Derivation and external validation of a simple risk score for predicting severe acute kidney injury after intravenous cisplatin: cohort study.

OBJECTIVE: To develop and externally validate a prediction model for severe cisplatin associated acute kidney injury (CP-AKI). DESIGN: Multicenter cohort study. SETTING: Six geographically diverse major academic cancer centers across the US. PARTICIPANTS: Adults (≥18 years) receiving their first dose of intravenous cisplatin, 2006-22. MAIN OUTCOME MEASURES: The primary outcome was CP-AKI, defined as a twofold or greater increase in serum creatinine or kidney replacement therapy within 14 days of a first dose of intravenous cisplatin. Independent predictors of CP-AKI were identified using a multivariable logistic regression model, which was developed in a derivation cohort and tested in an external validation cohort. For the primary model, continuous variables were examined using restricted cubic splines. A simple risk model was also generated by converting the odds ratios from the primary model into risk points. Finally, a multivariable Cox model was used to examine the association between severity of CP-AKI and 90 day survival. RESULTS: A total of 24 717 adults were included, with 11 766 in the derivation cohort (median age 59 (interquartile range (IQR) 50-67)) and 12 951 in the validation cohort (median age 60 (IQR 50-67)). The incidence of CP-AKI was 5.2% (608/11 766) in the derivation cohort and 3.3% (421/12 951) in the validation cohort. Each of the following factors were independently associated with CP-AKI in the derivation cohort: age, hypertension, diabetes mellitus, serum creatinine level, hemoglobin level, white blood cell count, platelet count, serum albumin level, serum magnesium level, and cisplatin dose. A simple risk score consisting of nine covariates was shown to predict a higher risk of CP-AKI in a monotonic fashion in both the derivation cohort and the validation cohort. Compared with patients in the lowest risk category, those in the highest risk category showed a 24.00-fold (95% confidence interval (CI) 13.49-fold to 42.78-fold) higher odds of CP-AKI in the derivation cohort and a 17.87-fold (10.56-fold to 29.60-fold) higher odds in the validation cohort. The primary model had a C statistic of 0.75 and showed better discrimination for CP-AKI than previously published models, the C statistics for which ranged from 0.60 to 0.68 (DeLong P<0.001 for each comparison). Greater severity of CP-AKI was monotonically associated with shorter 90 day survival (adjusted hazard ratio 4.63 (95% CI 3.56 to 6.02) for stage 3 CP-AKI versus no CP-AKI). CONCLUSION: This study found that a simple risk score based on readily available variables from patients receiving intravenous cisplatin could predict the risk of severe CP-AKI, the occurrence of which is strongly associated with death.

Differential impacts of COVID-19 lockdowns on PM 2.5 across the United States.

The COVID-19 pandemic has induced large-scale behavioral changes, presenting a unique opportunity to study how air pollution is affected by societal shifts. At 455 PM 2.5 monitoring sites across the United States, we conduct a causal inference analysis to determine the impacts of COVID-19 lockdowns on PM 2.5 . Our approach allows for rigorous confounding adjustment with highly spatio-temporally resolved effect estimates. We find that, with the exception of the Southwest, most of the US experienced increases in PM 2.5 compared to concentrations expected under business-as-usual. To investigate possible drivers of this phenomenon, we use a regression model to characterize the relationship of various factors with the observed impacts. Our findings have immense environmental policy relevance, suggesting that mobility reductions alone may be insufficient to substantially and uniformly reduce PM 2.5 .