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A Journal on Anesthesiology, Resuscitation, Analgesia and Intensive Care

Official Journal of the Italian Society of Anesthesiology, Analgesia, Resuscitation and Intensive Care
Indexed/Abstracted in: Current Contents/Clinical Medicine, EMBASE, PubMed/MEDLINE, Science Citation Index Expanded (SciSearch), Scopus
Impact Factor 2,036

Frequency: Monthly

ISSN 0375-9393

Online ISSN 1827-1596


Minerva Anestesiologica 2003 December;69(12):897-905



To verify four 5-year-old mathematical models to predict the outcome of ICU patients

Donati A. 1, Gabbanelli V. 1, Pantanetti S. 1, Scala C. 1, Carbini C. 1, Valentini I. 1, Antognini M. 1, Pelaia P. 1, Pietropaoli P. 2

1 Institute of Medical and Surgical Emergencies Marche Polytechnic University, Ancona, Italy
2 Institute of Anesthesiology and Intensive Care University “La Sapienza”, Rome, Italy

Aim. The aim of this study is to verify calibration and discrimination after 5 years in the case mix of patients admitted to the Intensive Care Unit (ICU) during the year 2000. In this way we want to perform a quality control of our ICU in order to justify the increased amount of money spent for intensive care.
Methods. A prospective study has been made on the 357 patients admitted to the ICU during the year 2000. The Apache II score was calculated within the first 24 hours and, depending on the length of stay in the ICU, on the 5th, 10th and 15th day after ICU admission. On the basis of the 4 mathematical models death risk has been calculated for each of the 4 times. The Hosmer-Lemeshow test was performed for calibration and ROC curves for discrimination, always for each of the 4 mathematical models.
Results. The 1st model, at 24 hours from ICU admission, showed a bad calibration (p=0.000088), while the ROC curve was 0.744±0.32. Also the 2nd model, at the 5th day from admission, showed a bad calibration (p=0.000588), with ROC curve of 0.827±0.04. The 3rd model (10th day), was well calibrated (p=0.112247) and discriminating (ROC=0.888 ±0.04). Finally the models at 15 days showed again a bad calibration (p=0.001422) but a very good discrimination (area=0.906±0.06).
Conclusion. Developing mathematical models to predict mortality within ICUs can be useful to assess quality of care, even if these models should not be the only ICU quality controls, but must be accompanied by other indicators, looking at quality of life of the patients after ICU discharge.

language: English, Italian

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