BackgroundThere is currently conflicting evidence surrounding the effects of obesity on postoperative outcomes. Previous studies have found obesity to be associated with adverse events, but others have found no association. The aim of this study was to determine whether increasing body mass index (BMI) is an independent risk factor for development of major postoperative complications.MethodsThis was a multicentre prospective cohort study across the UK and Republic of Ireland. Consecutive patients undergoing elective or emergency gastrointestinal surgery over a 4‐month interval (October–December 2014) were eligible for inclusion. The primary outcome was the 30‐day major complication rate (Clavien–Dindo grade III–V). BMI was grouped according to the World Health Organization classification. Multilevel logistic regression models were used to adjust for patient, operative and hospital‐level effects, creating odds ratios (ORs) and 95 per cent confidence intervals (c.i.).ResultsOf 7965 patients, 2545 (32·0 per cent) were of normal weight, 2673 (33·6 per cent) were overweight and 2747 (34·5 per cent) were obese. Overall, 4925 (61·8 per cent) underwent elective and 3038 (38·1 per cent) emergency operations. The 30‐day major complication rate was 11·4 per cent (908 of 7965). In adjusted models, a significant interaction was found between BMI and diagnosis, with an association seen between BMI and major complications for patients with malignancy (overweight: OR 1·59, 95 per cent c.i. 1·12 to 2·29, P = 0·008; obese: OR 1·91, 1·31 to 2·83, P = 0·002; compared with normal weight) but not benign disease (overweight: OR 0·89, 0·71 to 1·12, P = 0·329; obese: OR 0·84, 0·66 to 1·06, P = 0·147).ConclusionOverweight and obese patients undergoing surgery for gastrointestinal malignancy are at increased risk of major postoperative complications compared with those of normal weight.

Aims This study sought to review the literature for clinical prediction models for the diagnosis of patients with chronic heart failure in the community and to validate the models in a novel cohort of patients with a suspected diagnosis of chronic heart failure. Methods and results MEDLINE and Embase were searched from 1946 to Q4 2017. Studies were eligible if they contained at least one multivariable model for the diagnosis of chronic heart failure applicable to the primary care setting. The CHARMS checklist was used to evaluate models. We also validated models, where possible, in a novel cohort of patients with a suspected diagnosis of heart failure referred to a rapid access diagnostic clinic. In total, 5310 articles were identified with nine articles subsequently meeting the eligibility criteria. Three models had undergone internal validation, and four had undergone external validation. No clinical impact studies have been completed to date. Area under the curve (AUC) varied from 0.74 to 0.93 and from 0.60 to 0.65 in the novel cohort for clinical models alone with AUC up to 0.89 in combination with electrocardiogram and B‐type natriuretic peptide (BNP). The AUC for BNP was 0.86 (95% confidence interval 83.3–88.6%). Conclusions This review demonstrates that there are a number of clinical prediction rules relevant to the diagnosis of chronic heart failure in the literature. Clinical impact studies are required to compare the use of clinical prediction rules and biomarker strategies in this setting.

Background There is a need for effective primary care interventions that help older people combat frailty and build resilience. Objective To study the effectiveness of an optimised exercise and dietary protein intervention. Design Multicentre, randomised-controlled, parallel-arm trial. Setting Six primary care practices, Ireland. Methods Six general practitioners enrolled adults aged 65+ with Clinical Frailty Scale score ≤5 from December 2020 to May 2021. Participants were randomised to intervention or usual care with allocation concealed until enrolment. Intervention comprised a 3-month home-based exercise regime, emphasising strength, and dietary protein guidance (1.2 g/kg/day). Effectiveness was measured by comparing frailty levels, based on the SHARE-Frailty Instrument, on an intention-to-treat basis. Secondary outcomes included bone mass, muscle mass and biological age measured by bioelectrical impedance analysis. Ease of intervention and perceived health benefit were measured on Likert scales. Results Of the 359 adults screened, 197 were eligible and 168 enrolled; 156 (92.9%) attended follow-up (mean age 77.1; 67.3% women; 79 intervention, 77 control). At baseline, 17.7% of intervention and 16.9% of control participants were frail by SHARE-FI. At follow-up, 6.3 and 18.2% were frail, respectively. The odds ratio of being frail between intervention and control groups post-intervention was 0.23 (95% confidence interval: 0.07–0.72; P = 0.011), adjusting for age, gender and site. Absolute risk reduction was 11.9% (CI: 0.8%–22.9%). Number needed to treat was 8.4. Grip strength (P < 0.001) and bone mass (P = 0.040) improved significantly. 66.2% found the intervention easy, 69.0% reported feeling better. Conclusion A combination of exercises and dietary protein significantly reduced frailty and improved self-reported health.