A risk score to predict in-hospital mortality for percutaneous coronary interventions

Should We Use Aspirin or P2Y12 Inhibitor Monotherapy in Stable Ischemic Heart Disease?

PURPOSE OF REVIEW

To summarize the recent evidence and guideline recommendations on aspirin or P2Y12 inhibitor monotherapy in patients with stable ischemic heart disease and provide insights into future directions on this topic, which involves transition to a personalized assessment of bleeding and thrombotic risks.

RECENT FINDINGS

It has been questioned whether the evidence for aspirin as the foundational component of secondary prevention in patients with coronary artery disease aligns with contemporary pharmaco-invasive strategies. The recent HOST-EXAM study randomized patients who had received dual antiplatelet therapy for 6 to 18 months without ischemic or major bleeding events to either clopidogrel or aspirin for a further 24 months, and demonstrated that the patients in the clopidogrel arm had significantly lower rates of both thrombotic and bleeding complications compared to those in the aspirin arm. The patient-level PANTHER meta-analysis showed that in patients with established coronary artery disease, P2Y12 inhibitor monotherapy was associated with lower rates of myocardial infarction, stent thrombosis as well as gastrointestinal bleeding and hemorrhagic stroke compared to aspirin monotherapy, albeit with similar rates of all-cause mortality, cardiovascular mortality and major bleeding. Long-term low-dose aspirin is recommended for secondary prevention in patients with stable ischemic heart disease, with clopidogrel monotherapy being acknowledged as a feasible alternative. Dual antiplatelet therapy for six months after percutaneous coronary intervention remains the standard recommendation for patients with stable ischemic heart disease. However, the duration of dual antiplatelet therapy may be shortened and followed by P2Y12 inhibitor monotherapy or prolonged based on individualized evaluation of the patient's risk profile.

Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients

BACKGROUND

Machine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome.

METHODS

This study was performed on 13,682 patients at Tehran Heart Center from 2015 to 2021. Patients were split into 70:30 for testing and training. Four ML models were designed: a traditional Logistic Regression (LR) model, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ada Boost models. The importance of features was calculated using the RF feature selector and SHAP based on the XGBoost model. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) for the prediction on the testing dataset was the main measure of the model's performance.

RESULTS

From a total of 9,073 patients with >1-year follow-up, 340 participants died. Higher age and higher rates of comorbidities were observed in these patients. Body mass index and lipid profile demonstrated a U-shaped correlation with the outcome. Among the models, RF had the best discrimination (AUC 0.866), while the highest sensitivity (80.9%) and specificity (88.3%) were for LR and XGBoost models, respectively. All models had AUCs of >0.8.

CONCLUSION

ML models can predict 1-year mortality after PCI with high performance. A classic LR statistical approach showed comparable results with other ML models. The individual-level assessment of inter-variable correlations provided new insights into the non-linear contribution of risk factors to post-PCI mortality.

Critical appraisal and assessment of bias among studies evaluating risk prediction models for in-hospital and 30-day mortality after percutaneous coronary intervention: a systematic review

OBJECTIVE

We systematically assessed prediction models for the risk of in-hospital and 30-day mortality in post-percutaneous coronary intervention (PCI) patients.

DESIGN

Systematic review and narrative synthesis.

DATA SOURCES

Searched PubMed, Web of Science, Embase, Cochrane Library, CINAHL, CNKI, Wanfang Database, VIP Database and SinoMed for literature up to 31 August 2023.

ELIGIBILITY CRITERIA

The included literature consists of studies in Chinese or English involving PCI patients aged ≥18 years. These studies aim to develop risk prediction models and include designs such as cohort studies, case-control studies, cross-sectional studies or randomised controlled trials. Each prediction model must contain at least two predictors. Exclusion criteria encompass models that include outcomes other than death post-PCI, literature lacking essential details on study design, model construction and statistical analysis, models based on virtual datasets, and publications such as conference abstracts, grey literature, informal publications, duplicate publications, dissertations, reviews or case reports. We also exclude studies focusing on the localisation applicability of the model or comparative effectiveness.

DATA EXTRACTION AND SYNTHESIS

Two independent teams of researchers developed standardised data extraction forms based on CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies to extract and cross-verify data. They used Prediction model Risk Of Bias Assessment Tool (PROBAST) to assess the risk of bias and applicability of the model development or validation studies included in this review.

RESULTS

This review included 28 studies with 38 prediction models, showing area under the curve values ranging from 0.81 to 0.987. One study had an unclear risk of bias, while 27 studies had a high risk of bias, primarily in the area of statistical analysis. The models constructed in 25 studies lacked clinical applicability, with 21 of these studies including intraoperative or postoperative predictors.

CONCLUSION

The development of in-hospital and 30-day mortality prediction models for post-PCI patients is in its early stages. Emphasising clinical applicability and predictive stability is vital. Future research should follow PROBAST's low risk-of-bias guidelines, prioritising external validation for existing models to ensure reliable and widely applicable clinical predictions.

PROSPERO REGISTRATION NUMBER

CRD42023477272.

Contemporary Risk Models for In-Hospital and 30-Day Mortality After Percutaneous Coronary Intervention

PURPOSE OF REVIEW

Risk models for mortality after percutaneous coronary intervention (PCI) are underutilized in clinical practice though they may be useful during informed consent, risk mitigation planning, and risk adjustment of hospital and operator outcomes. This review analyzed contemporary risk models for in-hospital and 30-day mortality after PCI.

RECENT FINDINGS

We reviewed eight contemporary risk models. Age, sex, hemodynamic status, acute coronary syndrome type, heart failure, and kidney disease were consistently found to be independent risk factors for mortality. These models provided good discrimination (C-statistic 0.85-0.95) for both pre-catheterization and comprehensive risk models that included anatomic variables. There are several excellent models for PCI mortality risk prediction. Choice of the model will depend on the use case and population, though the CathPCI model should be the default for in-hospital mortality risk prediction in the United States. Future interventions should focus on the integration of risk prediction into clinical care.

Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models

Aims

Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting.

Methods and results

Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69-0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk.

Conclusion

Machine learning-derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted.

Registration

Clinicaltrial.gov identifier is NCT02188355.

New York Risk Model and Simplified Risk Score for In-Hospital/30-Day Mortality for Percutaneous Coronary Intervention

Risk models and risk scores derived from those models require periodic updating to account for changes in procedural performance, patient mix, and new risk factors added to existing systems. No risk model or risk score exists for predicting in-hospital/30-day mortality for percutaneous coronary interventions (PCIs) using contemporary data. This study develops an updated risk model and simplified risk score for in-hospital/30-day mortality following PCI. To accomplish this, New York's Percutaneous Coronary Intervention Reporting System was used to develop a logistic regression model and a simplified risk score model for predicting in-hospital/30-day mortality and to validate both models based on New York data from the previous year. A total of 54,770 PCI patients from 2019 were used to develop the models. Twelve different risk factors and 27 risk factor categories were used in the models. Both models displayed excellent discrimination for the development and validation samples (range from 0.894 to 0.896) and acceptable calibration, but the full logistic model had superior calibration, particularly among higher-risk patients. In conclusion, both the PCI risk model and its simplified risk score model provide excellent discrimination and although the full risk model requires the use of a hand-held device for estimating individual patient risk, it provides somewhat better calibration, especially among higher-risk patients.

Sex-Based Differences in Rotational Atherectomy and Long-Term Clinical Outcomes

Present research on the influence of gender on the treatment of coronary artery disease (CAD) and the outcome after percutaneous coronary intervention (PCI) is inconsistent. Sex differences in the presentation of CAD and the success after treatment have been described. We intend to compare the male and female sex in the procedure and the long-term outcome of Rotational Atherectomy (RA). A total of 597 consecutive patients (20.3% female and 79.7% male, mean age 75.3 ± 8.9 years vs. 72.7 ± 9 years, p < 0.001) undergoing Rotational Atherectomy between 2015 and 2020 were enrolled in the analysis. Demographic and clinical data were registered. In-hospital, 1-year, and 3-year MACCEs (major adverse cardiac and cerebrovascular events) were calculated. Women presented more often with myocardial infarction (23.9% vs. 14.9%, p = 0.017). The intervention was mainly performed via femoral access compared to radial access (65.4% vs. 33.6%, p = 0.002). Women had a smaller diameter of the balloon predilatation compared to men (2.8 ± 0.5 mm vs. 3.15 ± 2.4 mm, p < 0.05) and a smaller maximum diameter of the implanted stent (3.5 ± 1.2 mm vs. 4.10 ± 6.5 mm, p = 0.01). In-hospital, 1-year-, and 3-year MACCEs did not differ between the sexes. After a multivariate analysis, no difference between men and women could be detected. In conclusion, this analysis shows differences between women and men in periprocedural characteristics but does not show any differences after RA regarding in-hospital, 1-year-, and 3-year MACCEs.

New Criteria to Identify Patients at Higher Risk for Cardiovascular Complications After Percutaneous Coronary Intervention

A universal definition to identify patients at higher risk of complications after percutaneous coronary intervention (PCI) is lacking. We aimed to validate a recently developed score to identify patients at increased risk of all-cause death after PCI. All consecutive patients from a large PCI registry not presenting with ST-elevation myocardial infarction or cardiogenic shock were included. Each patient was assigned a score obtained by summing the points associated with the following variables: age >80 years (3 points), dialysis (6 points), left ventricular ejection fraction <30% (2 points), and multivessel PCI (2 points). Patients were stratified in 3 groups: low risk (score 0), intermediate risk (score 2 to 3), or high risk (score ≥4). The primary outcome was all-cause death, and the secondary outcomes were major adverse cardiovascular events and major bleeding. Events were assessed at 1 year after PCI. Between January 2014 and December 2019, 12,689 patients underwent PCI. Compared with the 9,884 patients at low risk, those at intermediate and high risk had a fourfold (hazard ratio 3.99, 95% confidence interval 2.95 to 5.38) and ninefold (hazard ratio 9.55, 95% confidence interval 6.89 to 13.2) higher hazard for all-cause death at 1 year, respectively. The score had a good predictive value for all-cause death at 1 year (area under the curve 0.70). The risk of major adverse cardiovascular events and major bleeding increased consistently from the low- to the high-risk group. In conclusion, in patients who underwent PCI for stable ischemic heart disease or non-ST-elevation acute coronary syndrome, a score based on 4 variables well predicted the risk of all-cause death at 1 year.

Risk prediction models in patients undergoing percutaneous coronary intervention: A collaborative analysis from a Japanese administrative dataset and nationwide academic procedure registry

BACKGROUND

Contemporary guidelines emphasize the importance of risk stratification in improving the quality of care for patients undergoing percutaneous coronary intervention (PCI). We aimed to investigate whether adding information from a procedure-based academic registry to administrative claims data would improve the performance of risk prediction model.

METHODS

We combined two nationally representative administrative and clinical databases. The study cohort comprised 43,095 patients; 18,719 and 23, 525 with acute [ACS] and chronic [CCS] coronary syndrome, respectively. Each population was randomly divided into the logistic regression model (derivation cohort, 80%) and model validation (validation cohort, 20%) groups. The performances of the following models were compared using C-statistics: (1) variables restricted to baseline claims data (model #1), (2) clinical registry data (model #2), and (3) expanded to both claims and clinical registry data (model #3). The primary outcomes were in-hospital mortality and bleeding.

RESULTS

The primary outcomes occurred in 3.7% (in-hospital mortality)/5.0% (bleeding) of patients with ACS and 0.21%/0.95% of CCS patients. For each event, the model performance was 0.65 (95% confidence interval [CI], 0.60-0.69) /0.67 (0.63-0.71) in ACS and 0.52 (0.35-0.76) /0.62 (0.54-0.70) for CCS patients in model #1, 0.83 (0.80-0.87) /0.77 (0.74-0.81) in ACS and 0.76 (0.60-0.92) /0.67 (0.59-0.75) in CCS for model #2, and 0.83 (0.79-0.86) /0.78 (0.75-0.81) in ACS and 0.76 (0.61-0.92) /0.67 (0.58-0.74) in CCS for model #3.

CONCLUSIONS

Combining clinical information from the academic registry with claims databases improved its performance in predicting adverse events.

A novel scoring system for identifying patients at risk for venous thromboembolism undergoing diverticular resection: an American College of Surgeons-National Surgical Quality Improvement Program Study

Following colorectal surgery, venous thromboembolism (VTE) is a serious complication occurring at an estimated incidence of 2-4%. There is a significant body of literature stratifying risk of VTE in specific populations undergoing colorectal resection for cancer or inflammatory bowel disease. There has been little research characterizing patients undergoing colorectal surgery for other indications, e.g. diverticulitis. We hypothesize that there exists a subgroup of patients with identifiable risk factors undergoing resection for diverticulitis that has relatively higher risks for VTE. We conducted a retrospective review of the American College of Surgeons National Surgical Quality Improvement Project database from 2006 to 2017 who underwent colorectal resection for diverticulitis. Patients with a primary indication for resection other than diverticulitis were excluded. Multivariate logistic regression modeling was conducted to determine the risk of VTE for each independent variable. A novel scoring system was developed and a receiver-operating-characteristic curve was generated. The rate of VTE was 1.49%. An 7-point scoring system was developed using identified significant variables. Patients scoring ≥ 6 on the developed scoring scale had a 3.12% risk of 30-day VTE development. A simple scoring system based on identified significant risk factors was specifically developed to predict the risk of VTE in patients undergoing diverticular colorectal resection. These patients are at significantly higher risk and may justify increased vigilance regarding VTE events, similar to patients undergoing colorectal resection for cancer or inflammatory bowel disease.

A nomogram predicting 30-day mortality in patients undergoing percutaneous coronary intervention

Background and aims

Early detection of mortality after percutaneous coronary intervention (PCI) is crucial, whereas most risk prediction models are based on outdated cohorts before the year 2000. This study aimed to establish a nomogram predicting 30-day mortality after PCI.

Materials and methods

In total, 10,444 patients undergoing PCI in National Center for Cardiovascular Diseases in China were enrolled to establish a nomogram to predict 30-day mortality after PCI. The nomogram was generated by incorporating parameters selected by logistic regression with the stepwise backward method.

Results

Five features were selected to build the nomogram, including age, male sex, cardiac dysfunction, STEMI, and TIMI 0–2 after PCI. The performance of the nomogram was evaluated, and the area under the curves (AUC) was 0.881 (95% CI: 0.8–0.961). Our nomogram exhibited better performance than a previous risk model (AUC = 0.7, 95% CI: 0.586–0.813) established by Brener et al. The survival curve successfully stratified the patients above and below the median score of 4.

Conclusion

A novel nomogram for predicting 30-day mortality was established in unselected patients undergoing PCI, which may help risk stratification in clinical practice.

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

Contemporary Clinical and Coronary Anatomic Risk Model for 30-Day Mortality After Percutaneous Coronary Intervention

BACKGROUND

Percutaneous coronary intervention (PCI) procedures are increasing in clinical and anatomic complexity, likely increasing the calculated risk of mortality. There is need for a real-time risk prediction tool that includes clinical and coronary anatomic information that is integrated into the electronic medical record system.

METHODS

We assessed 70 503 PCIs performed in 73 Veterans Affairs hospitals from 2008 to 2019. We used regression and machine-learning strategies to develop a prediction model for 30-day mortality following PCI. We assessed model performance with and without inclusion of the Veterans Affairs SYNTAX score (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery), an assessment of anatomic complexity. Finally, the discriminatory ability of the Veterans Affairs model was compared with the CathPCI mortality model.

RESULTS

The overall 30-day morality rate was 1.7%. The final model included 14 variables. Presentation status (salvage, emergent, urgent), ST-segment-elevation myocardial infarction, cardiogenic shock, age, congestive heart failure, prior valve disease, chronic kidney disease, chronic lung disease, atrial fibrillation, elevated international normalized ratio, and the Veterans Affairs SYNTAX score were all associated with increased risk of death, while increasing body mass index, hemoglobin level, and prior coronary artery bypass graft surgery were associated with lower risk of death. C-index for the development cohort was 0.93 (95% CI, 0.92-0.94) and for the 2019 validation cohort and the site validation cohort was 0.87 (95% CI, 0.83-0.92) and 0.86 (95% CI, 0.83-0.89), respectively. The positive likelihood ratio of predicting a mortality event in the top decile was 2.87% more accurate than the CathPCI mortality model. Inclusion of anatomic information in the model resulted in significant improvement in model performance (likelihood ratio test P<0.01).

CONCLUSIONS

This contemporary risk model accurately predicts 30-day post-PCI mortality using a combination of clinical and anatomic variables. This can be immediately implemented into clinical practice to promote personalized informed consent discussions and appropriate preparation for high-risk PCI cases.

Prognostic value of admission high-sensitivity troponin in patients with ST-elevation myocardial infarction

BACKGROUND AND AIM

Although the diagnostic usefulness of high-sensitivity cardiac troponin T (hs-cTnT) is well established in ST-segment elevation myocardial infarction (STEMI), its prognostic relevance in risk stratification of patients with STEMI remains obscure. This study sought to determine the prognostic value of pre-reperfusion (admission) and post-reperfusion (12-hour) hs-cTnT in patients with STEMI treated with primary percutaneous coronary intervention (PPCI).

METHODS

Retrospective observational longitudinal study including consecutive patients with STEMI treated with PPCI at a university hospital in the northeast of England. hs-cTnT was measured at admission to the catheterisation laboratory and 12 hours after PPCI. Clinical, procedural and laboratory data were prospectively collected during patient hospitalisation (June 2010-December 2014). Mortality data were obtained from the UK Office of National Statistics. The study endpoints were in-hospital and overall mortality.

RESULTS

A total of 3113 patients were included. Median follow-up was 53 months. Admission hs-cTnT >515 ng/L (fourth quartile) was independently associated with in-hospital mortality (HR=2.53 per highest to lower quartiles; 95% CI: 1.32 to 4.85; p=0.005) after multivariable adjustment for a clinical model of mortality prediction. Likewise, admission hs-cTnT >515 ng/L independently predicted overall mortality (HR=1.27 per highest to lower quartiles; 95% CI: 1.02 to 1.59; p=0.029). Admission hs-cTnT correctly reclassified risk for in-hospital death (net reclassification index (NRI)=0.588, p<0.001) and overall mortality (NRI=0.178, p=0.001). Conversely, 12-hour hs-cTnT was not independently associated with mortality.

CONCLUSION

Admission, but not 12-hour post-reperfusion, hs-cTnT predicts mortality and improves risk stratification in the PPCI era. These results support a prognostic role for admission hs-cTnT while challenge the cost-effectiveness of routine 12-hour hs-cTnT measurements in patients with STEMI.

Predicting postoperative surgical site infection with administrative data: a random forests algorithm

Background

Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health administrative data, using Machine Learning algorithms.

Methods

All patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk score system. The SSI risk models were validated in the validation datasets.

Results

Of 14,351 patients, 795 (5.5%) had an SSI. First, separate predictive models were built for three distinct administrative datasets. The final model, including hospitalization diagnostic, physician diagnostic and procedure codes, demonstrated excellent discrimination (C statistics, 0.91, 95% CI, 0.90–0.92) and calibration (Hosmer-Lemeshow χ² statistics, 4.531, p = 0.402).

Conclusion

We demonstrated that health administrative data can be effectively used to identify SSIs. Machine learning algorithms have shown a high degree of accuracy in predicting postoperative SSIs and can integrate and utilize a large amount of administrative data. External validation of this model is required before it can be routinely used to identify SSIs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01369-9.

Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation

After short-term, acute-care hospitalization for stroke, patients may be discharged home or other facilities for continued medical or rehabilitative management. The site of postacute care affects overall mortality and functional outcomes. Determining discharge disposition is a complex decision by the healthcare team. Early prediction of discharge destination can optimize poststroke care and improve outcomes. Previous attempts to predict discharge disposition outcome after stroke have limited clinical validations. In this study, readmission status was used as a measure of the clinical significance and effectiveness of a discharge disposition prediction. Low readmission rates indicate proper and thorough care with appropriate discharge disposition. We used Medicare beneficiary data taken from a subset of base claims in the years of 2014 and 2015 in our analyses. A predictive tool was created to determine discharge disposition based on risk scores derived from the coefficients of multivariable logistic regression related to an adjusted odds ratio. The top five risk scores were admission from a skilled nursing facility, acute heart attack, intracerebral hemorrhage, admission from "other" source, and an age of 75 or older. Validation of the predictive tool was accomplished using the readmission rates. A 75% probability for facility discharge corresponded with a risk score of greater than 9. The prediction was then compared to actual discharge disposition. Each cohort was further analyzed to determine how many readmissions occurred in each group. Of the actual home discharges, 95.7% were predicted to be there. However, only 47.8% of predictions for home discharge were actually discharged home. Predicted discharge to facility had 15.9% match to the actual facility discharge. The scenario of actual discharge home and predicted discharge to facility showed that 186 patients were readmitted. Following the algorithm in this scenario would have recommended continued medical management of these patients, potentially preventing these readmissions.

End-stage renal disease as an independent risk factor for in-hospital mortality after coronary drug-eluting stenting: Understanding and modeling the risk

OBJECTIVES

We sought to compare in-hospital outcomes between patients with and without end-stage renal disease (ESRD) undergoing coronary drug-eluting stent (DES) placement and to model risk of in-hospital adverse postpercutaneous coronary intervention (PCI) events in ESRD patients.

BACKGROUND

The effect of ESRD on the risk of in-hospital complications after DES PCI is relatively unclear, as is the ability to prospectively stratify risk in this population.

METHODS

Consecutive patients undergoing first-time DES between April 1, 2003 and June 30, 2018 at a single tertiary care hospital were included in a prospective registry. Outcomes in those with ESRD were compared to those without ESRD. The primary endpoint was in-hospital all-cause mortality; secondary endpoints included in-hospital major adverse cardiac events (MACE)-defined as cardiac death, myocardial infarction, or unplanned revascularization-and major bleeding. Multivariate logistic regression modeling was used to identify factors associated with each outcome and to generate risk scores.

RESULTS

Among 18,134 patients in the study population, 382 (2.1%) had ESRD. ESRD was associated with increased risk of in-hospital mortality (7.1 vs. 2.9%, p < .001), in-hospital MACE (6.3 vs. 2.1%, p < .001), and major bleeding (12.0 vs. 2.6%, p < .001). After multivariable risk adjustment, ESRD was independently associated with in-hospital mortality (odds ratio: 1.83, 95% confidence interval: 1.04-3.23, p = .04) but not MACE or major bleeding. Among patients with ESRD, risks of MACE and major bleeding were successfully modeled (c-statistics = .72 and .85, respectively).

CONCLUSIONS

ESRD is independently associated with increased risk of in-hospital mortality after coronary DES. Future studies are necessary to validate risk models derived to identify high-risk ESRD patients.

An Early Predictive Scoring Model for In-Hospital Cardiac Arrest of Emergent Hemodialysis Patients

BACKGROUND

Patients undergoing hemodialysis are prone to cardiac arrests.

METHODS

This study aimed to develop a risk score to predict in-hospital cardiac arrest (IHCA) in emergency department (ED) patients undergoing emergency hemodialysis. Patients were included if they received urgent hemodialysis within 24 h after ED arrival. The primary outcome was IHCA within three days. Predictors included three domains: comorbidity, triage information (vital signs), and initial biochemical results. The final model was generated from data collected between 2015 and 2018 and validated using data from 2019.

RESULTS

A total of 257 patients, including 52 with IHCA, were analyzed. Statistical analysis selected significant variables with higher sensitivity cutoff, and scores were assigned based on relative beta coefficient ratio: K > 5.5 mmol/L (score 1), pH < 7.35 (score 1), oxygen saturation < 85% (score 1), and mean arterial pressure < 80 mmHg (score 2). The final scoring system had an area under the curve of 0.78 (p < 0.001) in the primary group and 0.75 (p = 0.023) in the validation group. The high-risk group (defined as sum scores ≥ 3) had an IHCA risk of 47.2% and 41.7%, while the low-risk group (sum scores < 3) had 18.3% and 7%, in the primary and validation databases, respectively.

CONCLUSIONS

This predictive score model for IHCA in emergent hemodialysis patients could help healthcare providers to take necessary precautions and allocate resources.

Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome

Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events' prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.

Role of Post-Stent Physiological Assessment in a Risk Prediction Model After Coronary Stent Implantation

OBJECTIVES

The aim of this study was to develop a risk model incorporating clinical, angiographic, and physiological parameters to predict future clinical events after drug-eluting stent implantation.

BACKGROUND

Prognostic factors after coronary stenting have not been comprehensively investigated.

METHODS

A risk model to predict target vessel failure (TVF) at 2 years was developed from 2,200 patients who underwent second-generation drug-eluting stent implantation and post-stent fractional flow reserve (FFR) measurement. TVF was defined as a composite of cardiac death, target vessel myocardial infarction, and clinically driven target vessel revascularization. A random survival forest model with automatic feature selection by minimal depth analysis was used for risk model development.

RESULTS

During 2 years of follow-up, the cumulative incidence of TVF was 5.9%. From clinical, angiographic, and physiological parameters, 6 variables were selected for the risk model in order of importance within the model as follows: total stent length, post-stent FFR, age, post-stent percentage diameter stenosis, reference vessel diameter, and diabetes mellitus. Harrell's C index of the random survival forest model was 0.72 (95% confidence interval [CI]: 0.62 to 0.82). This risk model showed better prediction ability than models with clinical risk factors alone (Harrell's C index = 0.55; 95% CI: 0.41 to 0.59; p for comparison = 0.005) and with clinical risk factors and angiographic parameters (Harrell's C index = 0.65; 95% CI: 0.52 to 0.77; p for comparison = 0.045). When the patients were divided into 2 groups according to the median of total stent length (30 mm), post-stent FFR and total stent length showed the highest variable importance in the short- and long-stent groups, respectively.

CONCLUSIONS

A risk model incorporating clinical, angiographic, and physiological predictors can help predict the risk for TVF at 2 years after coronary stenting. Total stent length and post-stent FFR were the most important predictors. (International Post PCI FFR Registry; NCT04012281).

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Background

Currently, how to accurately determine the patient prognosis after a percutaneous coronary intervention (PCI) remains unclear and may vary among populations, hospitals, and datasets. The aim of this study was to establish a prediction model of in-hospital mortality risk after primary PCI in patients with acute ST-elevated myocardial infarction (STEMI).

Methods

This was a multicenter, observational study of patients with acute STEMI who underwent primary PCI. The outcome was in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome. A regression model was built using the selected variables to select the significant predictors of mortality. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.

Results

Totally, 1169 and 316 patients were enrolled in the training and validation sets, respectively. Fourteen predictors were identified by the LASSO analysis: sex, Killip classification, left main coronary artery disease (LMCAD), grading of thrombus, TIMI classification, slow flow, application of IABP, administration of β-blocker, ACEI/ARB, symptom-to-door time (SDT), symptom-to-balloon time (SBT), syntax score, left ventricular ejection fraction (LVEF), and CK-MB peak. The mortality risk prediction nomogram achieved good discrimination for in-hospital mortality (training set: C-statistic = 0.987; model calibration: P = 0.722; validation set: C-statistic = 0.984, model calibration: P = 0.669). Area under the curve (AUC) values for the training and validation sets are 0.987 (95% CI: 0.981–0.994, P = 0.003) and 0.990 (95% CI: 0.987–0.998, P = 0.007), respectively. DCA shows that the nomogram can achieve good net benefit.

Conclusions

A novel nomogram was developed and is a simple and accurate tool for predicting the risk of in-hospital mortality in patients with acute STEMI who underwent primary PCI.

Coronary anatomy and comorbidities impact on elective PCI outcomes in left main and multivessel coronary artery disease

INTRODUCTION

The effects of coronary anatomy, lesion complexity, and comorbidities on outcomes of elective percutaneous coronary intervention (PCI) in high-risk patients with left main (LM) and/or multivessel coronary artery disease (CAD) are not well studied, as these patients are typically underrepresented in the clinical trials.

METHODS

This cohort study involved 33,568 consecutive elective PCI cases, excluding patients with prior coronary artery bypass graft, acute coronary syndrome within 24 hr of index PCI, or shock. All data were obtained from the New York State's PCI Reporting System from the calendar year 2015. In-hospital mortality was the primary outcome of study. Logistic regression models were built to calculate odds ratios (OR) with 95% confidence intervals (CI) for in-hospital mortality after adjustment for coronary anatomy and significant clinical comorbidities.

RESULTS

In this cohort of elective PCI cases all cause in-hospital mortality was low (0.3%), with a clear mortality gradient according to the extent of CAD: 0.1% in 1 vessel disease, 0.4% in 2 vessel, 0.5% in 3 vessel disease, and 3.2% in patients with LM CAD (p < .001). Mortality was also significantly increased in patients with multiple comorbidities: 0.1% in patients with 1 comorbidity, 0.7% with 2, 2.5% with 3, and 7.4% with 4 or more studied comorbidities (p < .0001). When adjusted for coronary anatomy and lesion complexity, having any 4 or more comorbidities was associated with significantly increased odds of dying after elective PCI (OR 25.9, 95% CI 8.152-82.063, p < .0001). Furthermore, when compared to patients with 3-vessel CAD, and accounted for comorbidities, the patients with LM disease still had significantly increased (OR 5.254, 95% CI 3.104-8.891, p < .0001) odds of dying after elective PCI.

CONCLUSIONS

In patients undergoing elective PCI, multivessel CAD and particularly LM disease are associated with significantly increased all-cause mortality. Furthermore, when adjusted for the extent of CAD and lesion complexity, comorbidity burden remains an important predictor of mortality.

Ten-year survival of patients undergoing coronary angioplasty with first-generation sirolimus-eluting stents and bare-metal stents

INTRODUCTION

Compared to bare-metal stents (BMS), drug-eluting stents reduce stent restenosis and improve subsequent revascularization rates. The impact on patients' survival has been the subject of debate.

OBJECTIVE

To assess the long-term (10-year) survival of patients undergoing percutaneous coronary intervention (PCI) with first-generation sirolimus-eluting stents (SES) in comparison with BMS.

METHODS

In a single-center registry, 600 consecutive patients who underwent successful PCI with SES between April 2002 and February 2003 were compared to 594 patients who underwent PCI with BMS between January 2002 and April 2002, just before the introduction of SES. Clinical and procedural data were collected at the time of intervention and 10-year survival status was assessed via the national life status database.

RESULTS

All baseline characteristics were similar between groups except for smaller stent diameter (2.84±0.38 vs. 3.19±0.49 mm; p<0.001), greater stent length (18.50±8.2 vs. 15.96±6.10 mm; p<0.001) and higher number of stents per patient (1.95 vs. 1.46, p<0.001) in the SES group. Overall five- and 10-year all-cause mortality was 9.6% (n=110) and 22.7% (n=272), respectively. The adjusted HR for 10-year mortality in patients undergoing PCI with SES was 0.74 (95% CI 0.58-0.94; p=0.013), corresponding to a relative risk reduction of 19.8%. Other than PCI with BMS, older age, chronic kidney disease, chronic obstructive pulmonary disease and lower ejection fraction were independent predictors of 10-year mortality.

CONCLUSION

To date, this is the longest follow-up study ever showing a potential survival benefit of first-generation sirolimus-eluting stents versus bare-metal stents, supporting prior observations on their sustained efficacy and safety relative to contemporary BMS.

Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach

Background

The ability to accurately predict the occurrence of in‐hospital death after percutaneous coronary intervention is important for clinical decision‐making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System in order to elucidate the determinants of in‐hospital mortality in patients undergoing percutaneous coronary intervention across New York State.

Methods and Results

We examined 479 804 patients undergoing percutaneous coronary intervention between 2004 and 2012, utilizing traditional and advanced machine learning algorithms to determine the most significant predictors of in‐hospital mortality. The entire data were randomly split into a training (80%) and a testing set (20%). Tuned hyperparameters were used to generate a trained model while the performance of the model was independently evaluated on the testing set after plotting a receiver‐operator characteristic curve and using the output measure of the area under the curve (AUC) and the associated 95% CIs. Mean age was 65.2±11.9 years and 68.5% were women. There were 2549 in‐hospital deaths within the patient population. A boosted ensemble algorithm (AdaBoost) had optimal discrimination with AUC of 0.927 (95% CI 0.923–0.929) compared with AUC of 0.913 for XGBoost (95% CI 0.906–0.919, P=0.02), AUC of 0.892 for Random Forest (95% CI 0.889–0.896, P<0.01), and AUC of 0.908 for logistic regression (95% CI 0.907–0.910, P<0.01). The 2 most significant predictors were age and ejection fraction.

Conclusions

A big data approach that utilizes advanced machine learning algorithms identifies new associations among risk factors and provides high accuracy for the prediction of in‐hospital mortality in patients undergoing percutaneous coronary intervention.

See Editorial by Garratt and Schneider

Demystifying artificial intelligence in pharmacy

PURPOSE

To provide pharmacists and other clinicians with a basic understanding of the underlying principles and practical applications of artificial intelligence (AI) in the medication-use process.

SUMMARY

"Artificial intelligence" is a general term used to describe the theory and development of computer systems to perform tasks that normally would require human cognition, such as perception, language understanding, reasoning, learning, planning, and problem solving. Following the fundamental theorem of informatics, a better term for AI would be "augmented intelligence," or leveraging the strengths of computers and the strengths of clinicians together to obtain improved outcomes for patients. Understanding the vocabulary of and methods used in AI will help clinicians productively communicate with data scientists to collaborate on developing models that augment patient care. This primer includes discussion of approaches to identifying problems in practice that could benefit from application of AI and those that would not, as well as methods of training, validating, implementing, evaluating, and maintaining AI models. Some key limitations of AI related to the medication-use process are also discussed.

CONCLUSION

As medication-use domain experts, pharmacists play a key role in developing and evaluating AI in healthcare. An understanding of the core concepts of AI is necessary to engage in collaboration with data scientists and critically evaluating its place in patient care, especially as clinical practice continues to evolve and develop.

Impact of electrocardiographic morphology on clinical outcomes in patients with non-ST elevation myocardial infarction receiving coronary angiography and intervention: a retrospective study

Background

The impact of electrocardiography (ECG) morphology on clinical outcomes in patients with non-ST segment elevation myocardial infarction (NSTEMI) receiving percutaneous coronary intervention (PCI) is unknown. This study investigated whether different ST morphologies had different clinical outcomes in patients with NSTEMI receiving PCI.

Methods

This retrospective study analyzed record-linked data of 362 patients who had received PCI for NSTEMI between January 2008 and December 2010. ECG revealed ST depression in 67 patients, inverted T wave in 91 patients, and no significant ST-T changes in 204 patients. The primary endpoint was long-term all-cause mortality. The secondary endpoint was long-term cardiac death and non-fatal major adverse cardiac events.

Results

Compared to those patients whose ECG showed an inverted T wave and non-specific ST-T changes, patients whose ECG showed ST depression had more diabetes mellitus, advanced chronic kidney disease (CKD) and left main artery disease, as well as more in-hospital mortality, cardiac death and pulmonary edema during hospitalization. Patients with ST depression had a significantly higher rate of long-term total mortality and cardiac death. Finally, multiple stepwise Cox regression analysis showed that an advanced Killip score, age, advanced CKD, prior percutaneous transluminal coronary angioplasty and ST depression were independent predictors of the primary endpoint.

Conclusions

Among NSTEMI patients undergoing coronary angiography, those with ST depression had more in-hospital mortality and cardiac death. Long-term follow-up of patients with ST depression consistently reveals poor outcomes.

Three-year outcomes related to coronary stenting; a registry-based real-life population study

Objectives. Developments in medication and coronary interventions have improved coronary artery disease (CAD) treatment. We studied long-term outcomes in an observational, real-life population of CAD patients undergoing percutaneous coronary intervention (PCI) depending on the presentation and the stent type used. Design and results. Register included 789 consecutive patients undergoing PCI. Follow up period was three years with primary composite outcome (MACE) of all cause -mortality, myocardial infarction and target lesion revascularization. Mean age was 65 ± 11 and 69% were male. New-generation drug-eluting stents (DES-2) were associated with lower adjusted rates of MACE (HR 0.47; 95% CI 0.29-0.77) but not mortality (HR 0.50; 95% CI 0.22-1.14) in comparison to bare-metal stents. Patients with STEMI (14.4%) or NSTEMI (13.7%) had higher crude mortality rates than those with unstable (4.5%) or stable CAD (3.1%; p < .001). The association diminished after adjustments in NSTEMI (HR 2.01; 95% CI 0.88-4.58). Among smokers 45% quitted and 36% achieved recommended cholesterol levels. Conclusions. The overall prognosis was good. Irrespective of comorbidities, NSTEMI was not associated with worse outcome than stable CAD. DES-2 was associated with lower rates of MACE than BMS without affecting mortality rate. Patients succeeded better in smoking cessation than reaching recommended cholesterol levels.

The OPTIMIZE randomized trial to assess safety and efficacy of the Svelte IDS and RX Sirolimus-eluting coronary stent Systems for the Treatment of atherosclerotic lesions: Trial design and rationale

Coronary stenting without angioplasty pretreatment (direct stenting) may simplify procedures in appropriate lesions. Direct stenting is facilitated by smaller profile coronary stent platforms. The present study was designed for regulatory approval of a novel drug-eluting coronary stent and incorporates both randomized comparison for non-inferiority to an approved predicate device as well as a nested evaluation of subjects eligible for direct stenting. STUDY DESIGN AND OBJECTIVES: Prospective, single-blind, randomized, active-control, multi-center study designed to assess the safety and efficacy of the novel Svelte sirolimus-eluting stent (SES) systems. A total of 1630 subjects with up to 3 target lesions will be randomized 1:1 to the Svelte SES versus either the Xience or Promus everolimus-eluting stents (control). Randomization will be stratified by whether or not a direct stenting strategy is planned by the investigator. The primary endpoint is target lesion failure (TLF) at 12 months post index procedure, defined as cardiac death, target vessel myocardial infarction, or clinically driven target lesion revascularization, and the primary analysis is a non-inferiority test with a non-inferiority margin of 3.58%. Secondary clinical endpoints include individual components of TLF, stent thrombosis and measures of procedural resource utilization including contrast administration, fluoroscopy exposure and procedural resource utilization as well as costs. CONCLUSION: The OPTMIZE Trial will evaluate the safety, efficacy and clinical value of the novel Svelte SES in subjects with up to 3 lesions, and will provide a comparison of direct stenting between randomized devices.

Derivation and external validation of a novel risk score for prediction of 30-day mortality after percutaneous coronary intervention

AIMS

Early mortality after percutaneous coronary intervention (PCI) is relatively rare. Current risk prediction models for this event are outdated. We sought to derive a 30-day mortality risk score after PCI.

METHODS AND RESULTS

The score was derived from a pooled database of 21 randomised clinical trials using a logistic regression model incorporating clinical and angiographic variables. The score was validated in a separate unrestricted study population, the Assessment of Dual AntiPlatelet Therapy With Drug Eluting Stents (ADAPT-DES) registry. Of 32,882 eligible patients, 75% had data for all 19 variables used for score derivation. The independent predictors of 30-day mortality were age, presentation with ACS, diabetes mellitus, use of first-generation drug-eluting stents, left main or left anterior descending artery lesion, prior myocardial infarction (MI), and suboptimal flow in the artery before or after PCI. The median [interquartile range] score in the derivation cohort was 5 [3, 6] and overall mortality was 0.49%, ranging from 0.08% to 1.64% with scores of 0-16. The 30-day mortality rate was approximately tenfold higher in patients with a score at or above versus below the median of 5 (0.86% versus 0.08%, p<0.0001). Discrimination in both cohorts was very good (C statistic=0.848 and 0.828, respectively), and calibration was satisfactory.

CONCLUSIONS

A novel risk score incorporating eight readily available clinical and angiographic variables had high discrimination for 30-day death after PCI across a wide range of clinical scenarios.

Derivation and validation of a risk score for predicting mortality among inpatients following rapid response team activation

PURPOSE OF THE STUDY

Despite mature rapid response systems (RRS) for clinical deterioration, individuals activating RRS have poor outcomes, with up to one in four dying in hospital. We aimed to derive and validate a risk prediction tool for estimating risk of 28-day mortality among hospitalised patients following rapid response team (RRT) activation.

STUDY DESIGN

Analysis of prospectively collected data on 1151 consecutive RRT activations involving 800 inpatients at a tertiary adult hospital. Patient characteristics, RRT triggers and actions, and mortality were ascertained from medical records and death registries. A multivariable risk prediction regression model, derived from 600 randomly selected patients, was validated in the remaining 200 patients. Main outcome was accuracy of weighted risk score (measured by area under receiver operator curve (AUC)) and performance characteristics for various cut-off scores.

RESULTS

At 28 days, 150 (18.8%) patients had died. Increasing age, emergency admission, chronic liver disease, chronic kidney disease, malignancy, after-hours RRT activation, increasing National Early Warning Score, major/intense RRT intervention and multiple RRT activations were predictors of mortality. The risk score (0-105) in derivation and validation cohorts had AUCs 0.86 (95% CI 0.82 to 0.89) and 0.82 (95% CI 0.75 to 0.90), respectively. In the validation cohort, cut-off score of 32.5 or higher maximised sensitivity: 81.6% (95% CI 68.4% to 92.1%), specificity: 56.2% (95% CI 49.4% to 63.6%), positive likelihood ratio (LR): 1.9 (95% CI 1.5 to 2.3) and negative LR: 0.3 (95% CI 0.2 to 0.6).

CONCLUSION

A validated risk score predicted risk of post-RRT death with more than 80% accuracy, helping to identify patients for whom targeted rescue care may improve survival.

Subsample analysis of the Vascular Complications Risk Score at two public referral centers for interventional cardiology

OBJECTIVE

Evaluate the performance of the Vascular Complications Risk Score in two public referral centers for interventional cardiology.

METHOD

Subsample analysis of the Vascular Complications Risk Score, which was developed and validated in the catheterization laboratories of three cardiology referral centers (two public, one private) with a cutoff of <3 for no risk of developing vascular complications and ≥3 for risk. In this new analysis, we excluded data from the private facility, and only included participants from the original (validation) cohort of the two public hospitals.

RESULTS

Among the 629 patients studied, 11.8% had vascular complications; of these, 1.8% were major and 10% minor. Among the patients with a score <3, 310 (94.5%) presented no vascular complications; of those with a score ≥3, 50 (17%) developed complications. Of those who developed vascular complications, 18 scored <3; two of these had major complications.

CONCLUSION

This subanalysis confirms the ability of the Vascular Complications Risk core to predict low risk of vascular complications in patients with a score < 3.

Relation of Length of Stay to Unplanned Readmissions for Patients Who Undergo Elective Percutaneous Coronary Intervention

The cost of inpatient percutaneous coronary interventions (PCI) procedure is related to length of stay (LOS). It is unknown, how LOS may be associated with readmission rates and costs of index PCI and readmissions in elective PCI. This study aims to evaluate rates, predictors, causes, and costs associated with 30-day unplanned readmissions according to lLOS in patients, who underwent elective PCI. We included patients in the Nationwide Readmission Database, who were admitted to hospital from 2010 to 2014, who underwent uncomplicated elective PCI. LOS was defined as 0, 1, 2, and ≥3 days. A total of 324,345 patients were included in the analysis and the 30-day unplanned readmission was 4.75%, 4.67%, 6.44%, and 9.42% in the LOS groups 0, 1, 2, and ≥3 days, respectively. Prolonged LOS was associated with increased average total 30-day cost (index and readmission cost, 0 days $15,063, 1 day $14,693, 2 days $18,136, and ≥3 days $24,336). Compared with 0 days, the odds of readmissions were greater for 2 days (odds ratio 1.41, 95% confidence interval 1.07 to 1.87, p = 0.016) and ≥3 days (odds ratio 1.70, 95% confidence interval 1.28 to 2.24, p <0.001). Comorbidities were strong predictors of LOS and noncardiac causes, account for more than half of all causes for readmission. Longer LOS was associated with reduced incidence of readmissions for noncardiac causes such as noncardiac chest pain, but a greater rate of readmissions for heart failure. In conclusion, shorter length of stay was associated with reduced healthcare costs in elective PCI.

Development and Validation of a Novel Risk Score for Primary Percutaneous Coronary Intervention for ST-Elevation Myocardial Infarction

BACKGROUND

Primary percutaneous coronary intervention (PPCI) is the default treatment for patients with ST elevation myocardial infarction (STEMI) and carries a higher risk of adverse outcomes when compared with elective and urgent PCI. Conventional PCI risk scores tend to be complex and may underestimate the risk associated with PPCI due to under-representation of patients with STEMI in their datasets. This study aimed to develop a simple, practical and contemporary risk model to provide risk stratification in PPCI.

METHODS

Demographic, clinical and outcome data were collected for all patients who underwent PPCI between January 2009 and October 2013 at the Northern General Hospital, Sheffield. Multiple regression analysis was used to identify independent predictors of mortality and to construct a risk model. This model was then separately validated on an internal and external dataset.

RESULTS

The derivation cohort included 2870 patients with a 30-day mortality of 5.1% (145 patients). Only four variables were required to predict 30-day mortality: age [OR:1.047, 95% CI:1.031-1.063], call-to-balloon (CTB) time [OR:1.829, 95% CI:1.198-2.791], cardiogenic shock [OR:13.886, 95% CI:8.284-23.275] and congestive heart failure [OR:3.169, 95% CI:1.420-7.072]. Internal validation was performed in 693 patients and external validation in 660 patients undergoing PPCI. Our model showed excellent discrimination on ROC-curve analysis (C-Stat = 0.87 internal and 0.86, external), and excellent calibration on Hosmer-Lemeshow testing (p = 0.37 internal, 0.55 external).

CONCLUSIONS

We have developed a bedside risk model which can predict 30-day mortality after PPCI using only four variables: age, CTB time, congestive heart failure and shock.

Meta-Analysis Evaluating the Effect of Left Coronary Dominance on Outcomes After Percutaneous Coronary Intervention

Prognostic significance of coronary circulation dominance remains controversial. The primary objective of this meta-analysis was to pool all the available evidence to assess the influence of left coronary dominance (LD) on outcomes in patients who underwent percutaneous coronary intervention (PCI). MEDLINE, Cochrane CENTRAL, and Scopus databases were searched for all observational studies and randomized controlled trials that investigated the association between coronary dominance and outcomes in patients who underwent PCI. Odds ratios (OR) and 95% confidence intervals from individual studies were pooled using a random effects model. A total of nine studies including 266,119 patients were included. On pooled analysis, it was noted that LD was associated with significantly increased odds of in-hospital (OR: 1.54 [1.12, 2.11]; p = 0.007), 30-day (OR: 2.16 [1.22, 3.84]; p = 0.008), and long-term mortality (OR: 1.83 [1.33 to 2.50]; p < 0.001). LD patients also experienced a significantly higher incidence of major adverse cardiac events (OR: 1.27 [1.03, 1.58]; p = 0.03) and failed PCI (OR: 1.30 [1.03, 1.65]; p = 0.03). In contrast, no significant difference was noted between LD and non-LD patients in the incidence of stent thrombosis (OR: 1.28 [0.55, 3.01]; p = 0.57; I² = 0%) or reinfarction (OR: 1.73 [0.90, 3.35]; p = 0.10; I² = 63%). In conclusion, this meta-analysis suggests that patients with LD coronary anatomy are at significantly increased risk for mortality after PCI compared with patients with a non-LD anatomy.

A Risk Score to Predict Short-term Outcomes Following Emergency Department Discharge

Introduction

The emergency department (ED) is an inherently high-risk setting. Risk scores can help practitioners understand the risk of ED patients for developing poor outcomes after discharge. Our objective was to develop two risk scores that predict either general inpatient admission or death/intensive care unit (ICU) admission within seven days of ED discharge.

Methods

We conducted a retrospective cohort study of patients age > 65 years using clinical data from a regional, integrated health system for years 2009–2010 to create risk scores to predict two outcomes, a general inpatient admission or death/ICU admission. We used logistic regression to predict the two outcomes based on age, body mass index, vital signs, Charlson comorbidity index (CCI), ED length of stay (LOS), and prior inpatient admission.

Results

Of 104,025 ED visit discharges, 4,638 (4.5%) experienced a general inpatient admission and 531 (0.5%) death or ICU admission within seven days of discharge. Risk factors with the greatest point value for either outcome were high CCI score and a prolonged ED LOS. The C-statistic was 0.68 and 0.76 for the two models.

Conclusion

Risk scores were successfully created for both outcomes from an integrated health system, inpatient admission or death/ICU admission. Patients who accrued the highest number of points and greatest risk present to the ED with a high number of comorbidities and require prolonged ED evaluations.

Assessing Statewide All-Cause Future One-Year Mortality: Prospective Study With Implications for Quality of Life, Resource Utilization, and Medical Futility

Background

For many elderly patients, a disproportionate amount of health care resources and expenditures is spent during the last year of life, despite the discomfort and reduced quality of life associated with many aggressive medical approaches. However, few prognostic tools have focused on predicting all-cause 1-year mortality among elderly patients at a statewide level, an issue that has implications for improving quality of life while distributing scarce resources fairly.

Objective

Using data from a statewide elderly population (aged ≥65 years), we sought to prospectively validate an algorithm to identify patients at risk for dying in the next year for the purpose of minimizing decision uncertainty, improving quality of life, and reducing futile treatment.

Methods

Analysis was performed using electronic medical records from the Health Information Exchange in the state of Maine, which covered records of nearly 95% of the statewide population. The model was developed from 125,896 patients aged at least 65 years who were discharged from any care facility in the Health Information Exchange network from September 5, 2013, to September 4, 2015. Validation was conducted using 153,199 patients with same inclusion and exclusion criteria from September 5, 2014, to September 4, 2016. Patients were stratified into risk groups. The association between all-cause 1-year mortality and risk factors was screened by chi-squared test and manually reviewed by 2 clinicians. We calculated risk scores for individual patients using a gradient tree-based boost algorithm, which measured the probability of mortality within the next year based on the preceding 1-year clinical profile.

Results

The development sample included 125,896 patients (72,572 women, 57.64%; mean 74.2 [SD 7.7] years). The final validation cohort included 153,199 patients (88,177 women, 57.56%; mean 74.3 [SD 7.8] years). The c-statistic for discrimination was 0.96 (95% CI 0.93-0.98) in the development group and 0.91 (95% CI 0.90-0.94) in the validation cohort. The mortality was 0.99% in the low-risk group, 16.75% in the intermediate-risk group, and 72.12% in the high-risk group. A total of 99 independent risk factors (n=99) for mortality were identified (reported as odds ratios; 95% CI). Age was on the top of list (1.41; 1.06-1.48); congestive heart failure (20.90; 15.41-28.08) and different tumor sites were also recognized as driving risk factors, such as cancer of the ovaries (14.42; 2.24-53.04), colon (14.07; 10.08-19.08), and stomach (13.64; 3.26-86.57). Disparities were also found in patients’ social determinants like respiratory hazard index (1.24; 0.92-1.40) and unemployment rate (1.18; 0.98-1.24). Among high-risk patients who expired in our dataset, cerebrovascular accident, amputation, and type 1 diabetes were the top 3 diseases in terms of average cost in the last year of life.

Conclusions

Our study prospectively validated an accurate 1-year risk prediction model and stratification for the elderly population (≥65 years) at risk of mortality with statewide electronic medical record datasets. It should be a valuable adjunct for helping patients to make better quality-of-life choices and alerting care givers to target high-risk elderly for appropriate care and discussions, thus cutting back on futile treatment.

In-hospital outcomes after rotational atherectomy in patients with low ejection fraction

OBJECTIVES

This study evaluated angiographic success and in-hospital outcomes of percutaneous coronary intervention (PCI) with rotational atherectomy (RA) in patients with low left ventricular ejection fraction (LVEF).

DESIGN

Between January 2010 and March 2014, 272 consecutive patients with heavily calcified lesions underwent elective PCI with RA. Of these, 33 patients had LVEF ≤35% (low LVEF group), whereas 237 patients had LVEF >35% (preserved LVEF group). The primary endpoint was angiographic success and in-hospital major adverse cardiac events (MACE). MACE included death from any cause, postprocedure onset MI, emergency coronary artery bypass grafting, and target vessel revascularization. The secondary endpoints were MACE and the components within 30days after PCI. The components of MACE were evaluated.

RESULTS

Angiographic success, defined as <30% residual stenosis with thrombolysis in myocardial infarction flow 3 at final angiography, was achieved in all patients without fatal complications. Intra-aortic ballon pumping (IABP) was used significantly more frequently in the low LVEF group compared with the preserved LVEF group (15.2% vs. 2.1%, p = .003). There were no significant differences between groups regarding in-hospital and clinical outcomes within 30 days following PCI.

CONCLUSION

If medications and mechanical support were appropriately performed, the angiographic success rate and in-hospital MACE rate of PCI with RA in patients with low LVEF could be expected to have good outcomes similar to those for patients with preserved LVEF.

Transradial PCI and Same Day Discharge

PURPOSE OF REVIEW

The evolution of cardiac catheterization has led to the development of well-refined, more effective, and safer devices that allow cardiovascular interventionalists to deliver high-quality percutaneous interventions (PCI). Transradial PCI (TRI) has gained more popularity in the USA over the past 10 years, and as experience and volume of TRI grow, studies adopting same day radial PCI protocols have emerged and are showing promising results. We sought to review the current literature on TRI and same day discharge (SDD).

RECENT FINDINGS

This literature review was performed to evaluate the studies that were published over the last 17 years regarding TRI and SDD. A literature search using PubMed, Cochran database, Google Scholar, and Embase was performed for studies evaluating TRI and SDD from January 1, 2000, to August 1, 2017. Observational studies, randomized clinical trials, meta-analyses, and consensus statements were included in our review. We used the following terms in our search: "same day," "same day discharge," "outpatient," and "ambulatory radial PCI." Articles with data pertinent to the subject matter were included. We did not limit our searches to specific journals. The available literature supports SDD for selected radial PCI patients. The advancement in PCI devices and pharmacology has enhanced the safety of post-PCI disposition leading to the evolution from traditional overnight stays to the development of same day discharge programs. We conclude that outpatient TRI for appropriately selected patients will be the standard of care in the future. This will lead to increased patient satisfaction, improved hospital throughput, and reduced hospital costs, without increased procedural complications.

Development and validation of a clinical risk score to predict mortality after percutaneous coronary intervention

OBJECTIVE

To develop and validate a contemporary clinical risk score to predict mortality after percutaneous coronary intervention (PCI).

METHODS

Using data collected from patients undergoing PCI at the South Yorkshire Cardiothoracic Centre, Sheffield, UK, between January 2007 and September 2013, a risk score was developed to predict mortality. Logistic regression was used to evaluate the effect of each variable upon 30-day mortality. A backwards stepwise logistic regression model was then used to build a predictive model. The results were validated both internally and externally with data from Manchester Royal Infirmary, UK. 30-Day mortality status was determined from the UK Office of National Statistics.

RESULTS

The development data set comprised 6522 patients from Sheffield. Five risk factors, including cardiogenic shock, procedural urgency, history of renal disease, diabetes mellitus and age, were statistically significant to predict 30-day mortality. The risk score was validated internally on a further 3290 patients from Sheffield and externally on 3230 patients from Manchester. The discrimination of the model was high in the development (C-statistic=0.82, 95% CI 0.79 to 0.85), internal (C-statistic=0.81, 95% CI 0.76 to 0.86) and external (C statistics=0.90, 95% CI 0.87 to 0.93) cohorts. There was no significant difference between observed and predicted mortality in any group.

CONCLUSION

This contemporary risk score reliably predicts 30-day mortality after PCI using a small number of clinical variables obtainable prior to the procedure, without knowledge of the coronary anatomy.

Hospital Discharge Disposition of Stroke Patients in Tennessee

OBJECTIVES

Early determination of hospital discharge disposition status at an acute admission is extremely important for stroke management and the eventual outcomes of patients with stroke. We investigated the hospital discharge disposition of patients with stroke residing in Tennessee and developed a predictive tool for clinical adoption. Our investigational aims were to evaluate the association of selected patient characteristics with hospital discharge disposition status and predict such status at the time of an acute stroke admission.

METHODS

We analyzed 127,581 records of patients with stroke hospitalized between 2010 and 2014. Logistic regression was used to generate odds ratios with 95% confidence intervals to examine the factor outcome association. An easy-to-use clinical predictive tool was built by using integer-based risk scores derived from coefficients of multivariable logistic regression.

RESULTS

Among the 127,581 records of patients with stroke, 86,114 (67.5%) indicated home discharge and 41,467 (32.5%) corresponded to facility discharge. All considered patient characteristics had significant correlations with hospital discharge disposition status. Patients were at greater odds of being discharged to another facility if they were women; older; black; patients with a subarachnoid or intracerebral hemorrhage; those with the comorbidities of diabetes mellitus, heart disease, hypertension, chronic kidney disease, arrhythmia, or depression; those transferred from another hospital; or patients with Medicare as the primary payer. A predictive tool had a discriminatory capability with area under the curve estimates of 0.737 and 0.724 for derivation and validation cohorts, respectively.

CONCLUSIONS

Our investigation revealed that the hospital discharge disposition pattern of patients with stroke in Tennessee was associated with the key patient characteristics of selected demographics, clinical indicators, and insurance status. These analyses resulted in the development of an easy-to-use predictive tool for early determination of hospital discharge disposition status.

Fast Virtual Fractional Flow Reserve Based Upon Steady-State Computational Fluid Dynamics Analysis: Results From the VIRTU-Fast Study

Fractional flow reserve (FFR)-guided percutaneous intervention is superior to standard assessment but remains underused. The authors have developed a novel "pseudotransient" analysis protocol for computing virtual fractional flow reserve (vFFR) based upon angiographic images and steady-state computational fluid dynamics. This protocol generates vFFR results in 189 s (cf >24 h for transient analysis) using a desktop PC, with <1% error relative to that of full-transient computational fluid dynamics analysis. Sensitivity analysis demonstrated that physiological lesion significance was influenced less by coronary or lesion anatomy (33%) and more by microvascular physiology (59%). If coronary microvascular resistance can be estimated, vFFR can be accurately computed in less time than it takes to make invasive measurements.

Development and validation of a simple risk score to predict 30-day readmission after percutaneous coronary intervention in a cohort of medicare patients

OBJECTIVES

To develop a risk model that can be used to identify PCI patients at higher risk of readmission who may benefit from additional resources at the time of discharge.

BACKGROUND

A high proportion of patients undergoing PCI are readmitted within 30 days of discharge.

METHODS

The sample comprised patients aged ≥65 years who underwent PCI at a CathPCI Registry®-participating hospital and could be linked with 100% Medicare fee-for-service claims between 01/2007 and 12/2009. The sample (n = 388,078) was randomly divided into risk score development (n = 193,899) and validation (n = 194,179) cohorts. We did not count as readmissions those associated with staged revascularization procedures. Multivariable logistic regression models using stepwise selection models were estimated to identify variables independently associated with all-cause 30-day readmission.

RESULTS

The mean 30-day readmission rates for the development (11.36%) and validation (11.35%) cohorts were similar. In total, 19 variables were significantly associated with risk of 30-day readmission (P < 0.05), and model c-statistics were similar in the development (0.67) and validation (0.66) cohorts. The simple risk score based on 14 variables identified patients at high and low risk of readmission. Patients with a score of ≥13 (15.4% of sample) had more than an 18.5% risk of readmission, while patients with a score ≤6 (41.9% of sample) had less than an 8% risk of readmission.

CONCLUSION

Among PCI patients, risk of readmission can be estimated using clinical factors present at the time of the procedure. This risk score may guide clinical decision-making and resource allocation for PCI patients at the time of hospital discharge. © 2016 Wiley Periodicals, Inc.