Browsing by Author "Yu, Amy Y X"

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    Moderate sensitivity and high specificity of emergency department administrative data for transient ischemic attacks
    (2017-09-18) Yu, Amy Y X; Quan, Hude; McRae, Andrew; Wagner, Gabrielle O; Hill, Michael D; Coutts, Shelagh B
    Abstract Background Validation of administrative data case definitions is key for accurate passive surveillance of disease. Transient ischemic attack (TIA) is a condition primarily managed in the emergency department. However, prior validation studies have focused on data after inpatient hospitalization. We aimed to determine the validity of the Canadian 10th International Classification of Diseases (ICD-10-CA) codes for TIA in the national ambulatory administrative database. Methods We performed a diagnostic accuracy study of four ICD-10-CA case definition algorithms for TIA in the emergency department setting. The study population was obtained from two ongoing studies on the diagnosis of TIA and minor stroke versus stroke mimic using serum biomarkers and neuroimaging. Two reference standards were used 1) the emergency department clinical diagnosis determined by chart abstractors and 2) the 90-day final diagnosis, both obtained by stroke neurologists, to calculate the sensitivity, specificity, positive and negative predictive values (PPV and NPV) of the ICD-10-CA algorithms for TIA. Results Among 417 patients, emergency department adjudication showed 163 (39.1%) TIA, 155 (37.2%) ischemic strokes, and 99 (23.7%) stroke mimics. The most restrictive algorithm, defined as a TIA code in the main position had the lowest sensitivity (36.8%), but highest specificity (92.5%) and PPV (76.0%). The most inclusive algorithm, defined as a TIA code in any position with and without query prefix had the highest sensitivity (63.8%), but lowest specificity (81.5%) and PPV (68.9%). Sensitivity, specificity, PPV, and NPV were overall lower when using the 90-day diagnosis as reference standard. Conclusions Emergency department administrative data reflect diagnosis of suspected TIA with high specificity, but underestimate the burden of disease. Future studies are necessary to understand the reasons for the low to moderate sensitivity.
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    Systolic blood pressure as a predictor of transient ischemic attack/minor stroke in emergency department patients under age 80: a prospective cohort study
    (2019-10-25) Penn, Andrew M; Croteau, Nicole S; Votova, Kristine; Sedgwick, Colin; Balshaw, Robert F; Coutts, Shelagh B; Penn, Melanie; Blackwood, Kaitlin; Bibok, Maximilian B; Saly, Viera; Hegedus, Janka; Yu, Amy Y X; Zerna, Charlotte; Klourfeld, Evgenia; Lesperance, Mary L
    Abstract Background Elevated blood pressure (BP) at emergency department (ED) presentation and advancing age have been associated with risk of ischemic stroke; however, the relationship between BP, age, and transient ischemic attack/minor stroke (TIA/MS) is not clear. Methods A multi-site, prospective, observational study of 1084 ED patients screened for suspected TIA/MS (symptom onset < 24 h, NIHSS< 4) between December 2013 and April 2016. Systolic and diastolic BP measurements (SBP, DBP) were taken at ED presentation. Final diagnosis was consensus adjudication by stroke neurologists; patients were diagnosed as either TIA/MS or stroke-mimic (non-cerebrovascular conditions). Conditional inference trees were used to define age cut-points for predicting binary diagnosis (TIA/MS or stroke-mimic). Logistic regression models were used to estimate the effect of BP, age, sex, and the age-BP interaction on predicting TIA/MS diagnosis. Results Over a 28-month period, 768 (71%) patients were diagnosed with TIA/MS: these patients were older (mean 71.6 years) and more likely to be male (58%) than stroke-mimics (61.4 years, 41%; each p < 0.001). TIA/MS patients had higher SBP than stroke-mimics (p < 0.001). DBP did not differ between the two groups (p = 0.191). SBP was predictive of TIA/MS diagnosis in younger patients, after accounting for age and sex; an increase of 10 mmHg systolic increased the odds of TIA/MS 18% (odds ratio [OR] 1.18, 95% CI 1.00–1.39) in patients < 60 years, and 23% (OR 1.23, 95% CI 11.12–1.35) in those 60–79 years, while not affecting the odds of TIA/MS in patients ≥80 years (OR 0.99, 95% CI 0.89–1.07). Conclusions Raised SBP in patients younger than 80 with suspected TIA/MS may be a useful clinical indicator upon initial presentation to help increase clinicians’ suspicion of TIA/MS. Trial registration ClinicalTrials.gov NCT03050099 (10-Feb-2017) and NCT03070067 (3-Mar-2017). Retrospectively registered.
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    Using random forests to model 90-day hometime in people with stroke
    (2021-05-10) Holodinsky, Jessalyn K; Yu, Amy Y X; Kapral, Moira K; Austin, Peter C
    Abstract Background Ninety-day hometime, the number of days a patient is living in the community in the first 90 after stroke, exhibits a non-normal bucket-shaped distribution, with lower and upper constraints making its analysis difficult. In this proof-of-concept study we evaluated the performance of random forests regression in the analysis of hometime. Methods Using administrative data we identified stroke hospitalizations between 2010 and 2017 in Ontario, Canada. We used random forests regression to predict 90-day hometime using 15 covariates. Model accuracy was determined using the r-squared statistic. Variable importance in prediction and the marginal effects of each covariate were explored. Results We identified 75,745 eligible patients. Median 90-day hometime was 59 days (Q1: 2, Q3: 83). Random forests predicted hometime with reasonable accuracy (adjusted r-squared 0.3462); no implausible values were predicted but extreme values were predicted with low accuracy. Frailty, stroke severity, and age exhibited inverse non-linear relationships with hometime and patients arriving by ambulance had less hometime than those who did not. Conclusions Random forests may be a useful method for analyzing 90-day hometime and capturing the complex non-linear relationships which exist between predictors and hometime. Future work should compare random forests to other models and focus on improving the accuracy of predictions of extreme values of hometime.