Accurate classification of carotid endarterectomy indication using physician claims and hospital discharge data

Short-term outcomes of atrial fibrillation patients undergoing carotid stent placement or carotid endarterectomy in the United States

BACKGROUND

Patients with atrial fibrillation (AF) were excluded from the trials evaluating carotid artery stent placement (CAS) or carotid endarterectomy (CEA). We identified the prevalence of atrial fibrillation and how it might be associated with outcomes in patients with symptomatic internal carotid artery (ICA) stenosis.

METHODS

We analyzed the data from the National Inpatient Sample between 2016 and 2021 with stenosis of ICA who underwent CAS or CEA. We compared the end points of intra- and post-procedural cerebral infarction, hemorrhage, discharge home and death based on the presence or absence of AF. We also compared the outcomes in AF patients who had CAS with those who had CEA.

RESULTS

Atrial fibrillation was present in 3,785 (18.3%) of 20,645 patients who underwent either CAS or CEA between 2016 and 2021, while 16,860 did not have atrial fibrillation. The proportion of patients who developed acute myocardial infarction, respiratory failure, acute kidney injury, or required blood transfusion was higher in patients with atrial fibrillation among both CAS- and CEA-treated patients. There was no difference in odds of post-procedural stroke and/or death in patients with atrial fibrillation (compared with those without atrial fibrillation) who were treated with CAS (odds ratio [OR] 0.99, 95% confidence interval [CI] 0.62-1.60, p = 0.98) and those treated with CEA (OR 1.09, 95% CI 0.69-1.73, p = 0.72 in the multivariate analysis after adjusting for potential confounders. The length of stay and hospitalization cost were significantly higher in patients with atrial fibrillation. There was no difference in post-procedural stroke and/or death (10.7% versus 8.7%, p = 0.41) and discharge home (32.4% versus 26.8%, p = 0.13) in patients with atrial fibrillation who underwent CEA compared to those who underwent CAS in propensity-matched analysis.

CONCLUSION

One in five patients with symptomatic ICA stenosis who undergo CAS or CEA have AF. We did not identify higher risk of post-procedural stroke and/or death irrespective of the procedure, but resource utilization was higher in AF patients.

Investigation of treatment delay in a complex healthcare process using physician insurance claims data: an application to symptomatic carotid artery stenosis

BACKGROUND

Delays in diagnostic and therapeutic processes are a potentially preventable cause of morbidity and mortality. Process improvement depends on accurate knowledge about as-is processes, historically collected from front-line workers and summarized in flowcharts. Such flowcharts can now be generated by process discovery algorithms supplied with chronological records from real-world cases. However, these algorithms may generate incomprehensible flowcharts when applied to complex unstructured processes, which are common in healthcare. The aim of this study is to evaluate methods for analysing data from real-world cases to determine causes of delay in complex healthcare processes.

METHODS

Physician insurance claims and hospital discharge data were obtained for patients undergoing carotid endarterectomy at a single tertiary hospital between 2008 and 2014. All patients were recently symptomatic with vision loss. A chronological record of physician visits and diagnostic tests (activities) was generated for each patient using claims data. Algorithmic process discovery was attempted using the Heuristic Miner. The effect of activity selection on treatment delay was investigated from two perspectives: activity-specific effects were measured using linear regression, and patterns of activity co-occurrence were identified using K means clustering.

RESULTS

Ninety patients were included, with a median symptom-to-surgery treatment time of 34 days. Every patient had a unique sequence of activities. The flowchart generated by the Heuristic Miner algorithm was uninterpretable. Linear regression models of waiting time revealed beneficial effects of emergency and neurology visits, and detrimental effects of carotid ultrasound and post-imaging follow-up visits to family physicians and ophthalmologists. K-means clustering identified two co-occurrence patterns: emergency visits, neurology visits and CT angiography were more common in a cluster of rapidly treated patients (median symptom to surgery time of 18 days), whereas family physician visits, carotid ultrasound imaging and post-imaging follow-up visits to eye specialists were more common in a cluster of patients with treatment delay (median time of 57 days).

CONCLUSIONS

Routinely collected data provided a comprehensive account of events in the symptom-to-surgery process for carotid endarterectomy. Linear regression and K-means clustering can be used to analyze real-world data to understand causes of delay in complex healthcare processes.

The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis

BACKGROUND

Smoking is a risk factor for many chronic diseases. Multiple smoking status ascertainment algorithms have been developed for population-based electronic health databases such as administrative databases and electronic medical records (EMRs). Evidence syntheses of algorithm validation studies have often focused on chronic diseases rather than risk factors. We conducted a systematic review and meta-analysis of smoking status ascertainment algorithms to describe the characteristics and validity of these algorithms.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. We searched articles published from 1990 to 2022 in EMBASE, MEDLINE, Scopus, and Web of Science with key terms such as validity, administrative data, electronic health records, smoking, and tobacco use. The extracted information, including article characteristics, algorithm characteristics, and validity measures, was descriptively analyzed. Sources of heterogeneity in validity measures were estimated using a meta-regression model. Risk of bias (ROB) in the reviewed articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

RESULTS

The initial search yielded 2086 articles; 57 were selected for review and 116 algorithms were identified. Almost three-quarters (71.6%) of algorithms were based on EMR data. The algorithms were primarily constructed using diagnosis codes for smoking-related conditions, although prescription medication codes for smoking treatments were also adopted. About half of the algorithms were developed using machine-learning models. The pooled estimates of positive predictive value, sensitivity, and specificity were 0.843, 0.672, and 0.918 respectively. Algorithm sensitivity and specificity were highly variable and ranged from 3 to 100% and 36 to 100%, respectively. Model-based algorithms had significantly greater sensitivity (p = 0.006) than rule-based algorithms. Algorithms for EMR data had higher sensitivity than algorithms for administrative data (p = 0.001). The ROB was low in most of the articles (76.3%) that underwent the assessment.

CONCLUSIONS

Multiple algorithms using different data sources and methods have been proposed to ascertain smoking status in electronic health data. Many algorithms had low sensitivity and positive predictive value, but the data source influenced their validity. Algorithms based on machine-learning models for multiple linked data sources have improved validity.

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

Background: In recent years, the length of hospital stay (LOS) following endarterectomy has decreased significantly from 4 days to 1 day. LOS is influenced by several common complications and factors that can adversely affect the patient’s health and may vary from one healthcare facility to another. The aim of this work is to develop a forecasting model of the LOS value to investigate the main factors affecting LOS in order to save healthcare cost and improve management. Methods: We used different regression and machine learning models to predict the LOS value based on the clinical and organizational data of patients undergoing endarterectomy. Data were obtained from the discharge forms of the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital (Salerno, Italy). R² goodness of fit and the results in terms of accuracy, precision, recall and F1-score were used to compare the performance of various algorithms. Results: Before implementing the models, the preliminary correlation study showed that LOS was more dependent on the type of endarterectomy performed. Among the regression algorithms, the best was the multiple linear regression model with an R² value of 0.854, while among the classification algorithms for LOS divided into classes, the best was decision tree, with an accuracy of 80%. The best performance was obtained in the third class, which identifies patients with prolonged LOS, with a precision of 95%. Among the independent variables, the most influential on LOS was type of endarterectomy, followed by diabetes and kidney disorders. Conclusion: The resulting forecast model demonstrates its effectiveness in predicting the value of LOS that could be used to improve the endarterectomy surgery planning.