The Perioperative Quality Improvement Programme (PQIP patient study): protocol for a UK multicentre, prospective cohort study to measure quality of care and outcomes after major surgery.

INTRODUCTION: Major surgery accounts for a substantial proportion of health service activity, due not only to the primary procedure, but the longer-term health implications of poor short-term outcome. Data from small studies or from outside the UK indicate that rates of complications and failure to rescue vary between hospitals, as does compliance with best practice processes. Within the UK, there is currently no system for monitoring postoperative complications (other than short-term mortality) in major non-cardiac surgery. Further, there is variation between national audit programmes, in the emphasis placed on quality assurance versus quality improvement, and therefore the principles of measurement and reporting which are used to design such programmes. METHODS AND ANALYSIS: The PQIP patient study is a multi-centre prospective cohort study which recruits patients undergoing major surgery. Patient provide informed consent and contribute baseline and outcome data from their perspective using a suite of patient-reported outcome tools. Research and clinical staff complete data on patient risk factors and outcomes in-hospital, including two measures of complications. Longer-term outcome data are collected through patient feedback and linkage to national administrative datasets (mortality and readmissions). As well as providing a uniquely granular dataset for research, PQIP provides feedback to participating sites on their compliance with evidence-based processes and their patients' outcomes, with the aim of supporting local quality improvement. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Health Research Authority in the UK. Dissemination of interim findings (non-inferential) will form a part of the improvement methodology and will be provided to participating centres at regular intervals, including near-real time feedback of key process measures. Inferential analyses will be published in the peer-reviewed literature, supported by a comprehensive multi-modal communications strategy including to patients, policy makers and academic audiences as well as clinicians.

Delivery of drinking, eating and mobilising (DrEaMing) and its association with length of hospital stay after major noncardiac surgery: observational cohort study.

BACKGROUND: Enhanced recovery pathways are associated with improved postoperative outcomes. However, as enhanced recovery pathways have become more complex and varied, compliance has reduced. The 'DrEaMing' bundle re-prioritises early postoperative delivery of drinking, eating, and mobilising. We investigated relationships between DrEaMing compliance, postoperative hospital length of stay (LOS), and complications in a prospective multicentre major surgical cohort. METHODS: We interrogated the UK Perioperative Quality Improvement Programme dataset. Analyses were conducted in four stages. In an exploratory cohort, we identified independent predictors of DrEaMing. We quantified the association between delivery of DrEaMing (and its component variables) and prolonged LOS in a homogenous colorectal subgroup and assessed generalisability in multispecialty patients. Finally, LOS and complications were compared across hospitals, stratified by DrEaMing compliance. RESULTS: The exploratory cohort comprised 22 218 records, the colorectal subgroup 7230, and the multispecialty subgroup 5713. DrEaMing compliance was 59% (13 112 patients), 60% (4341 patients), and 60% (3421), respectively, but varied substantially between hospitals. Delivery of DrEaMing predicted reduced odds of prolonged LOS in colorectal (odds ratio 0.51 [0.43-0.59], P<0.001) and multispecialty cohorts (odds ratio 0.47 [0.41-0.53], P<0.001). At the hospital level, complications were not the primary determinant of LOS after colorectal surgery, but consistent delivery of DrEaMing was associated with significantly shorter LOS. CONCLUSIONS: Delivery of bundled and unbundled DrEaMing was associated with substantial reductions in postoperative LOS, independent of the effects of confounder variables. Consistency of process delivery, and not complications, predicted shorter hospital-level length of stay. DrEaMing may be adopted by perioperative health systems as a quality metric to support improved patient outcomes and reduced hospital length of stay.

Is DrEaMing (drinking, eating and mobilising) the dream?

The idea of patients drinking, eating and mobilising (DrEaMing) at 24 hours postoperatively is being used as a marker of functional recovery. This marker of recovery and quality is explored in this article.

Developing and validating subjective and objective risk-assessment measures for predicting mortality after major surgery: An international prospective cohort study.

BACKGROUND: Preoperative risk prediction is important for guiding clinical decision-making and resource allocation. Clinicians frequently rely solely on their own clinical judgement for risk prediction rather than objective measures. We aimed to compare the accuracy of freely available objective surgical risk tools with subjective clinical assessment in predicting 30-day mortality. METHODS AND FINDINGS: We conducted a prospective observational study in 274 hospitals in the United Kingdom (UK), Australia, and New Zealand. For 1 week in 2017, prospective risk, surgical, and outcome data were collected on all adults aged 18 years and over undergoing surgery requiring at least a 1-night stay in hospital. Recruitment bias was avoided through an ethical waiver to patient consent; a mixture of rural, urban, district, and university hospitals participated. We compared subjective assessment with 3 previously published, open-access objective risk tools for predicting 30-day mortality: the Portsmouth-Physiology and Operative Severity Score for the enUmeration of Mortality (P-POSSUM), Surgical Risk Scale (SRS), and Surgical Outcome Risk Tool (SORT). We then developed a logistic regression model combining subjective assessment and the best objective tool and compared its performance to each constituent method alone. We included 22,631 patients in the study: 52.8% were female, median age was 62 years (interquartile range [IQR] 46 to 73 years), median postoperative length of stay was 3 days (IQR 1 to 6), and inpatient 30-day mortality was 1.4%. Clinicians used subjective assessment alone in 88.7% of cases. All methods overpredicted risk, but visual inspection of plots showed the SORT to have the best calibration. The SORT demonstrated the best discrimination of the objective tools (SORT Area Under Receiver Operating Characteristic curve [AUROC] = 0.90, 95% confidence interval [CI]: 0.88-0.92; P-POSSUM = 0.89, 95% CI 0.88-0.91; SRS = 0.85, 95% CI 0.82-0.87). Subjective assessment demonstrated good discrimination (AUROC = 0.89, 95% CI: 0.86-0.91) that was not different from the SORT (p = 0.309). Combining subjective assessment and the SORT improved discrimination (bootstrap optimism-corrected AUROC = 0.92, 95% CI: 0.90-0.94) and demonstrated continuous Net Reclassification Improvement (NRI = 0.13, 95% CI: 0.06-0.20, p < 0.001) compared with subjective assessment alone. Decision-curve analysis (DCA) confirmed the superiority of the SORT over other previously published models, and the SORT-clinical judgement model again performed best overall. Our study is limited by the low mortality rate, by the lack of blinding in the 'subjective' risk assessments, and because we only compared the performance of clinical risk scores as opposed to other prediction tools such as exercise testing or frailty assessment. CONCLUSIONS: In this study, we observed that the combination of subjective assessment with a parsimonious risk model improved perioperative risk estimation. This may be of value in helping clinicians allocate finite resources such as critical care and to support patient involvement in clinical decision-making.