OBJECTIVE To identify, appraise, and synthesise the best available evidence on the efficacy of perioperative interventions to reduce postoperative pulmonary complications (PPCs) in adult patients undergoing non-cardiac surgery. DESIGNSystematic review and meta-analysis of randomised controlled trials. DATA SOURCESMedline, Embase, CINHAL, and CENTRAL from January 1990 to December 2017. ELIGIBILITY CRITERIARandomised controlled trials investigating short term, protocolised medical interventions conducted before, during, or after non-cardiac surgery were included. Trials with clinical diagnostic criteria for PPC outcomes were included. Studies of surgical technique or physiological or biochemical outcomes were excluded. DATA EXTRACTION AND SYNTHESISReviewers independently identified studies, extracted data, and assessed the quality of evidence. Metaanalyses were conducted to calculate risk ratios with 95% confidence intervals. Quality of evidence was summarised in accordance with GRADE methods. The primary outcome was the incidence of PPCs. Secondary outcomes were respiratory infection, atelectasis, length of hospital stay, and mortality. Trial sequential analysis was used to investigate the reliability and conclusiveness of available evidence. Adverse effects of interventions were not measured or compared. RESULTS117 trials enrolled 21 940 participants, investigating 11 categories of intervention. 95 randomised controlled trials enrolling 18 062 participants were included in meta-analysis; 22 trials were excluded from meta-analysis because the interventions were not sufficiently similar to be pooled. No high quality evidence was found for interventions to reduce the primary outcome (incidence of PPCs). Seven interventions had low or moderate quality evidence with confidence intervals indicating a probable reduction in PPCs: enhanced recovery pathways (risk ratio 0.35, 95% confidence interval 0.21 to 0.58), prophylactic mucolytics (0.40, 0.23 to 0.67), postoperative continuous positive airway pressure ventilation (0.49, 0.24 to 0.99), lung protective intraoperative ventilation (0.52, 0.30 to 0.88), prophylactic respiratory physiotherapy (0.55, 0.32 to 0.93), epidural analgesia (0.77, 0.65 to 0.92), and goal directed haemodynamic therapy (0.87, 0.77 to 0.98). Moderate quality evidence showed no benefit for incentive spirometry in preventing PPCs. Trial sequential analysis adjustment confidently supported a relative risk reduction of 25% in PPCs for prophylactic respiratory physiotherapy, epidural analgesia, enhanced recovery pathways, and goal directed haemodynamic therapies. Insufficient data were available to support or refute equivalent relative risk reductions for other interventions. CONCLUSIONSPredominantly low quality evidence favours multiple perioperative PPC reduction strategies. Clinicians may choose to reassess their perioperative care pathways, but the results indicate that new trials with a low risk of bias are needed to obtain conclusive evidence of efficacy for many of these interventions. STUDY REGIS...

Background Care bundles are small sets of evidence-based recommendations, designed to support the implementation of evidence-based best clinical practice. However, there is variation in the design and implementation of care bundles, which may impact on the fidelity of delivery and subsequently their clinical effectiveness. Methods A scoping review was carried out using the Arksey and O’Malley framework to identify the literature reporting on the design, implementation and evaluation of care bundles. The Embase, CINAHL, Cochrane and Ovid MEDLINE databases were searched for manuscripts published between 2001 and November 2017; hand-searching of references and citations was also undertaken. Data were initially assessed using a quality assessment tool, the Downs and Black checklist, prior to further analysis and narrative synthesis. Implementation strategies were classified using the Expert Recommendations for Implementing Change (ERIC) criteria. Results Twenty-eight thousand six hundred ninety-two publications were screened and 348 articles retrieved in full text. Ninety-nine peer-reviewed quantitative publications were included for data extraction. These consisted of one randomised crossover trial, one randomised cluster trial, one case-control study, 20 prospective cohort studies and 76 non-parallel cohort studies. Twenty-three percent of studies were classified as poor based on Downs and Black checklist, and reporting of implementation strategies lacked structure. Negative associations were found between the number of elements in a bundle and compliance (Spearman’s rho = − 0.47, non-parallel cohort and − 0.65, prospective cohort studies), and between the complexity of elements and compliance ( p < 0.001, chi-squared = 23.05). Implementation strategies associated with improved compliance included evaluative and iterative approaches, development of stakeholder relationships and education and training strategies. Conclusion Care bundles with a small number of simple elements have better compliance rates. Standardised reporting of implementation strategies may help to implement care bundles into clinical practice with high fidelity. Trial Registration This review was registered on the PROSPERO database: CRD 42015029963 in December 2015. Electronic supplementary material The online version of this article (10.1186/s13012-019-0894-2) contains supplementary material, which is available to authorized users.

ALH and HVC (Grant number: L-023032) is registered at ClinicalTrials.gov (ID: NCT02649231).We would furthermore like to thank Dr. Evgeny Krupitsky for his pioneering research into ketamine as a treatment and his input into the design of the study. This paper is dedicated to the memory of our colleague Dr. David Gilhooly.

CRD4201501277.

Acute postoperative pain is common, distressing and associated with increased morbidity. Targeted interventions can prevent its development. We aimed to develop and internally validate a predictive tool to preemptively identify patients at risk of severe pain following major surgery. We analysed data from the UK Perioperative Quality Improvement Programme to develop and validate a logistic regression model to predict severe pain on the first postoperative day using pre-operative variables. Secondary analyses included the use of peri-operative variables. Data from 17,079 patients undergoing major surgery were included. Severe pain was reported by 3140 (18.4%) patients; this was more prevalent in females, patients with cancer or insulindependent diabetes, current smokers and in those taking baseline opioids. Our final model included 25 preoperative predictors with an optimism-corrected c-statistic of 0.66 and good calibration (mean absolute error 0.005, p = 0.35). Decision-curve analysis suggested an optimal cut-off value of 20-30% predicted risk to identify high-risk individuals. Potentially modifiable risk factors included smoking status and patient-reported measures of psychological well-being. Non-modifiable factors included demographic and surgical factors. Discrimination was improved by the addition of intra-operative variables (likelihood ratio v 2 496.5, p < 0.001) but not by the addition of baseline opioid data. On internal validation, our pre-operative prediction model was well calibrated but discrimination was moderate. Performance was improved with the inclusion of peri-operative covariates suggesting pre-operative variables alone are not sufficient to adequately predict postoperative pain.

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Over 1.5 million major surgical procedures take place in the UK NHS each year and approximately 25% of patients develop at least one complication. The most widely used risk-adjustment model for postoperative morbidity in the UK is the physiological and operative severity score for the enumeration of mortality and morbidity. However, this model was derived more than 30 years ago and now overestimates the risk of morbidity. In addition, contemporary definitions of some model predictors are markedly different compared with when the tool was developed. A second model used in clinical practice is the American College of Surgeons National Surgical Quality Improvement Programme risk model; this provides a risk estimate for a range of postoperative complications. This model, widely used in North America, is not open source and therefore cannot be applied to patient populations in other settings. Data from a prospective multicentre clinical dataset of 118 NHS hospitals (the peri-operative quality improvement programme) were used to develop a bespoke risk-adjustment model for postoperative morbidity. Patients aged ≥ 18 years who underwent colorectal surgery were eligible for inclusion. Postoperative morbidity was defined using the postoperative morbidity survey at postoperative day 7. Thirty-one candidate variables were considered for inclusion in the model. Death or morbidity occurred by postoperative day 7 in 3098 out of 11,646 patients (26.6%). Twelve variables were incorporated into the final model, including (among others): Rockwood clinical frailty scale; body mass index; and index of multiple deprivation quintile. The C-statistic was 0.672 (95%CI 0.660-0.684), with a bootstrap optimism corrected C-statistic of 0.666 at internal validation. The model demonstrated good calibration across the range of morbidity estimates with a mean slope gradient of predicted risk of 0.959 (95%CI 0.894-1.024) with an index-corrected intercept of À0.038

BackgroundAs the prevalence of obesity is increasing, the number of patients requiring surgical intervention for obesity-related illness is also rising. The aim of this pilot study was to explore predictors of short-term morbidity and longer-term poor weight loss after bariatric surgery.MethodsThis was a single-centre prospective observational cohort pilot study in patients undergoing bariatric surgery. We assessed the accuracy (discrimination and calibration) of two previously validated risk prediction models (the Physiological and Operative Severity Score for the enumeration of Morbidity and Mortality, POSSUM score, and the Obesity Surgical Mortality Risk Score, OS-MS) for postoperative outcome (postoperative morbidity defined using the Post Operative Morbidity Survey). We then tested the relationship between postoperative morbidity and longer-term weight loss outcome adjusting for known patient risk factors.ResultsComplete data were collected on 197 patients who underwent surgery for obesity or obesity-related illnesses between March 2010 and September 2013. Results showed POSSUM and OS-MRS were less accurate at predicting Post Operative Morbidity Survey (POMS)-defined morbidity on day 3 than defining prolonged length of stay due to poor mobility and/or POMS-defined morbidity. Having fewer than 28 days alive and out of hospital within 30 days of surgery was predictive of poor weight loss at 1 year, independent of POSSUM-defined risk (odds ratio 2.6; 95% confidence interval 1.28–5.24).ConclusionsPOSSUM may be used to predict patients who will have prolonged postoperative LOS after bariatric surgery due to morbidity or poor mobility. However, independent of POSSUM score, having less than 28 days alive and out of hospital predicted poor weight loss outcome at 1 year. This adds to the literature that postoperative complications are independently associated with poor longer-term surgical outcomes.