Study On The Prognostic Risk Stratification Of Patients With Implantable Cardioverter-defibrillator

Part Ⅰ N-terminal pro-B-type natriuretic peptide in risk stratification of heart failure patients with implantable cardioverter-defibrillatorBackground:Prognostic value of N-terminal pro-B-type natriuretic peptide(NTproBNP)in heart failure is well-established.However,whether it could facilitate the risk stratification of heart failure patients with implantable cardioverter-defibrillator(ICD)is still unclear.Methods:All patients with ischemic or non-ischemic dilated cardiomyopathy disease implanted with ICD between January 1,2013 and September 1,2020 at Fuwai hospital were enrolled.Patients’ baseline characteristics,ICD interrogation information,and survival status were collected.NT-proBNP levels were categorized into quartiles and the first quartile was set as the reference group to evaluate its association with the outcomes of all-cause mortality and first appropriate ICD shock due to sustained ventricular tachycardia/ventricular fibrillation in ICD recipients.Restricted cubic splines and Wald tests were used to find the nonlinear relationships.Results:NT-proBNP was measured before ICD implant in 500 patients(mean age 60.2±12.0 years;415(83.0%)male;231(46.2%)non-ischemic dilated cardiomyopathy;136(27.2%)primary prevention).Over a median survival follow-up of 4.1(interquartile range[IQR]:2.8-5.7)years,106(21.2%)patients died.After adjusting for confounding factors,multivariable Cox regression showed a rise in NT-proBNP was associated with an increased risk of all-cause mortality.Compared with the lowest quartile,the hazard ratios with 95%confidence interval across increasing quartiles were 1.77(0.71,4.43),3.98(1.71,9.25),and 5.90(2.43,14.30)for NT-proBNP(P for trend<0.001).Restricted cubic spline demonstrated a similar pattern with an inflection point found at 3231.4 pg/mL,beyond which the increase in NT-proBNP was not associated with increased mortality(P for nonlinearity<0.001).The median interrogation follow-up was 1.7(IQR 0.8-3.5)years,and 89(17.8%)patients had their first appropriate shock.Fine-Gray regression was used to evaluate the association between NT-proBNP and first appropriate shock accounting for the competing risk of death.In the unadjusted,partial,and fully adjusted analysis,however,no significant association could be found regardless of NT-proBNP as a categorical variable or log-transformed continuous variable(all P>0.05).No nonlinearity was found,either(P=0.666).Conclusion:In heart failure patients with ICD,the rise in NT-proBNP is independently associated with increased mortality until it reaches the inflection point.However,its association with first appropriate shock was not found.Patients with higher NT-proBNP levels might derive less benefit from ICD implant.Part Ⅱ Competing risk nomogram predicting death and heart transplantation prior to appropriate ICD shock in dilated cardiomyopathyBackground:It’s still under debate whether non-ischemic dilated cardiomyopathy(DCM)patients would benefit from implantable cardioverter-defibrillators(ICD).Developing a simple risk score for predicting death and heart transplantation(HT)before receiving appropriate shock may help classifying potential ICD recipients.Methods:The primary endpoints included all-cause mortality and HT(whichever came first)without former appropriate shock.A total of 218 consecutive DCM patients implanted with ICD between 2010 and 2019 at Fuwai Hospital were retrospectively enrolled.Cox proportional-hazards model was primarily built to identify variables associated with death and HT.Then,a Fine-Gray model,accounting for the appropriate shock as a competing risk,was constructed using these selected variables along with implantation indication.Finally,a nomogram based on the Fine-Gray model was established to predict 1-,3-,and 5-year probabilities of primary endpoints.The area under the receiver operating characteristic(ROC)curve(AUC),Harrell’s C-index,and calibration curves were used to evaluate and internally validate the performance of this model.The decision curve analysis was applied to assess its clinical utility.Results:The 1-,3-,and 5-year cumulative incidence of all-cause mortality and HT without former appropriate shock were 5.3%(95%confidence interval[CI]2.9-9.9%),16.6%(95%CI 11-25.0%)and 25.3%(95%CI 17.2-37.1%),respectively.Five variables including implantation indication,left ventricular end-diastolic diameter,N-terminal probrain natriuretic peptide,angiotensin-converting enzyme inhibitor/angiotensin receptor blocker,and amiodarone treatment were independently associated with it(all P<0.05)and were used for constructing the nomogram.The 1-,3-,and 5-year AUC of the nomogram were 0.83(95%Cl 0.73-0.94,P<0.001),0.84(95%Cl 0.75-0.93,P<0.001),and 0.85(95%CI 0.77-0.94,P<0.001).respectively.The Harrell’s C-index was 0.788(95%Cl,0.697-0.877,P<0.001;0.762 for the optimism-corrected C-index),showing the good discriminative ability of the model.And the calibration was acceptable(optimismcorrected slope 0.896).Decision curve analysis identified our model was clinically useful within the entire range of potential treatment thresholds for ICD implantation.Three risk groups stratified by scores were significantly different between cumulative incidence curves(P<0.001).The identified high-risk group composed 17.9%of our population and did not derive long-term benefit from ICD.Conclusion:The proposed nomogram is a simple,useful risk stratification tool for selecting potential ICD recipients in DCM patients.It might facilitate the shared decisionmaking between patients and clinicians.Part Ⅲ Unsupervised learning in the risk stratification of patients with heart failure and secondary prevention implantable cardioverter-defibrillator implantationBackground:Previous studies have failed to implement risk stratification in patients with heart failure(HF)who are eligible for secondary implantable cardioverterdefibrillator(ICD)implantation.We aimed to evaluate whether machine learning-based phenomapping using routinely available clinical data can identify subgroups that differ in characteristics and prognoses.Methods:A total of 389 patients with chronic HF implanted with an ICD were included,and their clinical outcomes and forty-four baseline variables were collected.Phenomapping was performed using hierarchical k-means clustering based on factor analysis of mixed data(FAMD).The utility of phenomapping was validated by comparing the baseline features and outcomes of the first appropriate shock and all-cause death among the phenogroups.Results:During a median follow-up of 2.7 years for device interrogation and 5.1 years for survival status,142(36.5%)first appropriate shocks and 113(29.0%)all-cause deaths occurred.The first 12 principal components extracted using the FAMD,explaining 60.5%of the total variability,were left for phenomapping.Three mutually exclusive phenogroups were identified.Phenogroup 1 comprised the oldest patients with ischemic cardiomyopathy;had the highest proportion of diabetes mellitus,hypertension,and hyperlipidemia;and had the most favorable cardiac structure and function among the phenogroups.Phenogroup 2 included the youngest patients,mostly those with nonischemic cardiomyopathy,who had intermediate heart dimensions and function,and the fewest comorbidities.Phenogroup 3 had the worst HF progression.Kaplan-Meier curves revealed significant differences in the first appropriate shock(P=0.002)and all-cause death(P<0.001)across the phenogroups.After adjusting for medications in Cox regression,phenogroups 2 and 3 displayed a graded increase in appropriate shock risk(hazard ratio[HR]1.54,95%confidence interval[CI]1.03-2.28,P=0.033;HR 2.21,95%CI 1.42-3.43,P<0.001,respectively;P for trend<0.001)compared to phenogroup 1.Regarding mortality risk,phenogroup 3 was associated with an increased risk(HR 2.25,95%CI 1.45-3.49,P<0.001).In contrast,phenogroup 2 had a risk(P=0.124)comparable with phenogroup 1.Conclusion:Machine-learning-based phenomapping can identify distinct phenotype subgroups in patients with clinically heterogeneous HF with secondary prophylactic ICD therapy.This novel strategy may aid personalized medicine for these patients.Part Ⅳ:Supervised learning in the risk stratification of patients with implantable cardioverter-defibrillator implantationBackground:Current guideline-based implantable cardioverter-defibrillator(ICD)implant fails to meet the demands for precision medicine.We aimed to develop explainable machine learning(ML)models predicting mortality and the first appropriate shock and compare these to standard Cox proportional hazards(CPH)regression in ICD recipients.We also aimed to bring up a new bi-dimensional model predicting both death and shock risk.Methods:A total of 887 adult patients were finally enrolled and randomly split into a training set(n=665,75%)and a test set(n=222,25%).Forty-five routine clinical variables were collected.Four fine-tuned ML approaches(elastic net Cox regression,random survival forests,survival support vector machine,and XGBoost)were applied and compared with the CPH model on the test set using Harrell’s C-index.Shapley Additive exPlanation(SHAP)values were used to explain each variable’s contribution to prediction.Results:199 patients died(5.0 per 100 person-years)and 265 first appropriate shocks occurred(12.4 per 100 person-years)during the follow-up.Among ML models predicting death,XGBoost achieved the highest accuracy and outperformed the CPH model(C-index:0.794 vs.0.760,P<0.001).Other ML models did not perform worse than CPH model.For appropriate shock,survival support vector machine showed the highest accuracy,although not statistically different from the CPH model(0.621 vs.0.611,P=0.243).The performance of other ML models was either similar or slightly inferior to it.Feature contribution of ML models assessed by SHAP values at individual and overall levels was in accordance with established knowledge.Accordingly,a bi-dimensional risk matrix of nine scenarios integrating death and shock risk was built.This risk stratification framework ultimately classified patients into three recommendation situations(for/against/shared decision)due to different likelihood of benefiting from ICD implant.Conclusion:Explainable survival ML models offer a promising tool for risk stratification and feature interpretation in ICD-eligible patients and may aid clinical decision making in the future.