Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Applicability of the APACHE II model to a lower middle income country
Journal of Critical Care, Volume 42, Year 2017
Notification
URL copied to clipboard!
Description
Purpose To determine the utility of APACHE II in a low-and middle-income (LMIC) setting and the implications of missing data. Materials and methods Patients meeting APACHE II inclusion criteria admitted to 18 ICUs in Sri Lanka over three consecutive months had data necessary for the calculation of APACHE II, probabilities prospectively extracted from case notes. APACHE II physiology score (APS), probabilities, Standardised (ICU) Mortality Ratio (SMR), discrimination (AUROC), and calibration (C-statistic) were calculated, both by imputing missing measurements with normal values and by Multiple Imputation using Chained Equations (MICE). Results From a total of 995 patients admitted during the study period, 736 had APACHE II probabilities calculated. Data availability for APS calculation ranged from 70.6% to 88.4% for bedside observations and 18.7% to 63.4% for invasive measurements. SMR (95% CI) was 1.27 (1.17, 1.40) and 0.46 (0.44, 0.49), AUROC (95% CI) was 0.70 (0.65, 0.76) and 0.74 (0.68, 0.80), and C-statistic was 68.8 and 156.6 for normal value imputation and MICE, respectively. Conclusions An incomplete dataset confounds interpretation of prognostic model performance in LMICs, wherein imputation using normal values is not a suitable strategy. Improving data availability, researching imputation methods and developing setting-adapted and simpler prognostic models are warranted. © 2017 Elsevier Inc.
Authors & Co-Authors
Haniffa, Rashan
Sri Lanka, Colombo
National Intensive Care Surveillance
Thailand, Bangkok
Mahidol Oxford Tropical Medicine Research Unit
Sri Lanka, Colombo
University of Colombo
Pubudu de Silva, A.
Sri Lanka, Colombo
National Intensive Care Surveillance
Mukaka, Mavuto F.J.
Thailand, Bangkok
Mahidol Oxford Tropical Medicine Research Unit
Abayadeera, Anuja Unnathie
Sri Lanka, Colombo
University of Colombo
Jayasinghe, Saroj
Sri Lanka, Colombo
University of Colombo
de Keizer, Nicolet F.
Netherlands, Amsterdam
Amsterdam Umc - University of Amsterdam
Dondorp, A. M.
Thailand, Bangkok
Mahidol Oxford Tropical Medicine Research Unit
Statistics
Citations: 15
Authors: 7
Affiliations: 5
Identifiers
Doi:
10.1016/j.jcrc.2017.07.022
ISSN:
08839441