Key result
Machine-learning models predict normal potassium to safely identify ~27% of blood draws as avoidable.
Why the study?
The study was conducted to create a machine-learning model identifying potentially avoidable blood draws for serum potassium in pediatric patients after cardiac surgery.
Does a machine-learning model accurately identify potentially avoidable serum potassium blood draws in pediatric patients in the cardiac ICU following cardiac surgery?
Cohort (n=6,196)
No
Does a machine-learning model accurately identify potentially avoidable serum potassium blood draws in pediatric patients in the cardiac ICU following cardiac surgery?
Machine-learning models can accurately identify normal serum potassium levels in pediatric cardiac ICU patients, potentially avoiding 27.2% of routine blood draws and reducing associated costs, anemia, and infection risks.
Answers are grounded in this paper and cite their sources.
ML model may reduce unnecessary potassium testing post-pediatric cardiac surgery; leaves open prospective validation before clinical adoption.
Objectives: To create a machine-learning model identifying potentially avoidable blood draws for serum potassium among pediatric patients following cardiac surgery. Design: Retrospective cohort study. Setting: Tertiary-care center. Patients: All patients admitted to the cardiac ICU at Boston Children’s Hospital between January 2010 and December 2018 with a length of stay greater than or equal to 4 days and greater than or equal to two recorded serum potassium measurements. Interventions: None. Measurements and Main Results: We collected variables related to potassium homeostasis, including serum chemistry, hourly potassium intake, diuretics, and urine output. Using established machine-learning techniques, including random forest classifiers, and hyperparameter tuning, we created models predicting whether a patient’s potassium would be normal or abnormal based on the most recent potassium level, medications administered, urine output, and markers of renal function. We developed multiple models based on different age-categories and temporal proximity of the most recent potassium measurement. We assessed the predictive performance of the models using an independent test set. Of the 7,269 admissions (6,196 patients) included, serum potassium was measured on average of 1 (interquartile range, 0–1) time per day. Approximately 96% of patients received at least one dose of IV diuretic and 83% received a form of potassium supplementation. Our models predicted a normal potassium value with a median positive predictive value of 0.900. A median percentage of 2.1% measurements (mean 2.5%; interquartile range, 1.3–3.7%) was incorrectly predicted as normal when they were abnormal. A median percentage of 0.0% (interquartile range, 0.0–0.4%) critically low or high measurements was incorrectly predicted as normal. A median of 27.2% (interquartile range, 7.8–32.4%) of samples was correctly predicted to be normal and could have been potentially avoided. Conclusions: Machine-learning methods can be used to predict avoidable blood tests accurately for serum potassium in critically ill pediatric patients. A median of 27.2% of samples could have been saved, with decreased costs and risk of infection or anemia.
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Patel et al. (2020) conducted a cohort in Pediatric cardiac surgery admissions in the cardiac ICU (n=6,196). Machine-learning model (random forest classifiers) predicting normal vs abnormal serum potassium was evaluated on Prediction of normal serum potassium values and potentially avoidable blood draws (Median PPV 0.900). Machine-learning models predicted normal serum potassium with a median PPV of 0.900, identifying a median of 27.2% of blood draws as potentially avoidable.
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