Development and validation of a risk-prediction model for in-hospital major adverse cardiovascular events in patients admitted to hospital with acute myocardial infarction: results from China PEACE retrospective and prospective studies

The Lancet ◽  
2019 ◽  
Vol 394 ◽  
pp. S17
Author(s):  
Chaoqun Wu ◽  
Xiqian Huo ◽  
Jiamin Liu ◽  
Lihua Zhang ◽  
Jiapeng Lu ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Prediction Model ◽  
Risk Prediction ◽  
Prospective Studies ◽  
Cardiovascular Events ◽  
Risk Prediction Model ◽  
Major Adverse Cardiovascular Events ◽  
Development And Validation

Abstract 14508: Development of a Risk Prediction Model for 1-year Readmission Among Younger Adults Hospitalized for Acute Myocardial Infarction

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Rachel P Dreyer ◽  
Terrence E Murphy ◽  
Valeria Raparelli ◽  
Sui Tsang ◽  
Gail Onofrio ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Prediction Model ◽  
Risk Prediction ◽  
Bayesian Model Averaging ◽  
Risk Model ◽  
Young Patients ◽  
Risk Prediction Model ◽  
Younger Adults

Introduction: Although readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (18-55 yrs), there is no available risk prediction model for this age group. Existing risk models have been developed in older populations, have modest predictive ability, and exhibit methodological drawbacks. We developed a risk prediction model that considered a broad range of demographic, clinical, and psychosocial factors for readmission within 1-year of hospitalization for AMI among young adults. Methods: Young AMI adults (18-55 yrs) were enrolled from the prospective observational VIRGO study (2008-2012) of 3,572 patients. Data were obtained from medical record abstraction, interviews, and adjudicated hospitalization records. The outcome was all-cause readmission within 1-year. We used a two-stage selection process (LASSO followed by Bayesian Model Averaging) to develop a risk model. Results: The median age was 48 years (IQR: 44,52), 67.1% were women, and 20.1% were Non-white or Hispanic. Within 1-year, 906 patients (25.3%) were readmitted. Patients who were readmitted were more likely to be female, black, and had a clustering of adverse risk factors and co-morbidities. From 61 original variables considered, the final multivariable model of readmission within 1-year of discharge consisted of 14 predictors (Figure) . The model was well calibrated (Hosmer-Lemeshow P >0.05) with moderate discrimination (C statistic over 33 imputations: 0.69 development cohort). Conclusion: Adverse clinical risk factors such as diabetes, hypertension and prior AMI, but also female sex, access to specialist care, and major depression were associated with a higher risk of readmission at 1-year post AMI. This information is important to inform the development of interventions to reduce readmissions in young patients with AMI.

scholarly journals Development and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction

2021 ◽  
Vol 10 (18) ◽  
Author(s):  
Rachel P. Dreyer ◽  
Valeria Raparelli ◽  
Sui W. Tsang ◽  
Gail D’Onofrio ◽  
Nancy Lorenze ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Young Adults ◽  
Depressive Symptoms ◽  
Length Of Stay ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Model ◽  
Risk Prediction Model ◽  
Hospital Length Of Stay

Background Readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (≤55 years). Our aim was to develop/validate a risk prediction model that considered a broad range of factors for readmission within 1 year. Methods and Results We used data from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study, which enrolled young adults aged 18 to 55 years hospitalized with AMI across 103 US hospitals (N=2979). The primary outcome was ≥1 all‐cause readmissions within 1 year of hospital discharge. Bayesian model averaging was used to select the risk model. The mean age of participants was 47.1 years, 67.4% were women, and 23.2% were Black. Within 1 year of discharge for AMI, 905 (30.4%) of participants were readmitted and were more likely to be female, Black, and nonmarried. The final risk model consisted of 10 predictors: depressive symptoms (odds ratio [OR], 1.03; 95% CI, 1.01–1.05), better physical health (OR, 0.98; 95% CI, 0.97–0.99), in‐hospital complication of heart failure (OR, 1.44; 95% CI, 0.99–2.08), chronic obstructive pulmomary disease (OR, 1.29; 95% CI, 0.96–1.74), diabetes mellitus (OR, 1.23; 95% CI, 1.00–1.52), female sex (OR, 1.31; 95% CI, 1.05–1.65), low income (OR, 1.13; 95% CI, 0.89–1.42), prior AMI (OR, 1.47; 95% CI, 1.15–1.87), in‐hospital length of stay (OR, 1.13; 95% CI, 1.04–1.23), and being employed (OR, 0.88; 95% CI, 0.69–1.12). The model had excellent calibration and modest discrimination (C statistic=0.67 in development/validation cohorts). Conclusions Women and those with a prior AMI, increased depressive symptoms, longer inpatient length of stay and diabetes may be more likely to be readmitted. Notably, several predictors of readmission were psychosocial characteristics rather than markers of AMI severity. This finding may inform the development of interventions to reduce readmissions in young patients with AMI.

Development and Validation of a Risk Prediction Model for Patients with COVID-19 Using Linked Population Health Data (Preprint)

2021 ◽  
Author(s):  
Nikolaos Mastellos ◽  
Richard Betteridge ◽  
Prasanth Peddaayyavarla ◽  
Andrew Moran ◽  
Jurgita Kaubryte ◽  
...  
Keyword(s):  
Health Care ◽  
Prediction Model ◽  
Population Health ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Risk Prediction Model ◽  
Adult Patients ◽  
Internal Validation ◽  
Temporal Validation ◽  
Development And Validation

BACKGROUND The impact of the COVID-19 pandemic on health care utilisation and associated costs has been significant, with one in ten patients becoming severely ill and being admitted to hospital with serious complications during the first wave of the pandemic. Risk prediction models can help health care providers identify high-risk patients in their populations and intervene to improve health outcomes and reduce associated costs. OBJECTIVE To develop and validate a hospitalisation risk prediction model for adult patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). METHODS The model was developed using pre-linked and standardised data of adult patients with laboratory confirmed SARS-CoV-2 from Cerner’s population health management platform (HealtheIntent®) in the London Borough of Lewisham. A total of 14,203 patients who tested positive for SARS-CoV-2 between 1st March 2020 and 28th February 2021 were included in the development and internal validation cohorts. A second temporal validation cohort covered the period between 1st March 2021 to 30th April 2021. The outcome variable was hospital admission in adult patients with laboratory confirmed SARS-CoV-2. A generalised linear model was used to train the model. The predictive performance of the model was assessed using the area under the receiver operator characteristic curve (ROC-AUC). RESULTS Overall, 14,203 patients were included. Of those, 9,755 (68.7%) were assigned to the development cohort, 2,438 (17.2%) to the internal validation cohort, and 2,010 (14.1%) to the temporal validation cohort. A total of 917 (9.4%) patients were admitted to hospital in the development cohort, 210 (8.6%) in the internal validation cohort, and a further 204 (10.1%) in the temporal validation cohort. The model had a ROC-AUC of 0.85 in both the development and validation cohorts. The most predictive factors were older age, male sex, Asian or Other ethnic minority background, obesity, chronic kidney disease, hypertension and diabetes. CONCLUSIONS The COVID-19 hospitalisation risk prediction model demonstrated very good performance and can be used to stratify risk in the Lewisham population to help providers reduce unnecessary hospital admissions and associated costs, improve patient outcomes, and target those at greatest risk to ensure full vaccination against SARS-CoV-2. Further research may examine the external validity of the model in other populations.

P6424The association between long-term beta-blocker use and outcome in a contemporary large-scale cohort of patients with acute myocardial infarction

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
C X Song ◽  
R Fu ◽  
J G Yang ◽  
K F Dou ◽  
Y J Yang
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Beta Blocker ◽  
Cardiovascular Events ◽  
Beta Blockers ◽  
Left Ventricular Ejection ◽  
Major Adverse Cardiovascular Events ◽  
Baseline Characteristics

Abstract Background Controversy exists regarding the use of beta-blockers (BBs) among patients with acute myocardial infarction (AMI) in contemporary reperfusion era. Previous studies predominantly focused on beta-blockers prescribed at discharge, and the effect of long-term adherence to beta-blocker on major adverse cardiovascular events (MACE) remains unclear. Objective To explore the association between long-term beta-blocker use patterns and MACE among contemporary AMI patients. Methods We enrolled 7860 patients with AMI, who were discharged alive and prescribed with BBs based on CAMI registry from January 2013 to September 2014. Patients were divided into two groups according to BBs use pattern: Always users group (n=4476) were defined as patients reporting BBs use at both 6- and 12-month follow-up; Inconsistent users group were defined as patients reporting at least once not using BBs at 6- or 12-month follow-up. Primary outcome was defined as MACE at 24-month follow-up, including all-cause death, non-fatal MI and repeat-revascularization. Multivariable cox proportional hazards regression model was used to assess the association between BBs and MACE. Results Baseline characteristics are shown in table 1. At 2-year follow-up, 518 patients in inconsistent users group (15.6%) and 548 patients in always users group (12.3%) had MACE. After multivariable adjustment, inconsistent use of BBs was associated with higher risk of MACE (HR: 1.323, 95% CI: 1.171–1.493, p<0.001). Table 1 Baseline characteristics Variable Always user (N=4476) Inconsistent user (N=3384) P value Age (years) 60.6±12.0 61.2±12.2 <0.001 Male 3381 (75.7%) 2461 (74.3%) 0.084 Diabetes 892 (20.0%) 610 (18.4%) 0.003 Hypertension 2372 (53.2%) 1543 (46.6%) <0.001 Dyslipidemia 244 (5.5%) 126 (3.8%) <0.001 Prior myocardial infarction 351 (7.9%) 232 (7.0%) <0.001 Heart failure 88 (2.0%) 63 (1.9%) <0.001 Chronic obstructive pulmonary disease 66 (1.5%) 60 (1.8%) <0.001 Current smoker 2054 (46.1%) 1579 (47.8%) 0.179 Left ventricular ejection fraction (%) 53.7±11.48 54.0±10.9 <0.001 Major Adverse Cardiovascular Events 548 (12.3%) 518 (15.6%) <0.001 Conclusions Our results showed consistent BBs use was associated with reduced risk of MACE among patients with AMI managed by contemporary treatment. Acknowledgement/Funding CAMS Innovation Fund for Medical Sciences (CIFMS) (2016-I2M-1-009)

Sign in / Sign up

Export Citation Format

Share Document