Acute Myocardial Infarction In-hospital Mortality Research Articles

Impact of early death recording on international comparison of acute myocardial infarction mortality – administrative hospital data study using the example of Germany and the United States

BackgroundIn-hospital mortality from acute myocardial infarction (AMI) is widely used in international comparisons as an indicator of health system performance. Because of the high risk of early death after AMI, international comparisons may be biased by differences in the recording of early death cases in hospital inpatient data. This study examined whether differences in the recording of early deaths affect international comparisons of AMI in-hospital mortality by using the example of Germany and the United States, and explored approaches to address this issue.MethodsThe German Diagnosis-Related Groups Statistics (DRG Statistics), the U.S. National Inpatient Sample (NIS) and the U.S. Nationwide Emergency Department Sample (NEDS) were analysed from 2014 to 2019. Cases with treatment for AMI were identified in German and U.S. inpatient data. AMI deaths occurring in the emergency department (ED) without inpatient admission were extracted from NEDS data. 30-day in-hospital mortality figures were calculated according to the OECD indicator definition (unlinked data) and modified by including ED deaths, or excluding all same-day cases.ResultsGerman age-and-sex standardized 30-day in-hospital mortality was substantially higher compared to the U.S. (in 2019, 7.3% vs. 4.6%). The ratio of German vs. U.S. mortality was 1.6. After inclusion of ED deaths in U.S. data this ratio declined to 1.4. Exclusion of same-day cases in German and U.S. data led to a similar ratio.ConclusionsWhile short-duration treatments due to early death are generally recorded in German inpatient data, in U.S. inpatient data those cases are partially missing. Excluding cases with short-duration treatment from the calculation of mortality indicators could be a feasible approach to account for differences in the recording of early deaths, that might be existent in other countries as well.

The new biomarker that predicts in-hospital mortality in myocardial infarction: glucose/potassium ratio

Aims: Acute myocardial infarction (AMI) remains one of the leading causes of cardiovascular mortality. However, the parameters used to predict short-term mortality are limited. Glucose-potassium ratio (GPR) is a new biomarker that has been recently used. This study aimed to investigate the usefulness of GPR in predicting short-term mortality in patients diagnosed with AMI in the emergency department. Methods: This retrospective cohort study was conducted between January 1, 2022, and June 30, 2022, and included patients registered in the hospital emergency health system with IRB (Institutional Review Board) approval. Patients were identified based on inclusion criteria, and their demographic data, laboratory findings, and outcomes were recorded in the hospital registration system. Data comparisons between deceased and non-deceased patients were performed using the Student's t-test. Correlation analysis was performed using Spearman's correlation coefficients. A binary logistic regression model was created to determine the predictive parameters, and odds ratios and 95% confidence intervals (CI) were presented. Results: A total of 665 patients diagnosed with AMI were included in the study, with 75.9% of all patients being male and an average age of 62.14±11.62. A total of 6.6% of the patients included in the study died due to various causes during hospitalization. When laboratory values of deceased AMI patients were examined, statistically significant increases in glucose, urea, creatinine, WBC, neutrophil, lymphocyte, troponin, ckmb, and lactate levels were observed. The effects of GPR, NLR, PLR, lactate, and troponin levels on in-hospital mortality were analyzed using univariate logistic regression. GPR (95% CI 1.015-1.036, p=0.000), troponin (95% CI 1.001-1.001, p=0.000), and lactate (95% CI 1.437-1.908, p=0.000) were found to be the most important factors associated with mortality in patients with AMI. The ability to predict in-hospital mortality in AMI was compared, and GPR was found to have a higher predictive ability than troponin, a biomarker of myocardial damage (AUC: 0.729; P?=?0.000; Sensitivity:70%; Specificity:70%; Cut-off: 38.21). Conclusion: GPR, as a new and inexpensive biomarker, can be used to determine in-hospital mortality in patients with AMI. This index has a better predictive ability than troponin, NLR, and PLR, but is weaker than lactate.

Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction

BackgroundPersistent efforts are required to further reduce the in-hospital mortality of patients suffering from acute myocardial infarction (AMI), even in the face of a global trend of declining AMI-related fatalities. We investigated deep machine learning models for in-hospital death prediction in patients on their first AMI. MethodIn this 2-center retrospective analysis, first AMI patients from Hospital I and Hospital II were included; 4783 patients from Hospital 1 were split in a 7:3 ratio between the training and testing sets. Data from 1053 AMI patients in Hospital II was used for further validation. 70 clinical information and laboratory examination parameters as predictive indicators were included. Logistic Regression Classifier (LR), Random Forests Classifier (RF), eXtreme Gradient Boosting (XGB), Support Vector Machine Classifier (SVM), Multilayer Perceptron (MLP), Gradient Boosting Machine (GBM), Bootstrapped Aggregation (Bagging) models with AMI patients were developed. The importance of selected variables was obtained through the Shapley Additive exPlanations (SHAP) method. The performance of each model was shown using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (Average Precision; AP). Result: The in-hospital mortality for AMI in the training, testing, and validation sets were 5.7 %, 5.6 %, and 6.0 %, respectively. The top 8 predictors were D-dimer, brain natriuretic peptide, cardiogenic shock, neutrophil, prothrombin time, blood urea nitrogen, cardiac arrest, and phosphorus. In the testing cohort, the models of LR, RF, XGB, SVM, MLP, GBM, and Bagging yielded AUROC values of 0.929, 0.931, 0.907, 0.868, 0.907, 0.923, and 0.932, respectively. Bagging has good predictive value and certain clinical value in external validation with AUROC 0.893. ConclusionIn order to improve the forecasting accuracy of the risk of AMI patients, guide clinical nursing practice, and lower AMI inpatient mortality, this study looked into significant indicators and the optimal models for predicting AMI inpatient mortality.

Factors Associated With Hospital Mortality in Acute Myocardial Infarction

Aim To determine clinical and laboratory parameters associated with in-hospital mortality in patients with acute myocardial infarction and to develop a multifactorial prognostic model of in-hospital mortality.Material and methods This was a study based on the 2019-2020 Registry of acute coronary syndrome of the Tyumen Cardiology Research Center, a branch of the Tomsk National Research Medical Center. The study included 477 patients with ST-segment elevation acute myocardial infarction (AMI), 617 patients with non-ST segment elevation AMI, and 26 patients with unspecified AMI. In-hospital mortality was 6.0 % (n=67). Clinical and laboratory parameters were assessed on the day of admission. The separation power of indicators associated with in-hospital mortality was determined using a ROC analysis. The data array of each quantitative parameter was converted into a binary variable according to the obtained cut-off thresholds, followed by creation of a multifactorial model for predicting in-hospital mortality using a stepwise analysis with backward inclusion (Wald). The null hypothesis was rejected at p<0.05.Results The multivariate model for prediction of in-hospital mortality included age (cut-off, 72 years), OR 3.0 (95 % CI: 1.5-5.6); modified shock index (cut-off threshold, 0.87), OR 1.5 (95 % CI: 1.1-2.0); creatine phosphokinase-MB (cut-off threshold, 32.8 U / L), OR 4.1 (95 % CI: 2.2-7.7); hemoglobin (121.5 g / l), OR 1.7 (95 % CI: 1.2-2.3); leukocytes (11.5×109 / l), OR 1.9 (95 % CI: 1.3-2.6); glomerular filtration rate (60.9 ml / min), OR 1.7 (95 % CI: 1.2-2.2); left ventricular ejection fraction (42.5 %), OR 4.1 (95 % CI: 2.0-8.3); and size of myocardial asynergy (32.5 %), OR 2.6 (95 % CI: 1.4-5.0).Conclusions Independent predictors of in-hospital mortality in AMI are age, modified shock index, creatine phosphokinase-MB, peripheral blood leukocyte count, hemoglobin concentration, left ventricular ejection fraction, size of myocardial asynergy, and glomerular filtration rate. The in-hospital mortality model had a high predictive potential: AUC 0.930 (95 % CI: 0.905-0.954; p <0.001) with a cutoff threshold of 0.15; sensitivity 0.851, and specificity 0.850.

Abstract 11810: Is the Presence of Statin Use Before Onset of Disease Associated With In-Hospital Mortality in Elderly Patients With Acute Myocardial Infarction?

Introduction: The effect of statin on preventing cardiovascular events is clear, and aggressive lipid-lowering therapy with statins is strongly recommended for patients with coronary artery disease including of acute myocardial infarction (AMI). Hypothesis: The relationship between in-hospital mortality of AMI and statin use before onset of AMI in the elderly patients is unknown, we thus assessed. Methods: The subjects were 207 consecutive AMI patients aged 65 years or older who underwent successful percutaneous coronary intervention (PCI) at Kagoshima university hospital between January 2015 and March 2021. We investigated the association between in-hospital mortality after PCI and baseline characteristics including Global Registry of Acute Coronary Events (GRACE) risk score, which is widely used for estimating mortality in AMI patients. Results: Twenty-three patients (11%) died in-hospital. The mortality rate was significantly lower in patients who were taking statins at presentation compared to those who were not (3% vs. 16%, p=0.002). Univariate logistic regression analysis of risk factors for in-hospital mortality revealed that statin use before onset of AMI, level of serum albumin, level of high sensitivity C-reactive protein (hs-CRP) and GRACE risk score were associated with in-hospital mortality. Multivariate logistic analysis models adjusted for age, statin use, level of serum albumin, level of hs-CRP, and GRACE risk score showed that statin use [Odd's ratio (OR): 0.13, 95% confidence interval (CI): 0.02-0.81, p=0.029] and GRACE risk score (OR: 1.04, 95% CI: 1.02-1.06, p <0.001) were independently associated with in-hospital mortality. The optimal cut-off on the receiver-operating characteristic curve of GRACE risk score for predicting in-hospital mortality was 160. In addition, multivariate logistic analysis models using GRACE risk score ≥160 and non-use of statin before the onset of AMI showed greater risk of in-hospital mortality (HR 19.24; 95% CI 6.62-55.94; p <0.001) after PCI as compared to patients with GRACE risk score <160 and statins use. Conclusions: In elderly AMI patients undergoing PCI, statin use before the onset of AMI was an independent risk factor for in-hospital mortality, as was the GRACE risk score.

Leukoglycemic Index may be a Unique Parameter to Predict Mortality in Patients with Acute Myocardial Infarction: Single Operator Experience

Background and Aim: Predicting high-risk patients is crucial in acute myocardial infarction (AMI). We aimed to investigate whether the leukoglycemic index (LGI) has a unique ability and to compare it with other inflammatory parameters in predicting in-hospital mortality in AMI.
 Methods: In this single-center study, we retrospectively analyzed all AMI patients hospitalized and followed by a single operator. Patients were divided into two according to in-hospital outcomes. Other inflammatory parameters (systemic immune-inflammatory index, platelet-lymphocyte ratio, neutrophil-lymphocyte ratio, triglyceride-HDL ratio, and LDL-HDL ratio), C-reactive protein (CRP), and LGI were calculated according to previously described criteria. Univariable and multivariable logistic regression analyses were used to find independent predictors. The receiver operating characteristic (ROC) curve was used to find the cut-off point of LGI and other parameters in predicting mortality.
 Results: A total of 304 patients with AMI were included in the study. The mean age was 62.18±11.89 and 74 (24.3%) of patients were female. The total death rate was 19 (6.3%). In univariate variable analysis, LGI was found as a significant predictor of mortality (p

10-Year Temporal Trends of In-Hospital Mortality and Emergency Percutaneous Coronary Intervention for Acute Myocardial Infarction.

The mortality rate of acute myocardial infarction (AMI) has improved dramatically because of reperfusion therapy during the last 40 years; however, recent temporal trends for AMI have not been fully clarified in Japan. The purpose of this study was to elucidate the temporary trend in in-hospital mortality and treatment of AMI for the last decade in the Tokyo Metropolitan area. We enrolled 30,553 patients from the Tokyo Cardiovascular Care Unit Network Registry, diagnosed with AMI from 2007 to 2016, as part of an ongoing, multicenter, cohort study. We analyzed the temporal trends in basic characteristics, treatment, and in-hospital mortality of AMI. The overall emergency percutaneous coronary intervention (PCI) rate significantly increased (P< 0.001). In particular, it remarkably increased in patients older than 80 years of age (58.3% to 70.3%, P< 0.001) and patients with Killip III or IV (Killip III, 46.9% to 65.7%; Killip IV, 65.2% to 76.6%, P< 0.001 for both). The crude and age-adjusted in-hospital mortality remained low (5.2% to 8.2% and 3.4% to 5.5%, respectively) and significantly decreased during the decade (P< 0.001). The in-hospital mortality remarkably decreased in patients older than 80 years of age (17.3% to 12.7%, P< 0.001) and in those with cardiogenic shock (38.5% to 27.3%, P< 0.001). This large cohort study from Tokyo revealed that in-hospital mortality of AMI significantly decreased with the increase in emergency percutaneous coronary intervention rate over the decade, particularly for high-risk patients such as older patients and those with cardiogenic shock.

Assessment of the Impact of the Implementation of a Pre-Hospital Ambulance System on Acute Myocardial Infarction Mortality in a Developing Country.

The effective management of patients with acute myocardial infarction (AMI) is time-dependent. To assess the impacts of the implementation of prehospital care on admission rates and mortality associated with AMI. Retrospective, ecological study, which assessed data from the Brazilian Universal Health System, from all 853 municipalities of Minas Gerais, from 2008 to 2016. Excessive skewness of general and in-hospital mortality rates was smoothed using the empirical Bayes method. This study assessed the relationship between Mobile Emergency Care Service (SAMU) in each municipality and the following 3 outcomes: mortality rate due to AMI, AMI in-hospital mortality, and AMI hospitalization rate, using the Poisson hierarchical model. Rates were corrected by age structure and detrended by seasonality and temporal influences. A confidence interval of 95% was adopted. AMI mortality rates decreased throughout the study, on average 2% per year, with seasonal variation. AMI in-hospital mortality also showed a decreasing trend, from 13.81% in 2008 to 11.43% in 2016. SAMU implementation was associated with decreased AMI mortality (odds ratio [OR] = 0.967, 95% confidence interval [CI] 0.936 to 0.998) and AMI in-hospital mortality (OR = 0.914, 95% CI 0.845 to 0.986), with no relation with hospitalizations (OR = 1.003, 95% CI 0.927 to 1.083). SAMU implementation was associated with a modest but significant decrease in AMI in-hospital mortality. This finding reinforces the key role of prehospital care in AMI care and the need for investments on this service to improve clinical outcomes in low- and middle-income countries.

Hematological Parameter as Predictor Mortality in Acute Myocardial Infarction Patients.

PurposeAcute myocardial infarction (AMI) is one of the leading causes of death in the world. Several hematological parameters are involved in AMI conditions and can be explored for their ability to predict in-hospital mortality. We evaluated hematological parameter value as predictors of in-hospital mortality in AMI patients.Patients and MethodsAnalytical observational study on AMI patients that admitted to Dr. Saiful Anwar Hospital, June–August 2021. Complete blood count was taken at the beginning of hospital admission. Patients with sepsis were excluded from the study. The research subjects were divided into two groups, survivor group and non-survivor group (in-hospital mortality). Hematological parameters as predictors of mortality were analyzed using the ROC curve and odds ratio.ResultsAmong 44 study subjects, 19 (43.18%) patients were in the non-survivor group and 25 (56.82%) patients were in the survivor group. Hematological parameters that can be predictors of mortality include leukocytes (AUC = 0.744; p = 0.006), neutrophils (AUC = 0.729; p = 0.010), eosinophils (AUC = 0.696; p = 0.028), and eosinophils/leukocytes ratio (AUC = 0.772; p = 0.012). The cut-off value for leukocytes was 13,725/μL (OR: 9), neutrophils was 12,008/μL (OR: 8.14), eosinophils was 58.3/μL (OR: 4.06), and eosinophil/leukocyte ratio was 0.0025 (OR: 4.41) with sensitivity 63.25%, 52.6%, 78.9%, and 63.2%, respectively, and specificity 84%, 88%, 52%, and 72%, respectively.ConclusionLeukocyte, neutrophils, eosinophils count and eosinophil/leukocyte ratio can help predict in-hospital mortality of AMI patients.

Research progress of in-hospital mortality risk model in patients with acute myocardial infarction

The incidence of in-hospital death in acute myocardial infarction (AMI) is high, which seriously threatens the life and health of patients. At present, many countries and regions have established a variety of objective assessment models for predicting the in-hospital mortality of patients with AMI, providing important decision-making support for patients with different risk levels when formulating treatment plans. With the rise of artificial intelligence, many new modeling methods also show certain advantages over the traditional models. This article systematically introduces the commonly used and newly constructed risk prediction models for in-hospital mortality of AMI, in order to provide help for medical staff to assist decision-making in clinical practice, and provide reference for the establishment of a safe and more effective risk prediction model in the future.

Abstract 12474: Acute Myocardial Infarction Following Hospitalizations for Gastrointestinal Bleeding: Incidence, Predictors, Management, and Outcomes

Introduction: Patients hospitalized with gastrointestinal bleeding (GIB) are at risk for acute myocardial infarction (AMI). Clinical characteristics of patients with AMI after a GIB are poorly characterized. We sought to evaluate the incidence, management and outcomes of AMI following hospitalization for GIB. Methods: Patients admitted with a diagnosis of GIB were identified in the 2014 United States Nationwide Readmission Database using ICD-9 diagnosis codes. Hospital readmissions for AMI within 90 days of GIB were identified. Patients with AMI after GIB were compared to those without a prior GIB to determine differences in management and in-hospital outcomes. Invasive management of AMI was defined as invasive angiography or coronary revascularization. Logistic regression models were used to estimate odds of invasive management and all-cause, in-hospital morality after adjustment for clinical covariates. Results: A total of 644,622 patients with GIB were identified, of which 7,523 (1.2%) were readmitted for AMI within 90 days. Older age, diabetes mellitus, chronic kidney disease, coronary and peripheral artery disease, unknown GIB source, and AMI during the index GIB hospitalization were associated with 90-day AMI readmission. Compared to AMI patients without a recent GIB, patients with AMI within 90 days after GIB were older (74.4 vs. 68.4 years, p&lt;0.01), more likely to be women (47% vs. 41.1%, p&lt;0.01), have kidney disease (43.2% vs. 22.4%, p&lt;0.01), present with non-ST segment elevation MI (82% vs. 73%, p&lt;0.01), and were less likely to undergo invasive management of AMI (28% vs 63%, P&lt;0.01; adjusted odds ratio [aOR] 0.40, 95% CI 0.37-0.44). Recent hospitalization for GIB within 90 days was associated with higher all-cause in-hospital AMI mortality (22% vs 9%, P&lt;0.01; aOR 1.86 [1.68-2.06]). Conclusion: In the first 3 months after hospitalization for GIB, 1 of every 83 patients was readmitted with AMI. Patients with AMI after GIB were less likely to undergo invasive management of AMI and had higher mortality than those without recent GIB. Strategies to reduce morbidity and mortality of GIB-associated thrombosis are needed.

Estimated plasma volume status (ePVS) is a predictor for acute myocardial infarction in-hospital mortality: analysis based on MIMIC-III database

BackgroundEstimated plasma volume status (ePVS) has been reported that associated with poor prognosis in heart failure patients. However, no researchinvestigated the association of ePVS and prognosis in patients with acute myocardial infarction (AMI). Therefore, we aimed to determine the association between ePVS and in-hospital mortality in AMI patients.Methods and resultsWe extracted AMI patients data from MIMIC-III database. A generalized additive model and logistic regression model were used to demonstrate the association between ePVS levels and in-hospital mortality in AMI patients. Kaplan–Meier survival analysis was used to pooled the in-hospital mortality between the various group. ROC curve analysis were used to assessed the discrimination of ePVS for predicting in-hospital mortality. 1534 eligible subjects (1004 males and 530 females) with an average age of 67.36 ± 0.36 years old were included in our study finally. 136 patients (73 males and 63 females) died in hospital, with the prevalence of in-hospital mortality was 8.9%. The result of the Kaplan–Meier analysis showed that the high-ePVS group (ePVS ≥ 5.28 mL/g) had significant lower survival possibility in-hospital admission compared with the low-ePVS group (ePVS < 5.28 mL/g). In the unadjusted model, high-level of ePVS was associated with higher OR (1.09; 95% CI 1.06–1.12; P < 0.001) compared with low-level of ePVS. After adjusted the vital signs data, laboratory data, and treatment, high-level of ePVS were also associated with increased OR of in-hospital mortality, 1.06 (95% CI 1.03–1.09; P < 0.001), 1.05 (95% CI 1.01–1.08; P = 0.009), 1.04 (95% CI 1.01–1.07; P = 0.023), respectively. The ROC curve indicated that ePVS has acceptable discrimination for predicting in-hospital mortality. The AUC value was found to be 0.667 (95% CI 0.653–0.681).ConclusionHigher ePVS values, calculated simply from Duarte’s formula (based on hemoglobin/hematocrit) was associated with poor prognosis in AMI patients. EPVS is a predictor for predicting in-hospital mortality of AMI, and could help refine risk stratification.

Temporal Trends and Outcomes of Left Ventricular Aneurysm After Acute Myocardial Infarction

There are limited data on the prevalence and an outcome of left ventricular (LV) aneurysms following acute myocardial infarction (AMI). Using the National Inpatient Sample during 2000 to 2017, a retrospective cohort of AMI admissions was evaluated for LV aneurysms. Complications included ventricular arrhythmias, mechanical, cardiac arrest, pump failure, LV thrombus, and stroke. Outcomes of interest included in-hospital mortality, temporal trends, complications, hospitalization costs, and length of stay. A total 11,622,528 AMI admissions, with 17,626 (0.2%) having LV aneurysms were included. The LV aneurysm cohort was more often female, with higher comorbidity, and admitted to large urban hospitals (all p < 0.001). In 2017, compared with 2000, there was a slight increase in LV aneurysms prevalence in those with (adjusted odds ratio [aOR] 1.57 [95% confidence interval {CI} 1.41 to 1.76]) and without (aOR 1.13 [95% CI 1.00 to .127]) ST-segment-elevation AMI (p < 0.001 for trend). LV aneurysms were more commonly noted with anterior ST-segment-elevation AMI (31%) compared with inferior (12.3%) and other (7.9%). Ventricular arrhythmias (17.6% vs 8.0%), mechanical complications (2.6% vs 0.2%), cardiac arrest (7.1% vs 5.0%), pump failure (26.3% vs 16.1%), cardiogenic shock (10.0% vs 4.8%) were more common in the LV aneurysm cohort (all p < 0.001). Those with LV aneurysms had comparable in-hospital mortality compared with those without (7.4% vs 6.2%; aOR 1.02 [95% CI 0.90 to 1.14]; p = 0.43). The LV aneurysm cohort had longer length of hospital stay, higher hospitalization costs, and fewer discharges to home. In conclusion, LV aneurysms were associated with higher morbidity, more frequent complications, and greater in-hospital resource utilization, without any differences in in-hospital mortality in AMI.

Uncertainty in classification of death from fatal myocardial infarction: A nationwide analysis of regional variation in incidence and diagnostic support.

AimsThe usefulness of mortality statistics relies on the validity of death certificate diagnosis. However, diagnosing the causal sequence of conditions leading to death is not simple. We examined diagnostic support for fatal acute myocardial infarction (AMI) and investigated its association with regional variation.Methods and resultsFrom Danish nationwide registers, we identified the study population (N = 3,244,051) of whom 36,669 individuals were recorded with AMI as the underlying cause-of-death between 2002 and 2015. We included clinical diagnoses, procedures, and claimed prescriptions related to atherosclerotic disease to evaluate the level of diagnostic support for fatal AMI in three diagnostic groups (Definite; Plausible; Uncertain). Adjusted mortality rates, rate ratios, and odds ratios were estimated for each AMI category, stratified by hospital region using multivariable regression models. More than one-third (N = 12,827, 35%) of deaths reported as fatal AMI had uncertain diagnostic support. The largest regional variation in AMI mortality rate ratios, varying from 1.16 (95%CI:1.02;1.31) to 1.62 (95%CI:1.43;1.83), was found among cases with uncertain diagnostic supportive data. Substantial inter-regional differences in the degree to which death occurs outside hospital [OR: 1.01 (95%CI:0.92;1.12) - 1.49 (95%CI:1.36;1.63)] and general practitioners determining the cause-of-death at home were present. Minor regional differences [OR: 0.96 (95%CI:0.85;1.07) - 1.16 (95%CI:1.04;1.29)] in in-hospital AMI mortality were observed.ConclusionThere is significant regional variation associated with recording AMI as a cause-of-death. This variation is predominately based on death certificate diagnoses without diagnostic supportive evidence. Studies of fatal AMI should include a stratification on supportive evidence of the diagnosis.

Holiday and weekend effects on mortality for acute myocardial infarction in Shanxi, China: a cross-sectional study.

To examine the effects of holiday and weekend admission on in-hospital mortality for patients with acute myocardial infarction (AMI) in China. Patients with AMI in 31 tertiary hospitals in Shanxi, China from 2014 to 2017 were included (N = 54,968). Multivariable logistic regression models were used to examine the effects of holiday and weekend admission on in-hospital mortality. Compared to non-holiday and weekday admissions, holiday and weekend admissions, respectively, were associated with increases in risk-adjusted mortality rates. Chinese National Day was associated with an additional 10 deaths per 1000 admissions (95% confidence interval (CI): (0, 20))-a relative increase from baseline mortality of 64% (95% CI: (1%, 128%)). Sunday was associated with an additional 4 deaths per 1000 admissions (95% CI: (0, 7))-a relative increase from baseline mortality of 23% (95% CI: (3%, 45%)). We found no evidence of gender differences in holiday or weekend effects on mortality. Holiday and weekend admissions were associated with in-hospital AMI mortality. The admissions on Chinese National Day and Sunday contributed to the observed "holiday effect" and "weekend effect," respectively.

In-Hospital Mortality in Acute Myocardial Infarction According to Population Density and Primary Angioplasty Procedures Volume

Low population density may be associated with high mortality in acute myocardial infarction (AMI) patients. The purpose of this study was to investigate the effect of population density and hospital primary percutaneous coronary intervention (PCI) volume on AMI in-hospital mortality in Japan.Methods and Results:This is a retrospective study of 64,414 AMI patients transported to hospital by ambulances. The main outcome measure was in-hospital mortality. The median population density was 1,147 (interquartile range, 342-5,210) persons/km2. There was a significant negative relationship between population density and in-hospital mortality (OR for a quartile down in population density 1.086, 95% CI 1.042-1.132, P<0.001). Patients in less densely populated areas were more often transported to hospitals with a lower primary PCI volume, and they had a longer distance to travel. By using multivariable analysis, primary PCI volume was found to be significantly associated with in-hospital mortality, but distance to hospital was not. When divided into the low- and high-volume hospitals, using the cut-off value of 115 annual primary PCI procedures, the increase in in-hospital mortality associated with low population density was observed only in patients hospitalized in the low-volume hospitals. Increased in-hospital mortality related to low population density was observed only in AMI patients who were transported to the low primary PCI volume hospitals, but not in those who were transported to high-volume hospitals.

Frequency of Management of Cardiogenic Shock With Mechanical Circulatory Support Devices According to Race

Mechanical circulatory support (MCS) has influenced the management of cardiogenic shock (CS), but the association between race and MCS utilization is unknown. We sought to evaluate the effect of race on MCS utilization in CS and whether there are racial differences in in-hospital outcomes. Our study was a population-based retrospective cohort study that enrolled patients with CS, defined by International classification of disease, ninth Revision, clinical modification (ICD-9-CM) codes, between 2013 and 2015 from the National Inpatient Sample. Race was adjudicated by National Inpatient Sample and included White, Black, Hispanic, Asian, and Native American. The primary outcomes were the utilization of MCS devices in CS with and without acute myocardial infarction (AMI), and in-hospital mortality by race. The statistical adjustment was performed for clinical co-morbidities as well as in-hospital events using multivariate logistic regressions. Among 332,885 patients with CS, there were 71% white and 14% black patients, and AMI was present in 42% and MCS was utilized in 23% of patients. There was less utilization of MCS only in Black patients with CS, and with AMI after adjustment (odds ratio [OR] 0.84, 95% confidence interval [CI][0.79 to 0.89] and OR 0.85, 95% CI 0.78 to 0.92, respectively). In addition, only Black patients had greater in-hospital mortality in AMI after adjustment (OR 1.16, 95% CI [1.06 to 1.27]) whereas there was no statistically significant increase in in-hospital mortality in any other race. In conclusion, these results suggest that there is less utilization of MCS devices and, in parallel, increased odds of in-hospital mortality in Black patients in comparison to other races. Further steps may be needed to address possible implicit bias in acute clinical scenarios as new devices emerge, which carries new opportunities to improve clinical outcomes but there is a lack of clear guidelines.

Impact of disease-specific care certification on clinical outcome and healthcare performance of myocardial infarction in Taiwan.

The relationship between certification for specific disease care and clinical outcome was not well known. Previous studies regarding the effect of certification for acute stroke centers were limited by their cross-sectional design. This study aimed to investigate the effect of disease-specific care (DSC) certification on healthcare performance and clinical outcome of acute myocardial infarction (AMI). This retrospective, longitudinal, controlled study was performed by analyzing the nationwide Taiwan Clinical Performance Indicators dataset from 2011 to 2018. Hospitals undergoing DSC certification for coronary care and reporting AMI indicators 1 year before, during, and 1 year after certification were included in group C, whereas hospitals not seeking DSC certification but reporting AMI indicators during the same period were included in group U. The primary endpoint was in-hospital mortality of AMI. In total, 20 hospitals (9 in group C and 11 in group U) and up to 16 173 AMI cases were included for analysis. In-hospital mortality was similar between both groups at baseline. However, the in-hospital mortality was significantly improved during and after certification periods in comparison with that at baseline in group C (6.8% vs 8.4%, p = 0.04; 6.7% vs 8.4%, p = 0.02), whereas there was no significant change in group U, resulting in a statistically significant difference between both groups during and after certification periods (odds ratio = 0.74 [95% CI = 0.60-0.91] and 0.78 [95% CI = 0.64-0.96]). Compared with group U, the improvement in healthcare performance indicators, such as door-to-electrocardiography time <10 minutes, blood testing for low-density lipoprotein cholesterol level, prescribing a beta-blockade or a P2Y12 receptor inhibitor during hospitalization, prescribing a statin on discharge, and consultation for cardiac rehabilitation, was significant in group C. The current study demonstrated the beneficial effect of DSC certification on clinical outcome of AMI probably mediated through quality improvement during the healthcare process.

Association between time of hospitalization with acute myocardial infarction and in-hospital mortality.

To study the association between time of hospitalization and in-hospital mortality for acute myocardial infarction (AMI). Patients admitted with ST-elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI) across 243 hospitals in England and Wales between 1 January 2004 and 31 March 2013 were included. The outcome measure was in-hospital mortality. Adjusted odds ratios (ORs) for in-hospital mortality were estimated across six 4-hourly time periods over the 24-h clock using multilevel logistic regression, inverse-probability weighting propensity score, and instrumental variable analysis. Among 615035 patients [median age 70.0 years, interquartile range 59.0-80.0 years; 406519 (66.0%) men], there were 52777 (8.8%) in-hospital deaths. At night, patients with NSTEMI were more frequently comorbid, and for STEMI had longer symptom-onset-to-reperfusion times. For STEMI, unadjusted in-hospital mortality was highest between 20:00 and 23:59 [4-h period range 8.4-9.9%; OR compared with 00:00-03:59 reference 1.13, 95% confidence interval (CI) 1.07-1.20], and for NSTEMI highest between 12:00 and 15:59 (8.0-8.8%; OR compared with 00:00-03:59 reference 1.07, 95% CI 1.03-1.12). However, these differences were only apparent in the earlier years of the study, and were attenuated by adjustment for demographics, comorbidities, and clinical presentation. Differences were not statistically significant after adjustment for acute clinical treatment provided. There is little evidence to support an association between time of hospitalization and in-hospital mortality for AMI; variation in in-hospital mortality may be explained by case mix and the use of treatments.

Does Hospital Competition Harm Inpatient Quality? Empirical Evidence from Shanxi, China.

This study aimed to determine whether hospital competition is associated with improved in-hospital mortality in Shanxi, China. We included a total of 46,959 hospitalizations for acute myocardial infarction (AMI) and 44,063 hospitalizations for pneumonia from 2015 to 2017. Hospital competition was measured as Herfindahl–Hirschman Index based on the patient predicted flow approach. Two-level random-intercept logistic models were applied to explore the effects of hospital competition on quality for both AMI and pneumonia diagnoses. Hospital competition exerts negative or negligible effects on inpatient quality of care, and the pattern of competition effects on quality varies by specific diseases. While hospital competition is insignificantly correlated with lower AMI in-hospital mortality (odds ratio (OR): 0.94, 95% confidence interval (CI): 0.77–1.11), high hospital competition was, in fact, associated with higher in-hospital mortality for pneumonia patients (OR: 1.99, 95% CI: 1.51–2.64). Our study suggests that simply encouraging hospital competition may not provide effective channels to improve inpatient quality of health care in China’s current health care system.