Echocardiographic Indices in Patients with End-Stage Renal Disease and Their Association with Hemodialysis-to-Hemodiafiltration Transfer: A Prospective Observational Study.

Background and Objectives: The assessment of cardiac function in patients with end-stage renal disease (ESRD) is vital due to their high cardiovascular risk. However, contemporary echocardiographic indices and their association with hemodialysis-to-hemodiafiltration transfer are underreported in this population. Materials and Methods: This prospective cohort study enrolled 36 ESRD patients undergoing hemodialysis-to-hemodiafiltration transfer, with baseline and 3-month post-transfer comprehensive echocardiographic assessments. The key parameters included the global work index, global constructed work, global wasted work (GWW), global work efficiency (GWE), and global longitudinal strain (GLS), with secondary measures from conventional echocardiography. The baseline measures were compared to general population reference values and changes pre- to post-transfer were analyzed using the Mann-Whitney U test. Results: Patients exhibited significant deviations from reference ranges in GWW (179.0 vs. 53.0-122.2 mmHg%), GWE (90.0 vs. 53.0-122.2%), and GLS (-16.0 vs. -24.0-(-16.0)%). Post-transfer left ventricular myocardial work and longitudinal strain remained unchanged (p > 0.05), except for increased GWW (179.0, IQR 148.0-217.0 to 233.5, IQR 159.0-315.0 mmHg%, p = 0.037) and improved mid-inferior peak systolic longitudinal strain ((-17.0, IQR -19.0-(-11.0) to -18.7, IQR -20.0-(-18.0)%, p = 0.016). The enrolled patients also showed higher left atrial diameters, left ventricular volumes, and mass, with impaired systolic function in both ventricles compared to reference values. Conclusions: This study highlights baseline impairments in contemporary echocardiographic measures (GWW, GWE, GLS) in ESRD patients versus reference values, but found no association between hemodialysis-to-hemodiafiltration transfer and most myocardial work and strain parameters.

30-Day unplanned readmission rates, causes and outcomes of patients hospitalized for acute coronary syndrome based on the trial participation status.

BACKGROUND: This study aimed to investigate the association between index trial participation status and 30-day unplanned readmission rates, causes, and outcomes in acute coronary syndrome (ACS) patients. METHODS: The National Readmission Database was analysed for all index hospitalizations with a principal diagnosis of ACS between October 2015 to November 2019, stratified by index trial participation status (International Classification of Diseases - 10th edition code: Z00.6). The 30-day unplanned readmission rates, causes and outcomes were analysed, including the assessment of factors associated with readmission. Multivariable regression analyses were reported as adjusted odds ratios (aOR) with 95 % confidence intervals (95 % CI). All analyses were weighted and utilized hierarchical multi-level organization. RESULTS: A total of 2,066,328 cases with a principal diagnosis of ACS were included in the study, of which there were 4061 trial participants (0.2 %) and 189,240 (9.2 %) cases experienced unplanned 30-day readmission. Rates of unplanned 30-day readmission were similar between trial participants and non-participants (9.8 % vs. 9.2 %, p = 0.16). Consistently, after multivariable adjustment, there was no significant association between trial participation and unplanned 30-day readmissions (aOR 0.96, 95 % CI 0.86-1.07, p = 0.45). Compared with trial participants, the majority of readmissions in non-participants were related to cardiovascular conditions (55.2 % vs. 46.7 %, p = 0.005, respectively). There was no significant difference in all-cause mortality (5.5 % vs. 4.6 %, p = 0.368, respectively), but trial participants were more likely to develop major bleeding (3.5 % vs. 2.1 %, p = 0.044), ischemic stroke (4.0 % vs. 2.1 %, p = 0.008) and haemorrhagic stroke (2.0 % vs. 0.6 %, p < 0.001) at readmissions. CONCLUSION: Overall rates of unplanned 30-day readmissions after ACS are similar between trial participants and non-participants, but non-participation in trials was associated with a higher likelihood of cardiovascular readmission.

Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction.

BACKGROUND: Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis. METHODS: All adult discharges for STEMI in the National Inpatient Sample (October 2015 to December 2019) were included, excluding patients with prior myocardial infarction. Machine-learning clustering analysis was used to define clusters based on 21 clinical attributes of interest. Main outcomes of the study were cluster-based comparison of risk profile, in-hospital clinical outcomes and utilization of invasive management. Binomial hierarchical multivariable logistic regression with adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) was used to detect the between-cluster differences. RESULTS: Out of overall 470,960 STEMI cases, the machine-learning analysis revealed 4 different clusters with 205,640 (cluster 0: 'behavioural risk cluster'), 146,400 (cluster 1: 'least comorbidity cluster'), 45,100 (cluster 2: 'diabetes with end-organ damage cluster') and 73,820 (cluster 3: 'cardiometabolic cluster') cases. Attributes with the highest importance for clustering were hypertension and diabetes. After multivariable adjustment, patients from 'diabetes with end-organ damage cluster' exhibited the worst mortality, MACCE and ischemic stroke (p < 0.001 for all), as well as the lowest utilization of invasive management (p < 0.001 for all), in comparison to other clusters. Patients from 'behavioural risk cluster' exhibited the best in-hospital prognosis and the highest utilization of invasive management, compared to other clusters (p < 0.001 for all). CONCLUSIONS: Machine learning driven clustering of inpatients with STEMI reveals important population subgroups with distinct prevalence, risk profile, prognosis and management. Data driven approaches may identify high risk phenogroups and warrants further study.

Systematic Review of the Association of the Hospital Frailty Risk Score with Mortality in Patients with Cerebrovascular and Cardiovascular Disease.

BACKGROUND: There is limited systematic data on the association between the Hospital Frailty Risk Score (HFRS) and characteristics and mortality in patients with cerebrovascular and cardiovascular disease (CVD). This systematic review aimed to summarise the use of the HFRS in describing the prevalence of frailty in patients with CVD, the clinical characteristics of patients with CVD, and the association between frailty on the likelihood of mortality in patients with CVD. METHODS: A systematic literature search for observational studies using terms related to CVD, cerebrovascular disease, and the HFRS was conducted using 6 databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies were appraised using the Newcastle-Ottawa Scale (NOS). RESULTS: Seventeen observational studies were included, all rated 'good' quality according to the NOS. One study investigated 5 different CVD cohorts (atrial fibrillation (AF), heart failure (HF), hypotension, hypertension, and chronic ischemic heart disease), 1 study investigated 2 different CVD cohorts (AF and acute myocardial infarction (AMI)), 6 studies investigated HF, 3 studies investigated AMI, 4 studies investigated stroke, 1 study investigated AF, and 1 study investigated cardiac arrest. Increasing frailty risk category was associated with increased age, female sex, and non-white racial group across all CVD. Increasing frailty risk category is also associated with increased length of hospital stay, total costs, and increased odds of 30-day all-cause mortality across all CVD. CONCLUSIONS: The HFRS is an efficient and effective tool for stratifying frailty in patients with CVD and predicting adverse health outcomes.

Cardiovascular Risk in Patients with Hematological Malignancies: A Systematic Review and Meta-Analysis.

Patients with hematologic malignancies (HMs) are at risk of future cardiovascular (CV) events. We therefore conducted a systematic review and meta-analysis to quantify their risk of future CV events. We searched Medline and EMBASE databases from inception until January 31, 2023 for relevant articles using a combination of keywords and medical subject headings. Studies examining CV outcomes in patients with HM versus controls without HM were included. The outcomes of interest included acute myocardial infarction (AMI), heart failure (HF), and stroke. The outcomes were expressed as hazard ratios (HRs) and their 95% confidence intervals (CIs). This study is registered with PROSPERO at CRD42022307814. A total of 15 studies involving 1,960,144 cases (178,602 patients with HM and 1,781,212 controls) were included in the quantitative analysis. A total of 10 studies examined the risk of AMI, 5 examined HF, and 11 examined stroke. Compared with the control group, the HRs for HM for AMI, HF, and stroke were 1.65 (95% CI 1.29 to 2.09, p <0.001), 4.82 (95% CI 3.72 to 6.25, p <0.001), and 1.60 (95% CI 1.30 to 1.97, p <0.001), respectively. The sensitivity analysis of stroke risk based on lymphoma type showed an increased risk of stroke in patients with non-Hodgkin lymphoma compared with controls (HR 1.31, 95% CI 1.04 to 1.64, p = 0.03) but no significant difference for Hodgkin lymphoma (HR 1.67, 95% CI 0.86 to 3.23, p = 0.08). Patients with HM are at increased risk of future AMI, HF, and stroke, and these findings suggest that CV care of patients with HM should be considered as a growing priority.

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

Aims: Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting. Methods and results: Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69-0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk. Conclusion: Machine learning-derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted. Registration: Clinicaltrial.gov identifier is NCT02188355.

Prediction of clinical outcomes after percutaneous coronary intervention: Machine-learning analysis of the National Inpatient Sample.

BACKGROUND: This study aimed to develop a multiclass machine-learning (ML) model to predict all-cause mortality, ischemic and hemorrhagic events in unselected hospitalized patients undergoing percutaneous coronary intervention (PCI). METHODS: This retrospective study included 1,815,595 unselected weighted hospitalizations undergoing PCI from the National Inpatient Sample (2016-2019). Five most common ML algorithms (logistic regression, support vector machine (SVM), naive Bayes, random forest (RF), and extreme gradient boosting (XGBoost)) were trained and tested with 101 input features. The study endpoints were different combinations of all-cause mortality, ischemic cerebrovascular events (CVE) and major bleeding. An area under the curve (AUC) with 95% confidence interval (95% CI) was selected as a performance metric. RESULTS: The study population was split to a training cohort of 1,186,880 PCI discharges, validation cohort (for calibration) of 296,725 hospitalizations and a test cohort of 331,990 PCI discharges. A total of 98,180 (5.4%) hospital entries included study outcomes. Logistic regression, SVM, naive Bayes, and RF model demonstrated AUCs of 0.83 (95% CI 0.82-0.84), 0.84 (95% CI 0.83-0.86), 0.81 (95% CI 0.80-0.82), and 0.83 (95% CI 0.81-0.84), retrospectively. The XGBoost classifier performed the best with an AUC of 0.86 (95% CI 0.85-0.87) with excellent calibration. We then built a web-based application that provides predictions based on the XGBoost model. CONCLUSION: We derived the multi-task XGBoost classifier based on 101 features to predict different combinations of all-cause death, ischemic CVE and major bleeding. Such models may be useful in benchmarking and risk prediction using routinely collected administrative data.

Impact of Frailty on Emergency Department Encounters for Cardiovascular Disease: A Retrospective Cohort Study.

Data are limited on whether the causes of emergency department (ED) encounters for cardiovascular diseases (CVDs) and associated clinical outcomes vary by frailty status. Using the United States Nationwide ED Sample, selected CVD encounters (acute myocardial infarction [AMI], ischemic stroke, atrial fibrillation [AF], heart failure [HF], pulmonary embolism, cardiac arrest, and hemorrhagic stroke) were stratified by hospital frailty risk score (HFRS). Logistic regression was used to determine the adjusted odds ratios (aORs) and 95% confidence intervals (CIs) of ED mortality among the different frailty groups. A total of 8,577,028 selected CVD ED encounters were included. A total of 5,120,843 (59.7%) had a low HFRS (<5), 3,041,699 (35.5%) had an intermediate HFRS (5 to 15), and 414,485 (4.8%) had a high HFRS (>15). Ischemic stroke was the most common reason for the encounter in the high HFRS group (66.9%), followed by hemorrhagic stroke (11.7%) and AMI (7.2%). For the low HFRS group, AF was the most common reason for the encounter (30.2%), followed by AMI (23.6%) and HF (16.8%). Compared with the low-risk group, high-risk patients had a decreased ED mortality and an increased overall mortality across most CVD encounters (p <0.001). The strongest association with overall mortality was observed among patients with a high HFRS admitted for AF (aOR 27.14, 95% CI 25.03 to 29.43) and HF (aOR 13.71, 95% CI 12.95 to 14.51) compared with their low-risk counterparts. In conclusion, patients presenting to the ED with acute CVD have a significant frailty burden, with different patterns of CVD according to frailty status. Frailty is associated with an increased all-cause mortality in patients for most CVD encounters.

Trends In ST-elevation Myocardial Infarction Hospitalisation Among Young Adults: A Binational Analysis.

BACKGROUND: ST-segment myocardial infarction (STEMI) is typically associated with increased age, but there is an important group of patients that suffer STEMI under the age of fifty, that are not well characterized in studies. METHODS & RESULTS: We analysed results from Myocardial Ischemia National Audit Project (MINAP) from the United Kingdom (UK) between 2010-2017 and the National Inpatient Sample (NIS) from the United States (US) between 2010-2018. After exclusion criteria, there were 32,719 STEMI patients aged ≤50 from MINAP, and 238,952 patients' ≤50 from the NIS. We analysed temporal trends in demographics, management, and mortality. The proportion of females increased, 15.6% (2010-2012) to 17.6% (2016-2017) (UK) and 22.8% (2010-2012) to 23.1% (2016-2018) (US). The proportion of white patients decreased, from 86.7% (2010) to 79.1% (2017) (UK) and 72.1% (2010) to 67.1% (2017) (US). Invasive coronary angiography (ICA) rates increased in UK (2010-2012: 89.0%, 2016-2017: 94.3%), while decreased in US (2010-2012: 88.9%, 2016-2018: 86.2% (US). After adjusting for baseline characteristics and management strategies, there was no difference in all-cause mortality in the UK in 2016-2017 compared to 2010-2012 (OR:1.21, 95% CI:0.60-2.40), but there was a decrease in the US in 2016-2018 compared to 2010-2012 (OR: 0.84, 95% CI: 0.79-0.90). CONCLUSION: The demographics of young STEMI patients have temporally changed in the UK and US, with increased proportions of females and ethnic minorities. There was a significant increase in the frequency of diabetes mellitus over the respective time periods in both countries.

Association of Different Risk Scores and 30-Day Mortality in Kidney Transplant Recipients with COVID-19.

Background and Objectives: Clinical risk scores were poorly examined in kidney transplant recipients (KTR) with COVID-19. Materials and Methods: This observational study compared the association and discrimination of clinical risk scores (MEWS, qCSI, VACO, PSI/PORT, CCI, MuLBSTA, ISTH-DIC, COVID-GRAM and 4C) with 30-day mortality in 65 hospitalized KTRs with COVID-19. Cox regression was used to derive hazard ratios (HR) and 95% confidence intervals (95% CI), and discrimination was assessed by Harrell's C. Results: A significant association with 30-day mortality was demonstrated for MEWS (HR 1.65 95% CI 1.21-2.25, p = 0.002); qCSI (HR 1.32 95% CI 1.15-1.52, p < 0.001); PSI/PORT (HR 1.04 95% CI 1.02-1.07, p = 0.001); CCI (HR 1.79 95% CI 1.13-2.83, p = 0.013); MuLBSTA (HR 1.31 95% CI 1.05-1.64, p = 0.017); COVID-GRAM (HR 1.03 95% CI 1.01-1.06, p = 0.004); and 4C (HR 1.79 95% CI 1.40-2.31, p < 0.001). After multivariable adjustment, significant association persisted for qCSI (HR 1.33 95% CI 1.11-1.59, p = 0.002); PSI/PORT (HR 1.04 95% CI 1.01-1.07, p = 0.012); MuLBSTA (HR 1.36 95% CI 1.01-1.85, p = 0.046); and 4C Mortality Score (HR 1.93 95% CI 1.45-2.57, p < 0.001) risk scores. The best discrimination was observed with the 4C score (Harrell's C = 0.914). Conclusions: Risk scores such as qCSI, PSI/PORT and 4C showed the best association with 30-day mortality amongst KTRs with COVID-19.

Neurologic Biomarkers, Neuroimaging, and Cognitive Function in Persistent Atrial Fibrillation: A Cross-Sectional Study.

The aim of this study was to evaluate the specific neurologic biomarkers, neuroimaging findings, and cognitive function in patients with persistent atrial fibrillation (AF) undergoing electrical cardioversion, compared to control subjects. This cross-sectional study included 25 patients with persistent AF undergoing electrical cardioversion and 16 age- and sex-matched control subjects. Plasma levels of glial fibrillary acidic protein (GFAP), neurofilament light protein (NFL), and ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), as well as parameters of neuroimaging and cognitive function, were compared between the groups. Neuroimaging was performed using the standard magnetic resonance imaging (MRI) protocol. Cognitive function was assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) Cognitive Function Index. Further analysis of neurologic biomarkers was performed based on the subsequent electrical cardioversion. There was no significant difference in GFAP (median of 24.7 vs. 28.7 pg/mL, p = 0.347), UCH-L1 (median of 112.8 vs. 117.7 pg/mL, p = 0.885), and NFL (median of 14.2 vs. 15.4 pg/mL, p = 0.886) levels between AF patients and control subjects. Similarly, neuroimaging showed no between-group difference in large cortical and non-cortical lesions (n = 2, 8.0% vs. n = 0, 0.0%, p = 0.246), small non-cortical lesions (n = 5, 20.0% vs. n = 5, 31.3%, p = 0.413), white matter hyperintensity (n = 23, 92.0% vs. n = 14, 87.5%, p = 0.636), and thromboembolic lesions (n = 0, 0.0% vs. n = 1, 6.3%, p = 0.206). Cognitive assessment did not show any between-group difference in the PROMIS index (52.2 ± 9.6 vs. 51.2 ± 6.2, p = 0.706). Finally, there were no significant dynamics in neurologic biomarkers following electrical cardioversion (p > 0.05). This hypothesis-generating study did not find a significant difference in neurologic biomarkers, neuroimaging findings, or cognitive function between patients with persistent AF and controls. The restoration of sinus rhythm was not significantly associated with a change in neurologic biomarkers. Further powered longitudinal studies are needed to re-assess these findings in an AF population.

Association of Diabetes Mellitus and Its Types with In-Hospital Management and Outcomes of Patients with Acute Myocardial Infarction.

BACKGROUND: Diabetes mellitus (DM) is an important risk factor for adverse outcomes following acute myocardial infarction (AMI), but large-scale studies investigating the differential impact of Type 1 DM (T1DM) and Type 2 DM (T2DM) on AMI outcomes are lacking. METHODS: All adult discharges for AMI in the National Inpatient Sample (October 2015 to December 2018) were included and stratified into T1DM, T2DM and non-DM (NDM) groups. Outcomes of interests were all-cause mortality, major adverse cardiovascular and cerebrovascular events (MACCE), major bleeding and acute ischemic stroke, as well as invasive management. Binomial hierarchical multilevel multivariable logistic regression with adjusted odds ratios (aOR) and 95 % confidence intervals (95 % CI) was used to investigate the association between DM and its subtypes with the AMI outcomes. RESULTS: Out of 2,587,615 patients, there were 29,250 (1.1 %) T1DM and 1,032,925 (39.9 %) T2DM patients. After multivariable adjustment, patients with T1DM had increased odds of MACCE (aOR 1.20, 95 % CI 1.09-1.31), all-cause mortality (aOR 1.20, 95 % CI 1.08-1.33) and major bleeding (aOR 1.28, 95 % CI 1.13-1.44), whilst T2DM patients had increased odds of MACCE (aOR 1.03, 95 % CI 1.01-1.05) and ischemic stroke (aOR 1.09, 95 % CI 1.05-1.13), compared to NDM patients. The adjusted odds of receiving percutaneous coronary intervention were lower in both T1DM and T2DM patients (aOR 0.70, 95 % CI 0.66-0.75 and aOR 0.95, 95 % CI 0.94-0.96, respectively), but T2DM patients showed higher utilization of composite percutaneous and surgical revascularization (aOR 1.03, 95 % CI 1.03-1.04) compared to NDM patients. CONCLUSIONS: DM patients presenting with AMI have worse in-hospital clinical outcomes compared to NDM patients. There are important DM type-related differences with T1DM patients having overall worse outcomes and receiving less overall revascularization.

Association of Frailty Status on the Causes and Outcomes of Patients Admitted With Cardiovascular Disease.

Data are limited about the contemporary association between frailty and the causes and outcomes of patients admitted with cardiovascular diseases (CVD). Using the US National Inpatient Sample, CVD admissions of interest (acute myocardial infarction, ischemic stroke, atrial fibrillation (AF), heart failure, pulmonary embolism, cardiac arrest, and hemorrhagic stroke) were stratified by Hospital Frailty Risk Score (HFRS). Logistic regression was used to determine adjusted odds ratios (aORs) and 95% confidence intervals (CIs) of in-hospital mortality among different groups with frailty. The study included 9,317,398 hospitalizations. Of these, 5,573,033 (59.8%) had a low HFRS (<5); 3,422,700 (36.7%) had an intermediate HFRS (5 to 15); and 321,665 (3.5%) had a high HFRS (>15). Ischemic stroke was the most common admission for the groups with high risk (75.4%), whereas acute myocardial infarction was the most common admission for the group with low risk (36.9%). Compared with the group with low risk, patients with high risk had increased mortality across the most CVD admissions, except in patients admitted for cardiac arrest and hemorrhagic stroke (p <0.001). The strongest association with all-cause mortality was shown among patients with high risk admitted for AF (aOR 6.75, 95% CI 6.51 to 7.00, and aOR 17.69, 95% CI 16.08 to 19.45) compared with their counterparts with low risk. In conclusion, patients with CVD admissions have varying frailty risk according to cardiovascular cause of admission, with ischemic stroke being the most common among groups with frailty and high risk. Increased frailty is associated with all-cause mortality in patients with most CVD admissions, except for cardiac arrest and hemorrhagic stroke, with the strongest association seen in patients admitted with AF.

Association between cancer, CHA2DS2VASc risk, and in-hospital ischaemic stroke in patients hospitalized for atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is commonly encountered in cancer patients. We investigated the CHA2DS2VASc score, and its association with in-hospital ischaemic stroke in patients with cancer who were hospitalized for AF. METHODS AND RESULTS: Using the United States National Inpatient Sample, all hospitalizations with principal diagnosis of AF between October 2015 and December 2018 were stratified by cancer diagnosis, type, and CHA2DS2VASc risk categories (low risk, low-moderate risk, moderate-high risk). In-hospital ischaemic stroke and its association with the CHA2DS2VASc risk score was assessed across the groups using hierarchical multivariable logistic regression with adjusted odds ratios (aOR) and 95% confidence intervals (95% CI). Discrimination of CHA2DS2VASc score for in-hospital ischaemic stroke was evaluated with Receiver Operating Characteristic and Area Under the Curve (AUC). Among 1 341 870 included hospitalizations, 71 965 (5.4%) had comorbid cancer. Cancer patients had a higher proportion of moderate-high CHA2DS2VASc risk compared with their non-cancer counterparts (86.5% vs. 82.3%, P < 0.001). Compared with their low CHA2DS2VASc risk counterparts, cancer patients in low-moderate and moderate-high risk scores had similar odds of developing stroke (aOR 1.28 95% CI 0.22-7.63 and aOR 1.78 95% CI 0.41-7.66, respectively). The CHA2DS2VASc risk score had poor discrimination for ischaemic stroke in the cancer group (AUC 0.538 95% CI 0.477-0.598). CONCLUSION: Cancer patients with AF have high CHA2DS2VASc risk. Discrimination of CHA2DS2VASc for ischaemic stroke is lower in cancer than non-cancer patients, and CHA2DS2VASc may not be adequate in determining ischaemic risk in cancer population.

Invasive Management and In-Hospital Outcomes of Myocardial Infarction Patients in United States Safety-Net Hospitals.

AIM: Safety-net hospitals (SNHs) look after a higher proportion of uninsured patients and are often located in deprived areas. This study aimed to determine whether there are differences in the clinical characteristics, treatments and outcomes of patients presenting with acute myocardial infarction (AMI) in SNHs versus non-SNHs (N-SNHs). METHODS: All hospitalizations with a principal diagnosis of AMI in the United States' National Inpatient Sample between 2016 and 2019 were stratified by safety-net hospital status. Multivariable logistic regression with adjusted odds ratios (aOR) and 95 % confidence intervals (95 % CI) was conducted to investigate invasive management and clinical outcomes. RESULTS: A total of 2,544,009 weighted discharge records were analyzed, including 601,719 records from SNHs (23.7 %). Compared with N-SNHs, SNH AMI patients were younger (median 66 years vs. 67 years, p < 0.001), and had a higher proportion in the lowest quartile of median household income (37.3 % vs. 28.5 %, p < 0.001). Patients from SNHs were less likely to receive coronary angiography (aOR 0.92, 95 % CI 0.91-0.93, p < 0.001), percutaneous coronary intervention (aOR 0.94, 95 % CI 0.93-0.95, p < 0.001), and coronary artery bypass grafting (aOR 0.93, 95 % CI 0.92-0.94, p < 0.001). In addition, they had increased all-cause mortality (aOR 1.11, 95 % CI 1.09-1.12, p < 0.001), major adverse cardiovascular/cerebrovascular events (composite of mortality, stroke and reinfarction) (aOR 1.11, 95 % CI 1.09-1.12, p < 0.001), and stroke (aOR 1.11, 95 % CI 1.08-1.14, p < 0.001), while there was no difference in major bleeding (aOR 1.02, 95 % CI 1.00-1.04, p = 0.107). CONCLUSION: Among AMI patients, treatment in SNHs was associated with lower utilization of coronary angiography and revascularization and worse clinical outcomes.

Prevalence, characteristics and mortality of cancer patients undergoing pericardiocentesis in the United States between 2004 and 2017.

BACKGROUND: Pericardiocentesis is undertaken in patients with cancer for diagnostic and therapeutic purposes. However, there are limited data on the frequency, characteristics and mortality of patients with different cancers undergoing pericardiocentesis. METHODS: All hospitalisations of adult cancer patients (≥18 years) in the US National Inpatient Sample between January 2004 and December 2017 were included. The cohort was stratified by discharge code of pericardiocentesis and cancer, using the International Classification of Diseases. The prevalence of pericardiocentesis, patient characteristics, cancer types and in-hospital all-cause mortality were analysed between cancer patients undergoing pericardiocentesis versus not. RESULTS: A total of 19,773,597 weighted cancer discharges were analysed, out of which 18,847 (0.1%) underwent pericardiocentesis. The most common cancer types amongst the patients receiving pericardiocentesis were lung (51.3%), haematological (15.9%), breast (5.4%), mediastinum/heart (3.2%), gastroesophageal (2.2%) and female genital cancer (1.8%), whilst 'other' cancer types were present in 20.2% patients. Patients undergoing pericardiocentesis had significantly higher mortality (15.6% vs. 4.2%, p < 0.001) compared to their counterparts. The presence of metastatic disease (aOR 2.67 95% CI 1.79-3.97), weight loss (aOR 1.48 95% CI 1.33-1.65) and coagulopathy (aOR 3.22 95% CI 1.63-6.37) were each independently associated with higher mortality in patients who underwent pericardiocentesis. CONCLUSION: Pericardiocentesis is an infrequent procedure in cancer patients and is most commonly performed in patients with lung, haematological and breast cancer. Cancer patients undergoing pericardiocentesis have increased mortality, irrespective of the underlying cancer type.

Management and outcomes of patients admitted with type 2 myocardial infarction with and without standard modifiable risk factors.

BACKGROUND: Whilst it is known patients without standard modifiable cardiovascular risk factors (SMuRF; hypertension, diabetes, hypercholesterolaemia, smoking) have worse outcomes in Type 1 acute myocardial infarction (AMI), the relationship between type 2 AMI (T2AMI) and outcomes in patients with and without SMuRF is unknown. This study aimed to determine the prevalence, characteristics and clinical outcomes of patients hospitalised with T2AMI based on the presence of SMuRF. METHODS: Using the National Inpatient Sample, all hospitalizations with a primary discharge diagnosis of T2AMI were stratified according to SMuRF status (SMuRF and SMURF-less). Primary outcome was all-cause mortality while secondary outcomes were major adverse cardiovascular and cerebrovascular events (MACCE), major bleeding and ischemic stroke. Multivariable logistic regression was used to determine adjusted odds ratios (aOR) with 95% confidence intervals (95% CI). RESULTS: Among 17,595 included hospitalizations, 1345 (7.6%) were SMuRF-less and 16,250 (92.4%) were SMuRF. On adjusted analysis, SMuRF-less patients had increased odds of all-cause mortality (aOR 2.43, 95% CI 1.83 to 3.23), MACCE (aOR 2.32, 95% CI 1.79 to 2.90) and ischaemic stroke (aOR 2.57, 95% CI 1.56 to 4.24) compared to their SMuRF counterparts. Secondary diagnoses among both cohorts were similar, with respiratory disorders most prevalent followed by cardiovascular and renal disorders. CONCLUSIONS: T2AMI in the absence of SMuRF was associated with worse in-hospital outcomes compared to SMuRF-less patients. There was no SMuRF-based difference in the secondary diagnoses with the most common being respiratory, cardiovascular, and renal disorders. Further studies are warranted to improve overall care and outcomes of SMuRF-less patients.

The association between cancer diagnosis, care, and outcomes in 1 million patients hospitalized for acute pulmonary embolism.

OBJECTIVES: To evaluate the clinical care provided to cancer patients hospitalized for acute pulmonary embolism (PE), as well as the association between type of cancer, in-hospital care, and clinical outcomes. METHODS: This study examined the in-hospital care (systemic thrombolysis, catheter-directed thrombolysis, and surgical thrombectomy/embolectomy) and clinical outcomes (mortality, major bleeding, and hemorrhagic stroke) among adults hospitalized due to acute PE between October 2015 to December 2018 using the National Inpatient Sample (NIS). Multivariable logistic regression analysis was used to determine adjusted odds ratios (aOR) with 95% confidence interval (95% CI). RESULTS: Of 1,090,130 hospital records included in the analysis, 216,825 (19.9%) had current cancer diagnoses, including lung (4.7%), hematological (2.5%), colorectal (1.6%), breast (1.3%), prostate (0.8%), and 'other' cancer (9.0%). Cancer patients had lower adjusted odds of receiving systemic thrombolysis, catheter-directed therapy, and surgical thrombectomy/embolectomy compared with their non-cancer counterparts (P < 0.001), except for systemic thrombolysis (aOR 0.96, 95% CI 0.85-1.09, P = 0.553) and catheter-directed therapy (aOR 0.82, 95% CI 0.67-1.00, P = 0.053) for prostate cancer. Cancer patients had greater odds of mortality (P < 0.05). Lung cancer patients had the highest odds of mortality (aOR 2.68, 95% CI 2.61-2.76, P < 0.001) and hemorrhagic stroke (aOR 1.75, 95% CI 1.61-1.90, P < 0.001), while colorectal cancer patients had the greatest odds of bleeding (aOR 2.04, 95% CI 1.94-2.15, P < 0.001). CONCLUSION: Among those hospitalized for PE, cancer diagnoses were associated with lower odds of invasive management and poorer in-hospital outcomes, with metastatic status being an especially important determinant. Appropriateness of care could not be assessed in this study.

Invasive Management and In-Hospital Outcomes of Myocardial Infarction Patients in Rural Versus Urban Hospitals in the United States.

OBJECTIVES: The variation in the management and outcome of acute myocardial infarction (AMI) between rural and urban settings has been previously recognized, but there has previously been no nationwide data reported that is inclusive of the whole adult population. METHODS: All discharge records between 2004 and 2018 with AMI diagnosis were extracted from the National Inpatient Sample (NIS) database and stratified by hospital location. The primary outcome was in-hospital mortality, and secondary outcomes included (a) major adverse cardiovascular and cerebrovascular events (MACCE), (b) major bleeding, (c) acute ischemic stroke, the utilization of invasive management in the form of (d) coronary angiography (CA), and (e) percutaneous coronary intervention (PCI). The adjusted odds ratios (aOR) and 95 % confidence interval (95 % CI) were determined using multivariable logistic regression. RESULTS: 9,728,878 records with AMI were identified, of which 1,011,637 (10.4 %) discharges were from rural hospitals. Rural patients were older (median of 71 years vs. 67 years, p < 0.001) and had lower prevalence of the highest risk presentations of AMI than their urban counterparts. After multivariable adjustment, patients from rural hospitals had increased aOR of all-cause mortality (aOR 1.15 95 % CI 1.13-1.16) and MACCE (aOR 1.04 95 % CI 1.04-1.05), as well as the decreased aOR of coronary angiography (aOR 0.29, 95 % CI 0.29-0.29, p < 0.001) and PCI (aOR 0.40, 95 % CI 0.39-0.40, p < 0.001), compared to their urban counterparts. CONCLUSION: Between 2004 and 2018, the risk of in-hospital mortality and MACCE in AMI patients was significantly higher in rural hospitals, with considerably lower utilization of invasive angiography and revascularization.

Outcomes and Discriminatory Accuracy of the CHA2DS2VASc Score in Atrial Fibrillation and Cancer.

Background: Atrial fibrillation (AF) is highly prevalent among cancer patients. The role of traditional risk stratification scores in the context of different cancer types in these patients remains unknown. Objectives: The purpose of this study was to determine the discriminative accuracy of the CHA2DS2VASc score for ischemic stroke using receiver operating characteristic and area under the curve. Methods: The National Readmission Database (2015-2019) was used to identify all AF patients stratified by the cancer diagnosis, type, and CHA2DS2VASc category (low; moderate; high risk). Outcomes at 30-day readmission were compared between cancer and noncancer groups using hierarchical multivariable logistic regression to calculate adjusted odds ratios (aORs) and 95% CIs. Results: A total of 6,996,088 AF patients were identified at index admission. Of these, 4,242,630 (642,237 cancer, 3,600,393 noncancer) were readmitted at 30 days. Cancer patients (92.1%) had a higher proportion of high CHA2DS2VASc scores compared with their noncancer counterparts (89.8%, P < 0.001). The 30-day readmission rate and incidence of major bleeding in cancer patients were significantly higher compared with their corresponding noncancer group across all CHA2DS2VASc categories. Among the different cancer types, hematological and lung cancer had a high propensity for major bleeding. The odds of ischemic stroke were lower in the cancer group across high (1.9% vs 2.4%; aOR: 0.78; 95% CI: 0.76-0.79; P < 0.0001), moderate (0.8% vs 1.3%; aOR: 0.57; 95% CI: 0.50-0.64; P < 0.0001), and low (0.4% vs 0.9%; aOR: 0.46; 95% CI: 0.34-0.62; P < 0.0001) risk category relative to the noncancer group irrespective of type of cancer. CHA2DS2VASc category had a statistically significant discriminatory accuracy for ischemic stroke in both cancer and noncancer patients. Conclusions: Cancer patients with AF are at a higher risk of readmission and major bleeding. The risk of ischemic stroke during readmission appears to be lower than noncancer patients. These findings may have implications for anticoagulant therapy in cancer patients.