Background and scientific rationale:
Surgery is common, with a lifetime incidence of 60%, and projections suggesting that 1 in 5 adults over 75 will undergo surgery each year by 2040. Although perioperative care has become increasingly safe, complications (such as delirium, cardiovascular events, and death) still occur. Many of these, especially cognitive dysfunction or loss of independence, can have ongoing health consequences. Outcome is driven by the interaction of patient (such as comorbidity, frailty, or potentially genetic factors), procedural (severity or urgency), and process factors (such as timeliness of surgery or referral route).
The UK Biobank dataset contains a wealth of detailed biological measurements (including baseline biomarkers and genetics) that, via the hospital episode statistics link, can be explored for relevance within the perioperative period and on long-term health outcomes. Such data, as well as being potential drivers of risk in their own right, are vital to attempt to isolate any effect of process factors (such as referral route or delay) on outcome. At a time of increasing resource constraint the potential for variation in care processes to drive inequality in outcome is a real risk. Understanding these interactions is crucial to understand interventional strategies at both a biological and health system level to maximise the benefit of surgery.
Objectives:
1) Using hospital episode statistics data – curate a dataset of individuals undergoing surgical procedures, their severity, and
2) Determine if biological measurements (such as grip strength, muscle mass, or baseline inflammation) are associated with outcomes of surgery (such as days-alive-and-at home)
3) Determine if process measurements (such as waiting times or referral routes) are causally related to adverse outcomes after surgery