Suchergebnisse

IntroductionCorHealth Ontario, formerly Cardiac Care Network (CCN), maintains a registry of patients undergoing select cardiac procedures/surgeries in Ontario, Canada. This population-based database contains over 35 datasets with complex structure, linked by unique primary key or multiple keys.
Objectives and ApproachWe aimed to simplify the complex CorHealth database so that research analysts could create study cohorts more efficiently and effectively, and to enrich the study cohort by getting more clinical information through database linkage. Through internal linkage, we could combine clinical fields from multiple CorHealth datasets. While the CorHealth dataset may not have all the clinical information needed for a given study, we may link the CorHealth study cohort externally to other administration databases to obtain additional fields via the probability matching (i.e., identical patient ID, hospital ID and procedure/surgery date).
ResultsAfter identifying the primary keys on the relational database flowchart, we designed new data structures by combining similar topic datasets. The total number of datasets was reduced from 35 to 13. This simplified CorHealth dataset includes one main CorHealth dataset (including demographic information, referral data, comorbidities) plus 12 other linkable specific datasets (including stent, vessel, TAVI, STEMI). Through internal linkage, we can get the stent numbers, lengths and types of Percutaneous Coronary Interventions from the Stent dataset. Linking to Discharge Abstract Database (DAD), we can get the hospital length of stay and the episode of care of hospital transfer for each procedure; linking to The Ontario Health Insurance Plan database (OHIP), we can find the graft numbers and vessel types of Coronary Artery Bypass Graft.
Conclusion/ImplicationsTo improve the research capacity and increase the value of the CorHealth database, analysts could create enhanced cardiovascular study cohorts derived from the simplified CorHealth database, plus internal linkage from other CorHealth datasets, and external data linkage from population-based administrative sources. We have accomplished three reports (PCI/CABG/TAVI) accordingly in 2017/18.

ObjectiveAdjusting for stroke severity is critical in stroke outcomes research. The Passive Surveillance Stroke SeVerity (PaSSV) score is an administrative data-based measure of stroke severity, initially derived in Ontario, Canada using data between 2002-2013. We assessed its geographical and temporal external validity in British Columbia (BC), Nova Scotia (NS), and Ontario, Canada.
MethodsIn each province, we identified adult in-patients with ischemic stroke or intracerebral hemorrhage and admitted from an emergency department between 2014-2019 and calculated their PaSSV score using linked administrative data. We used Cox proportional hazards models to evaluate the association between the PaSSV score and the hazard of death over 30 days and the cause-specific hazard of admission to long-term care over 365 days. We assessed the models' discriminative values using Uno's c-statistic, comparing models with versus without PaSSV.
ResultsWe included 86,142 patients (n=18,387 in BC, n=65,082 in Ontario, n=2,673 in NS). The mean and median PaSSV were similar across provinces. Higher PaSSV score, reflecting lower stroke severity, was associated with a lower mortality (hazard ratio and 95% confidence intervals 0.70 [0.68-0.71] in BC, 0.69 [0.68-0.69] in Ontario, 0.72 [0.68-0.75] in NS) and long-term care admission (0.77 [0.76-0.79] in BC, 0.84 [0.83-0.85] in Ontario, 0.86 [0.79-0.93] in NS). Including PaSSV in the multivariable models improved model fit according to the c-statistics.
ConclusionWe showed that PaSSV has geographical and temporal validity. It is a useful tool for risk-adjustment in multi-jurisdiction stroke outcomes research, and a valuable addition to be included in the national algorithm inventory.