below 18years and 93 % were more than 18 years of age.Of
these 95 were unknown and 16 were unattended. The average
hospital stay of unknown and unattended was 13(1-368) and 21(7-
120) days respectively.The mean GCS of unknown patients during
admission who discharged later was 9(3-15) and who expired later
was 6(3-15).The mean GCS of unknown patients during discharge
was 13(1-15). The mean GCS of unattended patients during
admission and discharge was 12(13-15) and 14(3-15) respectively.
Of the 95 unknown patients, 69 %( 66) became known during
hospital stay. Of the 66 who became known, 21 %( 14) shifted to
rehabilitation centre as unattended, 15 %( 10) expired on hospital
and 59 %( 39) discharged to home. Of the 95 unknown patients,
31% (29) remained unknown; out of which 66 % (19) expired on
hospital and 34 % (10) shifted to rehabilitation centre as unknown.
Of the 16 unattended patients, 25% went to home, 63% shifted to
rehabilitation homes and 12% expired. The most common problems
faced during nursing care were aspiration (2%), corneal ulceration
(4%), contractures (7%), UTI (7%), pressure sores (8%) and VAP (20%)
mainly because of long hospital stay.
CONCLUSION: Patients remaining unknown/unattended is a unique
problem as far as developing countries are concerned. Managing
these patients is difficult as they occupy hospital beds for longer
duration and require more nursing care with higher mortality and
morbidity. It remains surprising that in spite of advancements in the
field of mass communication almost 31 % of the unknown remain
unidentified.
Determine the Patients' Satisfaction Concerning In-hospital Information Progr...iosrjce
IOSR Journal of Nursing and health Science is ambitious to disseminate information and experience in education, practice and investigation between medicine, nursing and all the sciences involved in health care.
Nursing & Health Sciences focuses on the international exchange of knowledge in nursing and health sciences. The journal publishes peer-reviewed papers on original research, education and clinical practice.
By encouraging scholars from around the world to share their knowledge and expertise, the journal aims to provide the reader with a deeper understanding of the lived experience of nursing and health sciences and the opportunity to enrich their own area of practice
This presentation focusses on the importance of diagnostic biomarkers for Alzheimer's disease. MRI, amyloid PET and CSF biomarkers are discussed in detail.
Clinician Satisfaction Before and After Transition from a Basic to a Comprehe...Allison McCoy
Healthcare organizations are transitioning from basic to comprehensive electronic health records (EHRs) to meet Meaningful Use requirements and improve patient safety. Yet, full adoption of EHRs is lagging and may be linked to clinician dissatisfaction. In depth assessment of satisfaction before, during, and after EHR transition is rarely done. Using an adapted published tool to assess adoption and satisfaction with EHRs, we surveyed clinicians at a large, non-profit academic medical center before (baseline) and 6-12 months (short-term follow-up) and 12-24 months (long-term follow-up) after transition from a basic, locally-developed to a comprehensive, commercial EHR. Satisfaction with the EHR (overall and by component) was captured at each interval. Overall satisfaction was highest at baseline (85%), lowest at short-term follow-up (66%), and increasing at long-term follow-up (79%). This trend was similar for satisfaction with EHR components designed to improve patient safety including clinical decision support, patient communication, health information exchange, and system reliability. Conversely, at baseline, short-term and long-term follow-up, perceptions of productivity, ability to provide better care with the EHR, and satisfaction with available resources, were lower at both short- and long-term follow-up compared to baseline. Persistent dissatisfaction with productivity and resources was identified. Addressing determinants of dissatisfaction may increase full adoption of EHRs. Further investigation in larger populations is warranted.
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
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Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Effect of Emergency Department and ICU Occupancy on Admission Decisions and Outcomes for Critically Ill Patients
1. Crit Care Med. 2018 Jan 30
Presented by PGY盧敬文
Supervisor: 蔡銘仁醫師
2018.04.10
2. Background
• The volume of ICU admissions from the emergency department (ED)
has increased by almost 50% between 2001 and 2009
• low availability leads to difficult ICU triage decisions often resulting in
the denial of patients who would otherwise be accepted to the ICU
• deny patients’ admission to the ICU has been shown to be associated
with increased hospital mortality
3. • The rise in ICU admissions has resulted in a 32% increase in ED length
of stay (LOS) for critically ill patients
• in higher volume and/or metropolitan area EDs, up to 87% of all patients
having delay of > 2 hours
• critically ill patients experiencing boarding times of > 6 hours have a
higher risk of inpatient mortality (but conflicting results)
• longer wait times for admission
higher cost, longer LOS, lower adherence to best practices
4. Puzzle ?
• For critically ill ED patients
1. the effect of ED crowding and ICU occupancy on ICU admission
decisions ?
2. the potential association of prolonged delays in admission on in-
hospital morbidity and mortality ?
5. Method:
Study setting and population
• single institution study
• an academic, urban, tertiary care center with a 14-bed closed medical
ICU(MICU)
• Other ICUs can serve as overflow units for patient admissions to the
MICU when there is no bed availability
• The ED contains a five bed area designated for high-acuity patients
6. • The patient cohort:
all adult ED patients (≧18 yr old) for whom MICU admission was
requested from October 1, 2013, to June 30, 2015
• final decision of ICU admission by ICU attending physician
• Post decision made board in the ED
7. Method:
Study design and measurements
• retrospective cohort study
• objectives:
1) identify predictors of ICU admission decisions (accept vs deny),
• examining the effect of ED and ICU volume on these decisions
2) measure the effect of postconsult ED boarding time on patient
outcome of in-hospital mortality or morbidity
• captured by the presence of persistent organ dysfunction and/or death at 28
days
8. Method:
Statistical methods
Predictors of ICU Admission Decision.
• Multivariable logistic regression was used to determine the odds of
receiving an ICU accept admission decision by patient- and hospital-
related characteristics
9. Predictors of Persistent Organ Dysfunction and/or Death.
• propensity score methods
• All predictors from the triage decision model were included as
candidate
• variables with low common support and high bias were dropped
• Individuals were stratified into quintiles who were similar with
respect to their baseline characteristics
10. RESULTS:
Baseline Characteristics
• A total of 854 consults for ICU admission
• representing 43.7% of all the ICU consults received
• 455 patients (53.3%) were accepted for ICU admission
• 57 patients (12.5%) requiring overflow admission to another ICU
11. Characteristics Accept N = 455 Deny N = 399
Patient-Related
Age, mean (SD)* 60.7 16.7 65.0 17.5
MPM0-III score, median (IQR) 0.15 (0.07, 0.30) 0.13 (0.06, 0.25)
Revised Charlson Score, Comorbidity Index, mean (SD) 3 (1, 5) 3 (1, 5)
Gender (N %)
Female 230 50.5 212 53.1
Male 225 49.5 187 46.9
Race/Ethnicity - (N %)
Caucasian, Non-Hispanic 117 25.7 103 25.8
African American, Non-Hispanic 131 28.8 124 31.1
Hispanic/Latino 146 32.1 114 28.6
Asian/Native American/Other, Non-Hispanic 51 11.2 42 10.5
Unknown 10 2.2 16 4.0
Insurance
Medicare/Private Payor 292 64.2 253 63.4
Medicaid 130 28.6 99 24.8
Other/Unknown 33 7.3 47 11.8
Nursing Home/Facility Pre-Hospital Origin* -
(N, %)
55 12.5 98 24.8
Code Status
No care limitations (e.g., FULL CODE) at time of consult* 443 97.4 341 85.5
No care limitations at time of ICU admission 371 81.5 n/a n/a
No care limitations at hospital discharge 334 73.4 247 61.9
Critical Care Diagnosis Category (N, %)*
Table A3: Baseline patient- and hospital-related characteristics for study cohort of ED patients for whom
Medical ICU admission consult was requested, between 10/2013 and 6/2015.
12. Pulmonary system 189 41.5 123 30.8
Sepsis/septic shock 64 14.1 26 6.5
Cardiac system 54 11.9 28 7.0
Gastrointestinal disorders 45 9.9 36 9.0
Endocrine (including electrolyte derangements) 22 4.8 12 3.0
Other 54 11.9 25 6.3
None 27 5.9 149 37.3
Nightshift timing of consult - (N, %) 247 54.3 215 53.9
ED LOS pre-ICU consult, median (IQR) (hours)* 3.1 (1.9, 5.7) 3.8 (2.1, 7.5)
Hospital-Related (at time of consult)
ED High Intensity Section at Full Capacity - (N, %) 160 35.2 146 36.6
Active ED Patient Volume(Quartiles)
Q1 (low) 118 25.9 97 24.3
Q2-Q3 (medium) 221 48.6 196 49.1
Q4 (high) 116 25.5 106 26.6
Medical ICU at Full Capacity* - (N, %) 117 25.7 131 32.8
Other ICU Patient Volume, percent capacity (Quartiles)
Q1 (low) 152 33.4 115 28.8
Q2-Q3 (medium) 229 50.3 206 51.6
Q4 (high) 74 16.3 78 19.5
Adult Inpatient Volume, percent occupancy (Quartiles)
Q1 (low) 27 5.9 24 6.0
Q2-Q3 (medium) 257 56.5 212 53.1
Q4 (high) 171 37.6 163 40.9
Hospital Course
ED boarding time post-consult, median (IQR) (hours)* 4.2 (2.8, 6.3) 11.7 (6.2, 20.3)
Death in ED* 9 2.0 28 7.0
Hospital LOS, median (IQR) (days)** 8.0 (4.3, 14.7) 6.2 (2.9, 12.5)
Primary Outcome
Persistent Organ Dysfunction/Death (POD+D) - (N, %) 189 41.5 178 44.6
*baseline differences statistically significant p < 0.05
17. TABLE 2.
Predictors of Persistent Organ
Dysfunction and/or Death for
Critically Ill Emergency Department
Patients for Whom Medical ICU
Admission Consult Was Completed,
Adjusted for Propensity Score (ICU
Admission): Results From the
Multivariate Regression Model
18. Discussion
• significant effect of MICU bed availability on ICU admission
decisions for critically ill ED patients
• Outcomes between those admitted to the MICU and those
admitted to another ICU as overflow were not significantly
different (36.8% vs 63.2%; p = 0.442).
potential opportunity to have improved coordination and collaboration
between ICUs to facilitate overflow to offload the ED
19. • use of propensity score analysis helps to account for the selection
bias associated with decision making around ICU admission
• using a composite outcome of mortality and 28-day morbidity
better elucidate the effect of boarding on negative patient outcomes
• Our model also documents the effect of severity of illness, diagnosis,
and surrogates for frailty (nursing home origin) on ICU decisions
• we did not see an increased effect of ED boarding on POD+D for more
frail or more severely ill patients, which may be related to inadequate
numbers
20. Limitation
1. observational study design and insufficient EHR documentation
• ED providers decided not to request ICU consult after their determination
that a patient may not benefit from ICU services
• detailed information is rarely found in the EHR for the clinical reasoning
2. inability to test for interactions between many of the patient-
related variables due to small sample sizes
3. did not contain a dynamic measure of clinical severity, nor
detailed accounting of the hospital course
4. patient goals of care are often revisited during the patient’s
hospital course
21. 5. We were limited in our investigation of secondary outcomes
related to resource utilization
did not have data on cost, transfers, readmissions, or other similar
metrics
6. our measures of ED crowdedness were taken at the time of ICU
consult and did not represent the dynamic changes
7. this study reflects a single institution’s ICU admission decision-
making process and an ED-led model of care for boarding critically
ill patients and may not be as applicable
22. Take-home message
1. Critically ill ED patients have lower odds of being accepted for ICU
admission in times of capacity strain in their target ICU, despite bed
availability in other units
2. For all these patients, longer ED boarding times have an
independent negative effect on inpatient mortality and morbidity