Prediction of individual COVID-19 diagnosis using baseline demographics and lab data.
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ABSTRACT:
SUBMITTER: Zhang J
PROVIDER: S-EPMC8260732 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.
| S-EPMC7556112 | biostudies-literature
Project description:BackgroundPrediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions.MethodsOur approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree.ResultsWe apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time.ConclusionWe presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.
| S-EPMC8046206 | biostudies-literature
Project description:At the COVID-19 pandemic onset, when individual-level data of COVID-19 patients were not yet available, there was already a need for risk predictors to support prevention and treatment decisions. Here, we report a hybrid strategy to create such a predictor, combining the development of a baseline severe respiratory infection risk predictor and a post-processing method to calibrate the predictions to reported COVID-19 case-fatality rates. With the accumulation of a COVID-19 patient cohort, this predictor is validated to have good discrimination (area under the receiver-operating characteristics curve of 0.943) and calibration (markedly improved compared to that of the baseline predictor). At a 5% risk threshold, 15% of patients are marked as high-risk, achieving a sensitivity of 88%. We thus demonstrate that even at the onset of a pandemic, shrouded in epidemiologic fog of war, it is possible to provide a useful risk predictor, now widely used in a large healthcare organization.
| S-EPMC7477233 | biostudies-literature
Project description:Risk of severe disease and death due to coronavirus 2019 (COVID-19) is increased in certain patient demographic groups, including those who are of advanced age, male sex, and obese body mass index. Investigations of the biological variations that contribute to this risk have been hampered by heterogeneous severity, with immunologic features of critical disease potentially obscuring differences between risk groups. To examine immune heterogeneity related to demographic risk factors, we enrolled 38 patients hospitalized with clinically homogeneous COVID-19 pneumonia – defined as oxygen saturation <94% on room air without respiratory failure, septic shock, or multiple organ dysfunction – and performed single-cell RNA-sequencing of leukocytes collected at admission. Examination of individual risk factors identified strong shifts within neutrophil and monocyte/dendritic cell compartments, revealing altered immune cell type-specific responses in higher-risk COVID-19 patient subgroups. Specifically, we found transcriptional evidence of altered neutrophil maturation in aged versus young patients and enhanced cytokine responses in monocytes/dendritic cells of male versus female patients. Such innate immune cell alterations may contribute to outcome differences linked to these risk factors. They also highlight the importance of diverse patient cohorts in studies of therapies targeting the immune response in COVID-19.
2023-10-11 | GSE236177 | GEO
Project description:We describe a method to estimate individual risks of hospitalization and death attributable to non-household and household transmission of SARS-CoV-2 using available public data on confirmed-case incidence data along with estimates of the clinical fraction, timing of transmission, isolation adherence, secondary infection risks, contact rates, and case-hospitalization and case-fatality ratios. Using the method, we estimate that risks for a 90-day period at the median daily summertime U.S. county confirmed COVID-19 case incidence of 10.8 per 100,000 and pre-pandemic contact rates range from 0.4 to 8.9 per 100,000 for the four deciles of age between 20 and 60 years. The corresponding 90-day period risk of hospitalization ranges from 13.7 to 69.2 per 100,000. Assuming a non-household secondary infection risk of 4% and pre-pandemic contact rates, the share of transmissions attributable to household settings ranges from 73% to 78%. These estimates are sensitive to the parameter assumptions; nevertheless, they are comparable to the COVID-19 hospitalization and fatality rates observed over the time period. We conclude that individual risk of hospitalization and death from SARS-CoV-2 infection is calculable from publicly available data sources. Access to publicly reported infection incidence data by setting and other exposure characteristics along with setting specific estimates of secondary infection risk would allow for more precise individual risk estimation.
| S-EPMC7721133 | biostudies-literature
Project description:IntroductionCoronavirus disease 2019 (COVID-19) can present as a range of symptoms, from mild to critical; lower pulmonary involvement, including pneumonia, is often associated with severe and critical cases. Understanding the baseline characteristics of patients hospitalized with COVID-19 illness is essential for effectively targeting clinical care and allocating resources. This study aimed to describe baseline demographics and clinical characteristics of US patients hospitalized with COVID-19 and pulmonary involvement.MethodsUS patients with COVID-19 and pulmonary involvement during an inpatient admission from December 1, 2019, to May 20, 2020, were identified using the IBM Explorys® electronic health records database. Baseline (up to 12 months prior to first COVID-19 hospitalization) demographics and clinical characteristics and preadmission (14 days to 1 day prior to admission) pulmonary diagnoses were assessed. Patients were stratified by sex, age, race, and geographic region.ResultsOverall, 3471 US patients hospitalized with COVID-19 and pulmonary involvement were included. The mean (SD) age was 63.5 (16.3) years; 51.2% of patients were female, 55.0% African American, 81.6% from the South, and 16.8% from the Midwest. The most common comorbidities included hypertension (27.7%), diabetes (17.3%), hyperlipidemia (16.3%), and obesity (9.7%). Cough (27.3%) and dyspnea (15.2%) were the most common preadmission pulmonary symptoms. African American patients were younger (mean [SD], 62.5 [15.4] vs. 67.8 [6.2]) with higher mean (SD) body mass index (33.66 [9.46] vs. 30.42 [7.86]) and prevalence of diabetes (19.8% vs. 16.7%) and lower prevalence of chronic obstructive pulmonary disease (5.6% vs. 8.2%) and smoking/tobacco use (28.1% vs. 37.2%) than White patients.ConclusionsAmong US patients primarily from the South and Midwest hospitalized with COVID-19 and pulmonary involvement, the most common comorbidities were hypertension, diabetes, hyperlipidemia, and obesity. Differences observed between African American and White patients should be considered in the context of the complex factors underlying racial disparities in COVID-19.
| S-EPMC7548311 | biostudies-literature
Project description:BackgroundCOVID-19, caused by SARS-CoV-2, presents distinct diagnostic challenges due to its wide range of clinical manifestations and the overlapping symptoms with other common respiratory diseases. This study focuses on addressing these difficulties by employing machine learning (ML) methodologies, particularly the XGBoost algorithm, to utilize Complete Blood Count (CBC) parameters for predictive analysis.MethodsWe performed a retrospective study involving 2114 COVID-19 patients treated between December 2022 and January 2023 at our healthcare facility. These patients were classified into fever (1057 patients) and pneumonia groups (1057 patients), based on their clinical symptoms. The CBC data were utilized to create predictive models, with model performance evaluated through metrics like Area Under the Receiver Operating Characteristics Curve (AUC), accuracy, sensitivity, specificity, and precision. We selected the top 10 predictive variables based on their significance in disease prediction. The data were then split into a training set (70% of patients) and a validation set (30% of patients) for model validation.ResultsWe identified 31 indicators with significant disparities. The XGBoost model outperformed others, with an AUC of 0.920 and high precision, sensitivity, specificity, and accuracy. The top 10 features (Age, Monocyte%, Mean Platelet Volume, Lymphocyte%, SIRI, Eosinophil count, Platelet count, Hemoglobin, Platelet Distribution Width, and Neutrophil count.) were crucial in constructing a more precise predictive model. The model demonstrated strong performance on both training (AUC = 0.977) and validation (AUC = 0.912) datasets, validated by decision curve analysis and calibration curve.ConclusionML models that incorporate CBC parameters offer an innovative and effective tool for data analysis in COVID-19. They potentially enhance diagnostic accuracy and the efficacy of therapeutic interventions, ultimately contributing to a reduction in the mortality rate of this infectious disease.
| S-EPMC10729447 | biostudies-literature
Project description:ObjectiveTo externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19.DesignTwo stage individual participant data meta-analysis.SettingSecondary and tertiary care.Participants46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021.Data sourcesMultiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ, and through PROSPERO, reference checking, and expert knowledge.Model selection and eligibility criteriaPrognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor.MethodsEight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters.Main outcome measures30 day mortality or in-hospital mortality.ResultsDatasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28).ConclusionThe prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.
| S-EPMC9273913 | biostudies-literature
Project description:ObjectivesEarly recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.MethodsAn artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19.ResultsA multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001).ConclusionsUsing CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment.Key point• AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.
| S-EPMC8256200 | biostudies-literature
Project description: Not available
| S-EPMC7891117 | biostudies-literature