Dataset Information

Derivation and validation of a combined in-hospital mortality and bleeding risk model in acute myocardial infarction.

ABSTRACT:

Background

In the potent new antiplatelet era, it is important issue how to balance the ischemic risk and the bleeding risk. However, previous risk models have been developed separately for in-hospital mortality and major bleeding risk. Therefore, we aimed to develop and validate a novel combined model to predict the combined risk of in-hospital mortality and major bleeding at the same time for initial decision making in patients with acute myocardial infarction (AMI).

Methods

Variables from the Korean Acute Myocardial Infarction Registry (KAMIR) - National Institute of Health (NIH) database were used to derive (n = 8955) and validate (n = 3838) a multivariate logistic regression model. Major adverse cardiovascular events (MACEs) were defined as in-hospital death and major bleeding.

Results

Seven factors were associated with MACE in the model: age, Killip class, systolic blood pressure, heart rate, serum glucose, glomerular filtration rate, and initial diagnosis. The risk model discriminated well in the derivation (c-static = 0.80) and validation (c-static = 0.80) cohorts. The KAMIR-NIH risk score was developed from the model and corresponded well with observed MACEs: very low risk (0.9%), low risk (1.7%), moderate risk (4.2%), high risk (8.6%), and very high risk (23.3%). In patients with MACEs, a KAMIR-NIH risk score ? 10 was associated with high bleeding risk, whereas a KAMIR-NIH risk score > 10 was associated with high in-hospital mortality.

Conclusion

The KAMIR-NIH in-hospital MACEs model using baseline variables stratifies comprehensive risk for in-hospital mortality and major bleeding, and is useful for guiding initial decision making.

SUBMITTER: Kim HN

PROVIDER: S-EPMC7907424 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

Derivation and validation of a combined in-hospital mortality and bleeding risk model in acute myocardial infarction.

Kim Hong Nyun HN Lee Jang Hoon JH Kim Hyeon Jeong HJ Park Bo Eun BE Jang Se Yong SY Bae Myung Hwan MH Yang Dong Heon DH Park Hun Sik HS Cho Yongkeun Y Jeong Myung Ho MH Park Jong-Seon JS Kim Hyo-Soo HS Hur Seung-Ho SH Seong In-Whan IW Cho Myeong-Chan MC Kim Chong-Jin CJ Chae Shung Chull SC

International journal of cardiology. Heart & vasculature 20210222

<h4>Background</h4>In the potent new antiplatelet era, it is important issue how to balance the ischemic risk and the bleeding risk. However, previous risk models have been developed separately for in-hospital mortality and major bleeding risk. Therefore, we aimed to develop and validate a novel combined model to predict the combined risk of in-hospital mortality and major bleeding at the same time for initial decision making in patients with acute myocardial infarction (AMI).<h4>Methods</h4>Var ...[more]

PMID: 33665352

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Project description:BACKGROUND:Bleeding complications in patients with acute ST-segment elevation myocardial infarction (STEMI) have been associated with increased risk of subsequent adverse consequences. OBJECTIVE:The objective of our study was to develop and externally validate a diagnostic model of in-hospital bleeding. METHODS:We performed multivariate logistic regression of a cohort for hospitalized patients with acute STEMI in the emergency department of a university hospital. Participants: The model development data set was obtained from 4262 hospitalized patients with acute STEMI from January 2002 to December 2013. A set of 6015 hospitalized patients with acute STEMI from January 2014 to August 2019 were used for external validation. We used logistic regression analysis to analyze the risk factors of in-hospital bleeding in the development data set. We developed a diagnostic model of in-hospital bleeding and constructed a nomogram. We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS:In-hospital bleeding occurred in 112 of 4262 participants (2.6%) in the development data set. The strongest predictors of in-hospital bleeding were advanced age and high Killip classification. Logistic regression analysis showed differences between the groups with and without in-hospital bleeding in age (odds ratio [OR] 1.047, 95% CI 1.029-1.066; P<.001), Killip III (OR 3.265, 95% CI 2.008-5.31; P<.001), and Killip IV (OR 5.133, 95% CI 3.196-8.242; P<.001). We developed a diagnostic model of in-hospital bleeding. The area under the receiver operating characteristic curve (AUC) was 0.777 (SD 0.021, 95% CI 0.73576-0.81823). We constructed a nomogram based on age and Killip classification. In-hospital bleeding occurred in 117 of 6015 participants (1.9%) in the validation data set. The AUC was 0.7234 (SD 0.0252, 95% CI 0.67392-0.77289). CONCLUSIONS:We developed and externally validated a diagnostic model of in-hospital bleeding in patients with acute STEMI. The discrimination, calibration, and DCA of the model were found to be satisfactory. TRIAL REGISTRATION:ChiCTR.org ChiCTR1900027578; http://www.chictr.org.cn/showprojen.aspx?proj=45926.

Project description:BackgroundBefore outcomes-based measures of quality can be used to compare and improve care, they must be risk-standardized to account for variations in patient characteristics. Despite the importance of health-related quality of life (HRQL) outcomes among patients with acute myocardial infarction (AMI), no risk-standardized models have been developed.Methods and resultsWe assessed disease-specific HRQL using the Seattle Angina Questionnaire at baseline and 1 year later in 2693 unselected AMI patients from 24 hospitals enrolled in the Translational Research Investigating Underlying disparities in acute Myocardial infarction Patients' Health status (TRIUMPH) registry. Using 57 candidate sociodemographic, economic, and clinical variables present on admission, we developed a parsimonious, hierarchical linear regression model to predict HRQL. Eleven variables were independently associated with poor HRQL after AMI, including younger age, previous coronary artery bypass graft surgery, depressive symptoms, and financial difficulties (R(2)=20%). The model demonstrated excellent internal calibration and reasonable calibration in an independent sample of 1890 AMI patients in a separate registry, although the model slightly overpredicted HRQL scores in the higher deciles. Among the 24 TRIUMPH hospitals, 1-year unadjusted HRQL scores ranged from 67-89. After risk-standardization, HRQL score variability narrowed substantially (range=79-83), and the group of hospital performance (bottom 20%/middle 60%/top 20%) changed in 14 of the 24 hospitals (58% reclassification with risk-standardization).ConclusionsIn this predictive model for HRQL after AMI, we identified risk factors, including economic and psychological characteristics, associated with HRQL outcomes. Adjusting for these factors substantially altered the rankings of hospitals as compared with unadjusted comparisons. Using this model to compare risk-standardized HRQL outcomes across hospitals may identify processes of care that maximize this important patient-centered outcome.

Project description:Background Bleeding risk stratification in acute coronary syndrome is of highest clinical interest but current risk scores have limitations. We sought to develop and validate a new in-hospital bleeding risk score for patients with acute myocardial infarction. Methods and Results From the nationwide SWEDEHEART (Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) register, 97,597 patients with acute myocardial infarction enrolled from 2009 until 2014 were selected. A full model with 23 predictor variables and 8 interaction terms was fitted using logistic regression. The full model was approximated by a model with 5 predictors and 1 interaction term. Calibration, discrimination, and clinical utility was evaluated and compared with the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) and CRUSADE (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Ad verse Outcomes With Early Implementation of the ACC /AHA Guidelines) scores. Internal and temporal validity was assessed. In-hospital major bleeding, defined as fatal, intracranial, or requiring surgery or blood transfusion, occurred in 1356 patients (1.4%). The 5 predictors in the approximate model that constituted the SWEDEHEART score were hemoglobin, age, sex, creatinine, and C-reactive protein. The ACTION and CRUSADE scores were poorly calibrated in the derivation cohort and therefore were recalibrated. The SWEDEHEART score showed higher discriminative ability than both recalibrated scores, overall ( C-index 0.80 versus 0.73/0.72) and in all predefined subgroups. Decision curve analysis demonstrated consistently positive and higher net benefit for the SWEDEHEART score compared with both recalibrated scores across all clinically relevant decision thresholds. The original ACTION and CRUSADE scores showed negative net benefit. Conclusions The 5-item SWEDEHEART score discriminates in-hospital major bleeding in patients with acute myocardial infarction and has superior model performance compared with the recalibrated ACTION and CRUSADE scores.

Project description:BackgroundPublicly reported hospital risk-standardized mortality rates (RSMRs) for acute myocardial infarction (AMI) are calculated for Medicare beneficiaries. Outcomes for older patients with AMI may not reflect general outcomes.ObjectiveTo examine the relationship between hospital 30-day RSMRs for older patients (aged ≥65 years) and those for younger patients (aged 18 to 64 years) and all patients (aged ≥18 years) with AMI.DesignRetrospective cohort study.Setting986 hospitals in the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) Registry-Get With the Guidelines.ParticipantsAdults hospitalized for AMI from 1 October 2010 to 30 September 2014.MeasurementsHospital 30-day RSMRs were calculated for older, younger, and all patients using an electronic health record measure of AMI mortality endorsed by the National Quality Forum. Hospitals were ranked by their 30-day RSMRs for these 3 age groups, and agreement in rankings was plotted. The correlation in hospital AMI achievement scores for each age group was also calculated using the Hospital Value-Based Purchasing (HVBP) Program method computed with the electronic health record measure.Results267 763 and 276 031 AMI hospitalizations among older and younger patients, respectively, were identified. Median hospital 30-day RSMRs were 9.4%, 3.0%, and 6.2% for older, younger, and all patients, respectively. Most top- and bottom-performing hospitals for older patients were neither top nor bottom performers for younger patients. In contrast, most top and bottom performers for older patients were also top and bottom performers for all patients. Similarly, HVBP achievement scores for older patients correlated weakly with those for younger patients (R = 0.30) and strongly with those for all patients (R = 0.92).LimitationMinority of U.S. hospitals.ConclusionHospital mortality rankings for older patients with AMI inconsistently reflect rankings for younger patients. Incorporation of younger patients into assessment of hospital outcomes would permit further examination of the presence and effect of age-related quality differences.Primary funding sourceAmerican College of Cardiology.

Project description:Despite recent improvements in survival after acute myocardial infarction (AMI), U.S. hospitals vary 2-fold in their 30-day risk-standardized mortality rates (RSMRs). Nevertheless, information is limited on hospital-level factors that may be associated with RSMRs.To identify hospital strategies that were associated with lower RSMRs.Cross-sectional survey of 537 hospitals (91% response rate) and weighted multivariate regression by using data from the Centers for Medicare & Medicaid Services to determine the associations between hospital strategies and hospital RSMRs.Acute care hospitals with an annualized AMI volume of at least 25 patients.Patients hospitalized with AMI between 1 January 2008 and 31 December 2009.Hospital performance improvement strategies, characteristics, and 30-day RSMRs.In multivariate analysis, several hospital strategies were significantly associated with lower RSMRs and in aggregate were associated with clinically important differences in RSMRs. These strategies included holding monthly meetings to review AMI cases between hospital clinicians and staff who transported patients to the hospital (RSMR lower by 0.70 percentage points), having cardiologists always on site (lower by 0.54 percentage points), fostering an organizational environment in which clinicians are encouraged to solve problems creatively (lower by 0.84 percentage points), not cross-training nurses from intensive care units for the cardiac catheterization laboratory (lower by 0.44 percentage points), and having physician and nurse champions rather than nurse champions alone (lower by 0.88 percentage points). Fewer than 10% of hospitals reported using at least 4 of these 5 strategies.The cross-sectional design demonstrates statistical associations but cannot establish causal relationships.Several strategies, which are currently implemented by relatively few hospitals, are associated with significantly lower 30-day RSMRs for patients with AMI.The Agency for Healthcare Research and Quality, the United Health Foundation, and the Commonwealth Fund.

Project description:BackgroundCurrently, how to accurately determine the patient prognosis after a percutaneous coronary intervention (PCI) remains unclear and may vary among populations, hospitals, and datasets. The aim of this study was to establish a prediction model of in-hospital mortality risk after primary PCI in patients with acute ST-elevated myocardial infarction (STEMI).MethodsThis was a multicenter, observational study of patients with acute STEMI who underwent primary PCI. The outcome was in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome. A regression model was built using the selected variables to select the significant predictors of mortality. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.ResultsTotally, 1169 and 316 patients were enrolled in the training and validation sets, respectively. Fourteen predictors were identified by the LASSO analysis: sex, Killip classification, left main coronary artery disease (LMCAD), grading of thrombus, TIMI classification, slow flow, application of IABP, administration of ?-blocker, ACEI/ARB, symptom-to-door time (SDT), symptom-to-balloon time (SBT), syntax score, left ventricular ejection fraction (LVEF), and CK-MB peak. The mortality risk prediction nomogram achieved good discrimination for in-hospital mortality (training set: C-statistic?=?0.987; model calibration: P?=?0.722; validation set: C-statistic?=?0.984, model calibration: P?=?0.669). Area under the curve (AUC) values for the training and validation sets are 0.987 (95% CI: 0.981-0.994, P?=?0.003) and 0.990 (95% CI: 0.987-0.998, P?=?0.007), respectively. DCA shows that the nomogram can achieve good net benefit.ConclusionsA novel nomogram was developed and is a simple and accurate tool for predicting the risk of in-hospital mortality in patients with acute STEMI who underwent primary PCI.

Project description:Language barriers between patients and providers may influence the process and quality of care.To examine the association of language preference with length of stay (LOS) and in-hospital mortality for patients admitted for acute myocardial infarction (AMI).Electronic administrative hospital discharge data for all non-disabled Medicaid beneficiaries age 35 years and older admitted to all acute care California hospitals with a diagnosis of AMI between 1994 and 1998.We used multivariate regression to explore whether observed differences in the hospital LOS and in-hospital mortality between non-English preference (NEP) and English preference (EP) individuals could be explained by individual and/or hospital level factors. We adjusted for patient level characteristics using 24 covariates from a previously validated prediction model of mortality after hospitalization for AMI.Of 12,609 Medicaid patients across 401 California hospitals, 2,757 (22%) had NEP. NEP was associated with a 3.9% increased LOS (95% CI 0.7, 7.1; p = 0.02) in unadjusted analysis and a 3.8% increased LOS (95% CI 0.3, 7.3; p = 0.03) after controlling for patient level characteristics. Differences in LOS were no longer significant after adjusting receipt of cardiac procedure/ surgery (2.8%; 95% CI -0.6, 6.2; p = 0.1) or after adjusting for hospital (0.9%; 95% CI -2.5, 4.3; p = 0.6). Non-English language preference was associated with lower in-hospital mortality in unadjusted analysis (odds ratio [OR] = 0.80; 95% CI 0.69, 0.94; p = 0.005), but was not significant after adjusting for patient level characteristics (adjusted OR [AOR] 0.95; 95% CI 0.78, 1.27; p = 0.6). Adjusting for receipt of cardiac procedure/ surgery (AOR 0.97; 95% CI 0.79, 1.18; p = 0.7) and hospital (AOR 0.97; 95% CI 0.78; 1.21; p = 0.8) did not alter this finding.Language preference is not associated with AMI mortality, and the small increase in length of stay associated with non-English preference is accounted for by hospital level factors. Our results suggest that system level differences are important to consider in studies of the effect of language barriers in the health care setting.

Dataset Information

Derivation and validation of a combined in-hospital mortality and bleeding risk model in acute myocardial infarction.

Background

Methods

Results

Conclusion

Publications

Derivation and validation of a combined in-hospital mortality and bleeding risk model in acute myocardial infarction.

Similar Datasets

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