Dataset Information

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.

ABSTRACT:

Background

For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic.

Objective

This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases.

Methods

Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19-like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features.

Results

Compared to the COVID-19-like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19-like patients. In the COVID-19-like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19-like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values.

Conclusions

We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

SUBMITTER: Sang S

PROVIDER: S-EPMC7901593 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.

Sang Shengtian S Sun Ran R Coquet Jean J Carmichael Harris H Seto Tina T Hernandez-Boussard Tina T

Journal of medical Internet research 20210222 2

<h4>Background</h4>For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particula ...[more]

PMID: 33534724

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Project description:BackgroundUnlike well-established diseases that base clinical care on randomized trials, past experiences, and training, prognosis in COVID19 relies on a weaker foundation. Knowledge from other respiratory failure diseases may inform clinical decisions in this novel disease. The objective was to predict 48-hour invasive mechanical ventilation (IMV) within 48 h in patients hospitalized with COVID-19 using COVID-like diseases (CLD).MethodsThis retrospective multicenter study trained machine learning (ML) models on patients hospitalized with CLD to predict IMV within 48 h in COVID-19 patients. CLD patients were identified using diagnosis codes for bacterial pneumonia, viral pneumonia, influenza, unspecified pneumonia and acute respiratory distress syndrome (ARDS), 2008-2019. A total of 16 cohorts were constructed, including any combinations of the four diseases plus an exploratory ARDS cohort, to determine the most appropriate cohort to use. Candidate predictors included demographic and clinical parameters that were previously associated with poor COVID-19 outcomes. Model development included the implementation of logistic regression and three ensemble tree-based algorithms: decision tree, AdaBoost, and XGBoost. Models were validated in hospitalized COVID-19 patients at two healthcare systems, March 2020-July 2020. ML models were trained on CLD patients at Stanford Hospital Alliance (SHA). Models were validated on hospitalized COVID-19 patients at both SHA and Intermountain Healthcare.ResultsCLD training data were obtained from SHA (n = 14,030), and validation data included 444 adult COVID-19 hospitalized patients from SHA (n = 185) and Intermountain (n = 259). XGBoost was the top-performing ML model, and among the 16 CLD training cohorts, the best model achieved an area under curve (AUC) of 0.883 in the validation set. In COVID-19 patients, the prediction models exhibited moderate discrimination performance, with the best models achieving an AUC of 0.77 at SHA and 0.65 at Intermountain. The model trained on all pneumonia and influenza cohorts had the best overall performance (SHA: positive predictive value (PPV) 0.29, negative predictive value (NPV) 0.97, positive likelihood ratio (PLR) 10.7; Intermountain: PPV, 0.23, NPV 0.97, PLR 10.3). We identified important factors associated with IMV that are not traditionally considered for respiratory diseases.ConclusionsThe performance of prediction models derived from CLD for 48-hour IMV in patients hospitalized with COVID-19 demonstrate high specificity and can be used as a triage tool at point of care. Novel predictors of IMV identified in COVID-19 are often overlooked in clinical practice. Lessons learned from our approach may assist other research institutes seeking to build artificial intelligence technologies for novel or rare diseases with limited data for training and validation.

Project description:BackgroundCOVID-19 has been associated with Interstitial Lung Disease features. The immune transcriptomic overlap between Idiopathic Pulmonary Fibrosis (IPF) and COVID-19 has not been investigated.Methodswe analyzed blood transcript levels of 50 genes known to predict IPF mortality in three COVID-19 and two IPF cohorts. The Scoring Algorithm of Molecular Subphenotypes (SAMS) was applied to distinguish high versus low-risk profiles in all cohorts. SAMS cutoffs derived from the COVID-19 Discovery cohort were used to predict intensive care unit (ICU) status, need for mechanical ventilation, and in-hospital mortality in the COVID-19 Validation cohort. A COVID-19 Single-cell RNA-sequencing cohort was used to identify the cellular sources of the 50-gene risk profiles. The same COVID-19 SAMS cutoffs were used to predict mortality in the IPF cohorts.Findings50-gene risk profiles discriminated severe from mild COVID-19 in the Discovery cohort (P = 0·015) and predicted ICU admission, need for mechanical ventilation, and in-hospital mortality (AUC: 0·77, 0·75, and 0·74, respectively, P < 0·001) in the COVID-19 Validation cohort. In COVID-19, 50-gene expressing cells with a high-risk profile included monocytes, dendritic cells, and neutrophils, while low-risk profile-expressing cells included CD4+, CD8+ T lymphocytes, IgG producing plasmablasts, B cells, NK, and gamma/delta T cells. Same COVID-19 SAMS cutoffs were also predictive of mortality in the University of Chicago (HR:5·26, 95%CI:1·81-15·27, P = 0·0013) and Imperial College of London (HR:4·31, 95%CI:1·81-10·23, P = 0·0016) IPF cohorts.Interpretation50-gene risk profiles in peripheral blood predict COVID-19 and IPF outcomes. The cellular sources of these gene expression changes suggest common innate and adaptive immune responses in both diseases.FundingThis work was supported in part by National Institute for Health Research Clinician Scientist Fellowship NIHR: CS-2013-13-017 (TMM); Action for Pulmonary Fibrosis Mike Bray fellowship (PLM); The National Heart, Lung, and Blood Institute (NHLBI) through award K01-HL-130704 (AJ); The University of South Florida (USF) Academic Support Fund and the USF Foundation, Ubben Fibrosis Fund (JHM).

Project description:BackgroundPrioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available.ObjectiveThe purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics-baseline demographics, comorbidities, and symptoms.MethodsA retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC).ResultsA total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively.ConclusionsWe developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.

Project description:BackgroundThe COVID-19 pandemic remains an immediate and present concern, yet as of now there is still no approved therapeutic available for the treatment of COVID-19.This study aimed to investigate and report evidence concerning demographic characteristics and currently-used medications that contribute to the ultimate outcomes of COVID-19 ICU patients.MethodsA retrospective cohort study was conducted among all COVID-19 patients in the Intensive Care Unit (ICU) of Asir Central Hospital in Saudi Arabia between the 1st and 30th of June 2020. Data extracted from patients' medical records included their demographics, home medications, medications used to treat COVID-19, treatment durations, ICU stay, hospital stay, and ultimate outcome (recovery or death).Descriptive statistics and regression modelling were used to analyze and compare the results. The study was approved by the Institutional Ethics Committees at both Asir Central Hospital and King Khalid University.ResultsA total of 118 patients with median age of 57 years having definite clinical and disease outcomes were included in the study. Male patients accounted for 87% of the study population, and more than 65% experienced at least one comorbidity. The mean hospital and ICU stay was 11.4 and 9.8 days, respectively. The most common drugs used were tocilizumab (31.4%), triple combination therapy (45.8%), favipiravir (56.8%), dexamethasone (86.7%), and enoxaparin (83%). Treatment with enoxaparin significantly reduced the length of ICU stay (p = 0.04) and was found to be associated with mortality reduction in patients aged 50-75 (p = 0.03), whereas the triple regimen therapy and tocilizumab significantly increased the length of ICU stay in all patients (p = 0.01, p = 0.02 respectively).ConclusionCOVID-19 tends to affect males more significantly than females. The use of enoxaparin is an important part of COVID-19 treatment, especially for those above 50 years of age, while the use of triple combination therapy and tocilizumab in COVID-19 protocols should be reevaluated and restricted to patients who have high likelihood of benefit.

Project description:BackgroundThe current COVID-19 pandemic is unprecedented; under resource-constrained settings, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients; however, there are only few risk scores derived from a substantially large electronic health record (EHR) data set, using simplified predictors as input.ObjectiveThe objectives of this study were to develop and validate simplified machine learning algorithms that predict COVID-19 adverse outcomes; to evaluate the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration of the algorithms; and to derive clinically meaningful thresholds.MethodsWe performed machine learning model development and validation via a cohort study using multicenter, patient-level, longitudinal EHRs from the Optum COVID-19 database that provides anonymized, longitudinal EHR from across the United States. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, intensive care unit (ICU) admission, respiratory failure, and mechanical ventilator usages at inpatient setting. Data from patients who were admitted from February 1, 2020, to September 7, 2020, were randomly sampled into development, validation, and test data sets; data collected from September 7, 2020, to November 15, 2020, were reserved as the postdevelopment prospective test data set.ResultsOf the 3.7 million patients in the analysis, 585,867 patients were diagnosed or tested positive for SARS-CoV-2, and 50,703 adult patients were hospitalized with COVID-19 between February 1 and November 15, 2020. Among the study cohort (n=50,703), there were 6204 deaths, 9564 ICU admissions, 6478 mechanically ventilated or EMCO patients, and 25,169 patients developed acute respiratory distress syndrome or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC 0.89, 95% CI 0.89-0.89 on the test data set [n=10,752]), consistent prediction through the second wave of the pandemic from September to November (AUC 0.85, 95% CI 0.85-0.86) on the postdevelopment prospective test data set [n=14,863], great clinical relevance, and utility. Besides, a comprehensive set of 386 input covariates from baseline or at admission were included in the analysis; the end-to-end pipeline automates feature selection and model development. The parsimonious model with only 10 input predictors produced comparably accurate predictions; these 10 predictors (age, blood urea nitrogen, SpO2, systolic and diastolic blood pressures, respiration rate, pulse, temperature, albumin, and major cognitive disorder excluding stroke) are commonly measured and concordant with recognized risk factors for COVID-19.ConclusionsThe systematic approach and rigorous validation demonstrate consistent model performance to predict even beyond the period of data collection, with satisfactory discriminatory power and great clinical utility. Overall, the study offers an accurate, validated, and reliable prediction model based on only 10 clinical features as a prognostic tool to stratifying patients with COVID-19 into intermediate-, high-, and very high-risk groups. This simple predictive tool is shared with a wider health care community, to enable service as an early warning system to alert physicians of possible high-risk patients, or as a resource triaging tool to optimize health care resources.

Dataset Information

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.

Background

Objective

Methods

Results

Conclusions

Publications

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.

Similar Datasets

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