Thirty-day Readmission Rates for Carotid Endarterectomy Versus Carotid Artery Stenting

Qualitative Analysis of Length of Stay and Readmission after Carotid Endarterectomy

BACKGROUND

Length of stay (LOS) and readmissions are common measures to evaluate quality of health care. The objective of this study was to evaluate factors related to hospital LOS and readmission within 90 days following carotid endarterectomy (CEA) in patients who have not had a stroke.

METHODS

Using a single institution database, patients who underwent CEA for carotid stenosis between 2014 and 2019 were identified. Asymptomatic carotid stenosis (no history of any stroke or transient ischemic attack (TIA) within 6 months prior to CEA), and patients who had a TIA without stroke were included. Demographic and perioperative factors were collected. Primary outcomes analyzed were increased LOS (>1 day) and readmission within 90 days after surgery.

RESULTS

There were 125 patients identified who underwent CEA for 133 carotid stenosis, and 8 patients had bilateral CEA; of which 36.8% were asymptomatic carotid stenosis with the remaining being operated on for TIA without any stroke. The mean age was 68 years old and 36.1% of cases were female. The median postoperative LOS was 2 days. Increased LOS occurred in 81 cases (60.9%). Increased LOS, compared to no increased LOS, occurred more often in patients with diabetes (48.1% vs. 30.8%, P = 0.047), in those with operations starting after 12:00 pm (45.7% vs. 21.2%, P = 0.004) and those with any minor complications such as neck swelling, neck pain, and urinary retention (30.9% vs. 15.4%, P = 0.044). Readmission within 90 days after CEA occurred in 24 (18%) of cases. Readmission within 90 days, compared to no readmission within 90 days, occurred more often in patients with a history of coronary artery disease (58.3% vs. 27.5%, P = 0.004), congestive heart failure (37.5% vs. 11%, P = 0.001), and atrial fibrillation (29.2% vs. 8.3%, P = 0.004).

CONCLUSIONS

More than half of patients undergoing CEA for carotid stenosis were discharged after postoperative day 1. Interventions on modifiable clinical risk factors, such as morning CEA scheduling and management of comorbidities, may decrease LOS and 90-day readmission rates.

Unplanned readmission after carotid stenting versus endarterectomy: analysis of the United States Nationwide Readmissions Database

BACKGROUND

Hospital readmissions are costly and reflect negatively on care delivered.

OBJECTIVE

To have a better understanding of unplanned readmissions after carotid revascularization, which might help to prevent them.

METHODS

The Nationwide Readmissions Database was used to determine rates and reasons for unplanned readmission following carotid endarterectomy (CEA) and carotid artery stenting (CAS). Trends were assessed by annual percent change, modified Poisson regression was used to estimate risk ratios (RR) for readmission, and propensity scores were used to match cohorts.

RESULTS

Analysis yielded 522 040 asymptomatic and 55 485 symptomatic admissions for carotid revascularization between 2010 and 2015. Higher 30-day readmission rates were noted after CAS versus CEA in both symptomatic (9.1% vs 7.7%, p<0.001) and asymptomatic (6.8% vs 5.7%, p<0.001) patients. Readmission rates trended lower over time, significantly so for 90-day readmissions in symptomatic patients undergoing CEA. The most common cause for 30-day readmission was stroke in both symptomatic (5.5%) and asymptomatic (3.9%) patients. Factors associated with a higher risk of readmission included age over 80; male gender; Medicaid health insurance; and increases in severity of illness, mortality risk, and comorbidity indices. Analysis of matched cohorts showed that CAS had higher readmission than CEA (RR=1.14 (95% CI 1.06 to 1.22); p<0.001) only in asymptomatic patients. Adverse events during initial admission which predicted 30-day readmission included acute renal failure and acute respiratory failure in asymptomatic patients; hematoma and cardiac events were additional predictive adverse events in symptomatic patients.

CONCLUSIONS

Readmission is not uncommon after carotid revascularization, occurs more often after CAS, and is predicted by baseline factors and by preventable adverse events at initial admission.

Female Gender Increases Risk of Stroke and Readmission after CEA and CAS

OBJECTIVES

Carotid endarterectomy (CEA) has historically demonstrated a higher rate of perioperative adverse events for female patients. However, recent evidence suggests similar outcomes for CEA between genders. In contrast, fewer studies have examined gender in carotid artery stenting (CAS). Using contemporary data from the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database, we aim to determine if gender impacts differences in postoperative complications in patients who undergo CEA or CAS.

METHODS

The ACS NSQIP database was queried from 2005-2017 using Current Procedural Terminology (CPT) and International Classification of Diseases (ICD) codes for retrospective review. Patients with carotid intervention (CEA or CAS) were stratified into asymptomatic vs symptomatic cohorts to determine the effect of gender on 30-day postoperative outcomes. Symptomatic patients were defined as those with perioperative transient cerebral ischemic attack or stenosis of carotid artery with cerebral infarction. Descriptive statistics were calculated. Risk-adjusted odds of 30-day postoperative outcomes were calculated using multivariate regression analysis with fixed effects for age, race, and comorbidities.

RESULTS

There were 106,568 patients with CEA or CAS (104,412 CEA and 2,156 CAS). Average age was 70.9 years old and female patients accounted for 39.9% of the population. For asymptomatic patients that underwent CEA or CAS, female gender was associated with significantly higher rates of CVA/stroke (13%, p=0.005), readmission (10%, p=0.004), bleeding complication (32%, p=0.001), and UTI (54%, p=0.001) as well as less infection (26%, p=0.001). In the symptomatic cohort, female gender was associated with significantly higher rates of CVA/stroke (32%, p=0.034), bleeding complication (203%, p=0.001), and UTI (70%, p=0.011), while female gender was associated with a lower rate of pneumonia (39%, p=0.039). Subset analysis found that, compared to male patients, female patients <75yo have an increased rate of CVA/stroke (21%, p=0.001) and readmission (15%, p<0.001), while female patients ≥75yo did not. In asymptomatic and symptomatic patients that underwent CEA, female gender was associated with significantly higher rates of CVA/stroke (13%, p=0.006 and 31%, p=0.044, respectively), but this finding was present not in patients undergoing CAS.

CONCLUSION

In patients undergoing carotid intervention, female gender was associated with significantly increased rates of postoperative CVA/stroke in the asymptomatic and symptomatic cohorts as well as readmission in the asymptomatic cohort. Female gender was associated with higher rates of CVA/stroke following CEA, but not CAS. We recommend that randomized control trials ensure adequate representation of female patients to better understand gender-based disparities in carotid intervention.

Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence

Introduction

This study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-term readmissions. The prediction of unplanned readmissions after index CAS remains challenging. There is a need to leverage deep machine learning algorithms in order to develop robust prediction tools for early readmissions.

Methods

Patients undergoing inpatient CAS during the year 2017 in the US Nationwide Readmission Database (NRD) were evaluated for the rates, predictors, and costs of unplanned 30-day readmission. Logistic regression, support vector machine (SVM), deep neural network (DNN), random forest, and decision tree models were evaluated to generate a robust prediction model.

Results

We identified 16,745 patients who underwent CAS, of whom 7.4% were readmitted within 30 days. Depression [p < 0.001, OR 1.461 (95% CI 1.231–1.735)], heart failure [p < 0.001, OR 1.619 (95% CI 1.363–1.922)], cancer [p < 0.001, OR 1.631 (95% CI 1.286–2.068)], in-hospital bleeding [p = 0.039, OR 1.641 (95% CI 1.026–2.626)], and coagulation disorders [p = 0.007, OR 1.412 (95% CI 1.100–1.813)] were the strongest predictors of readmission. The artificial intelligence machine learning DNN prediction model has a C-statistic value of 0.79 (validation 0.73) in predicting the patients who might have all-cause unplanned readmission within 30 days of the index CAS discharge.

Conclusions

Machine learning derived models may effectively identify high-risk patients for intervention strategies that may reduce unplanned readmissions post carotid artery stenting.

Central Illustration

Figure 2: ROC and AUPRC analysis of DNN prediction model with other classification models on 30-day readmission data for CAS subjects

Supplementary Information

The online version contains supplementary material available at 10.1007/s12325-021-01709-7.

We present a novel deep neural network-based artificial intelligence prediction model to help identify a subgroup of patients undergoing carotid artery stenting who are at risk for short-term unplanned readmissions. Prior studies have attempted to develop prediction models but have used mainly logistic regression models and have low prediction ability. The novel model presented in this study boasts 79% capability to accurately predict individuals for unplanned readmissions post carotid artery stenting within 30 days of discharge.

Risk factors associated with 30-day hospital readmission after carotid endarterectomy

OBJECTIVE

The current study evaluated all-cause 30-day readmissions after carotid endarterectomy.

METHODS

Patients undergoing carotid endarterectomy were selected from the Cerner Health Facts® database using ICD-9-CM procedure codes from their index admission. Readmission within 30 days of discharge was determined. Chi-square analysis determined characteristics of the index admission (demographics, diagnoses, postoperative medications, and laboratory results) associated with readmission. Multivariate logistic regression models were used to identify characteristics independently associated with readmission.

RESULTS

In total, 5257 patients undergoing elective carotid endarterectomy were identified. Readmission within 30 days was 3.1%. After multivariable adjustment, readmission was associated with end-stage renal disease (OR: 3.21, 95% CI: 1.01-10.2), hemorrhage or hematoma (OR: 2.34, 95% CI: 1.15-4.77), procedural complications (OR: 3.07, 95% CI: 1.24-7.57), use of bronchodilators (OR: 1.48, 95% CI: 1.03-2.11), increased Charlson index scores (OR: 1.22, 95% CI: 1.08-1.38), and electrolyte abnormalities (hyponatremia < 135 mEq/L (OR: 1.69, 95% CI: 1.07-2.67) and hypokalemia less than 3.7 mEq/L (OR: 2.26, 95% CI: 1.03-4.98)).

CONCLUSIONS

Factors associated with readmission following carotid endarterectomy included younger age, increased comorbidity burden, end-stage renal disease, electrolyte disorders, the use of bronchodilators, and complications including bleeding (hemorrhage or hematoma). Of note, in this real-world study, only 40% of the patients received protamine, despite evidence-based literature demonstrating the reduced risk of bleeding complications. As healthcare moves towards quality of care-driven reimbursement, physician modifiable targets such as protamine utilization to reduce bleeding are greatly needed to reduce readmission, and failure to reduce preventable physician-driven complications after carotid interventions may be associated with decreased reimbursement.

Thirty-Day Readmissions After Carotid Artery Stenting Versus Endarterectomy: Analysis of the 2013-2014 Nationwide Readmissions Database

BACKGROUND

Contemporary, nationally representative 30-day readmissions data after carotid artery stenting (CAS) and carotid endarterectomy (CEA) are lacking.

METHODS

Patients undergoing CAS or CEA were identified from the 2013 to 2014 Nationwide Readmissions Databases. Propensity matching was used to balance baseline clinical characteristics. Thirty-day nonelective readmission rates, length of stay, and causes of readmission were compared.

RESULTS

Overall, 85 337 (national estimate of 194 332) patients were identified before propensity score matching, 11 490 (13.4%) of whom underwent CAS and 73 847 (86.6%) of whom underwent CEA. Crude 30-day readmission rates were higher for patients treated with CAS than CEA (8.3% versus 6.8%; P<0.001), but these differences were negated in the propensity-matched cohort (n=22 214; 8.4% versus 7.9%, P=0.20), and readmission length of stay was longer for CEA than CAS (2 versus 1 day, respectively; P=0.002). The most common reasons for readmission were neurological and cardiac events; readmission reasons varied by revascularization modality. Readmission due to a stroke or transient ischemic attack was more common among patients treated with CAS than CEA (1.2% versus 0.9%; P=0.042), while readmission for procedural or medical complications occurred more often following CEA than CAS (1.1% versus 0.5%; P<0.001); readmission rates for cardiac causes were similar between groups.

CONCLUSIONS

Less than 8% of patients are readmitted within 30 days of a carotid revascularization procedure. After adjusting for baseline differences, readmission rates are similar for CAS and CEA although readmission length of stay is longer after the latter. Readmission for neurological causes was more common following CAS while readmission for procedural or medical complications occurred more often following CEA. Higher annual institutional CEA volumes were associated with lower risk for 30-day readmission; in contrast, institutional CAS volumes were not related to readmission risk. These data provide important insights into the short-term, outcomes of patients following carotid artery revascularization.

Socioeconomic Disparities in Carotid Revascularization Procedures

OBJECTIVES

Several studies have demonstrated healthcare disparities in postoperative outcomes after carotid endarterectomy and carotid artery stenting, including increased hospital mortality, postoperative stroke, and readmission rates. The objective of the present study was to examine the intersectionality between race/ethnicity, insurance status, and postoperative outcomes in carotid procedures.

DESIGN

Records of adults from 2007 to 2014 were retrospectively identified, and patients with appropriate International Classification of Diseases Ninth Revision Clinical Modification codes for carotid endarterectomy or carotid artery stenting were identified. Primary outcomes were unadjusted rates and adjusted odds ratios (aORs) of postoperative in-hospital mortality, stroke, combined stroke/mortality, and cardiovascular complications.

SETTING

Data were sourced from the State Inpatient Databases data from California, Florida, Kentucky, Maryland, and New York during the years 2007 to 2014.

PARTICIPANTS

Patients undergoing carotid revascularization procedures.

INTERVENTIONS

The effects of race and insurance status as independent variables and as effect modifiers on postoperative outcomes.

MEASUREMENTS AND MAIN RESULTS

Multivariable logistic regression models were used to examine the associations between race and/or insurance status with respect to study outcomes. Race, but not payer status, was significantly associated with adverse outcomes after carotid artery procedures, with blacks, Hispanics, and other non-Caucasian races demonstrating a significantly greater risk of postoperative stroke and mortality (aOR range 1.24-1.59). This relationship persisted even when stratified by procedure type (aOR range 1.25-1.56) and symptomatology (aOR range 1.51-1.63).

CONCLUSIONS

These results suggest that disparities in postoperative outcomes after carotid artery procedures are associated with race but not with primary insurance status. Multiple contributing factors exist, including racial inequities in prevalence of comorbidities, health literacy, and procedure type performed.

Comparison of outcomes after carotid endarterectomy between type 2 diabetic and non-diabetic patients with significant carotid stenosis

BACKGROUND

We aimed to compare early and late outcomes after carotid endarterectomy (CEA) between Korean type 2 diabetic and non-diabetic patients and to investigate the impact of diabetes on the overall incidence of cardiovascular events after CEA.

METHODS

We retrospectively analyzed 675 CEAs, which were performed on 613 patients with significant carotid stenosis between January 2007 and December 2014. The CEAs were divided into a type 2 diabetes mellitus (DM) group (n = 265, 39.3%) and a non-DM group (n = 410, 60.7%). The study outcomes included the incidence of major adverse events (MAEs), defined as fatal or nonfatal stroke or myocardial infarction or all-cause mortality, during the perioperative period and within 4 years after CEA.

RESULTS

Patients in the DM and non-DM groups did not differ significantly in the incidence of MAEs or any of the individual MAE manifestations during the perioperative period. However, within 4 years after CEA, the difference in the MAE incidence was significantly greater in the DM group (P = 0.040). Analysis of the individual MAE manifestations indicated a significantly higher risk of stroke in the DM group (P = 0.006). Multivariate analysis indicated that diabetes was not associated with MAEs or individual MAE manifestations during the perioperative period, whereas within 4 years after CEA, diabetes was an independent risk factor for MAEs overall (hazard ratio [HR], 1.62; 95% confidence interval [CI] 1.06-2.48; P = 0.026) and stroke (HR, 2.55; 95% CI 1.20-5.41; P = 0.015) in particular.

CONCLUSIONS

Diabetic patients were not at greater risk of perioperative MAEs after CEA; however, the risk of late MAE occurrence was significantly greater in these patients. Within 4 years after CEA, DM was an independent risk factor for the occurrence of MAEs overall and stroke in particular.