Performance evaluation of a multinational data platform for critical care in Asia
Collaboration for Research, Implementation and Training in Critical Care - Asia investigators (CCA), ; Pisani, L.; Rashan, T.; Shamal, M.; Ghose, A.; Vijayaraghavan, B. K.; Tripathy, S.; Aryal, D.; Hashmi, M.; Nor, B.; Lam Minh, Y.; Haniffa, R.; Beane, A.
Show abstract
ObjectiveWe aimed to evaluate the quality of a multinational intensive care unit (ICU) network of registries of critically ill patients established in seven Asian low and middle income countries (LMICs). MethodsThe Critical Care Asia federated registry platform enables ICUs to collect clinical, outcome and process data for aggregate and unit-level analysis. The evaluation used the standardised criteria of the Directory of Clinical Databases (DoCDat) and a framework for data quality assurance in medical registries. Six reviewers assessed structure, coverage, reliability and validity of the ICU registry data. Case mix and process measures on patient episodes from June to December 2020 were analysed. ResultsData on 20,507 consecutive patient episodes from 97 ICUs in Afghanistan, Bangladesh, India, Malaysia, Nepal, Pakistan and Vietnam were included. The quality level achieved according to the ten prespecified DoCDat criteria was high (average score 3.4 out of 4) as was the structural and organizational performance -- comparable to ICU registries in high-income countries. Identified strengths were types of variables included, reliability of coding, data completeness and validation. Potential improvements include extension of national coverage. ConclusionThe Critical Care Asia platform evaluates well using standardised frameworks for data quality and equally to registries in resource-rich settings. FundingThis work was undertaken as part of the existing Wellcome Innovations Flagship award, Collaboration for Research, Improvement and Training in Critical CARE in ASIA (ref. 215522/Z/19/Z). The funder had no role in the decision to publish or in the preparation of this manuscript.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Development and validation of automated computer aided-risk score for predicting the risk of in-hospital mortality using first electronically recorded blood test results and vital signs for COVID-19 hospital admissions: a retrospective development and validation study 94%
- Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study 94%
- Performance of digital Early Warning Score (NEWS2) in a cardiac specialist setting: retrospective cohort study 93%
Similar papers in this journal
- Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study 93%
- Derivation and validation of a triage tool for acutely ill adults with suspected COVID-19: The PRIEST observational cohort study 93%
- LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting 93%
Similar papers in this journal
- A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda 93%
- Use of a Continuous Single Lead Electrocardiogram Analytic to Predict Patient Deterioration Requiring Rapid Response Team Activation 92%
- ePOCT+ and the medAL-suite: Development of an electronic clinical decision support algorithm and digital platform for pediatric outpatients in low- and middle-income countries 92%
Similar papers in this journal
- Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score 91%
- Clinical Decision Support in Cardiovascular Medicine: Effectiveness, Implementation Barriers, and Regulation 90%
- Prone positioning of patients with moderate hypoxia due to COVID-19: A multicenter pragmatic randomized trial [COVID-PRONE] 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.