Abstract
Background
Risk factors for emergent readmissions or death after acute myocardial infarction (AMI) are important in identifying patients at risk for major adverse events. However, there has been limited investigation conducted of prospective clinical registries to determine relevant risk factors.
Hypothesis
We hypothesize 30‐day readmission or death could be predicted using patient, procedural, and process factors.
Methods
Patients presenting with ST‐elevation myocardial infarction (STEMI) from 2006 to 2011 were prospectively enrolled in a STEMI registry (1271 patients). Thirty‐day readmission was ascertained by administrative claims data. Death was determined by linking to the Social Security Death Master File. Univariate and stepwise multivariate logistic regression was conducted with Hosmer‐Lemeshow goodness‐of‐fit statistics for model calibration and receiver operating characteristic (ROC) curve for model discrimination.
Results
The combined end point of 30‐day readmission or postdischarge death included 135 patients (10.6%), including 109 emergent readmissions and 26 deaths. Factors associated with an increase risk of 30‐day readmission or postdischarge death included age ≥80 years, diabetes, chest pain or cardiac arrest at presentation, and 3‐vessel disease found at initial angiography. Factors associated with a decreased risk of 30‐day readmission or postdischarge death included transfer to the catheterization lab from another emergency department, clopidogrel given during the procedure hypercholesterolemia, and receiving aspirin, β‐blockers, and angiotensin‐converting enzyme or angiotensin receptor blocker inhibitors at discharge. Index admission outcomes indicative of readmission or death postdischarge only included a new diagnosis of congestive heart failure. The model discriminated well with an ROC of 0.71 (95% confidence interval: 0.66‐0.76).
Conclusions
Prehospitalization factors are overlooked and are important factors to incorporate in routine risk prediction models for readmission or death within 30 days following an AMI.
Introduction
Approximately 20% of Medicare beneficiaries are rehospitalized within 30 days of a hospital admission.1 Beginning in fiscal year 2012, hospital readmissions following acute myocardial infarction (AMI) and other conditions will be subject to marked reductions in reimbursement from the Center for Medicaid and Medicare Services (section 3025 of the Patient Protection and Affordable Care Act).2, 3 Current risk modeling for 30‐day readmission following AMI has focused on patient risk factors derived from claims.4 Other investigations in all angioplasty patients have reported limited success in discrimination and ability to predict 30‐day readmissions alone.5, 6 However, if patient safety is the goal, prediction models should focus on both readmission and the competing risk of mortality in the first 30 days after discharge. Currently, there has been little evidence developed to investigate readmission and the competing risk of death following ST‐segment elevated myocardial infarction (STEMI),7, 8 and limited evidence regarding risk factors for or the underlying principal causes of readmission following STEMI.9, 10 Moreover, evidence has been sparse in describing these phenomena in rural tertiary care settings. Therefore, we sought to determine the underlying principal causes of readmission in a regional STEMI registry and develop a comprehensive predictive model for 30‐day emergent readmission or postdischarge death.
Methods
Dartmouth‐Hitchcock Medical Center is a rural tertiary care setting. Since 2001, all patients presenting initially to the Dartmouth‐Hitchcock Medical Center emergency department, as well as all emergency transfers who have not received thrombolytic therapy, have been treated with a strategy of immediate primary percutaneous coronary intervention (PCI). All emergency transfers who have received thrombolytic therapy at the referring facility have been treated with a strategy of pharmacoinvasive PCI. All patients presenting to the Dartmouth‐Hitchcock Medical Center cardiac catheterization laboratory with a diagnosis of STEMI between 2006 and 2011 were prospectively enrolled in the regional STEMI registry. Of the 1415 patients who were enrolled in the registry during the study period, 106 patients were excluded due to false STEMI diagnosis and 38 index admission deaths, leaving 1271 STEMI patients for analysis. The study was reviewed and approved by the Center for Protection of Human Subjects.
Preadmission emergency medical service, index hospital, and procedural data were collected by clinical staff and recorded in a prospective STEMI registry. Death following the index admission was obtained from the Social Security Death Master File. Hospital readmissions were defined by the time from STEMI admission to 30 days. Readmissions were obtained through Dartmouth‐Hitchcock's electronic data warehouse. Once readmission was determined, 2 investigators adjudicated the principal reason for readmission (Table 1). All readmissions were included in the principal causes of readmission. Only emergent readmissions and postdischarge death occurring within 30 days of the STEMI admission were included for the development of the prediction model. Primary cause of death within 30 days was not available.
Table 1.
Principal Causes of Readmission
| Events | Percent | |
|---|---|---|
| Ischemic heart disease | 54 | 43% |
| Chest pain | 20 | 16% |
| Gastrointestinal | 10 | 8% |
| Heart failure | 9 | 7% |
| Arrhythmia | 4 | 3% |
| Atherosclerosis | 4 | 3% |
| Valve disease | 3 | 2% |
| Renal failure | 3 | 2% |
| Respiratory | 3 | 2% |
| Medication | 3 | 2% |
| Shock | 2 | 2% |
| Cerebral | 2 | 2% |
| Bleeding | 2 | 2% |
| Other | 8 | 6% |
Statistical Methods
Patient demographics, comorbidities, procedural variables, in‐hospital outcomes, and discharge location were evaluated for inclusion in the prediction model using χ2 and Student t test comparisons and univariate logistic regression.11, 12 Using only factors with univariate associations with the composite end point of death or readmission, with an α of 0.10, both forward and backward step logistic regression were conducted to determine factors to be included in the multivariable model using an α of 0.10. Factors remaining in the multivariate models were evaluated, and only factors with an alpha of <0.10 were included in the final prediction model. First‐order interaction terms were evaluated. Model calibration was assessed using plots of observed and expected by deciles of predicted risk using the methods by Lemeshow and Hosmer.13 The discriminating ability of the final multivariate logistic regression model was assessed by estimating the area under the receiver operating characteristic (ROC) curve C‐statistic (ROC area).14 The bootstrapping method was used to calculate the 95% confidence interval (CI) around the ROC curve.15
Results
There were 14 elective readmissions, 109 emergent readmissions, and 26 postdischarge deaths within 30 days of STEMI. The principal causes of any readmission are listed in Table 1, the most common of which included ischemic heart disease (43% of all readmissions), chest pain (20%), gastrointestinal complications (8%), and heart failure (7%). The combined end point of 30‐day emergent readmission or death included 135 patients (11.7%), including 109 emergent readmissions and 26 deaths. Patient demographics and characteristics are reported in Table 2. Table 3 summarizes periprocedural characteristics, hospitalization risk factors, and discharge medications. Patient, procedural, and hospital factors included in the final multivariate model are reported in Table 4. Risk factors associated with 30‐day emergent readmission or postdischarge death following acute hospitalization for STEMI included age ≥80 years, diabetes, chest pain at presentation, cardiac arrest at presentation, and 3‐vessel disease. Preadmission transfer to the catheterization lab from another emergency department and hypercholesterolemia were factors that lowered the risk of readmission or postdischarge death. Clopidogrel given during the procedure was protective against readmission and postdischarge death. Index admission outcomes indicative of readmission or death only included a new diagnosis of congestive heart failure. The final multivariate model for readmission or death had good calibration with the Hosmer‐Lemeshow χ2 of 8.0 and P = 0.4. The model discriminated well with an ROC of 0.71 and 95% CI: 0.66‐0.76.
Table 2.
Patient and Disease Characteristics
| Patient Factors | Readmitted or Death Within 30 Days | Not Readmitted and Alive at 30 Days | P Valuea |
|---|---|---|---|
| Alive discharges (N = 1271) | 135 (10.6%) | 1136 | |
| Patient comorbidities | |||
| Age ± SD, y | 66.3 ± 14.8 | 62.3 ± 12.9 | <0.001 |
| Age ≥80 years, % | 23.0 | 10.6 | <0.001 |
| Female, % | 37.3 | 26.9 | 0.006 |
| Angina, % | 28.0 | 26.3 | 0.646 |
| Prior myocardial infarction, % | 29.8 | 23.6 | 0.082 |
| Prior coronary artery bypass surgery, % | 3.1 | 3.9 | 0.639 |
| Prior cerebrovascular accident, % | 11.2 | 5.9 | 0.011 |
| Diabetes mellitus, % | 28.0 | 20.9 | 0.042 |
| Hypertension, % | 63.4 | 59.0 | 0.288 |
| Underweight (BMI < 18.5), % | 1.9 | 1.6 | 0.772 |
| Obese (BMI >30), % | 36.7 | 33.4 | 0.411 |
| Aspirin therapy, % | 39.8 | 37.9 | 0.649 |
| Hypercholesterolemia, % | 51.4 | 51.4 | 0.114 |
| Statin therapy, % | 33.5 | 30.8 | 0.488 |
| Chest pain, % | 95.0 | 92.0 | 0.179 |
| Smoker, % | 31.7 | 36.0 | 0.280 |
| Presentation | |||
| Transfer patient | 69.6 | 78.0 | 0.017 |
| Cardiac arrest, % | 16.8 | 6.2 | <0.001 |
| Shortness of breath, % | 54.7 | 42.4 | 0.003 |
| Syncope, % | 13.7 | 6.6 | 0.001 |
| Symptom duration ± SD, min | 267 ± 474 | 252 ± 577 | 0.752 |
| Anterior ST‐elevation, % | 45.3 | 35.8 | 0.018 |
| Inferior ST‐elevation, % | 49.1 | 60.0 | 0.008 |
| Lateral ST‐elevation, % | 23.6 | 21.7 | 0.591 |
| Left bundle branch block, % | 3.1 | 2.8 | 0.820 |
| Anterior ST‐depression, % | 28.0 | 29.0 | 0.791 |
| Sinus rhythm, % | 80.8 | 88.4 | 0.006 |
| Systolic blood pressure ± SD | 118 ± 40 | 127 ± 39 | 0.005 |
| Diastolic blood pressure ± SD | 69 ± 24 | 75 ± 24 | 0.009 |
| Heart rate ± SD | 74 ± 26 | 73 ± 24 | 0.571 |
| Killip class IV, % | 12.4 | 3.3 | <0.001 |
| Primary PCI, % | 52.6 | 42.0 | 0.019 |
| Full dose lytic given, % | 21.7 | 29.4 | 0.044 |
| Half dose lytic given, % | 19.9 | 22.5 | 0.455 |
| Persistent pain, % | 50.9 | 49.4 | 0.712 |
| Persistent ST‐elevations, % | 68.3 | 65.7 | 0.511 |
| Shock, % | 22.4 | 7.3 | <0.001 |
| Intubation, % | 19.9 | 6.1 | <0.001 |
| TIMI flow pre | |||
| 0 | 47.8 | 43.0 | 0.096 |
| 1 | 11.8 | 7.7 | |
| 2 | 19.3 | 22.0 | |
| 3 | 21.1 | 27.4 | |
| Coronary disease | |||
| Left main disease >50%, % | 8.7 | 5.5 | 0.105 |
| 2‐Vessel disease >70%, % | 25.5 | 29.9 | 0.241 |
| 3‐vessel disease >70%, % | 38.5 | 23.1 | <0.001 |
| Infarcted left anterior descending, % | 42.9 | 33.0 | 0.013 |
| Infarcted left circumflex, % | 14.3 | 14.4 | 0.981 |
| Infarcted right coronary, % | 36.7 | 43.1 | 0.121 |
| Infarcted ramus, % | 0.6 | 0.6 | 0.941 |
| Infarcted left main, % | 1.9 | 0.6 | 0.070 |
| TIMI flow post | |||
| 0 | 10.6 | 13.1 | 0.001 |
| 1 | 1.9 | 0.2 | |
| 2 | 3.7 | 1.4 | |
| 3 | 83.9 | 85.4 |
Abbreviations: BMI, body mass index; PCI, percutaneous coronary intervention; SD, standard deviation; TIMI, Thrombolysis in Myocardial Infarction. TIMI Flow Pre, TIMI flow measured prior to intervention; TIMI Flow Post, TIMI flow measured after intervention.
Pearson χ2 or Student t test and SD.
Table 3.
Procedural Characteristics, Outcomes, and Discharge Medications
| Procedural Factors and Outcomes | Readmitted or Death Within 30 Days | Not Readmitted and Alive at 30 Days | P Valuea |
|---|---|---|---|
| Procedure characteristics | |||
| Door‐to‐balloon time within 90 minutes, % | 14.9 | 12.7 | 0.437 |
| Cardiac catheterization within 4 hours, % | 88.8 | 89.4 | 0.817 |
| Attempted PCI, % | 90.1 | 87.8 | 0.401 |
| Stent, % | 85.1 | 84.7 | 0.907 |
| 2b3a Inhibitor during catheterization, % | 46.0 | 46.1 | 0.973 |
| Antithrombotic during catheterization, % | 93.8 | 92.9 | 0.666 |
| Clopidogrel during catheterization, % | 54.7 | 65.5 | 0.007 |
| Heparin, % | 62.2 | 65.3 | 0.476 |
| Bivalirudin, % | 18.5 | 18.2 | 0.933 |
| Thrombectomy, % | 31.1 | 32.3 | 0.779 |
| Intra‐aortic balloon pump, % | 19.9 | 7.6 | <0.001 |
| Intubation during catheterization, % | 10.6 | 3.2 | <0.001 |
| In‐hospital outcomes | |||
| Recurrent MI, % | 1.2 | 1.2 | 0.574 |
| Persistent neurological deficit % | 3.7 | 1.2 | 0.010 |
| Cerebrovascular accident, % | 5.0 | 1.2 | <0.001 |
| Intracranial hemorrhage, % | 1.2 | 0.4 | 0.162 |
| Congestive heart failure, % | 26.7 | 9.2 | <0.001 |
| Coronary artery bypass graft surgery, % | 3.7 | 2.1 | 0.177 |
| Left ventricular gram, % | 78.3 | 71.8 | 0.083 |
| Ejection fraction (%) ± SD | 46.0 ± 13.9 | 51.4 ± 11.3 | <0.001 |
| Ejection fraction <40%, % | 25.5 | 12.3 | <0.001 |
| Length of stay (days) ± SD | 5.7 ± 4.8 | 4.6 ± 3.1 | <0.001 |
| Length of stay >6 days, % | 28.0 | 15.8 | <0.001 |
| Discharge medications | |||
| Aspirin, % | 87.9 | 97.5 | <0.001 |
| β‐Blockers, % | 87.9 | 96.0 | <0.001 |
| ACE or ARB inhibitors, % | 74.0 | 80.7 | 0.078 |
| Aspirin, BB, and ACE or ARB combined, % | 64.4 | 74.6 | 0.012 |
| Calcium channel blockers, % | 6.7 | 7.1 | 0.859 |
| Lipid‐lowering agents, % | 87.1 | 96.6 | <0.001 |
| Antiarrhythmics, % | 5.8 | 2.3 | 0.023 |
| Platelet inhibitors, % | 94.1 | 95.7 | 0.441 |
| Warfarin, % | 22.0 | 16.1 | 0.088 |
Abbreviations: ACE, angiotensin‐converting enzyme; ARB, angiotensin receptor blocker; BB, β‐blocker; MI, myocardial infarction; PCI, percutaneous coronary intervention; SD, standard deviation.
Pearson χ2 or Student t test and SD.
Table 4.
Final Multivariate Logistic Model for Emergent 30‐Day Readmission or Death
| Risk Factor | Adjusted Odds Ratio | 95% CI | P Value |
|---|---|---|---|
| Patient comorbidities | |||
| Age ≥80 years | 2.25 | 1.39‐3.63 | 0.001 |
| Diabetes | 1.90 | 1.21‐2.96 | 0.005 |
| Hypercholesterolemia | 0.61 | 0.41‐0.91 | 0.016 |
| Presentation and disease | |||
| Transfer patient | 0.56 | 0.37‐0.85 | 0.006 |
| Chest pain | 3.37 | 1.35‐8.43 | 0.009 |
| Cardiac arrest | 3.02 | 1.71‐5.34 | <0.001 |
| 3‐Vessel disease | 1.95 | 1.31‐2.90 | 0.001 |
| Clopidogrel given during procedure | 0.69 | 0.47‐1.02 | 0.061 |
| In‐hospital outcomes | |||
| In‐hospital new heart failure | 2.15 | 1.32‐3.51 | 0.002 |
| Discharge medications | |||
| Aspirin, β‐blocker, ACE or ARB inhibitors combined | 0.67 | 0.45‐0.99 | 0.050 |
Abbreviations: ACE, angiotensin‐converting enzyme; ARB, angiotensin receptor blocker; CI, confidence interval.
Discussion
We have developed a comprehensive model for estimating risk of 30‐day emergent readmission or postdischarge death after STEMI. Because this combined end point encompasses nearly all patients with major adverse events, the creation of this model is intended to assist in addressing the problem of readmission without ignoring patient safety. We report on patient factors in addition to STEMI presentation factors, and procedural and index hospitalization risk factors for readmission or postdischarge death within 30 days of STEMI with an ROC of 0.71 and 95% CI: 0.66‐0.76. We are among the first to report on novel risk factors for readmission and death including presenting a history of chest pain, shortness of breath, cardiac arrest, and whether or not the patient was transferred from another facility. The incorporation of these factors into routine clinical risk models for 30‐day readmission or death should be considered.
There is sparse evidence on the principal causes of readmission following acute hospitalization for STEMI. We demonstrated in Table 1 the principal causes of 30‐day readmission, including elective (staged) and emergent readmissions. We found the most common causes of readmission were ischemic heart disease, chest pain, gastrointestinal complications, and heart failure (7%). We observed similar results to previous reports on PCI causes of readmissions listing ischemic heart disease, chest pain, and heart failure as 3 of the leading causes of readmission after PCI.5, 10 However, we expanded our modeling to incorporate 30‐day mortality in an effort to cluster 2 major accepted 30‐day quality measures of patient safety, and move the field toward the development of predictive models targeting patient safety measures and not readmissions alone, which could be used to help institutions reduce readmissions but not at the expense of patient safety.
Recently, there has been an emergence of evidence from major state‐based registries and hospitals reporting on the risk factors associated with 30‐day readmission after AMI. However, these reports have predominantly focused their modeling efforts on the entire PCI population and were not restricted to PCI following STEMI.5, 10 Various investigations that have developed models to predict 30‐day readmission have been moderate in performance including reports using Medicare claims (ROC 0.63)16 and state registries where all PCI procedures reported a readmission rate of 12.4% to 15.6%, however with poor discrimination with ROC curves ranging from 0.64 to 0.67.5, 10 Others have reported the composite outcome of readmission or death within 30 days at 9.4% and 0.68%, respectively.6 Four common risk scores have been developed to predict 30‐day and 1‐year mortality after STEMI, including the Controlled Abciximab and Device Investigation to Lower Late Angioplasty complications (CADILLAC),17 Thrombolysis In Myocardial Infarction (TIMI),18 Global Registry for Acute Coronary Events (GRACE),19 and Primary Angioplasty in Myocardial Infarction (PAMI).20 These risk scores have been externally validated and perform with moderate to good discrimination, with ROCs from 0.72 to 0.82.21 Several shared risk factors in these models with our composite outcome include older age, 3‐vessel disease, and diabetes. We expanded on the science by contributing risk factors derived from the time of presentation with STEMI, including whether or not the patient was transferred from another facility, chest pain, and cardiac arrest at the time of STEMI presentation. We also expanded the research in this field by combining 2 major patient safety quality measures, 30‐day readmission and 30‐day all‐cause mortality after alive discharge, in an effort to redirect the field to routinely model early postdischarge adverse events that are threats to patient safety and not solely on readmission. Unfortunately, the majority of these preliminary reports on readmission do not separate out acute hospitalization for AMI or primary PCI.5, 6, 10, 22 We contributed to the literature a clinical risk model for STEMI 30‐day readmission or death and evaluate risk factors at the time of presentation with STEMI.
There are several limitations of this investigation to consider. First, we presented results from a single rural academic tertiary medical center receiving STEMI patients from Vermont and New Hampshire. Nearly all of our patients were treated with an initial strategy of either primary PCI or pharmacoinvasive PCI, and therefore our report may not be generalizable to medical centers performing predominantly primary PCI for STEMI or those treating predominantly with a stand‐alone lytic therapy strategy. Regardless, our results appear to be consistent with the PCI models reported from other regions including New York, Massachusetts, and Minnesota. Moreover, we used prospectively collected STEMI registry data to measure, for the first time, STEMI presentation and prehospitalization measures with readmission. Although it is possible that unmeasured factors during the index admission or postdischarge may be associated with readmission (eg, discharge planning and quality, medications, medication compliance, and referrals to primary care and cardiac rehabilitation services), we believe that along with the inclusion of STEMI processes of care, using patient and procedural factors is a comprehensive approach toward developing a prediction model for STEMI readmission, and contributes novel evidence on modeling readmission and mortality. In our modeling, we observed transfer patients to be a protective factor against 30‐day death or readmission. There are several reasons our transfer patients may be a protective population. We limited our analysis to patients alive at discharge. Patients transferred to our hospital from an outlying hospital must have survived both the transit and index admission to be included in the analysis. It is likely that high‐risk patients or patients in shock died prior to arrival during a transfer or did not survive the index admission, leaving only low‐ to moderate‐risk patients alive at discharge among the transfer patients. Over the study period we also noted a marked improvement in transfer 30‐day mortality and reduction in door‐to‐needle and door‐to‐balloon times. Finally, the ascertainment of readmission was conducted using hospital administration data. In our region, all major cardiac complications are referred back to our tertiary medical center for observation, readmission, or repeat angiography with or without angioplasty. As noted by Desai and colleagues,9 conducting readmission investigations in the United States requires the use of Medicare data to completely ascertain the end point of readmission. Although we did not link our data to Medicare claims, in doing so, the ascertainment of readmission would only supplement patients over the age of 65 years and not include HMO‐covered beneficiaries under Medicare (in some geographic regions, the HMO penetration for Medicare beneficiaries is more than 50%), and will still underestimate the ascertainment of readmission for the uninsured, those under 65 years, and those over 65 years HMO‐covered Medicare beneficiaries.9
Conclusion
We present a prediction model for 30‐day emergent readmission or postdischarge death following acute hospitalization for STEMI. We report on novel patient and modifiable risk factors derived from the time of presentation with STEMI and their utility in predicting readmission or death. Our model can be used to develop routine surveillance systems when planning for STEMI discharge to determine the likelihood of near‐term adverse events that may eminently jeopardize patient safety.
Acknowledgments
The authors acknowledge Sheila Conley and the Dartmouth‐Hitchcock Medical Center STEMI Registry team members and staff for contributing patient, procedural, and outcomes data to the registry.
Dr. Brown is supported by grant number K01HS018443 from the Agency for Healthcare Research and Quality. Funding for the STEMI registry is supported by the Section of Cardiology, Dartmouth‐Hitchcock Medical Center.
The authors have no other funding, financial relationships, or conflicts of interest to disclose.
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