Clinical pathology selected abstracts

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Editor: Deborah Sesok-Pizzini, MD, MBA, chief medical officer, Labcorp Diagnostics, Burlington, NC, and adjunct professor, Department of Clinical Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.

Risk estimation of severe COVID-19 based on biomarker assessment across demographics

January 2024—People respond differently to SARS-CoV-2 infection, with some having a very severe clinical course and sequelae while others recover quickly. Several research studies have used laboratory data to identify patient populations most at risk for severe outcome from COVID-19. However, many of these studies were conducted in China and did not represent the demographics of the U.S. population. Among the drawbacks of these studies were that most analyzed variance between two patient groups, yet statistical differences don’t always correlate with clinically useful predictions. Furthermore, these studies used data from throughout patients’ disease course, and clinicians would like to identify patients at risk during their initial interaction. The authors of this study sought to determine which demographic, clinical, and laboratory variables, at the time of initial patient contact, may help predict severe versus mild COVID-19. They studied patients from a large integrated health care delivery network that included four hospitals. Deidentified patient data were collected retrospectively for all patients who tested positive or negative for SARS-CoV-2 using a polymerase chain reaction assay from March 2020 through September 2021. The authors studied data from 14,147 patients and analyzed 58 variables, including demographics, clinical parameters, and biomarker test results. Four statistical models—inclusive, receiver operating characteristic, specific, and sensitive—were generated using backward stepwise logistic regression to predict severe disease (death or 90 or more hospital days) versus mild disease (alive and less than one hospital day). The authors found that of the 14,147 patients, including whites and Blacks and people of Hispanic ethnicity, 2,546 (18 percent) had severe outcomes and 3,395 (24 percent) had mild outcomes. The testing parameters present in all models were age, albumin, diastolic blood pressure, ferritin, lactate dehydrogenase, socioeconomic status, procalcitonin, B-type natriuretic peptide, and platelet count. The authors concluded that the biomarkers in the sensitive and specific statistical models are most useful to health care providers when they initially evaluate the severity of COVID-19. Moreover, most of the tests, including albumin, C-reactive protein, D-dimer, ferritin, procalcitonin, and platelet count, are inexpensive and readily available. Variables such as race, ethnicity, and clinical parameters did not further inform the modeling.

Kroll MH, Bi C, Salm AE, et al. Risk estimation of severe COVID-19 based on initial biomarker assessment across racial and ethnic groups. Arch Pathol Lab Med. 2023;147:1109–1118.

Correspondence: Dr. Hema Kapoor at hemaxkapoor@gmail.com