Early predictors of in-hospital mortality in patients with COVID-19 in a large American cohort

Abstract

Coronavirus disease (COVID-19) has aggressively spread across the United States with numerous fatalities. Risk factors for mortality are poorly described. This was a multicentered cohort study identifying patient characteristics and diagnostic markers present on initial evaluation associated with mortality in hospitalized COVID-19 patients. Epidemiological, demographic, clinical, and laboratory characteristics of survivors and non-survivors were obtained from electronic medical records and a multivariable survival regression analysis was conducted to identify risk factors of in-hospital death. Of 1629 consecutive hospitalized adult patients with confirmed COVID-19 from March 1st thru March 31, 2020, 1461 patients were included in final analysis. 327 patients died during hospitalization and 1134 survived to discharge. Median age was 62 years (IQR 50.0, 74.0) with 56% of hospitalized patients under the age of 65. 47% were female and 63% identified as African American. Most patients (55%) had either no or one comorbidity. In multivariable analysis, older age, admission respiratory status including elevated respiratory rate and oxygen saturation ≤ 88%, and initial laboratory derangements of creatinine > 1.33 mg/dL, alanine aminotransferase > 40 U/L, procalcitonin > 0.5 ng/mL, and lactic acid ≥ 2 mmol/L increased risk of in-hospital death. This study is one of the largest analyses in an epicenter for the COVID-19 pandemic. Older age, low oxygen saturation and elevated respiratory rate on admission, and initial lab derangements including renal and hepatic dysfunction and elevated procalcitonin and lactic acid are risk factors for in-hospital death. These factors can help clinicians prognosticate and should be considered in management strategies.

Keywords: COVID-19; Mortality; Predictors; Scoring system.

Fig. 1

Flow diagram of study patients. Figure shows study inclusions and exclusions leading to the final cohort, and patient disposition

Fig. 2

Daily patient influx by unit type and discharge disposition over the course of the study period. Figure shows probing daily change on hospital admission, locations of admission and transfer-in, and hospital discharge. In the upper panel, blue vertical lines stacked with black lines indicate the cumulative number of admitted patients prior to a specific date (blue lines) and the increment of patients on a specific date (black lines) from March 1st to March 31st, 2020. Other colorful vertical lines (red; green; plum; orange) stacked with black lines indicate the cumulative number on death, home discharge, skilled nursing home (SNF) discharge, and hospice discharge, respectively, prior to a specific date (red lines; green lines; plum lines; orange lines) and the increment of new occurrence (black lines) on a specific date until April 23rd, 2020. Similarly, in the bottom panel, colorful vertical lines (dark gray; gray; brown) stacked with black lines indicate the cumulative observations of admission/transfer-in to regular floor, progressive care, and intensive care unit (ICU), respectively, prior to a specific date (dark gray lines; gray lines; brown lines) and number of occurrence (black lines) on a specific date until April 23rd, 2020. Figure shows a daily influx of patients by unit type (Regular floor, Progressive floor, ICU) including admissions from the ED and transfers within the hospital and hospital system. Daily efflux of patients is also captured with discharge disposition including death, discharge to home, hospice, and skilled nursing facility/ rehabilitation units

Fig. 3

Kaplan–Meier survival curve for mortality for three risk groups. Figure shows overall survival of study patients associated with three risk groups (score: ≤ 12, 13 to 26, ≥ 27) during study period. The estimated survival cures were pooled from 20 imputed datasets