Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

A highly efficient L-estimator for the location parameter of the Cauchy distribution

Asymptotic efficiency, The Cramér-Rao lower bound, Maximum likelihood estimator, Mean square error, Order statistics,

Does child gender determine household decision for health care in rural Thatta, Pakistan?

Background: In South Asia, gender disparity in child mortality is highest in Pakistan. We examined the influence of child gender on household decision regarding health care. Methods: Prevalence ratios were calculated for 3740 children aged 1-59 months from 92 randomly selected villages of rural Pakistan using a cluster-adjusted log-binomial model. Level 1 variables included child and household characteristics and level 2 included village characteristics. Results: There were 25 more girl deaths than boys per 1000 live births (95% CI: 13.9, 48.6) among post-neonates and 38 more among children aged 12-59 months (95% CI: 10.5, 65.5). However, in adjusted analysis, gender was not a significant predictor of illness reporting, visit to health facilities, choice of provider, hospitalization and health expenditure. Significant predictors of health care were child\u27s age, illness characteristics, number of children in the family, household socio-economic status and absence of girls\u27 school in the village. Conclusions: Differential care seeking for boys and girls is not seen in Thatta despite clear differences in mortality ratios. This calls for more creative research to identify pathways for gender differential in child mortality. Factors identified as influencing child health care and amenable to modification include poverty alleviation and girls\u27 education