A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children.

Significance Statement Because AKI in hospitalized children is associated with poor outcomes, a tool allowing early identification of children at risk of developing AKI may facilitate timely interventions. The authors describe various machine learning techniques used to build a parsimonious model predictive of pediatric AKI. From an initial pool of 720 potential variables, they evaluated multiple feature selection techniques to create a ten-feature logistic regression model that could predict, in time-updated fashion, the risk of AKI in the next 48 hours. A machine learning-based genetic algorithm (reflecting the process of natural selection) was the best variable selection method, using ten factors extracted from electronic health records to use for AKI prediction. Risk-stratifying hospitalized children might allow clinicians to implement targeted and timely interventions prior to AKI development. Background Timely prediction of AKI in children can allow for targeted interventions, but the wealth of data in the electronic health record poses unique modeling challenges. Methods We retrospectively reviewed the electronic medical records of all children younger than 18 years old who had at least two creatinine values measured during a hospital admission from January 2014 through January 2018. We divided the study population into derivation, and internal and external validation cohorts, and used five feature selection techniques to select 10 of 720 potentially predictive variables from the electronic health records. Model performance was assessed by the area under the receiver operating characteristic curve in the validation cohorts. The primary outcome was development of AKI (per the Kidney Disease Improving Global Outcomes creatinine definition) within a moving 48-hour window. Secondary outcomes included severe AKI (stage 2 or 3), inpatient mortality, and length of stay. Results Among 8473 encounters studied, AKI occurred in 516 (10.2%), 207 (9%), and 27 (2.5%) encounters in the derivation, and internal and external validation cohorts, respectively. The highest-performing model used a machine learning-based genetic algorithm, with an overall receiver operating characteristic curve in the internal validation cohort of 0.76 [95% confidence interval (CI), 0.72 to 0.79] for AKI, 0.79 (95% CI, 0.74 to 0.83) for severe AKI, and 0.81 (95% CI, 0.77 to 0.86) for neonatal AKI. To translate this prediction model into a clinical risk-stratification tool, we identified high- and low-risk threshold points. Conclusions Using various machine learning algorithms, we identified and validated a time-updated prediction model of ten readily available electronic health record variables to accurately predict imminent AKI in hospitalized children.