This study, which examines the rate and costs of 30-day readmissions after index CAS, finds that 7.4% of patients get readmitted within 30 days of discharge after undergoing CAS. The major causes for 30-day unplanned readmission were septicemia or cerebral infarction/hemorrhagic cerebrovascular bleed. Using machine learning approaches, we can develop a risk prediction model that can identify patients at high risk of unplanned readmissions with a C-statistic of 0.802 using DNN, to the best of our knowledge, the first of its kind in many ways. This is the most contemporary analysis looking at 30-day unplanned readmissions. The first study uses nationally representative data to develop risk prediction models using advanced machine learning like DNNs for CAS. On the basis of AUROC and AUPRC metrics, DNN shows superior performance to commonly used statistical or machine learning methods in modeling CAS readmission rates.
Prior studies looking at readmissions have shown a variety of readmission rates, including vascular interventions in Medicare patients (24%) , endovascular aortic aneurysm repair (10.2%) , lower extremity bypass (14.8%) , endovascular or surgical revascularization for chronic mesenteric ischemia (19.5%) , and revascularization for critical limb ischemia (20.4%) . Looking specifically at the readmission rates in the CAS population, most prior studies have compared CEA readmission rates versus CAS. These studies have demonstrated rates of 12.0% and 8.3% for Medicare-only and nationally representative data, respectively, for the CAS cohorts [5, 22]. Other studies have also shown similar readmission rates for CAS patients in the range of 9.6% in the Pennsylvania Health Care Cost Containment Council study by Hintze et al. , 10.75% by Greenleaf et al. , and 11.11% by Galinanes et al. . All these prior studies have used patient-level data from before 2015 when ICD-9 was in use. Our study gives a glimpse into the most contemporary nationally representative data using ICD-10 codes. In addition, our national evaluation of unplanned 30-day readmissions after CAS has several key findings. Our observation showed that 7.4% of patients undergoing CAS had unplanned readmissions within 30 days of hospital discharge. The decline in readmission rates observed in our study, as compared to aforementioned prior studies, may be related to increased operator and/or hospital experience or may be due to strict inclusion/exclusion criteria employed in our study [26, 27].
In our study, sepsis was found to be one of the leading causes for readmission post CAS. Interestingly literature review showed that postoperative surgical-site infection, sepsis/septic shock, pneumonia, and urinary tract infection are known associations with readmissions after CEA . Quiroz et al. looked into hidden readmissions after CEA and CAS, and found infectious etiologies amounting to 9.9% as a cause for readmission (wound complication 3.7%, sepsis 3.1%, urinary tract infection/pyelonephritis 0.5%, and other infections 2.6%). This proves that the infection/sepsis rates found in our study were not in excess of those in the existing literature .
There are over 40 models for predicting short- and long-term outcomes after carotid revascularization . However, the prediction models available have the following potential limitations. Firstly, most of the models have used patient databases that are not representative of the national population. The models have used logistic regression, and none of them have used artificial intelligence to improve the quality of predictions. The existing models have used data from the ICD-9 era wherein the specificity of diagnostic codes was significantly inferior to ICD-10 codes. None of the short-term models have looked at all-cause readmissions and can only predict stroke or death. C-statistic (or area under the curve) is considered an important discriminating factor for the accuracy of a prediction model with scores of < 0.50, > 0.50–0.70, > 0.70–0.80, < 0.80–0.90, and > 0.90 representing no, poor, low, excellent, and outstanding discrimination, respectively [31, 32]. Volkers et al. presented an excellent external validation study evaluating 30 prediction models for CEA, CAS, or both and found that not a single model had C-statistic over 0.67 (poor prediction capability) during external validation proving that although there are many models to choose from none of them truly qualify for being substantially useful by clinicians .
Despite having a plethora of existing state-of-the-art statistical and machine learning methods in modeling readmission rates, it is noteworthy that our novel implementation of DNN helped us build a model with increased predictive power and, at the same time, facilitated the identification of features that are clinically both relevant and important to their association with the event of readmission post CAS. It was also interesting to see that although DNN had a lower or similar level of accuracy compared to traditional methods, the score of our model with respect to performance metrics such as AUROC and AUPRC was significantly higher. One plausible reasoning for such an observation stems from the fact that the accuracy of existing models such as logistic regression is heavily biased towards the majority class’s proportion as opposed to the minority class samples. Although it enriches the accuracy score, this inherent bias has downstream consequences in high misclassification rates, subsequently resulting in low predictive power . In contrast, DNN is agnostic to such biases. It uses the entire dataset to find out the complex patterns between the variables and then further utilizes this pattern to classify the outcome labels even if the data is highly imbalanced. Therefore, on the basis of our strong evidence, DNN should serve as a premium choice in building more robust and adaptive predictive models for accurate predictions in complex data architectures such as 30-day readmission post CAS.
Patients with comorbidities, including depression, heart failure, cancer, in-hospital bleeding, and coagulation disorders, were the strongest predictors of readmission based on logistic regression, as shown in Fig. 1. The logistic regression analysis did not include hospital-level factors like teaching status, control ownership, or hospital location. Also evident was that most comorbidities had overlapping power, which would make it challenging to develop a robust prediction model using this analysis alone. This meant that logistic regression-based prediction models would not perform well in a clinical setting. Our study further improved upon this and used DNN and hospital-level data to identify novel predictors for early readmission, as shown in Fig. 3. The DNN model provided a zoomed-in view with refined results and showed that factors like hospital rural–urban designation, control/ownership, and teaching status form one of the strongest predictors in addition to newly identified comorbidities to identify patients at risk of early readmissions post CAS. Patients with these comorbidities were more likely to get readmitted, which may have been due to disease progression. Further prospective research would be needed to determine real impact and causal associations.
Reasons for readmission based on the primary diagnosis code were septicemia (8.6%), cerebral infarction (8.6%), heart failure (5.9%), acute hemorrhagic cerebrovascular disease (4.2%), acute renal failure (4.2%), and gastrointestinal hemorrhage (3.6%). These further affirm the need to develop robust prediction models to help decrease unplanned readmissions and comorbidities. Interestingly, in our study, patients with different insurance status (Medicare, Medicaid, or private) and hospital bed size had no significant impact on unplanned readmissions. In contrast, patients with higher scores on mortality/severity of illness subclass of APRDRG scores had worse outcomes in terms of all-cause readmissions. Patients treated at private hospitals compared to government, nonfederal hospitals, and those at metropolitan hospitals were at increased risk for unplanned readmissions. This may be attributed to the difference in the practice patterns at different hospital locations or subtypes or secondary to the number of cases being done at that location and physician experience [26, 27].
Multiple prediction models have been developed in the past, mostly looking at outcomes like recurrent stroke, myocardial infarction, or death. Unfortunately, none of them have a prediction tool to help with short-term readmission risk due to all-cause readmissions. The studies also lack prediction power, especially when evaluated with external validation. A study by Volkers et al. presented an excellent external validation study evaluating 30 prediction models for CEA, CAS, or both and found that not a single model had AUC over 0.67 during external validation . This further proves the point that although there are many models to choose from, none of them truly qualify for being substantially useful in current day practice. Our prediction model is novel in many ways. First, it is the first model to use nationally representative data in the contemporary ICD-10 era and uses machine learning models to predict all-cause unplanned short-term readmissions. The AUC score of 0.79 for DNN is very robust in predicting our primary outcome.
NRD is in a format of annualized data with a maximum follow-up of 1 year. As with any observational data, the results do not suggest a causal relationship as there can be other unmeasured confounders. The NRD database does not provide pharmacological data/lesion-level data that may impact readmissions. Lastly, the presented risk scores have not been externally validated and currently stand applicable only to the US population.