A biomarker-based age, biomarkers, clinical history, sex (ABCS)-mortality risk score for patients with coronavirus disease 2019.

backgroundEarly identification and timely therapeutic strategies for potential critical patients with coronavirus disease 2019 (COVID-19) are of crucial importance to reduce mortality. We aimed to develop and validate a prediction tool for 30-day mortality for these patients on admission.

methodsConsecutive hospitalized patients admitted to Tongji Hospital and Hubei Xinhua Hospital from January 1 to March 10, 2020, were retrospective analyzed. They were grouped as derivation and external validation set. Multivariate Cox regression was applied to identify the risk factors associated with death, and a nomogram was developed and externally validated by calibration plots, C-index, Kaplan-Meier curves and decision curve.

resultsData from 1,717 patients at the Tongji Hospital and 188 cases at the Hubei Xinhua Hospital were included in our study. Using multivariate Cox regression with backward stepwise selection of variables in the derivation cohort, age, sex, chronic obstructive pulmonary disease (COPD), as well as seven biomarkers (aspartate aminotransferase, high-sensitivity C-reactive protein, high-sensitivity troponin I, white blood cell count, lymphocyte count, D-dimer, and procalcitonin) were incorporated in the model. An age, biomarkers, clinical history, sex (ABCS)-mortality score was developed, which yielded a higher C-index than the conventional CURB-65 score for predicting 30-day mortality in both the derivation cohort {0.888 [95% confidence interval (CI), 0.869-0.907]

conclusionsThe ABCS-mortality score might be offered to clinicians to strengthen the prognosis-based clinical decision-making, which would be helpful for reducing mortality of COVID-19 patients.