Early Predictors of Mortality for Hospitalized Patients Suffering Cardiopulmonary Arrest

CHEST Journal ◽  
1990 ◽  
Vol 97 (2) ◽  
pp. 413-419 ◽  
Author(s):  
Daniel Roberts ◽  
Kevin Landolfo ◽  
R. Bruce Light ◽  
Karen Dobson
Keyword(s):  
Cardiopulmonary Arrest ◽  
Hospitalized Patients ◽  
Predictors Of Mortality ◽  
Early Predictors

scholarly journals Fr402 MELD-NA SCORE VERSUS SOFA SCORE AND APACHE II SCORE AS EARLY PREDICTORS OF MORTALITY IN COVID-19 PATIENTS WITH LIVER INVOLVEMENT

2021 ◽  
Vol 160 (6) ◽  
pp. S-312-S-313
Author(s):  
Sandra R. Gomez ◽  
Eric Lam ◽  
Luis Gonzalez Mosquera ◽  
Joshua Fogel ◽  
Paul Mustacchia
Keyword(s):  
Sofa Score ◽  
Apache Ii ◽  
Liver Involvement ◽  
Apache Ii Score ◽  
Predictors Of Mortality ◽  
Early Predictors

scholarly journals Predictors of mortality in hospitalized patients with acute exacerbation of bronchiectasis

2010 ◽  
Vol 104 (6) ◽  
pp. 816-821 ◽  
Author(s):  
James D. Finklea ◽  
Gul Khan ◽  
Sheree Thomas ◽  
Juhee Song ◽  
Dennis Myers ◽  
...  
Keyword(s):  
Acute Exacerbation ◽  
Hospitalized Patients ◽  
Predictors Of Mortality

Early Predictors of Mortality in Geriatric Patients With Trauma

2016 ◽  
Vol 30 (9) ◽  
pp. e299-e304 ◽  
Author(s):  
Matthew S. Wilson ◽  
Sanjit R. Konda ◽  
Rachel B. Seymour ◽  
Madhav A. Karunakar
Keyword(s):  
Geriatric Patients ◽  
Predictors Of Mortality ◽  
Early Predictors

Early Predictors of Mortality After Spine Trauma

Spine ◽  
2013 ◽  
Vol 38 (2) ◽  
pp. 169-177 ◽  
Author(s):  
Jin W. Tee ◽  
Patrick C. H. Chan ◽  
Russell L. Gruen ◽  
Mark C. B. Fitzgerald ◽  
Susan M. Liew ◽  
...  
Keyword(s):  
Spine Trauma ◽  
Predictors Of Mortality ◽  
Early Predictors

scholarly journals Fatality rate and predictors of mortality in a large Italian cohort of hospitalized COVID-19 patients

2020 ◽  
Author(s):  
COVID-UPO Clinical Team ◽  
Pier Paolo Sainaghi
Keyword(s):  
Laboratory Data ◽  
Northern Italy ◽  
Hospitalized Patients ◽  
Fatality Rate ◽  
Multicenter Cohort Study ◽  
Current Smoking ◽  
Predictors Of Mortality ◽  
History Of ◽  
Diagnosis Of Cancer ◽  
Study Population

Abstract Substantial discrepancies are evident in the clinical features and natural history of coronavirus disease 2019 (COVID-19), among different countries, so we aimed to evaluate the fatality rate and to identify predictors of mortality in a cohort of hospitalized patients. We performed a retrospective multicenter cohort study on medical records of patients admitted between 1st March and 28th April 2020, involving three hospitals in Northern Italy. We included 1697 patients older than 18 years of age and with a confirmed diagnosis of SARS-CoV-2 infection by reverse-transcriptase polymerase chain reaction (RT-PCR). During the study period we observed 504 deaths, with a CFR of 29.7%. We further looked for predictors of mortality in a subset of 486 patients (239 males, 59%); the median age of the study population was 71 [58-80] years. Among demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were independent predictors of mortality. During the COVID-19 outbreak, the CFR of hospitalized patients in Northern Italy was high, approaching 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk.

scholarly journals 392. Predictors of Mortality in Hospitalized Patients with COVID-19; A Single Centered Retrospective Analysis

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S265-S266
Author(s):  
Farah N Harmouch ◽  
Kashyap Shah ◽  
Harsh Goel
Keyword(s):  
Logistic Regression ◽  
Multivariate Analysis ◽  
Retrospective Analysis ◽  
Inflammatory Markers ◽  
Clinical Characteristics ◽  
Conditional Logistic Regression ◽  
Hospitalized Patients ◽  
University Hospital ◽  
Categorical Variables ◽  
Predictors Of Mortality

Abstract Background The Coronavirus disease-2019 (COVID-19) has been responsible for the death of over 400,000 people with a continuous rise in prevalence and mortality globally. Identifying hospitalized patients at high mortality risk is critical for triage and health-care resource management regionally, nationally, and globally. We present a retrospective analysis of predictors of mortality in hospitalized COVID-19 patients. Methods Electronic health records (EHR) of patients admitted between March 1 and April 18, 2020 to St. Luke’s University Hospital with a primary diagnosis of COVID-19 were reviewed for medical co-morbidities and initial biochemical/inflammatory markers. Survivors vs non-survivors were compared using χ 2 test, Student’s t-test, and Mann-Whitney U-test as appropriate. Univariate logistic regression was used to identify candidate variables for multivariate analysis, which were then included in stepwise backward logistic regression. Statistical analyses were done on SPSS v26 software (IBM, Armonk, NY). Results Clinical characteristics, biochemical abnormalities and results of univariate regression in our cohort of 560 patients are noted in table 1. Multivariate regression revealed age, congestive heart failure (CHF), and creatinine≥ 1.5 mg/dl as significant predictors of mortality while race (Caucasian), vascular disease, lymphopenia, and elevated ferritin approached significance (Table 2). Table 1: Baseline clinical characteristics, overall and by mortality. Continuous variables are presented as median (25th-75th percentile), and categorical variables as n (%) Significance of difference between subgroups (survivors versus non-survivors) *p≤0.05, **p≤0.01, ***p≤0.001 Table 2: Results of stepwise backward conditional logistic regression for predicting mortality among hospitalized COVID-19 patients. (n=334, 287 survivors and 47 non-survivors). ALC – Absolute lymphocyte count, S.E. – Standard error of B. Conclusion We present one of the largest cohorts to date of hospitalized COVID-19 patients. Age, CHF, and renal disease were significant independent predictors of mortality. Though several inflammatory markers (d-dimer, CRP, procalcitonin) initially predicted mortality, they failed in multivariate analysis, questioning their role in risk-stratifying COVID-19 hospitalized patients. Interestingly, IL-6 used in those severely ill patients to assess candidacy for IL-6 inhibitor therapy (Tocilizumab) failed to predict mortality in our study. Our analysis was limited due to its retrospective nature and unfortunately large amounts of data were missing for some variables (ESR, BNP, IL-6 levels). The missing data was due to rapidly evolving institutional protocols early during the pandemic, leading to non-uniform assessment of these markers. Disclosures All Authors: No reported disclosures

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