Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.

BACKGROUND: Acute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines. METHODS: EHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predictors, such as creatinine rate of change, to automatically assess real-time AKI risk. The primary outcome is prediction of moderate to severe AKI (Stage 2/3), and secondary outcomes are prediction of any AKI (Stage 1/2/3) and requirement of renal replacement therapy (RRT). Predictions generate alerts allowing fast assessment and reduction of AKI risk, such as: "patient has 90% risk of developing AKI in the next 48 h" along with contextual information and suggested response such as "patient on aminoglycosides, suggest check level and review dose and indication". RESULTS: The model was successful in predicting Stage 2/3 AKI prior to detection by conventional criteria with a median lead-time of 30 h at AUROC of 0.89. The model predicted 70% of subsequent RRT episodes, 58% of Stage 2/3 episodes, and 41% of any AKI episodes. The ratio of false to true alerts of any AKI episodes was approximately one-to-one (PPV 47%). Among patients predicted, 79% received potentially nephrotoxic medication after being identified by the model but before development of AKI. CONCLUSIONS: As the first multi-center validated AKI prediction model for all pediatric critical care patients, the machine learning model described in this study accurately predicts moderate to severe AKI up to 48 h in advance of AKI onset. The model may improve outcome of pediatric AKI by providing early alerting and actionable feedback, potentially preventing or reducing AKI by implementing early measures such as medication adjustment.

Impact of computerized orders for pediatric continuous drug infusions on detecting infusion pump programming errors: a simulated study.

Continuous infusion medications are associated with fatal adverse events in pediatric intensive care units. The effect of computerized orders on detecting infusion pumps programming errors has never been studied. Using a crossover design, we examined the effect of using computerized orders for continuous infusions as compared with that of using handwritten orders on nurse ability to detect infusion pump programming errors, time required to verify pump settings, and user satisfaction. The computerized orders saved nurses time but did not improve their ability to detect infusion pumps programming errors. Nurses preferred computerized orders. High error rate was related to manual calculations and inconsistent use of computerized orders.

Computerized orders with standardized concentrations decrease dispensing errors of continuous infusion medications for pediatrics.

OBJECTIVES: The use of continuous infusion medications with individualized concentrations may increase the risk for errors in pediatric patients. The objective of this study was to evaluate the effect of computerized prescriber order entry (CPOE) for continuous infusions with standardized concentrations on frequency of pharmacy processing errors. In addition, time to process handwritten versus computerized infusion orders was evaluated and user satisfaction with CPOE as compared to handwritten orders was measured. METHODS: Using a crossover design, 10 pharmacists in the pediatric satellite within a university teaching hospital were given test scenarios of handwritten and CPOE order sheets and asked to process infusion orders using the pharmacy system in order to generate infusion labels. Participants were given three groups of orders: five correct handwritten orders, four handwritten orders written with deliberate errors, and five correct CPOE orders. Label errors were analyzed and time to complete the task was recorded. RESULTS: Using CPOE orders, participants required less processing time per infusion order (2 min, 5 sec ± 58 sec) compared with time per infusion order in the first handwritten order sheet group (3 min, 7 sec ± 1 min, 20 sec) and the second handwritten order sheet group (3 min, 26 sec ± 1 min, 8 sec), (p<0.01). CPOE eliminated all error types except wrong concentration. With CPOE, 4% of infusions processed contained errors, compared with 26% of the first group of handwritten orders and 45% of the second group of handwritten orders (p<0.03). Pharmacists were more satisfied with CPOE orders when compared with the handwritten method (p=0.0001). CONCLUSIONS: CPOE orders saved pharmacists' time and greatly improved the safety of processing continuous infusions, although not all errors were eliminated. pharmacists were overwhelmingly satisfied with the CPOE orders.