Survival outcomes post percutaneous coronary intervention: Why the hype about stent type? Lessons from a healthcare system in India

The effects of dual antiplatelet therapy (DAPT) adherence on survival in patients undergoing revascularization and the determinants of DAPT adherence

BACKGROUND

The prevalence and burden of coronary heart disease (CHD) has increased substantially in India, accompanied with increasing need for percutaneous coronary interventions (PCI). Although a large government-funded insurance scheme in Maharashtra, India covered the cost of PCI for low-income patients, the high cost of post-PCI treatment, especially Dual Antiplatelet Therapy (DAPT), still caused many patients to prematurely discontinue the secondary prevention. Our study aimed to investigate the effectiveness of DAPT adherence on all-cause mortality among post-PCI patients and explore the potential determinants of DAPT adherence in India.

METHOD

We collected clinical data of 4,595 patients undergoing PCI in 110 participating medical centers in Maharashtra, India from 2012 to 2015 by electronic medical records. We surveyed 2527 adult patients who were under the insurance scheme by telephone interview, usually between 6 to 12 months after their revascularization. Patients reporting DAPT continuation in the telephone survey were categorized as DAPT adherence. The outcome of the interest was all-cause mortality within 1 year after the index procedure. Multivariate Cox proportional hazard (PH) model with adjustment of potential confounders and standardization were used to explore the effects of DAPT adherence on all-cause mortality. We further used a multivariate logistic model to investigate the potential determinants of DAPT adherence.

RESULTS

Out of the 2527 patients interviewed, 2064 patients were included in the analysis, of whom 470 (22.8%) discontinued DAPT prematurely within a year. After adjustment for baseline confounders, DAPT adherence was associated with lower one-year all-cause mortality compared to premature discontinuation (less than 6-month), with an adjusted hazard ratio (HR) of 0.52 (95% Confidence Interval (CI) = (0.36, 0.67)). We also found younger patients (OR per year was 0.99 (0.97, 1.00)) and male (vs. female, OR of 1.30 (0.99, 1.70)) had higher adherence to DAPT at one year as did patients taking antihypertensive medications (vs. non medication, OR of 1.57 (1.25, 1.95)).

CONCLUSION

These findings suggest the protective effects of DAPT adherence on 1-year mortality among post-PCI patients in a low-income setting and indicate younger age, male sex and use of other preventive treatments were predictors of higher DAPT adherence.

Application of Extreme Learning Machine in the Survival Analysis of Chronic Heart Failure Patients With High Percentage of Censored Survival Time

Objective: To explore the application of the Cox model based on extreme learning machine in the survival analysis of patients with chronic heart failure.

Methods: The medical records of 5,279 inpatients diagnosed with chronic heart failure in two grade 3 and first-class hospitals in Taiyuan from 2014 to 2019 were collected; with death as the outcome and after the feature selection, the Lasso Cox, random survival forest (RSF), and the Cox model based on extreme learning machine (ELM Cox) were constructed for survival analysis and prediction; the prediction performance of the three models was explored based on simulated data with three censoring ratios of 25, 50, and 75%.

Results: Simulation results showed that the prediction performance of the three models decreased with increasing censoring proportion, and the ELM Cox model performed best overall; the ELM Cox model constructed with 21 highly influential survival predictors screened from actual chronic heart failure data showed the best performance with C-index and Integrated Brier Score (IBS) of 0.775(0.755, 0.802) and 0.166(0.150, 0.182), respectively.

Conclusion: The ELM Cox model showed good discrimination performance in the survival analysis of patients with chronic heart failure; it performs consistently for data with a high proportion of censored survival time; therefore, the model could help physicians identify patients at high risk of poor prognosis and target therapeutic measures to patients as early as possible.

Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model

Background

Due to the limited number of studies with long term follow-up of patients undergoing Percutaneous Coronary Intervention (PCI), we investigated the occurrence of Major Adverse Cardiac and Cerebrovascular Events (MACCE) during 10 years of follow-up after coronary angioplasty using Random Survival Forest (RSF) and Cox proportional hazards models.

Methods

The current retrospective cohort study was performed on 220 patients (69 women and 151 men) undergoing coronary angioplasty from March 2009 to March 2012 in Farchshian Medical Center in Hamadan city, Iran. Survival time (month) as the response variable was considered from the date of angioplasty to the main endpoint or the end of the follow-up period (September 2019). To identify the factors influencing the occurrence of MACCE, the performance of Cox and RSF models were investigated in terms of C index, Integrated Brier Score (IBS) and prediction error criteria.

Results

Ninety-six patients (43.7%) experienced MACCE by the end of the follow-up period, and the median survival time was estimated to be 98 months. Survival decreased from 99% during the first year to 39% at 10 years' follow-up. By applying the Cox model, the predictors were identified as follows: age (HR = 1.03, 95% CI 1.01–1.05), diabetes (HR = 2.17, 95% CI 1.29–3.66), smoking (HR = 2.41, 95% CI 1.46–3.98), and stent length (HR = 1.74, 95% CI 1.11–2.75). The predictive performance was slightly better by the RSF model (IBS of 0.124 vs. 0.135, C index of 0.648 vs. 0.626 and out-of-bag error rate of 0.352 vs. 0.374 for RSF). In addition to age, diabetes, smoking, and stent length, RSF also included coronary artery disease (acute or chronic) and hyperlipidemia as the most important variables.

Conclusion

Machine-learning prediction models such as RSF showed better performance than the Cox proportional hazards model for the prediction of MACCE during long-term follow-up after PCI.

Did India's price control policy for coronary stents create unintended consequences?

In February 2017, India capped the retail price of coronary stents and restricted the channel margin to bring Percutaneous Transluminal Coronary Angioplasty (PTCA) procedure, which uses coronary stents, within reach of millions of patients who previously could not afford it. Prior research shows that care providers respond to such regulations in a way that compensates for their loss in profits because of price control. Therefore, price control policies often introduce unintended consequences, such as distortions in clinical decision making. We investigate such distortions through empirical analysis of claims data from a representative public insurance program in the Indian state of Karnataka. Our data comprises 25,769 insurance claims from 69 private and seven public hospitals from February 2016 to February 2018. The public insurance context is ideal for investigating distortions in clinical decisions as the price paid by patients, and thereby access to the treatment, does not change after price control. We find that the change in the average volume of PTCA procedures per hospital per month after price control disproportionately increased when compared to the change in the clinical alternative - Coronary Artery Bypass Graft (CABG) procedures. This increase corresponds to 6% of the average number of PTCA procedures and 28% of the average number of CABG procedures before the price control. In addition, disproportionate increase in PTCA procedures occurred only among private hospitals, indicating the possibility of profit-maximization intentions driving the clinical choices. Such clinical distortions can have negative implications for patient health outcomes in the long run. We discuss alternative policies to improve access and affordability to healthcare products and services which are likely to not suffer from similar distortions.