Improving ICU services in resource-limited settings: Perceptions of ICU workers from low-middle-, and high-income countries.

PURPOSE: To evaluate perceptions of intensive care unit (ICU) workers from low-and-middle income countries (LMICs) and high income countries (HICs). MATERIALS AND METHODS: A cross sectional design. Data collected from doctors using an anonymous online, questionnaire. RESULTS: Hundred seventy-five from LMICs and 43 from HICs participated. Barriers in LMICs were lack of formal training (Likert score median 3 [inter quartile range 3]), lack of nurses (3[3]) and low wages (3[4]). Strategies for LMICs improvement were formal training of ICU staff (4[3]), an increase in number of ICU nurses (4[2]), collection of outcome data (3[4]), as well as maintenance of available equipment [3(3)]. The most useful role of HIC ICU staff was training of LMIC staff (4[2]). Donation of equipment [2(4)], drugs [2(4)], and supplies (2[4]) perceived to be of limited usefulness. The most striking difference between HIC and LMIC staff was the perception on the lack of physician leadership as an obstacle to ICU functioning (4[3] vs. 0[2], p<0.005). CONCLUSION: LMICs ICU workers perceived lack of training, lack of nurses, and low wages as major barriers to functioning. Training, increase of nurse workforce, and collection of outcome data were proposed as useful strategies to improve LMIC ICU services.

Applicability of the APACHE II model to a lower middle income country.

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.