Adherence to healthcare quality measures is needed to reduce the burden of cerebrovascular disease and improve clinical outcomes. Electronic health records (EHRs) can facilitate the standardization of care provision and the improvement of disease prediction and prevention. Although the EHRs in clinical settings are increasingly prevalent in China, they are rarely used for healthcare research. the investigators aimed to conduct an EHR-based registry study to improve the healthcare and outcomes for cerebrovascular diseases.~Twenty-four hospitals were enrolled in the PLATFORM-CVD Study in January 2018. Data collection began on February 1st, 2019. Historical data from January 2017 are abstracted first and prospective data are continuously reported until May 20th, 2020. Data were abstracted from the medical records, including hospital information system, laboratory information management system, and picture archiving and communication systems by an extract-transform-load tool. The EHR system included diagnostic information for cerebral infarctions (I63), nontraumatic intracerebral hemorrhages (I61), nontraumatic subarachnoid hemorrhages (I60), transient cerebral ischemic attacks and related syndromes (G45), intracranial and intraspinal phlebitis and thrombophlebitis (G08), vascular dementia (F01), and other aneurysms (I72). The quality of stroke care was assessed by 21 evidence-based performance measures. In-hospital outcomes were calculated including mortality, length of stay, and costs.~The PLATFORM-CVD Study leverages EHRs to better understand incident cerebrovascular diseases in China. Data from this cohort will serve as a unique platform for quality assessment and improvement for acute treatment and secondary prevention of cerebrovascular diseases, as well as in-hospital outcome risk predictions and health economic evaluations.
In this protocol, the investigators present methods and preliminary results from the PLATFORM-CVD Study, an EHR-based multicenter cohort. This study will focus on assessing the distribution of major cerebrovascular diseases, determining the risk factors associated with disease incidence and worse in-hospital outcomes, as well as describing the quality of care. Data from this cohort will be used to develop suitable prediction models for cerebrovascular diseases using real-world data and to understand how outcomes for cerebrovascular diseases would change with quality improvement interventions.