ArticleBMC medical informatics and decision making2019
Electronic medical record-based model to predict the risk of 90-day readmission for patients with heart failure.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for hospital readmissions in patients with heart disease: a systematic review and meta-analysis.BMJ open · 2021Pooled it
- The relationship between discharge destination and rehospitalization in Chinese patients with heart failure: a cohort study.Frontiers in cardiovascular medicine · 2026Article
- Predicting death or readmission following heart failure hospitalisation: the VancOuver CoastAL Acute Heart Failure (VOCAL-AHF) registry.Open heart · 2025Article
- Influencing factors of 90-day readmission of patients with heart failure based on the MIMIC-IV database.Journal of thoracic disease · 2025Article
- Prediction of 90 day readmission in heart failure with preserved ejection fraction by interpretable machine learning.ESC heart failure · 2024Article
- Article
- Association of red cell distribution width with the risk of 3-month readmission in patients with heart failure: A retrospective cohort study.Frontiers in cardiovascular medicine · 2023Article
- Prevalence and impact of malnutrition on readmission among hospitalized patients with heart failure in China.ESC heart failure · 2022Article
- Readmission Risk Prediction Model for Patients with Chronic Heart Failure: A Systematic Review and Meta-Analysis.Iranian journal of public health · 2022Review
- Prediction of unplanned 30-day readmission for ICU patients with heart failure.BMC medical informatics and decision making · 2022Article
- Machine Learning Patient-Specific Prediction of Heart Failure Hospitalization Using Cardiac MRI-Based Phenotype and Electronic Health Information.Frontiers in cardiovascular medicine · 2022Article
- Current Trends in Readmission Prediction: An Overview of Approaches.Arabian journal for science and engineering · 2021Article
- Predicting 90 day acute heart failure readmission and death using machine learning-supported decision analysis.Clinical cardiology · 2021Article
- Development and Validation of a Nomogram to Predict the 180-Day Readmission Risk for Chronic Heart Failure: A Multicenter Prospective Study.Frontiers in cardiovascular medicine · 2021Article
- A Model for the Prediction of Mortality and Hospitalization in Chinese Heart Failure Patients.Frontiers in cardiovascular medicine · 2021Article
- A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction.BMC medical informatics and decision making · 2020Article
- miR-130a-3p Exacerbates Sepsis-Induced Cardiac Dysfunction by Promoting Inflammation and Targeting PDK4.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
- Predicting readmission rates in critically ill heart failure patients during a 90-day vulnerable phase using interpretable machine learning models.Clinics (Sao Paulo, Brazil)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral heart failure (HF) risk models exist, however, most of them perform poorly when applied to real-world situations. This study aimed to develop a convenient and efficient risk model to identify patients with high readmission risk within 90 days of HF.
methodsA multivariate logistic regression model was used to predict the risk of 90-day readmission. Data were extracted from electronic medical records from January 1, 2017 to December 31, 2017 and follow-up records of patients with HF within 3 months after discharge. Model performance was evaluated using a receiver operating characteristic curve. All statistical analysis was done using R version 3.5.0.
resultsA total of 350 patients met the inclusion criterion of being readmitted within in 90 days. All data sets were randomly divided into derivation and validation cohorts at a 7/3 ratio. The baseline data were fairly consistent among the derivation and validation cohorts. The variables most clearly related to readmission were logarithm of serum N-terminal pro b-type natriuretic peptide (NT-proBNP) level, red cell volume distribution width (RDW-CV), and Charlson comorbidity index (CCI). The model had good discriminatory ability (C-statistic = 0.73).
conclusionsWe developed and validated a multivariate logistic regression model to predict the 90-day readmission risk for Chinese patients with HF. The predictors included in the model are derived from electronic medical record (EMR) admission data, making it easier for physicians and pharmacists to identify high-risk patients and tailor more intensive precautionary strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.