Reducing Adverse Events Following Thoracic Surgery
Thoracic surgery is high-stakes,
high-risk surgery, with 30-60% incidence of postoperative adverse events (AEs)
depending on surgical procedure. Dr. Seely and team defined all thoracic
surgery AEs and their severity (see
https://ottawatmm.org/) (Seely
et al.,
Ann
Thor Surg, 2010) and have long monitored all AEs after all thoracic surgery
in Ottawa. They built software to enable AE data collection, harmonized with
international organizations (Sigler
et al.,
JTCVS Open, 2021) and
helped demonstrate how AEs augment the risk of mortality, worsen length of
stay, readmissions, and oncologic outcomes, impair patient
experience, and raise hospital costs (Grigor
et al.,
Ann Thor Surg,
2017).
To
help utilize this data to improve care, Dr. Seely led the initiation of
positive deviance (PD) quality improvement (QI) interventions, which combine
best experience (i.e. PD) with best evidence to generate recommendations to
improve care. This collegial and novel QI approach demonstrated improvements in
AEs in Thoracic surgery (Ivanovic
et al.,
Ann Thor Surg, 2015;
Ivanovic et al., J Healthc Qual, 2018) and
OR times in Orthopedics (Gold
et al.,
Can J Surg, 2023), in Ottawa.
Embracing the importance of a national learning network (Seely
et al.,
Can J Respir Crit Care Sleep Med,
2021), 14 Canadian thoracic centres spanning 7 provinces (two-thirds of
Canadian thoracic surgeons) now collect identical AE data using
Ottawa-developed definitions and software, with 5 more centers working to join
(see
https://www.canadianthoracicsurgeons.ca/cats-qipdb/).
Dr. Seely and team led the implementation of national PD QI interventions to
utilize this data to improve care, and results from multiple studies to date
demonstrate reductions in AEs (Seely
et al.,
BMJ Open Qual, 2023; Jones
et al.,
BMJ Open Qual, 2023) Dr.
Seely and Co-PIs Drs. Jamie Brehaut and Monica Taljaard were awarded a CIHR
Project Grant to evaluate this national PD QI program on care and further
improve it.
Variability-derived
Predictive Clinical Decision Support
Dr. Seely and team and
many others have shown that preserved degree and
complexity of heart and respiratory rate variability (HRV & RRV) are
associated with health, and illness is characterized by a reduction in
variability. Dr. Seely initiated his research to utilize commonly discarded
monitored waveform data to improve care, by developing
variability-derived predictive models and clinical decision support (CDS) tools
(Ahmad
et al.,
PLoS ONE, 2009; Bravi
et al.,
Biomed Eng Online, 2011; Bravi
et al.,
PLoS One, 2012; Seely
et al.,
Crit Care, 2014). Dr. Seely and his team have further
developed and implemented and evaluated three variability-derived CDS tools,
helping to establish a roadmap for the evolution of monitoring (Seely
et al.,
Crit Care, 2024).
Timely
and safe extubation (i.e. endotracheal tube removal) in critically ill patients
is vitally important, as prolonged mechanical ventilation and failed extubation
(i.e. re-intubation<48 hrs; 15% incidence) are associated with increased,
morbidity, mortality, costs, intensive care unit (ICU) stays. To address this
challenge, Dr. Seely and his team developed
Extubation
Advisor (EA), a CDS software tool which provides a standardized
and improved assessment of extubation readiness based on RRV (Seely
et al.,
Crit Care, 2014). Dr. Seely, with co-PI Dr. Karen Burns, completed
an observational study (n=117; 2 ICUs) and an interventional Phase I study
(n=29; 3 ICUs) (Sarti
et al.,
BMJ
Open, 2021; Hryciw
et al., J
Intensive Care Med, 2024) and they are currently conducting a CIHR-funded multicentre
pilot RCT (10 sites, n=110) of Extubation Advisor nearing completion. EA became a patented regulatory approved
commercial product in Europe and Health Canada in 2023, commercialized by the
company Dr. Seely founded, Therapeutic Monitoring Systems, with patent
licensing agreements with the OHRI.
Donation
after circulatory determined death (DCD) allows for
organ retrieval after death determination after withdrawal of life sustaining
measures (WLSM), with subsequent transplantation; however, uncertainty in
predicting time to death leads to decreased efficiency, interest and
acceptability of DCD programs, distress for donor families and waste healthcare
resources. In 2022, Dr. Seely and team derived and validated a variability-derived
predictive model to provide a probability of dying within 30, 60 and 120 min
after WLSM (Scales
et al.,
Crit Care
Explor, 2022). Dr. Seely and team, along with co-PI Dr. Sonny Dhanani,
developed
Donation
Advisor (DA), which identifies DCD candidates likely to become
successful organ donors and objectively characterizes organ ischemia
during DCD. They are currently performing a multicenter feasibility RCT of
DA.
Given infection is a common presentation in emergency departments (EDs), and given its potential to
develop into life-threatening sepsis, ED and ICU physicians have the difficult
task of prognosticating these patients daily. A critical decision
for physicians is whether to admit or transfer a patient to a high-volume ICU
with improved outcomes, yet
unnecessary hospital transport is both costly and harmful. Based on HRV and bloodwork in the ED, Dr.
Seely and team developed and implemented a novel disposition CDS tool called
Sepsis
Advisor (SA) which quantifies the risk of future deterioration in patients
with infection based on HRV and bloodwork (Barnaby
et al.,
Shock, 2018). This SA tool has been
submitted to Health Canada in 2025 for regulatory approval, and is proposed for
hospital and region wide implementation in Ottawa.
Understanding
Variability
Dr. Seely has long been interested in theoretical
research, initially hypothesizing the importance of variability monitoring in
critical care within the framework in complex systems science (Seely
et al.,
Crit Care Med, 2000), then focussing on the importance of embracing
uncertainty (Seely,
Perspect Biol Med, 2013),
and more recently, on the vital importance of human entropy production (Brodeur
et al.,
Entropy, 2023; Seely,
Entropy 2020). Dr. Seely has hypothesized that multiscale
self-similar fractal structure contained in HRV & RRV is related to its
capacity to maximize entropy production, and that health is characterized by
robust entropy production at rest combined with the ability to elevate it in
response to exercise or heat stress (i.e. both function & adaptability).
Supported by a New Frontiers Research Fund Exploration grant, Dr. Seely and
co-PIs Drs. Glen Kenny (physiologist) and Andre Longtin (physicist) are
pioneering groundbreaking research.