Relationships between age, other predictive variables, and the 90-day functional outcome after intravenous thrombolysis for acute ischemic stroke

Roshan Gunathilake, Venkatesh Krishnamurthy, Christopher Oldmeadow, Erin Kerr, Chandrasekhara Padmakumar, John Attia, Mark Parsons and Christopher Levi

Departments of General Medicine, Neurology, and Geriatric Medicine John Hunter Hospital ANDHunter Medical Research InstituteNewcastle, NSW

Introduction

• Strong data to support use of IV thrombolysis regardless of age1

• Absolute benefit is attenuated in older patients, but the relative benefit is retained2

• Careful patient selection is important• Traditional statistical methods used in previous

studies have limited utility in classifying patients into risk categories3

Objective• To examine the relationships between – age–other recognised predictor variables, and –90-day outcome following IV thrombolysis among acute stroke victims presenting to an Australian tertiary centre

• using classification and regression trees (CART)4

Methods• Design: observational study using

prospectively collected data (TIPS database)• Setting: Acute Stroke Service, John Hunter

Hospital, NSW• Subjects: All acute stroke patients treated with

rt-PA between Jan 2011 – Jun 2013• Thrombolysis protocol5: rt-PA within 4.5h, no

upper age limit , NCCT+ CTA

Methods• Follow up: 90 day clinical review/ telephone interview• Ethics approval: HNE Human Research Ethics Committee• Measurements: – Baseline clinical variables – Alberta Stroke Program Early CT Scale (ASPECTS)6 – Modified leptomeningeal collateral score7

– 90-day modified Rankin score [dichotomized to <2 (good outcome) or ≥2 (poor outcome)

Imaging analysis

• Baseline NCCT and CTA independently reviewed and scored by consensus by 2 authors (RG, VK)

• ASPECTS6 to assess the EIC in 2 axial cuts (score 0- 10)

• Leptomeningeal collateral Grading (modified after Tan et al.)7 based on contrast opacification in arteries distal to an occlusion (score 0- 2)

Statistical analysis• Student’s t test, Mann Whitney U test and Fisher’s

exact test for descriptive statistics• CART analysis (RPART library R2.15) to determine

which baseline variables predicted 90-day outcome (dichotomized according to mRS <2 or ≥2)

• Variables included in CART: Age1, gender, premorbid mRS, NIHSS8, ASPECTS6, Collateral grade7, BP9, glucose10, onset-to needle time11

• Decision tree pruned using cost-complexity parameter to minimize over fitting

<75 years ≥75 years P valueNumber of subjects 60 57 Age (years) 62.6±10.5 82.6±5.2 Male 32 (53%) 31 (54%) Premorbid mRS 0 (0) 0 (0) 0.2Baseline NIHSS 14 (11.5) 14 (11.5) 0.2ASPECTS 10 (0) 10 (0) 0.6Collateral score 1(2) 1 (2) 0.08Systolic BP (mmHg) 150±25 144±23 0.18Diastolic BP (mmHg) 85±15 79±13 0.02Baseline blood glucose 7.1±2.7 6.9±1.8 0.5Aspirin use 17 (28%) 26 (45%) 0.06Anticoagulant use 7 (12%) 6 (10%) 1.0Hypertension 32(53%) 36 (63%) 0.3Diabetes 12 (20%) 7 (12%) 0.3Hyperlipidaemia 17 (28%) 16 (28%) 1.0Smoking history 24 (40%) 8 (14%) 0.002Atrial fibrillation 19 (32%) 15 (26%) 0.5Heart failure 1 (2%) 4 (7%) 0.2Previous stroke 6 (10%) 7 (12%) 0.8Time to treatment min 184 ±69 174±60 0.4

Baseline characteristics

<75 years ≥75 years P value24 h NIHSS 5 (11) 4 (8.5) 0.6sICH 5 (8%) 10 (17%) 0.2Discharge mRS<2 14 (23%) 12 (21%) 0.890 day mRS<2† † 29 (48%) 18 (32%) 0.0690 day mortality‡ 6 (10%) 17 (30%) 0.01

Outcome of IV thrombolysis

† unadjusted OR 0.49, 95% CI 0.23-1.04‡ unadjusted OR 3.82, 95% CI 1.38-10.57

Classification tree for the outcome mRS at 90 days

Classification tree forcing the first split at age 75 years

Conclusion

CART analysis based on standard baseline clinico-radiological characteristics identifies a number of prognostically disparate subgroups of patients following IV thrombolysis

Strengths and weaknesses• CART analysis identified dominant predictors , patterns of

interactions, and subsets where a given variable was of greatest importance

• Able to develop decision rules that can assist clinical decision-making

• Single centre study, sample size, potential bias towards selecting older patients with better premorbid functional status, loss to follow up

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