TY - JOUR
T1 - Applicability of the APACHE II model to a lower middle income country
AU - Haniffa, Rashan
AU - Pubudu de Silva, A.
AU - Weerathunga, Prasad
AU - Mukaka, Mavuto
AU - Athapattu, Priyantha
AU - Munasinghe, Sithum
AU - Mahesh, Buddhika
AU - Mahipala, Palitha
AU - de Silva, Terrence
AU - Abayadeera, Anuja
AU - Jayasinghe, Saroj
AU - de Keizer, Nicolette
AU - Dondorp, Arjen M.
PY - 2017
Y1 - 2017
N2 - 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. (c) 2017 Elsevier Inc. All rights reserved
AB - 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. (c) 2017 Elsevier Inc. All rights reserved
U2 - https://doi.org/10.1016/j.jcrc.2017.07.022
DO - https://doi.org/10.1016/j.jcrc.2017.07.022
M3 - Article
C2 - 28755619
SN - 0883-9441
VL - 42
SP - 178
EP - 183
JO - Journal of critical care
JF - Journal of critical care
ER -