Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: The AMI “READMITS” (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus
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            Nonmale Sex
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            Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score

Nguyen, Oanh Kieu; Makam, Anil N.; Clark, Christopher; Zhang, Song; Das, Sandeep R; Halm, Ethan A.

doi:10.1161/jaha.118.008882

Cited by 21 publications

(38 citation statements)

References 38 publications

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“…We divided these studies on the basis of their population cohort into all patient populations (n=17, including one intensive care unit and one emergency department readmission) and patient specific populations (n=24). Most patient specific models were for heart conditions (n=13) 24252627303234373843454647. The remainder were based on readmission among patients with diabetes (n=4),28334041 kidney transplantation (1),44 hemodialysis (1),29 low back surgery (1),36 pneumonia (2),3135 lupus (1),39 and psychiatric conditions (1) 42.…”

Section: Resultsmentioning

confidence: 99%

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Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Mahmoudi

Kamdar

Kim

et al. 2020

BMJ

122

128

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Objective To provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission. Design Systematic review. Data source Ovid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019. Eligibility criteria for selecting studies All studies of predictive models for 28 day or 30 day hospital readmission that used EMR data. Outcome measures Characteristics of included studies, methods of prediction, predictive features, and performance of predictive models. Results Of 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval −0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77). Conclusions On average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.

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Section: Resultsmentioning

confidence: 99%

“…All validations were done internally; most were conducted through retrospective validation (n=37) and used split sample (n=24) or cross validation (n=11) methods. The C statistics ranged between 0.52 and 0.90,2324 with 17 studies reporting a C statistic of 0.75 or greater 1112141516171923293334363742434647…”

Section: Resultsmentioning

confidence: 99%

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Mahmoudi

Kamdar

Kim

et al. 2020

BMJ

122

128

View full text Add to dashboard Cite

show abstract

“…The 920 unique records after eliminating 184 duplication literatures would be further removed in a stepwise by the title, abstract, full‐text according to pre‐specified study inclusion and exclusion criteria. Ultimately, 14 observational studies met the eligibility criteria and were comprised in this systematic review, which the detailed filtering process was presented in Figure . Ten of the 14 studies were performed in the America, which the rest of individual studies were conducted Chain, France, Spain, and the United Kingdom (UK).…”

Section: Resultsmentioning

confidence: 99%

The prevalence of 30‐day readmission after acute myocardial infarction: A systematic review and meta‐analysis

Wang

Zhao

Wei

et al. 2019

Clinical Cardiology

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Objective The 30‐day readmission is associated with increased medical costs, which has become an important quality metric in several medical institutions. This current study is aimed at clarifying the prevalence, the underlying risk factors, and reasons of the 30‐day readmission after acute myocardial infarction (AMI). Methods PubMed, Cochrane Library, and EMBASE were systematically searched to identify eligible studies. Random‐effect models were employed to perform pooled analyses. Means and 95% confidence intervals (CIs) were used to estimate prevalence and reasons for 30‐day readmission. We also used Odds ratios (ORs) to explore the potential significant predictors of risk factors of 30‐day readmission after AMI. Potential publication bias was assessed using funnel plot and Begg'test. Results A total of 14 relevant studies were included in this systematic review and meta‐analysis. The pooled 30‐day readmission rate of AMI was 12% (95% CI 0.11‐0.14). Acute coronary syndrome (ACS), angina and acute ischemic heart disease, and heart failure (HF) were the principal cardiovascular reasons of 30‐day readmission. Meanwhile, non‐specific chest pain was regarded as the significant cause among non‐cardiovascular reasons. The common co‐morbidities kidney disease, HF and diabetes mellitus were significant risk factors for 30‐day readmission. No significant publication bias was found by funnel plot and statistical tests. Conclusions The 30‐day readmission rate of post‐AMI ranged from 11% to 14% and can be mainly attributed to cardiovascular and non‐cardiovascular events. The common co‐morbidities, such as kidney disease, HF, and diabetes mellitus were significant risk factors for 30‐day readmission.

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“…A recent predictive model for hospital readmission for acute myocardial infarction similarly pointed to the use of emergency services as a predictive factor (Nguyen et al, 2018). Frequent emergencies increase the chances of readmission for general causes (van Walraven et al, 2010) but are still an indicator insufficiently used by health professionals to predict further hospitalisations.…”

Section: Discussionmentioning

confidence: 99%

“…Although the medical diagnosis of diabetes mellitus was not significant for readmissions in the present model, it is evident that the glycaemic value is related to the outcome. In other predictive models for similar outcomes (Nguyen, 2018; Stewart, 2000), this variable was not investigated.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical analysis of factors associated with hospital readmissions for coronary heart disease: A case–control study

Albuquerque

Araújo

Lopes

et al. 2020

Journal of Clinical Nursing

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Aims and objectives To analyse, hierarchically, factors associated with hospital readmissions for acute coronary syndrome. Background Hospital readmissions have risen, especially in patients with multiple comorbidities, which are most often chronic. The leading causes of hospital readmission include acute coronary syndrome, which is costly and often preventable. Determining clinical and nonclinical variables that increase the chances of readmission is important to assess and evaluate patients hospitalised for coronary heart diseases. Design A case–control study whose dependent variable was hospital readmission for acute coronary syndrome. Methods The study included 277 inpatients, of whom 132 were in their first hospitalisation and 145 had already been hospitalised for acute coronary syndrome. The independent variables for this hierarchical model were sociodemographic conditions, life habits, access to health services and physical health measures. Data were obtained by interviews, anthropometric measurements and patient records. Logistic regression analysis was performed using the stepwise technique, with Microsoft Excel and R version 3.2.3. The research was reported via strengthening the reporting of observational studies in epidemiology (STROBE). Results In the final hierarchical logistic model, the following risk factors were associated with readmission for acute coronary syndrome: inadequate drug therapy adherence, stress, history of smoking for 30 years or more, and the lack of use of primary healthcare services. Conclusions Clinical and nonclinical variables are related to hospital readmission for acute coronary syndrome and can increase the chance of readmission by up to six times. Relevance to clinical practice The predictive model can be used to avoid readmission for acute coronary syndrome, and it represents an advance in the prediction of the occurrence of the outcome. This implies the need for a reorientation of the network for postdischarge care in the first hospitalisation for acute coronary syndrome.

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Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: The AMI “READMITS” (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus , Nonmale Sex , Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score

Cited by 21 publications

References 38 publications

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

The prevalence of 30‐day readmission after acute myocardial infarction: A systematic review and meta‐analysis

Hierarchical analysis of factors associated with hospital readmissions for coronary heart disease: A case–control study

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