Dataset Information

Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.

ABSTRACT:

Background and aims

Identification of SARS-CoV-2-infected patients at high-risk of poor prognosis is crucial. We aimed to establish predictive models for COVID-19 pneumonia severity in hospitalized patients.

Methods

Retrospective study of 430 patients admitted in Vall d'Hebron Hospital (Barcelona) between 03-12-2020 and 04-28-2020 due to COVID-19 pneumonia. Two models to identify the patients who required high-flow-oxygen-support were generated, one using baseline data and another with also follow-up analytical results. Calibration was performed by a 1000-bootstrap replication model.

Results

249 were male, mean age 57.9 years. Overall, 135 (31.4%) required high-flow-oxygen-support. The baseline predictive model showed a ROC of 0.800 based on: SpO2/FiO2 (adjusted Hazard Ratio-aHR = 8), chest x-ray (aHR = 4), prior immunosuppressive therapy (aHR = 4), obesity (aHR = 2), IL-6 (aHR = 2), platelets (aHR = 0.5). The cut-off of 11 presented a specificity of 94.8%. The second model included changes on the analytical parameters: ferritin (aHR = 7.5 if ?200ng/mL) and IL-6 (aHR = 18 if ?64pg/mL) plus chest x-ray (aHR = 2) showing a ROC of 0.877. The cut-off of 12 exhibited a negative predictive value of 92%.

Conclusions

SpO2/FiO2 and chest x-ray on admission or changes on inflammatory parameters as IL-6 and ferritin allow us early identification of COVID-19 patients at risk of high-flow-oxygen-support that may benefit from a more intensive disease management.

SUBMITTER: Riveiro-Barciela M

PROVIDER: S-EPMC7769554 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.

Riveiro-Barciela Mar M Labrador-Horrillo Moisés M Camps-Relats Laura L González-Sans Didac D Ventura-Cots Meritxell M Terrones-Peinador María M Nuñez-Conde Andrea A Martínez-Gallo Mónica M Hernández Manuel M Antón Andrés A González Antonio A Pujol-Borrell Ricardo R Martínez-Valle Fernando F

PloS one 20201228 12

<h4>Background and aims</h4>Identification of SARS-CoV-2-infected patients at high-risk of poor prognosis is crucial. We aimed to establish predictive models for COVID-19 pneumonia severity in hospitalized patients.<h4>Methods</h4>Retrospective study of 430 patients admitted in Vall d'Hebron Hospital (Barcelona) between 03-12-2020 and 04-28-2020 due to COVID-19 pneumonia. Two models to identify the patients who required high-flow-oxygen-support were generated, one using baseline data and another ...[more]

PMID: 33370397

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Project description:Highlights • COVID-19 outbreak belongs to the simple universality class of the SIR model and extensions thereof.• The unpredictable non-stationarity of the testing frames behind the figures reported by national authorities sets a fundamental limitation to any theoretical approach.• The time evolution of the reporting rates controls the occurrence of the apparent epidemic peak, which typically follows the true one in countries that were not vigorous enough in their testing at the onset of the outbreak. When the novel coronavirus disease SARS-CoV2 (COVID-19) was officially declared a pandemic by the WHO in March 2020, the scientific community had already braced up in the effort of making sense of the fast-growing wealth of data gathered by national authorities all over the world. However, despite the diversity of novel theoretical approaches and the comprehensiveness of many widely established models, the official figures that recount the course of the outbreak still sketch a largely elusive and intimidating picture. Here we show unambiguously that the dynamics of the COVID-19 outbreak belongs to the simple universality class of the SIR model and extensions thereof. Our analysis naturally leads us to establish that there exists a fundamental limitation to any theoretical approach, namely the unpredictable non-stationarity of the testing frames behind the reported figures. However, we show how such bias can be quantified self-consistently and employed to mine useful and accurate information from the data. In particular, we describe how the time evolution of the reporting rates controls the occurrence of the apparent epidemic peak, which typically follows the true one in countries that were not vigorous enough in their testing at the onset of the outbreak. The importance of testing early and resolutely appears as a natural corollary of our analysis, as countries that tested massively at the start clearly had their true peak earlier and less deaths overall.

Project description:BACKGROUND:COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU. OBJECTIVE:The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care. METHODS:Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations. RESULTS:Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00). CONCLUSIONS:Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.

Dataset Information

Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.

Background and aims

Methods

Results

Conclusions

Publications

Simple predictive models identify patients with COVID-19 pneumonia and poor prognosis.

Similar Datasets

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