Abstract
Background
Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after coronary artery bypass graft surgery (CABG) early in a hospitalization would enable hospitals to enhance discharge planning.
Methods
We developed different models to predict 30-day inpatient readmission to our institution in patients who underwent CABG between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from STS Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent CABG between May 2013 and September 2015. Our cohort included 1277 CABG patients: 1159 in the derivation cohort and 1018 in the validation cohort.
Results
The discriminative ability of the admission model was reasonable (C-index of 0.673). The c-indices for the discharge and STS models were slightly better. (C-index of 0.700 and 0.714 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.641, 0.659 and 0.670 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.573, 0.605 and 0.595 respectively).
Conclusions
Risk prediction models based on data available early on admission are predictive for readmission risk. Adding registry data did not improved the performance of these models. These simplified models may be sufficient to identify patients at highest risk of readmission following coronary revascularization early in the hospitalization.
1
Introduction
Coronary artery bypass graft (CABG) surgery is one of the most expensive procedures, with mean charges of nearly $45,358 for the index admission . CABG is known to be associated with reasonably high short-term readmission rates . Hospital readmissions in the US are associated with significant increase in cost and are responsible for an estimate of $26 billion to the Medicare program alone . The Patient Protection and Affordable Care Act linked many quality outcomes including 30-day readmission rates to hospital reimbursement . The Center for Medicare and Medicaid Services (CMS) publicly reports hospital level 30-day readmission rates for congestive heart failure (CHF), acute myocardial infarction (AMI), and for patients undergoing percutaneous coronary intervention (PCI) with expectation that readmission rates after CABG may be reported in the future .
The key for reducing readmission rates depends on delivering high quality care in the inpatient setting and improving the transitional care upon discharge. Given the limitation in resources, early identification of patients at risk for readmission after CABG becomes crucial to direct potential interventions that may help in reducing readmission and improving hospital quality of care. Although many studies have identified strong predictors for readmissions risk for CABG , to date there are only two risk models specifically designed to predict readmission risk for patients undergoing CABG . However these risk models used registry data at the time of discharge to identify patients at high risk for readmission. These risk models did not address the possibility of using data available prior to or early after admission to create a model that may be helpful in identifying those at risk early in an admission.
We sought to develop and validate a risk model to predict readmission risk after CABG using available clinical and administrative data available within our hospital system at the time of admission, and to determine the incremental benefit to risk assessment of adding 1) clinical information available at the time of discharge, and 2) registry data from the STS (Society Of Thoracic Surgeons) Registry. Our findings will inform the use of clinical data within hospital systems to prospectively risk-stratify patients to support the cost-effective application of care management or other resources with the intent to reduce readmission.
2
Methods
2.1
Study design, population, and setting
We conducted a retrospective cohort of all patients with revascularization with CABG at Christiana Care Health System between January 1, 2010 and April 1, 2015. Christiana Care is a large system that comprises two hospitals with more than 1100 beds as well as a variety of outpatient and other services in facilities and provides the majority of cardiovascular care in Delaware and the surrounding area with an estimate of 1700 PCI and more than 600 open heart surgery annually. We identified all patients who were discharged alive following CABG. We further divided the cohort into 1) a derivation cohort that was used to develop the 3 separate prediction models and included CABG patients admitted between April 1, 2010 and April 30, 2013; and 2) a validation cohort that was used to test the prediction models and included those admitted between May 1, 2013 and September 30, 2015. Importantly, the validation cohort included patients that were included in a longitudinal care management program for patients following coronary revascularization. Patients were enrolled in the program during the hospitalization and followed with telephonic care management following discharge. The Christiana Care Institutional Review Board approved the study.
2.2
Outcomes
We identified inpatient, non-elective readmissions to Christiana Care within 30 days of discharge from the index procedure. We identified readmissions at our own system and we used QualityNet Data from CMS to identify readmissions at other hospitals.
2.3
Candidate variables and model derivation
Candidate variables for the prediction model were drawn from three sources: 1) administrative and billing data from the data warehouse at Christiana Care (demographics, previous utilization, and comorbidities; 2) clinical data including initial and discharge laboratory and vital signs from key clinical systems; and 3) registry information from the STS Registry for data concerning anatomic and procedural information. Comorbidities were classified from administrative data using the Elixhauser classification .
In order to determine the incremental value of additional information gathered across the hospital visit, we developed three models that sequentially added information available during the hospitalization: 1) an admission model that included only variables available at the time the patient arrived at the hospital, 2) a discharge model that included administrative and clinical information available at the time of discharge; and 3) a discharge model that also included anatomic and procedural information from the STS Registry. This progression of models was chosen based on the timing of availability of these data in the clinical setting. STS registry information, for example, is collected by staff following discharge and is not available for operational purposes at the time of discharge. These variables and the progress in building each model are shown in Table 1 .
| Variables by data class | ||
|---|---|---|
| Baseline/Admission | Discharge | STS |
| Age | Length of stay | Post-op complication |
| Sex | AMI indication | LVEF |
| Race | Any ICU stay | ACE/ARB post-op |
| Insurance | Weekend discharge | Beta Blocker post-op |
| Elective status | Discharge location Home Home with services Skilled nursing facility Others facility | ADP post-op |
| Previous AMI | Updated Elixhauser comorbidities ▪ Comorbidity count ▪ CHF ▪ COPD ▪ Diabetes ▪ Renal failure ▪ Perivascular disease ▪ Valve disease ▪ Electrolyte imbalance ▪ Obesity | Antiarrhythmic med post-op |
| Previous PCI | ||
| Previous CABG | ||
| Weekend admission | Lipid-lowering med post-op | |
| Previous hospitalization within 6 months | Statin at discharge | |
| Coumadin post-op | ||
| Canadian Classification System Angina Class | ||
| Model | Data classes included | |
| 1 | Baseline – admission | |
| 2 | Baseline – admission/discharge | |
| 3 | Baseline – admission/discharge/STS Registry | |
Hierarchical logistic regression was used to model readmissions (a patient may have had more than one). Derivation models were developed by a combination of forward selection and backward elimination of variables. Variables were entered if p ≤ .2 and removed if p > .2. Reduced models were compared to larger models by likelihood ratio tests. Fractional polynomial (FP) regression was used to assess non-linearity of continuous variables. Cubic splines were then used to determine categories for nonlinear continuous variables. Although adding variables to the sequential models will likely change the estimation of odds ratios (as well as contribution to the predictive ability of the model), variables were retained in subsequent models regardless of their contribution to predictive ability. Model discrimination was assessed by the c-statistic and model calibration was assessed by plotting observed readmission rates with deciles of model-predicted rates.
Models were developed for CABG patients admitted to the hospital between April 1, 2010 and April 30, 2013. Internal model validity was assessed by bootstrap methods – 500 bootstrap replicates with replacement were drawn to calculate bias-corrected c-indices. The derivation models were then applied to patients admitted between May 1, 2013 and September 30, 2015 to assess external validity.
2
Methods
2.1
Study design, population, and setting
We conducted a retrospective cohort of all patients with revascularization with CABG at Christiana Care Health System between January 1, 2010 and April 1, 2015. Christiana Care is a large system that comprises two hospitals with more than 1100 beds as well as a variety of outpatient and other services in facilities and provides the majority of cardiovascular care in Delaware and the surrounding area with an estimate of 1700 PCI and more than 600 open heart surgery annually. We identified all patients who were discharged alive following CABG. We further divided the cohort into 1) a derivation cohort that was used to develop the 3 separate prediction models and included CABG patients admitted between April 1, 2010 and April 30, 2013; and 2) a validation cohort that was used to test the prediction models and included those admitted between May 1, 2013 and September 30, 2015. Importantly, the validation cohort included patients that were included in a longitudinal care management program for patients following coronary revascularization. Patients were enrolled in the program during the hospitalization and followed with telephonic care management following discharge. The Christiana Care Institutional Review Board approved the study.
2.2
Outcomes
We identified inpatient, non-elective readmissions to Christiana Care within 30 days of discharge from the index procedure. We identified readmissions at our own system and we used QualityNet Data from CMS to identify readmissions at other hospitals.
2.3
Candidate variables and model derivation
Candidate variables for the prediction model were drawn from three sources: 1) administrative and billing data from the data warehouse at Christiana Care (demographics, previous utilization, and comorbidities; 2) clinical data including initial and discharge laboratory and vital signs from key clinical systems; and 3) registry information from the STS Registry for data concerning anatomic and procedural information. Comorbidities were classified from administrative data using the Elixhauser classification .
In order to determine the incremental value of additional information gathered across the hospital visit, we developed three models that sequentially added information available during the hospitalization: 1) an admission model that included only variables available at the time the patient arrived at the hospital, 2) a discharge model that included administrative and clinical information available at the time of discharge; and 3) a discharge model that also included anatomic and procedural information from the STS Registry. This progression of models was chosen based on the timing of availability of these data in the clinical setting. STS registry information, for example, is collected by staff following discharge and is not available for operational purposes at the time of discharge. These variables and the progress in building each model are shown in Table 1 .
| Variables by data class | ||
|---|---|---|
| Baseline/Admission | Discharge | STS |
| Age | Length of stay | Post-op complication |
| Sex | AMI indication | LVEF |
| Race | Any ICU stay | ACE/ARB post-op |
| Insurance | Weekend discharge | Beta Blocker post-op |
| Elective status | Discharge location Home Home with services Skilled nursing facility Others facility | ADP post-op |
| Previous AMI | Updated Elixhauser comorbidities ▪ Comorbidity count ▪ CHF ▪ COPD ▪ Diabetes ▪ Renal failure ▪ Perivascular disease ▪ Valve disease ▪ Electrolyte imbalance ▪ Obesity | Antiarrhythmic med post-op |
| Previous PCI | ||
| Previous CABG | ||
| Weekend admission | Lipid-lowering med post-op | |
| Previous hospitalization within 6 months | Statin at discharge | |
| Coumadin post-op | ||
| Canadian Classification System Angina Class | ||
| Model | Data classes included | |
| 1 | Baseline – admission | |
| 2 | Baseline – admission/discharge | |
| 3 | Baseline – admission/discharge/STS Registry | |
Hierarchical logistic regression was used to model readmissions (a patient may have had more than one). Derivation models were developed by a combination of forward selection and backward elimination of variables. Variables were entered if p ≤ .2 and removed if p > .2. Reduced models were compared to larger models by likelihood ratio tests. Fractional polynomial (FP) regression was used to assess non-linearity of continuous variables. Cubic splines were then used to determine categories for nonlinear continuous variables. Although adding variables to the sequential models will likely change the estimation of odds ratios (as well as contribution to the predictive ability of the model), variables were retained in subsequent models regardless of their contribution to predictive ability. Model discrimination was assessed by the c-statistic and model calibration was assessed by plotting observed readmission rates with deciles of model-predicted rates.
Models were developed for CABG patients admitted to the hospital between April 1, 2010 and April 30, 2013. Internal model validity was assessed by bootstrap methods – 500 bootstrap replicates with replacement were drawn to calculate bias-corrected c-indices. The derivation models were then applied to patients admitted between May 1, 2013 and September 30, 2015 to assess external validity.
3
Results
The total number of CABG patients was 1277 including 1159 in the derivation cohort and 1018 in the validation cohort. These patients had a total of 2183 hospitalizations; 1163 in the derivation cohort and 1020 in the validation cohort. The readmission rate was 14.2%. Table 2 shows the demographic and clinical characteristics of the derivation and validation cohorts.
