Dataset Information

Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country.

ABSTRACT:

Background

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.

Methods

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.

Results

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.

Conclusion

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.

SUBMITTER: Tirupakuzhi Vijayaraghavan BK

PROVIDER: S-EPMC7775074 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country.

Tirupakuzhi Vijayaraghavan Bharath Kumar BK Priyadarshini Dilanthi D Rashan Aasiyah A Beane Abi A Venkataraman Ramesh R Ramakrishnan Nagarajan N Haniffa Rashan R

PloS one 20201231 12

<h4>Background</h4>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.<h4>Methods ...[more]

PMID: 33382834

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Project description:BackgroundAlthough around 70% of the world's prison population live in low- and middle-income countries (LMICs), risk assessment tools for criminal recidivism have been developed and validated in high-income countries (HICs). Validating such tools in LMIC settings is important for the risk management of people released from prison, development of evidence-based intervention programmes, and effective allocation of limited resources.MethodsWe aimed to externally validate a scalable risk assessment tool, the Oxford Risk of Recidivism (OxRec) tool, which was developed in Sweden, using data from a cohort of people released from prisons in Tajikistan. Data were collected from interviews (for predictors) and criminal records (for some predictors and main outcomes). Individuals were first interviewed in prison and then followed up over a 1-year period for post-release violent reoffending outcomes. We assessed the predictive performance of OxRec by testing discrimination (area under the receiver operating characteristic curve; AUC) and calibration (calibration statistics and plots). In addition, we calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for different predetermined risk thresholds.ResultsThe cohort included 970 individuals released from prison. During the 12-month follow-up, 144 (15%) were reincarcerated for violent crimes. The original model performed well. The discriminative ability of OxRec Tajikistan was good (AUC = 0.70; 95% CI 0.66-0.75). The calibration plot suggested an underestimation of observed risk probabilities. However, after recalibration, model performance was improved (Brier score = 0.12; calibration in the large was 1.09). At a selected risk threshold of 15%, the tool had a sensitivity of 60%, specificity of 65%, PPV 23% and NPV 90%. In addition, OxRec was feasible to use, despite challenges to risk prediction in LMICs.ConclusionIn an external validation in a LMIC, the OxRec tool demonstrated good performance in multiple measures. OxRec could be used in Tajikistan to help prioritize interventions for people who are at high-risk of violent reoffending after incarceration and screen out others who are at lower risk of violent reoffending. The use of validated risk assessment tools in LMICs could improve risk stratification and inform the development of future interventions tailored at modifiable risk factors for recidivism, such as substance use and mental health problems.

Project description:BackgroundPrior research has demonstrated that low- and low-middle-income countries (LLMICs) bear a higher burden of critical illness and have a higher rate of mortality from critical illness than high-income countries (HICs). There is a pressing need for improved critical care delivery in LLMICs to reduce this inequity. This systematic review aimed to characterise the range of critical care interventions and services delivered within LLMIC health care systems as reported in the literature.MethodsA search strategy using terms related to critical care in LLMICs was implemented in multiple databases. We included English language articles with human subjects describing at least one critical care intervention or service in an LLMIC setting published between 1 January 2008 and 1 January 2020.ResultsA total of 1620 studies met the inclusion criteria. Among the included studies, 45% of studies reported on pediatric patients, 43% on adults, 23% on infants, 8.9% on geriatric patients and 4.2% on maternal patients. Most of the care described (94%) was delivered in-hospital, with the remainder (6.2%) taking place in out-of-hospital care settings. Overall, 49% of critical care described was delivered outside of a designated intensive care unit. Specialist physicians delivered critical care in 60% of the included studies. Additional critical care was delivered by general physicians (40%), as well as specialist physician trainees (22%), pharmacists (16%), advanced nursing or midlevel practitioners (8.9%), ambulance providers (3.3%) and respiratory therapists (3.1%).ConclusionsThis review represents a comprehensive synthesis of critical care delivery in LLMIC settings. Approximately 50% of critical care interventions and services were delivered outside of a designated intensive care unit. Specialist physicians were the most common health care professionals involved in care delivery in the included studies, however generalist physicians were commonly reported to provide critical care interventions and services. This study additionally characterised the quality of the published evidence guiding critical care practice in LLMICs, demonstrating a paucity of interventional and cost-effectiveness studies. Future research is needed to understand better how to optimise critical care interventions, services, care delivery and costs in these settings.RegistrationPROSPERO CRD42019146802.

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Dataset Information

Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country.

Background

Methods

Results

Conclusion

Publications

Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country.

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