Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests.
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ABSTRACT:
SUBMITTER: Yao H
PROVIDER: S-EPMC7411005 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveScreening for chronic kidney disease (CKD) requires an estimated glomerular filtration rate (eGFR, mL/min/1.73 m2) from a blood sample and a proteinuria level from a urinalysis. We developed machine-learning models to detect CKD without blood collection, predicting an eGFR less than 60 (eGFR60 model) or 45 (eGFR45 model) using a urine dipstick test.Materials and methodsThe electronic health record data (n = 220 018) obtained from university hospitals were used for XGBoost-derived model construction. The model variables were age, sex, and 10 measurements from the urine dipstick test. The models were validated using health checkup center data (n = 74 380) and nationwide public data (KNHANES data, n = 62 945) for the general population in Korea.ResultsThe models comprised 7 features, including age, sex, and 5 urine dipstick measurements (protein, blood, glucose, pH, and specific gravity). The internal and external areas under the curve (AUCs) of the eGFR60 model were 0.90 or higher, and a higher AUC for the eGFR45 model was obtained. For the eGFR60 model on KNHANES data, the sensitivity was 0.93 or 0.80, and the specificity was 0.86 or 0.85 in ages less than 65 with proteinuria (nondiabetes or diabetes, respectively). Nonproteinuric CKD could be detected in nondiabetic patients under the age of 65 with a sensitivity of 0.88 and specificity of 0.71.Discussion and conclusionsThe model performance differed across subgroups by age, proteinuria, and diabetes. The CKD progression risk can be assessed with the eGFR models using the levels of eGFR decrease and proteinuria. The machine-learning-enhanced urine-dipstick test can become a point-of-care test to promote public health by screening CKD and ranking its risk of progression.
| S-EPMC10198537 | biostudies-literature
Project description:SubjectsEarly detection of coronavirus disease 2019 in patients likely to develop severe manifestations enables appropriate interventions, including rapid ICU admission. This study was conducted to determine whether noninvasive urine biomarkers can predict the clinical severity of coronavirus disease 2019.InterventionsNot applicable.Measurements and main resultsThis is single-center study, national center hospital designated for infectious disease. Fifty-eight patients who tested positive for severe acute respiratory syndrome coronavirus 2 in respiratory specimens through real-time reverse transcription-polymerase chain reaction were retrospectively studied. Urinary β2-microglobulin, liver-type fatty acid-binding protein were serially measured. Serum interferon-γ and monocyte chemotactic protein-1 were also evaluated. The 58 patients were assigned into three groups. Patients requiring intensive care were assigned to the severe group (n = 12). Patients treated with oxygen were assigned to the moderate group (n = 13). Other patients were assigned to the mild group (n = 33). Urine tests revealed that low β2-microglobulin and liver-type fatty acid-binding protein levels were associated with mild disease, whereas high levels were associated with severe disease. In severe cases, liver-type fatty acid-binding protein tended to be persistently high. The resulting cutoff values were β2-microglobulin; severe versus moderate + mild: 2,457 μg/dL (specificity 76.9% and sensitivity 90.0%, area under the receiver operating characteristic curve 85.9%), liver-type fatty acid-binding protein; severe versus moderate + mild: 22.0 μg/gCre (specificity 84.6% and sensitivity 90%, area under the receiver operating characteristic curve 91.8%). Urinary β2-microglobulin and serum interferon-γ/monocyte chemotactic protein-1 showed a similar trend.ConclusionsEvaluating urinary biomarkers such as β2-microglobulin and liver-type fatty acid-binding protein may allow determination of coronavirus disease 2019 patients with active cytokines and recognition of patients likely to become critically ill and requiring careful observation and early intervention.
| S-EPMC7402421 | biostudies-literature
Project description:Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected the operational ROC point at a sensitivity of 81.9% and a specificity of 97.9%. The cross-validated AUC was 0.97. The five most useful routine blood parameters for COVID-19 diagnosis according to the feature importance scoring of the XGBoost algorithm were: MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. t-SNE visualization showed that the blood parameters of the patients with a severe COVID-19 course are more like the parameters of a bacterial than a viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results represent a significant contribution to improvements in COVID-19 diagnosis.
| S-EPMC8144373 | biostudies-literature
Project description:ObjectivesThe present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results.MethodsWe developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil.ResultsThe accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%).ConclusionsML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses.
| S-EPMC8690000 | biostudies-literature
Project description:Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
| S-EPMC8960704 | biostudies-literature
Project description:With widespread usage of online social networks and its popularity, social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. In this study, we have proposed a cyberbullying detection framework to generate features from Twitter content by leveraging a pointwise mutual information technique. Based on these features, we developed a supervised machine learning solution for cyberbullying detection and multi-class categorization of its severity in Twitter. In the study we applied Embedding, Sentiment, and Lexicon features along with PMI-semantic orientation. Extracted features were applied with Naïve Bayes, KNN, Decision Tree, Random Forest, and Support Vector Machine algorithms. Results from experiments with our proposed framework in a multi-class setting are promising both with respect to Kappa, classifier accuracy and f-measure metrics, as well as in a binary setting. These results indicate that our proposed framework provides a feasible solution to detect cyberbullying behavior and its severity in online social networks. Finally, we compared the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection.
| S-EPMC7591033 | biostudies-literature
Project description:Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients we built a machine learning predictive model for the diagnosis of brain tumours. We validated the model by retrospective analysis of 68 consecutive brain tumour and 215 control patients presenting to the neurological emergency service. Only patients with head imaging and routine blood test data were included in the validation sample. The sensitivity and specificity of the adapted tumour model in the validation group were 96% and 74%, respectively. Our data demonstrate the feasibility of brain tumour diagnosis from routine blood tests using machine learning. The reported diagnostic accuracy is comparable and possibly complementary to that of imaging studies. The presented machine learning approach opens a completely new avenue in the diagnosis of these grave neurological diseases and demonstrates the utility of valuable information obtained from routine blood tests.
| S-EPMC6785553 | biostudies-literature
Project description:ObjectiveThis study aimed to implement and evaluate machine learning based-models to predict COVID-19' diagnosis and disease severity.MethodsCOVID-19 test samples (positive or negative results) from patients who attended a single hospital were evaluated. Patients diagnosed with COVID-19 were categorised according to the severity of the disease. Data were submitted to exploratory analysis (principal component analysis, PCA) to detect outlier samples, recognise patterns, and identify important variables. Based on patients' laboratory tests results, machine learning models were implemented to predict disease positivity and severity. Artificial neural networks (ANN), decision trees (DT), partial least squares discriminant analysis (PLS-DA), and K nearest neighbour algorithm (KNN) models were used. The four models were validated based on the accuracy (area under the ROC curve).ResultsThe first subset of data had 5,643 patient samples (5,086 negatives and 557 positives for COVID-19). The second subset included 557 COVID-19 positive patients. The ANN, DT, PLS-DA, and KNN models allowed the classification of negative and positive samples with >84% accuracy. It was also possible to classify patients with severe and non-severe disease with an accuracy >86%. The following were associated with the prediction of COVID-19 diagnosis and severity: hyperferritinaemia, hypocalcaemia, pulmonary hypoxia, hypoxemia, metabolic and respiratory acidosis, low urinary pH, and high levels of lactate dehydrogenase.ConclusionOur analysis shows that all the models could assist in the diagnosis and prediction of COVID-19 severity.
| S-EPMC8164361 | biostudies-literature
Project description:The purpose of this study was to identify miRNAs that were dysregulated after the onset of COVID-19 and thus potentially be used for risk stratification (i.e., mortality). Therefore, we conducted a multi-center, retrospective longitudinal cohort study enrolling 142 patients with laboratory-confirmed SARS-CoV-2 infection who presented to two Canadian hospitals from May 2020 – December 2020 along with a cohort of 27 SARS-CoV-2 patients with mild upper respiratory tract symptoms and 69 SARS-CoV-2-negative patients from the ICU. Blood was biobanked from SARS-CoV-2 positive patients in the emergency department (mild), ward (moderate) or intensive care unit (severe). Assessment of miRNA expression and co-regulatory network generation revealed significant transcriptome dyregulation in pateints with severe COVID-19 that was largely different from SARS-CoV-2 negative patients in the ICU.
2022-04-08 | GSE178246 | GEO
Project description:Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.
| S-EPMC8469072 | biostudies-literature