Predictors of Treatment Delay in Aneurysmal Subarachnoid Hemorrhage Patients

Sarmiento, J. Manuel; Mukherjee, Debraj; Nosova, Kristin; Schievink, Wouter I.; Alexander, Michael J.; Patil, Chirag G.; Nuño, Miriam A

doi:10.1055/s-0034-1372438

Cited by 15 publications

(22 citation statements)

References 38 publications

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“…An increased availability of diagnostic imaging is also suggested to have a positive impact on the diagnosis of aSAH. [65,66] However, within rural and regional hospitals, this availability does not always include appropriate radiologist services with experience in interpreting cerebral imaging. [67] Physician led, rather than radiologist led, interpretation of imaging has previously been identified as a leading cause of diagnostic errors in emergency settings.…”

Section: Modifiable Health Risk Factorsmentioning

confidence: 99%

Determining rural risk for aneurysmal subarachnoid hemorrhages: A structural equation modeling approach

Nichols

Gall

Stirling

2016

Journal of Neurosciences in Rural Practice

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An aneurysmal subarachnoid hemorrhage (aSAH) carries a high disability burden. The true impact of rurality as a predictor of outcome severity is unknown. Our aim is to clarify the relationship between the proposed explanations of regional and rural health disparities linked to severity of outcome following an aSAH. An initial literature search identified limited data directly linking geographical location, rurality, rural vulnerability, and aSAH. A further search noting parallels with ischemic stroke and acute myocardial infarct literature presented a number of diverse and interrelated predictors. This a priori knowledge informed the development of a conceptual framework that proposes the relationship between rurality and severity of outcome following an aSAH utilizing structural equation modeling. The presented conceptual framework explores a number of system, environmental, and modifiable risk factors. Socioeconomic characteristics, modifiable risk factors, and timely treatment that were identified as predictors of severity of outcome following an aSAH and within each of these defined predictors a number of contributing specific individual predictors are proposed. There are considerable gaps in the current knowledge pertaining to the impact of rurality on the severity of outcome following an aSAH. Absent from the literature is any investigation of the cumulative impact and multiplicity of risk factors associated with rurality. The proposed conceptual framework hypothesizes a number of relationships between both individual level and system level predictors, acknowledging that intervening predictors may mediate the effect of one variable on another.

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Section: Modifiable Health Risk Factorsmentioning

confidence: 99%

Determining rural risk for aneurysmal subarachnoid hemorrhages: A structural equation modeling approach

Nichols

Gall

Stirling

2016

Journal of Neurosciences in Rural Practice

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“…A number of recent studies have evaluated the aneurysm treatment experience at a nationwide level, noting factors resulting in procedural delays 9–11. Although studies have suggested that surgical clipping is associated with increased wait times when compared with aneurysm coiling, the specific factors associated with these delays have not yet been determined 10. We set out to evaluate time to clipping and time to coiling following SAH in a national cohort according to hospital and patient demographics.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of time to aneurysm treatment following subarachnoid hemorrhage: comparison of patients treated with clipping versus coiling

Attenello

Reid

Wen

et al. 2015

J NeuroIntervent Surg

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“…Delayed treatment for aSAH appears to be common. In one study from a single hospital in Melbourne, only half the people with aSAH were treated within 24 hours of onset (Phillips et al, 2011) while in a country-wide study in the UK more than 60% were treated >2 days after onset of symptoms (Langham et al, 2009) and in the US 10-25% of people are treated at >48 hours after admission (Sarmiento et al, 2015;Siddiq et al, 2012). Our understanding of the causes of these delays remains limited, in turn affecting our ability to develop interventions targeted at reducing delays and improving outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…being female, older age, living in urban areas, having inter-facility transfer) had a bidirectional influence on time to treatment . Other potential predictors of delays in aSAH treatment include pre-hospital delays (Germans et al, 2014), admission on the weekend (Sarmiento et al, 2015;Siddiq et al, 2012), having pre-treatment complications (Sonig et al, 2018) or having surgical clipping (Sarmiento et al, 2015;Sonig et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Advancing convergent mixed methods design using the Diamond Approach for clinical multiple case studies: an example using data on time to treatment in aneurysmal subarachnoid haemorrhage

Nguyen

Stirling

Kitsos

et al. 2021

Preprint

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Backgrounds and aims: Delays in treatment of aSAH appear common but the causes are not well understood. We explored facilitators and barriers to timely treatment of aSAH. Methods: We used a mixed-methods multiple case study approach including in-depth interviews with stakeholders involved in individual aSAH cases, focusing on events from onset to treatment. Quantitative data were extracted from medical records including date and times. Within-case analysis identified barriers and facilitators in 4 phases (pre-hospital, presentation, transfer, in-hospital), which then being triangulated with the quantified time in each phase to determine significant influencing factors. Finally, we conducted thematic analysis across cases in early (<12h) and delayed (>12h) treatment group using a case-study matrix. Results: Twenty-seven cases (74.1% female) with 89 interviewees were included. Median (IQR) time to treatment was 15.1 (9.0, 24.1) hours. Only 37% of cases had treatment within 12 hours of onset. Qualitative and quantitative data triangulation identified key themes influencing timely treatment of aSAH. Early recognition of aSAH and good coordination during pre-hospital and diagnosis phases, and availability of resources for treatment during in-hospital period were main facilitators for treatment within 12 hours from onset. Lack of recognition of aSAH at onset and lack of resources for immediate in-hospital treatment were major barriers for more delayed treatment. Conclusions: Using a robust mixed-methods approach, we identified the most significant factors affecting more timely treatment within 12 hours from onset of aSAH. The factors are potentially modifiable and may improve timely treatment of aSAH.

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Predictors of Treatment Delay in Aneurysmal Subarachnoid Hemorrhage Patients

Abstract: Risk factors associated with treatment delay in aSAH patients include older age, nonwhite race, surgical clipping, and admission to low surgical volume hospitals.

Cited by 15 publications

References 38 publications

Determining rural risk for aneurysmal subarachnoid hemorrhages: A structural equation modeling approach

Determining rural risk for aneurysmal subarachnoid hemorrhages: A structural equation modeling approach

Evaluation of time to aneurysm treatment following subarachnoid hemorrhage: comparison of patients treated with clipping versus coiling

Advancing convergent mixed methods design using the Diamond Approach for clinical multiple case studies: an example using data on time to treatment in aneurysmal subarachnoid haemorrhage

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