Dataset Information

Five-year mortality and readmission rates in patients with heart failure in India: Results from the Trivandrum heart failure registry.

ABSTRACT:

Introduction

Heart failure (HF) has emerged as an important and increasing disease burden in India. We present the 5-year outcomes of patients hospitalized for HF in India.

Methods

The Trivandrum Heart Failure Registry (THFR) recruited consecutive patients admitted for acute HF among 16 hospitals in Trivandrum, Kerala in 2013. Guideline-directed medical therapy (GDMT) was defined as the combination of beta-blockers (BB), renin angiotensin system blockers (RAS), and mineralocorticoid receptor antagonists (MRA) in patients with HF with reduced ejection fraction (HFrEF, EF < 40%) at discharge. We used Cox proportional hazards models and Kaplan-Meier survival plots for analysis. The MAGGIC risk score variables were included as exposure variables.

Results

Among 1205 patients [69% male, mean (SD) age = 61.2 (13.7) years], HFrEF constituted 62% of patients and among them, 25% received GDMT. The 5-year mortality rate was 59% (n = 709 deaths), and median survival was 3.1 years. Sudden cardiac death and pump failure caused 46% and 49% of the deaths, respectively. In the multivariate Cox model, components of GDMT associated with lower 5-year mortality risks were discharge prescription of BB, RAS blocker, and MRA. Older age, lower systolic blood pressure, NYHA class III or IV, and higher serum creatinine were also associated with higher 5-year mortality.

Conclusions

Three out of every 5 patients had died during 5-years of follow-up with a median survival of approximately 3 years. Lack of GDMT in patients with HFrEF and frequent readmissions were associated with higher 5-year mortality. Quality improvement programmes with strategies to improve adherence to GDMT and reduction in readmissions may improve HF outcomes in this region.

SUBMITTER: Harikrishnan S

PROVIDER: S-EPMC10424638 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

Five-year mortality and readmission rates in patients with heart failure in India: Results from the Trivandrum heart failure registry.

Harikrishnan Sivadasanpillai S Jeemon Panniyammakal P Ganapathi Sanjay S Agarwal Anubha A Viswanathan Sunitha S Sreedharan Madhu M Vijayaraghavan Govindan G Bahuleyan Charantharayil G CG Biju Ramabhadran R Nair Tiny T Pratapkumar N N Krishnakumar K K Rajalekshmi N N Suresh Krishnan K Huffman Mark D MD

International journal of cardiology 20201010

<h4>Introduction</h4>Heart failure (HF) has emerged as an important and increasing disease burden in India. We present the 5-year outcomes of patients hospitalized for HF in India.<h4>Methods</h4>The Trivandrum Heart Failure Registry (THFR) recruited consecutive patients admitted for acute HF among 16 hospitals in Trivandrum, Kerala in 2013. Guideline-directed medical therapy (GDMT) was defined as the combination of beta-blockers (BB), renin angiotensin system blockers (RAS), and mineralocortico ...[more]

PMID: 33049297

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Project description:AimsThe aim of this study was to assess the performance of these main scores in predicting prognosis in patients with heart failure (HF).Methods and resultsA total of 2008 patients who were admitted to the Fourth People's Hospital of Zigong, Sichuan, from December 2016 to June 2019 and diagnosed with HF were included in the study. We compared the prognostic predictive performance of Seattle Heart Failure Model (SHFM), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, Get With the Guidelines-Heart Failure programme (GWTG-HF), Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure (ASCEND) risk scores, the Acute Decompensated Heart Failure National Registry (ADHERE) model, Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator, and Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure (GISSI-HF) for the endpoints. The primary endpoint was 1 year all-cause mortality and the secondary endpoint was the incidence of 28 day readmission post-discharge. At 1 year follow-up, 44 (2.21%) patients with HF died. Discrimination analyses showed that all risk scores performed reasonably well in predicting 1 year mortality, with areas under the receiver operating characteristic curve (AUCs) fluctuating between 0.757 and 0.822. GISSI-HF showed the best discrimination with the AUC of 0.822 (0.768-0.876), followed by MAGGIC-HF, BCN-Bio-HF, ASCEND, SHFM, GWTG-HF, and ADHERE with AUCs of 0.819 (0.756-0.883), 0.812 (0.758-0.865), 0.802 (0.742-0.862), 0.787 (0.725-0.849), 0.762 (0.684-0.840), and 0.757 (0.681-0.833), respectively. All risk scores were similarly predictive of 28 day emergency readmissions, with AUCs fluctuating between 0.609 and 0.680. Overestimation of mortality occurred in all scores except the ASCEND. The risk scores remained with good prognostic discrimination in patients with biventricular HF and in the subgroup of patients taking angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker.ConclusionsCurrently assessed risk scores have limited clinical utility, with fair accuracy and calibration in assessing patients' 1 year risk of death and poor accuracy in assessing patients' risk of readmission. There is a need to incorporate more patient-level information, use more advanced technologies, and develop models for different subgroups of patients to achieve more practical, innovative, and accurate risk assessment tools.

Project description: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.

Dataset Information

Five-year mortality and readmission rates in patients with heart failure in India: Results from the Trivandrum heart failure registry.

Introduction

Methods

Results

Conclusions

Publications

Five-year mortality and readmission rates in patients with heart failure in India: Results from the Trivandrum heart failure registry.

Similar Datasets

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