Factors associated with delay in carotid endarterectomy for patients with symptomatic severe internal carotid artery stenosis: a case–control study

Meyer, Daniel; Karreman, Erwin; Kopriva, David

doi:10.9778/cmajo.20170060

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…This result is consistent with previous research that identifies presentation to a nonemergency setting is associated with significant delays in carotid intervention. 18,22 Although a substantial proportion of the delay among patients presenting to a nonemergency setting relates back to gaps in patient knowledge or slower response times in seeking medical attention after a TIA or stroke event, limited access to diagnostic services, including vascular imaging, in the primary care or other nonemergency settings is another important gap.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Early vascular imaging and key system strategies expedite carotid revascularization after transient ischemic attack and stroke

Gocan

Bourgoin

Shamloul

et al. 2020

Journal of Vascular Surgery

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Section: Discussionmentioning

confidence: 99%

“…16 In other regions of Canada, delays to carotid surgery are similar, with only 30% to 39% of patients meeting benchmarks. 17,18 The purpose of this study was to explore the influence of health system factors on carotid revascularization timelines.…”

mentioning

confidence: 99%

Early vascular imaging and key system strategies expedite carotid revascularization after transient ischemic attack and stroke

Gocan

Bourgoin

Shamloul

et al. 2020

Journal of Vascular Surgery

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Determinants of prehospital and in-hospital delay in patients with symptomatic carotid stenosis and their influence on the outcome after elective carotid endarterectomy

Kirchhoff,

Knappich,

Kallmayer

et al. 2024

Stroke Vasc Neurol

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BackgroundThis study analyses the determinants of prehospital (index event to admission) and in-hospital delay (admission to carotid endarterectomy (CEA)). In addition, the analysis addresses the association between prehospital or in-hospital delay and outcomes after CEA for symptomatic patients in German hospitals.Materials and methodsThis retrospective analysis is based on the nationwide German statutory quality assurance database. 55 437 patients were included in the analysis. Prehospital delay was grouped as follows: 180–15, 14–8, 7–3, 2–0 days or ‘in-hospital index event’. In-hospital delay was divided into: 0–1, 2–3 and >3 days. The primary outcome event (POE) was in-hospital stroke or death. Univariate and multivariable regression analyses were performed for statistical analysis. The slope of the linear regression line is given as the β-value, and the rate parameter of the logistic regression is given as the adjusted OR (aOR).ResultsPrehospital delay was 0–2 days in 34.9%, 3–14 days in 29.5% and >14 days in 18.6%. Higher age (β=−1.08, p<0.001) and a more severe index event (transitory ischaemic attack: β=−4.41, p<0.001; stroke: β=−6.05, p<0.001, Ref: amaurosis fugax) were determinants of shorter prehospital delay. Higher age (β=0.28, p<0.001) and female sex (β=0.09, p=0.014) were associated with a longer in-hospital delay. Index event after admission (aOR 1.23, 95% CI: 1.04 to 1.47) and an intermediate in-hospital delay of 2–3 days (aOR 1.15, 95% CI: 1.00 to 1.33) were associated with an increased POE risk.ConclusionsThis study revealed that older age, higher American Society of Anesthesiology (ASA) stage, increasing severity of symptoms and ipsilateral moderate stenosis were associated with shorter prehospital delay. Non-specific symptoms were associated with a longer prehospital delay. Regarding in-hospital delay, older age, higher ASA stage, contralateral occlusion, preprocedural examination by a neurologist and admission on Fridays or Saturdays were associated with lagged treatment. A very short (<2 days) prehospital and intermediate in-hospital delay (2–3 days) were associated with increased risk of perioperative stroke or death.

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

van Gaal,

Alimohammadi,

Karim

et al. 2024

BMC Health Serv Res

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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.

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Factors associated with delay in carotid endarterectomy for patients with symptomatic severe internal carotid artery stenosis: a case–control study

Cited by 3 publications

References 21 publications

Early vascular imaging and key system strategies expedite carotid revascularization after transient ischemic attack and stroke

Early vascular imaging and key system strategies expedite carotid revascularization after transient ischemic attack and stroke

Determinants of prehospital and in-hospital delay in patients with symptomatic carotid stenosis and their influence on the outcome after elective carotid endarterectomy

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

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