Evidence map›Paper›PMID 40417569›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Sepsis Prediction Models are Trained on Labels that Diverge from Clinician-Recommended Treatment Times.

Gary E Weissman, Rebecca A Hubbard, Blanca E Himes, Kelly L Goodman-O'Leary, Michael O Harhay, Jennifer C Ginestra, Rachel Kohn, Andrew J Admon, Stephanie Parks Taylor, Scott D Halpern

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Gary E WeissmanUniversity of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Rebecca A HubbardUniversity of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Blanca E HimesUniversity of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Kelly L Goodman-O'LearyUniversity of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Michael O HarhayUniversity of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Jennifer C GinestraUniversity of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Rachel KohnUniversity of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Andrew J AdmonDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Stephanie Parks TaylorDepartment of Internal Medicine, Atrium Health's Carolinas Medical Center, Charlotte, North Carolina, United States of America.
Scott D HalpernUniversity of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.

Funding

Training in Critical Care Health Policy ResearchT32HL098054 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI Scott D Halpern · 2010 to 2026
$8.2M
Using natural language processing and machine learning to identify potentially preventable hospital admissions among outpatients with chronic lung diseasesK23HL141639 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI WEISSMAN, GARY · 2018 to 2022
$933k
Characterizing the Impact of Fragmented Care in Acute Respiratory FailureK08HL155407 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ADMON, ANDREW JOHN · 2021 to 2025
$848k
NHLBI NIH HHS K08 HL155407NHLBI NIH HHS K23 HL141639NHLBI NIH HHS T32 HL098054
6 · The paper itself

Abstract

Many sepsis prediction models use the Sepsis-3 definition or its variants as a training label. However, among the few sepsis models ever deployed in practice, there is scant evidence that they offer clinically meaningful decision support at the bedside. As a potential mechanism to explain this limitation, we hypothesized that clinician-recommended treatment times for sepsis would diverge from onset time defined by Sepsis-3. We conducted an electronic survey that was completed by 153 clinicians at three large and geographically diverse medical centers using vignettes derived from eight real cases of sepsis. After reviewing these vignettes, participants suggested antibiotic treatment to start an average of 7.0 hours (95% confidence interval 5.3 to 8.8) before the Sepsis-3 definition onset. Thus, predicting Sepsis-3 onset as a treatment prompt could lead to inappropriate and delayed treatment recommendations. Building predictive decision support systems that identify outcomes aligned with bedside decisions would increase their clinical utility.

Indexed as

Decision Support Systems, ClinicalSepsisAnti-Bacterial AgentsHumansTime FactorsAnti-Bacterial Agents

Identifiers

PMID40417569
PMCPMC12099352

What OpenQuestion holds

Textmetadata
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.