Abstract
The use of severity of illness scoring systems such as the Acute Physiology and Chronic Health Evaluation in lower-middle income settings comes with important limitations, primarily due to data burden, missingness of key variables and lack of resources. To overcome these challenges, in Asia, a simplified model, designated as e-TropICS was previously developed. We sought to externally validate this model using data from a multi-centre critical care registry in India. Seven ICUs from the Indian Registry of IntenSive care(IRIS) contributed data to this study. Patients > 18 years of age with an ICU length of stay > 6 hours were included. Data including age, gender, co-morbidity, diagnostic category, type of admission, vital signs, laboratory measurements and outcomes were collected for all admissions. e-TropICS was calculated as per original methods. The area under the receiver operator characteristic curve was used to express the model's power to discriminate between survivors and non-survivors. For all tests of significance, a 2-sided P less than or equal to 0.05 was considered to be significant. AUROC values were considered poor when ≤ to 0.70, adequate between 0.71 to 0.80, good between 0.81 to 0.90, and excellent at 0.91 or higher. Calibration was assessed using Hosmer-Lemeshow C -statistic. We included data from 2062 consecutive patient episodes. The median age of the cohort was 60 and predominantly male (n = 1350, 65.47%). Mechanical Ventilation and vasopressors were administered at admission in 504 (24.44%) and 423 (20.51%) patients respectively. Overall, mortality at ICU discharge was 10.28% (n = 212). Discrimination (AUC) for the e-TropICS model was 0.83 (95% CI 0.812-0.839) with an HL C statistic p value of < 0.05. The best sensitivity and specificity (84% and 72% respectively) were achieved with the model at an optimal cut-off for probability of 0.29. e-TropICS has utility in the care of critically unwell patients in the South Asia region with good discriminative capacity. Further refinement of calibration in larger datasets from India and across the South-East Asia region will help in improving model performance.
Highlights
Severity of illness scoring systems such as the Simple Acute Physiology Score (SAPS) [1] and the Acute Physiology and Chronic Health Evaluation (APACHE) [2] help in risk prediction, benchmarking, quality improvement and patient selection for research
Over the past three decades, several iterations of these models have been developed and validated based on changes in the epidemiology of critical illness and substantial improvements in survival [3, 4]. Most of these models have been evaluated in the context of high-income countries (HICs)
2094 consecutive patient episodes were reported to the Indian Registry of IntenSive care (IRIS) registry from the seven participating centres
Summary
Severity of illness scoring systems such as the Simple Acute Physiology Score (SAPS) [1] and the Acute Physiology and Chronic Health Evaluation (APACHE) [2] help in risk prediction, benchmarking, quality improvement and patient selection for research. There are several limitations to the use of these models in middle income and lower-middle income countries (LMICs) such as differences in epidemiology of critical illness, including the high burden of tropical infections, the lack of resources for data collection, burden of data collection due to the large number of variables, missing variables and the absence of electronic health records that would otherwise facilitate seamless data flow [5] To overcome these problems, researchers have developed and tested newer simplified models in LMICs [6, 7]. The use of severity of illness scoring systems such as the Acute Physiology and Chronic Health Evaluation in lower-middle income settings comes with important limitations, primarily due to data burden, missingness of key variables and lack of resources To overcome these challenges, in Asia, a simplified model, designated as e-TropICS was previously developed.
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