A Model Predicting Mortality of Hospitalized Covid-19 Patients Four Days After Admission: Development, Internal and Temporal-External Validation

Abstract

Objective: To develop and validate a prognostic model for in-hospital mortality after four days based on age, fever at admission and five haematological parameters routinely measured in hospitalized Covid-19 patients during the first four days after admission.

Methods: Haematological parameters measured during the first 4 days after admission were subjected to a linear mixed model to obtain patient-specific intercepts and slopes for each parameter. A prediction model was built using logistic regression with variable selection and shrinkage factor estimation supported by bootstrapping. Model development was based on 481 survivors and 97 non-survivors, hospitalized before the occurrence of mutations. Internal validation was done by 10-fold cross-validation. The model was temporally-externally validated in 299 survivors and 42 non-survivors hospitalized when the Alpha variant (B.1.1.7) was prevalent.

Results: The final model included age, fever on admission as well as the slope or intercept of lactate dehydrogenase, platelet count, C-reactive protein, and creatinine. Tenfold cross validation resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.92, a mean calibration slope of 1.0023 and a Brier score of 0.076. At temporal-external validation, application of the previously developed model showed an AUROC of 0.88, a calibration slope of 0.95 and a Brier score of 0.073. Regarding the relative importance of the variables, the (apparent) variation in mortality explained by the six variables deduced from the haematological parameters measured during the first four days is higher (explained variation 0.295) than that of age (0.210).

Conclusions: The presented model requires only variables routinely acquired in hospitals, which allows immediate and wide-spread use as a decision support for earlier discharge of low-risk patients to reduce the burden on the health care system.

Clinical trial registration: Austrian Coronavirus Adaptive Clinical Trial (ACOVACT); ClinicalTrials.gov, identifier NCT04351724.

Keywords: COVID-19; blood parameter; hospitalized patients; logistic regression; prediction model; survival.

Figure 1

Model development and validation strategy.

Figure 2

Model development. Candidate variables in bold were selected as they remained in the model after backward elimination in more than 50% of all bootstrap samples. Coefficients were shrunk according to a linear shrinkage factor, which was 0.7974 (Steyerberg et al., 2001). The constant was recalibrated that the mean estimated mortality probability corresponded to the mortality in the training sample. The boxplots show median, interquartile range as well as 10^th and 90^th percentile of continuous candidate variables or percentages for fever on admission according to outcome.

Figure 3

Model performance. Discrimination and calibration in another cohort admitted to hospital while the B.1.1.7/Alpha variant of SARS CoV-2 was widespread. Fav., Clinic Favoriten; Kep., Kepler University Clinics.