Cargando…

Assessment of performance of four mortality prediction systems in a Saudi Arabian intensive care unit

INTRODUCTION: The purpose of this study is to assess the performance of Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, Mortality Probability Model MPM II(0) and MPM II(24) systems in a major tertiary care hospital in Riyadh, Saudi Arabia. MET...

Descripción completa

Detalles Bibliográficos
Autores principales: Arabi, Yaseen, Haddad, Samir, Goraj, Radoslaw, Al-Shimemeri, Abdullah, Al-Malik, Salim
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2002
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC111184/
https://www.ncbi.nlm.nih.gov/pubmed/11983044
_version_ 1782120226688598016
author Arabi, Yaseen
Haddad, Samir
Goraj, Radoslaw
Al-Shimemeri, Abdullah
Al-Malik, Salim
author_facet Arabi, Yaseen
Haddad, Samir
Goraj, Radoslaw
Al-Shimemeri, Abdullah
Al-Malik, Salim
author_sort Arabi, Yaseen
collection PubMed
description INTRODUCTION: The purpose of this study is to assess the performance of Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, Mortality Probability Model MPM II(0) and MPM II(24) systems in a major tertiary care hospital in Riyadh, Saudi Arabia. METHODS: The following data were collected prospectively on all consecutive patients admitted to the Intensive Care Unit between 1 March 1999 and 31 December 2000: demographics, APACHE II and SAPS II scores, MPM variables, ICU and hospital outcome. Predicted mortality was calculated using original regression formulas. Standardized mortality ratio (SMR) was computed with 95% confidence intervals (CI). Calibration was assessed by calculating Lemeshow–Hosmer goodness-of-fit C statistics. Discrimination was evaluated by calculating the Area Under the Receiver Operating Characteristic Curves (ROC AUC). RESULTS: Predicted mortality by all systems was not significantly different from actual mortality [SMR for MPM II(0): 1.00 (0.91–1.10), APACHE II: 1.00 (0.8–1.11), SAPS II: 1.09 (0.97–1.21), MPM II(24) 0.92 (0.82–1.03)]. Calibration was best for MPM II(24) (C-statistic: 14.71, P = 0.06). Discrimination was best for MPM II(0) (ROC AUC:0.85) followed by MPM II(24) (0.84), APACHE II (0.83) then SAPS II (0.79). CONCLUSIONS: In our ICU population: 1) Overall mortality prediction, estimated by standardized mortality ratio, was accurate, especially for MPM II(0) and APACHE II. 2) MPM II(24) has the best calibration. 3) SAPS II has the lowest calibration and discrimination. The local performance of MPM II(24) in addition to its ease-to-use makes it an attractive model for mortality prediction in Saudi Arabia.
format Text
id pubmed-111184
institution National Center for Biotechnology Information
language English
publishDate 2002
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-1111842002-05-17 Assessment of performance of four mortality prediction systems in a Saudi Arabian intensive care unit Arabi, Yaseen Haddad, Samir Goraj, Radoslaw Al-Shimemeri, Abdullah Al-Malik, Salim Crit Care Research INTRODUCTION: The purpose of this study is to assess the performance of Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, Mortality Probability Model MPM II(0) and MPM II(24) systems in a major tertiary care hospital in Riyadh, Saudi Arabia. METHODS: The following data were collected prospectively on all consecutive patients admitted to the Intensive Care Unit between 1 March 1999 and 31 December 2000: demographics, APACHE II and SAPS II scores, MPM variables, ICU and hospital outcome. Predicted mortality was calculated using original regression formulas. Standardized mortality ratio (SMR) was computed with 95% confidence intervals (CI). Calibration was assessed by calculating Lemeshow–Hosmer goodness-of-fit C statistics. Discrimination was evaluated by calculating the Area Under the Receiver Operating Characteristic Curves (ROC AUC). RESULTS: Predicted mortality by all systems was not significantly different from actual mortality [SMR for MPM II(0): 1.00 (0.91–1.10), APACHE II: 1.00 (0.8–1.11), SAPS II: 1.09 (0.97–1.21), MPM II(24) 0.92 (0.82–1.03)]. Calibration was best for MPM II(24) (C-statistic: 14.71, P = 0.06). Discrimination was best for MPM II(0) (ROC AUC:0.85) followed by MPM II(24) (0.84), APACHE II (0.83) then SAPS II (0.79). CONCLUSIONS: In our ICU population: 1) Overall mortality prediction, estimated by standardized mortality ratio, was accurate, especially for MPM II(0) and APACHE II. 2) MPM II(24) has the best calibration. 3) SAPS II has the lowest calibration and discrimination. The local performance of MPM II(24) in addition to its ease-to-use makes it an attractive model for mortality prediction in Saudi Arabia. BioMed Central 2002 2002-03-13 /pmc/articles/PMC111184/ /pubmed/11983044 Text en Copyright © 2002 Arabi et al., licensee BioMed Central Ltd
spellingShingle Research
Arabi, Yaseen
Haddad, Samir
Goraj, Radoslaw
Al-Shimemeri, Abdullah
Al-Malik, Salim
Assessment of performance of four mortality prediction systems in a Saudi Arabian intensive care unit
title Assessment of performance of four mortality prediction systems in a Saudi Arabian intensive care unit
title_full Assessment of performance of four mortality prediction systems in a Saudi Arabian intensive care unit
title_fullStr Assessment of performance of four mortality prediction systems in a Saudi Arabian intensive care unit
title_full_unstemmed Assessment of performance of four mortality prediction systems in a Saudi Arabian intensive care unit
title_short Assessment of performance of four mortality prediction systems in a Saudi Arabian intensive care unit
title_sort assessment of performance of four mortality prediction systems in a saudi arabian intensive care unit
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC111184/
https://www.ncbi.nlm.nih.gov/pubmed/11983044
work_keys_str_mv AT arabiyaseen assessmentofperformanceoffourmortalitypredictionsystemsinasaudiarabianintensivecareunit
AT haddadsamir assessmentofperformanceoffourmortalitypredictionsystemsinasaudiarabianintensivecareunit
AT gorajradoslaw assessmentofperformanceoffourmortalitypredictionsystemsinasaudiarabianintensivecareunit
AT alshimemeriabdullah assessmentofperformanceoffourmortalitypredictionsystemsinasaudiarabianintensivecareunit
AT almaliksalim assessmentofperformanceoffourmortalitypredictionsystemsinasaudiarabianintensivecareunit