Evidence map›Paper›PMID 37698862›Full record

Trial reportJAMA network open2023

Hospital Readmissions by Variation in Engagement in the Health Care Hotspotting Trial: A Secondary Analysis of a Randomized Clinical Trial.

Qiang Yang, Dawn Wiest, Anna C Davis, Aaron Truchil, John L Adams

Registry-linked trialOpen access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02090426 (Health Care Hotspotting), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.2field-weighted citation impact, top 12% 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.

NCT02090426 nacompletednot on this map

Health Care Hotspotting: A Randomized Controlled Trial

TypeinterventionalSponsorAbdul Latif Jameel Poverty Action LabRan2014 to 2020Enrolled800ConditionsHigh Utilizers of Hospital CareArmsLink2Care
3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  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

5 authors at 2 institutions in 1 country.

Qiang YangCamden Coalition, Camden, New Jersey.
Dawn WiestCamden Coalition, Camden, New Jersey.
Anna C DavisCenter for Effectiveness and Safety Research, Kaiser Permanente, Pasadena, California.
Aaron TruchilCamden Coalition, Camden, New Jersey.
John L AdamsCenter for Effectiveness and Safety Research, Kaiser Permanente, Pasadena, California.
Camden County College · USKaiser Permanente · US

Funding

A Randomized Controlled Trial of Health Care HotspottingR01AG049897 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI FINKELSTEIN, AMY N. · 2015 to 2019
$2.7M
NIA NIH HHS R01 AG049897
6 · The paper itself

Abstract

Importance: Variability in intervention participation within care management programs can complicate standard analysis strategies. Objective: To evaluate whether care management was associated with reduced hospital readmissions among individuals with higher participation probabilities. Design, Setting, and Participants: A total of 800 hospitalized patients aged 18 years and older were randomized as part of the Health Care Hotspotting randomized clinical trial, which was conducted in Camden, New Jersey, from June 2014 to September 2017. Data were collected through October 2018. In this new analysis performed between April 6, 2022, and April 23, 2023, the distillation method was applied to account for variable intervention participation. A gradient-boosting machine learning model produced predicted probabilities of engaged participation using baseline covariates only. Predicted probabilities were used to trim both intervention and control populations in an equivalent manner, and intervention effects were reevaluated within study population subsets that were increasingly concentrated with patients having higher participation probabilities. Patients had 2 or more hospitalizations in the 6-month preenrollment period and documented evidence of chronic illness and social complexity. Intervention: Multidisciplinary teams provided services to patients in the intervention arm for a mean 120 days after hospital discharge. Patients in the control group received usual postdischarge care. Main Outcomes and Measures: Hospital readmission rates and counts 30, 90, and 180 days postdischarge. Results: Of 800 eligible patients, 782 had complete discharge information and were included in this analysis (mean [SD] age, 56.6 [12.7] years; 395 [50.5%] female). In the intent-to-treat analysis, the unadjusted 180-day readmission rate for treatment and control groups was 60.1% vs 61.7% (adjusted odds ratio, 0.95; 95% CI, 0.71-1.28; P = .73) and the mean (SD) number of 180-day readmissions was 1.45 (1.89) vs 1.48 (1.94) (adjusted incidence rate ratio, 0.99, 95% CI, 0.88-1.12; P = .86). Among the population with the highest participation probabilities, the mean (SD) 180-day readmission count was 1.22 (1.74) vs 1.57 (1.74) and the incidence rate ratio attained statistical significance (adjusted incidence rate ratio, 0.74; 95% CI, 0.56-0.99; P = .045). Adjusted odds ratios and adjusted incidence rate ratios for 30- and 90-day outcomes reached statistical significance after population distillation. Conclusions and Relevance: This secondary analysis of a randomized clinical trial found that care management was associated with reduced readmissions among patients with higher participation probabilities, suggesting that program operation could be improved by addressing barriers to participation and refining inclusion criteria to identify patients most likely to benefit. Trial Registration: ClinicalTrials.gov Identifier: NCT02090426.

Indexed as

AftercarePatient ReadmissionDelivery of Health CareFemaleHospitalizationHumansMaleMiddle AgedPatient Discharge

Identifiers

PMID37698862
PMCPMC10498327
OpenAlexW4386623896

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.