Emily Hladkowicz,Gurlavine Kidd,Alana Flexman,Allan Garland,Julie Hallet,Daniel Kobewka,Matthew McGarr,Robert Talarico,Carl van Walraven,Duminda N Wijeysundera,Camilla L Wong,Daniel I McIsaac,,
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引用次数: 0
Abstract
BACKGROUND
Older adults prioritise independent return home after surgery. Most discharge outcomes are binary composites that do not incorporate temporal information. We defined and validated a novel ordinal outcome, the Postoperative Discharge Recovery State, and prioritised its temporal measurement, to overcome these limitations.
METHODS
This retrospective cohort study was conducted with patient partnership. Adults ≥65 yr having major, elective, noncardiac, non-orthopaedic surgery were identified from 2012 to 2022 using linked, routinely collected data in Ontario, Canada. Construct, convergent, and predictive validity were estimated. A multivariable ordinal regression model was derived and internally-externally validated.
RESULTS
We included 84 422 older adult surgical patients. At the patient-prioritised postoperative day 90, the distribution of patients across Postoperative Discharge Recovery State categories was: (1) dead (2718; 3.2%); (2) hospitalised (1696; 2.0%); (3) in long-term care (179; 0.2%); (4) in rehabilitation (593; 0.7%); and (5) at home (79 236; 93.9%). Directionally expected associations with baseline characteristics supported construct validity. Consistency in associations over time supported reliability. Relationships with days alive and at home supported convergent (ρ=0.373) and predictive (fewer days at home with worse recovery state) validity. A prespecified ordinal logistic regression model had inadequate accuracy (c-statistic 0.700, poor calibration) to support its clinical use.
CONCLUSIONS
The Postoperative Discharge Recovery State is a 5-level ordinal outcome that can be applied at key time points after surgery to quantify the proportion of patients in patient-prioritised discharge locations. Validity and reliability support utility, but further development will be required to maximise information gain relative to binary outcomes.
期刊介绍:
The British Journal of Anaesthesia (BJA) is a prestigious publication that covers a wide range of topics in anaesthesia, critical care medicine, pain medicine, and perioperative medicine. It aims to disseminate high-impact original research, spanning fundamental, translational, and clinical sciences, as well as clinical practice, technology, education, and training. Additionally, the journal features review articles, notable case reports, correspondence, and special articles that appeal to a broader audience.
The BJA is proudly associated with The Royal College of Anaesthetists, The College of Anaesthesiologists of Ireland, and The Hong Kong College of Anaesthesiologists. This partnership provides members of these esteemed institutions with access to not only the BJA but also its sister publication, BJA Education. It is essential to note that both journals maintain their editorial independence.
Overall, the BJA offers a diverse and comprehensive platform for anaesthetists, critical care physicians, pain specialists, and perioperative medicine practitioners to contribute and stay updated with the latest advancements in their respective fields.