Derivation and validation of pragmatic clinical models to predict hospital length of stay after cardiac surgery in Ontario, Canada: a population-based cohort study.

BACKGROUND: Cardiac surgery is resource intensive and often requires multidisciplinary involvement to facilitate discharge. To facilitate evidence-based resource planning, we derived and validated clinical models to predict postoperative hospital length of stay (LOS). METHODS: We used linked, population-level databases with information on all Ontario residents and included patients aged 18 years or older who underwent coronary artery bypass grafting, valvular or thoracic aorta surgeries between October 2008 and September 2019. The primary outcome was hospital LOS. The models were derived by using patients who had surgery before Sept. 30, 2016, and validated after that date. To address the rightward skew in LOS data and to identify top-tier resource users, we used logistic regression to derive a model to predict the likelihood of LOS being more than the 98th percentile (> 30 d), and γ regression in the remainder to predict continuous LOS in days. We used backward stepwise variable selection for both models. RESULTS: Among 105 193 patients, 2422 (2.3%) had an LOS of more than 30 days. Factors predicting prolonged LOS included age, female sex, procedure type and urgency, comorbidities including frailty, high-risk acute coronary syndrome, heart failure, reduced left ventricular ejection fraction and psychiatric and pulmonary circulatory disease. The C statistic was 0.92 for the prolonged LOS model and the mean absolute error was 2.4 days for the continuous LOS model. INTERPRETATION: We derived and validated clinical models to identify top-tier resource users and predict continuous LOS with excellent accuracy. Our models could be used to benchmark clinical performance based on expected LOS, rationally allocate resources and support patient-centred operative decision-making.

Derivation and validation of predictive indices for cardiac readmission after coronary and valvular surgery - A multicenter study.

Objective: To derive and validate models to predict the risk of a cardiac readmission within one year after specific cardiac surgeries using information that is commonly available from hospital electronic medical records. Methods: In this retrospective cohort study, we derived and externally validated clinical models to predict the likelihood of cardiac readmissions within one-year of isolated CABG, AVR, and combined CABG+AVR in Ontario, Canada, using multiple clinical registries and routinely collected administrative databases. For all adult patients who underwent these procedures, multiple Fine and Gray subdistribution hazard models were derived within a competing-risk framework using the cohort from April 2015 to March 2018 and validated in an independent cohort (April 2018 to March 2020). Results: For the model that predicted post-CABG cardiac readmission, the c-statistic was 0.73 in the derivation cohort and 0.70 in the validation cohort at one-year. For the model that predicted post-AVR cardiac readmission, the c-statistic was 0.74 in the derivation and 0.73 in the validation cohort at one-year. For the model that predicted cardiac readmission following CABG+AVR, the c-statistic was 0.70 in the derivation and 0.66 in the validation cohort at one-year. Conclusions: Prediction of one-year cardiac readmission for isolated CABG, AVR, and combined CABG+AVR can be achieved parsimoniously using multidimensional data sources. Model discrimination was better than existing models derived from single and multicenter registries.

Predictors of cumulative cost for patients with severe aortic stenosis referred for surgical or transcatheter aortic valve replacement: a population-based study in Ontario, Canada.

AIMS: Transcatheter aortic valve replacement (TAVR) as an alternative to surgical aortic valve replacement (SAVR) has transformed severe aortic stenosis (AS) management. Our aim was understand AS cost drivers from referral to 1-year post-procedure. METHODS AND RESULTS: We identified patients referred for either TAVR/SAVR between 1 April 2015 and 31 March 2018, with follow-up until 31 March 2019 in Ontario, Canada. We stratified costs into (i) a referral phase, (ii) a procedural phase from the procedure date to 60 days post-procedure, and (iii) post-procedure phase from 61 days to 1 year. Multivariable regression modelling using generalized linear models with a log link gamma distribution was used to identify cost drivers in each phase. The study cohort included 12 086 AS patients; 4832 were referred for TAVR and 7254 were referred for SAVR. The median cost for TAVR was higher than SAVR in the referral ($3593 vs. $2944) and post-procedural ($5938 vs. $3257) phases. In contrast, for the procedural phase, SAVR had a median cost of $29 756 vs. $27 907 for TAVR. Predictors of high cost in the referral phase were longer wait-time, and an urgent in-hospital procedure. In the procedural phase, procedural complications were the major drivers of higher cost. In the post-procedural phase, patient co-morbidities were the major drivers, specifically dialysis, liver disease, cancer, peripheral vascular disease, and diabetes mellitus. CONCLUSION: We identified distinct patterns of cost accumulation and modifiable drivers for SAVR compared with TAVR; these drivers may guide clinical and health policy decisions to make AS care more efficient.

Implementation of an agency to improve chronic kidney disease care in Ontario: lessons learned by the Ontario Renal Network.

In 2009, Ontario's Ministry of Health and Long-Term Care initiated the transfer of oversight and coordination of chronic kidney disease (CKD) care to the Ontario Renal Network (ORN) under the auspices of Cancer Care Ontario (CCO). The aim was to replicate the quality improvement and change management practices used for cancer control within CKD. Much of the ORN's first three years were dedicated to building the infrastructure necessary to bridge the gap between provincial policy and clinical practice. This article explores the accomplishments, challenges and lessons learned over that period. The results, which are applicable to the management of chronic diseases in Ontario, Canada, and internationally, confirm that sustainable change takes time and requires strong leadership, transparency, accountability and communication, supported by a solid foundation of data and evidence.