Background Acute myocardial infarction (AMI) infrequently occurs after acute stroke. The Heart‐brain team approach has a potential to appropriately manage this poststroke cardiovascular complication. However, clinical outcomes of AMI complicating acute stroke (AMI‐CAS) with the heart‐brain team approach have not been characterized. The current study investigated cardiovascular outcomes in patients with AMI‐CAS managed by a heart‐brain team. Methods and Results We retrospectively analyzed 2390 patients with AMI at our institute (January 1, 2007–September 30, 2020). AMI‐CAS was defined as the occurrence of AMI within 14 days after acute stroke. Major adverse cerebral/cardiovascular events (cardiac‐cause death, nonfatal myocardial infarction, and nonfatal stroke) and major bleeding events were compared in subjects with AMI‐CAS and those without acute stroke. AMI‐CAS was identified in 1.6% of the subjects. Most AMI‐CASs (37/39=94.9%) presented ischemic stroke. Median duration of AMI from the onset of acute stroke was 2 days. Patients with AMI‐CAS less frequently received primary percutaneous coronary intervention (43.6% versus 84.7%; P <0.001) and dual‐antiplatelet therapy (38.5% versus 85.7%; P <0.001), and 33.3% of them did not receive any antithrombotic agents (versus 1.3%; P <0.001). During the observational period (median, 2.4 years [interquartile range, 1.1–4.4 years]), patients with AMI‐CAS exhibited a greater likelihood of experiencing major adverse cerebral/cardiovascular events (hazard ratio [HR], 3.47 [95% CI, 1.99–6.05]; P <0.001) and major bleeding events (HR, 3.30 [95% CI, 1.34–8.10]; P =0.009). These relationships still existed even after adjusting for clinical characteristics and medication use (major adverse cerebral/cardiovascular event: HR, 1.87 [95% CI, 1.02–3.42]; P =0.04; major bleeding: HR, 2.67 [95% CI, 1.03–6.93]; P =0.04). Conclusions Under the heart‐brain team approach, AMI‐CAS was still a challenging disease, reflected by less adoption of primary percutaneous coronary intervention and antithrombotic therapies, with substantially elevated cardiovascular and major bleeding risks. Our findings underscore the need for a further refined approach to mitigate their ischemic/bleeding risks.
Background: Quality indicators (QIs) are an accepted tool for measuring a hospital’s performance in routine care. We examined national trends in adherence to the QIs developed by the Close The Gap-Stroke program by combining data from the health insurance claims database and electronic medical records, and the association between adherence to these QIs and early outcomes in patients with acute ischemic stroke in Japan. Methods: In the present study, patients with acute ischemic stroke who received acute reperfusion therapy in 351 Close The Gap-Stroke-participating hospitals were analyzed retrospectively. The primary outcomes were changes in trends for adherence to the defined QIs by difference-in-difference analysis and the effects of adherence to distinct QIs on in-hospital outcomes at the individual level. A mixed logistic regression model was adjusted for patient and hospital characteristics (eg, age, sex, number of beds) and hospital units as random effects. Results: Between 2013 and 2017, 21 651 patients (median age, 77 years; 43.0% female) were assessed. Of the 25 defined measures, marked and sustainable improvement in the adherence rates was observed for door-to-needle time, door-to-puncture time, proper use of endovascular thrombectomy, and successful revascularization. The in-hospital mortality rate was 11.6%. Adherence to 14 QIs lowered the odds of in-hospital mortality (odds ratio [95% CI], door-to-needle <60 min, 0.80 [0.69–0.93], door-to-puncture <90 min, 0.80 [0.67–0.96], successful revascularization, 0.40 [0.34–0.48]), and adherence to 11 QIs increased the odds of functional independence (modified Rankin Scale score 0–2) at discharge. Conclusions: We demonstrated national marked and sustainable improvement in adherence to door-to-needle time, door-to-puncture time, and successful reperfusion from 2013 to 2017 in Japan in patients with acute ischemic stroke. Adhering to the key QIs substantially affected in-hospital outcomes, underlining the importance of monitoring the quality of care using evidence-based QIs and the nationwide Close The Gap-Stroke program.
BACKGROUND: Intravascular imaging has shown better response of coronary atheroma to statin-mediated lowering of low-density lipoprotein cholesterol in women. However, its detailed mechanism remains to be determined yet. Modifiability of coronary atheroma under lipid-lowering therapies is partly driven by lipidic plaque component. Given a smaller plaque volume in women, lipidic plaque features including their density may differ between sex. Therefore, the current study sought to characterize sex-related differences in the density of lipidic plaque. METHODS: We analyzed 1429 coronary lesions (culprit/nonculprit lesions=825/604) in 758 coronary artery disease patients (men/women=608/150) from the REASSURE-NIRS multicenter registry (Revelation of Pathophysiological Phenotypes of Vulnerable Lipid-Rich Plaque on Near-Infrared Spectroscopy). Total atheroma volume at 4-mm segment, maximum 4-mm-lipid-core burden index, and lipid plaque density index (=maximum 4-mm-lipid-core burden index/total atheroma volume at 4-mm segment) on near-infrared spectroscopy/intravascular ultrasound imaging at culprit and nonculprit lesions were compared in men and women. RESULTS: Statin and high-intensity statin were used in 72.4 ( P =0.81) and 22.9% ( P =0.32) of study subjects, respectively. Women exhibited a smaller adjusted total atheroma volume at 4-mm segment (culprit lesions: 50.3±0.4 versus 54.2±0.3mm 3 , P <0.001, nonculprit lesions: 31.5±3.0 versus 44.4±2.1mm 3 , P <0.001), whereas their adjusted maximum 4-mm-lipid-core burden index did not differ between sex (culprit lesions: 544.7±29.9 versus 501.7±19.1, P =0.11, nonculprit lesions: 288.8±26.7 versus 272.7±18.9, P =0.51). Furthermore, a greater adjusted lipid plaque density index was observed in women (culprit lesions: 18.2±0.9 versus 9.8±0.6, P <0.001, nonculprit lesions: 23.0±2.0 versus 7.8±1.4, P <0.001). These adjustments of total atheroma volume at 4-mm segment, maximum 4-mm-lipid-core burden index, and lipid plaque density index included age, body mass index, hypertension, dyslipidemia, diabetes, smoking, a history of myocardial infarction and chronic kidney disease, low-density lipoprotein cholesterol level, statin and ezetimibe use, vessel volume, and hospital unit. The aforementioned plaque features consistently existed in both acute coronary syndrome and stable coronary artery disease subjects. CONCLUSIONS: Women harbored greater condensed lipidic plaque features, accompanied by smaller atheroma volume. These observations indicate potentially better modifiable disease in women, which underscores the need to intensify their lipid-lowering therapies for further improving their outcomes. REGISTRATION: URL: https://www.clinicaltrials.gov/ ; Unique identifier: NCT04864171
Background: Despite advances in pre- and post-resuscitation care, percentage of survival to hospital discharge after OHCA was extremely low. Development of an accurate system to predict the daily incidence of out-of-hospital cardiac arrest (OHCA) might provide a significant public health benefit. Here, we developed and validated a machine learning (ML) predictive model for daily OHCA incidence using high-resolution meteorological, chronological, and geographical data. Methods: We analyzed a dataset from the United States that combined an OHCA nationwide registry, high-resolution meteorological data, chronological data, and geographical data. We developed a model to predict daily OHCA incidence with a training dataset for 2013?2017 using the eXtreme Gradient Boosting algorithm. A dataset for 2018?2019 was used to test the predictive model. The main outcome was the predictive accuracy for the number of daily OHCA events, based on root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In general, a model with MAPE less than 10% is considered highly accurate. Results: Among the 446,830 OHCAs of non-traumatic cause where resuscitative efforts were initiated by a 911 responder, 264,916 in the training dataset and 181,914 in the testing dataset were included in the analysis. The ML model with combined meteorological, chronological, and geographical data had high predictive accuracy in relation to nationwide incidence rate per 100,000 at the nationwide level) in the training dataset (RMSE, 0.016; MAE, 0.013; and MAPE, 7.61%) and in the testing dataset (RMSE, 0.018; MAE, 0.014; and MAPE, 6.52%). Conclusions: A ML predictive model using comprehensive daily meteorological, chronological, and geographical data allows for highly precise estimates of OHCA incidence in the United States.
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