• Rashan Haniffa, Pubudu De Silva A A National Intensive Care Surveillance, Quality Secretariat Building, Castle Street Ho... more spital for Women, Colombo 08, Sri Lanka; Intensive Care National A, Prasad Weerathunga, Mavuto Mukaka, Priyantha Athapattu, Sithum Munasinghe, Buddhika Mahesh, Palitha Mahipala, Terrence De Silva, Anuja Abayadeera, Saroj Jayasinghe, Nicolette de Keizer, and Arjen M Dondorp. less
• National Intensive Care Surveillance, Quality Secretariat Building, Castle Street Hospital for Women, Colombo 08, Sri Lanka; Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Ma... more hidol University, 3/F, 60th Anniversary Chalermprakiat Building, 420/6 Rajvithi Road, Bangkok 10400, Thailand; Department of Clinical Medicine, Faculty of Medicine, University of Colombo, No. 25, Kynsey Road, Colombo 08, Sri Lanka. Electronic address: rashan@nicslk.com. less
• J Crit Care. 2017 Dec 1; 42: 178-183.

PurposeTo determine the utility of APACHE II in a low-and middle-income (LMIC) setting and the implications of missing data.Materials And MethodsPatients 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).ResultsFrom 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.ConclusionsAn 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.Copyright © 2017 Elsevier Inc. All rights reserved.

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