ASSESSMENT OF THE PROGNOSIS OF CORONARY PATIENTS - PERFORMANCE AND CUSTOMIZATION OF GENERIC SEVERITY INDEXES

Citation
X. Sarmiento et al., ASSESSMENT OF THE PROGNOSIS OF CORONARY PATIENTS - PERFORMANCE AND CUSTOMIZATION OF GENERIC SEVERITY INDEXES, Chest, 111(6), 1997, pp. 1666-1671
Citations number
18
Categorie Soggetti
Respiratory System
Journal title
ChestACNP
ISSN journal
00123692
Volume
111
Issue
6
Year of publication
1997
Pages
1666 - 1671
Database
ISI
SICI code
0012-3692(1997)111:6<1666:AOTPOC>2.0.ZU;2-I
Abstract
Study objective: To assess the prognostic performance of general sever ity systems (APACHE II [acute physiology and chronic health evaluation ], simplified acute physiology score [SAPS II], and mortality probabil ity models [MPM II]) in coronary patients and to derive new customized indexes for coronary patients using a reduced number of variables, De sign: Inception cohort, Setting: Adult medical and surgical ICUs in 17 hospitals in Catalonia and the Balearic Islands, Patients: Four hundr ed fifty-six patients with acute myocardial infarction. Measurements a nd results: The APACHE II, SAPS II, and MPM II variables and survival status at hospital discharge have been collected, Performance of the s everity systems was assessed by evaluating calibration and discriminat ion. Logistic I egression was used to customize the MPM II24 and SAPS II indexes, Discrimination was high enough for all of the models. Howe ver, calibration of the MPM II24 was not as satisfactory as for the ot her models, The MPM II24 and SAPS II were both reduced to five variabl es (MPM II24 cor: age, PaO2, continuous vasoactive drugs, urinary outp ut, and mechanical ventilation; SAPS IIcor: age, PaO2/FIo(2) ratio, sy stolic BP, Glasgow coma score, and urinary output), Both models showed better calibration and discrimination than the original ones, Conclus ions: Prognostic indexes developed for multidisciplinary patients show good performance when applied to patients with acute myocardial infar ction, but customization can reduce the number of variables necessary to compute them without a loss of, and a possible improvement in, prog nostic accuracy.