OC 23.2 - Development and Validation of a Risk Prediction Model for Ischemic Stroke after Cancer Diagnosis

Sunday, June 23, 2024

15:00 – 15:15 ICT

Room: Ballroom B3

Presenting Author(s)

Deborah Siegal, MD MSc FRCPC

Associate Professor
University of Ottawa and Ottawa Hospital Research Institute
Ottawa, Ontario, Canada

Co-Author(s)

RL

Ronda Lun

Vascular Neurology Fellow
Stanford Medicine
Palo Alto, California, United States
Jenneke Leentjens

Internist and Junior Principal Clinician
Department of Internal Medicine, Radboud Institute for Health Sciences, Radboud University Medical Centre
Nijmegen, Gelderland, Netherlands
Marc Carrier, MD

Professor
Department of Medicine, University of Ottawa at the The Ottawa Hospital
Ottawa, Ontario, Canada
JC

Joshua O. Cerasuolo

Staff Scientist
ICES North
Sudbury, Ontario, Canada
DK

David Kirkwood

Analytic Epidemiologist
ICES McMaster
Hamilton, Ontario, Canada
MK

Moira Kapral

Professor
University of Toronto
Toronto, Ontario, Canada
RS

Rinku Sutradhar

Senior Core Scientist
ICES Central
Toronto, Ontario, Canada

Background: The risk of ischemic stroke is increased after cancer diagnosis, but there are no tools to identify individuals at highest risk.

Aims: To develop and validate a risk prediction model for ischemic stroke within 1 year after cancer diagnosis.

Methods: Using linked clinical and administrative health databases, we conducted a population-based retrospective cohort study of adults in Ontario, Canada with newly diagnosed cancer from 2010 – 2021 (excluding non-melanoma skin cancer & central nervous system malignancies). Patients were randomly selected for model derivation (60%) or validation (40%). The final model predicting ischemic stroke within 1 year following cancer diagnosis was derived using multivariable Fine-Gray regression with candidate predictors selected via backward elimination. Sub-distribution adjusted hazard ratios (aHR) and 95% confidence intervals (CI) were calculated, where all-cause mortality was treated as a competing event. Model performance was assessed in the validation cohort using C-statistic and calibration plots.

Results: Of the 698,566 eligible patients, 418,911 were randomly allocated to derivation, and 279,576 to validation. The overall rate of stroke per 1000 person-years was 6.7 (6.4 – 6.9) for the derivation cohort. The final model included 22 predictors: age, sex, long-term care residency, history of heart failure, hypertension, dementia, asthma, atrial fibrillation, dyslipidemia, liver disease, ischemic stroke, transient ischemic attack, valvular disease, venous thromboembolism, hospitalization within the last 3 months, cancer type, cancer stage, cancer surgery or chemotherapy 3 months following diagnosis, and several 2-way interactions with age and cancer type (Table 1). Discrimination was good (C-statistic 0.73). The model was well calibrated, with points following the 45-degree line (Figure 1).

Conclusion(s): Our risk prediction model for ischemic stroke after cancer diagnosis showed good discrimination. Although external validation is required, it has the potential to identify patients at high risk who may benefit from prevention treatments that can be evaluated in clinical trials.