min-SIA: a Lightweight Algorithm to Predict the Risk of 6-Month Mortality at the Time of Hospital Admission.
Background: Predicting 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 serio...
| Publicado en: | JGIM: Journal of General Internal Medicine Vol. 35; no. 5; pp. 1413 - 1419 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143113206&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143113206 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08848734 4BF jtl: JGIM: Journal of General Internal Medicine issn: 08848734 maglogo: N pubinfo: dt: May2020 vid: 35 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143113206 143113206 NLM32157649 143113206 10.1007/s11606-020-05733-1 NLM32157649 143113206 ppf: 1413 ppct: 6 formats: tig: atl: min-SIA: a Lightweight Algorithm to Predict the Risk of 6-Month Mortality at the Time of Hospital Admission. aug: au: Sahni, Nishant Tourani, Roshan Sullivan, Donald Simon, Gyorgy affil: Division of General Internal Medicine, University of Minnesota, Delaware Street SE, MMC 741, Minneapolis, MN, USA sug: subj: Hospitalization Algorithms Risk Assessment Prospective Studies Retrospective Design Hospitals Hospital Mortality Human Interview Guides Funding Source ab: Background: Predicting 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.Objective: The 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.Design: This is a retrospective study using electronic medical record data linked with the state death registry.Participants: Participants 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 Results: Model 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.Conclusion: min-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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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