Literature DB >> 22315120

Severity scoring in the critically ill: part 2: maximizing value from outcome prediction scoring systems.

Michael J Breslow1, Omar Badawi2.   

Abstract

Part 2 of this review of ICU scoring systems examines how scoring system data should be used to assess ICU performance. There often are two different consumers of these data: lCU clinicians and quality leaders who seek to identify opportunities to improve quality of care and operational efficiency, and regulators, payors, and consumers who want to compare performance across facilities. The former need to know how to garner maximal insight into their care practices; this includes understanding how length of stay (LOS) relates to quality, analyzing the behavior of different subpopulations, and following trends over time. Segregating patients into low-, medium-, and high-risk populations is especially helpful, because care issues and outcomes may differ across this severity continuum. Also, LOS behaves paradoxically in high-risk patients (survivors often have longer LOS than nonsurvivors); failure to examine this subgroup separately can penalize ICUs with superior outcomes. Consumers of benchmarking data often focus on a single score, the standardized mortality ratio (SMR). However, simple SMRs are disproportionately affected by outcomes in high-risk patients, and differences in population composition, even when performance is otherwise identical, can result in different SMRs. Future benchmarking must incorporate strategies to adjust for differences in population composition and report performance separately for low-, medium- and high-acuity patients. Moreover, because many ICUs lack the resources to care for high-acuity patients (predicted mortality >50%), decisions about where patients should receive care must consider both ICU performance scores and their capacity to care for different types of patients.

Entities:  

Mesh:

Year:  2012        PMID: 22315120     DOI: 10.1378/chest.11-0331

Source DB:  PubMed          Journal:  Chest        ISSN: 0012-3692            Impact factor:   9.410


  12 in total

1.  Hospital mortality prediction for intermediate care patients: Assessing the generalizability of the Intermediate Care Unit Severity Score (IMCUSS).

Authors:  David N Hager; Varshitha Tanykonda; Zeba Noorain; Sarina K Sahetya; Catherine E Simpson; Juan Felipe Lucena; Dale M Needham
Journal:  J Crit Care       Date:  2018-05-19       Impact factor: 3.425

2.  Anesthetic variation and potential impact of anesthetics used during endovascular management of acute ischemic stroke.

Authors:  Chitra Sivasankar; Michael Stiefel; Todd A Miano; Guy Kositratna; Sukanya Yandrawatthana; Robert Hurst; W Andrew Kofke
Journal:  J Neurointerv Surg       Date:  2015-11-27       Impact factor: 5.836

3.  PICU Length of Stay: Factors Associated With Bed Utilization and Development of a Benchmarking Model.

Authors:  Murray M Pollack; Richard Holubkov; Ron Reeder; J Michael Dean; Kathleen L Meert; Robert A Berg; Christopher J L Newth; John T Berger; Rick E Harrison; Joseph Carcillo; Heidi Dalton; David L Wessel; Tammara L Jenkins; Robert Tamburro
Journal:  Pediatr Crit Care Med       Date:  2018-03       Impact factor: 3.624

4.  Gender differences in outcome and use of resources do exist in Swedish intensive care, but to no advantage for women of premenopausal age.

Authors:  Carolina Samuelsson; Folke Sjöberg; Göran Karlström; Thomas Nolin; Sten M Walther
Journal:  Crit Care       Date:  2015-03-30       Impact factor: 9.097

Review 5.  State of the art review: the data revolution in critical care.

Authors:  Marzyeh Ghassemi; Leo Anthony Celi; David J Stone
Journal:  Crit Care       Date:  2015-03-16       Impact factor: 9.097

6.  What every intensivist should know about prognostic scoring systems and risk-adjusted mortality.

Authors:  Mark T Keegan; Marcio Soares
Journal:  Rev Bras Ter Intensiva       Date:  2016-09

7.  External validation of a prognostic model for intensive care unit mortality: a retrospective study using the Ontario Critical Care Information System.

Authors:  Fran Priestap; Raymond Kao; Claudio M Martin
Journal:  Can J Anaesth       Date:  2020-05-07       Impact factor: 5.063

8.  Development of an open-heart intraoperative risk scoring model for predicting a prolonged intensive care unit stay.

Authors:  Sirirat Tribuddharat; Thepakorn Sathitkarnmanee; Kriangsak Ngamsangsirisup; Somrat Charuluxananan; Cameron P Hurst; Suparit Silarat; Ganjana Lertmemongkolchai
Journal:  Biomed Res Int       Date:  2014-04-10       Impact factor: 3.411

9.  APACHE IV score in postoperative kidney transplantation.

Authors:  Edison Moraes Rodrigues-Filho; Anderson Garcez
Journal:  Rev Bras Ter Intensiva       Date:  2018 Apr-Jun

10.  Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.

Authors:  Ben J Marafino; Miran Park; Jason M Davies; Robert Thombley; Harold S Luft; David C Sing; Dhruv S Kazi; Colette DeJong; W John Boscardin; Mitzi L Dean; R Adams Dudley
Journal:  JAMA Netw Open       Date:  2018-12-07
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.