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

Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: A systematic review and meta-analysis

BACKGROUND

Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the significant imbalance in data, the predictive accuracy of these models remains controversial.

OBJECTIVE

This study aimed to review the accuracy of ML in predicting in-hospital mortality risk in MI patients and to provide evidence-based advices for the development or updating of clinical tools.

METHODS

PubMed, Embase, Cochrane, and Web of Science databases were searched, up to June 4, 2024. PROBAST and ChAMAI checklist are utilized to assess the risk of bias in the included studies. Since the included studies constructed models based on severely unbalanced datasets, subgroup analyses were conducted by the type of dataset (balanced data, unbalanced data, model type).

RESULTS

This meta-analysis included 32 studies. In the validation set, the pooled C-index, sensitivity, and specificity of prediction models based on balanced data were 0.83 (95 % CI: 0.795-0.866), 0.81 (95 % CI: 0.79-0.84), and 0.82 (95 % CI: 0.78-0.86), respectively. In the validation set, the pooled C-index, sensitivity, and specificity of ML models based on imbalanced data were 0.815 (95 % CI: 0.789-0.842), 0.66 (95 % CI: 0.60-0.72), and 0.84 (95 % CI: 0.83-0.85), respectively.

CONCLUSIONS

ML models such as LR, SVM, and RF exhibit high sensitivity and specificity in predicting in-hospital mortality in MI patients. However, their sensitivity is not superior to well-established scoring tools. Mitigating the impact of imbalanced data on ML models remains challenging.

Tryptase as a Biomarker for Adverse Prognosis in ST-Segment Elevation Myocardial Infarction Patients: A Prospective Cohort Study

Background

Acute ST-segment elevation myocardial infarction (STEMI) is characterized by a rapid inflammatory response, with mast cells (MCs) playing a significant role. However, the relationship between MC activation and the adverse outcomes remains unclear. This study investigated the association between the MC activation biomarker, tryptase, and major adverse cardiovascular events (MACE).

Methods

This prospective study included patients with STEMI who underwent primary percutaneous coronary intervention (PPCI) at Peking University Third Hospital between July 2020 and July 2023. Tryptase levels were detected from plasma samples collected 6 hours post-PPCI and using ELISA method. All patients were followed up every 6 months, with MACE as the primary endpoint.

Results

The study enrolled 514 patients with STEMI who underwent PPCI (mean age: 59.27 ± 13.26 years, 16.93% female). The median follow-up time was 13.28 (10.47-37.61) months, during which 85 patients (16.54%) experienced MACE. Patients in the higher tryptase group had a higher risk of MACE (HR 2.60 [1.68-4.01], P < 0.001). Tryptase was an independent risk factor of MACE (HR 1.56 [1.29-1.88] per 1-unit increase, P < 0.001). Subgroup analysis revealed the prognostic value of tryptase among different age groups, left ventricular ejection levels, and patients with hypertension, hyperlipidemia, smoking and diabetes. The addition of tryptase to the basic model had an incremental effect on the predictive value for MACE (AUC: 0.763 vs 0.702, P = 0.002).

Conclusion

In this study, elevated tryptase levels, a biomarker of MC activation, were identified as a significant predictor of MACE in STEMI patients undergoing PPCI.

Trial Registration

(ClinicalTrials.gov NCT05802667).

Nomogram Predicting In-Hospital Mortality in Patients with Myocardial Infarction Treated with Primary Coronary Interventions Based on Logistic and Angiographic Predictors

Background: Systems developed in recent years to assess the risk of in-hospital death in patients with myocardial infarction (MI) are mainly based on angiographic, electrocardiographic, and laboratory variables. Risk systems based on contemporary angiographic data and logistic variables have not been reported. The aim of this study was to develop and validate a system to assess the risk of in-hospital death in patients across the entire clinical spectrum of MI treated with primary coronary intervention (PCI) based on modern angiographic and logistic predictors. Methods: A subgroup of patients from the observational single-centre registry of MI treated with PCIs (from 1 February 2019 until 31 January 2020) was used to develop a multivariate logistic regression model predicting in-hospital mortality. The population (603 patients) was divided, with 60% of the sample used for model derivation and the remaining 40% used for internal model validation. Results: The main findings were as follows: (1) coronary angiography results and suboptimal flow after PCI were important predictors of in-hospital mortality; (2) the time of PCI as well as the mode of presentation of patients with MI contributed to in-hospital mortality; and (3) the discrimination (C statistic = 0.848, 95% CI: [0.765, 0.857]) and calibration (χ2 = 2.78, pHL = 0.94) were good in the derivation set, while the discrimination (C statistic = 0.6438, 95% CI: [0.580, 0.703]) in the validation set was satisfactory. Conclusions: A novel clinical nomogram based on four available logistic and angiographic variables was developed and validated for in-hospital mortality after PCIs in a wide range of MIs.

Construction and validation of a personalized risk prediction model for in-hospital mortality in patients with acute myocardial infarction undergoing percutaneous coronary intervention

BACKGROUND

Although emergency Percutaneous Coronary Intervention (PCI) has been shown to reduce mortality in patients with Acute Myocardial Infarction (AMI), the risk of in-hospital death remains high. In this study, the authors aimed to identify risk factors associated with in-hospital mortality in AMI patients who underwent PCI, develop a nomogram prediction model, and evaluate its effectiveness.

METHODS

The authors retrospectively analyzed data from 1260 patients who underwent emergency PCI at Dongyang People's Hospital between June 1, 2013, and December 31, 2021. Patients were divided into two groups based on in-hospital mortality: the death group (n = 61) and the survival group (n = 1199). Clinical data between the two groups were compared. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select non-zero coefficients of predictive factors. Multivariable logistic regression analysis was then performed to identify independent risk factors for in-hospital mortality in AMI patients after emergency PCI. A nomogram model for predicting the risk of in-hospital mortality in AMI patients after PCI was constructed, and its predictive performance was evaluated using the c-index. Internal validation was performed using the bootstrap method with 1000 resamples. The Hosmer-Lemeshow test was used to assess the goodness of fit, and a calibration curve was plotted to evaluate the model's calibration.

RESULTS

LASSO regression identified d-dimer, B-type natriuretic peptide, white blood cell count, heart rate, aspartate aminotransferase, systolic blood pressure, and the presence of postoperative respiratory failure as important predictive factors for in-hospital mortality in AMI patients after PCI. Multivariable logistic regression analysis showed that d-dimer, B-type natriuretic peptide, white blood cell count, systolic blood pressure, and the presence of postoperative respiratory failure were independent factors for in-hospital mortality. A nomogram model for predicting the risk of in-hospital mortality in AMI patients after PCI was constructed using these independent predictive factors. The Hosmer-Lemeshow test yielded a Chi-Square value of 9.43 (p = 0.331), indicating a good fit for the model, and the calibration curve closely approximated the ideal model. The c-index for internal validation was 0.700 (0.560‒0.834), further confirming the predictive performance of the model. Clinical decision analysis demonstrated that the nomogram model had good clinical utility, with an area under the ROC curve of 0.944 (95 % CI 0.903‒0.963), indicating excellent discriminative ability.

CONCLUSION

This study identified B-type natriuretic peptide, white blood cell count, systolic blood pressure, d-dimer, and the presence of respiratory failure as independent factors for in-hospital mortality in AMI patients undergoing emergency PCI. The nomogram model based on these factors showed high predictive accuracy and feasibility.

Temporal trends of in-hospital mortality and its determinants following percutaneous coronary intervention in patients with acute coronary syndrome in England and Wales: A population-based study between 2006 and 2021

BACKGROUND

There is limited data around drivers of changes in mortality over time. We aimed to examine the temporal changes in mortality and understand its determinants over time.

METHODS

743,149 PCI procedures for patients from the British Cardiovascular Intervention Society (BCIS) database who were aged between 18 and 100 years and underwent Percutaneous Coronary Intervention (PCI) for Acute Coronary Syndrome (ACS) in England and Wales between 2006 and 2021 were included. We decomposed the contributing factors to the difference in the observed mortality proportions between 2006 and 2021 using Fairlie decomposition method. Multiple imputation was used to address missing data.

RESULTS

Overall, there was an increase in the mortality proportion over time, from 1.7% (95% CI: 1.5% to 1.9%) in 2006 to 3.1% (95% CI: 3.0% to 3.2%) in 2021. 61.2% of this difference was explained by the variables included in the model. ACS subtypes (percentage contribution: 14.67%; 95% CI: 5.76% to 23.59%) and medical history (percentage contribution: 13.50%; 95% CI: 4.33% to 22.67%) were the strongest contributors to the difference in the observed mortality proportions between 2006 and 2021. Also, there were different drivers to mortality changes between different time periods. Specifically, ACS subtypes and severity of presentation were amongst the strongest contributors between 2006 and 2012 while access site and demographics were the strongest contributors between 2012 and 2021.

CONCLUSIONS

Patient factors and the move towards ST-elevated myocardial infarction (STEMI) PCI have driven the short-term mortality changes following PCI for ACS the most.

Accuracy of machine learning in predicting outcomes post-percutaneous coronary intervention: a systematic review

Background

Recent studies have shown potential in introducing machine learning (ML) algorithms to predict outcomes post-percutaneous coronary intervention (PCI).

Aims

We aimed to critically appraise current ML models' effectiveness as clinical tools to predict outcomes post-PCI.

Methods

Searches of four databases were conducted for articles published from the database inception date to 29 May 2021. Studies using ML to predict outcomes post-PCI were included. For individual post-PCI outcomes, measures of diagnostic accuracy were extracted. An adapted checklist comprising existing frameworks for new risk markers, diagnostic accuracy, prognostic tools and ML was used to critically appraise the included studies along the stages of the translational pathway: development, validation, and impact. Quality of training data and methods of dealing with missing data were evaluated.

Results

Twelve cohorts from 11 studies were included with a total of 4,943,425 patients. ML models performed with high diagnostic accuracy. However, there are concerns over the development of the ML models. Methods of dealing with missing data were problematic. Four studies did not discuss how missing data were handled. One study removed patients if any of the predictor variable data points were missing. Moreover, at the validation stage, only three studies externally validated the models presented. There could be concerns over the applicability of these models. None of the studies discussed the cost-effectiveness of implementing the models.

Conclusions

ML models show promise as a useful clinical adjunct to traditional risk stratification scores in predicting outcomes post-PCI. However, significant challenges need to be addressed before ML can be integrated into clinical practice.

In-Hospital Mortality in Patients With Acute Myocardial Infarction: A Literature Overview

Acute myocardial infarction (AMI) continues to be a predominant cause of global morbidity and mortality, with in-hospital mortality (IHM) serving as a pivotal metric for patient outcomes. This review explores the influence of several clinical variables on IHM in individuals with AMI. Factors such as age, gender, body mass index (BMI), smoking habits, existing comorbidities, prior coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), and biomarkers, including high-sensitivity cardiac troponin T (hs-cTnT) and creatine kinase MB (CK-MB), significantly affect the prognosis of the patient. Advanced age and comorbid conditions such as diabetes and hypertension exacerbate myocardial damage and systemic impacts, thus increasing IHM. Gender and BMI are also critical, and women and patients with obesity face different risks. Smoking increases both the risk of AMI and IHM, underscoring the importance of cessation interventions. ST-elevation myocardial infarction is associated with elevated IHM and requires immediate reperfusion therapy, while non-ST-elevation myocardial infarction requires customized management for risk assessment. Previous CABG and PCI add complexity to AMI treatment and elevate IHM due to pre-existing coronary pathology and the intricacies of the procedures involved. The application of biomarker-centered techniques facilitates the swift identification of individuals at elevated risk, improves therapeutic planning, and reduces IHM for patients with AMI. Understanding and incorporating these clinical determinants are essential to optimize the management of AMI, minimize IHM, and improve patient outcomes. This all-encompassing strategy requires ongoing research, quality improvement efforts, and personalized care approaches.

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.

Nomogram to Predict Outcomes After Staged Revascularization in ST-Segment Elevation Myocardial Infarction and Multivessel Coronary Artery Disease

Objective

Approximately 50% of ST-segment elevation myocardial infarction (STEMI) patients have multivessel coronary artery disease (MVD). The management strategy for these patients remains controversial. This study aimed to develop predictive models and nomogram of outcomes in STEMI patients with MVD for better identification and classification.

Methods

The least absolute shrinkage and selection operator (LASSO) method was used to select the features most significantly associated with the outcomes. A Cox regression model was built using the selected variables. One nomogram was computed from each model, and individual risk scores were obtained by applying the nomograms to the cohort. After regrouping patients based on nomogram risk scores into low- and high-risk groups, we used the Kaplan-Meier method to perform survival analysis.

Results

The C-index of the major adverse cardiovascular event (MACE)-free survival model was 0·68 (95% CI 0·62-0·74) and 0·65 [0·62-0·68]) at internal validation, and that of the overall survival model was 0·75 (95% CI 0·66-0·84) and (0·73 [0·65-0·81]). The predictions of both models correlated with the observed outcomes. Low-risk patients had significantly lower probabilities of 1-year or 3-year MACEs (4% versus 11%, P= 0.003; 7% versus 15%, P=0.01, respectively) and 1-year or 3-year all-cause death (1% versus 3%, P=0.048; 2% versus 7%, respectively, P=0.001) than high-risk patients.

Conclusion

Our nomograms can be used to predict STEMI and MVD outcomes in a simple and practical way for patients who undergo primary PCI for culprit vessels and staged PCI for non-culprit vessels.

Development and validation of a prognostic model for predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI)

BACKGROUND

Accurately predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) remains a complex and critical challenge. The primary objective of this study was to develop and validate a robust risk prediction model to assess the 12-month and 24-month mortality risk in STEMI patients after hospital discharge.

METHODS

A retrospective study was conducted on 664 STEMI patients who underwent PPCI at Xiangtan Central Hospital Chest Pain Center between 2020 and 2022. The dataset was randomly divided into a training cohort (n = 464) and a validation cohort (n = 200) using a 7:3 ratio. The primary outcome was all-cause mortality following hospital discharge. The least absolute shrinkage and selection operator (LASSO) regression model was employed to identify the optimal predictive variables. Based on these variables, a regression model was constructed to determine the significant predictors of mortality. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

RESULTS

The prognostic model was developed based on the LASSO regression results and further validated using the independent validation cohort. LASSO regression identified five important predictors: age, Killip classification, B-type natriuretic peptide precursor (NTpro-BNP), left ventricular ejection fraction (LVEF), and the usage of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors (ACEI/ARB/ARNI). The Harrell's concordance index (C-index) for the training and validation cohorts were 0.863 (95% CI: 0.792-0.934) and 0.888 (95% CI: 0.821-0.955), respectively. The area under the curve (AUC) for the training cohort at 12 months and 24 months was 0.785 (95% CI: 0.771-0.948) and 0.812 (95% CI: 0.772-0.940), respectively, while the corresponding values for the validation cohort were 0.864 (95% CI: 0.604-0.965) and 0.845 (95% CI: 0.705-0.951). These results confirm the stability and predictive accuracy of our model, demonstrating its reliable discriminative ability for post-discharge all-cause mortality risk. DCA analysis exhibited favorable net benefit of the nomogram.

CONCLUSION

The developed nomogram shows potential as a tool for predicting post-discharge mortality in STEMI patients undergoing PPCI. However, its full utility awaits confirmation through broader external and temporal validation.

Impact of Naples Prognostic Score at Admission on In-Hospital and Follow-Up Outcomes Among Patients with ST-Segment Elevation Myocardial Infarction

The Naples prognostic score (NPS) consists of cholesterol level, albumin concentration, lymphocyte-to-monocyte and neutrophil-to-lymphocyte ratios and reflects systemic inflammation, malnutrition, and survival for various conditions. We investigated the relationship of NPS at admission with in-hospital and follow-up outcomes among ST-segment elevation myocardial infarction (STEMI) patients. This retrospective study included 1887 consecutive patients diagnosed with STEMI and who underwent primary percutaneous coronary intervention between March 2020 and May 2022. The study population was divided by NPS into 2; low (0-1-2) and high (3-4). In-hospital adverse events and all-cause mortality rates during follow-up were extracted from the registry. The Median follow-up time was 15 months. The overall mortality rate was 14.6%. The proportions of in-hospital events that included acute respiratory failure, acute kidney injury, malignant arrhythmia, and mortality were significantly higher in the high NPS group than in the low NPS group. Compared with the baseline model, in the full model of Cox regression analysis; NPS was an independent predictor of all-cause mortality (adjusted hazard ratio (aHR): 2.49, 95%CI, 1.75-3.50, P < .001), with a significant improvement in model performance (likelihood ratio χ2, P < .001) and better calibration. In conclusion, we found an association between NPS and in-hospital and follow-up outcomes in STEMI patients.

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.

The Prognostic Value of Creatine Kinase-MB Dynamics after Primary Angioplasty in ST-Elevation Myocardial Infarctions

BACKGROUND

In STEMIs, the evaluation of the relationship between biomarkers of myocardial injury and patients' prognoses has not been completely explored. Increased levels of CK-MB in patients with a STEMI undergoing primary angioplasty are known to be associated with higher mortality rates, yet the correlation of these values with short-term evolution remains unknown.

MATERIAL AND METHODS

The research encompassed a sample of 80 patients diagnosed with STEMIs, and its methodology entailed a retrospective analysis of the data collected during their hospital stays. The study population was then categorized into three distinct analysis groups based on the occurrence or absence of acute complications and fatalities.

RESULTS

The findings indicated that there is a notable correlation between rising levels of CK-MB upon admission and peak CK-MB levels with a reduction in left ventricular ejection fraction. Moreover, the CK-MB variation established a point of reference for anticipating complications at 388 U/L, and a cut-off value for predicting death at 354 U/L.

CONCLUSION

CK-MB values are reliable indicators of the progress of patients with STEMIs. Furthermore, the difference between the peak and admission CK-MB levels demonstrates a high accuracy of predicting complications and has a significant predictive power to estimate mortality risk.

PTH Predicts the in-Hospital MACE After Primary Percutaneous Coronary Intervention for Acute ST-Segment Elevation Myocardial Infarction

Objective

To investigate the correlation between serum parathyroid hormone (PTH) levels and in-hospital major adverse cardiovascular events (MACE) in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary percutaneous coronary intervention (PCI), and establish a risk prediction model based on parameters such as PTH for in-hospital MACE.

Methods

This observational retrospective study consecutively enrolled 340 patients who underwent primary PCI for STEMI between January 2016 and December 2020, divided into a MACE group (n=92) and a control group (n=248). The least absolute shrinkage and selection operator (LASSO) and logistic regression analyses were used to determine the risk factors for MACE after primary PCI. The rms package in R-studio statistical software was used to construct a nomogram, to detect the line chart C-index, and to draw a calibration curve. The decision curve analysis (DCA) method was used to evaluate the clinical application value and net benefit.

Results

Correlation analysis revealed that PTH level positively correlated with the occurrence of in-hospital MACE. Receiver operating characteristic curve analyses revealed that PTH had a good predictive value for in-hospital MACE. Multivariate logistic regression analysis indicated that Killip class II-IV, and FBG were independently associated with in-hospital MACE after primary PCI. A nomogram model was constructed using the above parameters. The model C-index was 0.894 and the calibration curve indicated that the model was well calibrated. The DCA curve suggested that the nomogram model was better than TIMI score model in terms of net clinical benefit.

Conclusion

Serum PTH levels in patients with STEMI are associated with in-hospital MACE after primary PCI, and the nomogram risk prediction model based on PTH demonstrated good predictive ability with obvious clinical practical value.

Nomograms referenced by cardiac magnetic resonance in the prediction of cardiac injuries in patients with ST-elevation myocardial infarction

BACKGROUND

Evaluation of cardiac injuries is essential in patients with ST-elevation myocardial infarction (STEMI). Cardiac magnetic resonance (CMR) has become the gold standard for quantifying cardiac injuries; however, its routine application is limited. A nomogram is a useful tool for prognostic prediction based on the comprehensive utilization of clinical data. We presumed that the nomogram models established using CMR as a reference could precisely predict cardiac injuries.

METHODS

This analysis included 584 patients with acute STEMI from a CMR registry study for STEMI (NCT03768453). The patients were divided into training (n = 408) and testing (n = 176) datasets. The least absolute shrinkage and selection operator method and multivariate logistic regression were used to construct nomograms for predicting left ventricular ejection fraction (LVEF) ≤40%, infarction size (IS) ≥ 20% on the LV mass, and microvascular dysfunction.

RESULTS

The nomogram for predicting LVEF≤40%, IS≥20%, and microvascular dysfunction comprised 14, 10, and 15 predictors, respectively. With the nomograms, the individual risk probability of developing specific outcomes could be calculated, and the weight of each risk factor was demonstrated. The C-index of the nomograms in the training dataset were 0.901, 0.831, and 0.814, respectively, and were comparable in the testing set, showing good nomogram discrimination and calibration. The decision curve analysis demonstrated good clinical effectiveness. Online calculators were also constructed.

CONCLUSIONS

With the CMR results as the reference standard, the established nomograms demonstrated good effectiveness in predicting cardiac injuries after STEMI and could provide physicians with a new option for individual risk stratification.

From Classic to Modern Prognostic Biomarkers in Patients with Acute Myocardial Infarction

Despite all the important advances in its diagnosis and treatment, acute myocardial infarction (AMI) is still one of the most prominent causes of morbidity and mortality worldwide. Early identification of patients at high risk of poor outcomes through the measurement of various biomarker concentrations might contribute to more accurate risk stratification and help to guide more individualized therapeutic strategies, thus improving prognoses. The aim of this article is to provide an overview of the role and applications of cardiac biomarkers in risk stratification and prognostic assessment for patients with myocardial infarction. Although there is no ideal biomarker that can provide prognostic information for risk assessment in patients with AMI, the results obtained in recent years are promising. Several novel biomarkers related to the pathophysiological processes found in patients with myocardial infarction, such as inflammation, neurohormonal activation, myocardial stress, myocardial necrosis, cardiac remodeling and vasoactive processes, have been identified; they may bring additional value for AMI prognosis when included in multi-biomarker strategies. Furthermore, the use of artificial intelligence algorithms for risk stratification and prognostic assessment in these patients may have an extremely important role in improving outcomes.

Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention

BACKGROUND

The machine learning algorithm (MLA) was implemented to establish an optimal model to predict the no reflow (NR) process and in-hospital death that occurred in ST-elevation myocardial infarction (STEMI) patients who underwent primary percutaneous coronary intervention (pPCI).

METHODS

The data were obtained retrospectively from 854 STEMI patients who underwent pPCI. MLA was applied to predict the potential NR phenomenon and confirm the in-hospital mortality. A random sampling method was used to split the data into the training (66.7%) and testing (33.3%) sets. The final results were an average of 10 repeated procedures. The area under the curve (AUC) and the associated 95% confidence intervals (CIs) of the receiver operator characteristic were measured.

RESULTS

A random forest algorithm (RAN) had optimal discrimination for the NR phenomenon with an AUC of 0.7891 (95% CI: 0.7093-0.8688) compared with 0.6437 (95% CI: 0.5506-0.7368) for the decision tree (CTREE), 0.7488 (95% CI: 0.6613-0.8363) for the support vector machine (SVM), and 0.681 (95% CI: 0.5767-0.7854) for the neural network algorithm (NNET). The optimal RAN AUC for in-hospital mortality was 0.9273 (95% CI: 0.8819-0.9728), for SVM, 0.8935 (95% CI: 0.826-0.9611); NNET, 0.7756 (95% CI: 0.6559-0.8952); and CTREE, 0.7885 (95% CI: 0.6738-0.9033).

CONCLUSIONS

The MLA had a relatively higher performance when evaluating the NR risk and in-hospital mortality in patients with STEMI who underwent pPCI and could be utilized in clinical decision making.

In-Hospital Mortality in Patients With Acute ST-Elevation Myocardial Infarction With or Without Mitral Regurgitation

Background Mitral regurgitation (MR) is a common complication in hospitalized cardiac patients with ST-segment elevation myocardial infarction (STEMI); however, the patient outcomes depend on various factors that vary across facilities and regions. There is an acute need to stratify STEMI patients by risk of in-hospital mortality. We conducted this study to compare the mortality of patients with acute STEMI with or without MR admitted to different units of the Cardiology Department at Lady Reading Hospital (LRH) in Peshawar. Methods In this prospective study, we compared the mortality rates of STEMI patients with and without MR from June 5 to October 30, 2021. All patients with different types of STEMI treated at LRH were enrolled in the study regardless of age and gender. ST-elevation was confirmed via electrocardiogram, and MR was confirmed via echocardiography. We excluded any patients with primary organic valve disease or congenital heart disease. We also collected patient demographic and clinical characteristics. We used IBM SPSS Statistics for Windows, Version 24.0 (IBM Corp., Armonk, NY) for statistical analyses. Results Our study population included 228 patients with a mean age of 62.4 ± 12.3 years. Most of the patients were men (n=140; 61.4%), and only 78 (38.6%) were women. The prevalence of MR was 29.4%. Hypertension was the most common comorbidity (63.6%), and inferior wall myocardial infarction (MI) was the most common type of MI (49.1%). Hypertension, prehospital cardiopulmonary resuscitation (CPR), and Killip class ≥ 2 were significantly associated with MR (p<.001). In-hospital mortality was 29.8%, significantly associated with MR (p=.0001). Patients who needed CPR prior to hospitalization and those with Killip class ≥ 2 were less likely to survive (p=.0001). Conclusions MR is common following MI, especially in cases of inferior wall MI. Patients with MR have a poorer prognosis than those without MR following MI, more so when combined with other comorbidities. Regarding its relation to MI complications, an assessment of the MR is necessary to make an appropriate decision for treatment.

Association between total ischemic time and in-hospital mortality after emergency PCI in patients with acute ST-segment elevation myocardial infarction: a retrospective study

Background

Symptom-to-balloon time (SBT) represents the total ischemic time in ST-elevated myocardial infarction (STEMI) and is associated with poor long-term outcomes. The study aimed to explore the association between SBT and in-hospital mortality after emergency percutaneous coronary intervention (PCI) in patients with acute STEMI.

Methods

This retrospective, multicenter, observational study included patients admitted to the Hebei General Hospital, Baoding No. 1 Central Hospital, and Cangzhou Central Hospital from January 2016 to December 2018. The outcome was all-cause mortality during the hospital stay. Logistic regression models were established to explore the association between SBT and all-cause mortality during the hospital stay.

Results

This study included 1169 patients: 876 males of 59.6 ± 11.4 years of age, and 293 females 66.3 ± 13.3 years of age. A first analysis showed EF had an interaction with SBT (P = 0.01). In patients with EF ≥ 50%, SBT was not an independent risk factor for postoperative all-cause mortality in the hospital (all P > 0.05). In patients with EF < 50%, SBT was an independent risk factor for postoperative all-cause mortality in the hospital [model 3: 1.51 (1.17, 1.54), P for trend = 0.01].

Conclusions

SBT was independently associated with all-cause mortality in the hospital after PCI in patients with acute STEMI and EF < 50%. Specifically, the risk of in-hospital mortality for those with SBT ≥ 361 min is increased by 51% compared with those with SBT ≤ 120 min.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Development of a nomogram for the prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary percutaneous coronary intervention: a multicentre, retrospective, observational study in Hebei province, China

OBJECTIVES

To establish a clinical prognostic nomogram for predicting in-hospital mortality after primary percutaneous coronary intervention (PCI) among patients with ST-elevation myocardial infarction (STEMI).

DESIGN

Retrospective, multicentre, observational study.

SETTING

Thirty-nine hospitals in Hebei province.

PARTICIPANTS

Patients with STEMI who underwent PCI from January 2018 to December 2019.

INTERVENTIONS

A multivariable logistic regression model was used to identify the factors associated with in-hospital mortality, and a nomogram was established using these factors. The performance of the nomogram was evaluated by the discrimination, calibration and clinical usefulness.

PRIMARY AND SECONDARY OUTCOME MEASURES

The outcome was the factors associated with in-hospital mortality.

RESULTS

This study included 855 patients, among whom 223 died in hospital. Age, body mass index, systolic pressure on admission, haemoglobin, random blood glucose on admission, ejection fraction after PCI, use aspirin before admission, long lesions, thrombolysis in myocardial infarction flow grade and neutrophils/lymphocytes ratio were independently associated with in-hospital mortality (all p<0.05). In the training set, the nomogram showed a C-index of 0.947, goodness-of-fit of 0.683 and area under the receiver operating characteristic curve (AUC) of 0.947 (95% CI 0.927 to 0.967). In the testing set, the C-index was 0.891, goodness-of-fit was 0.462 and AUC was 0.891 (95% CI 0.844 to 0.939). The results indicate that the nomogram had good discrimination and good prediction accuracy and could achieve a good net benefit.

CONCLUSIONS

A nomogram to predict in-hospital mortality in patients with STEMI after PCI was developed and validated in Hebei, China and showed a satisfactory performance. Prospective studies will be necessary to confirm the performance and clinical applicability and practicality of the nomogram.

The Association Between Hematologic Indices with TIMI Flow in STEMI Patients who Undergo Primary Percutaneous Coronary Intervention

BACKGROUND

The Primary Percutaneous Coronary Intervention (PPCI) is the preferred therapeutic strategy for patients who experienced ST-Elevation Myocardial Infarction (STEMI).

OBJECTIVE

We aimed to evaluate the association of hematological indices, including hemoglobin level, platelets, White Blood Cells (WBCs) count, and MPV before PPCI with the TIMI grade flow after PPCI.

METHODS

STEMI patients who experienced PPCI were included in the present retrospective crosssectional study. Then participants were divided into three groups based on their post-procedural TIMI flow grades. Demographic data and hematologic indices of patients before PPCI were collected and their association with the TIMI grade flow after PPCI was evaluated. To compare the quantitative and qualitative variables, chi-square and t-tests were performed, respectively.

RESULTS

We found that elevated levels of hemoglobin and decreased levels of MPV had a significant association with an advanced grade of TIMI flow. Interestingly, in the normal range, there was a significant association between higher platelet count and TIMI-flow grade 1. Besides, TIMI flow grades 2 and 3 had a significant association with low and moderate platelets count, respectively.

CONCLUSION

In conclusion, evaluating MPV, platelets, and hemoglobin levels before PPCI as easy and accessible parameters may be able to identify high-risk STEMI patients undergoing PPCI.

Factors affecting STEMI performance in six hospitals within one healthcare system

BACKGROUND

How quickly percutaneous coronary intervention is performed in patients with ST-elevation myocardial infarction (STEMI) is a quality measure, reported as door-to-balloon (D2B) time.

OBJECTIVES

To explore factors affecting STEMI performance in six hospitals in one healthcare system.

METHODS

This was a retrospective chart review of clinical features and D2B times. Predictors for D2B times were identified using multivariate linear regression.

RESULTS

The median D2B time for all six hospitals was 63 minutes and all hospitals surpassed the minimal recommended percentage of patients achieving D2B time ≤90 minutes (87.8%vs75%,p<0.001). Patient confounders adversely affect D2B times (+21.5 minutes, p<0.001). Field ECG/activation with emergency department (ED) transport (-22.0 minutes) or direct cardiac catheterization laboratory (CCL) transport (-27.3 minutes) was superior to ED ECG/activation (p<0.001).

CONCLUSION

Field ECG/STEMI activation significantly shortened D2B time. To improve D2B time, hospital and Emergency Medical Service collaboration should be advocated to increase field activation and direct patient transportation to CCL.