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Record W4387893051 · doi:10.1097/aln.0000000000004674

Healthcare Costs of New Functional Impairment in the Year after Noncardiac Surgery

2023· article· en· W4387893051 on OpenAlexaffabout
Julian F. Daza, Harindra C. Wijeysundera, Calvin Diep, Karim S. Ladha, Brian H. Cuthbertson, Duminda N. Wijeysundera

Bibliographic record

VenueAnesthesiology · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineFunctional impairmentHealth careIntensive care medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Postoperative functional impairment is a patient-centered outcome associated with important sequelae such as poor quality of life,1 yet its impact on healthcare expenditure is poorly understood. Such data could shed light on the potential cost savings of interventions that support patients either before surgery (e.g., prehabilitation) or during their recovery period (e.g., home care, rehabilitation) to enhance functional recovery. Therefore, we sought to characterize the healthcare costs associated with postoperative functional impairment in a contemporary cohort of surgical patients.We conducted a cost-of-illness analysis of data from the Measurement of Exercise Tolerance before Surgery (METS) study—a prospective multicenter cohort study that evaluated the prognostic validity of preoperative measures of functional capacity.2 For this present analysis, we used data from participants in Ontario, Canada (32% of the entire METS cohort) that were linked to administrative healthcare databases (Supplemental Digital Content 1, https://links.lww.com/ALN/D203). In these databases, patient-level healthcare costs are calculated according to validated algorithms.3 Thus, we provide a comprehensive summary of healthcare expenditure given that in Ontario nearly 100% of hospital care, community and hospital physician services, and prescription drugs for most adults are publicly funded. The Unity Health Toronto Research Ethics Board approved this secondary analysis (#12-319).Eligible participants were adults (40 yr or older) who had one or more cardiovascular risk factors and underwent elective inpatient noncardiac surgery (orthopedic, abdomino-pelvic, head-neck, vascular, intrathoracic).2 The EQ-5D-3L4 was administered on enrollment and at 30 days and 1 yr after surgery. We defined postoperative functional impairment as a one or more level decline at 1 yr (from baseline) in the performance of either mobility, self-care, or usual activities as measured by the EQ-5D-3L.We used mean values and SDs to summarize 1-yr healthcare costs. We fit quasi-Poisson regression with an identity link to model mean cost differences. We also used the log link to assess mean cost ratios, or the multiplicative cost difference between groups. Point estimates are presented with 95% CIs. To further characterize which areas of healthcare generated these costs, five cost subcategories were predefined: acute care, ambulatory, provider, continuing care, and emergency services (Supplementary Table 1, https://links.lww.com/ALN/D203). For the subcategories of continuing care and emergency costs, we fit two-part models due to excess of zero-cost observations.5 Missing functional status was handled using listwise deletion, with an additional sensitivity analysis using multiple imputation by chained equations (10 imputations).6 All monetary values were inflation-adjusted to 2016 Canadian dollars.3The analytic cohort included 443 participants, of whom 100 (22.6%) had new functional impairment at 1 yr. Most healthcare costs were incurred from acute care and ambulatory settings as well as provider billings. Continuing care and emergency costs made up a smaller proportion of the total costs (table 1).The total 1-yr healthcare costs per participant were $32,901.09 ± $27,116.38 for those with functional impairment and $24,863.98 ± $17,819.45 for those without, corresponding to a difference in mean costs of $8,037.11 (CI $3,380.99 to $12,986.17; table 1). Except for the first 29 days, costs were higher among participants with functional impairment compared to those without functional impairment at all timepoints after surgery (fig. 1). Notably, there were no significant cost differences before surgery (P = 0.65). Participants with functional impairment had 58% higher ambulatory costs (CI, 16 to 113%) and 28% higher provider costs (CI, 11 to 47%) compared to those without functional impairment. Functional impairment was associated with a 73% increase (CI, 18 to 150%) in continuing care costs, compared to those without functional impairment, among participants who required at least one of these services. Participants with functional impairment also had 2.32 higher odds (CI, 1.48 to 3.67) of emergency service utilization.Postoperative functional status was missing for 39 (8.1%) participants. A sensitivity analysis using multiple imputation yielded qualitatively similar results (Supplementary Table 2, https://links.lww.com/ALN/D203).In summary, one in five participants experienced new functional impairment in the year after surgery, which was associated with a 32% increase in healthcare costs corresponding to an increase in expenditure of $8,040 per patient. These disparities were not evident before surgery. Notably, cost differences were largely attributed to provider billings, ambulatory costs, and continuing care services. Although emergency-related costs made up a small proportion of total healthcare cost, participants with functional impairment more frequently utilized these services. Overall, our findings provide supporting evidence that interventions for preventing postoperative functional impairment may plausibly be associated with significant cost savings. Considering that more than 900,000 inpatient surgeries happen in Canada each year,7 this may represent incremental healthcare costs of more than $1.45 billion annually across Canada. Future studies should also evaluate patient-level expenditure, for example, income loss, which is not captured in our current analysis.This study contracted Institute for Clinical Evaluative Sciences (ICES) Data and Analytic Services and used data from the ICES Data Repository, which is managed by ICES with support from its funders and partners: Canada’s Strategy for Patient Oriented Research, the Ontario Strategy for Patient Oriented Research Support Unit (Toronto, Ontario), the Canadian Institutes of Health Research (Ottawa, Ontario), and the Government of Ontario (Toronto, Ontario). The opinions, results and conclusions reported are those of the authors. No endorsement by ICES or any of its funders or partners is intended or should be inferred. Parts of this material are based on data and information compiled and provided by Canadian Institute for Health Information (Toronto, Ontario). However, the analyses, conclusions, opinions, and statements expressed herein are those of the author, and not necessarily those of Canadian Institute for Health Information. Parts of this material are based on data and information provided by Cancer Care Ontario (Toronto, Ontario). The opinions, results, view, and conclusions reported in this paper are those of the authors and do not necessarily reflect those of Cancer Care Ontario. No endorsement by Cancer Care Ontario is intended or should be inferred.Dr. Daza is supported by a Vanier Graduate Scholarship and a research scholarship from the Division of General Surgery at the University of Toronto (Toronto, Ontario, Canada). Drs. Ladha, Cuthbertson, and Wijeysundera are supported in part by Merit Awards from the Department of Anesthesiology and Pain Medicine at the University of Toronto. Dr. Wijeysundera is supported in part by the Endowed Chair in Translational Anesthesiology Research at St. Michael’s Hospital (Toronto, Ontario, Canada) and University of Toronto. The METS study was supported by grants from the Canadian Institutes of Health Research (Ottawa, Ontario, Canada); Heart and Stroke Foundation of Canada (Toronto, Ontario, Canada); Ontario Ministry of Health and Long-Term Care (Toronto, Ontario, Canada); Ontario Ministry of Research, Innovation and Science (Toronto, Ontario, Canada); United Kingdom National Institute of Academic Anaesthesia (London, United Kingdom); United Kingdom Clinical Research Collaboration (London, United Kingdom); Australian and New Zealand College of Anaesthetists; and Monash University (Melbourne, Victoria, Australia). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.Dr. Ladha is a co-principal investigator of an observational study on medical cannabis funded by Shoppers Drug Mart (Toronto, Ontario, Canada). The analyses, conclusions, opinions, and statements expressed herein are those of the author, and not of Shoppers Drug Mart. The other authors declare no competing interests.Supplementary Tables, https://links.lww.com/ALN/D203

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.286
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2023
Admission routes2
Has abstractyes

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