Incidence and Predictors of Unplanned Hospital Readmission after Percutaneous Coronary Intervention

Using the Super Learner algorithm to predict risk of major adverse cardiovascular events after percutaneous coronary intervention in patients with myocardial infarction

BACKGROUND

The primary treatment for patients with myocardial infarction (MI) is percutaneous coronary intervention (PCI). Despite this, the incidence of major adverse cardiovascular events (MACEs) remains a significant concern. Our study seeks to optimize PCI predictive modeling by employing an ensemble learning approach to identify the most effective combination of predictive variables.

METHODS AND RESULTS

We conducted a retrospective, non-interventional analysis of MI patient data from 2018 to 2021, focusing on those who underwent PCI. Our principal metric was the occurrence of 1-year postoperative MACEs. Variable selection was performed using lasso regression, and predictive models were developed using the Super Learner (SL) algorithm. Model performance was appraised by the area under the receiver operating characteristic curve (AUC) and the average precision (AP) score. Our cohort included 3,880 PCI patients, with 475 (12.2%) experiencing MACEs within one year. The SL model exhibited superior discriminative performance, achieving a validated AUC of 0.982 and an AP of 0.971, which markedly surpassed the traditional logistic regression models (AUC: 0.826, AP: 0.626) in the test cohort. Thirteen variables were significantly associated with the occurrence of 1-year MACEs.

CONCLUSION

Implementing the Super Learner algorithm has substantially enhanced the predictive accuracy for the risk of MACEs in MI patients. This advancement presents a promising tool for clinicians to craft individualized, data-driven interventions to better patient outcomes.

Activity counseling early postelective percutaneous coronary intervention (ACE-PCI): Mixed-methods pilot randomized controlled trial

Background

Physical activity (PA) levels of people with coronary heart disease are low in the first 30 days after percutaneous coronary intervention (PCI), increasing the risk of recurrent cardiac events. Following PCI, PA counseling delivered by a physiotherapist before discharge may increase the PA levels of patients. Preliminary work is required to determine the effects of the counseling session compared to usual care.

Objectives

To investigate the feasibility and potential efficacy of a brief physiotherapist-led PA counseling session immediately after an elective PCI compared to usual care for improved PA early post-PCI.

Methods

Using concealed allocation and blinded assessments, eligible participants (n = 30) were randomized to a physiotherapist-led PA counseling session (30 min) or usual care (nurse-led PA advice < 5 min). The primary outcome was daily minutes of moderate-to-vigorous PA (accelerometry; 3 weeks). Secondary outcomes included cardiac rehabilitation intention, anxiety and depression levels (Hospital Anxiety and Depression Scale), and quality-of-life (MacNew questionnaire). Recruitment, retention, and attrition were assessed for feasibility. Semistructured interviews were conducted with 13 participants to determine intervention acceptability, and barriers and enablers to PA.

Results

Between and within-group comparisons were not significant in intention-to-treat analyses. All feasibility criteria were met except for retention and attrition of participants. At 3 weeks, only 25% of participants were planning to attend cardiac rehabilitation, with no between-group differences. Increased PA at 3 weeks was associated with participants that were younger, without other chronic disease,s and more active immediately following discharge. Interviews revealed personal, environmental, and program-based themes for barriers and enablers to PA.

Conclusions

A physiotherapist-led PA counseling session may not improve PA levels early post-elective PCI compared to very brief PA advice delivered by nurses. A larger multicentre randomized controlled trial is feasible with minor modifications to participant follow-up. Further research is required.

Early vs. Late Readmission following Percutaneous Coronary Intervention: Predictors and Impact on Long-Term Outcomes

BACKGROUND

Readmissions within 1 year after percutaneous coronary intervention (PCI) are common (18.6-50.4% in international series) and a burden to patients and health services, however their long-term implications are not well characterised. We compared predictors of 30-day (early) and 31-day to 1-year (late) unplanned readmission and the impact of unplanned readmission on long-term clinical outcomes post-PCI.

METHODS

Patients enrolled in the GenesisCare Cardiovascular Outcomes Registry (GCOR-PCI) from 2008 to 2020 were included in the study. Multivariate logistic regression analysis was performed to identify predictors of early and late unplanned readmission. A Cox proportion hazards regression model was used to explore the impact of any unplanned readmission during the first year post-PCI on the clinical outcomes at 3 years. Finally, patients with early and late unplanned readmission were compared to determine which group was at the highest risk of adverse long-term outcomes.

RESULTS

The study comprised 16,911 consecutively enrolled patients who underwent PCI between 2009-2020. Of these, 1422 patients (8.5%) experienced unplanned readmission within 1-year post-PCI. Overall, the mean age was 68.9 ± 10.5 years, 76.4% were male and 45.9% presented with acute coronary syndromes. Predictors of unplanned readmission included increasing age, female gender, previous CABG, renal impairment and PCI for acute coronary syndromes. Unplanned readmission within 1 year of PCI was associated with an increased risk of MACE (adjusted HR 1.84 (1.42-2.37), p < 0.001) and death over a 3-year follow-up (adjusted HR 1.864 (1.34-2.59), p < 0.001) compared with those without readmission within 1-year post-PCI. Late compared with early unplanned readmission within the first year of PCI was more frequently associated with subsequent unplanned readmission, MACE and death between 1 and 3 years post-PCI.

CONCLUSIONS

Unplanned readmissions in the first year following PCI, particularly those occurring more than 30 days after discharge, were associated with a significantly higher risk of adverse outcomes, such as MACE and death at 3 years. Strategies to identify patients at high risk of readmission and interventions to reduce their greater risk of adverse events should be implemented post-PCI.

The Association of Sex with Unplanned Cardiac Readmissions following Percutaneous Coronary Intervention in Australia: Results from a Multicentre Outcomes Registry (GenesisCare Cardiovascular Outcomes Registry)

Background and aim: Unplanned cardiac readmissions in patients with percutaneous intervention (PCI) is very common and is seen as a quality indicator of in-hospital care. Most studies have reported on the 30-day cardiac readmission rates, with very limited information being available on 1-year readmission rates and their association with mortality. The aim of this study was to investigate the impact of biological sex at 1-year post-PCI on unplanned cardiac readmissions. Methods and results: Patients enrolled into the GenesisCare Cardiovascular Outcomes Registry (GCOR-PCI) from December 2008 to December 2020 were included in the study. A total of 13,996 patients completed 12 months of follow-up and were assessed for unplanned cardiac readmissions. All patients with unplanned cardiac readmissions in the first year of post-PCI were followed in year 2 (post-PCI) for survival status. The rate of unplanned cardiac readmissions was 10.1%. Women had a 29% higher risk of unplanned cardiac readmission (HR 1.29, 95% CI 1.11 to 1.48; p = 0.001), and female sex was identified as an independent predictor of unplanned cardiac readmissions. Any unplanned cardiac readmission in the first year was associated with a 2.5-fold higher risk of mortality (HR 2.50, 95% CI 1.67 to 3.75; p < 0.001), which was similar for men and women. Conclusion: Unplanned cardiac readmissions in the first year post-PCI was strongly associated with increased all-cause mortality. Whilst the incidence of all-cause mortality was similar between women and men, a higher incidence of unplanned cardiac readmissions was observed for women, suggesting distinct predictors of unplanned cardiac readmissions exist between women and men.

Predicting the Risk of Unplanned Readmission at 30 Days After PCI: Development and Validation of a New Predictive Nomogram

Objective

This study aimed to develop and validate a risk prediction model that can be used to identify percutaneous coronary intervention (PCI) patients at high risk for 30-day unplanned readmission.

Patients and Methods

We developed a prediction model based on a training dataset of 1348 patients after PCI. The data were collected from January 2020 to December 2020. Clinical characteristics, laboratory data and risk factors were collected using the hospital database. The LASSO regression method was applied to filter variables and select predictors, and feature selection for a 30-day readmission risk model was optimized using least absolute shrinkage. Multivariate logistic regression was used to construct a nomogram. The performance and clinical utility of the nomogram were evaluated with a receiver operating characteristic (ROC) curve, a calibration curve, and decision curve analysis (DCA). Internal validation of the predictive accuracy was performed using bootstrapping validation.

Results

The predictors included in the prediction nomogram were medical insurance, length of stay, left ventricular ejection fraction on admission, history of hypertension, the presence of chronic lung disease, the presence of anemia, and serum creatinine level on admission. The area under the receiver operating characteristic curve for the predictive model was 0.735 (95% CI: 0.711–0.759). The P value of the Hosmer–Lemeshow goodness of fit test was 0.326, indicating good calibration, and the calibration curves showed good agreement between the classifications and actual observations. DCA also demonstrated that the nomogram was clinically useful. A high c-index value of 0.723 was obtained during the internal validation.

Conclusion

We developed an easy-to-use nomogram model to predict the risk of readmission 30 days after discharge for PCI patients. This risk prediction model may serve as a guide for screening high-risk patients and allocating resources for PCI patients at the time of hospital discharge and may provide a reference for preventive care interventions.

Prognostic Impact of Multiple Lymphocyte-Based Inflammatory Indices in Acute Coronary Syndrome Patients

Background

The aim of this study was to evaluate the prognostic values of five lymphocyte-based inflammatory indices (platelet-lymphocyte ratio [PLR], neutrophil-lymphocyte ratio [NLR], monocyte-lymphocyte ratio [MLR], systemic immune inflammation index [SII], and system inflammation response index [SIRI]) in patients with acute coronary syndrome (ACS).

Methods

A total of 1,701 ACS patients who underwent percutaneous coronary intervention (PCI) were included in this study and followed up for major adverse cardiovascular events (MACE) including all-cause death, non-fatal ischemic stroke, and non-fatal myocardial infarction. The five indices were stratified by the optimal cutoff value for comparison. The association between each of the lymphocyte-based inflammatory indices and MACE was assessed by the Cox proportional hazards regression analysis.

Results

During the median follow-up of 30 months, 107 (6.3%) MACE were identified. The multivariate COX analysis showed that all five indices were independent predictors of MACE, and SIRI seemingly performed best (Hazard ratio [HR]: 3.847; 95% confidence interval [CI]: [2.623–5.641]; p < 0.001; C-statistic: 0.794 [0.731–0.856]). The addition of NLR, MLR, SII, or SIRI to the Global Registry of Acute Coronary Events (GRACE) risk score, especially SIRI (C-statistic: 0.699 [0.646–0.753], p < 0.001; net reclassification improvement [NRI]: 0.311 [0.209–0.407], p < 0.001; integrated discrimination improvement [IDI]: 0.024 [0.010–0.046], p < 0.001), outperformed the GRACE risk score alone in the risk predictive performance.

Conclusion

Lymphocyte-based inflammatory indices were significantly and independently associated with MACE in ACS patients who underwent PCI. SIRI seemed to be better than the other four indices in predicting MACE, and the combination of SIRI with the GRACE risk score could predict MACE more accurately.

Estimating the economic impacts of percutaneous coronary intervention in Australia: a registry-based cost burden study

OBJECTIVES

In this study, we sought to evaluate the costs of percutaneous coronary intervention (PCI) across a variety of indications in Victoria, Australia, using a direct per-person approach, as well as to identify key cost drivers.

DESIGN

A cost-burden study of PCI in Victoria was conducted from the Australian healthcare system perspective.

SETTING

A linked dataset of patients admitted to public hospitals for PCI in Victoria was drawn from the Victorian Cardiac Outcomes Registry (VCOR) and the Victorian Admitted Episodes Dataset. Generalised linear regression modelling was used to evaluate key cost drivers. From 2014 to 2017, 20 345 consecutive PCIs undertaken in Victorian public hospitals were captured in VCOR.

PRIMARY OUTCOME MEASURES

Direct healthcare costs attributed to PCI, estimated using a casemix funding method.

RESULTS

Key cost drivers identified in the cost model included procedural complexity, patient length of stay and vascular access site. Although the total procedural cost increased from $A55 569 740 in 2014 to $A72 179 656 in 2017, mean procedural costs remained stable over time ($A12 521 in 2014 to $A12 185 in 2017) after adjustment for confounding factors. Mean procedural costs were also stable across patient indications for PCI ($A9872 for unstable angina to $A15 930 for ST-elevation myocardial infarction) after adjustment for confounding factors.

CONCLUSIONS

The overall cost burden attributed to PCIs in Victoria is rising over time. However, despite increasing procedural complexity, mean procedural costs remained stable over time which may be, in part, attributed to changes in clinical practice.

Capturing rich person-centred discharge information: exploring the challenges in developing a new model

Purpose

Capture, consumption and use of person-centred information presents challenges for hospitals when operating within the scope of limited resources and the push for organisational routines and efficiencies. This paper explores these challenges for patients with Acute Coronary Syndrome (ACS) and the examination of information that supports successful hospital discharge. It aims to determine how the likelihood of readmission may be prevented through the capturing of rich, person-specific information during in-patient care to improve the process for discharge to home.

Design/methodology/approach

The authors combine four research data collection and analysis techniques: one, an analysis of the patient record; two, semi-structured longitudinal interviews; three, an analysis of the patient's journey using process mining to provide analytics about the discharge process, and four, a focus group with nurses to validate and confirm our findings.

Findings

The authors’ contribution is to show that information systems which support discharge need to consider models focused on individual patient stressors. The authors find that current discharge information capture does not provide the required person-centred information to support a successful discharge. Data indicate that rich, detailed information about the person acquired through additional nursing assessments are required to complement data provided about the patient's journey in order to support the patients’ post-discharge recovery at home.

Originality/value

Prior research has focused on information collection constrained by pre-determined limitations and barriers of system design. This work has not considered the information provided by multiple sources during the whole patient journey as a mechanism to reshape the discharge process to become more person-centred. Using a novel combination of research techniques and theory, the authors have shown that patient information collected through multiple channels across the patient care journey may significantly extend the quality of patient care beyond hospital discharge. Although not assessed in this study, rich, person-centred discharge information may also decrease the likelihood of patient readmission.