Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, but validated, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI in hospitalized patients with coronavirus disease 2019 and assess its performance over time with the emergence of vaccines and the Delta variant.

STUDY DESIGN

Longitudinal cohort study.

SETTING & PARTICIPANTS: Hospitalized patients with a positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction result between March 1, 2020, and August 20, 2021 at 19 hospitals in Texas.

EXPOSURES

Comorbid conditions, baseline laboratory data, inflammatory biomarkers.

OUTCOMES

AKI defined by KDIGO (Kidney Disease: Improving Global Outcomes) creatinine criteria.

ANALYTICAL APPROACH

Three nested models for AKI were built in a development cohort and validated in 2 out-of-time cohorts. Model discrimination and calibration measures were compared among cohorts to assess performance over time.

RESULTS

Of 10,034 patients, 5,676, 2,917, and 1,441 were in the development, validation 1, and validation 2 cohorts, respectively, of whom 776 (13.7%), 368 (12.6%), and 179 (12.4%) developed AKI, respectively ( = 0.26). Patients in the validation cohort 2 had fewer comorbid conditions and were younger than those in the development cohort or validation cohort 1 (mean age, 54 ± 16.8 years vs 61.4 ± 17.5 and 61.7 ± 17.3 years, respectively,  < 0.001). The validation cohort 2 had higher median high-sensitivity C-reactive protein level (81.7 mg/L) versus the development cohort (74.5 mg/L;  < 0.01) and higher median ferritin level (696 ng/mL) versus both the development cohort (444 ng/mL) and validation cohort 1 (496 ng/mL;  < 0.001). The final model, which added high-sensitivity C-reactive protein, ferritin, and D-dimer levels, had an area under the curve of 0.781 (95% CI, 0.763-0.799). Compared with the development cohort, discrimination by area under the curve (validation 1: 0.785 [0.760-0.810],  = 0.79, and validation 2: 0.754 [0.716-0.795],  = 0.53) and calibration by estimated calibration index (validation 1: 0.116 [0.041-0.281],  = 0.11, and validation 2: 0.081 [0.045-0.295],  = 0.11) showed stable performance over time.

LIMITATIONS

Potential billing and coding bias.

CONCLUSIONS

We developed and externally validated a model to accurately predict AKI in patients with coronavirus disease 2019. The performance of the model withstood changes in practice patterns and virus variants.