Trial reportPloS one2018
Readmissions of adults within three age groups following hospitalization for pneumonia: Analysis from the Nationwide Readmissions Database.
Trial report in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- 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
- Risk factors for hospital readmissions in pneumonia patients: A systematic review and meta-analysis.World journal of clinical cases · 2022Article
- Longitudinal course of GDF15 levels before acute hospitalization and death in the general population.GeroScience · 2021Article
- Article
- Readmissions and costs among younger and older adults for targeted conditions during the enactment of the hospital readmission reduction program.BMC health services research · 2021Article
- Article
- Readmission of older acutely admitted medical patients after short-term admissions in Denmark: a nationwide cohort study.BMC geriatrics · 2020Article
- Community-acquired pneumonia in critically ill very old patients: a growing problem.European respiratory review : an official journal of the European Respiratory Society · 2020Review
- Prediction of 30-Day Hospital Readmissions for All-Cause Dental Conditions using Machine Learning.Risk management and healthcare policy · 2020Article
- A machine learning model for predicting severity-adjusted in-hospital mortality in pneumonia patients.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundWhile 30-day readmissions following hospitalization for pneumonia have been well-studied in the elderly, their burden in young adults remains poorly understood.
objectiveTo study patterns of readmissions following hospitalization for pneumonia across age groups and insurance payers.
methodsIn the Nationwide Readmission Database for the years 2013 and 2014 we identified all adults (≥18 years) discharged alive after a hospitalization with the primary diagnosis of pneumonia, and examined rates of readmissions within 30-days of discharge. Using covariates included in the Center for Medicare & Medicaid Services risk-adjustment model for pneumonia readmissions in a multivariable regression model for survey data, we identified predictors of 30-day readmission.
resultsWe identified 629,939 index pneumonia hospitalizations with a weighted estimate of 1,472,069 nationally. Overall, 16.2% of patients were readmitted within 30 days of their hospitalization for pneumonia, with 30-day readmission rates of 12.4% in the 18-44 year age-group, 16.1% in the 45-64 year age-group, and 16.7% in the ≥65-year age-group. In risk-adjusted analyses, compared with elderly, middle-aged adults were more likely to be readmitted (risk-adjusted OR 1.05, 95% CI 1.03-1.07). Mean cost per readmission was also highest for this age group at $15,976.
conclusionMiddle-aged adults experience substantial rates of 30-day readmission that are comparable to those over 65 years of age, with a higher cost per readmission event. Future efforts are needed to identify potential interventions to alleviate the high burden of pneumonia readmissions in middle-aged adults.
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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.