Incidence, associated outcomes, and predictors of upper gastrointestinal bleeding following acute myocardial infarction: a SWEDEHEART-based nationwide cohort study

Association between Blood Urea Nitrogen Level and In-Hospital Mortality in Patients with Acute Myocardial Infarction and Subsequent Gastrointestinal Bleeding

Background

Limited studies have explored the association between blood urea nitrogen (BUN) levels and in-hospital mortality in patients with acute myocardial infarction (AMI) and subsequent gastrointestinal bleeding (GIB). Our objective was to explore this correlation.

Methods

276 individuals with AMI and subsequent GIB were retrospectively included between January 2012 and April 2023. The predictive value of BUN for in-hospital mortality was assessed through receiver operating characteristic (ROC) curve. Logistic regression models were constructed to assess the relationship between BUN and in-hospital mortality. Propensity score weighting (PSW), sensitivity and subgroup analyses were used to further explore the association.

Results

Fifty-three (19.2%) patients died in the hospital. BUN levels were higher in non-survivors compared with the survivors [(11.17 ± 6.17) vs (8.09 ± 4.24), p = 0.001]. The ROC curve suggested that the optimal cut-off for BUN levels to predict in-hospital mortality was 8.45 mmol/L (AUC [area under the ROC curve] 0.678, 95% confidence interval [CI] 0.595-0.761, p < 0.001). Multivariable logistic regression showed that elevated BUN levels ( ≥ 8.45 mmol/L) were positively association with in-hospital mortality (odds ratio [OR] 4.01, 95% CI 1.55-10.42, p = 0.004). After PSW, sensitivity and subgroup analyses, the association remained significant.

Conclusions

Elevated BUN levels were associated with in-hospital mortality in patients with AMI and subsequent GIB.

CT Image-Based Radiomic Analysis for Detecting PD-L1 Expression Status in Bladder Cancer Patients

RATIONALE AND OBJECTIVES

The role of Programmed death-ligand 1 (PD-L1) expression is crucial in guiding immunotherapy selection. This study aims to develop and evaluate a radiomic model, leveraging Computed Tomography (CT) imaging, with the objective of predicting PD-L1 expression status in patients afflicted with bladder cancer.

MATERIALS AND METHODS

The study encompassed 183 subjects diagnosed with histologically confirmed bladder cancer, among which the PD-L1(+) cohort constituted 60.1% of the total population. Stratified random sampling was utilized at a 7:3 ratio. We employed five diverse machine learning algorithms-Decision Tree, Random Forest, Linear Support Vector Classification, Support Vector Machine, and Logistic Regression-to establish radiomic models on the training dataset. These models endeavored to predict PD-L1 expression status premised on radiomic features derived from region-of-interest segmentation. Subsequent to this, the predictive performance of these models was examined on a validation set employing the receiver operating characteristic (ROC) curve. The DeLong test was utilized to contrast ROC curves, thereby pinpointing the model with superior predictive accuracy.

RESULTS

16 features were chosen for the model construction. All five models revealed strong performance in the training set (AUC, 0.920-1) and commendable predictive ability in the validation set (AUC, 0.753-0.766). As per the DeLong test, no statistically significant disparities were observed among any of the models (P > 0.05) in the validation set. Additional verification through the calibration curve and decision curve analysis indicated that the Logistic Regression model exhibited extraordinary precision and practicality.

CONCLUSION

Our machine learning model, grounded on radiomic features, demonstrated its proficiency in accurately distinguishing bladder cancer patients with high PD-L1 expression. Future research, incorporating more exhaustive datasets, could potentially augment the predictive efficiency of radiomic algorithms, thereby advancing their clinical utility.

Application of Machine Learning Based on Structured Medical Data in Gastroenterology

The era of big data has led to the necessity of artificial intelligence models to effectively handle the vast amount of clinical data available. These data have become indispensable resources for machine learning. Among the artificial intelligence models, deep learning has gained prominence and is widely used for analyzing unstructured data. Despite the recent advancement in deep learning, traditional machine learning models still hold significant potential for enhancing healthcare efficiency, especially for structured data. In the field of medicine, machine learning models have been applied to predict diagnoses and prognoses for various diseases. However, the adoption of machine learning models in gastroenterology has been relatively limited compared to traditional statistical models or deep learning approaches. This narrative review provides an overview of the current status of machine learning adoption in gastroenterology and discusses future directions. Additionally, it briefly summarizes recent advances in large language models.

Six-month cardiovascular prognostic impact of type 1 And type 2 myocardial infarction in patients hospitalized for gastrointestinal bleeding

BACKGROUND

Patients with gastrointestinal bleeding (GIB) are at an increased risk of cardiovascular events and myocardial infarction (MI). Myocardial supply-demand mismatch results in type 2 MI(T2MI) and atherosclerotic plaque rupture leads to type 1 MI(T1MI). Data comparing the prognostic impact of these MI types in GIB are sparse.

METHODS

Patients hospitalized for GIB were identified in the 2019 US Nationwide Readmissions Sample. In this population, we studied the differences in management of T1MI and T2MI, and the association of these MI types with in-hospital mortality and risk for 6-month MI and MI-related mortality.

RESULTS

Of 444,475 patients admitted for a GIB, 12,860 (2.9%) had an MI (1.7% T2MI, 1.2% T1MI). Patients with T1MI were more likely to receive coronary angiography and revascularization than patients with T2MI. In-hospital mortality occurred in 2.0% patients, at a significantly higher rate in patients with an MI (7.9% vs 1.8%; P < 0.001), and higher with T1MI (11.9%) than T2MI (5.3%; P < 0.001). Among the survivors, 2.2% patient had an MI within 6 months, at a significantly higher rate in patients with index MI (13.1% vs 2.0%, adjusted OR 4.3 95% CI 3.83-4.90; P < 0.001). Mortality during the subsequent MI occurred in 0.3% of all patients (12% with an MI), at a 6-fold higher rate in patients with index MI (1.7% vs 0.3%; adjusted OR 3.69 95% CI 2.75-4.95; P < 0.001). The elevated risks were associated with both MI types. The risks for 6-month MI and related mortality were similar between T1MI and T2MI (6-month AMI: adjusted OR for T2MI = 1.03, 95% 0.83-1.29; fatal MI: adjusted OR for T2MI = 1.5, 95% CI 0.85-2.7).

CONCLUSION

The occurrence of an MI is associated with a substantially elevated risk for subsequent AMI and related mortality in patients hospitalized for a GIB. This future prognostic impact was similar between T1MI and T2MI.

Results from the "Me & My Heart" (eMocial) Study: a Randomized Evaluation of a New Smartphone-Based Support Tool to Increase Therapy Adherence of Patients with Acute Coronary Syndrome

PURPOSE

This study evaluated whether patient support, administered via an electronic device-based app, increased adherence to treatment and lifestyle changes in patients with acute coronary syndrome (ACS) treated with ticagrelor in routine clinical practice.

METHODS

Patients (aged ≥ 18 years) with diagnosed ACS treated with ticagrelor co-administered with low-dose acetylsalicylic acid were randomized into an active group (with support tool app for medication intake reminders and motivational messages) and a control group (without support tool app), and observed for 48 weeks (ClinicalTrials.gov Identifier: NCT02615704). Patients were asked to complete the 36-item Short-Form Health Survey (SF-36) and Lifestyle Changes Questionnaire (LSQ), and were assessed for blood pressure and body mass index (BMI) at baseline (visit 1) and at the end of the study (visit 2). Medication adherence was measured using the Brilique Adherence Questionnaire (BAQ).

RESULTS

Patients (N = 676) were randomized to an active (n = 342) or a control (n = 334) group. BAQ data were available for 174 patients in the active group and 174 patients in the control group. Over the 48-week period, mean (standard deviation) adherence for the active and control groups was 96.4% (13.2%) and 91.5% (23.1%), respectively (effect of app intervention, p < 0.05). There were no significant differences in blood pressure and BMI between visits. General improvements in SF-36 and LSQ scores were observed for both groups.

CONCLUSION

The patient support tool app was associated with significant improvements in patient-reported treatment adherence compared with a data collection app alone in patients prescribed ticagrelor for ACS.

Drug-Associated Gastropathy: Diagnostic Criteria

Drugs are widely used to treat different diseases in modern medicine, but they are often associated with adverse events. Those located in the gastrointestinal tract are common and often mild, but they can be serious or life-threatening and determine the continuation of treatment. The stomach is often affected not only by drugs taken orally but also by those administered parenterally. Here, we review the mechanisms of damage, risk factors and specific endoscopic, histopathological and clinical features of those drugs more often involved in gastric damage, namely NSAIDs, aspirin, anticoagulants, glucocorticosteroids, anticancer drugs, oral iron preparations and proton pump inhibitors. NSAID- and aspirin-associated forms of gastric damage are widely studied and have specific features, although they are often hidden by the coexistence of Helicobacter pylori infection. However, the damaging effect of anticoagulants and corticosteroids or oral iron therapy on the gastric mucosa is controversial. At the same time, the increased use of new antineoplastic drugs, such as checkpoint inhibitors, has opened up a new area of gastrointestinal damage that will be seen more frequently in the near future. We conclude that there is a need to expand and understand drug-induced gastrointestinal damage to prevent and recognize drug-associated gastropathy in a timely manner.

Effect Produced by a Cyclooctyne Derivative on Both Infarct Area and Left Ventricular Pressure via Calcium Channel Activation

BACKGROUND

There are reports which indicate that some cyclooctyne derivatives may exert changes in cardiovascular system; however, its molecular mechanism is not very clear.

OBJECTIVE

The aim of this study was to evaluate the biological activity of four cyclooctyne derivatives (compounds 1: to 4: ) produced on infarct area and left ventricular pressure.

METHODS

Biological activity produced by cyclooctyne derivatives on infarct area was determinate using an ischemia/reperfusion injury model. In addition, to characterize the molecular mechanism of this effect, the following strategies were carried out as follows; i) biological activity produced by cyclooctyne derivative (compound 4: ) on either perfusion pressure or left ventricular pressure was evaluated using an isolated rat heart; ii) theoretical interaction of cyclooctyne derivative with calcium channel (1t0j protein surface) using a docking model.

RESULTS

The results showed that cyclooctyne derivative (compound 4: ) decrease infarct area of in a dose-dependent manner compared with compound 1: to 3: . Besides, this cyclooctyne derivative increase both perfusion pressure and left ventricular pressure which was inhibited by nifedipine. Other theoretical data suggests that cyclooctyne derivative could interact with some aminoacid residues (Met₈₃, Ile₈₅, Ser₈₆, Leu₁₀₈, Glu₁₁₄) involved in 1t0j protein surface.

CONCLUSIONS

All these data indicate that cyclooctyne derivative increase left ventricular pressure via calcium channel activation and this phenomenon could be translated as a decrease of infarct area.

Acute myocardial infarction post-gastrointestinal bleeding: A clinical dilemma with poor prognosis

BACKGROUND

Gastrointestinal bleeding (GIB) complicating acute myocardial infarction (AMI) is a severe clinical condition with treatment contradiction and poor prognosis. This study aimed to evaluate the rate of in-hospital mortality in patients with GIB who subsequently suffered from AMI and to explore the potential risk factors for this condition.

METHODS

In this retrospective study, a total of 77 patients diagnosed with GIB, who subsequently suffered from AMI, were enrolled from January 2013 to March 2022. Demographic, laboratory, and clinical data were collected. The in-hospital mortality was the outcome of interest. Logistic regression analysis was used to investigate the potential risk factors of in-hospital mortality.

RESULTS

Among the 77 patients included in this study, 62 (80.52%) were males. The mean age of patients was 65.88 ± 12.15 years, and 48 patients (62.34%) were non-ST-segment elevation myocardial infarction (NSTEMI). There were 16 (20.78%) cases of in-hospital deaths. The subjects who died showed higher levels of white blood cell count (13.05 ± 5.76 vs. 9.31 ± 4.07 × 10⁹/L, P = 0.003) and troponin I (TnI) (9.23 ± 9.17 vs. 4.12 ± 5.03 μg/L, P = 0.003). Besides, there were higher proportions of cardiogenic shock (81.25% vs. 26.23%, P < 0.001) and mechanical ventilator usage (75.0% vs. 11.48%, P < 0.001) among the patients who died. The multivariate logistic regression analysis showed that white blood cell count (odds ratio [OR] 1.19, 95% confidence interval [CI] 1.02-1.39, P = 0.030), cardiogenic shock (OR 12.18, 95% CI 3.06-48.39, P = 0.017), and mechanical ventilator usage (OR 7.21, 95% CI 1.28-40.51, P = 0.025) were independently associated with in-hospital mortality.

CONCLUSIONS

The in-hospital mortality of patients with GIB who subsequently develop AMI is high. White blood cell count, cardiogenic shock, and mechanical ventilator usage are independent predictors of in-hospital mortality.

External validation of the SWEDEHEART score for predicting in-hospital major bleeding among East Asian patients with acute myocardial infarction

Background

Risk scores for predicting in-hospital major bleeding in patients with acute myocardial infarction (AMI) are rare. The Swedish web-system for the enhancement and development of evidence-based care in heart disease evaluated according to recommended therapies (SWEDEHEART) score (SS), consisting of five common clinical variables, is a novel model for predicting in-hospital major bleeding. External validation of SS has not yet been completed.

Methods and results

A retrospective study recruiting consecutive East Asian patients diagnosed with AMI was conducted in the Second Affiliated Hospital, Zhejiang University. The primary endpoint was the ability of SS to predict in-hospital major bleeding, which was defined as Bleeding Academic Research Consortium (BARC) type 3 or 5 bleeding. To validate SS, the discrimination and calibration were assessed in the overall population and several subgroups. The receiver operating characteristic (ROC) curves and the areas under ROC curves (AUCs) were calculated for discrimination. The calibration of SS was evaluated with the unreliability U test. A total of 2,841 patients diagnosed with AMI during hospitalization were included, and 1.94% (55) of them experienced in-hospital major bleeding events. The AUC of SS for the whole population was only 0.60 [95% confidence interval (CI), 0.52-0.67], without an acceptable calibration (p = 0.001). Meanwhile, the highest AUC (0.72; 95% CI, 0.61-0.82) of SS for the primary endpoint was found in the diabetes subgroup, with an acceptable calibration (p = 0.87).

Conclusion

This external validation study showed that SS failed to exhibit sufficient accuracy in predicting in-hospital major bleeding among East Asian patients with AMI despite demonstrating acceptable performance in the diabetic subgroup of patients. Studies to uncover optimal prediction tools for in-hospital major bleeding risk in AMI are urgently warranted.

Clinical characteristics and risk factors of in-hospital gastrointestinal bleeding in patients with acute myocardial infarction

Background

Gastrointestinal bleeding (GIB) is one of the most serious complications of acute myocardial infarction (AMI) and is correlated with poor outcomes.

Objective

To evaluate the prevalence, risk factors and in-hospital mortality of GIB in patients with AMI.

Methods

This observational case-control study retrospectively enrolled consecutive patients with AMI from the Department of Cardiovascular Medicine and Cardiovascular Surgery of the First Affiliated Hospital of Xi'an Jiaotong University from January 2015 to December 2020. GIB after AMI was identified by International Classification of Diseases (ICD) codes from inpatient medical settings and validated by medical record review. AMI patients without GIB were accordingly classified as the control group. Propensity score matching (PSM) was used to match with the GIB group and the control group. All anonymized clinical data were provided by the Biobank of the First Affiliated Hospital of Xi'an Jiaotong University.

Results

A total of 5,868 AMI patients were enrolled, 0.87% (51/5,868) of whom developed GIB after AMI. On the univariate analysis, history of diabetes, chronic kidney disease, Killip IV, a lower hemoglobin concentration, a higher serum level of creatinine, blood urea nitrogen and D-dimer were closely associated with the risk of GIB (P < 0.05). On the multivariable analysis, a lower hemoglobin concentration (OR: 0.93, 95% CI: 0.89–0.96, P < 0.001) was independently associated with the risk of GIB. Patients with GIB had a much higher in-hospital mortality rate than those without GIB (14.3 vs. 2.1%, P = 0.047). In-hospital mortality among patients with GIB after AMI appeared to be associated with a decreased hemoglobin concentration (OR: 0.93, 95% CI: 0.86–0.99, P = 0.045) and Killip IV (OR: 51.59, 95% CI: 2.65–1,005.30, P = 0.009).

Conclusion

The history of diabetes, poor renal function and heart failure were associated with the high risk of GIB in patients experiencing AMI. The in-hospital mortality in patients with AMI complicating GIB was higher than that in patients without GIB and was associated with a decreased hemoglobin concentration and high Killip classification.

Clinical Characteristics and Risk Factors of In-Hospital Mortality in Patients With Acute Myocardial Infarction With Subsequent Gastrointestinal Bleeding: A Single-Center Experience

Objective

Gastrointestinal bleeding (GIB) post acute myocardial infarction (AMI) is a severe clinical condition with a poor prognosis. The purpose of the study was to evaluate the rate of in-hospital mortality in patients with GIB post-AMI and to identify the potential risk factors of this situation.

Methods

In this single-center retrospective study, a total of 154 patients diagnosed with AMI who subsequently suffered GIB were enrolled from October 2013 to December 2021. Demographic, laboratory, and clinical data were collected. The in-hospital mortality was the outcome of interest. Logistic regression analysis was used to investigate the potential risk factors of in-hospital mortality.

Results

Among the 154 subjects included in the final analysis, the mean age was 65.58 ± 11.20 years, and 104 (67.53%) were males. GIB occurred in 11 patients after thrombolytic therapy, 50 patients after percutaneous coronary intervention (PCI), and 93 patients during drug conservative treatment. A total of 41 patients died in the hospital. The in-hospital mortality rate of the thrombolysis group, PCI group, and drug conservative treatment group was 27.27% (3/11), 28.00% (14/50), and 25.81% (24/93), respectively. There was no difference in the in-hospital mortality among the three groups. The multivariate logistic regression analysis showed that the peak levels of TnI (OR 1.07, 95% CI 1.02–1.12, P = 0.011), condition of cardiogenic shock after admission (OR 14.52, 95% CI 3.36–62.62, P < 0.001), and the use of the mechanical ventilator (OR 8.14, 95% CI 2.03–32.59, P = 0.003) were significantly associated with in-hospital mortality.

Conclusion

Regardless of the treatment strategy for AMI, once GIB occurred, the prognosis was poor. High in-hospital mortality in patients with GIB post-AMI was independently associated with the peak levels of TnI, condition of cardiogenic shock, and the use of a mechanical ventilator.

Outcomes and Resource Utilization in Patients Hospitalized with Gastrointestinal Bleeding Complicated by Types 1 and 2 Myocardial Infarction

BACKGROUND

Types 1 and 2 myocardial infarction (MI) may occur in the setting of gastrointestinal bleeding (GIB). There is a paucity of data pertinent to the contemporary prevalence and impact of types 1 and 2 MI following GIB. We examined clinical profiles and the prognostic impact of both MI types on outcomes of patients hospitalized with GIB.

METHODS

The 2018 Nationwide Readmission Database was queried for patients hospitalized for the primary diagnosis of GIB and had concomitant diagnoses of type 1 or type 2 MI. Baseline characteristics, in-hospital mortality, resource utilization, and 30-day all-cause readmissions were compared among groups.

RESULTS

Of 381,867 primary GIB hospitalizations, 2902 (0.75%) had type 1 MI and 3963 (1.0%) had type 2 MI. GIB patients with type 1 and type 2 MI had significantly higher in-hospital mortality compared to their counterparts without MI (adjusted odds ratios [aOR]: 4.72, 95% confidence interval [CI] 3.43-6.48; and aOR: 2.17, 95% CI 1.48-3.16, respectively). Both types 1 and 2 MI were associated with higher rates of discharge to a nursing facility (aOR of type 1 vs. no MI: 1.65, 95% CI 1.45-1.89, and aOR of type 2 vs no MI: 1.37, 95% CI 1.22-1.54), longer length of stay, higher hospital costs, and more 30-day all-cause readmissions (aOR of type 1 vs no MI: 1.22, 95% CI 1.08-1.38; aOR of type 2 vs no MI: 1.17, 95% CI 1.05-1.30).

CONCLUSION

Types 1 and 2 MI are associated with higher in-hospital mortality and resource utilization among patients hospitalized with GIB in the United States.

Cardiogenic Shock Does Not Portend Poor Long-Term Survival in Patients Undergoing Primary Percutaneous Coronary Intervention

Although a strong association of cardiogenic shock (CS) with in-hospital mortality in patients with acute coronary syndrome (ACS) is well established, less attention has been paid to its prognostic influence on long-term outcome. We evaluated the impact of CS in 1173 patients undergoing primary percutaneous coronary interventions between 1997 and 2009. Patients were followed up until the primary study endpoint (cardiovascular mortality) was reached. Within the entire study population, 112 (10.4%) patients presented with CS at admission. After initial survival, CS had no impact on mortality (non-CS: 23.5% vs. CS: 24.0%; p = 0.923), with an adjusted hazard ratio of 1.18 (95% CI: 0.77–1.81; p = 0.457). CS patients ≥ 55 years (p = 0.021) with moderately or severely impaired left ventricular function (LVF; p = 0.039) and chronic kidney disease (CKD; p = 0.013) had increased risk of cardiovascular mortality during follow-up. The present investigation extends currently available evidence that cardiovascular survival in CS is comparable with non-CS patients after the acute event. CS patients over 55 years presenting with impaired LVF and CKD at the time of ACS are at increased risk for long-term mortality and could benefit from personalized secondary prevention.

Predicting 30-Day Readmission for Stroke Using Machine Learning Algorithms: A Prospective Cohort Study

Background

Machine learning algorithms for predicting 30-day stroke readmission are rarely discussed. The aims of this study were to identify significant predictors of 30-day readmission after stroke and to compare prediction accuracy and area under the receiver operating characteristic (AUROC) curve in five models: artificial neural network (ANN), K nearest neighbor (KNN), random forest (RF), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models.

Methods

The subjects of this prospective cohort study were 1,476 patients with a history of admission for stroke to one of six hospitals between March, 2014, and September, 2019. A training dataset (n = 1,033) was used for model development, and a testing dataset (n = 443) was used for internal validation. Another 167 patients with stroke recruited from October, to December, 2019, were enrolled in the dataset for external validation. A feature importance analysis was also performed to identify the significance of the selected input variables.

Results

For predicting 30-day readmission after stroke, the ANN model had significantly (P &lt; 0.001) higher performance indices compared to the other models. According to the ANN model results, the best predictor of 30-day readmission was PAC followed by nasogastric tube insertion and stroke type (P &lt; 0.05). Using a machine learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients.

Conclusion

Using a machine-learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients. For stroke patients who are candidates for PAC rehabilitation, these predictors have practical applications in educating patients in the expected course of recovery and health outcomes.

Association of Genetic Polymorphisms with Complications of Implanted LVAD Devices in Patients with Congestive Heart Failure: A Kazakhstani Study

The left ventricular assist device (LVAD) is one of the alternative treatments for heart failure (HF) patients. However, LVAD support is followed by thrombosis, and bleeding complications which are caused by high non-physiologic shear stress and antithrombotic/anticoagulant therapy. A high risk of complications occurs in the presence of the genotype polymorphisms which are involved in the coagulation system, hemostasis function and in the metabolism of the therapy. The aim of the study was to investigate the influence of single-nucleotide polymorphisms (SNP) in HF patients with LVAD complications. We analyzed 21 SNPs in HF patients (n = 98) with/without complications, and healthy controls (n = 95). SNPs rs9934438; rs9923231 in VKORC1, rs5918 in ITGB3 and rs2070959 in UGT1A6 demonstrated significant association with HF patients’ complications (OR (95% CI): 3.96 (1.42–11.02), p = 0.0057), (OR (95% CI): 3.55 (1.28–9.86), p = 0.011), (OR (95% CI): 5.37 (1.79–16.16), p = 0.0056) and OR (95% CI): 4.40 (1.06–18.20), p = 0.044]. Genotype polymorphisms could help to predict complications at pre- and post-LVAD implantation period, which will reduce mortality rate. Our research showed that patients can receive treatment with warfarin and aspirin with a personalized dosage and LVAD complications can be predicted by reference to their genotype polymorphisms in VKORC1, ITGB3 and UGT1A6 genes.

Comparative effectiveness of generic nifedipine versus Adalat long-acting nifedipine for hypertension treatment: A multi-institutional cohort study

This retrospective multi‐institutional database analysis aimed to evaluate the blood‐pressure‐lowering efficacy and clinical outcomes of a generic versus brand‐name nifedipine for hypertension management. A total of 12 693 patients who were prescribed a generic or brand‐name nifedipine between January 1, 2011, and December 31, 2018, were identified from the Chang Gung Research Database of Chang Gung Memorial Hospitals, Taiwan. Among them, 2112 (21.4%) were prescribed generic nifedipine. After propensity score matching, both the generic and brand‐name groups consisted of 2102 patients. At a mean follow‐up of 3 years, the changes in office systolic (p for interaction = .791) and diastolic blood pressure (p for interaction = .689) did not differ significantly between the patients who received the generic and the brand‐name nifedipine. There was no significant difference between the two study groups regarding the composite of all‐cause mortality, acute myocardial infarction, stroke, coronary revascularization, or hospitalization for heart failure (hazard ratio 0.98, 95% confidence interval 0.85–1.13; p = .774). In conclusion, the generic nifedipine was comparable to its brand‐name counterpart regarding office blood pressure reduction and the composite cardiovascular outcome for the treatment of patients with hypertension.

ARNI in HFrEF—One-Centre Experience in the Era before the 2021 ESC HF Recommendations

Background: Sacubitril/valsartan, an angiotensin receptor–neprilysin inhibitor (ARNI), has demonstrated a survival benefit and reduces heart failure hospitalization in patients with heart failure with reduced left ventricular ejection fraction (HFrEF); however, our experience in this field is limited. This study aimed to summarize a real clinical practice of the use of ARNI in HFrEF patients hospitalized due to HFrEF in the era before the 2021 ESC HF recommendations, as well as assess their clinical outcome with regard to ARNI administration. Methods and Materials: Overall, 613 patients with HFrEF hospitalized in 2018–2020 were enrolled into a retrospective one-centre cross-sectional analysis. The study population was categorized into patients receiving (82/13.4%) and not-receiving (531/82.6%) ARNI. Clinical outcomes defined as rehospitalization, number of rehospitalizations, time to the first rehospitalization and death from any cause were analysed in the 1–2 year follow-up in the ARNI and non-ARNI groups, matched as to age and LVEF. Results: Clinical characteristics revealed the following differences between ARNI and non-ARNI groups: A higher percentage of cardiovascular implantable electronic devices (CIED) (p = 0.014) and defibrillators with cardiac resynchronization therapy (CRT-D) (p = 0.038), higher frequency of atrial fibrillation (p = 0.002) and history of stroke (p = 0.024) were in the ARNI group. The percentage of patients with HFrEF NYHA III/IV presented an increasing trend to be higher in the ARNI (64.1%) as compared to the non-ARNI group (51.5%, p = 0.154). Incidence of rehospitalization, number of rehospitalizations and time to the first rehospitalization were comparable between the groups. There were no differences between the numbers of deaths of any cause in the ARNI (28%) and non-ARNI (28%) groups. The independent negative predictor of death in the whole population of ARNI and non-ARNI groups was the coexistence of coronary artery disease (CAD) (beta= −0.924, HR 0.806, p = 0.011). Conclusions: Our current positive experience in ARNI therapy is limited to extremely severe patients with HFrEF. Regardless of the more advanced HF and HF comorbidities, the patients treated with ARNI presented similar mortality and rehospitalizations as the patients treated by standard therapy.

Investigating Genetic and Other Determinants of First-Onset Myocardial Infarction in Malaysia: Protocol for the Malaysian Acute Vascular Events Risk Study

BACKGROUND

Although the burden of premature myocardial infarction (MI) is high in Malaysia, direct evidence on the determinants of MI in this multi-ethnic population remains sparse.

OBJECTIVE

The Malaysian Acute Vascular Events Risk (MAVERIK) study is a retrospective case-control study established to investigate the genomic, lipid-related, and other determinants of acute MI in Malaysia. In this paper, we report the study protocol and early results.

METHODS

By June 2019, we had enrolled approximately 2500 patients with their first MI and 2500 controls without cardiovascular disease, who were frequency-matched by age, sex, and ethnicity, from 17 hospitals in Malaysia. For each participant, serum and whole blood have been collected and stored. Clinical, demographic, and behavioral information has been obtained using a 200-item questionnaire.

RESULTS

Tobacco consumption, a history of diabetes, hypertension, markers of visceral adiposity, indicators of lower socioeconomic status, and a family history of coronary disease were more prevalent in cases than in controls. Adjusted (age and sex) logistic regression models for traditional risk factors indicated that current smoking (odds ratio [OR] 4.11, 95% CI 3.56-4.75; P<.001), previous smoking (OR 1.34, 95% CI 1.12-1.60; P=.001), a history of high blood pressure (OR 2.13, 95% CI 1.86-2.44; P<.001), a history of diabetes mellitus (OR 2.72, 95% CI 2.34-3.17; P<.001), a family history of coronary heart disease (OR 1.28, 95% CI 1.07-1.55; P=.009), and obesity (BMI >30 kg/m²; OR 1.19, 95% CI 1.05-1.34; P=.009) were associated with MI in age- and sex-adjusted models.

CONCLUSIONS

The MAVERIK study can serve as a useful platform to investigate genetic and other risk factors for MI in an understudied Southeast Asian population. It should help to hasten the discovery of disease-causing pathways and inform regionally appropriate strategies that optimize public health action.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/31885.

Heart Team for Left Atrial Appendage Occlusion: A Patient-Tailored Approach

BACKGROUND AND PURPOSE

Left atrial appendage occlusion (LAAO) is an accepted therapeutic option for stroke prevention; however, the ideal technique and device have not yet been identified. In this study we evaluate the potential role of a heart team approach for patients contraindicated for oral anticoagulants and indicated for left atrial appendage closure, to minimize risk and optimize benefit in a patient-centered decision-making process.

METHODS

Forty patients were evaluated by the heart team for appendage occlusion. Variables considered were CHA2DS2VASc, HASBLED, documented blood transfusions, comorbidities, event forcing anticoagulant interruption, past medical history, anatomy of the left atrial appendage, and patient quality of life. Twenty patients had their appendage occluded percutaneously (65% male, mean age 72.3 ± 7.5, mean CHA2DS2VASc 4.2 ± 1.5, mean HASBLED 3.5 ± 1.1). The other twenty underwent thoracoscopic occlusion (65% male, mean age of 74.9 ± 8, mean CHA2DS2VASc 6.0 ± 1.5, HASBLED mean 5.4 ± 1.4). Percutaneous patients were on dual antiplatelet therapy for the first three months and aspirin thereafter, whereas the others received no anticoagulant/antiplatelet therapy from the day of surgery. Follow up included TEE, CT scan, and periodical clinical evaluation.

RESULTS

Mean duration of procedures and hospital stay were comparable. All patients had complete exclusion of the appendage; at a mean follow up of 33.1 ± 14.1 months, no neurological or hemorrhagic events were reported.

CONCLUSIONS

A heart team approach may improve the decision-making process for stroke and hemorrhage prevention, where LAAO is a therapeutic option. Percutaneous and thoracoscopic appendage occlusion seem to be comparably safe and effective. An epicardial LAAO could be advisable in patients for whom the risk of bleeding is estimated as being too high for post-procedural antiplatelet therapy.

Artificial intelligence's role in vascular surgery decision-making

Artificial intelligence (AI) is the next great advance informing medical science. Several disciplines, including vascular surgery, use AI-based decision-making tools to improve clinical performance. Although applied widely, AI functions best when confronted with voluminous, accurate data. Consistent, predictable analytic technique selection also challenges researchers. This article contextualizes AI analyses within evidence-based medicine, focusing on "big data" and health services research, as well as discussing opportunities to improve data collection and realize AI's promise.

Gastro-Cardiology: A Novel Perspective for the Gastrocardiac Syndrome

The gastrocardiac syndrome was coined originally at the beginning of the 19th century to describe an alleged gastric-cardiopathy with reflux heartburn mimicking cardiac chest pain. Today, a wider perspective of gastrocardiac syndrome has emerged. First, the cardiovascular risk factor chronic systemic inflammation may reflect gastroenterological inflammatory conditions, such as inflammatory bowel disease and gastrointestinal infections, in particular, chronic Helicobacter pylori infection. Furthermore, since contemporary treatment of cardiovascular disease commonly includes potent antithrombotic medications, the cardiovascular benefit in terms of a decrease in the incidence of recurrent ischemic events and death needs to be carefully balanced with an increased risk of gastrointestinal bleeding. Several strategies to target chronic gastrointestinal inflammation and to diagnose and treat Helicobacter pylori to reduce the risk of cardiovascular events and gastrointestinal bleeding are available but residual controversy remains and large-scale gastro-cardiology trials are needed to determine the optimal treatment approaches. In perspective, the centennial gastrocardiac syndrome is more relevant than ever in a contemporary gastroenterology and cardiology setting. A collaborative subspecialty, namely Gastro-cardiology, would introduce novel unique means to study, diagnose and treat gastrocardiac conditions with the aim to reduce the risk of cardiovascular and bleeding events to improve the prognosis for gastro-cardiology patients.

The process of sourcing and preparing EHR data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

Background

Health care data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes. However, the differences that exist in each individual’s health records, combined with the lack of health data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. Although these problems exist throughout health care, they are especially prevalent within maternal health and exacerbate the maternal morbidity and mortality crisis in the United States.

Objective

This study aims to demonstrate that patient records extracted from the electronic health records (EHRs) of a large tertiary health care system can be made actionable for the goal of effectively using ML to identify maternal cardiovascular risk before evidence of diagnosis or intervention within the patient’s record. Maternal patient records were extracted from the EHRs of a large tertiary health care system and made into patient-specific, complete data sets through a systematic method.

Methods

We outline the effort that was required to define the specifications of the computational systems, the data set, and access to relevant systems, while ensuring that data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for their use by a proprietary risk stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions.

Results

Patient records can be made actionable for the goal of effectively using ML, specifically to identify cardiovascular risk in pregnant patients.

Conclusions

Upon acquiring data, including their concatenation, anonymization, and normalization across multiple EHRs, the use of an ML-based tool can provide early identification of cardiovascular risk in pregnant patients.

Does Oxygen Content Play a Role in Spontaneous Closure of Perimembranous Ventricular Septal Defects?

(1) Background: the impact of a series of laboratory parameters (haemoglobin, haematocrit, foetal haemoglobin, peripheral oxygen saturation, iron, transferrin, ferritin, and albumin) on perimembranous ventricular septal defects spontaneous healing was tested. (2) Methods: one hundred and seven patients were enrolled in the study (57% males; mean age 2.1 ± 0.4 years) and were subsequently subdivided into two groups: self-healing (n = 36) and in need of intervention (n = 71). Self-healing subjects were defined on the basis of an absence of residual shunts at colorDoppler across the previous defect. (3) Results: no statistically significant differences were reported in the size of perimembranous ventricular septal defects between the two groups (p = ns). Conversely, prevalence of anaemia was significantly higher in those requiring intervention than in the self-healing group (p < 0.03), while haemoglobin, iron, ferritin, and albumin levels were lower (p < 0.001, p < 0.05, p < 0.02, p < 0.007, respectively). In multivariable linear regression analysis, only haemoglobin and albumin were found to be associated with spontaneous closure (p < 0.005 and p < 0.02, respectively). In multiple logistic regression analysis, haemoglobin independently increased the probability of self-healing of perimembranous ventricular septal defects (p = 0.03). All patients needing an interventional closure of perimembranous ventricular septal defects presented with haemoglobin <12.7 g/dL. (4) Conclusion: the self-resolution of perimembranous ventricular septal defects seems to rely on numerous factors, including oxygen content, which is likely to promote cell proliferation as well as tissue regeneration. Haemoglobin blood concentration seems to influence the natural history of perimembranous ventricular septal defects and improvement of anaemia by supplementation of iron intake might represent a simple and reliable method to promote self-healing.

Gastrointestinal Bleeding, but Not Other Gastrointestinal Symptoms, Is Associated With Worse Outcomes in COVID-19 Patients

Background: Patients with coronavirus disease 2019 (COVID-19) can present with gastrointestinal (GI) symptoms. However, the prevalence of GI symptoms and their association with outcomes remain controversial in COVID-19 patients. Methods: All COVID-19 patients consecutively admitted to the Wuhan Huoshenshan hospital from February 2020 to April 2020 were collected. Disease severity and outcomes were compared between COVID-19 patients with and without GI symptoms. Logistic regression analyses were performed to evaluate the association of GI symptoms with the composite endpoint and death in COVID-19 patients. A composite endpoint was defined as transfer to intensive care unit, requirement of mechanical ventilation, and death. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results: Overall, 2,552 COVID-19 patients were included. The prevalence of GI symptoms was 21.0% (537/2,552). Diarrhea (8.9%, 226/2,552) was the most common GI symptom. Patients with GI symptoms had significantly higher proportions of severe COVID-19 and worse outcomes than those without. Univariate logistic regression analyses demonstrated that GI symptoms were significantly associated with the composite endpoint (OR = 2.426, 95% CI = 1.608-3.661; P < 0.001) and death (OR = 2.137, 95% CI = 1.209-3.778; P = 0.009). After adjusting for age, sex, and severe/critical COVID-19, GI symptoms were still independently associated with the composite endpoint (OR = 2.029, 95% CI = 1.294-3.182; P = 0.002), but not death (OR = 1.726, 95% CI = 0.946-3.150; P = 0.075). According to the type of GI symptoms, GI bleeding was an independent predictor of the composite endpoint (OR = 8.416, 95% CI = 3.465-20.438, P < 0.001) and death (OR = 6.640, 95% CI = 2.567-17.179, P < 0.001), but not other GI symptoms (i.e., diarrhea, abdominal discomfort, nausea and/or vomiting, constipation, acid reflux and/or heartburn, or abdominal pain). Conclusion: GI symptoms are common in COVID-19 patients and may be associated with their worse outcomes. Notably, such a negative impact of GI symptoms on the outcomes should be attributed to GI bleeding.