Literature DB >> 29194147

Severity of Illness Scores May Misclassify Critically Ill Obese Patients.

Rodrigo Octávio Deliberato1,2, Stephanie Ko2,3, Matthieu Komorowski2,4, M A Armengol de La Hoz2,5,6,7, Maria P Frushicheva2,8, Jesse D Raffa2, Alistair E W Johnson2, Leo Anthony Celi2,9, David J Stone2,10.   

Abstract

OBJECTIVE: Severity of illness scores rest on the assumption that patients have normal physiologic values at baseline and that patients with similar severity of illness scores have the same degree of deviation from their usual state. Prior studies have reported differences in baseline physiology, including laboratory markers, between obese and normal weight individuals, but these differences have not been analyzed in the ICU. We compared deviation from baseline of pertinent ICU laboratory test results between obese and normal weight patients, adjusted for the severity of illness.
DESIGN: Retrospective cohort study in a large ICU database.
SETTING: Tertiary teaching hospital. PATIENTS: Obese and normal weight patients who had laboratory results documented between 3 days and 1 year prior to hospital admission.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Seven hundred sixty-nine normal weight patients were compared with 1,258 obese patients. After adjusting for the severity of illness score, age, comorbidity index, baseline laboratory result, and ICU type, the following deviations were found to be statistically significant: WBC 0.80 (95% CI, 0.27-1.33) × 10/L; p = 0.003; log (blood urea nitrogen) 0.01 (95% CI, 0.00-0.02); p = 0.014; log (creatinine) 0.03 (95% CI, 0.02-0.05), p < 0.001; with all deviations higher in obese patients. A logistic regression analysis suggested that after adjusting for age and severity of illness at least one of these deviations had a statistically significant effect on hospital mortality (p = 0.009).
CONCLUSIONS: Among patients with the same severity of illness score, we detected clinically small but significant deviations in WBC, creatinine, and blood urea nitrogen from baseline in obese compared with normal weight patients. These small deviations are likely to be increasingly important as bigger data are analyzed in increasingly precise ways. Recognition of the extent to which all critically ill patients may deviate from their own baseline may improve the objectivity, precision, and generalizability of ICU mortality prediction and severity adjustment models.

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Year:  2018        PMID: 29194147     DOI: 10.1097/CCM.0000000000002868

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


  7 in total

1.  An Evaluation of the Influence of Body Mass Index on Severity Scoring.

Authors:  Rodrigo Octavio Deliberato; Ary Serpa Neto; Matthieu Komorowski; David J Stone; Stephanie Q Ko; Lucas Bulgarelli; Carolina Rodrigues Ponzoni; Renato Carneiro de Freitas Chaves; Leo Anthony Celi; Alistair E W Johnson
Journal:  Crit Care Med       Date:  2019-02       Impact factor: 7.598

2.  The impact of body mass index on the prognostic performance of the Simplified Acute Physiology Score 3: A prospective cohort study.

Authors:  Isabella B B Ferreira; Rodrigo C Menezes; Matheus L Otero; Thomas A Carmo; Gabriel A Agareno; Gabriel P Telles; Bruno V B Fahel; María B Arriaga; Kiyoshi F Fukutani; Licurgo Pamplona Neto; Sydney Agareno; Kevan M Akrami; Nivaldo M Filgueiras Filho; Bruno B Andrade
Journal:  Heliyon       Date:  2022-03-28

Review 3.  Predicting adverse hemodynamic events in critically ill patients.

Authors:  Joo H Yoon; Michael R Pinsky
Journal:  Curr Opin Crit Care       Date:  2018-06       Impact factor: 3.687

4.  Performance of intensive care unit severity scoring systems across different ethnicities.

Authors:  Rahuldeb Sarkar; Christopher Martin; Heather Mattie; Judy Wawira Gichoya; David J Stone; Leo Anthony Celi
Journal:  medRxiv       Date:  2021-01-20

5.  Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study.

Authors:  Rahuldeb Sarkar; Christopher Martin; Heather Mattie; Judy Wawira Gichoya; David J Stone; Leo Anthony Celi
Journal:  Lancet Digit Health       Date:  2021-04

6.  OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality.

Authors:  Yasser El-Manzalawy; Mostafa Abbas; Ian Hoaglund; Alvaro Ulloa Cerna; Thomas B Morland; Christopher M Haggerty; Eric S Hall; Brandon K Fornwalt
Journal:  BMC Med Inform Decis Mak       Date:  2021-05-13       Impact factor: 3.298

7.  Understanding the complexity of sepsis mortality prediction via rule discovery and analysis: a pilot study.

Authors:  Ying Wu; Shuai Huang; Xiangyu Chang
Journal:  BMC Med Inform Decis Mak       Date:  2021-11-28       Impact factor: 2.796

  7 in total

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