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4 results on '"Salles, TD"'

1. Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets

Author

Reps, J, Kim, C, Williams, R, Markus, A, Yang, C, Salles, TD, Falconer, T, Jonnagaddala, J, Williams, A, Fernández-Bertolín, S, DuVall, S, Kostka, K, Rao, G, Shoaibi, A, Ostropolets, A, Spotnitz, M, Zhang, L, Casajust, P, Steyerberg, E, Nyberg, F, Kaas-Hansen, BS, Choi, YH, Morales, D, Liaw, S-T, Fernandes Abrahão, MT, Areia, C, Matheny, M, Aragón, M, Park, RW, Hripcsak, G, Reich, C, Suchard, M, You, SC, Ryan, P, Prieto-Alhambra, D, Rijnbeek, P, Reps, J, Kim, C, Williams, R, Markus, A, Yang, C, Salles, TD, Falconer, T, Jonnagaddala, J, Williams, A, Fernández-Bertolín, S, DuVall, S, Kostka, K, Rao, G, Shoaibi, A, Ostropolets, A, Spotnitz, M, Zhang, L, Casajust, P, Steyerberg, E, Nyberg, F, Kaas-Hansen, BS, Choi, YH, Morales, D, Liaw, S-T, Fernandes Abrahão, MT, Areia, C, Matheny, M, Aragón, M, Park, RW, Hripcsak, G, Reich, C, Suchard, M, You, SC, Ryan, P, Prieto-Alhambra, D, and Rijnbeek, P

Abstract

Background

SARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision making during the pandemic. However, the model is at high risk of bias according to the Prediction model Risk Of Bias ASsessment Tool and has not been externally validated.

Methods

We followed the OHDSI framework for external validation to assess the reliability of the C-19 model. We evaluated the model on two different target populations: i) 41,381 patients that have SARS-CoV-2 at an outpatient or emergency room visit and ii) 9,429,285 patients that have influenza or related symptoms during an outpatient or emergency room visit, to predict their risk of hospitalization with pneumonia during the following 0 to 30 days. In total we validated the model across a network of 14 databases spanning the US, Europe, Australia and Asia.

Findings

The internal validation performance of the C-19 index was a c-statistic of 0.73 and calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data the model obtained c-statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US and South Korean datasets respectively. The calibration was poor with the model under-estimating risk. When validated on 12 datasets containing influenza patients across the OHDSI network the c-statistics ranged between 0.40-0.68.

Interpretation

The results show that the discriminative performance of the C-19 model is low for influenza cohorts, and even worse amongst COVID-19 patients in the US, Spain and South Korea. These results suggest that C-19 s

Published
2020

2. Seek COVER: Development and validation of a personalized risk calculator for COVID-19 outcomes in an international network

Author

Williams, R, Markus, A, Yang, C, Salles, TD, DuVall, S, Falconer, T, Jonnagaddala, J, Kim, C, Rho, Y, Williams, A, Alberga, A, An, MH, Aragón, M, Areia, C, Burn, E, Choi, YH, Drakos, I, Fernandes Abrahão, MT, Fernández-Bertolín, S, Hripcsak, G, Kaas-Hansen, BS, Kandukuri, P, Kors, J, Kostka, K, Liaw, S-T, Lynch, K, Machnicki, G, Matheny, M, Morales, D, Nyberg, F, Park, RW, Prats-Uribe, A, Pratt, N, Rao, G, Reich, C, Rivera, M, Seinen, T, Shoaibi, A, Spotnitz, M, Steyerberg, E, Suchard, M, You, SC, Zhang, L, Zhou, L, Ryan, P, Prieto-Alhambra, D, Reps, J, Rijnbeek, P, Williams, R, Markus, A, Yang, C, Salles, TD, DuVall, S, Falconer, T, Jonnagaddala, J, Kim, C, Rho, Y, Williams, A, Alberga, A, An, MH, Aragón, M, Areia, C, Burn, E, Choi, YH, Drakos, I, Fernandes Abrahão, MT, Fernández-Bertolín, S, Hripcsak, G, Kaas-Hansen, BS, Kandukuri, P, Kors, J, Kostka, K, Liaw, S-T, Lynch, K, Machnicki, G, Matheny, M, Morales, D, Nyberg, F, Park, RW, Prats-Uribe, A, Pratt, N, Rao, G, Reich, C, Rivera, M, Seinen, T, Shoaibi, A, Spotnitz, M, Steyerberg, E, Suchard, M, You, SC, Zhang, L, Zhou, L, Ryan, P, Prieto-Alhambra, D, Reps, J, and Rijnbeek, P

Abstract

Objective

To develop and externally validate COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission (COVER-H), requiring intensive services (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis.

Methods

We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries. We developed and validated 3 scores using 6,869,127 patients with a general practice, emergency room, or outpatient visit with diagnosed influenza or flu-like symptoms any time prior to 2020. The scores were validated on patients with confirmed or suspected COVID-19 diagnosis across five databases from South Korea, Spain and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death iii) death in the 30 days after index date.

Results

Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved high performance in influenza. When transported to COVID-19 cohorts, the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration was overall acceptable.

Conclusions

A 9-predictor model performs well for COVID-19 patients for predicting hospitalization, intensive services and fatality. The models could aid in providing reassurance for low risk patients and shield high risk patients from COVID-19 during de-confinement to reduce the virus’ impact on morbidity and mortality.

Published
2020

3. Integrating real-world data from Brazil and Pakistan into the OMOP common data model and standardized health analytics framework to characterize COVID-19 in the Global South.

Author

Junior EPP, Normando P, Flores-Ortiz R, Afzal MU, Jamil MA, Bertolin SF, Oliveira VA, Martufi V, de Sousa F, Bashir A, Burn E, Ichihara MY, Barreto ML, Salles TD, Prieto-Alhambra D, Hafeez H, and Khalid S

Subjects
Male, Humans, Female, Aged, Brazil epidemiology, Pakistan epidemiology, Intensive Care Units, Delivery of Health Care, COVID-19 epidemiology
Abstract

Objectives: The aim of this work is to demonstrate the use of a standardized health informatics framework to generate reliable and reproducible real-world evidence from Latin America and South Asia towards characterizing coronavirus disease 2019 (COVID-19) in the Global South., Materials and Methods: Patient-level COVID-19 records collected in a patient self-reported notification system, hospital in-patient and out-patient records, and community diagnostic labs were harmonized to the Observational Medical Outcomes Partnership common data model and analyzed using a federated network analytics framework. Clinical characteristics of individuals tested for, diagnosed with or tested positive for, hospitalized with, admitted to intensive care unit with, or dying with COVID-19 were estimated., Results: Two COVID-19 databases covering 8.3 million people from Pakistan and 2.6 million people from Bahia, Brazil were analyzed. 109 504 (Pakistan) and 921 (Brazil) medical concepts were harmonized to Observational Medical Outcomes Partnership common data model. In total, 341 505 (4.1%) people in the Pakistan dataset and 1 312 832 (49.2%) people in the Brazilian dataset were tested for COVID-19 between January 1, 2020 and April 20, 2022, with a median [IQR] age of 36 [25, 76] and 38 (27, 50); 40.3% and 56.5% were female in Pakistan and Brazil, respectively. 1.2% percent individuals in the Pakistan dataset had Afghan ethnicity. In Brazil, 52.3% had mixed ethnicity. In agreement with international findings, COVID-19 outcomes were more severe in men, elderly, and those with underlying health conditions., Conclusions: COVID-19 data from 2 large countries in the Global South were harmonized and analyzed using a standardized health informatics framework developed by an international community of health informaticians. This proof-of-concept study demonstrates a potential open science framework for global knowledge mobilization and clinical translation for timely response to healthcare needs in pandemics and beyond., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Published
2023
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4. Molecular Cytogenetic Approach to Characterize Novel and Cryptic Chromosome Abnormalities in Childhood Myeloid Malignances of Fanconi Anemia.

Author

Borges ML, Capela de Matos RR, Amaral BD, Soares-Ventura EM, Leite EP, Silva MO, Cornélio MT, Silva ML, Liehr T, and Marques-Salles TD

Subjects
Adolescent, Asymptomatic Diseases, Cell Transformation, Neoplastic, Child, Child, Preschool, Chromosomal Instability genetics, Chromosomes, Human ultrastructure, Clone Cells pathology, Dengue complications, Fanconi Anemia complications, Fatal Outcome, Female, Genetic Predisposition to Disease, Humans, Karyotyping, Leukemia, Myeloid, Acute etiology, Male, Myelodysplastic Syndromes etiology, Neoplastic Stem Cells pathology, Abnormal Karyotype, Fanconi Anemia genetics, Leukemia, Myeloid, Acute genetics, Myelodysplastic Syndromes genetics
Abstract

Myeloid malignancies can be either primary or secondary, whether or not a specific cause can be determined. Fanconi anemia (FA), a rare constitutional bone marrow failure, usually presents an increased possibility of clonal evolution, due to the increase in chromosomal instability, TP53 activation, and cell death. The evolution of FA may include aplastic anemia by the progressive failure of the bone marrow and myelod neoplasias, such as acute myeloid leukemia and myelodysplastic syndrome. Chromosome abnormalities, particularly of chromosomes, 1, 3, and 7, during the aplastic phase of the disease are predictive of evolution to acute myeloid leukemia/myelodysplastic syndrome. Cytogenetic studies are indispensable to characterize chromosome abnormalities, and thus an important part of the clinical management, and for planning of therapeutic interventions. Here, clinical data and outcomes of 4 FA, 3 of them with myeloid malignances and 1 asymptomatic, and detailed characterization of their chromosome abnormalities using cytogenetics techniques are described.

Published
2017
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