Evidence map›Paper›PMID 37395923›Full record

ArticleJournal of medical systems2023

Can a 5-to-90-day Mortality Predictor Perform Consistently Across Time and Equitably Across Populations?

Jonathan Handler, Olivia J Lee, Sheena Chatrath, Jeremy McGarvey, Tyler Fitch, Divya Jose, John Vozenilek

Abstract read
In one paragraph

Article in Journal of medical systems, 2023. 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. Review
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

7 authors.

Jonathan HandlerClinical Intelligence and Advanced Data Lab, OSF Healthcare System, 1306 N Berkeley Ave, Peoria, IL, 61603, USA. jonathan.a.handler@osfhealthcare.org.ORCID http://orcid.org/0000-0002-0304-7539
Olivia J LeeUniversity of Illinois College of Medicine at Peoria, Peoria, IL, USA.ORCID http://orcid.org/0009-0008-5270-3118
Sheena ChatrathUniversity of Illinois College of Medicine at Peoria, Peoria, IL, USA.ORCID http://orcid.org/0000-0002-1227-1719
Jeremy McGarveyMinistry Healthcare Analytics, OSF HealthCare System, Peoria, IL, USA.ORCID http://orcid.org/0000-0003-2475-1071
Tyler FitchInternal Medicine and Pediatrics, OSF Healthcare System, Peoria, IL, USA.
Divya JoseBusiness Intelligence Consulting, Indus Group, Wheeling, IL, USA.ORCID http://orcid.org/0009-0007-9242-1767
John VozenilekUniversity of Illinois College of Medicine at Peoria, Peoria, IL, USA.ORCID http://orcid.org/0000-0001-7955-4089

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advance care planning (ACP) facilitates end-of-life care, yet many die without it. Timely and accurate mortality prediction may encourage ACP. However, performance of predictors typically differs among sub-populations (e.g., rural vs. urban) and worsens over time ("concept drift"). Therefore, we assessed performance equity and consistency for a novel 5-to-90-day mortality predictor across various demographies, geographies, and timeframes (n = 76,812 total encounters). Predictions were made for the first day of included adult inpatient admissions on a retrospective dataset. AUC-PR remained at 29% both pre-COVID (throughout 2018) and during COVID (8 months in 2021). Pre-COVID-19 recall and precision were 58% and 25% respectively at the 12.5% certainty cutoff, and 12% and 44% at the 37.5% cutoff. During COVID-19, recall and precision were 59% and 26% at the 12.5% cutoff, and 11% and 43% at the 37.5% cutoff. Pre-COVID, compared to the overall population, recall was lower at the 12.5% cutoff in the White, non-Hispanic subgroup and at both cutoffs in the rural subgroup. During COVID-19, precision at the 12.5% cutoff was lower than that of the overall population for the non-White and non-White female subgroups. No other significant differences were seen between subgroups and the corresponding overall population. Overall performance during COVID was unchanged from pre-pandemic performance. Although some comparisons (especially precision at the 37.5% cutoff) were underpowered, precision at the 12.5% cutoff was equitable across most demographies, regardless of the pandemic. Mortality prediction to prioritize ACP conversations can be provided consistently and equitably across many studied timeframes and sub-populations.

Indexed as

Advance Care PlanningCOVID-19AdultFemaleHospitalizationHumansRetrospective StudiesAdvance care planningCOVID-19Health equityMachine learningMortalityPrognosis

Identifiers

PMID37395923
PMCPMC10317873

What OpenQuestion holds

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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.