Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers

Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians

BACKGROUND

Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population.

OBJECTIVE

To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores.

METHODS

We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined.

RESULTS

Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration.

CONCLUSIONS

In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.

Adding the value of the Charlson Comorbidity Index to the GRACE score for mortality prediction in acute coronary syndromes

BACKGROUND

Scarce and conflicting data still exist about the role of the Charlson Comorbidity Index (CCI) when added to the traditional Global Registry of Acute Coronary Events (GRACE) risk score for outcome prediction in patients with acute coronary syndrome (ACS).

METHODS

All consecutive admissions due to ACS, from 1 January 2018 to 31 December 2020 were retrospectively reviewed from an internal database of a tertiary cardiac center in Salerno (Italy). Logistic and Cox proportional regression analyses were performed in order to assess the contribution of the CCI on 30-day and long-term mortality. The CCI adding value to the GRACE score was analyzed with several measures of improvement in discrimination: increase in the area under the receiver-operating characteristic curve (AUC), the integrated discrimination improvement (IDI), and the categorical and continuous net reclassification improvement (cNRI) more than 0. Robustness of the results was assessed through an internal validation procedure with bootstrapping.

RESULTS

One thousand three hundred and ten patients were identified. The median age was 68 (58-78) years. One hundred and twenty (9.2%) and 113 (9.5%) deaths occurred, respectively, during the first 30 days from admission and during long-term follow-up (median follow-up time: 13 months; interquartile range: 9-24). After multivariate regression analysis, the CCI was not associated with short-term mortality, while it was significantly and independently associated with long-term mortality along with the GRACE score (hazard ratio: 1.34, 95% confidence interval: 1.22-1.47; P  < 0.001). An additive effect of CCI with the GRACE risk score was observed in predicting long-term mortality: AUC from 0.768 to 0.819 ( P  = 0.003), category-based NRI: 0.215, cNRI>0: 0.669 ( P  < 0.001), IDI: 0.066 ( P  < 0.001).

CONCLUSION

The CCI is a predictor of long-term mortality and improves risk stratification of patients with ACS over the GRACE risk score.

The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis

BACKGROUND

Acute coronary syndromes (ACS) are the leading cause of global death. Optimizing mortality risk prediction and early identification of high-risk patients is essential for developing targeted prevention strategies. Many researchers have built machine learning (ML) models to predict the mortality risk in ACS patients. Our meta-analysis aimed to evaluate the predictive value of various ML models in predicting death in ACS patients at different times.

METHODS

PubMed, Embase, Web of Science, and Cochrane Library were searched systematically from database establishment to March 12, 2022 for studies developing or validating at least one ML predictive model for death in ACS patients. We used PROBAST to assess the risk of bias in the reported predictive models and a random-effects model to assess the pooled C-index and accuracy of these models.

RESULTS

Fifty papers were included, involving 216 ML prediction models, 119 of which were externally validated. The combined C-index of the ML models in the validation cohort predicting the in-hospital mortality, 30-day mortality, 3- or 6-month mortality, and 1 year or above mortality in ACS patients were 0.8633 (95% CI 0.8467-0.8802), 0.8296 (95% CI 0.8134-0.8462), 0.8205 (95% CI 0.7881-0.8541), and 0.8197 (95% CI 0.8042-0.8354), respectively, with the corresponding combined accuracy of 0.8569 (95% CI 0.8411-0.8715), 0.8282 (95% CI 0.7922-0.8591), 0.7303 (95% CI 0.7184-0.7418), and 0.7837 (95% CI 0.7455-0.8175), indicating that the ML models were relatively excellent in predicting ACS mortality at different times. Furthermore, common predictors of death in ML models included age, sex, systolic blood pressure, serum creatinine, Killip class, heart rate, diastolic blood pressure, blood glucose, and hemoglobin.

CONCLUSIONS

The ML models had excellent predictive power for mortality in ACS, and the methodologies may need to be addressed before they can be used in clinical practice.

A bibliometric analysis of acute myocardial infarction in women from 2000 to 2022

Background

Plenty of publications had been written in the last several decades on acute myocardial infarction (AMI) in women. However, there are few bibliometric analyses in such field. In order to solve this problem, we attempted to examine the knowledge structure and development of research about AMI in women based on analysis of related publications.

Method

The Web of Science Core Collection was used to extract all publications regarding AMI in women, ranging from January 2000 to August 2022. Bibliometric analysis was performed using VOSviewer, Cite Space, and an online bibliometric analysis platform.

Results

A total of 14,853 publications related to AMI in women were identified from 2000 to 2022. Over the past 20 years, the United States had published the most articles in international research and participated in international cooperation the most frequently. The primary research institutions were Harvard University and University of Toronto. Circulation was the most cited journal and had an incontrovertible academic impact. 67,848 authors were identified, among which Harlan M Krumholz had the most significant number of articles and Thygesen K was co-cited most often. And the most common keywords included risk factors, disease, prognosis, mortality, criteria and algorithm.

Conclusion

The research hotspots and trends of AMI in women were identified and explored using bibliometric and visual methods. Researches about AMI in women are flourishing. Criteria and algorithms might be the focus of research in the near future, which deserved great attentions.

Artificial Intelligence Technologies in Cardiology

As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types-supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.

The Clinical Value of Systemic Inflammatory Response Index and Inflammatory Prognosis Index in Predicting 3-Month Outcome in Acute Ischemic Stroke Patients with Intravenous Thrombolysis

Purpose

Systemic inflammatory response index (SIRI) was an independent predictor of the prognosis of many diseases. Inflammatory prognostic index (IPI) was a new inflammatory prognostic marker with certain clinical significance. We aimed to explore the association between SIRI, IPI and early stage severity of stroke as well as 3-month outcome of AIS patients.

Patients and Methods

A total of 63 AIS patients who treated with alteplase were selected. The patients were divided into mild group and moderate to severe group according to the National Institutes of Health Stroke Scale (NIHSS) scores. According to the modified Rankin scale (mRS) score, patients were divided into the good prognosis group and the poor prognosis group. Spearman correlation statistically analyzed the correlation between SIRI, IPI and NIHSS score. Univariate and multivariate logistic regression analyzed the risk factors of 3-month prognosis. ROC curve was adopted to predict the effect of SIRI and IPI levels on poor prognosis in AIS patients.

Results

Spearman analysis showed that there was positively correlated with NIHSS score and IPI in mild AIS group (r=0.541, P<0.05). Compared with the mild group, SIRI and IPI in the moderate to severe group was significantly higher (P<0.05). The SIRI and IPI in the poor prognosis group were significantly higher than those in the good prognosis group (P<0.05). Univariate and multivariate logistic regression analysis showed that SIRI and IPI were independent prognostic factors for the 3-month prognosis of AIS patients (P< 0.05). The ROC curve showed that the areas under the 3-month prognosis curve predicted by SIRI and IPI were 0.685, 0.774 respectively.

Conclusion

IPI is correlated with stroke severity at admission. SIRI and IPI are independent predictors of short-term prognosis in AIS patients. SIRI and IPI can be a novel the good short-term prognostic biomarker for AIS patients treated with intravenous thrombolysis.

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions-from heart failure to stroke-has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.

Machine learning-based prediction of 1-year mortality for acute coronary syndrome✰

BACKGROUND

Clinical risk assessment with quantitative formal risk scores may add to intuitive physician risk assessment and are advised by the international guidelines for the management of acute coronary syndrome (ACS) patients. Most previous studies have used the binary regression/classification approach (dead/alive) for long-term mortality post-ACS, without considering the time-to-event as in survival analysis. The use of machine learning (ML)-based survival models has yet to be validated. The primary objective was to compare survival prediction performance of 1-year mortality following ACS of two newly developed ML-based models [random survival forest (RSF) and deep learning (DeepSurv)] with the traditional Cox-proportional hazard (CPH) model. The secondary objective was external validation of the findings.

METHODS

This was a retrospective, supervised learning data mining study based on the Acute Coronary Syndrome Israeli Survey (ACSIS) and the Myocardial Ischemia National Audit Project (MINAP). The ACSIS data were divided to train/test in a 70/30 fashion. Next, the models were externally validated on the MINAP data. Harrell's C-index, inverse probability of censoring weighting (IPCW), and the Brier-score were used for models' performance comparison.

RESULTS

RSF performed best among the three models, with Harrell's C-index on training and testing sets reaching 0.953 and 0.924 respectively, followed by CPH multivariate selected model (0.805/0.849), CPH Univariate selected model (0.828/0.806), DeepSurv model (0.801/0.804), and the traditional CPH model (0.826/0.738). The RSF model also had the highest performance on the validation data set with 0.811 for Harrell's C-index, 0.844 for IPCW, and 0.093 for Brier score. The CPH model performance on the validation set had C-index range between 0.689 to 0.790, 0.713 to 0.826 for IPCW, and 0.094 to 0.103 Brier score.

CONCLUSIONS

RSF survival predictions for long-term mortality post-ACS show improved model performance compared with the classic statistical method. This may benefit patients by allowing better risk stratification and tailored therapy, however further prospective evaluations are required.

Research on the Segmentation of Biomarker for Chronic Central Serous Chorioretinopathy Based on Multimodal Fundus Image

At present, laser surgery is one of the effective ways to treat the chronic central serous chorioretinopathy (CSCR), in which the location of the leakage area is of great importance. In order to alleviate the pressure on ophthalmologists to manually label the biomarkers as well as elevate the biomarker segmentation quality, a semiautomatic biomarker segmentation method is proposed in this paper, aiming to facilitate the accurate and rapid acquisition of biomarker location information. Firstly, the multimodal fundus images are introduced into the biomarker segmentation task, which can effectively weaken the interference of highlighted vessels in the angiography images to the location of biomarkers. Secondly, a semiautomatic localization technique is adopted to reduce the search range of biomarkers, thus enabling the improvement of segmentation efficiency. On the basis of the above, the low-rank and sparse decomposition (LRSD) theory is introduced to construct the baseline segmentation scheme for segmentation of the CSCR biomarkers. Moreover, a joint segmentation framework consisting of the above method and region growing (RG) method is further designed to improve the performance of the baseline scheme. On the one hand, the LRSD is applied to offer the initial location information of biomarkers for the RG method, so as to ensure that the RG method can capture effective biomarkers. On the other hand, the biomarkers obtained by RG are fused with those gained by LRSD to make up for the defect of undersegmentation of the baseline scheme. Finally, the quantitative and qualitative ablation experiments have been carried out to demonstrate that the joint segmentation framework performs well than the baseline scheme in most cases, especially in the sensitivity and F1-score indicators, which not only confirms the effectiveness of the framework in the CSCR biomarker segmentation scene but also implies its potential application value in CSCR laser surgery.

Hematological Biomarkers, Mortality, Transfusion and Acute Heart Disease

BACKGROUND

Patients hospitalized with acute heart disease [acute myocardial infarction (MI); heart disease exacerbation] may require red blood cell (RBC) transfusion. These patients are at increased risk for morbidity and mortality. Hematological biomarkers may help to identify increased mortality risk. The aim of the study was to evaluate the association between hematological biomarkers and survival in these patients.

METHODS

A historical cohort study of all patients admitted to an internal medicine department, who were diagnosed with acute heart disease and requiring RBC transfusion, was carried out in a tertiary medical center between 2009-2014. The association between hematological biomarkers and 30-, 90-day and 5-year mortality was studied.

RESULTS

A total of 254 patients (median age 80 years, IQR 74-86.25; 40.9% females; acute MI 24.8%), were included. During the 5-year follow-up 212(83.5%) patients died. In a multivariate analysis the lower platelet to neutrophil ratio (PNR) was significantly associated with increased 30-, 90-day and 5-year mortality (p<0.001, 0.041, 0.003 respectively). A higher red cell distribution width (RDW) was significantly associated with 30- and 90-day mortality (p=0.003, 0.023 respectively), while higher neutrophil to lymphocyte ratio (NLR) was associated with increased 30-day and 5-year mortality (p= 0.036, 0.033 respectively).

CONCLUSIONS

Hematological biomarkers may help to identify increased mortality risk of acute heart disease patients, receiving RBC transfusions in an internal medicine department.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Clinical impact of estimated plasma volume status and its additive effect with the GRACE risk score on in-hospital and long-term mortality for acute myocardial infarction

BACKGROUND

Estimated plasma volume status (ePVS) is a well-validated prognostic indicator in heart failure. However, it remains unclear whether ePVS has prognostic significance in patients with acute myocardial infarction (AMI). Moreover, there is no available information on its additive effect with the Global Registry of Acute Coronary Events (GRACE) risk score in AMI patients.

METHODS

Data were obtained from the Osaka Acute Coronary Insufficiency Study (OACIS) registry database. Patients whose data were available for ePVS derived from Hakim's formula and the GRACE risk score were studied. The primary endpoints were in-hospital and 5-year mortality.

RESULTS

Of 3930 patients, 206 and 200 patients died during hospitalization and 5 years after discharge, respectively. After adjustment, ePVS remained an independent predictor of in-hospital death (OR:1.02, 95% CI: 1.00-1.04, p = 0.036), and 5-year mortality(HR:1.03, 95% CI: 1.01-1.04, p < 0.001). An additive effect of ePVS with the GRACE risk score was observed in predicting the 5-year mortality with an area under the receiver operating characteristic curve (AUC) from 0.744 to 0.763 (p = 0.026), but not in-hospital mortality (the AUC changed from 0.875 to 0.875, p = 0.529). The incremental predictive value of combining ePVS and the GRACE risk score for 5-year mortality was significantly improved, as shown by the net reclassification improvement (NRI:0.378, p < 0.001) and integrated discrimination improvement (IDI:0.014, p < 0.001).

CONCLUSIONS

In patients with AMI, ePVS independently predicted in-hospital and long-term mortality. In addition, ePVS had an additive effect with the GRACE risk score on long-term mortality. Therefore, ePVS may be useful for identifying high-risk subjects for intensive treatment.

Machine Learning to Predict Treatment in Oropharyngeal Squamous Cell Carcinoma

OBJECTIVES

This study seeks to (1) demonstrate how machine learning (ML) can be used for prediction modeling by predicting the treatment patients with T1-2, N0-N1 oropharyngeal squamous cell carcinoma (OPSCC) receive and (2) assess the impact patient, socioeconomic, regional, and institutional factors have in the treatment of this population.

METHODS

A retrospective cohort of adults diagnosed with T1-2, N0-N1 OPSCC from 2004 to 2013 was obtained using the National Cancer Database. The data was split into 80/20 distribution for training and testing, respectively. Various ML algorithms were explored for development. Area under the curve (AUC), accuracy, precision, and recall were calculated for the final model.

RESULTS

Among the 19,111 patients in the study, the mean (standard deviation) age was 61.3 (10.8) years, 14,034 (73%) were male, and 17,292 (91%) were white. Surgery was the primary treatment in 9,533 (50%) cases and radiation in 9,578 (50%) cases. The model heavily utilized T-stage, primary site, N-stage, grade, and type of treatment facility to predict the primary treatment modality. The final model yielded an AUC of 78% (95% CI, 77-79%), accuracy of 71%, precision of 72%, and recall of 71%.

CONCLUSION

This study created a ML model utilizing clinical variables to predict primary treatment modality for T1-2, N0-N1 OPSCC. This study demonstrates how ML can be used for prediction modeling while also highlighting that tumor and facility realted variables impact the decision making process on a national level.

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

Aims

This study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling.

Methods and results

Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full‐text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time‐point comparisons, ML‐derived c‐indices were higher than CSM‐derived c‐indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in‐hospital mortality to 1 year survival; of these nine, seven reported higher c‐indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c‐indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c‐indices 0.913 vs. 0.835).

Conclusions

ML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML‐based studies of prediction modelling. We suggest that ML‐based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867

Clinical applications of artificial intelligence in cardiology on the verge of the decade

Artificial intelligence (AI) has been hailed as the fourth industrial revolution and its influence on people's lives is increasing. The research on AI applications in medicine is progressing rapidly. This revolution shows promise for more precise diagnoses, streamlined workflows, increased accessibility to healthcare services and new insights into ever-growing population-wide datasets. While some applications have already found their way into contemporary patient care, we are still in the early days of the AI-era in medicine. Despite the popularity of these new technologies, many practitioners lack an understanding of AI methods, their benefits, and pitfalls. This review aims to provide information about the general concepts of machine learning (ML) with special focus on the applications of such techniques in cardiovascular medicine. It also sets out the current trends in research related to medical applications of AI. Along with new possibilities, new threats arise - acknowledging and understanding them is as important as understanding the ML methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI-powered tools.

Inflammatory signatures are associated with increased mortality after transfemoral transcatheter aortic valve implantation

Aims

Systemic inflammatory response, identified by increased total leucocyte counts, was shown to be a strong predictor of mortality after transcatheter aortic valve implantation (TAVI). Yet the mechanisms of inflammation‐associated poor outcome after TAVI are unclear. Therefore, the present study aimed at investigating individual inflammatory signatures and functional heterogeneity of circulating myeloid and T‐lymphocyte subsets and their impact on 1 year survival in a single‐centre cohort of patients with severe aortic stenosis undergoing TAVI.

Methods and results

One hundred twenty‐nine consecutive patients with severe symptomatic aortic stenosis admitted for transfemoral TAVI were included. Blood samples were obtained at baseline, immediately after, and 24 h and 3 days after TAVI, and these were analysed for inflammatory and cardiac biomarkers. Myeloid and T‐lymphocyte subsets were measured using flow cytometry. The inflammatory parameters were first analysed as continuous variables; and in case of association with outcome and area under receiver operating characteristic (ROC) curve (AUC) ≥ 0.6, the values were dichotomized using optimal cut‐off points. Several baseline inflammatory parameters, including high‐sensitivity C‐reactive protein (hsCRP; HR = 1.37, 95% CI: 1.15–1.63; P < 0.0001) and IL‐6 (HR = 1.02, 95% CI: 1.01–1.03; P = 0.003), lower counts of Th2 (HR = 0.95, 95% CI: 0.91–0.99; P = 0.009), and increased percentages of Th17 cells (HR = 1.19, 95% CI: 1.02–1.38; P = 0.024) were associated with 12 month all‐cause mortality. Among postprocedural parameters, only increased post‐TAVI counts of non‐classical monocytes immediately after TAVI were predictive of outcome (HR = 1.03, 95% CI: 1.01–1.05; P = 0.003). The occurrence of SIRS criteria within 48 h post‐TAVI showed no significant association with 12 month mortality (HR = 0.57, 95% CI: 0.13–2.43, P = 0.45). In multivariate analysis of discrete or dichotomized clinical and inflammatory variables, the presence of diabetes mellitus (HR = 3.50; 95% CI: 1.42–8.62; P = 0.006), low left ventricular (LV) ejection fraction (HR = 3.16; 95% CI: 1.35–7.39; P = 0.008), increased baseline hsCRP (HR = 5.22; 95% CI: 2.09–13.01; P < 0.0001), and low baseline Th2 cell counts (HR = 8.83; 95% CI: 3.02–25.80) were significant predictors of death. The prognostic value of the linear prediction score calculated of these parameters was superior to the Society of Thoracic Surgeons score (AUC: 0.88; 95% CI: 0.78–0.99 vs. 0.75; 95% CI: 0.64–0.86, respectively; P = 0.036). Finally, when analysing LV remodelling outcomes, ROC curve analysis revealed that low numbers of Tregs (P = 0.017; AUC: 0.69) and increased Th17/Treg ratio (P = 0.012; AUC: 0.70) were predictive of adverse remodelling after TAVI.

Conclusions

Our findings demonstrate an association of specific pre‐existing inflammatory phenotypes with increased mortality and adverse LV remodelling after TAVI. Distinct monocyte and T‐cell signatures might provide additive biomarkers to improve pre‐procedural risk stratification in patients referred to TAVI for severe aortic stenosis.

Prognostic value of MELD-XI score in patients referring to the emergency department with acute ST elevation myocardial infarction

Objective:

We aimed to evaluate Model for End-stage Liver Disease excluding international normalized ratio (MELD-XI) score for prediction of 30-day in-hospital mortality in a cohort of patients with ST elevation myocardial infarction.

Methods:

The medical records of a total of 256 patients admitted with ST elevation myocardial infarction to the emergency department between January 2015 and January 2019 were retrospectively reviewed. A total of 111 patients were found eligible for the study. MELD-XI score was analyzed and compared on the basis of survival status.

Results:

A total of 111 patients with a mean age of 62.5 ± 2.55 years were included in the study. In total, 81% (n = 90) of the patients were male and 19% (n = 21) were female. The mean MELD-XI score of the patients was 10.1 ± 1.1. A total of 12 patients (12.9%) died within 30 days after hospitalization. The median MELD-XI score of the patients who died in the hospital was significantly higher than the patients survived (11.0 (10.5–11.6) vs 9.5 (9.4–13.8); p < 0.01). However, Gensini score was not significantly different between the surviving and deceased patients (p > 0.05). MELD-XI score was significantly correlated to left ventricular ejection fraction (r = −232, p < 0.01), and both parameters and age were significant independent predictors of in-hospital mortality (odds ratio: 1.73, 95% confidence interval: 1.25–2.39, p < 0.05; odds ratio: 0.89, 95% confidence interval: 0.81–0.99, p < 0.05; and odds ratio: 1.07, 95% confidence interval: 0.99–1.15, p < 0.05, respectively). A MELD-XI cut-off point of 10 had a sensitivity of 100% and a specificity of 78.8% for in-hospital mortality (area under receiver operating characteristics curve: 0.92, 95% confidence interval: 0.87–0.97, p < 0.05). A survival analysis based on a MELD-XI threshold of 10 revealed that the patients in the high-MELD-XI group had a significantly worse in-hospital survival (log rank test p < 0.001).

Conclusion:

MELD-XI score is a useful tool for in-hospital mortality prediction in patients referring to emergency medicine with acute ST elevation myocardial infarction.