In-Hospital Mortality and Prediction in an Urban U.S. Population With COVID-19.

Reynold Panettieri,Abhishek Bhurwal,Carolyn Catalano,You Li,Kapil Gupta,Carlos D Minacapelli,Vinod Rustgi,Michael Makar

doi:10.7759/cureus.11786

Abstract

Coronavirus disease 2019 (COVID-19) has touched every aspect of society, and as the pandemic continues around the globe, many of the clinical factors that influence the disease course remain unclear. A useful clinical decision-making tool is a risk stratification model to determine in-hospital mortality as defined in this study. The study was performed at Robert Wood Johnson University Hospital (RWJUH) in New Brunswick, New Jersey, USA. Data was extracted from our electronic medical records on 44 variables that included demographic, clinical, laboratory tests, treatments, and mortality information. We used the least absolute shrinkage and selection operator regression with corrected Akaike’s information criterion to identify a subset of variables that yielded the smallest estimated prediction error for the risk of in-hospital mortality. During the study period, 808 COVID-19 patients were admitted to RWJUH. The sample size was limited to patients with at least one confirmed in-house positive nasopharyngeal swab COVID-19 test. Pregnant patients or those who were transferred to our facility were excluded. Patients who were in observation and were discharged from the emergency room were also excluded. A total of 403 patients had complete values for all variables and were eligible for the study. We identified significant clinical, laboratory, and radiologic variables determining severe outcomes and mortality. An in-hospital mortality risk calculator was created after the identification of significant factors for the specific cohort, which were abnormal CT scan or chest X-ray, chronic kidney disease, age, white blood cell count, platelet count, alanine aminotransferase, and aspartate transaminase with a sensitivity, specificity, and negative predictive value of 82%, 72%, and 93%, respectively. While numerous reports from around the globe have helped outline the pandemic, demographic factors vary widely. This study is more applicable to an urban, highly diverse population in the United States.

Highlights

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1], previously known as the 2019 novel coronavirus (2019-nCoV) [2], has wreaked havoc
An in-hospital mortality risk calculator was created after the identification of significant factors for the specific cohort, which were abnormal computed tomography (CT) scan or chest X-ray, chronic kidney disease, age, white blood cell count, platelet count, alanine aminotransferase, and aspartate transaminase with a sensitivity, specificity, and negative predictive value of 82%, 72%, and 93%, respectively
Clinicians, scientists, data scientists, vaccine experts, public policy specialists, and others, as well as the highest levels of governments globally are focused on COVID-19 [3] as it has touched every aspect of society

Summary

Introduction

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1], previously known as the 2019 novel coronavirus (2019-nCoV) [2], has wreaked havoc. Coronaviruses are enveloped, positive-sense single-stranded RNA viruses that are classified together on the basis of the crown or halo-like appearance of the spike envelope glycoproteins [4]. The name is derived from the Latin word corona, which means crown. Seven human coronaviruses have been identified, and based on the published information, SARS-CoV-2 is the third zoonotic human coronavirus of the century [5]. This new agent causes symptoms ranging from a dry cough to dyspnea to a syndrome with protean manifestations including severe respiratory distress, thrombotic conditions, and other clinical problems that are still being identified [6,7]

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