ArticlePloS one2017
Survival analysis of heart failure patients: A case study.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Smiling in time: the interactive development of infant smiling with mothers, fathers and strangers.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Article
- GBC-AST: a cluster-based oversampling method for heart failure prediction in imbalanced medical data sets.BMC medical informatics and decision making · 2026Article
- A Unified Framework for Survival Prediction: Combining Machine Learning Feature Selection with Traditional Survival Analysis in Heart Failure and METABRIC Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Why follow-up matters in survival analysis: comparing cox proportional hazard regression and random survival forest for predicting heart failure outcomes.BMC cardiovascular disorders · 2025Article
- Enhancing remote patient monitoring with AI-driven IoMT and cloud computing technologies.Scientific reports · 2025Article
- Survival parametric modeling for patients with heart failure based on Kernel learning.BMC medical research methodology · 2025Article
- Development a nomogram prognostic model for survival in heart failure patients based on the HF-ACTION data.BMC medical informatics and decision making · 2024Article
- Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network.BMC medical informatics and decision making · 2024Article
- On variable selection in a semiparametric AFT mixture cure model.Lifetime data analysis · 2024Article
- Clinical Predictive Modeling of Heart Failure: Domain Description, Models' Characteristics and Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach.Clinical cardiology · 2024Article
- Analysis of the survival time of patients with heart failure with reduced ejection fraction: a Bayesian approach via a competing risk parametric model.BMC cardiovascular disorders · 2024Article
- Semiparametrically Efficient Method for Enveloped Central Space.Journal of the American Statistical Association · 2024Article
- Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study.Frontiers in medicine · 2024Article
- Heart failure survival prediction using novel transfer learning based probabilistic features.PeerJ. Computer science · 2024Article
- Interpretable prediction of 3-year all-cause mortality in patients with chronic heart failure based on machine learning.BMC medical informatics and decision making · 2023Article
- Heart failure patients monitoring using IoT-based remote monitoring system.Scientific reports · 2023Article
- Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System.Sensors (Basel, Switzerland) · 2023Article
- Sensitivity analysis of unmeasured confounding in causal inference based on exponential tilting and super learner.Journal of applied statistics · 2023Article
- Improvement of a prediction model for heart failure survival through explainable artificial intelligence.Frontiers in cardiovascular medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study was focused on survival analysis of heart failure patients who were admitted to Institute of Cardiology and Allied hospital Faisalabad-Pakistan during April-December (2015). All the patients were aged 40 years or above, having left ventricular systolic dysfunction, belonging to NYHA class III and IV. Cox regression was used to model mortality considering age, ejection fraction, serum creatinine, serum sodium, anemia, platelets, creatinine phosphokinase, blood pressure, gender, diabetes and smoking status as potentially contributing for mortality. Kaplan Meier plot was used to study the general pattern of survival which showed high intensity of mortality in the initial days and then a gradual increase up to the end of study. Martingale residuals were used to assess functional form of variables. Results were validated computing calibration slope and discrimination ability of model via bootstrapping. For graphical prediction of survival probability, a nomogram was constructed. Age, renal dysfunction, blood pressure, ejection fraction and anemia were found as significant risk factors for mortality among heart failure patients.
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