A Novel Composite Indicator of Predicting Mortality Risk for Heart Failure Patients With Diabetes Admitted to Intensive Care Unit Based on Machine Learning

Association between Red Blood Cell Distribution Width and In-Hospital Mortality among Congestive Heart Failure Patients with Diabetes among Patients in the Intensive Care Unit: A Retrospective Cohort Study

Background

Elevated red blood cell distribution width (RDW) levels are strongly associated with an increased risk of mortality in patients with congestive heart failure (CHF). Additionally, heart failure has been closely linked to diabetes. Nevertheless, the relationship between RDW and in-hospital mortality in the intensive care unit (ICU) among patients with both congestive heart failure (CHF) and diabetes mellitus (DM) remains uncertain.

Methods

This retrospective study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, a comprehensive critical care repository. RDW was assessed as both continuous and categorical variables. The primary outcome of the study was in-hospital mortality at the time of hospital discharge. We examined the association between RDW on ICU admission and in-hospital mortality using multivariable logistic regression models, restricted cubic spline analysis, and subgroup analysis.

Results

The cohort consisted of 7,063 patients with both DM and CHF (3,135 females and 3,928 males). After adjusting for potential confounders, we found an association between a 9% increase in mortality rate and a 1 g/L increase in RDW level (OR = 1.09; 95% CI, 1.05∼1.13), which was associated with 11 and 58% increases in mortality rates in Q2 (OR = 1.11, 95% CI: 0.87∼1.43) and Q3 (OR = 1.58, 95% CI: 1.22∼2.04), respectively, compared with that in Q1. Moreover, we observed a significant linear association between RDW and in-hospital mortality, along with strong stratified analyses to support the findings.

Conclusions

Our findings establish a positive association between RDW and in-hospital mortality in patients with DM and CHF.

An Overview of Diabetic Cardiomyopathy

Diabetic cardiomyopathy (DCM) is a myocardial disorder that is characterised by structural and functional abnormalities of the heart muscle in the absence of hypertension, valvular heart disease, congenital heart defects, or coronary artery disease (CAD). After witnessing a particular form of cardiomyopathy in diabetic individuals, Rubler et al. came up with the moniker diabetic cardiomyopathy in 1972. Four stages of DCM are documented, and the American College of Cardiology/American Heart Association Stage and New York Heart Association Class for HF have some overlap. Diabetes is linked to several distinct forms of heart failure. Around 40% of people with heart failure with preserved ejection fraction (HFpEF) have diabetes, which is thought to be closely associated with the pathophysiology of HFpEF. Diabetes and HF are uniquely associated in a bidirectional manner. When compared to the general population without diabetes, those with diabetes have a risk of heart failure that is up to four times higher. A biomarker is a trait that is reliably measured and assessed as a predictor of healthy biological activities, pathological processes, or pharmacologic responses to a clinical treatment. Several biomarker values have been discovered to be greater in patients with diabetes than in control subjects among those who have recently developed heart failure. Myocardial fibrosis and hypertrophy are the primary characteristics of DCM, and structural alterations in the diabetic myocardium are often examined by non-invasive, reliable, and reproducible procedures. An invasive method called endomyocardial biopsy (EMB) is most often used to diagnose many cardiac illnesses.

Predicting 1-year mortality of patients with diabetes mellitus in Kazakhstan based on administrative health data using machine learning

Diabetes mellitus (DM) affects the quality of life and leads to disability, high morbidity, and premature mortality. DM is a risk factor for cardiovascular, neurological, and renal diseases, and places a major burden on healthcare systems globally. Predicting the one-year mortality of patients with DM can considerably help clinicians tailor treatments to patients at risk. In this study, we aimed to show the feasibility of predicting the one-year mortality of DM patients based on administrative health data. We use clinical data for 472,950 patients that were admitted to hospitals across Kazakhstan between mid-2014 to December 2019 and were diagnosed with DM. The data was divided into four yearly-specific cohorts (2016-, 2017-, 2018-, and 2019-cohorts) to predict mortality within a specific year based on clinical and demographic information collected up to the end of the preceding year. We then develop a comprehensive machine learning platform to construct a predictive model of one-year mortality for each year-specific cohort. In particular, the study implements and compares the performance of nine classification rules for predicting the one-year mortality of DM patients. The results show that gradient-boosting ensemble learning methods perform better than other algorithms across all year-specific cohorts while achieving an area under the curve (AUC) between 0.78 and 0.80 on independent test sets. The feature importance analysis conducted by calculating SHAP (SHapley Additive exPlanations) values shows that age, duration of diabetes, hypertension, and sex are the top four most important features for predicting one-year mortality. In conclusion, the results show that it is possible to use machine learning to build accurate predictive models of one-year mortality for DM patients based on administrative health data. In the future, integrating this information with laboratory data or patients' medical history could potentially boost the performance of the predictive models.

Prediction of 30-day mortality in heart failure patients with hypoxic hepatitis: Development and external validation of an interpretable machine learning model

Background

This study aimed to explore the impact of hypoxic hepatitis (HH) on survival in heart failure (HF) patients and to develop an effective machine learning model to predict 30-day mortality risk in HF patients with HH.

Methods

In the Medical Information Mart for Intensive Care (MIMIC)-III and IV databases, clinical data and survival situations of HF patients admitted to the intensive care unit (ICU) were retrospectively collected. Propensity Score Matching (PSM) analysis was used to balance baseline differences between HF patients with and without HH. Kaplan Meier analysis and multivariate Cox analysis were used to determining the effect of HH on the survival of CF patients. For developing a model that can predict 30-day mortality in CF patients with HH, the feature recurrence elimination (RFE) method was applied to feature selection, and seven machine learning algorithms were employed to model construction. After training and hyper-parameter optimization (HPO) of the model through cross-validation in the training set, a performance comparison was performed through internal and external validation. To interpret the optimal model, Shapley Additive Explanations (SHAP) were used along with the Local Interpretable Model-agnostic Explanations (LIME) and the Partial Dependence Plot (PDP) techniques.

Results

The incidence of HH was 6.5% in HF patients in the MIMIC cohort. HF patients with HH had a 30-day mortality rate of 33% and a 1-year mortality rate of 51%, and HH was an independent risk factor for increased short-term and long-term mortality risk in HF patients. After RFE, 21 key features (21/56) were selected to build the model. Internal validation and external validation suggested that Categorical Boosting (Catboost) had a higher discriminatory capability than the other models (internal validation: AUC, 0.832; 95% CI, 0.819–0.845; external validation: AUC, 0.757 95% CI, 0.739–0.776), and the simplified Catboost model (S-Catboost) also had good performance in both internal validation and external validation (internal validation: AUC, 0.801; 95% CI, 0.787–0.813; external validation: AUC, 0.729, 95% CI, 0.711–0.745).

Conclusion

HH was associated with increased mortality in HF patients. Machine learning methods had good performance in identifying the 30-day mortality risk of HF with HH. With interpretability techniques, the transparency of machine learning models has been enhanced to facilitate user understanding of the prediction results.