Evidence map›Paper›PMID 32157649›Full record

ArticleJournal of general internal medicine2020

min-SIA: a Lightweight Algorithm to Predict the Risk of 6-Month Mortality at the Time of Hospital Admission.

Nishant Sahni, Roshan Tourani, Donald Sullivan, Gyorgy Simon

Abstract read
In one paragraph

Article in Journal of general internal medicine, 2020. 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

4 authors.

Nishant SahniDivision of General Internal Medicine, University of Minnesota, Delaware Street SE, MMC 741, Minneapolis, MN, USA. nishant.sahni@gmail.com.ORCID 0000-0002-2384-0243
Roshan TouraniInstitute of Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Donald SullivanOregon Health Sciences University, Portland, OR, USA.
Gyorgy SimonInstitute of Health Informatics, University of Minnesota, Minneapolis, MN, USA.

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR000114 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R · 2012 to 2015
$35.0M
University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
NCATS NIH HHS UL1 TR000114NCATS NIH HHS UL1 TR002494
6 · The paper itself

Abstract

backgroundPredicting death in a cohort of clinically diverse, multi-condition hospitalized patients is difficult. This frequently hinders timely serious illness care conversations. Prognostic models that can determine 6-month death risk at the time of hospital admission can improve access to serious illness care conversations.

objectiveThe objective is to determine if the demographic, vital sign, and laboratory data from the first 48 h of a hospitalization can be used to accurately quantify 6-month mortality risk.

designThis is a retrospective study using electronic medical record data linked with the state death registry.

participantsParticipants were 158,323 hospitalized patients within a 6-hospital network over a 6-year period. MAIN MEASURES: Main measures are the following: the first set of vital signs, complete blood count, basic and complete metabolic panel, serum lactate, pro-BNP, troponin-I, INR, aPTT, demographic information, and associated ICD codes. The outcome of interest was death within 6 months. KEY

resultsModel performance was measured on the validation dataset. A random forest model-mini serious illness algorithm-used 8 variables from the initial 48 h of hospitalization and predicted death within 6 months with an AUC of 0.92 (0.91-0.93). Red cell distribution width was the most important prognostic variable. min-SIA (mini serious illness algorithm) was very well calibrated and estimated the probability of death to within 10% of the actual value. The discriminative ability of the min-SIA was significantly better than historical estimates of clinician performance.

conclusionmin-SIA algorithm can identify patients at high risk of 6-month mortality at the time of hospital admission. It can be used to improved access to timely, serious illness care conversations in high-risk patients.

Indexed as

AlgorithmsHospitalizationCohort StudiesHospital MortalityHospitalsHumansRetrospective StudiesRisk Assessmentdata mininghospital outcomespalliative carepredictive models

Identifiers

PMID32157649
PMCPMC7210334

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.