Dataset Information

Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer.

ABSTRACT: To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. Methods: We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 variables was analyzed. Data were randomly split into training (70%) and testing (30%) sets. Model performance was assessed by confusion matrix metrics on the testing data for each model as positive predictive value, sensitivity and F1-score. Results: We showed that the five selected models outperformed classical statistical methods of predictive validity and logistic regression was the most effective, being able to classify with an accuracy of 81%. The most relevant result was finding a patient-proof where python function was able to obtain the exact dose of low weight molecular heparin to be administered and thereby to prevent the occurrence of VTE. Conclusions: The world of machine learning and artificial intelligence is constantly developing. The identification of a specific LMWH dose for preventing VTE in very high-risk populations, such as the COVID-19 and active cancer population, might improve with the use of new training ML-based algorithms. Larger studies are needed to confirm our exploratory results.

SUBMITTER: Imbalzano E

PROVIDER: S-EPMC8746167 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer.

Imbalzano Egidio E Orlando Luana L Sciacqua Angela A Nato Giuseppe G Dentali Francesco F Nassisi Veronica V Russo Vincenzo V Camporese Giuseppe G Bagnato Gianluca G Cicero Arrigo F G AFG Dattilo Giuseppe G Vatrano Marco M Versace Antonio Giovanni AG Squadrito Giovanni G Di Micco Pierpaolo P

Journal of clinical medicine 20211231 1

To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. <b>Methods:</b> We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 varia ...[more]

PMID: 35011959

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Project description:ImportanceRandomized clinical trials (RCTs) of therapeutic-dose heparin in patients hospitalized with COVID-19 produced conflicting results, possibly due to heterogeneity of treatment effect (HTE) across individuals. Better understanding of HTE could facilitate individualized clinical decision-making.ObjectiveTo evaluate HTE of therapeutic-dose heparin for patients hospitalized for COVID-19 and to compare approaches to assessing HTE.Design, setting, and participantsExploratory analysis of a multiplatform adaptive RCT of therapeutic-dose heparin vs usual care pharmacologic thromboprophylaxis in 3320 patients hospitalized for COVID-19 enrolled in North America, South America, Europe, Asia, and Australia between April 2020 and January 2021. Heterogeneity of treatment effect was assessed 3 ways: using (1) conventional subgroup analyses of baseline characteristics, (2) a multivariable outcome prediction model (risk-based approach), and (3) a multivariable causal forest model (effect-based approach). Analyses primarily used bayesian statistics, consistent with the original trial.ExposuresParticipants were randomized to therapeutic-dose heparin or usual care pharmacologic thromboprophylaxis.Main outcomes and measuresOrgan support-free days, assigning a value of -1 to those who died in the hospital and the number of days free of cardiovascular or respiratory organ support up to day 21 for those who survived to hospital discharge; and hospital survival.ResultsBaseline demographic characteristics were similar between patients randomized to therapeutic-dose heparin or usual care (median age, 60 years; 38% female; 32% known non-White race; 45% Hispanic). In the overall multiplatform RCT population, therapeutic-dose heparin was not associated with an increase in organ support-free days (median value for the posterior distribution of the OR, 1.05; 95% credible interval, 0.91-1.22). In conventional subgroup analyses, the effect of therapeutic-dose heparin on organ support-free days differed between patients requiring organ support at baseline or not (median OR, 0.85 vs 1.30; posterior probability of difference in OR, 99.8%), between females and males (median OR, 0.87 vs 1.16; posterior probability of difference in OR, 96.4%), and between patients with lower body mass index (BMI <30) vs higher BMI groups (BMI ≥30; posterior probability of difference in ORs >90% for all comparisons). In risk-based analysis, patients at lowest risk of poor outcome had the highest propensity for benefit from heparin (lowest risk decile: posterior probability of OR >1, 92%) while those at highest risk were most likely to be harmed (highest risk decile: posterior probability of OR <1, 87%). In effect-based analysis, a subset of patients identified at high risk of harm (P = .05 for difference in treatment effect) tended to have high BMI and were more likely to require organ support at baseline.Conclusions and relevanceAmong patients hospitalized for COVID-19, the effect of therapeutic-dose heparin was heterogeneous. In all 3 approaches to assessing HTE, heparin was more likely to be beneficial in those who were less severely ill at presentation or had lower BMI and more likely to be harmful in sicker patients and those with higher BMI. The findings illustrate the importance of considering HTE in the design and analysis of RCTs.Trial registrationClinicalTrials.gov Identifiers: NCT02735707, NCT04505774, NCT04359277, NCT04372589.

Project description:BackgroundIdentification of distinct clinical phenotypes of diseases can guide personalized treatment. This study aimed to classify hospitalized COVID-19 pneumonia subgroups using an unsupervised machine learning approach.MethodsWe included hospitalized COVID-19 pneumonia patients from July to September 2021. K-means clustering, an unsupervised machine learning method, was performed to identify clinical phenotypes based on clinical and laboratory variables collected within 24 hours of admission. Variables were normalized before clustering to ensure equal contribution to the analysis. The optimal number of clusters was determined using the elbow method and Silhouette scores. Cox proportional hazard models were used to compare the risk of intubation and 90-day mortality across the identified clusters.ResultsThree clinically distinct clusters were identified among 538 hospitalized COVID-19 pneumonia patients. Cluster 1 (N = 27) consisted predominantly of males and showed significantly elevated serum liver enzymes and LDH levels. Cluster 2 (N = 370) was characterized by lower chest x-ray scores and higher serum albumin levels. Cluster 3 (N = 141) was characterized by older age, diabetes mellitus, higher chest x-ray scores, more severe vital signs, higher creatinine levels, lower hemoglobin levels, lower lymphocyte counts, higher C-reactive protein, higher D-dimer, and higher LDH levels. When compared to cluster 2, cluster 3 was significantly associated with increased risk of 90-day mortality (HR, 6.24; 95% CI, 2.42-16.09) and intubation (HR, 5.26; 95% CI 2.37-11.72). In contrast, cluster 1 had a 100% survival rate with a non-significant increase in intubation risk compared to cluster 2 (HR, 1.40, 95% CI, 0.18-11.04).ConclusionsWe identified three distinct clinical phenotypes of COVID-19 pneumonia patients, with cluster 3 associated with an increased risk of respiratory failure and mortality. These findings may guide tailored clinical management strategies.

Dataset Information

Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer.

Publications

Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer.

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