Literature DB >> 25365725

Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ?

Andrew A Kramer1, Thomas L Higgins, Jack E Zimmerman.   

Abstract

OBJECTIVES: To compare ICU performance using standardized mortality ratios generated by the Acute Physiology and Chronic Health Evaluation IVa and a National Quality Forum-endorsed methodology and examine potential reasons for model-based standardized mortality ratio differences.
DESIGN: Retrospective analysis of day 1 hospital mortality predictions at the ICU level using Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models on the same patient cohort.
SETTING: Forty-seven ICUs at 36 U.S. hospitals from January 2008 to May 2013. PATIENTS: Eighty-nine thousand three hundred fifty-three consecutive unselected ICU admissions.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: We assessed standardized mortality ratios for each ICU using data for patients eligible for Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum predictions in order to compare unit-level model performance, differences in ICU rankings, and how case-mix adjustment might explain standardized mortality ratio differences. Hospital mortality was 11.5%. Overall standardized mortality ratio was 0.89 using Acute Physiology and Chronic Health Evaluation IVa and 1.07 using National Quality Forum, the latter having a widely dispersed and multimodal standardized mortality ratio distribution. Model exclusion criteria eliminated mortality predictions for 10.6% of patients for Acute Physiology and Chronic Health Evaluation IVa and 27.9% for National Quality Forum. The two models agreed on the significance and direction of standardized mortality ratio only 45% of the time. Four ICUs had standardized mortality ratios significantly less than 1.0 using Acute Physiology and Chronic Health Evaluation IVa, but significantly greater than 1.0 using National Quality Forum. Two ICUs had standardized mortality ratios exceeding 1.75 using National Quality Forum, but nonsignificant performance using Acute Physiology and Chronic Health Evaluation IVa. Stratification by patient and institutional characteristics indicated that units caring for more severely ill patients and those with a higher percentage of patients on mechanical ventilation had the most discordant standardized mortality ratios between the two predictive models.
CONCLUSIONS: Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models yield different ICU performance assessments due to differences in case-mix adjustment. Given the growing role of outcomes in driving prospective payment patient referral and public reporting, performance should be assessed by models with fewer exclusions, superior accuracy, and better case-mix adjustment.

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Year:  2015        PMID: 25365725     DOI: 10.1097/CCM.0000000000000694

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


  10 in total

1.  Investigating associations between ICU level and quality of care in the Netherlands: reporting only SMRs is not the whole story.

Authors:  Armand R J Girbes; Margreeth B Vroom; Michael A Kuiper; Annemarie M G A de Smet; Marcus J Schultz
Journal:  Intensive Care Med       Date:  2015-06-12       Impact factor: 17.440

2.  Response to Girbes et al.: Investigating associations between ICU level and quality of care in the Netherlands: reporting only SMRs is not the whole story.

Authors:  Georg Heinrich Kluge; John P W Vogelaar; Emiel S Boon
Journal:  Intensive Care Med       Date:  2015-06-24       Impact factor: 17.440

3.  Retrospective Descriptive Study of an Intensive Care Unit at a Ugandan Regional Referral Hospital.

Authors:  Stephen S Ttendo; Adam Was; Mark A Preston; Emmanuel Munyarugero; Vanessa B Kerry; Paul G Firth
Journal:  World J Surg       Date:  2016-12       Impact factor: 3.352

4.  Personalized mortality prediction driven by electronic medical data and a patient similarity metric.

Authors:  Joon Lee; David M Maslove; Joel A Dubin
Journal:  PLoS One       Date:  2015-05-15       Impact factor: 3.240

5.  Detecting organisational innovations leading to improved ICU outcomes: a protocol for a double-blinded national positive deviance study of critical care delivery.

Authors:  Howard Chiou; Jeffrey K Jopling; Jennifer Yang Scott; Meghan Ramsey; Kelly Vranas; Todd H Wagner; Arnold Milstein
Journal:  BMJ Open       Date:  2017-06-14       Impact factor: 2.692

6.  Validation of the Pediatric Index of Mortality 3 in a Single Pediatric Intensive Care Unit in Korea.

Authors:  Ok Jeong Lee; Minyoung Jung; Minji Kim; Hae Kyoung Yang; Joongbum Cho
Journal:  J Korean Med Sci       Date:  2017-02       Impact factor: 2.153

7.  Developing well-calibrated illness severity scores for decision support in the critically ill.

Authors:  Christopher V Cosgriff; Leo Anthony Celi; Stephanie Ko; Tejas Sundaresan; Miguel Ángel Armengol de la Hoz; Aaron Russell Kaufman; David J Stone; Omar Badawi; Rodrigo Octavio Deliberato
Journal:  NPJ Digit Med       Date:  2019-08-15

8.  Variation in severity-adjusted resource use and outcome in intensive care units.

Authors:  Matti Reinikainen; Stephan M Jakob; Jukka Takala; André Moser; Rahul Raj; Ville Pettilä; Irina Irincheeva; Tuomas Selander; Olli Kiiski; Tero Varpula
Journal:  Intensive Care Med       Date:  2021-10-18       Impact factor: 17.440

9.  Criticality: A New Concept of Severity of Illness for Hospitalized Children.

Authors:  Eduardo A Trujillo Rivera; Anita K Patel; James M Chamberlain; T Elizabeth Workman; Julia A Heneghan; Douglas Redd; Hiroki Morizono; Dongkyu Kim; James E Bost; Murray M Pollack
Journal:  Pediatr Crit Care Med       Date:  2021-01-01       Impact factor: 3.971

10.  Severity Trajectories of Pediatric Inpatients Using the Criticality Index.

Authors:  Eduardo A Trujillo Rivera; Anita K Patel; Qing Zeng-Treitler; James M Chamberlain; James E Bost; Julia A Heneghan; Hiroki Morizono; Murray M Pollack
Journal:  Pediatr Crit Care Med       Date:  2021-01-01       Impact factor: 3.971

  10 in total

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