Improving irregular temporal modeling by integrating synthetic data to the electronic medical record using conditional GANs: a case study of fluid overload prediction in the intensive care unit

Rafiei, Alireza; Rad, Milad Ghiasi; Sikora, Andrea; Kamaleswaran, Rishikesan

doi:10.1101/2023.06.20.23291680

Cited by 3 publications

(1 citation statement)

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“…These methods have been previously published. [9, 17] This evaluation followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension reporting frameworks, as applicable. [18, 19]…”

Section: Methodsmentioning

confidence: 99%

Augmenting mortality prediction with medication data and machine learning models

Murray,

Zhang,

Most

et al. 2024

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Background: In critically ill patients, complex relationships exist among patient disease factors, medication management, and mortality. Considering the potential for nonlinear relationships and the high dimensionality of medication data, machine learning and advanced regression methods may offer advantages over traditional regression techniques. The purpose of this study was to evaluate the role of different modeling approaches incorporating medication data for mortality prediction. Methods: This was a single-center, observational cohort study of critically ill adults. A random sample of 991 adults admitted ≥ 24 hours to the intensive care unit (ICU) from 10/2015 to 10/2020 were included. Models to predict hospital mortality at discharge were created. Models were externally validated against a temporally separate dataset of 4,878 patients. Potential mortality predictor variables (n=27, together with 14 indicators for missingness) were collected at baseline (age, sex, service, diagnosis) and 24 hours (illness severity, supportive care use, fluid balance, laboratory values, MRC-ICU score, and vasopressor use) and included in all models. The optimal traditional (equipped with linear predictors) logistic regression model and optimal advanced (equipped with nature splines, smoothing splines, and local linearity) logistic regression models were created using stepwise selection by Bayesian information criterion (BIC). Supervised, classification-based ML models [e.g., Random Forest, Support Vector Machine (SVM), and XGBoost] were developed. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared among different mortality prediction models. Results: A model including MRC-ICU in addition to SOFA and APACHE II demonstrated an AUROC of 0.83 for hospital mortality prediction, compared to AUROCs of 0.72 and 0.81 for APACHE II and SOFA alone. Machine learning models based on Random Forest, SVM, and XGBoost demonstrated AUROCs of 0.83, 0.85, and 0.82, respectively. Accuracy of traditional regression models was similar to that of machine learning models. MRC-ICU demonstrated a moderate level of feature importance in both XGBoost and Random Forest. Across all ten models, performance was lower on the validation set. Conclusions: While medication data were not included as a significant predictor in regression models, addition of MRC-ICU to severity of illness scores (APACHE II and SOFA) improved AUROC for mortality prediction. Machine learning methods did not improve model performance relative to traditional regression methods.

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Section: Methodsmentioning

confidence: 99%

Augmenting mortality prediction with medication data and machine learning models

Murray,

Zhang,

Most

et al. 2024

Preprint

Self Cite

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Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction

Keats,

Deng,

Chen

et al. 2024

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INTRODUCTION: Intravenous (IV) medications are a fundamental cause of fluid overload (FO) in the intensive care unit (ICU); however, the association between IV medication use (including volume), administration timing, and FO occurrence remains unclear. METHODS: This retrospective cohort study included consecutive adults admitted to an ICU ≥72 hours with available fluid balance data. FO was defined as a positive fluid balance ≥7% of admission body weight within 72 hours of ICU admission. After reviewing medication administration record (MAR) data in three-hour periods, IV medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess for temporal clusters associated with FO using the Wilcoxon rank sum test. Exploratory analyses of the medication cluster most associated with FO for medications frequently appearing and used in the first 24 hours was conducted. RESULTS: FO occurred in 127/927 (13.7%) of the patients enrolled. Patients received a median (IQR) of 31 (13-65) discrete IV medication administrations over the 72-hour period. Across all 47,803 IV medication administrations, ten unique IV medication clusters were identified with 121-130 medications in each cluster. Among the ten clusters, cluster 7 had the greatest association with FO; the mean number of cluster 7 medications received was significantly greater in patients in the FO cohort compared to patients who did not experience FO (25.6 vs.10.9. p<0.0001). 51 of the 127 medications in cluster 7 (40.2%) appeared in > 5 separate 3-hour periods during the 72-hour study window. The most common cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of cluster 7 medications to a prediction model with APACHE II score and receipt of diuretics improved the ability for the model to predict fluid overload (AUROC 5.65, p =0.0004). CONCLUSIONS: Using ML approaches, a unique IV medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict development of fluid overload in ICU patients compared with traditional prediction models. This method may be further developed into real-time clinical applications to improve early detection of adverse outcomes.

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Prediction of pharmacist medication interventions using medication regimen complexity

Zhao,

Shen,

Devlin

et al. 2024

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Background: Critically ill patients are managed with complex medication regimens that require medication management to optimize safety and efficacy. When performed by a critical care pharmacist (CCP), discrete medication management activities are termed medication interventions. The ability to define CCP workflow and intervention timeliness depends on the ability to predict the medication management needs of individual intensive care unit (ICU) patients. The purpose of this study was to develop prediction models for the number and intensity of medication interventions in critically ill patients. Methods: This was a retrospective, observational cohort study of adult patients admitted to an ICU between June 1, 2020 and June 7, 2023. Models to predict number of pharmacist interventions using both patient and medication related predictor variables collected at either baseline or in the first 24 hours of ICU stay were created. Both regression and supervised machine learning models (Random Forest, Support Vector Machine, XGBoost) were developed. Root mean square derivation (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) were calculated. Results: In a cohort of 13,373 patients, the average number of interventions was 4.7 (standard deviation (SD) 7.1) and intervention intensity was 24.0 (40.3). Among the ML models, the Random Forest model had the lowest RMSE (9.26) while Support Vector Machine had the lowest MAE (4.71). All machine learning models performed similarly to the stepwise logistic regression model, and these performed better than a base model combining severity of illness with medication regimen complexity scores. Conclusions: Intervention quantity can be predicted using patient-specific factors. While inter-institutional variation in intervention documentation precludes external validation, our results provide a framework workload modeling at any institution.

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Improving irregular temporal modeling by integrating synthetic data to the electronic medical record using conditional GANs: a case study of fluid overload prediction in the intensive care unit

Cited by 3 publications

References 36 publications

Augmenting mortality prediction with medication data and machine learning models

Augmenting mortality prediction with medication data and machine learning models

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction

Prediction of pharmacist medication interventions using medication regimen complexity

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