Evidence map›Paper›PMID 28619703›Full record

ArticleJMIR research protocols2017

Identifying Nonclinical Factors Associated With 30-Day Readmission in Patients with Cardiovascular Disease: Protocol for an Observational Study.

Matthew E Dupre, Alicia Nelson, Scott M Lynch, Bradi B Granger, Hanzhang Xu, Janese M Willis, Lesley H Curtis, Eric D Peterson

Abstract read
In one paragraph

Article in JMIR research protocols, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
  4. Article
  5. 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

8 authors.

Matthew E DupreDuke Clinical Research Institute, Duke University, Durham, NC, United States.ORCID http://orcid.org/0000-0002-0976-4715
Alicia NelsonDepartment of Community and Family Medicine, Duke University, Durham, NC, United States.ORCID http://orcid.org/0000-0002-5455-5007
Scott M LynchDepartment of Sociology, Duke University, Durham, NC, United States.ORCID http://orcid.org/0000-0002-6795-1199
Bradi B GrangerDuke University School of Nursing, Duke University, Durham, NC, United States.ORCID http://orcid.org/0000-0003-0828-6851
Hanzhang XuDuke University School of Nursing, Duke University, Durham, NC, United States.ORCID http://orcid.org/0000-0001-9617-247X
Janese M WillisDepartment of Community and Family Medicine, Duke University, Durham, NC, United States.ORCID http://orcid.org/0000-0001-6980-294X
Lesley H CurtisDuke Clinical Research Institute, Duke University, Durham, NC, United States.ORCID http://orcid.org/0000-0002-3286-9371
Eric D PetersonDuke Clinical Research Institute, Duke University, Durham, NC, United States.ORCID http://orcid.org/0000-0002-5415-4721

Funding

STALLARD Admin Supp: Predicting Future Care Needs and Costs for Individual Medicare Enrollees with Incident Suspected Alzheimer's DiseaseP30AG034424 · NIA · DUKE UNIVERSITY · PI Tyson Brown · 2009 to 2026
$12.2M
An Innovative Model to Predict Readmissions in Adults with Cardiovascular DiseaseK01HL114750 · NHLBI · DUKE UNIVERSITY · PI DUPRE, MATTHEW E. · 2013 to 2017
$590k
NHLBI NIH HHS K01 HL114750NIA NIH HHS P30 AG034424
6 · The paper itself

Abstract

backgroundCardiovascular disease (CVD) is the leading cause of hospitalization in older adults and high readmission rates have attracted considerable attention as actionable targets to promote efficiency in care and to reduce costs. Despite a plethora of research over the past decade, current strategies to predict readmissions have been largely ineffective and efforts to identify novel clinical predictors have been largely unsuccessful.

objectiveThe objective of this study is to examine a wide array of socioeconomic, psychosocial, behavioral, and clinical factors to predict risks of 30-day hospital readmission in cardiovascular patients.

methodsThe study includes patients (aged 18 years and older) admitted for the treatment of cardiovascular-related illnesses at the Duke Heart Center, which is among the nation's largest and top-ranked cardiovascular care hospitals. The study uses a novel standardized survey to ascertain data on a comprehensive array of patient characteristics that will be linked to their electronic medical records. A series of univariate and multivariate models will be used to estimate the associations between the patient-level factors and 30-day readmissions. The performance of the risk models will be examined based on 2 components of accuracy-model calibration and discrimination-to determine how closely the predicted outcome agrees with the observed (actual) outcome and how well the model distinguishes patients who were readmitted and those who were not. The purpose of this paper is to present the protocol for the implementation of this study.

resultsThe study was launched in February 2014 and is actively recruiting patients from the Heart Center. Approximately 550 patients have been enrolled to date and the study is expected to continue recruitment until February 2018. Preliminary results show that participants in the study were aged 63.6 years on average (SD 14.0), predominately male (61.2%), and primarily non-Hispanic white (64.6%) or non-Hispanic black (31.7%). The demographic characteristics of study participants were not significantly different from all patients admitted to the Heart Center during this period with an average age of 65.0 years (SD 15.3) and predominately male (58.6%), non-Hispanic white (62.9%) or non-Hispanic black (31.8%) The integration of the interview data with clinical data from the patient electronic medical records is currently underway. The study has received funding and ethical approval.

conclusionsMany US hospitals continue to struggle with high readmission rates in patients with cardiovascular disease. The primary objective of this study is to collect and integrate a comprehensive array of patient attributes to develop a powerful yet parsimonious model to stratify risks of rehospitalization in cardiovascular patients. The results of this research also have the potential to identify actionable targets for tailored interventions to improve patient outcomes.

Indexed as

cardiovascular diseaseeHealthobservational studypsychosocial factorsreadmissionsocioeconomic statusUnited States

Identifiers

PMID28619703
PMCPMC5491895

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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.