Evidence map›Paper›PMID 37090521›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Predicting Hospital Readmission among Patients with Sepsis using Clinical and Wearable Data.

Fatemeh Amrollahi, Supreeth Prajwal Shashikumar, Haben Yhdego, Arshia Nayebnazar, Nathan Yung, Gabriel Wardi, Shamim Nemati

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Fatemeh AmrollahiDivision of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093.
Supreeth Prajwal ShashikumarDivision of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093.
Haben YhdegoDivision of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093.
Arshia NayebnazarDivision of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093.
Nathan YungDepartment of Emergency Medicine, UC San Diego Health, La Jolla, CA 92093.
Gabriel WardiDepartment of Emergency Medicine, UC San Diego Health, La Jolla, CA 92093.
Shamim NematiDivision of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093.
University of California San Diego · USUC San Diego Health System · US

Funding

VentNet: A Real-Time Multimodal Data Integration Model for Prediction of Respiratory Failure in Patients with COVID-19R01HL157985 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MALHOTRA, ATUL, NEMATI, SHAMIM · 2022 to 2025
$2.9M
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
NHLBI NIH HHS R01 HL157985NIGMS NIH HHS K23 GM146092NIGMS NIH HHS R35 GM143121NLM NIH HHS R01 LM013998
6 · The paper itself

Abstract

Sepsis is a life-threatening condition that occurs due to a dysregulated host response to infection. Recent data demonstrate that patients with sepsis have a significantly higher readmission risk than other common conditions, such as heart failure, pneumonia and myocardial infarction and associated economic burden. Prior studies have demonstrated an association between a patient's physical activity levels and readmission risk. In this study, we show that distribution of activity level prior and post-discharge among patients with sepsis are predictive of unplanned rehospitalization in 90 days (P-value<1e-3). Our preliminary results indicate that integrating Fitbit data with clinical measurements may improve model performance on predicting 90 days readmission.

Indexed as

Activity levelClinical relevance SepsisHospital readmissionWearable data

Identifiers

PMID37090521
PMCPMC10120792
OpenAlexW4365148739

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.