Observational studyJournal of hospital medicine2017
Predicting 30-Day Pneumonia Readmissions Using Electronic Health Record Data.
Observational study in Journal of hospital medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review.BMJ (Clinical research ed.) · 2020Pooled it
- Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance.Circulation. Cardiovascular quality and outcomes · 2018Pooled it
- Article
- Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database.BMC medical informatics and decision making · 2022Article
- Leveraging Advanced Data Analytics to Predict the Risk of All-Cause Seven-Day Emergency Readmissions.Cureus · 2022Article
- Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France.JAMA network open · 2022Article
- Man vs. Machine: Comparing Physician vs. Electronic Health Record-Based Model Predictions for 30-Day Hospital Readmissions.Journal of general internal medicine · 2021Article
- Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN).International journal of environmental research and public health · 2021Article
- Predictors of 30-day readmission following hospitalisation with community-acquired pneumonia.BMJ open respiratory research · 2021Article
- Article
- Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool.Journal of personalized medicine · 2020Article
- Article
- Associations between biomarkers at discharge and co-morbidities and risk of readmission after community-acquired pneumonia: a retrospective cohort study.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2018Article
- Predicting 30-Day Hospital Readmissions in Acute Myocardial Infarction: The AMI "READMITS" (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes MellitusJournal of the American Heart Association · 2018Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundReadmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction.
objectiveTo develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay").
designObservational cohort study using stepwise-backward selection and cross-validation. SUBJECTS: Consecutive pneumonia hospitalizations from 6 diverse hospitals in north Texas from 2009-2010. MEASURES: All-cause nonelective 30-day readmissions, ascertained from 75 regional hospitals.
resultsOf 1463 patients, 13.6% were readmitted. The first-day pneumonia-specific model included sociodemographic factors, prior hospitalizations, thrombocytosis, and a modified pneumonia severity index; the full-stay model included disposition status, vital sign instabilities on discharge, and an updated pneumonia severity index calculated using values from the day of discharge as additional predictors. The full-stay pneumonia-specific model outperformed the first-day model (C statistic 0.731 vs 0.695; P = 0.02; net reclassification index = 0.08). Compared to a validated multi-condition readmission model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores, the full-stay pneumonia-specific model had better discrimination (C statistic range 0.604-0.681; P < 0.01 for all comparisons), predicted a broader range of risk, and better reclassified individuals by their true risk (net reclassification index range, 0.09-0.18).
conclusionsEHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores. Journal of Hospital Medicine 2017;12:209-216.
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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.