Six-Month Predictive Value of Diuretic Resistance Formulas in Discharged Heart Failure Patients after an Acute Decompensation

Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database

Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599-0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.

A Machine Learning Model to Predict Diuretic Resistance

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.

Toward quantification of loop diuretic responsiveness for congestive heart failure

Diuretics, such as furosemide, are routinely administered to dogs with congestive heart failure (CHF). Traditionally, dose and determination of efficacy primarily are based on clinical signs rather than quantitative measures of drug action. Treatment of human CHF patients increasingly is guided by quantification of urine sodium concentration (uNa) and urine volume after diuretic administration. Use of these and other measures of diuretic responsiveness is associated with decreased duration of hospitalization, complication rates, future rehospitalization, and mortality. At their core, loop diuretics act through natriuresis, and attention to body sodium (Na) stores and handling offers insight into the pathophysiology of CHF and pharmacology of diuretics beyond what is achievable from clinical signs alone. Human patients with low diuretic responsiveness or diuretic resistance are at risk for difficult or incomplete decongestion that requires diuretic intensification or other remedial strategies. Identification of the specific etiology of resistance in a patient can help tailor personalized interventions. In this review, we advance the concept of loop diuretic responsiveness by highlighting Na and natriuresis. Specifically, we review body water homeostasis and congestion in light of the increasingly recognized role of interstitial Na, propose definitions for diuretic responsiveness and resistance in veterinary subjects, review relevant findings of recent studies, explain how the particular cause of resistance can guide treatment, and identify current knowledge gaps. We believe that a quantitative approach to loop diuretic usage primarily involving natriuresis will advance our understanding and care of dogs with CHF.

Correction: Feola, M., et al. Six-Month Predictive Value of Diuretic Resistance Formulas in Discharged Heart Failure Patients after an Acute Decompensation. J. Clin. Med. 2020, 9, 2932

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