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

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 A...

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Autores principales: Tirupakuzhi Vijayaraghavan, Bharath Kumar, Priyadarshini, Dilanthi, Rashan, Aasiyah, Beane, Abi, Venkataraman, Ramesh, Ramakrishnan, Nagarajan, Haniffa, Rashan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7775074/
https://www.ncbi.nlm.nih.gov/pubmed/33382834
http://dx.doi.org/10.1371/journal.pone.0244989
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author Tirupakuzhi Vijayaraghavan, Bharath Kumar
Priyadarshini, Dilanthi
Rashan, Aasiyah
Beane, Abi
Venkataraman, Ramesh
Ramakrishnan, Nagarajan
Haniffa, Rashan
author_facet Tirupakuzhi Vijayaraghavan, Bharath Kumar
Priyadarshini, Dilanthi
Rashan, Aasiyah
Beane, Abi
Venkataraman, Ramesh
Ramakrishnan, Nagarajan
Haniffa, Rashan
author_sort Tirupakuzhi Vijayaraghavan, Bharath Kumar
collection PubMed
description 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.
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spelling pubmed-77750742021-01-11 Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country Tirupakuzhi Vijayaraghavan, Bharath Kumar Priyadarshini, Dilanthi Rashan, Aasiyah Beane, Abi Venkataraman, Ramesh Ramakrishnan, Nagarajan Haniffa, Rashan PLoS One Research Article 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. Public Library of Science 2020-12-31 /pmc/articles/PMC7775074/ /pubmed/33382834 http://dx.doi.org/10.1371/journal.pone.0244989 Text en © 2020 Vijayaraghavan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tirupakuzhi Vijayaraghavan, Bharath Kumar
Priyadarshini, Dilanthi
Rashan, Aasiyah
Beane, Abi
Venkataraman, Ramesh
Ramakrishnan, Nagarajan
Haniffa, Rashan
Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country
title Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country
title_full Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country
title_fullStr Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country
title_full_unstemmed Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country
title_short Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country
title_sort validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7775074/
https://www.ncbi.nlm.nih.gov/pubmed/33382834
http://dx.doi.org/10.1371/journal.pone.0244989
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