Machine Learning Identifies Clinical Parameters to Predict Mortality in Patients Undergoing Transcatheter Mitral Valve Repair

Association between echocardiographic velocity time integral ratio of mitral valve and left ventricular outflow tract and clinical outcomes post transcatheter edge-to-edge mitral valve repair

Background

Residual mitral regurgitation (MR) is frequent after transcatheter edge-to-edge repair (TEER). There is controversy regarding the clinical impact of residual MR and its quantitative assessment by transthoracic echocardiography (TTE), which is often challenging with multiple eccentric jets and artifact from the clip. The utility of the velocity time integral (VTI) ratio between the mitral valve (MV) and left ventricular outflow tract (LVOT), (VTIMV/LVOT), a simple Doppler measurement that increases with MR, has not been assessed post TEER.

Methods

Baseline characteristics, clinical outcomes, and TTE data from patients who underwent TEER between 2014 and 2021 across three academic centers were retrospectively analyzed. Post-procedure TTEs were evaluated for VTIMV/LVOT in the first three months after TEER. One-year outcomes including all-cause and cardiac mortality, major adverse cardiac events, and MV reintervention were compared between patients with high VTIMV/LVOT (≥2.5) and low (<2.5).

Results

In total, 372 patients were included (mean age 78.7 ± 8.8 years, 68 % male, mean pre-TEER ejection fraction of 50.5 ± 14.7 %). Follow up TTEs were performed at a median of 37.5 (IQR 30-48) days post-procedure. Patients with high VTIMV/LVOT had significantly higher all-cause mortality (HR 2.10, p = 0.003), cardiac mortality (HR 3.03, p = 0.004) and heart failure admissions (HR 2.28, p < 0.001) at one-year post-procedure. There was no association between raised VTIMV/LVOT and subsequent MV reintervention.

Conclusion

High VTIMV/LVOT has clinically significant prognostic value at one year post TEER. This tool could be used to select patients for consideration of repeat intervention.

Comparative analysis of different risk prediction tools after mitral Transcatheter edge-to-edge repair

BACKGROUND

Transcatheter edge-to-edge repair (TEER) has become an established treatment for primary and secondary mitral regurgitation (PMR and SMR). The objective of this study was to compare the accuracy of different risk scores for predicting 1-year mortality and the composite endpoint of 1-year mortality and/or heart failure (HF) hospitalization after TEER.

METHODS

We analyzed data from 206 patients treated for MR at a tertiary European center between 2011 and 2023 and compared the accuracy of different mitral and surgical risk scores: EuroSCORE II, GRASP, MITRALITY, MitraScore, TAPSE/PASP-MitraScore, and STS for predicting 1-year mortality and the composite of 1-year mortality and/or HF hospitalization in PMR and SMR. A subanalysis of SMR-only patients with the addition of COAPT Risk Score and baseline N-Terminal pro-Brain Natriuretic Peptide (NT-proBNP) list was also performed.

RESULTS

MITRALITY had the best discriminative ability for 1-year mortality and the composite endpoint of 1-year mortality and/or HF hospitalization, with an area under the curve (AUC) of 0.74 and 0.74, respectively, in a composed group of PMR and SMR. In a SMR-only population, MITRALITY also presented the best AUC for 1-year mortality and the composite endpoint of 1-year mortality and/or HF hospitalization, with values of 0.72 and 0.72, respectively.

CONCLUSION

MITRALITY was the best mitral TEER risk model for both 1-year mortality and the composite endpoint of 1-year mortality and/or HF hospitalization in a population of PMR and SMR patients, as well as in SMR patients only. Surgical risk scores, MitraScore, TAPSE/PASP-MitraScore and NT-proBNP alone showed poor predictive values.

Prognostic impact of MitraScore in elderly Asian patients with heart failure: sub-analysis of FRAGILE-HF

AIMS

MitraScore is a novel, simple, and manually calculatable risk score developed as a prognostic model for patients undergoing transcatheter edge-to-edge repair (TEER) for mitral regurgitation. As its components are considered prognostic in heart failure (HF), we aimed to investigate the usefulness of the MitraScore in HF patients.

METHODS AND RESULTS

We calculated MitraScore for 1100 elderly patients (>65 years old) hospitalized for HF in the prospective multicentre FRAGILE-HF study and compared its prognostic ability with other simple risk scores. The primary endpoint was all-cause deaths, and the secondary endpoints were the composite of all-cause deaths and HF rehospitalization and cardiovascular deaths. Overall, the mean age of 1100 patients was 80 ± 8 years, and 58% were men. The mean MitraScore was 3.2 ± 1.4, with a median of 3 (interquartile range: 2-4). A total of 326 (29.6%), 571 (51.9%), and 203 (18.5%) patients were classified into low-, moderate-, and high-risk groups based on the MitraScore, respectively. During a follow-up of 2 years, 226 all-cause deaths, 478 composite endpoints, and 183 cardiovascular deaths were observed. MitraScore successfully stratified patients for all endpoints in the Kaplan-Meier analysis (P < 0.001 for all). In multivariate analyses, MitraScore was significantly associated with all endpoints after covariate adjustments [adjusted hazard ratio (HR) (95% confidence interval): 1.22 (1.10-1.36), P < 0.001 for all-cause deaths; adjusted HR 1.17 (1.09-1.26), P < 0.001 for combined endpoints; and adjusted HR 1.24 (1.10-1.39), P < 0.001 for cardiovascular deaths]. The Hosmer-Lemeshow plot showed good calibration for all endpoints. The net reclassification improvement (NRI) analyses revealed that the MitraScore performed significantly better than other manually calculatable risk scores of HF: the GWTG-HF risk score, the BIOSTAT compact model, the AHEAD score, the AHEAD-U score, and the HANBAH score for all-cause and cardiovascular deaths, with respective continuous NRIs of 0.20, 0.22, 0.39, 0.39, and 0.29 for all-cause mortality (all P-values < 0.01) and 0.20, 0.22, 0.42, 0.40, and 0.29 for cardiovascular mortality (all P-values < 0.02).

CONCLUSIONS

MitraScore developed for patients undergoing TEER also showed strong discriminative power in HF patients. MitraScore was superior to other manually calculable simple risk scores and might be a good choice for risk assessment in clinical practice for patients receiving TEER and those with HF.

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

There are limited data available regarding the connection between hypertension and heavy metal exposure. The authors intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that identifies hypertension based on heavy metal exposure. Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013-2020.3). The authors developed 5 ML models for hypertension identification by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, the authors chose the optimally performing model after parameter adjustment by Genetic Algorithm (GA) for identification. Finally, in order to visualize the model's ability to make decisions, the authors used SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 19 368 participants in total. A best-performing eXtreme Gradient Boosting (XGB) with GA for hypertension identification by 16 heavy metals was selected (AUC: 0.774; 95% CI: 0.772-0.776; accuracy: 87.7%). According to SHAP values, Barium (0.02), Cadmium (0.017), Lead (0.017), Antimony (0.008), Tin (0.007), Manganese (0.006), Thallium (0.004), Tungsten (0.004) in urine, and Lead (0.048), Mercury (0.035), Selenium (0.05), Manganese (0.007) in blood positively influenced the model, while Cadmium (-0.001) in urine negatively influenced the model. Study participants' hypertension associated with heavy metal exposure was identified by an efficient, robust, and interpretable GA-XGB model with SHAP and LIME. Barium, Cadmium, Lead, Antimony, Tin, Manganese, Thallium, Tungsten in urine, and Lead, Mercury, Selenium, Manganese in blood are positively correlated with hypertension, while Cadmium in blood is negatively correlated with hypertension.

Secondary Mitral Regurgitation and Transcatheter Mitral Valve Therapies: Do They Have a Role in Advanced Heart Failure with Reduced Ejection Fraction?

Transcatheter mitral valve repair should be considered for patients with severe secondary mitral regurgitation with symptomatic heart failure with reduced ejection fraction for symptom improvement and survival benefit. Patients with a higher severity of secondary mitral regurgitation relative to the degree of left ventricular dilation are more likely to benefit from transcatheter mitral valve repair. A multidisciplinary Heart Team should participate in patient selection for transcatheter mitral valve therapy.

A deep learning-based interpretable decision tool for predicting high risk of chemotherapy-induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy

OBJECTIVE

This study aims to develop a risk prediction model for chemotherapy-induced nausea and vomiting (CINV) in cancer patients receiving highly emetogenic chemotherapy (HEC) and identify the variables that have the most significant impact on prediction.

METHODS

Data from Tianjin Medical University General Hospital were collected and subjected to stepwise data preprocessing. Deep learning algorithms, including deep forest, and typical machine learning algorithms such as support vector machine (SVM), categorical boosting (CatBoost), random forest, decision tree, and neural network were used to develop the prediction model. After training the model and conducting hyperparameter optimization (HPO) through cross-validation in the training set, the performance was evaluated using the test set. Shapley additive explanations (SHAP), partial dependence plot (PDP), and Local Interpretable Model-Agnostic Explanations (LIME) techniques were employed to explain the optimal model. Model performance was assessed using AUC, F1 score, accuracy, specificity, sensitivity, and Brier score.

RESULTS

The deep forest model exhibited good discrimination, outperforming typical machine learning models, with an AUC of 0.850 (95%CI, 0.780-0.919), an F1 score of 0.757, an accuracy of 0.852, a specificity of 0.863, a sensitivity of 0.784, and a Brier score of 0.082. The top five important features in the model were creatinine clearance (Ccr), age, gender, anticipatory nausea and vomiting, and antiemetic regimen. Among these, Ccr had the most significant predictive value. The risk of CINV decreased with increased Ccr and age, while it was higher in the presence of anticipatory nausea and vomiting, female gender, and non-standard antiemetic regimen.

CONCLUSION

The deep forest model demonstrated good discrimination in predicting the risk of CINV in cancer patients prescribed HEC. Kidney function, as represented by Ccr, played a crucial role in the model's prediction. The clinical application of this predictive tool can help assess individual risks and improve patient care by proactively optimizing the use of antiemetics in cancer patients receiving HEC.

ACC/AHA and ESC/EACTS Guidelines for the Management of Valvular Heart Diseases: JACC Guideline Comparison

Valvular heart disease (VHD) is common and poses important challenges from the standpoints of diagnosis and therapeutic management. Clinical practice guidelines have been developed to help health care professionals to overcome these challenges and provide optimal management to patients with VHD. The American College of Cardiology, in collaboration with the American Heart Association, and the European Society of Cardiology, in collaboration with the European Association for Cardio-Thoracic Surgery, recently updated their guidelines on the management of VHD. Although these 2 sets of guidelines are generally concordant, there are some substantial differences between these guidelines, which may have significant implications for clinical practice. This review prepared on behalf of the EuroValve Consortium describes the consistencies and discrepancies between the guidelines and highlights the gaps in these guidelines and the future research perspectives to fill these gaps.

Mitral Interventions in Heart Failure

Patients with heart failure with reduced ejection fraction who have secondary mitral regurgitation (SMR) have poorer outcomes and quality of life than those without SMR. Guideline-directed medical therapy is the cornerstone of SMR treatment. Careful evaluation of landmark trials using mitral transcatheter edge-to-edge repair in SMR has led to an improved understanding of who will benefit from percutaneous interventions with emphasis on a multidisciplinary approach. The success with mitral transcatheter edge-to-edge repair in SMR has also spurred the evaluation of its role in populations that were not initially studied, such as end-stage heart failure and cardiogenic shock. A spectrum of transcatheter devices in development and clinical trials promise to further provide a growing array of management options for heart failure with reduced ejection fraction patients with symptomatic SMR.

Prediction of mortality and heart failure hospitalisations in patients undergoing M-TEER: external validation of the COAPT risk score

BACKGROUND

A risk score was recently derived from the Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients with Functional Mitral Regurgitation (COAPT) Trial. However, external validation of this score is still lacking.

AIMS

We aimed to validate the COAPT risk score in a large multicentre population undergoing mitral transcatheter edge-to-edge repair (M-TEER) for secondary mitral regurgitation (SMR).

METHODS

The Italian Society of Interventional Cardiology (GIse) Registry of Transcatheter Treatment of Mitral Valve RegurgitaTiOn (GIOTTO) population was stratified according to COAPT score quartiles. The performance of the COAPT score for 2-year all-cause death or heart failure (HF) hospitalisation was evaluated in the overall population and in patients with or without a COAPT-like profile.

RESULTS

Among the 1,659 patients included in the GIOTTO registry, 934 had SMR and complete data for a COAPT risk score calculation. The incidence of 2-year all-cause death or HF hospitalisation progressively increased through the COAPT score quartiles in the overall population (26.4% vs 44.5% vs 49.4% vs 59.7%; log-rank p<0.001) and COAPT-like patients (24.7% vs 32.4% vs 52.3% vs. 53.4%; log-rank p=0.004), but not in those with a non-COAPT-like profile. The COAPT risk score had poor discrimination and good calibration in the overall population, moderate discrimination and good calibration in COAPT-like patients and very poor discrimination and poor calibration in non-COAPT-like patients.

CONCLUSIONS

The COAPT risk score has a poor performance in the prognostic stratification of real-world patients undergoing M-TEER. However, after application to patients with a COAPT-like profile, moderate discrimination and good calibration were observed.

Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair

AIMS

Patients with mitral regurgitation (MR) present with considerable heterogeneity in cardiac damage depending on underlying aetiology, disease progression, and comorbidities. This study aims to capture their cardiopulmonary complexity by employing a machine-learning (ML)-based phenotyping approach.

METHODS AND RESULTS

Data were obtained from 1426 patients undergoing mitral valve transcatheter edge-to-edge repair (MV TEER) for MR. The ML model was developed using 609 patients (derivation cohort) and validated on 817 patients from two external institutions. Phenotyping was based on echocardiographic data, and ML-derived phenotypes were correlated with 5-year outcomes. Unsupervised agglomerative clustering revealed four phenotypes among the derivation cohort: Cluster 1 showed preserved left ventricular ejection fraction (LVEF; 56.5 ± 7.79%) and regular left ventricular end-systolic diameter (LVESD; 35.2 ± 7.52 mm); 5-year survival in Cluster 1, hereinafter serving as a reference, was 60.9%. Cluster 2 presented with preserved LVEF (55.7 ± 7.82%) but showed the largest mitral valve effective regurgitant orifice area (0.623 ± 0.360 cm2) and highest systolic pulmonary artery pressures (68.4 ± 16.2 mmHg); 5-year survival ranged at 43.7% (P-value: 0.032). Cluster 3 was characterized by impaired LVEF (31.0 ± 10.4%) and enlarged LVESD (53.2 ± 10.9 mm); 5-year survival was reduced to 38.3% (P-value: <0.001). The poorest 5-year survival (23.8%; P-value: <0.001) was observed in Cluster 4 with biatrial dilatation (left atrial volume: 312 ± 113 mL; right atrial area: 46.0 ± 8.83 cm2) although LVEF was only slightly reduced (51.5 ± 11.0%). Importantly, the prognostic significance of ML-derived phenotypes was externally confirmed.

CONCLUSION

ML-enabled phenotyping captures the complexity of extra-mitral valve cardiac damage, which does not necessarily occur in a sequential fashion. This novel phenotyping approach can refine risk stratification in patients undergoing MV TEER in the future.

Mitral valve transcatheter edge-to-edge repair

Mitral regurgitation (MR) is the most prevalent valvular heart disease and, when left untreated, results in reduced quality of life, heart failure, and increased mortality. Mitral valve transcatheter edge-to-edge repair (M-TEER) has matured considerably as a non-surgical treatment option since its commercial introduction in Europe in 2008. As a result of major device and interventional improvements, as well as the accumulation of experience by the interventional cardiologists, M-TEER has emerged as an important therapeutic strategy for patients with severe and symptomatic MR in the current European and American guidelines. Herein, we provide a comprehensive up-do-date overview of M-TEER. We define preprocedural patient evaluation and highlight key aspects for decision-making. We describe the currently available M-TEER systems and summarise the evidence for M-TEER in both primary mitral regurgitation (PMR) and secondary mitral regurgitation (SMR). In addition, we provide recommendations for device selection, intraprocedural imaging and guiding, M-TEER optimisation and management of recurrent MR. Finally, we provide information on major unsolved questions and "grey areas" in M-TEER.

Development of an interpretable machine learning model associated with heavy metals' exposure to identify coronary heart disease among US adults via SHAP: Findings of the US NHANES from 2003 to 2018

Limited information is available on the links between heavy metals' exposure and coronary heart disease (CHD). We aim to establish an efficient and explainable machine learning (ML) model that associates heavy metals' exposure with CHD identification. Our datasets for investigating the associations between heavy metals and CHD were sourced from the US National Health and Nutrition Examination Survey (US NHANES, 2003-2018). Five ML models were established to identify CHD by heavy metals' exposure. Further, 11 discrimination characteristics were used to test the strength of the models. The optimally performing model was selected for identification. Finally, the SHapley Additive exPlanations (SHAP) tool was used for interpreting the features to visualize the selected model's decision-making capacity. In total, 12,554 participants were eligible for this study. The best performing random forest classifier (RF) based on 13 heavy metals to identify CHD was chosen (AUC: 0.827; 95%CI: 0.777-0.877; accuracy: 95.9%). SHAP values indicated that cesium (1.62), thallium (1.17), antimony (1.63), dimethylarsonic acid (0.91), barium (0.76), arsenous acid (0.79), total arsenic (0.01) in urine, and lead (3.58) and cadmium (4.66) in blood positively contributed to the model, while cobalt (-0.15), cadmium (-2.93), and uranium (-0.13) in urine negatively contributed to the model. The RF model was efficient, accurate, and robust in identifying an association between heavy metals' exposure and CHD among US NHANES 2003-2018 participants. Cesium, thallium, antimony, dimethylarsonic acid, barium, arsenous acid, and total arsenic in urine, and lead and cadmium in blood show positive relationships with CHD, while cobalt, cadmium, and uranium in urine show negative relationships with CHD.

Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm

Aims

The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute Type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortality based on distinct risk stratifications.

Methods and results

A total of 3771 ATAAD patients who underwent aortic surgery retrospectively included were randomly divided into training and testing cohorts at a ratio of 7:3 for the development and validation of the risk model based on multiple circulating biomarkers and conventional clinical factors. Extreme gradient boosting was used to generate the risk models. Subgroup analyses were performed by risk stratifications (low vs. middle-high risk) and arch repair strategies (proximal vs. extensive arch repair). Addition of multiple biomarkers to a model with conventional factors fitted an ABC risk model consisting of platelet-leucocyte ratio, mean arterial pressure, albumin, age, creatinine, creatine kinase-MB, haemoglobin, lactate, left ventricular end-diastolic dimension, urea nitrogen, and aspartate aminotransferase, with adequate discrimination ability {area under the receiver operating characteristic curve (AUROC): 0.930 [95% confidence interval (CI) 0.906-0.954] and 0.954, 95% CI (0.930-0.977) in the derivation and validation cohort, respectively}. Compared with proximal arch repair, the extensive repair was associated with similar mortality risk among patients at low risk [odds ratio (OR) 1.838, 95% CI (0.559-6.038); P = 0.316], but associated with higher mortality risk among patients at middle-high risk [OR 2.007, 95% CI (1.460-2.757); P < 0.0001].

Conclusion

In ATAAD patients, the simultaneous addition of circulating biomarkers of inflammatory, cardiac, hepatic, renal, and metabolic abnormalities substantially improved risk stratification and individualized arch repair strategy.

Therapie der sekundären Mitralklappeninsuffizienz – Strategien eines interuniversitären Verbundes

Die sekundäre Mitralinsuffizienz ist bei Patienten mit Herzinsuffizienz häufig und mit einem schlechten Verlauf assoziiert. Aufgrund des hohen OP-Risikos war die Therapie traditionell auf eine Behandlung der Herzinsuffizienz beschränkt. Die Entwicklung von kathetergestützten Techniken ermöglicht nun die Behandlung mit geringem Risiko. Wenngleich die Studienevidenz immer noch begrenzt ist, erfolgte in den aktuellen Leitlinien der europäischen Fachgesellschaften eine Aufwertung der kathetergestützten Therapie für ausgewählte Patienten mit hohem OP-Risiko und hoher Wahrscheinlichkeit für ein Therapieansprechen. Dennoch bleiben viele Fragen offen, was die Rolle der chirurgischen Behandlung und auch die Patientenselektion für kathetergestützte Therapieverfahren angeht. Hier beschreiben wir den aktuellen Stand der Behandlung der sekundären Mitralinsuffizienz und zeigen Strategien von transuniversitären Verbundprojekten mit dem Ziel, Evidenz für die Behandlung dieser Patienten zu entwickeln.

Contemporary Applications of Machine Learning for Device Therapy in Heart Failure

Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of "big data" and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.