Literature DB >> 19363210

Mortality probability model III and simplified acute physiology score II: assessing their value in predicting length of stay and comparison to APACHE IV.

Eduard E Vasilevskis1, Michael W Kuzniewicz2, Brian A Cason3, Rondall K Lane4, Mitzi L Dean5, Ted Clay5, Deborah J Rennie5, Eric Vittinghoff6, R Adams Dudley7.   

Abstract

BACKGROUND: To develop and compare ICU length-of-stay (LOS) risk-adjustment models using three commonly used mortality or LOS prediction models.
METHODS: Between 2001 and 2004, we performed a retrospective, observational study of 11,295 ICU patients from 35 hospitals in the California Intensive Care Outcomes Project. We compared the accuracy of the following three LOS models: a recalibrated acute physiology and chronic health evaluation (APACHE) IV-LOS model; and models developed using risk factors in the mortality probability model III at zero hours (MPM(0)) and the simplified acute physiology score (SAPS) II mortality prediction model. We evaluated models by calculating the following: (1) grouped coefficients of determination; (2) differences between observed and predicted LOS across subgroups; and (3) intraclass correlations of observed/expected LOS ratios between models.
RESULTS: The grouped coefficients of determination were APACHE IV with coefficients recalibrated to the LOS values of the study cohort (APACHE IVrecal) [R(2) = 0.422], mortality probability model III at zero hours (MPM(0) III) [R(2) = 0.279], and simplified acute physiology score (SAPS II) [R(2) = 0.008]. For each decile of predicted ICU LOS, the mean predicted LOS vs the observed LOS was significantly different (p <or= 0.05) for three, two, and six deciles using APACHE IVrecal, MPM(0) III, and SAPS II, respectively. Plots of the predicted vs the observed LOS ratios of the hospitals revealed a threefold variation in LOS among hospitals with high model correlations.
CONCLUSIONS: APACHE IV and MPM(0) III were more accurate than SAPS II for the prediction of ICU LOS. APACHE IV is the most accurate and best calibrated model. Although it is less accurate, MPM(0) III may be a reasonable option if the data collection burden or the treatment effect bias is a consideration.

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Year:  2009        PMID: 19363210      PMCID: PMC3198495          DOI: 10.1378/chest.08-2591

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


  33 in total

1.  Validation, calibration, revision and combination of prognostic survival models.

Authors:  H C van Houwelingen
Journal:  Stat Med       Date:  2000-12-30       Impact factor: 2.373

2.  Development and testing of a hierarchical method to code the reason for admission to intensive care units: the ICNARC Coding Method. Intensive Care National Audit & Research Centre.

Authors:  J D Young; C Goldfrad; K Rowan
Journal:  Br J Anaesth       Date:  2001-10       Impact factor: 9.166

3.  National and state trends in quality of care for acute myocardial infarction between 1994-1995 and 1998-1999: the medicare health care quality improvement program.

Authors:  Dale R Burwen; Deron H Galusha; Jennifer M Lewis; Marjorie R Bedinger; Martha J Radford; Harlan M Krumholz; JoAnne Micale Foody
Journal:  Arch Intern Med       Date:  2003-06-23

4.  Internal and external validation of predictive models: a simulation study of bias and precision in small samples.

Authors:  Ewout W Steyerberg; Sacha E Bleeker; Henriëtte A Moll; Diederick E Grobbee; Karel G M Moons
Journal:  J Clin Epidemiol       Date:  2003-05       Impact factor: 6.437

5.  Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders.

Authors:  Michael W Kuzniewicz; Eduard E Vasilevskis; Rondall Lane; Mitzi L Dean; Nisha G Trivedi; Deborah J Rennie; Ted Clay; Pamela L Kotler; R Adams Dudley
Journal:  Chest       Date:  2008-04-10       Impact factor: 9.410

Review 6.  Physician staffing patterns and clinical outcomes in critically ill patients: a systematic review.

Authors:  Peter J Pronovost; Derek C Angus; Todd Dorman; Karen A Robinson; Tony T Dremsizov; Tammy L Young
Journal:  JAMA       Date:  2002-11-06       Impact factor: 56.272

7.  Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome.

Authors:  Roy G Brower; Michael A Matthay; Alan Morris; David Schoenfeld; B Taylor Thompson; Arthur Wheeler
Journal:  N Engl J Med       Date:  2000-05-04       Impact factor: 91.245

8.  The implications of regional variations in Medicare spending. Part 1: the content, quality, and accessibility of care.

Authors:  Elliott S Fisher; David E Wennberg; Thérèse A Stukel; Daniel J Gottlieb; F L Lucas; Etoile L Pinder
Journal:  Ann Intern Med       Date:  2003-02-18       Impact factor: 25.391

9.  Evaluation of predicted and actual length of stay in 22 Scottish intensive care units using the APACHE III system. Acute Physiology and Chronic Health Evaluation.

Authors:  A W Woods; F N MacKirdy; B M Livingston; J Norrie; J C Howie
Journal:  Anaesthesia       Date:  2000-11       Impact factor: 6.955

10.  Intensive communication: four-year follow-up from a clinical practice study.

Authors:  Craig M Lilly; L A Sonna; K J Haley; A F Massaro
Journal:  Crit Care Med       Date:  2003-05       Impact factor: 7.598

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  26 in total

1.  Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and influence of resuscitation status on model performance.

Authors:  Mark T Keegan; Ognjen Gajic; Bekele Afessa
Journal:  Chest       Date:  2012-10       Impact factor: 9.410

2.  The effect of race and ethnicity on outcomes among patients in the intensive care unit: a comprehensive study involving socioeconomic status and resuscitation preferences.

Authors:  Sara E Erickson; Eduard E Vasilevskis; Michael W Kuzniewicz; Brian A Cason; Rondall K Lane; Mitzi L Dean; Deborah J Rennie; R Adams Dudley
Journal:  Crit Care Med       Date:  2011-03       Impact factor: 7.598

3.  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

4.  Comparative effectiveness of aminoglycosides, polymyxin B, and tigecycline for clearance of carbapenem-resistant Klebsiella pneumoniae from urine.

Authors:  Michael J Satlin; Christine J Kubin; Jill S Blumenthal; Andrew B Cohen; E Yoko Furuya; Stephen J Wilson; Stephen G Jenkins; David P Calfee
Journal:  Antimicrob Agents Chemother       Date:  2011-10-03       Impact factor: 5.191

5.  N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.

Authors:  Ben J Marafino; Jason M Davies; Naomi S Bardach; Mitzi L Dean; R Adams Dudley
Journal:  J Am Med Inform Assoc       Date:  2014-04-30       Impact factor: 4.497

6.  Predictors of early postdischarge mortality in critically ill patients: a retrospective cohort study from the California Intensive Care Outcomes project.

Authors:  Eduard E Vasilevskis; Michael W Kuzniewicz; Brian A Cason; Rondall K Lane; Mitzi L Dean; Ted Clay; Deborah J Rennie; R Adams Dudley
Journal:  J Crit Care       Date:  2010-08-16       Impact factor: 3.425

7.  [Predicting prolonged length of intensive care unit stay via machine learning].

Authors:  J Y Wu; Y Lin; K Lin; Y H Hu; G L Kong
Journal:  Beijing Da Xue Xue Bao Yi Xue Ban       Date:  2021-12-18

8.  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

9.  Variations in the length of stay of intensive care unit nonsurvivors in three Scandinavian countries.

Authors:  Kristian Strand; Sten M Walther; Matti Reinikainen; Tero Ala-Kokko; Thomas Nolin; Jan Martner; Petteri Mussalo; Eldar Søreide; Hans K Flaatten
Journal:  Crit Care       Date:  2010-10-04       Impact factor: 9.097

Review 10.  Prediction of intensive care units length of stay: a concise review.

Authors:  Igor Tona Peres; Silvio Hamacher; Fernando Luiz Cyrino Oliveira; Fernando Augusto Bozza; Jorge Ibrain Figueira Salluh
Journal:  Rev Bras Ter Intensiva       Date:  2021 Apr-Jun
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