Predictors of readmission in hospitalized heart failure patients

Analysis of the survival time of patients with heart failure with reduced ejection fraction: a Bayesian approach via a competing risk parametric model

PURPOSE

Heart failure (HF) is a widespread ailment and is a primary contributor to hospital admissions. The focus of this study was to identify factors affecting the extended-term survival of patients with HF, anticipate patient outcomes through cause-of-death analysis, and identify risk elements for preventive measures.

METHODS

A total of 435 HF patients were enrolled from the medical records of the Rajaie Cardiovascular Medical and Research Center, covering data collected between March and August 2018. After a five-year follow-up (July 2023), patient outcomes were assessed based on the cause of death. The survival analysis was performed with the AFT method with the Bayesian approach in the presence of competing risks.

RESULTS

Based on the results of the best model for HF-related mortality, age [time ratio = 0.98, confidence interval 95%: 0.96-0.99] and ADHF [TR = 0.11, 95% (CI): 0.01-0.44] were associated with a lower survival time. Chest pain in HF-related mortality [TR = 0.41, 95% (CI): 0.10-0.96] and in non-HF-related mortality [TR = 0.38, 95% (CI): 0.12-0.86] was associated with a lower survival time. The next significant variable in HF-related mortality was hyperlipidemia (yes): [TR = 0.34, 95% (CI): 0.13-0.64], and in non-HF-related mortality hyperlipidemia (yes): [TR = 0.60, 95% (CI): 0.37-0.90]. CAD [TR = 0.65, 95% (CI): 0.38-0.98], CKD [TR = 0.52, 95% (CI): 0.28-0.87], and AF [TR = 0.53, 95% (CI): 0.32-0.81] were other variables that were directly related to the reduction in survival time of patients with non-HF-related mortality.

CONCLUSION

The study identified distinct predictive factors for overall survival among patients with HF-related mortality or non-HF-related mortality. This differentiated approach based on the cause of death contributes to the estimation of patient survival time and provides valuable insights for clinical decision-making.

Machine learning based readmission and mortality prediction in heart failure patients

This study intends to predict in-hospital and 6-month mortality, as well as 30-day and 90-day hospital readmission, using Machine Learning (ML) approach via conventional features. A total of 737 patients remained after applying the exclusion criteria to 1101 heart failure patients. Thirty-four conventional features were collected for each patient. First, the data were divided into train and test cohorts with a 70-30% ratio. Then train data were normalized using the Z-score method, and its mean and standard deviation were applied to the test data. Subsequently, Boruta, RFE, and MRMR feature selection methods were utilized to select more important features in the training set. In the next step, eight ML approaches were used for modeling. Next, hyperparameters were optimized using tenfold cross-validation and grid search in the train dataset. All model development steps (normalization, feature selection, and hyperparameter optimization) were performed on a train set without touching the hold-out test set. Then, bootstrapping was done 1000 times on the hold-out test data. Finally, the obtained results were evaluated using four metrics: area under the ROC curve (AUC), accuracy (ACC), specificity (SPE), and sensitivity (SEN). The RFE-LR (AUC: 0.91, ACC: 0.84, SPE: 0.84, SEN: 0.83) and Boruta-LR (AUC: 0.90, ACC: 0.85, SPE: 0.85, SEN: 0.83) models generated the best results in terms of in-hospital mortality. In terms of 30-day rehospitalization, Boruta-SVM (AUC: 0.73, ACC: 0.81, SPE: 0.85, SEN: 0.50) and MRMR-LR (AUC: 0.71, ACC: 0.68, SPE: 0.69, SEN: 0.63) models performed the best. The best model for 3-month rehospitalization was MRMR-KNN (AUC: 0.60, ACC: 0.63, SPE: 0.66, SEN: 0.53) and regarding 6-month mortality, the MRMR-LR (AUC: 0.61, ACC: 0.63, SPE: 0.44, SEN: 0.66) and MRMR-NB (AUC: 0.59, ACC: 0.61, SPE: 0.48, SEN: 0.63) models outperformed the others. Reliable models were developed in 30-day rehospitalization and in-hospital mortality using conventional features and ML techniques. Such models can effectively personalize treatment, decision-making, and wiser budget allocation. Obtained results in 3-month rehospitalization and 6-month mortality endpoints were not astonishing and further experiments with additional information are needed to fetch promising results in these endpoints.

One-year trajectories of self-care behaviours and unplanned hospital readmissions among patients with heart failure: A prospective longitudinal study

AIM AND OBJECTIVES

This study aimed to identify the associations between longitudinal trajectories of self-care behaviours and unplanned hospital readmissions in patients with heart failure.

BACKGROUND

Adherence to self-care behaviours is crucial to prevent hospital readmissions; however, self-care behaviours remain unsatisfactory among patients with heart failure. Studies of long-term trajectories of self-care behaviours and their influence on hospital readmissions are limited in this population.

DESIGN

A prospective, longitudinal observational study.

METHODS

Among 137 participants with heart failure (mean age 67.36 years, 62% men), we analysed the 1-year follow-up data to determine the association between 1-year trajectories of self-care behaviours and hospital readmissions using Kaplan-Meier analysis and multivariable Cox regression, adjusted for confounding variables.

RESULTS

Self-care behaviour trajectories of heart failure patients were classified as 'high-stable' (58.4%) or 'low-sustained' (41.6%). The cumulative rate of readmissions for the low-sustained class was higher than that of the high-stable class for all periods. Factors influencing readmissions included anaemia, cognitive function, frailty and self-care behaviours trajectories. The low-sustained class had a 2.77 times higher risk of readmissions within 1 year than that in the high-stable class.

CONCLUSIONS

Longitudinal self-care behaviours pattern trajectories of heart failure patients were stratified as high-stable and low-sustained. Routine follow-up assessment of patients' self-care behavioural patterns, including anaemia and frailty, and cognitive function can minimise unplanned hospital readmissions.

RELEVANCE TO CLINICAL PRACTICE

Identification of trajectory patterns of self-care behaviours over time and provision of timely and individualised care can reduce readmissions for heart failure patients. Healthcare professionals should recognise the significance of developing tailored strategies incorporating longitudinal self-care behavioural patterns in heart failure patients.

REPORTING METHOD

The study has been reported in accordance with the STROBE checklist (Appendix S1).

PATIENT OR PUBLIC CONTRIBUTION

Patients have completed a self-reported questionnaire after providing informed consent.

Correlation analysis between the static and the changed neutrophil-to-lymphocyte ratio and in-hospital mortality in critical patients with acute heart failure

OBJECTIVE

Association between neutrophil-to-lymphocyte ratio (NLR) on admission and poor prognosis in patients with acute heart failure (AHF) has been well established. However, the relationship between dynamic changes in NLR and in-hospital mortality in AHF patients has not been studied. Our purpose was to determine if an early change in NLR within the first week after AHF patients was admitted to intensive care unit (ICU) was associated with in-hospital mortality.

METHODS

Data from the medical information mart for intensive care IV (the MIMIC-IV) database was analyzed. The effect of baseline NLR on in-hospital mortality in critical patients with AHF was evaluated utilizing smooth curve fitting and multivariable logistic regression analysis. Moreover, comparison of the dynamic change in NLR among survivors and non-survivors was performed using the generalized additive mixed model (GAMM).

RESULTS

There were 1169 participants who took part in the present study, 986 of whom were in-hospital survivors and 183 of whom were in-hospital non-survivors. The smooth curve fitting revealed a positive relationship between baseline NLR and in-hospital mortality, and multivariable logistic regression analysis indicated that baseline NLR was an independent risk factor for in-hospital mortality (OR 1.04, 95% CI 1.02,1.07, P-value = 0.001). After adjusting for confounders, GAMM showed that the difference in NLR between survivors and non-survivors grew gradually during the first week after ICU admission, and the difference grew by an average of 0.51 per day (β = 0.51, 95% CI 0.45-0.56, P-value <0.001).

CONCLUSIONS

Baseline NLR was associated with poor prognosis in critical patients with AHF. Early rises in NLR were linked to higher in-hospital mortality, which suggests that keeping track of how NLR early changes might help identify short-term prognosis of critical patients with AHF.