Time series forecasting of weight for diuretic dose adjustment using bidirectional long short-term memory.

Loop diuretics are prevailing drugs to manage fluid overload in heart failure. However, adjusting to loop diuretic doses is strenuous due to the lack of a diuretic guideline. Accordingly, we developed a novel clinician decision support system for adjusting loop diuretics dosage with a Long Short-Term Memory (LSTM) algorithm using time-series EMRs. Weight measurements were used as the target to estimate fluid loss during diuretic therapy. We designed the TSFD-LSTM, a bi-directional LSTM model with an attention mechanism, to forecast weight change 48 h after heart failure patients were injected with loop diuretics. The model utilized 65 variables, including disease conditions, concurrent medications, laboratory results, vital signs, and physical measurements from EMRs. The framework processed four sequences simultaneously as inputs. An ablation study on attention mechanisms and a comparison with the transformer model as a baseline were conducted. The TSFD-LSTM outperformed the other models, achieving 85% predictive accuracy with MAE and MSE values of 0.56 and 1.45, respectively. Thus, the TSFD-LSTM model can aid in personalized loop diuretic treatment and prevent adverse drug events, contributing to improved healthcare efficacy for heart failure patients.

Fully automated quantitative coronary angiography versus optical coherence tomography guidance for coronary stent implantation (FLASH): Study protocol for a randomized controlled noninferiority trial.

BACKGROUND: Artificial intelligence-based quantitative coronary angiography (AI-QCA) has been developed to provide a more objective and reproducible data about the severity of coronary artery stenosis and the dimensions of the vessel for intervention in real-time, overcoming the limitations of significant inter- and intraobserver variability, and time-consuming nature of on-site QCA, without requiring extra time and effort. Compared with the subjective nature of visually estimated conventional CAG guidance, AI-QCA guidance provides a more practical and standardized angiography-based approach. Although the advantage of intravascular imaging-guided PCI is increasingly recognized, their broader adoption is limited by clinical and economic barriers in many catheterization laboratories. METHODS: The FLASH (fully automated quantitative coronary angiography versus optical coherence tomography guidance for coronary stent implantation) trial is a randomized, investigator-initiated, multicenter, open-label, noninferiority trial comparing the AI-QCA-assisted PCI strategy with optical coherence tomography-guided PCI strategy in patients with significant coronary artery disease. All operators will utilize a novel, standardized AI-QCA software and PCI protocol in the AI-QCA-assisted group. A total of 400 patients will be randomized to either group at a 1:1 ratio. The primary endpoint is the minimal stent area (mm2), determined by the final OCT run after completion of PCI. Clinical follow-up and cost-effectiveness evaluations are planned at 1 month and 6 months for all patients enrolled in the study. RESULTS: Enrollment of a total of 400 patients from the 13 participating centers in South Korea will be completed in February 2024. Follow-up of the last enrolled patients will be completed in August 2024, and primary results will be available by late 2024. CONCLUSION: The FLASH is the first clinical trial to evaluate the feasibility of AI-QCA-assisted PCI, and will provide the clinical evidence on AI-QCA assistance in the field of coronary intervention. CLINICAL TRIAL REGISTRATION: URL: https://www. CLINICALTRIALS: gov. Unique identifier: NCT05388357.

Circulating miRNA-4701-3p as a predictive biomarker of cardiovascular disease which induces angiogenesis by inhibition of TOB2.

Angiogenesis is mainly regulated by the delivery of VEGF-dependent signaling to cells. However, the angiogenesis mechanism regulated by VEGF-induced miRNA is still not understood. After VEGF treatment in HUVECs, we screened the changed miRNAs through small-RNA sequencing and found VEGF-induced miR-4701-3p. Furthermore, the GFP reporter gene was used to reveal that TOB2 expression was regulated by miR-4701-3p, and it was found that TOB2 and miR-4701-3p modulation could cause angiogenesis in an in-vitro angiogenic assay. Through the luciferase assay, it was confirmed that the activation of the angiogenic transcription factor MEF2 was regulated by the suppression and overexpression of TOB2 and miR-4701-3p. As a result, MEF2 downstream gene mRNAs that induce angiogenic function were regulated. We used the NCBI GEO datasets to reveal that the expression of TOB2 and MEF2 was significantly changed in cardiovascular disease. Finally, it was confirmed that the expression of circulating miR-4701-3p in the blood of myocardial infarction patients was remarkably increased. In patients with myocardial infarction, circulating miR-4701-3p was increased regardless of age, BMI, and sex, and showed high AUC levels in specificity and sensitivity analysis (AUROC) (AUC = 0.8451, 95 % CI 0.78-0.90). Our data showed TOB2-mediated modulation of MEF2 and its angiogenesis by VEGF-induced miR-4701-3p in vascular endothelial cells. In addition, through bioinformatics analysis using GEO data, changes in TOB2 and MEF2 were revealed in cardiovascular disease. We suggest that circulating miR-4701-3p has high potential as a biomarker for myocardial infarction.

Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. METHODS: We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. RESULTS: We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. CONCLUSIONS: We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

Quantitative Coronary Angiography vs Intravascular Ultrasonography to Guide Drug-Eluting Stent Implantation: A Randomized Clinical Trial.

Importance: Although intravascular ultrasonography (IVUS) guidance promotes favorable outcomes after percutaneous coronary intervention (PCI), many catheterization laboratories worldwide lack access. Objective: To investigate whether systematic implementation of quantitative coronary angiography (QCA) to assist angiography-guided PCI could be an alternative strategy to IVUS guidance during stent implantation. Design, Setting, and Participants: This randomized, open-label, noninferiority clinical trial enrolled adults (aged ≥18 years) with chronic or acute coronary syndrome and angiographically confirmed native coronary artery stenosis requiring PCI. Patients were enrolled in 6 cardiac centers in Korea from February 23, 2017, to August 23, 2021, and follow-up occurred through August 25, 2022. All principal analyses were performed according to the intention-to-treat principle. Interventions: After successful guidewire crossing of the first target lesion, patients were randomized in a 1:1 ratio to receive either QCA- or IVUS-guided PCI. Main Outcomes and Measures: The primary outcome was target lesion failure at 12 months, defined as a composite of cardiac death, target vessel myocardial infarction, or ischemia-driven target lesion revascularization. The trial was designed assuming an event rate of 8%, with the upper limit of the 1-sided 97.5% CI of the absolute difference in 12-month target lesion failure (QCA-guided PCI minus IVUS-guided PCI) to be less than 3.5 percentage points for noninferiority. Results: The trial included 1528 patients who underwent PCI with QCA guidance (763; mean [SD] age, 64.1 [9.9] years; 574 males [75.2%]) or IVUS guidance (765; mean [SD] age, 64.6 [9.5] years; 622 males [81.3%]). The post-PCI mean (SD) minimum lumen diameter was similar between the QCA- and IVUS-guided PCI groups (2.57 [0.55] vs 2.60 [0.58] mm, P = .26). Target lesion failure at 12 months occurred in 29 of 763 patients (3.81%) in the QCA-guided PCI group and 29 of 765 patients (3.80%) in the IVUS-guided PCI group (absolute risk difference, 0.01 percentage points [95% CI, -1.91 to 1.93 percentage points]; hazard ratio, 1.00 [95% CI, 0.60-1.68]; P = .99). There was no difference in the rates of stent edge dissection (1.2% vs 0.7%, P = .25), coronary perforation (0.2% vs 0.4%, P = .41), or stent thrombosis (0.53% vs 0.66%, P = .74) between the QCA- and IVUS-guided PCI groups. The risk of the primary end point was consistent regardless of subgroup, with no significant interaction. Conclusions and Relevance: Findings of this randomized clinical trial indicate that QCA and IVUS guidance during PCI showed similar rates of target lesion failure at 12 months. However, due to the lower-than-expected rates of target lesion failure in this trial, the findings should be interpreted with caution. Trial Registration: ClinicalTrials.gov Identifier: NCT02978456.

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning.

BACKGROUND: Quantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance. METHODS: AI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL). RESULTS: AI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections. CONCLUSION: AI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.

Are polypharmacy side effects predicted by public data still valid in real-world data?

Background and Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions: Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.

Association of metabolic health and obesity with coronary heart disease in adult cancer survivors.

BACKGROUND: The metabolically healthy obese (MHO) phenotype is associated with an increased risk of coronary heart disease (CHD) in the general population. However, association of metabolic health and obesity phenotypes with CHD risk in adult cancer survivors remains unclear. We aimed to investigate the associations between different metabolic health and obesity phenotypes with incident CHD in adult cancer survivors. METHODS: We used National Health Insurance Service (NHIS) to identify a cohort of 173,951 adult cancer survivors aged more than 20 years free of cardiovascular complications. Metabolically healthy nonobese (MHN), MHO, metabolically unhealthy nonobese (MUN), metabolically unhealthy obese (MUO) phenotypes were created using as at least three out of five metabolic health criteria along with obesity (body mass index ≥ 25.0 kg/m2). We used Cox proportional hazards model to assess CHD risk in each metabolic health and obesity phenotypes. RESULTS: During 1,376,050 person-years of follow-up, adult cancer survivors with MHO phenotype had a significantly higher risk of CHD (hazard ratio [HR] = 1.52; 95% confidence intervals [CI]: 1.41 to 1.65) as compared to those without obesity and metabolic abnormalities. MUN (HR = 1.81; 95% CI: 1.59 to 2.06) and MUO (HR = 1.92; 95% CI: 1.72 to 2.15) phenotypes were also associated with an increased risk of CHD among adult cancer survivors. CONCLUSIONS: Adult cancer survivors with MHO phenotype had a higher risk of CHD than those who are MHN. Metabolic health status and obesity were jointly associated with CHD risk in adult cancer survivors.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.