A Population Cohort Study to Evaluate the Risk of Ischemic Stroke Among Individuals with a New Diagnosis of Cancer Compared to Matched Cancer-Free Controls: Impact of Prior Stroke History
Notice bibliographique
Résumé
Abstract Cancer is a risk factor for thrombosis, but unlike venous thrombosis, its association with ischemic stroke, and its impact on stroke management and post-stroke outcomes are not well characterized. The primary objective of this study was to measure and compare the risk of ischemic stroke in individuals with a new diagnosis of cancer and those without a history of cancer in two separate matched cohorts based on the absence (Matched Cohort 1) or presence (Matched Cohort 2) of a prior diagnosis of ischemic stroke (Figure 1). Methods: We conducted a population-based matched cohort study of adults ≥18 years using linked clinical and administrative health databases in Ontario, Canada (2010 to 2019). Individuals with a new diagnosis of cancer were matched (1:1) to cancer-free controls by age and sex. Cancer diagnoses were determined using ICD-O-3 diagnostic codes (basal and squamous cell carcinoma and primary central nervous system tumors were excluded). For cancer patients, the index date was the diagnosis date and a corresponding dummy index date was assigned to the matched cancer-free control. The primary outcome was the incidence of ischemic stroke (time to ischemic stroke following index) determined using validated ICD-9 or -10 diagnostic codes from hospitalizations or emergency department visits. Analyses were conducted separately, in parallel, for each cohort. Standardized differences were used to compare the distributions of baseline characteristics. Cumulative incidence function (CIF) curves were generated for ischemic stroke and all-cause mortality. Competing risk methods were used to calculate sub-distribution adjusted hazard ratios (aHR) and 95% confidence intervals (CI) for ischemic stroke, where death was treated as a competing event. Interactions with the timeline at 1.5 years after index were incorporated to assess if the hazard ratio for the main exposure (cancer vs cancer-free) varied before and after the 1.5-year mark. Regression analyses were adjusted using baseline characteristics for which there was imbalance between groups. Results: Matched Cohort 1 included 620,647 individuals with cancer and 620,647 controls. Matched Cohort 2 included 13,924 individuals with cancer and 13,924 controls. Baseline characteristics were generally well balanced (Table 1). Use of antithrombotic medications (prior to index) among individuals ≥66 years was similar between the groups (data not shown). CIF curves for ischemic stroke and mortality are shown in Figure 2. In Matched Cohort 1 (no history of stroke), the risk of ischemic stroke was increased in cancer patients compared to cancer-free controls 1.5 years post-index (aHR 1.40, 95%CI 1.34-1.47). From 1.5 years to 5 years, the risk of ischemic stroke was lower in cancer patients compared to controls (aHR 0.72, 95%CI 0.69-0.74). In Matched Cohort 2 (prior history of ischemic stroke), the risk of ischemic stroke was similar at 1.5 years after the index date in individuals with and without cancer (aHR 1.00, 95%CI 0.88-1.14). From 1.5 to 5 years, the risk of ischemic stroke was reduced (aHR 0.53, 95%CI 0.46-0.62) in cancer patients compared to controls. Conclusions: Compared to cancer-free controls, the risk of ischemic stroke among individuals with a new cancer diagnosis depended on the presence or absence of prior history of ischemic stroke, a novel finding not previously reported. In individuals without a prior history of stroke, those with cancer had a 1.5-fold higher risk of ischemic stroke at 1.5 years compared to controls. In individuals with a prior history of stroke, those with cancer had a similar risk of stroke compared to controls. At 5 years post-index, the risk of ischemic stroke was lower in cancer patients in both cohorts which may reflect high early mortality rates and lower stroke risk among long-term survivors of cancer. The risk of death among cancer patients was highest during the first 1.5 years after the index date in both cohorts. However, the magnitude of the risk increase was higher in individuals without a prior history of stroke (10-fold) compared to those with a prior history of cancer (5-fold). Limitations include unknown causes of death and unavailability of some covariates (e.g. smoking and antithrombotic use in individuals aged <65 years). Future analyses will explore risk of ischemic stroke within common cancer subtypes (breast, prostate, colon, pancreas, lymphoma), and assess stroke risk factors, treatments and outcomes. Figure 1 Figure 1. Disclosures Siegal: BMS-Pfizer: Honoraria; Leo Pharma: Honoraria; Novartis: Honoraria; Portola: Honoraria; Servier: Honoraria. Carrier: Pfizer: Honoraria, Research Funding; BMS: Honoraria, Research Funding; Servier: Honoraria; Leo Pharma: Honoraria, Research Funding; Bayer: Honoraria; Sanofi: Honoraria. Gross: Valeo: Honoraria; Leo Pharma: Honoraria; Bayer: Honoraria; BMS-Pfizer: Honoraria.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».