Do Multidisciplinary Team (MDT) processes influence survival in patients with colorectal cancer? A population-based experience

BACKGROUND: MDT (multidisciplinary team) meetings are considered an essential component of care for patients with cancer. However there is remarkably little direct evidence that such meetings improve outcomes. We assessed whether or not MDT (multidisciplinary team) processes influenced survival in a cohort of patients with colorectal cancer. METHODS: Observational study of a population-based cohort of 586 consecutive patients with colorectal cancer diagnosed in Tayside (Scotland) during 2006 and 2007. RESULTS: Recommendations from MDT meetings were implemented in 411/586 (70.1 %) of patients, the MDT+ group. The remaining175/586 (29.9 %) were either never discussed at an MDT, or recommendations were not implemented, MDT- group. The 5-year cause-specific survival (CSS) rates were 63.1 % (MDT+) and 48.2 % (MDT-), p < 0.0001. In analysis confined to patients who survived >6 weeks after diagnosis, the rates were 63.2 % (MDT+) and 57.7 % (MDT-), p = 0.064. The adjusted hazard rate (HR) for death from colorectal cancer was 0.73 (0.53 to 1.00, p = 0.047) in the MDT+ group compared to the MDT- group, in patients surviving >6 weeks the adjusted HR was 1.00 (0.70 to 1.42, p = 0.987). Any benefit from the MDT process was largely confined to patients with advanced disease: adjusted HR ((early)) 1.32 (0.69 to 2.49, p = 0.401); adjusted HR((advanced)) 0.65 (0.45 to 0.96, p = 0.031). CONCLUSIONS: Adequate MDT processes are associated with improved survival for patients with colorectal cancer. However, some of this effect may be more apparent than real – simply reflecting selection bias. The MDT process predominantly benefits the 40 % of patients who present with advanced disease and conveys little demonstrable advantage to patients with early tumours. These results call into question the current belief that all new patients with colorectal cancer should be discussed at an MDT meeting

Encouraging female entrepreneurship in Jordan: environmental factors, obstacles and challenges

The number of female entrepreneurs and their contribution to the economy is steadily rising. Yet research suggests that female entrepreneurs face more challenges and barriers than their male counterparts. This is expected to be even more prevalent in Islamic contexts, which are characterised by conservative and patriarchal societies. In this research, 254 female business students from a private and a public university responded to a questionnaire that gauges their perceptions about potential barriers to entrepreneurship in Jordan and whether the business education they are receiving helps to prepare them for future entrepreneurial activity. Our results help to form a basis on which a deeper understanding of the phenomena can be achieved through more in depth future research. Among the main environmental factors that worry potential female entrepreneurs are the weakness of Jordanian economy, lack of finance, fear of risk, gender inequality and inability to maintain a work and private life balance. Our results also show that students are really not aware of the opportunities available to them and are unable to make a proper assessment. We call on both universities and the Jordanian government to put more emphasis on practical entrepreneurial education and encouraging women to play a much more active role within the workforce

Selective droplet splitting using single layer microfluidic valves

Droplet microfluidics, with its small scale isolated samples, offers huge potential in the further miniaturisation of high throughput screening. The challenge is to deliver multiple samples in a manner such that reactions can be performed in numerous permutations. The present study investigates the use of single layer valves to break up individual droplets selectively. This splitting of large droplets, allows the main sample volume to navigate around the chip, with smaller daughter droplets being removed at desired locations. As such, the mother droplet is no longer an isolated sample akin to an on-chip test tube, but rather a mobile sample delivery system akin to an on-chip pipette. The partitioning takes place at the entrance to a bypass loop of the main channel. Under normal operating conditions the droplet passes the entrance intact, however, when a valve located at the entrance to the bypass loop is actuated, the geometry changes causes the droplet to split. We analyse this transition in behaviour for a range of oil and water inlets, and valve actuation pressures, showing that the valve can be actuated such that the next droplet to pass the bypass loop will be split, but subsequent droplets will not be

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments

Multivariable and Bayesian network analysis of outcome predictors in acute aneurysmal subarachnoid hemorrhage: review of a pure surgical series in the Post- International Subarachnoid Aneurysm Trial Era

Background: Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatmentmodalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping. Objective: To update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning. Methods: We reviewed a single surgeon\u27s case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R2 scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning. Results: Importance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong\u27s P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters. Conclusion: Our study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation