Towards a standardised method of patient prioritisation that accounts for clinical harm

Transcatheter Aortic Valve Implantation Wait-Time Management: Derivation and Validation of the Canadian TAVI Triage Tool (CAN3T)

BACKGROUND

Transcatheter aortic valve implantation (TAVI) has seen indication expansion and thus exponential growth in demand over the past decade. In many jurisdictions, the growing demand has outpaced capacity, increasing wait times and preprocedural adverse events. In this study, we derived prediction models that estimate the risk of adverse events on the waitlist and developed a triage tool to identify patients who should be prioritized for TAVI.

METHODS AND RESULTS

We included adult patients in Ontario, Canada referred for TAVI and followed up until one of the following events first occurred: death, TAVI procedure, removal from waitlist, or end of the observation period. We used subdistribution hazards models to find significant predictors for each of the following outcomes: (1) all-cause death while on the waitlist; (2) all-cause hospitalization while on the waitlist; (3) receipt of urgent TAVI; and (4) a composite outcome. The median predicted risk at 12 weeks was chosen as a threshold for a maximum acceptable risk while on the waitlist and incorporated in the triage tool to recommend individualized wait times. Of 13 128 patients, 586 died while on the waitlist, and 4343 had at least 1 hospitalization. A total of 6854 TAVIs were completed, of which 1135 were urgent procedures. We were able to create parsimonious models for each outcome that included clinically relevant predictors.

CONCLUSIONS

The Canadian TAVI Triage Tool (CAN3T) is a triage tool to assist clinicians in the prioritization of patients who should have timely access to TAVI. We anticipate that the CAN3T will be a valuable tool as it may improve equity in access to care, reduce preventable adverse events, and improve system efficiency.

A pragmatic digital health informatics based approach for aiding clinical prioritisation and reducing backlog of care: A study in cohort of 4022 people with diabetes

BACKGROUND AND AIMS

The backlog of care in resource stretched healthcare systems requires innovative approaches to aid clinical prioritisation. Our aim was to develop an informatics tool to identify and prioritise people with diabetes who are likely to deteriorate whilst awaiting an appointment to optimise clinical outcomes and resources.

MATERIALS AND METHODS

Using data from electronic health care records we identified 6 risk-factors that could be addressed in 4022 people (52% male, 30% non-Caucasian) with diabetes attending a large university hospital in London. The risk-factors were new clinical events/data occurring since their last routine clinic visit. To validate and compare data-led prioritisation tool to a traditional 'clinical approach' a sample of 450 patients were evaluated.

RESULTS

Of the 4022 people, 549 (13.6%) were identified as having one or more risk events/factors. People with risk were more likely to be non-Caucasian and had greater socio-economic deprivation. Taking clinical prioritisation as the gold standard, informatics tool identified high risk patients with a sensitivity of 83% and lower risk patients with a specificity of 81%. An operational pilot pathway over 3 months using this approach demonstrated in 101 high risk people that 40% received interventions/care optimisation to prevent deterioration in health.

CONCLUSION

A pragmatic data-driven method identifies people with diabetes at highest need for clinical prioritisation within restricted resources. Health informatics systems such as our can enhance care and improve operational efficiency and better healthcare delivery for people with diabetes.