Evidence map›Paper›PMID 38531636›Full record

ArticleRespiratory care2024

Predicting Hospital Readmission in Medicaid Patients With COPD Using Administrative and Claims Data.

Daniel F Heitjan, Yifei Wang, Jaehyeon Yun

Open access · goldAbstract read
In one paragraph

Article in Respiratory care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.4field-weighted citation impact, top 21% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Observational
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Daniel F HeitjanDepartment of Statistics and Data Science, Southern Methodist University, Dallas, Texas; and Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas. dheitjan@smu.edu.
Yifei WangDepartment of Statistics and Data Science, Southern Methodist University, Dallas, Texas.
Jaehyeon YunGainwell Technologies, Irving, Texas.
Southern Methodist University · USZIN Technologies (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe goals of this study were to develop a model that predicts the risk of 30-d all-cause readmission in hospitalized Medicaid patients diagnosed with COPD and to create a predictive model in a retrospective study of a population cohort.

methodsWe analyzed 2016-2019 Medicaid claims data from 7 United States states. A

resultsAmong 12,283 COPD hospitalizations contributed by 9,437 subjects, 2,534 (20.6%) were 30-d readmissions. The final model included demographics, comorbidities, claims history, admission and discharge variables, length of stay, and seasons of admission and discharge. The observed versus predicted plot showed reasonable fit, and the estimated area under the ROC curve of 0.702 was robust in sensitivity analyses.

conclusionsOur model identified with acceptable accuracy hospitalized Medicaid patients with a diagnosis of COPD who are at high risk of readmission. One can use the model to develop post-discharge management interventions for reducing readmissions, for adjusting comparisons of readmission rates between sites/providers or over time, and to guide a patient-centered approach to patient care.

Indexed as

MedicaidPatient ReadmissionPulmonary Disease, Chronic ObstructiveAdultAgedComorbidityFemaleHumansInsurance Claim ReviewLength of StayLogistic ModelsMaleMiddle AgedRetrospective StudiesRisk AssessmentROC Curveburden of COPDclaims database analysishospital admissionsMedicaidprediction model

Identifiers

PMID38531636
PMCPMC11147616
OpenAlexW4393187929

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.