A risk score for prediction of symptomatic intracerebral haemorrhage following thrombolysis. (December 2021)

• Record Type: Journal Article
• Authors: Soni, Mukesh; Wijeratne, Tissa; Ackland, David C.
• Is Part Of: International journal of medical informatics. Volume 156(2021)
• Publication Date: 2021-12
• Subjects: Cerebral infarct -- Prognosis -- Stroke management -- Database -- Modelling; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2021.104586
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Risk factors for SICH following rt-PA treatment identified. An accurate risk score for SICH following rt-PA therapy developed and validated. SICH risk following rt-PA treatment predicted using readily available clinical data. The SICH prediction tool developed may be used in patient screening prior to rt-PA therapy. Abstract: Background and purpose: Intravenous recombinant tissue plasminogen activator (rt-PA) remains the only FDA approved pharmacological therapy for acute ischemic stroke (AIS), but this treatment is associated with symptomatic intracerebral haemorrhage (SICH). The aim of this study was to derive and validate an accurate measure of SICH risk in ischemic stroke patients treated with rt-PA using data readily available from patient clinical records. Methods: Demographics, physiological parameters, and clinical data were obtained from 1, 270 ischemic stroke patients treated with thrombolysis at 20 hospitals. This included age, sex, weight, blood pressure, glucose levels, smoking preferences, and presence of previous clinical conditions. Using a bivariate analysis on a training dataset of 890 patients, SICH cases were compared against SICH-free patients and key risk factors associated with SICH were identified. Continuous variables were stratified using k-means clustering, and odds ratios computed for each of the categorical risk factors employed in the risk score. The SICH risk score, which was assessed using an independent validation dataset comprising 380 patients, was defined between 0 and 53, and stratified into 4 categories: very low risk (0–6), low risk (7–12), moderate risk (13–19), and high risk (>20). Results: Older age (age > 75 years), higher blood pressure, higher severity of stroke, pre-treatment antithrombotic and history of hypertension and hyperlipidaemia, were shown to be significant risk factors for SICH following rt-PA treatment (p < 0.05). A number of interaction effects with age produced greater overall SICH risk than that of individual variables alone, including age*weight, age*NIHSS, age*diastolic blood pressure, and age*hypertension. The SICH prediction tool demonstrated a C-statistic of 0.75 for continuous risk scoring (0–53) and 0.71 for stratified risk levels. Conclusion: A novel, computationally efficient risk score utilising data readily available from patient clinical records was shown to predict SICH risk following thrombolysis treatment with high accuracy. This tool may be useful for pre-screening patients for SICH risk to reduce the morbidity and mortality associated with thrombolysis treatment.