Evidence map›Paper›PMID 35081441›Full record

ArticleJournal of pain and symptom management2022

Palliative Care Exposure Relative to Predicted Risk of Six-Month Mortality in Hospitalized Adults.

Rajiv Agarwal, Henry J Domenico, Sreenivasa R Balla, Daniel W Byrne, Jennifer G Whisenant, Marcella C Woods, Barbara J Martin, Mohana B Karlekar, Marc L Bennett

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pain and symptom management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

9 authors.

Rajiv AgarwalDepartment of Medicine (R.A., J.G.W., M.B.K.), Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt-Ingram Cancer Center (R.A., J.G.W.), Nashville, Tennessee, USA. Electronic address: rajiv.agarwal@vumc.org.
Henry J DomenicoDepartment of Biostatistics (H.J.D., D.W.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Office of Quality, Safety, and Risk Prevention (H.J.D., M.C.W., B.J.M., M.L.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Sreenivasa R BallaHealth Information Technology (S.R.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Daniel W ByrneDepartment of Biostatistics (H.J.D., D.W.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Jennifer G WhisenantDepartment of Medicine (R.A., J.G.W., M.B.K.), Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt-Ingram Cancer Center (R.A., J.G.W.), Nashville, Tennessee, USA.
Marcella C WoodsVanderbilt Office of Quality, Safety, and Risk Prevention (H.J.D., M.C.W., B.J.M., M.L.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Barbara J MartinVanderbilt Office of Quality, Safety, and Risk Prevention (H.J.D., M.C.W., B.J.M., M.L.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Mohana B KarlekarDepartment of Medicine (R.A., J.G.W., M.B.K.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Marc L BennettVanderbilt Office of Quality, Safety, and Risk Prevention (H.J.D., M.C.W., B.J.M., M.L.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Otolaryngology Head and Neck Surgery (M.L.B.), Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Funding

Vanderbilt Clinical Oncology Research Career Development ProgramK12CA090625 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Debra L. Friedman, Paula Jill Hurley · 2001 to 2026
$16.9M
NCI NIH HHS K12 CA090625
6 · The paper itself

Abstract

contextThe optimal strategy for implementing mortality-predicting algorithms to facilitate clinical care, prognostic discussions, and palliative care interventions remains unknown.

objectivesTo develop and validate a real-time predictive model for 180 day mortality using routinely available clinical and laboratory admission data and determine if palliative care exposure varies with predicted mortality risk.

methodsAdult admissions between October 1, 2013 and October.1, 2017 were included for the model derivation. A separate cohort was collected between January 1, 2018 and July 31, 2020 for validation. Patients were followed for 180 days from discharge, and logistic regression with selected variables was used to estimate patients' risk for mortality.

resultsIn the model derivation cohort, 7963 events of 180 day mortality (4.5% event rate) were observed. Median age was 53.0 (IQR 24.0-66.0) with 92,734 females (52.5%). Variables with strongest association with 180 day mortality included: Braden Score (OR 0.83; 95% CI 0.82-0.84); admission Do Not Resuscitate orders (OR 2.61; 95% CI 2.43-2.79); admission service and admission status. The model yielded excellent discriminatory ability in both the derivation (c-statistic 0.873; 95% CI 0.870-0.877; Brier score 0.04) and validation cohorts (c-statistic 0.844; 95% CI 0.840-0.847; Brier score 0.072). Inpatient palliative care consultations increased from 3% of minimal-risk encounters to 41% of high-risk encounters (P < 0.01).

conclusionWe developed and temporally validated a predictive mortality model for adults from a large retrospective cohort, which helps quantify the potential need for palliative care referrals based on risk strata. Machine learning algorithms for mortality require clinical interpretation, and additional studies are needed to design patient-centered and risk-specific interventions.

Indexed as

Machine LearningPalliative CareAdultCohort StudiesFemaleHospitalizationHumansMaleMiddle AgedRetrospective StudiesRisk Assessmentend-of-lifegoals of carePalliative carepredictive modeling

Identifiers

PMID35081441
PMCPMC9018538

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.