Janet Halliday

On- versus Off-Hour Patient Cohorts at a Primary Stroke Center: Onset-to-Treatment Duration and Clinical Outcomes after IV Thrombolysis.

BACKGROUND The symptom onset-to-treatment (OTT) duration predicts symptomatic intracerebral hemorrhage (sICH) and adverse outcomes after ischemic stroke. Previous studies found disparities in OTT durations and clinical outcomes between stroke patients with symptom onset during on-hours versus off-hours, which led to the initiation of nationwide efforts to provide consistent 24-hour stroke care. GOAL Our objective is to compare OTT durations and clinical outcomes in ischemic stroke patients whose symptoms originated during on- versus off-hours at a primary stroke center. METHODS We analyzed clinical data from 210 consecutive patients receiving intravenous recombinant tissue plasminogen activator therapy between January 2009 and December 2013 at Yale-New Haven Stroke Center, a primary stroke center. Stroke severity was assessed by baseline National Institutes of Health Stroke Scale (NIHSS) scores. Clinical outcomes were assessed by presence of sICH and by stroke-related fatalities. OTT durations and clinical outcomes were compared using Mann-Whitney tests, 2-sample tests of proportions, and 2-sample t-tests after testing for equal variance. FINDINGS We found no significant differences in OTT durations between on-hour and off-hour patient cohorts (137 minutes versus 145 minutes, P = .53). There were also no differences in stroke severity (mean NIHSS score 12.4 versus 11.3, P = .27), sICH rates (4.6% versus 6.5%, P = .56), or stroke fatality rates (9.2% versus 9.8%, P = .89) between the 2 cohorts. CONCLUSIONS Our results represent progress in emergency response and acute stroke care, and reinforce ongoing nationwide efforts to increase stroke awareness and provide consistent quality care for patients with acute stroke.

Modest Association between the Discharge Modified Rankin Scale Score and Symptomatic Intracerebral Hemorrhage after Intravenous Thrombolysis

BACKGROUND Thirty- and 90-day modified Rankin Scale (mRS) scores are used to monitor adverse outcome or symptomatic intracerebral hemorrhage (sICH) in ischemic stroke patients after intravenous (IV) thrombolytic therapy. Discharge mRS scores are more readily available and could serve as a proxy for 30- or 90-day mRS data. Our goal was to evaluate agreement between the discharge mRS score and sICH. Additionally, we tested for correlations between the discharge mRS score and 8 clinical scores developed to predict sICH or adverse outcomes based on 90-day mRS data. METHODS Clinical data were analyzed from 210 patients receiving IV thrombolysis from January 2009 till December 2013 at the Yale New Haven Hospital. Agreement between sICH and the discharge mRS score was assessed using linear kappa. Eight clinical scores were calculated for each patient and compared with the discharge mRS score by univariate logistic regression. Goodness of fit was tested by receiver operating characteristic (ROC) analysis and by Hosmer-Lemeshow statistics. RESULTS We found only modest agreement between sICH and unfavorable discharge mRS scores (mRS ≥ 5), with kappa .22, P = .0001. All 8 clinical scores tested showed good agreement with discharge mRS score of 5 or more (ROC area >.7). CONCLUSIONS The discharge mRS score shows only modest agreement with sICH and therefore cannot be recommended as a proxy for 30- or 90-day mRS data. However, the discharge mRS score correlates strongly with clinical scores predicting long-term adverse outcome; therefore, assessment of discharge mRS scores may be of some clinical benefit.

Risk rtPA: An iOS mobile application based on TURN for predicting 90-day outcome after IV thrombolysis

OBJECTIVE We recently developed Thrombolysis risk Using mRS and NIHSS (TURN), a simple score using only prestroke mRS scores and admission NIHSS scores to predict 90-day outcome after IV thrombolysis in ischemic stroke patients. Our purpose was to develop and test a mobile application for utilization of TURN at the bedside. METHODS We developed Risk rtPA, an iOS mobile application based on TURN for prediction of 90-day excellent and severe outcome after IV thrombolysis. Excellent outcome was defined as 90-day mRS≤1. Severe outcome was defined as 90-day mRS≥5. Predictors for excellent and severe outcome were calculated using the inverse logit of -TURN and TURN respectively. We retrospectively validated our mobile application using data from 303 patients who received IV rt-PA during the NINDS rt-PA trial. Sensitivity and specificity analyses were performed using receiver operating characteristic (ROC) curves. RESULTS Prediction of excellent and severe outcome using Risk rtPA followed an S-shaped curve as expected. We confirmed this finding using data from the NINDS trial. Cutoffs selected after ROC analysis predicted severe outcome with sensitivity of 94.4% and specificity of 52.2%, and excellent outcome with specificity of 83.9% and sensitivity of 61.2%. CONCLUSION The Risk rtPA mobile application predicted 90-day excellent and severe outcome in most clinically relevant cases. This mobile application brings the TURN score to the bedside for prediction of 90-day outcome in ischemic stroke patients being evaluated for IV thrombolysis.

Validation of TURN, a simple predictor of symptomatic intracerebral hemorrhage after IV thrombolysis.

OBJECTIVE We recently described TURN (Thrombolysis risk Using mRS and NIHSS), a computationally simple tool for predicting symptomatic intracerebral hemorrhage (sICH) after IV thrombolysis (rt-PA). Our objective was to compare TURN to existing scores for predicting sICH. METHODS Our internal dataset consisted of 210 ischemic stroke patients receiving IV rt-PA from January 2009 until July 2013 at Yale New Haven Hospital. Our external dataset included 303 patients who received IV rt-PA during the NINDS rt-PA trial. Predictive ability and goodness of fit were quantified by odds ratios (OR) and areas under the receiver operating characteristic curve (AUROC), and compared using unequal variance two-sample t-tests. RESULTS TURN predicted sICH with a higher OR than ASTRAL in the internal dataset (2.72 versus 1.10, P=0.05). We found no other significant differences in OR or AUROC between TURN and other scores in both datasets. CONCLUSION Despite its computational simplicity, TURN predicts sICH with accuracy comparable to existing scores.