Dataset Information

Machine-Learning-Based In-Hospital Mortality Prediction for Transcatheter Mitral Valve Repair in the United States.

ABSTRACT:

Background

Transcatheter mitral valve repair (TMVR) utilization has increased significantly in the United States over the last years. Yet, a risk-prediction tool for adverse events has not been developed. We aimed to generate a machine-learning-based algorithm to predict in-hospital mortality after TMVR.

Methods

Patients who underwent TMVR from 2012 through 2015 were identified using the National Inpatient Sample database. The study population was randomly divided into a training set (n = 636) and a testing set (n = 213). Prediction models for in-hospital mortality were obtained using five supervised machine-learning classifiers.

Results

A total of 849 TMVRs were analyzed in our study. The overall in-hospital mortality was 3.1%. A naïve Bayes (NB) model had the best discrimination for fifteen variables, with an area under the receiver-operating curve (AUC) of 0.83 (95% CI, 0.80-0.87), compared to 0.77 for logistic regression (95% CI, 0.58-0.95), 0.73 for an artificial neural network (95% CI, 0.55-0.91), and 0.67 for both a random forest and a support-vector machine (95% CI, 0.47-0.87). History of coronary artery disease, of chronic kidney disease, and smoking were the three most significant predictors of in-hospital mortality.

Conclusions

We developed a robust machine-learning-derived model to predict in-hospital mortality in patients undergoing TMVR. This model is promising for decision-making and deserves further clinical validation.

SUBMITTER: Hernandez-Suarez DF

PROVIDER: S-EPMC7736498 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Machine-Learning-Based In-Hospital Mortality Prediction for Transcatheter Mitral Valve Repair in the United States.

Hernandez-Suarez Dagmar F DF Ranka Sagar S Kim Yeunjung Y Latib Azeem A Wiley Jose J Lopez-Candales Angel A Pinto Duane S DS Gonzalez Maday C MC Ramakrishna Harish H Sanina Cristina C Nieves-Rodriguez Brenda G BG Rodriguez-Maldonado Jovaniel J Feliu Maldonado Roberto R Rodriguez-Ruiz Israel J IJ da Luz Sant'Ana Istoni I Wiley Karlo A KA Cox-Alomar Pedro P Villablanca Pedro A PA Roche-Lima Abiel A

Cardiovascular revascularization medicine : including molecular interventions 20200615

<h4>Background</h4>Transcatheter mitral valve repair (TMVR) utilization has increased significantly in the United States over the last years. Yet, a risk-prediction tool for adverse events has not been developed. We aimed to generate a machine-learning-based algorithm to predict in-hospital mortality after TMVR.<h4>Methods</h4>Patients who underwent TMVR from 2012 through 2015 were identified using the National Inpatient Sample database. The study population was randomly divided into a training ...[more]

PMID: 32591310

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Project description:BackgroundClinical trials have demonstrated health status benefit of transcatheter mitral valve repair (TMVr) with MitraClip in patients with mitral valve regurgitation. Real-world site-level variability in health status outcomes for TMVr, and factors associated with this variability, are unknown.MethodsAll patients undergoing TMVr procedure with MitraClip between November 2013 and March 2019 in the Transcatheter Valve Therapy Registry were included. Health status was measured at baseline and 30 days with the Kansas City Cardiomyopathy Questionnaire (KCCQ) Overall Summary (OS) score. Site-level variability in 30-day change in KCCQ-OS was examined by calculating the median odds ratio from a hierarchical logistic regression model, with ≥20-point improvement as the dependent variable. To define the extent to which patient characteristics, procedural characteristics (residual mitral valve regurgitation, periprocedural bleeding), site volume, and patients' baseline health status accounted for variability in outcomes, the proportion of variability (R2) explained by sequentially adding these variables to the model was quantified.ResultsAcross 339 sites, 12 415 patients (mean age 79.0±9.5 years, 46.1%. females, 89.5% White) completed baseline and 30-day health status assessments. Mean KCCQ-OS score was 43.0±24.4 at baseline and 67.0±24.9 at 30-day follow-up. Across sites, the proportion of patients achieving a ≥20-point improvement in KCCQ-OS ranged from 12.5% to 100% and the adjusted median odds ratio was 1.58 (95% CI, 1.46-1.69). The greatest contribution to the variability in health status outcomes was from patients' baseline KCCQ-OS score (R2=25%) with <1% of the variability explained by patient and procedural characteristics, and annual site volume.ConclusionsThere is moderate variation across sites in their patients' achievement of health status benefits from TMVr, with patient's baseline health status accounting for the largest proportion of this variation. This underscores the importance of patient selection in supporting more consistent health status benefit from TMVr.

Project description:ImportanceThere are limited data on the outcomes of transcatheter edge-to-edge mitral valve repair for degenerative mitral regurgitation (MR) in a real-world setting.ObjectiveTo evaluate the outcomes of transcatheter mitral valve repair for degenerative MR.Design, setting, and participantsCohort study of consecutive patients in the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapies Registry who underwent nonemergent transcatheter mitral valve repair for degenerative MR in the US from 2014 through 2022.ExposureTranscatheter edge-to-edge mitral valve repair with the MitraClip device (Abbott).Main outcomes and measuresThe primary end point was MR success, defined as moderate or less residual MR and a mean mitral gradient of less than 10 mm Hg. Clinical outcomes were evaluated based on the degree of residual MR (mild or less MR or moderate MR) and mitral valve gradients (≤5 mm Hg or >5 to <10 mm Hg).ResultsA total of 19 088 patients with isolated moderate to severe or severe degenerative MR who underwent transcatheter mitral valve repair were analyzed (median age, 82 years; 48% women; median Society of Thoracic Surgeons predicted risk of mortality with surgical mitral valve repair, 4.6%). MR success was achieved in 88.9% of patients. At 30 days, the incidence of death was 2.7%; stroke, 1.2%; and mitral valve reintervention, 0.97%. MR success compared with an unsuccessful procedure was associated with significantly lower mortality (14.0% vs 26.7%; adjusted hazard ratio, 0.49; 95% CI, 0.42-0.56; P < .001) and heart failure readmission (8.4% vs 16.9%; adjusted hazard ratio, 0.47; 95% CI, 0.41-0.54; P < .001) at 1 year. Among patients with MR success, the lowest mortality was observed in patients who had both mild or less residual MR and mean mitral gradients of 5 mm Hg or less compared with those with an unsuccessful procedure (11.4% vs 26.7%; adjusted hazard ratio, 0.40; 95% CI, 0.34-0.47; P < .001).Conclusions and relevanceIn this registry-based study of patients with degenerative MR undergoing transcatheter mitral valve repair, the procedure was safe and resulted in successful repair in 88.9% of patients. The lowest mortality was observed in patients with mild or less residual MR and low mitral gradients.

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Project description:ImportanceVolume metrics may have relevance in the evaluation of valve center expertise. However, a paucity of data exists regarding the quantity, volume, and geographic location of mitral valve (MV) surgical centers in the United States and the proportion of underserved populations they treat.ObjectivesTo evaluate the hospital, patient, and procedural characteristics of mitral valve repair or replacement (MVRR) in the United States as a function of hospital procedure volume.Design, setting, and participantsThis cross-sectional, multicenter observational study was conducted from July 2014 to June 2018. Patients in the Society of Thoracic Surgeons Adult Cardiac Surgery Database undergoing any surgical procedure involving MVRR in the United States were included.Main outcomes and measuresVolume distribution of MVRR by hospital and hospital referral region.ResultsThere were 165 405 MVRRs performed in 1082 centers during the study period, of which 86 488 (52.3%) were MV repairs. There were 575 centers (53.1%) that performed 25 or more MVRRs per year. The geographic distribution of centers performing 25 or more MVRRs per year differed from those performing fewer than 25 MVRRs per year. Of 304 designated hospital referral regions, 235 (77.3%) had at least 1 center performing 25 or more MVRRs per year, representing accessibility to 1 or more such centers for 296.4 million of 320.1 million US residents (92.6% of the US population; Midwest, 60.0 million of 68.0 million [88.4%]; South, 112.6 million of 122.6 million [91.9%]; West, 68.6 million of 72.9 million [94.1%]; and Northeast, 54.9 million of 56.6 million [97.1%]). Of 304 hospital referral regions, 168 (55.3%) had at least 1 center performing 40 or more MVRRs per year, representing accessibility to 1 or more such centers for 259.8 million of 317.90 million (81.7%) of the US population (Midwest, 50.5 million of 67.9 million [74.5%]; South, 94.5 million of 121.1 million [78.1%]; West, 64.0 million of 72.8 million [88.0%]; Northeast, 50.1 million of 56.3 million [90.2%]). More black and Hispanic patients received operations in centers performing 25 or more MVRRs per year (22 984) vs those performing fewer than 25 MVRRs per year (3227), yet the proportion was higher in lower-volume centers (22 984 of 148 385 [15.5%] vs 3227 of 17 020 [19.0%]; P < .001). In centers performing 25 or more MVRRs per year vs fewer than 25 MVRRs per year, there was a lower percentage of Medicare and Medicaid patients (47 920 of 148 385 [32.3%] vs 6183 of 17 020 [.3%]; P < .001) and patients from rural zip codes (21 208 of 148 385 [14.3%] vs 3146 of 17 020 [18.5%]; P < .001).Conclusions and relevanceFifty-three percent of all centers performed 25 or more MVRRs per year, and 92.6% of the US population lived in an hospital referral region with at least 1 such center. Disparities in race/ethnicity, rurality, and insurance status exist among patients being treated at centers with different volumes. These data indicate that efforts to centralize care based on volume metrics will need to balance access vs quality.

Dataset Information

Machine-Learning-Based In-Hospital Mortality Prediction for Transcatheter Mitral Valve Repair in the United States.

Background

Methods

Results

Conclusions

Publications

Machine-Learning-Based In-Hospital Mortality Prediction for Transcatheter Mitral Valve Repair in the United States.

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