Development and internal validation of clinical prediction models for outcomes of complicated intra-abdominal infection.

BACKGROUND: Complicated intra-abdominal infections (cIAIs) are associated with significant morbidity and mortality. The aim of this study was to describe the clinical characteristics of patients with cIAI in a multicentre study and to develop clinical prediction models (CPMs) to help identify patients at risk of mortality or relapse. METHODS: A multicentre observational study was conducted from August 2016 to February 2017 in the UK. Adult patients diagnosed with cIAI were included. Multivariable logistic regression was performed to develop CPMs for mortality and cIAI relapse. The c-statistic was used to test model discrimination. Model calibration was tested using calibration slopes and calibration in the large (CITL). The CPMs were then presented as point scoring systems and validated further. RESULTS: Overall, 417 patients from 31 surgical centres were included in the analysis. At 90 days after diagnosis, 17.3 per cent had a cIAI relapse and the mortality rate was 11.3 per cent. Predictors in the mortality model were age, cIAI aetiology, presence of a perforated viscus and source control procedure. Predictors of cIAI relapse included the presence of collections, outcome of initial management, and duration of antibiotic treatment. The c-statistic adjusted for model optimism was 0.79 (95 per cent c.i. 0.75 to 0.87) and 0.74 (0.73 to 0.85) for mortality and cIAI relapse CPMs. Adjusted calibration slopes were 0.88 (95 per cent c.i. 0.76 to 0.90) for the mortality model and 0.91 (0.88 to 0.94) for the relapse model; CITL was -0.19 (95 per cent c.i. -0.39 to -0.12) and - 0.01 (- 0.17 to -0.03) respectively. CONCLUSION: Relapse of infection and death after complicated intra-abdominal infections are common. Clinical prediction models were developed to identify patients at increased risk of relapse or death after treatment, these now require external validation.

Rectal stump management in inflammatory bowel disease: a cohort study, systematic review and proportional analysis of perioperative complications.

BACKGROUND: The aim of this study was to analyse local single-institution data and perform a systematic review of the literature to calculate precise risk estimates of rectal stump-related morbidity and mortality following subtotal colectomy in patients with inflammatory bowel disease (IBD), including Crohn's colitis, ulcerative colitis and indeterminate colitis. METHODS: Institutional information systems were interrogated to obtain local patient data. A systematic review of MEDLINE and EMBASE was performed to identify relevant articles. Fixed-effects or random-effects meta-analysis of proportions was performed to calculate pooled incidence estimates, including local data. RESULTS: Sixty-one patients were included locally and all had their rectal stump closed intra-abdominally. Four patients (8.3%) had a rectal stump perforation and 30-day mortality was 0. Fourteen papers were included in our review alongside local data, with a total of 1330 patients included. Pooled mortality was 1.7% (95% confidence interval, CI 1.0-2.8), pooled incidence of pelvic abscess/sepsis, stump leak and wound infection was 5.7% (95% CI 4.4-7.3), 4.9% (95% CI 3.7-6.6) and 11.3% (95% CI 7.8-16), respectively. Subcutaneous placement of the stump was associated with the highest incidence of stump leak (12.6%, 95% CI 8.3-18.6), and closure of the stump with both staples and suture was associated with the highest incidence of pelvic abscess (11.1%, 95% CI 5.8-20.3). Mortality and the incidence of wound infection were similar across stump closure techniques. There was evidence suggesting considerable heterogeneity and publication bias among studies. CONCLUSIONS: This study provides estimates of morbidity associated with the rectal stump after subtotal colectomy for IBD. A closed intra-abdominal stump seems to be associated with the highest rate of pelvic abscess/sepsis. Further work in form of an international collaborative project would allow individual patient data analysis and identification of risk factors for complications.