Dataset Information

Individualized prediction of COVID-19 adverse outcomes with MLHO.

ABSTRACT: The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients' past medical records (before their COVID-19 infection). MLHO's architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.

SUBMITTER: Estiri H

PROVIDER: S-EPMC7935934 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Individualized prediction of COVID-19 adverse outcomes with MLHO.

Estiri Hossein H Strasser Zachary H ZH Murphy Shawn N SN

Scientific reports 20210305 1

The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements i ...[more]

PMID: 33674708

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Project description:Risk calculators can facilitate shared medical decision-making 1 . Demographics, comorbidities, medication use, geographic region, and other factors may increase the risk for COVID-19-related complications among patients with IBD 2,3 . Develop an individualized prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with IBD. This study developed and validated prognostic penalized logistic regression models 4 using reports to Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease (SECURE-IBD) from March-October 2020. Model development was done using a training data set (85% of cases reported March 13 - September 15, 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported September 16-October 20, 2020). COVID-19 related:Hospitalization+: composite outcome of hospitalization, ICU admission, mechanical ventilation, or deathICU+: composite outcome of ICU admission, mechanical ventilation, or deathDeathWe assessed the resulting models' discrimination using the area under the curve (AUC) of the receiver-operator characteristic (ROC) curves and reported the corresponding 95% confidence intervals (CIs). We included 2709 cases from 59 countries (mean age 41.2 years [s.d. 18], 50.2% male). A total of 633 (24%) were hospitalized, 137 (5%) were admitted to the ICU or intubated, and 69 (3%) died. 2009 patients comprised the training set and 700 the test set.The models demonstrated excellent discrimination, with a test set AUC (95% CI) of 0.79 (0.75, 0.83) for Hospitalization+, 0.88 (0.82, 0.95) for ICU+, and 0.94 (0.89, 0.99) for Death. Age, comorbidities, corticosteroid use, and male gender were associated with higher risk of death, while use of biologic therapies was associated with a lower risk. Prognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of IBD patients. A free online risk calculator ( https://covidibd.org/covid-19-risk-calculator/ ) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with IBD patients. The tool numerically and visually summarizes the patient's probabilities of adverse outcomes and associated CIs. Helping physicians identify their highest-risk patients will be important in the coming months as cases rise in the US and worldwide. This tool can also serve as a model for risk stratification in other chronic diseases. Question: How well can a multivariable risk model predict the risk of hospitalization, intensive care unit (ICU) stay, or death due to COVID-19 in patients with inflammatory bowel disease (IBD)?Findings: Multivariable prediction models developed using data from an international voluntary registry of IBD patients and available for use online ( https://covidibd.org/ ) have very good discrimination for predicting hospitalization (Test set AUC 0.79) and excellent discrimination for ICU admission (Test set AUC 0.88) and death (Test set AUC 0.94). The models were developed with a training sample of 2009 cases and validated in an independent test sample of 700 cases comprised of a random sub-sample of cases and all cases entered in the registry during a one-month period after model development. Meaning: This risk prediction model may serve as an effective tool for healthcare providers to facilitate conversations about COVID-19-related risks with IBD patients.

Project description:ObjectivesDevelop an individualised prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with inflammatory bowel disease (IBD).Design and settingThis study developed and validated prognostic penalised logistic regression models using reports to the international Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease voluntary registry from March to October 2020. Model development was done using a training data set (85% of cases reported 13 March-15 September 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported 16 September-20 October 2020).ParticipantsWe included 2709 cases from 59 countries (mean age 41.2 years (SD 18), 50.2% male). All submitted cases after removing duplicates were included.Primary and secondary outcome measuresCOVID-19 related: (1) Hospitalisation+: composite outcome of hospitalisation, ICU admission, mechanical ventilation or death; (2) Intensive Care Unit+ (ICU+): composite outcome of ICU admission, mechanical ventilation or death; (3) Death. We assessed the resulting models' discrimination using the area under the curve of the receiver operator characteristic curves and reported the corresponding 95% CIs.ResultsOf the submitted cases, a total of 633 (24%) were hospitalised, 137 (5%) were admitted to the ICU or intubated and 69 (3%) died. 2009 patients comprised the training set and 700 the test set. The models demonstrated excellent discrimination, with a test set area under the curve (95% CI) of 0.79 (0.75 to 0.83) for Hospitalisation+, 0.88 (0.82 to 0.95) for ICU+ and 0.94 (0.89 to 0.99) for Death. Age, comorbidities, corticosteroid use and male gender were associated with a higher risk of death, while the use of biological therapies was associated with a lower risk.ConclusionsPrognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of patients with IBD. A free online risk calculator (https://covidibd.org/covid-19-risk-calculator/) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with patients with IBD.

Project description:BackgroundLittle is known about the association between acute mental changes and adverse outcomes in hospitalized adults with COVID-19.ObjectivesTo investigate the occurrence of delirium in hospitalized patients with COVID-19 and explore its association with adverse outcomes.DesignLongitudinal observational study.SettingTertiary university hospital dedicated to the care of severe cases of COVID-19 in São Paulo, Brazil.ParticipantsA total of 707 patients, aged 50 years or older, consecutively admitted to the hospital between March and May 2020.MeasurementsWe completed detailed reviews of electronic medical records to collect our data. We identified delirium occurrence using the Chart-Based Delirium Identification Instrument (CHART-DEL). Trained physicians with a background in geriatric medicine completed all CHART-DEL assessments. We complemented our baseline clinical information using telephone interviews with participants or their proxy. Our outcomes of interest were in-hospital death, length of stay, admission to intensive care, and ventilator utilization. We adjusted all multivariable analyses for age, sex, clinical history, vital signs, and relevant laboratory biomarkers (lymphocyte count, C-reactive protein, glomerular filtration rate, D-dimer, and albumin).ResultsOverall, we identified delirium in 234 participants (33%). On admission, 86 (12%) were delirious. We observed 273 deaths (39%) in our sample, and in-hospital mortality reached 55% in patients who experienced delirium. Delirium was associated with in-hospital death, with an adjusted odds ratio of 1.75 (95% confidence interval = 1.15-2.66); the association held both in middle-aged and older adults. Delirium was also associated with increased length of stay, admission to intensive care, and ventilator utilization.ConclusionDelirium was independently associated with in-hospital death in adults aged 50 years and older with COVID-19. Despite the difficulties for patient care during the pandemic, clinicians should routinely monitor delirium when assessing severity and prognosis of COVID-19 patients.

Project description:Developing an accurate and interpretable model to predict an individual's risk for Coronavirus Disease 2019 (COVID-19) is a critical step to efficiently triage testing and other scarce preventative resources. To aid in this effort, we have developed an interpretable risk calculator that utilized de-identified electronic health records (EHR) from the University of Alabama at Birmingham Informatics for Integrating Biology and the Bedside (UAB-i2b2) COVID-19 repository under the U-BRITE framework. The generated risk scores are analogous to commonly used credit scores where higher scores indicate higher risks for COVID-19 infection. By design, these risk scores can easily be calculated in spreadsheets or even with pen and paper. To predict risk, we implemented a Credit Scorecard modeling approach on longitudinal EHR data from 7,262 patients enrolled in the UAB Health System who were evaluated and/or tested for COVID-19 between January and June 2020. In this cohort, 912 patients were positive for COVID-19. Our workflow considered the timing of symptoms and medical conditions and tested the effects by applying different variable selection techniques such as LASSO and Elastic-Net. Within the two weeks before a COVID-19 diagnosis, the most predictive features were respiratory symptoms such as cough, abnormalities of breathing, pain in the throat and chest as well as other chronic conditions including nicotine dependence and major depressive disorder. When extending the timeframe to include all medical conditions across all time, our models also uncovered several chronic conditions impacting the respiratory, cardiovascular, central nervous and urinary organ systems. The whole pipeline of data processing, risk modeling and web-based risk calculator can be applied to any EHR data following the OMOP common data format. The results can be employed to generate questionnaires to estimate COVID-19 risk for screening in building entries or to optimize hospital resources.

Dataset Information

Individualized prediction of COVID-19 adverse outcomes with MLHO.

Publications

Individualized prediction of COVID-19 adverse outcomes with MLHO.

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