Literature DB >> 23222263

An electronic Simplified Acute Physiology Score-based risk adjustment score for critical illness in an integrated healthcare system.

Vincent Liu1, Benjamin J Turk, Arona I Ragins, Patricia Kipnis, Gabriel J Escobar.   

Abstract

OBJECTIVE: Risk adjustment is essential in evaluating the performance of an ICU; however, assigning scores is time-consuming. We sought to create an automated ICU risk adjustment score, based on the Simplified Acute Physiology Score 3, using only data available within the electronic medical record (Kaiser Permanente HealthConnect). DESIGN, SETTING, AND PATIENTS: The eSimplified Acute Physiology Score 3 was developed by adapting Kaiser Permanente HealthConnect structured data to Simplified Acute Physiology Score 3 criteria. The model was tested among 67,889 first-time ICU admissions at 21 hospitals between 2007 and 2011 to predict hospital mortality. Model performance was evaluated using published Simplified Acute Physiology Score 3 global and North American coefficients; a first-level customized version of the eSimplified Acute Physiology Score 3 was also developed in a 40% derivation cohort and tested in a 60% validation cohort. MEASUREMENTS: Electronic variables were considered "directly" available if they could be mapped exactly within Kaiser Permanente HealthConnect; they were considered "adapted" if no exact electronic corollary was identified. Model discrimination was evaluated with area under receiver operating characteristic curves; calibration was assessed using Hosmer-Lemeshow goodness-of-fit tests. MAIN
RESULTS: Mean age at ICU admission was 65 ± 17 yrs. Mortality in the ICU was 6.2%; total in-hospital mortality was 11.2%. The majority of Simplified Acute Physiology Score 3 variables were considered "directly" available; others required adaptation based on diagnosis coding, medication records, or procedure tables. Mean eSimplified Acute Physiology Score 3 scores were 45 ± 13. Using published Simplified Acute Physiology Score 3 global and North American coefficients, the eSimplified Acute Physiology Score 3 demonstrated good discrimination (area under the receiver operating characteristic curve, 0.80-0.81); however, it overpredicted mortality. The customized eSimplified Acute Physiology Score 3 score demonstrated good discrimination (area under the receiver operating characteristic curve, 0.82) and calibration (Hosmer-Lemeshow goodness-of-fit chi-square p = 0.57) in the validation cohort. The eSimplified Acute Physiology Score 3 demonstrated stable performance when cohorts were limited to specific hospitals and years.
CONCLUSIONS: The customized eSimplified Acute Physiology Score 3 shows good potential for providing automated risk adjustment in the intensive care unit.

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Year:  2013        PMID: 23222263     DOI: 10.1097/CCM.0b013e318267636e

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  21 in total

1.  Guiding Principles for a Pediatric Neurology ICU (neuroPICU) Bedside Multimodal Monitor: Findings from an International Working Group.

Authors:  Zachary M Grinspan; Yonina C Eldar; Daniel Gopher; Amihai Gottlieb; Rotem Lammfromm; Halinder S Mangat; Nimrod Peleg; Steven Pon; Igal Rozenberg; Nicholas D Schiff; David E Stark; Peter Yan; Hillel Pratt; Barry E Kosofsky
Journal:  Appl Clin Inform       Date:  2016-05-18       Impact factor: 2.342

2.  Impact of nurse-led remote screening and prompting for evidence-based practices in the ICU*.

Authors:  Jeremy M Kahn; Scott R Gunn; Holly L Lorenz; Jeffrey Alvarez; Derek C Angus
Journal:  Crit Care Med       Date:  2014-04       Impact factor: 7.598

3.  Keeping Score of Severity Scores: Taking the Next Step.

Authors:  Vincent Liu
Journal:  Crit Care Med       Date:  2016-03       Impact factor: 7.598

4.  Validation of Administrative Definitions of Invasive Mechanical Ventilation across 30 Intensive Care Units.

Authors:  Meeta Prasad Kerlin; Gary E Weissman; Katherine A Wonneberger; Saida Kent; Vanessa Madden; Vincent X Liu; Scott D Halpern
Journal:  Am J Respir Crit Care Med       Date:  2016-12-15       Impact factor: 21.405

5.  Comparing Hospital Processes and Outcomes in California Medicare Beneficiaries: Simulation Prompts Reconsideration.

Authors:  Gabriel J Escobar; Jennifer M Baker; Benjamin J Turk; David Draper; Vincent Liu; Patricia Kipnis
Journal:  Perm J       Date:  2017

6.  The Impact of Acute Organ Dysfunction on Long-Term Survival in Sepsis.

Authors:  Alejandro Schuler; David A Wulf; Yun Lu; Theodore J Iwashyna; Gabriel J Escobar; Nigam H Shah; Vincent X Liu
Journal:  Crit Care Med       Date:  2018-06       Impact factor: 7.598

7.  Enhanced Recovery After Surgery Program Implementation in 2 Surgical Populations in an Integrated Health Care Delivery System.

Authors:  Vincent X Liu; Efren Rosas; Judith Hwang; Eric Cain; Anne Foss-Durant; Molly Clopp; Mengfei Huang; Derrick C Lee; Alex Mustille; Patricia Kipnis; Stephen Parodi
Journal:  JAMA Surg       Date:  2017-07-19       Impact factor: 14.766

Review 8.  Using existing data to address important clinical questions in critical care.

Authors:  Colin R Cooke; Theodore J Iwashyna
Journal:  Crit Care Med       Date:  2013-03       Impact factor: 7.598

9.  The Natural History of Changes in Preferences for Life-Sustaining Treatments and Implications for Inpatient Mortality in Younger and Older Hospitalized Adults.

Authors:  Yan S Kim; Gabriel J Escobar; Scott D Halpern; John D Greene; Patricia Kipnis; Vincent Liu
Journal:  J Am Geriatr Soc       Date:  2016-04-27       Impact factor: 5.562

10.  Evaluation Following Staggered Implementation of the "Rethinking Critical Care" ICU Care Bundle in a Multicenter Community Setting.

Authors:  Vincent Liu; David Herbert; Anne Foss-Durant; Gregory P Marelich; Anandray Patel; Alan Whippy; Benjamin J Turk; Arona I Ragins; Patricia Kipnis; Gabriel J Escobar
Journal:  Crit Care Med       Date:  2016-03       Impact factor: 7.598

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