Is American College of Surgeons NSQIP organ space infection a surrogate for pancreatic fistula?

BACKGROUND: In the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP), pancreatic fistula has not been monitored, although organ space infection (OSI) data are collected. Therefore, the purpose of this analysis was to determine the relationship between ACS NSQIP organ space infection and pancreatic fistulas. STUDY DESIGN: From 2007 to 2011, 976 pancreatic resection patients were monitored via ACS NSQIP at our institution. From this database, 250 patients were randomly chosen for further analysis. Four patients were excluded because they underwent total pancreatectomy. Data on OSI were gathered prospectively. Data on pancreatic fistulas and other intra-abdominal complications were determined retrospectively. RESULTS: Organ space infections (OSIs) were documented in 22 patients (8.9%). Grades B (n = 26) and C (n = 5) pancreatic fistulas occurred in 31 patients (12.4%); grade A fistulas were observed in 38 patients (15.2%). Bile leaks and gastrointestinal (GI) anastomotic leaks each developed in 5 (2.0%) patients. Only 17 of 31 grade B and C pancreatic fistulas (55%), and none of 38 grade A fistulas were classified as OSIs in ACS NSQIP. In addition, only 2 of 5 bile leaks (40%) and 2 of 5 GI anastomotic leaks (40%) were OSIs. Moreover, 3 OSIs were due to bacterial peritonitis, a chyle leak, and an ischemic bowel. CONCLUSIONS: This analysis suggests that the sensitivity (55%) and specificity (45%) of organ space infection (OSI) in ACS NSQIP are too low for OSI to be a surrogate for grade B and C pancreatic fistulas. We concluded that procedure-specific variables will be required for ACS NSQIP to improve outcomes after pancreatectomy

Exercise Following Bariatric Surgery: Systematic Review

The contribution of physical activity on the degree of weight loss following bariatric surgery is unclear. To determine impact of exercise on postoperative weight loss. Medline search (1988–2009) was completed using MeSH terms including bariatric procedures and a spectrum of patient factors with potential relationship to weight loss outcomes. Of the 934 screened articles, 14 reported on exercise and weight loss outcomes. The most commonly used instruments to measure activity level were the Baecke Physical Activity Questionnaire, the International Physical Activity Questionnaire, and a variety of self-made questionnaires. The definition of an active patient varied but generally required a minimum of 30 min of exercise at least 3 days per week. Thirteen articles reported on exercise and degree of postoperative weight loss (n = 4,108 patients). Eleven articles found a positive association of exercise on postoperative weight loss, and two did not. Meta-analysis of three studies revealed a significant increase in 1-year postoperative weight loss (mean difference = 4.2% total body mass index (BMI) loss, 95% confidence interval (CI; 0.26–8.11)) for patients who exercise postoperatively. Exercise following bariatric surgery appears to be associated with a greater weight loss of over 4% of BMI. While a causal relationship cannot be established with observational data, this finding supports the continued efforts to encourage and support patients’ involvement in post-surgery exercise. Further research is necessary to determine the recommended activity guidelines for this patient population

An Ensembling Architecture Incorporating Machine Learning Models and Genetic Algorithm Optimization for Forex Trading

Algorithmic trading has become the standard in the financial market. Traditionally, most algorithms have relied on rule-based expert systems which are a set of complex if/then rules that need to be updated manually to changing market conditions. Machine learning (ML) is the natural next step in algorithmic trading because it can directly learn market patterns and behaviors from historical trading data and factor this into trading decisions. In this paper, a complete end-to-end system is proposed for automated low-frequency quantitative trading in the foreign exchange (Forex) markets. The system utilizes several State of the Art (SOTA) machine learning strategies that are combined under an ensemble model to derive the market signal for trading. Genetic Algorithm (GA) is used to optimize the strategies for maximizing profits. The system also includes a money management strategy to mitigate risk and a back-testing framework to evaluate system performance. The models were trained on EUR–USD pair Forex data from Jan 2006 to Dec 2019, and subsequently evaluated on unseen samples from Jan 2020 to Dec 2020. The system performance is promising under ideal conditions. The ensemble model achieved about 10% nett P&L with −0.7% drawdown level based on 2020 trading data. Further work is required to calibrate trading costs & execution slippage in real market conditions. It is concluded that with the increased market volatility due to the global pandemic, the momentum behind machine learning algorithms that can adapt to a changing market environment will become even stronger