Citations

Abstract The objective of this retrospective analysis was to describe the development and implementation of an anesthesiologist‐led multidisciplinary committee to evaluate high‐risk surgical patients in order to improve surgical appropriateness. The study was conducted in an anesthesia preoperative evaluation clinic at an academic comprehensive cancer center. One hundred sixty‐seven high‐risk surgical patients with cancer‐related diagnoses were evaluated and discussed at a High‐Risk Committee (HRC) meeting to determine surgical appropriateness and optimize perioperative care. The HRC is an anesthesiologist‐led model for multidisciplinary review of high‐risk patients developed at Roswell Park Comprehensive Cancer Center. The group of high‐risk patients in which surgery was not performed had, on average, a greater percentage of hypertension, smoking history, dyspnea, heart failure, chronic obstructive pulmonary disease, diabetes, renal failure, and sleep apnea than the group in whom surgery was performed. Only one of 107 high‐risk patients who had surgery died within the first 30 days after surgery. A smaller percentage of patients died in the group that had surgery versus the group in which surgery was canceled. For all patients discussed by the HRC, the mortality was less than 2% within the first 30 days after the HRC.

Biomimetic biomaterials require good biocompatibility and bioactivity to serve as appropriate scaffolds for tissue engineering applications.

BackgroundFor cancers of the distal gastroesophageal junction or the proximal stomach, proximal gastrectomy can be performed. It is associated with several perioperative benefits compared with total gastrectomy. The use of laparoscopic proximal gastrectomy (LPG) has become an increasingly popular approach for select tumors.MethodsWe describe our method of LPG, including the preoperative work‐up, illustrated depictions of the key steps of the surgery, and our postoperative pathway.ResultsA total of 6 patients underwent LPG between July, 2013 to June, 2017. Five patients had early‐stage adenocarcinoma, and 1 patient had a gastrointestinal stromal tumor. The median age of the cohort was 70, and each patient had significant comorbidities. Conversion to open was required for 1 patient. All patients had negative final margins and an adequate lymph node dissection (median number of nodes examined was 15, range 12‐22). The median postoperative length of stay was 7 days (range 4‐7). Two patients developed anastomotic strictures requiring intervention, and 1 patient experienced significant reflux. At a median follow‐up of 11 months, there was 1 recurrence. Three patients were alive without evidence of disease, and 2 patients died from other causes.ConclusionsFor carefully selected patients, LPG is a safe and reasonable alternative to total gastrectomy, which is associated with similar oncologic outcomes and low morbidity.

Many targeted therapies—and, more recently, immunotherapies—have been approved by the U.S. Food and Drug Administration (FDA) with companion diagnostic tests. Next-generation sequencing (NGS) platforms are now approved to screen for many of these abnormalities, and they are increasingly being applied to guide therapeutic decision-making outside of these intended uses. The results provided by NGS testing can vary significantly based on the exact test performed and the analysis of the sequencing data. Given the complexities associated with interpreting NGS test results and acting on them, academic and community molecular tumor boards have been developed to provide multidisciplinary expertise for this endeavor. NGS test results may identify FDA-approved therapies, guide clinical trial recommendations, or prompt consideration of expanded access to investigational agents or off-label use of therapies approved for other indications. Many clinical trials now include NGS testing to assign treatments to patients based on the molecular profiles of their tumors. Although NGS testing may eventually help realize the development of individualized treatment regimens based on combinations of targeted therapies, the use of unproven and nonapproved combinations can be toxic and expensive. Given the increasing reliance on genetic biomarkers to guide therapeutic recommendations for FDA-approved therapies or enrollment into clinical trials, NGS will remain an integral part of the evolving medical oncology practice.

AbstractMotivationThe use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP–SNP interactions.ResultsWe tested two candidate approaches: the ‘Five-Full’ and ‘AA9int’ method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP–SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP–SNP interactions in large-scale studies.Availability and implementationThe ‘AA9int’ and ‘parAA9int’ functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/.Supplementary informationSupplementary data are available at Bioinformatics online.