There are 28 non-cardiology medications from multiple families costing more than $13 billion annually in Canada, categorized as 'Known' QT-prolonging medications (QTPmeds) based on very low levels of evidence. The association between many commonly used medications listed as known QTPmeds and actual major adverse cardiac events (MACE) is weak. Meanwhile, QTPmeds-related warnings are ubiquitous in every healthcare setting, triggering 'hard stop' disruption millions of times per day to front line clinicians. Poor quality medication safety alerts are increasingly recognized as a source of inferior patient care and provider burnout which detracts from healthcare sustainability.

In this study, anonymized hospital electronic medical record data from more than 990,000 adult patients across Ontario will be used to compare patients who experience MACE with those who do not, measuring their real-time exposure to QT-prolonging drugs. Additionally, machine-learning techniques will also be used to find which patient or treatment factors best predict risk.

The objectives of this study are to 1) Investigate whether exposure to one or more 'Known' QTPmed is associated with an increased risk of MACE after adjusting for confounders; and 2) Identify predictors and their relative importance for QTPmeds-associated MACE.

In summary, QT-prolonging medications have the potential to cause very serious adverse events, including death. However, it is not sufficiently clear which patients under which circumstances suffer events, or when is QT prolongation a useful surrogate marker for harm. Meanwhile, ubiquitous medication alerts related to QT-prolonging medications are at best imprecise and at worst, misleading, costly and potentially dangerous. Now that data resources are available with the data elements, structure and sample size required to rigorously assess this association, this study will address this question to improve patient safety, provider satisfaction and the cost-effectiveness of care.

This study's investigators have formed the largest hospital electronic medical record (EMR) research network in Canada to be able to address this important patient safety issue. The team's combined use of the Ontario GEMINI network and Epic-Dovetale EMR data has huge power because of the nearly 1 million patients, the rich data including medication exposure, outcomes and risk factor determination that is not available elsewhere, and the development of data structure that allows for future international collaborations.

The investigators plan a nested case-control analysis using the Epic-Dovetale EMR and the GEMINI hospital electronic medical record (EMR) data repository (N > 990,000 eligible adult patients) comparing patients with a MACE to those without a MACE and their relative exposure to one or more Known QTPmed during a hospitalization. In addition, the investigators will do time-varying estimates of the risk of MACE for those patients with periods of Known QTPmed exposure and non-exposure during the same hospitalization. The MACE primary outcome is a QTP-relevant composite of death, non-fatal cardiac arrest, ventricular arrhythmias, and syncope. A second analysis will use machine learning algorithms to predict QTPmeds-associated MACE and determine the relative importance of each predictor.

This project will not only improve patient safety dramatically by improving the accuracy of QTPmeds-related alerts, it will also improve productivity and work satisfaction for thousands of healthcare providers internationally. In addition, the continued expansion and improvement of the largest Canadian hospital EMR research data platform is a huge new opportunity for future high-quality research.

Inclusion Criteria:

• Adult patients 18 years of age or older
• Admitted to St. Joseph's Healthcare Hamilton or GEMINI hospitals between December 2017 and March 2025

Exclusion Criteria:

• Patients <18 years old
• Outpatient encounters

This is a retrospective observational study using de-identified electronic-medical-record data from St. Joseph's Healthcare Hamilton and GEMINI hospitals; no direct participant enrollment or consent is required.

It is not permitted for datasets from Epic-Dovetale and GEMINI to be shared openly as they contain sensitive patient health information. This is based on the data governance policies of the Epic-Dovetale and GEMINI research network as well as its research ethics board-approved study protocols. The data can only be accessed in the secure Epic-Dovetale and GEMINI research environment. Summary analyses will be shareable.