A Machine Learning Model to Predict Diuretic Resistance. Issue 1 (January 2023)

• Record Type: Journal Article
• Authors: Mercier, Joey A.; Ferguson, Thomas W.; Tangri, Navdeep
• Is Part Of: Kidney360. Volume 4:Issue 1(2023)
• Page Start: 15
• Page End: 22
• Publication Date: 2023-01
• Subjects: acute kidney injury and ICU nephrology -- diuretics -- loop diuretic resistance -- machine learning -- prediction; 616.61
• DOI: 10.34067/KID.0005562022
• Languages: English
• ISSNs: 2641-7650

Abstract

Key Points: Our machine learning algorithm was able to quickly predict those at risk for diuretic resistance using common variables. Our machine learning algorithm could easily be implemented within electrical medical software (or as an online calculator). Our study provides a proof of concept/framework for the development of future machine learning models. Abstract : Background: Volume overload is a common complication encountered in hospitalized patients, and the mainstay of therapy is diuresis. Unfortunately, the diuretic response in some individuals is inadequate despite a typical dose of loop diuretics, a phenomenon called diuretic resistance. An accurate prediction model that predicts diuretic resistance using predosing variables could inform the right diuretic dose for a prospective patient. Methods: Two large, deidentified, publicly available, and independent intensive care unit (ICU) databases from the United States were used—the Medical Information Mart for Intensive Care III (MIMIC) and the Philips eICU databases. Loop diuretic resistance was defined as <1400 ml of urine per 40 mg of diuretic dose in 24 hours. Using 24-hour windows throughout admission, commonly accessible variables were obtained and incorporated into the model. Data imputation was performed using a highly accurate machine learning method. Using XGBoost, several models were created using train and test datasets from the eICU database. These were then combined into an ensemble model optimized for increased specificity and then externally validated on the MIMIC database. Results: The final ensemble model was composed of four separate models, each using 21 commonly available variables. The ensemble model outperformed individual models during validation. Higher serum creatinine, lower systolic blood pressure, lower serum chloride, higher age, and female sex were the most important predictors of diuretic resistance (in that order). The specificity of the model on external validation was 92%, yielding a positive likelihood ratio of 3.46 while maintaining overall discrimination (C-statistic 0.69). Conclusions: A diuretic resistance prediction model was created using machine learning and was externally validated in ICU populations. The model is easy to use, would provide actionable information at the bedside, and would be ready for implementation in existing electronic medical records. This study also provides a framework for the development of future machine learning models.