Evidence map›Paper›PMID 38787299›Full record

ArticleCritical care explorations2024

Prediction of Readmission Following Sepsis Using Social Determinants of Health.

Fatemeh Amrollahi, Brent D Kennis, Supreeth Prajwal Shashikumar, Atul Malhotra, Stephanie Parks Taylor, James Ford, Arianna Rodriguez, Julia Weston, Romir Maheshwary, Shamim Nemati and 2 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Critical care explorations, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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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

12 authors.

Fatemeh AmrollahiDepartment of Biomedical Informatics, University of California San Diego, La Jolla, CA.
Brent D KennisSchool of Medicine, University of California San Diego, La Jolla, CA.
Supreeth Prajwal ShashikumarDepartment of Biomedical Informatics, University of California San Diego, La Jolla, CA.
Atul MalhotraDivision of Pulmonary, Critical Care and Sleep Medicine, University of California at San Diego, La Jolla, CA.
Stephanie Parks TaylorDivision of Hospital Medicine, University of Michigan, Ann Arbor, MI.
James FordDepartment of Emergency Medicine, University of California, San Francisco, San Francisco, CA.
Arianna RodriguezDepartment of Medicine, University of California San Diego, La Jolla, CA.
Julia WestonDepartment of Medicine, University of California San Diego, La Jolla, CA.
Romir MaheshwaryDepartment of Medicine, University of California San Diego, La Jolla, CA.
Shamim NematiDepartment of Biomedical Informatics, University of California San Diego, La Jolla, CA.
Gabriel WardiDivision of Pulmonary, Critical Care and Sleep Medicine, University of California at San Diego, La Jolla, CA.
Angela MeierDepartment of Anesthesiology, Division of Critical Care, University of California, San Diego, La Jolla, CA.

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
GeneRAlizable Sepsis Phenotyping (GRASP) using Electronic Health Records and Continuous Monitoring SensorsR35GM143121 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NEMATI, SHAMIM · 2021 to 2025
$2.2M
RAAB-AI: Reducing Automation and Anchoring Bias in AI SystemsR01LM013998 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2022 to 2026
$1.7M
Implementation of Continuum of Care Sepsis Phenotyping and Risk StratificationK23GM146092 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Gabriel Wardi · 2022 to 2026
$898k
NIGMS NIH HHS K23 GM146092NIGMS NIH HHS K23GM146092NIGMS NIH HHS R35 GM143121NLM NIH HHS R01 LM013998NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

objectivesTo determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables.

designMulticenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data. SETTINGS: Thirty-five hospitals across the United States from 2017 to 2021. PATIENTS: Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization.

interventionsNone. MEASUREMENTS AND MAIN

resultsUnplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission.

conclusionsIn this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables.

Indexed as

Patient ReadmissionSepsisSocial Determinants of HealthAdultAgedCohort StudiesFemaleHumansLogistic ModelsMaleMiddle AgedRetrospective StudiesRisk FactorsUnited States

Identifiers

PMID38787299
PMCPMC11132367

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.