Key result
Dynamic EHR-based model accurately predicts hospital-acquired stage 3 AKI with ~0.86 validation AUC.
Why the study?
Existing models predicting hospital-acquired AKI in non-critically ill patients fail to identify those at risk of severe HA-AKI stage 3.
Cohort (n=209,462)
Yes
Effect estimate: AUC 0.906 (95% CI 0.904-0.908)
The development and external validation of this dynamic EHR-based predictive model enables real-time, accurate individual risk assessment for severe hospital-acquired AKI in non-critically ill patients.
May aid real-time HA-AKI risk stratification in non-critically ill inpatients; leaves open prospective trials of clinical impact.
Background. The current models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill fail to identify the patients at risk of severe HA-AKI stage 3. Objective. To develop and externally validate a model to predict the individual probability of developing HA-AKI stage 3 through the integration of electronic health databases. Methods. Study set: 165,893 non-critically ill hospitalized patients. Using stepwise logistic regression analyses, including demography, chronic comorbidities, and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI stage 3. This model was then externally validated in 43,569 non-critical patients admitted to the validation center. Results. The incidence of HA-AKI stage 3 in the study set was 0.6%. Among chronic comorbidities, the highest odds ratios were conferred by ischemic heart disease, ischemic cerebrovascular disease, chronic congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, major surgery and exposure to nephrotoxic drugs. The model showed an AUC of 0.906 (95% CI 0.904 to 0.908), a sensitivity of 89.1 (95% CI 87.0–91.0) and a specificity of 80.5 (95% CI 80.2–80.7) to predict HA-AKI stage 3, but tended to overestimate the risk at low-risk categories with an adequate goodness-of-fit for all risk categories (Chi2: 16.4, p: 0.034). In the validation set, incidence of HA-AKI stage 3 was 0.62%. The model showed an AUC of 0.861 (95% CI 0.859–0.863), a sensitivity of 83.0 (95% CI 80.5–85.3) and a specificity of 76.5 (95% CI 76.2–76.8) to predict HA-AKI stage 3 with an adequate goodness of fit for all risk categories (Chi2: 15.42, p: 0.052). Conclusions. Our study provides a model that can be used in clinical practice to obtain an accurate dynamic assessment of the individual risk of HA-AKI stage 3 along the hospital stay period in non-critically ill patients.
No takes yet. Share an insight, caveat, or question.
Carpio et al. (2021) conducted a cohort in Hospital-acquired acute kidney injury (HA-AKI) stage 3 (n=209,462). Clinical risk factors (demographics, comorbidities, acute complications) was evaluated on Prediction of hospital-acquired AKI stage 3 (model performance) (AUC 0.906, 95% CI 0.904-0.908). A dynamic electronic health record-based model accurately predicted hospital-acquired AKI stage 3, showing an AUC of 0.906 in the derivation cohort and 0.861 in the external validation cohort.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: