Evidence map›Paper›PMID 23754777›Full record

ArticleClinical cardiology2013

Predicting readmission or death after acute ST-elevation myocardial infarction.

Jeremiah R Brown, Sheila M Conley, Nathaniel W Niles

Open access · bronzeAbstract read
In one paragraph

Article in Clinical cardiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 3 pooled it
3.2field-weighted citation impact, top 8% 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

18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. An Evaluation of the Use of Fibrinolytic Therapy for Treatment of STEMI in Northern New England Over Two Decades.Journal of the Society for Cardiovascular Angiography & Interventions · 2026
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  12. Predictors of Posthospital Transitions of Care in Patients With Advanced Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2018
    Article
  13. Article
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  15. Article
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  17. Development and implementation of a real-time 30-day readmission predictive model.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014
    Article
  18. Risk factors and prediction nomogram model for 1-year readmission for major adverse cardiovascular events in patients with STEMI after PCI.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    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 1 institution in 1 country.

Jeremiah R BrownThe Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth,Lebanon, New Hampshire; Section of Cardiology, Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
Sheila M Conley
Nathaniel W Niles
Dartmouth–Hitchcock Medical Center · US

Funding

Information Extraction from EMRs to Predict Readmission following Acute Myocardial InfarctionR01HL130828 · NHLBI · DARTMOUTH COLLEGE · PI BROWN, JEREMIAH R, MATHENY, MICHAEL E. · 2016 to 2019
$3.1M
Patient Safety and Acute Kidney InjuryK01HS018443 · AHRQ · DARTMOUTH COLLEGE · PI BROWN, JEREMIAH R · 2009 to 2013
$696k
AHRQ HHS K01HS018443NHLBI NIH HHS R01 HL130828
6 · The paper itself

Abstract

backgroundRisk factors for emergent readmissions or death after acute myocardial infarction (AMI) are important in identifying patients at risk for major adverse events. However, there has been limited investigation conducted of prospective clinical registries to determine relevant risk factors. HYPOTHESIS: We hypothesize 30-day readmission or death could be predicted using patient, procedural, and process factors.

methodsPatients presenting with ST-elevation myocardial infarction (STEMI) from 2006 to 2011 were prospectively enrolled in a STEMI registry (1271 patients). Thirty-day readmission was ascertained by administrative claims data. Death was determined by linking to the Social Security Death Master File. Univariate and stepwise multivariate logistic regression was conducted with Hosmer-Lemeshow goodness-of-fit statistics for model calibration and receiver operating characteristic (ROC) curve for model discrimination.

resultsThe combined end point of 30-day readmission or postdischarge death included 135 patients (10.6%), including 109 emergent readmissions and 26 deaths. Factors associated with an increase risk of 30-day readmission or postdischarge death included age ≥ 80 years, diabetes, chest pain or cardiac arrest at presentation, and 3-vessel disease found at initial angiography. Factors associated with a decreased risk of 30-day readmission or postdischarge death included transfer to the catheterization lab from another emergency department, clopidogrel given during the procedure hypercholesterolemia, and receiving aspirin, β-blockers, and angiotensin-converting enzyme or angiotensin receptor blocker inhibitors at discharge. Index admission outcomes indicative of readmission or death postdischarge only included a new diagnosis of congestive heart failure. The model discriminated well with an ROC of 0.71 (95% confidence interval: 0.66-0.76).

conclusionsPrehospitalization factors are overlooked and are important factors to incorporate in routine risk prediction models for readmission or death within 30 days following an AMI.

Indexed as

Patient ReadmissionAgedAged, 80 and overArea Under CurveChi-Square DistributionHumansLogistic ModelsMiddle AgedMultivariate AnalysisMyocardial InfarctionProspective StudiesRegistriesRisk AssessmentRisk FactorsROC CurveTime Factors

Identifiers

PMID23754777
PMCPMC5585865
OpenAlexW1588217394

What OpenQuestion holds

Textmetadata
LicenceTDM
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.