Development of a patient and institutional-based model for estimation of operative times for robot-assisted radical cystectomy: results from the International Robotic Cystectomy Consortium.

OBJECTIVES: To design a methodology to predict operative times for robot-assisted radical cystectomy (RARC) based on variation in institutional, patient, and disease characteristics to help in operating room scheduling and quality control. PATIENTS AND METHODS: The model included preoperative variables and therefore can be used for prediction of surgical times: institutional volume, age, gender, body mass index, American Society of Anesthesiologists score, history of prior surgery and radiation, clinical stage, neoadjuvant chemotherapy, type, technique of diversion, and the extent of lymph node dissection. A conditional inference tree method was used to fit a binary decision tree predicting operative time. Permutation tests were performed to determine the variables having the strongest association with surgical time. The data were split at the value of this variable resulting in the largest difference in means for the surgical time across the split. This process was repeated recursively on the resultant data sets until the permutation tests showed no significant association with operative time. RESULTS: In all, 2 134 procedures were included. The variable most strongly associated with surgical time was type of diversion, with ileal conduits being 70 min shorter (P < 0.001). Amongst patients who received neobladders, the type of lymph node dissection was also strongly associated with surgical time. Amongst ileal conduit patients, institutional surgeon volume (>66 RARCs) was important, with those with a higher volume being 55 min shorter (P < 0.001). The regression tree output was in the form of box plots that show the median and ranges of surgical times according to the patient, disease, and institutional characteristics. CONCLUSION: We developed a method to estimate operative times for RARC based on patient, disease, and institutional metrics that can help operating room scheduling for RARC.

Loss of expression of the tumour suppressor gene AIMP3 predicts survival following radiotherapy in muscle-invasive bladder cancer.

The aim of this study was to test the utility of AIMP3, an upstream regulator of DNA damage response following genotoxic stress, as a clinical biomarker in muscle-invasive bladder cancer (MIBC). AIMP3 was identified from a meta-analysis of a global gene-expression dataset. AIMP3 protein expression was determined by immunohistochemistry on a customised bladder cancer tissue-microarray (TMA). The mechanism of gene silencing was probed using methylation-specific PCR. The association between AIMP3 expression, Tp53 transactivity and genomic stability was analysed. In vitro AIMP3 translocation to the nucleus in response to ionising radiation was demonstrated using immunofluorescence. Radiosensitisation effects of siRNA-mediated AIMP3-knockdown were measured using colony forming assays. TMAs derived from patients enrolled in BCON, a Phase III multicentre radiotherapy trial in bladder cancer (ISRCTN45938399) were used to evaluate the association between AIMP3 expression and survival. The prognostic value of AIMP3 expression was determined in a TMA derived from patients treated by radical cystectomy. Loss of AIMP3 expression was frequent in MIBC and associated with impaired Tp53 transactivity and genomic instability. AIMP3-knockdown was associated with an increase in radioresistance. Loss of AIMP3 expression was associated with survival in MIBC patients following radiotherapy (HR = 0.53; 95% CI: 0.36 to 0.78, p = 0.002) but was not prognostic in the cystectomy set. In conclusion, AIMP3 expression is lost in a subset of bladder cancers and is significantly predictive of survival following radiotherapy in MIBC patients.

Minimum design requirements for a poroelastic mimic of articular cartilage.

The exceptional functional performance of articular cartilage (load-bearing and lubrication) is attributed to its poroelastic structure and resulting interstitial fluid pressure. Despite this, there remains no engineered cartilage repair material capable of achieving physiologically relevant poroelasticity. In this work we develop in silico models to guide the design approach for poroelastic mimics of articular cartilage. We implement the constitutive models in FEBio, a PDE solver for multiphasic mechanics problems in biological and soft materials. We investigate the influence of strain rate, boundary conditions at the contact interface, and fiber modulus on the reaction force and load sharing between the solid and fluid phases. The results agree with the existing literature that when fibers are incorporated the fraction of load supported by fluid pressure is greatly amplified and increases with the fiber modulus. This result demonstrates that a stiff fibrous phase is a primary design requirement for poroelastic mimics of articular cartilage. The poroelastic model is fit to experimental stress-relaxation data from bovine and porcine cartilage to determine if sufficient design constraints have been identified. In addition, we fit experimental data from FiHy™, an engineered material which is claimed to be poroelastic. The fiber-reinforced poroelastic model was able to capture the primary physics of these materials and demonstrates that FiHy™ is beginning to approach a cartilage-like poroelastic response. We also develop a fiber-reinforced poroelastic model with a bonded interface (rigid contact) to fit stress relaxation data from an osteochondral explant and FiHy™ + bone substitute. The model fit quality is similar for both the chondral and osteochondral configurations and clearly captures the first order physics. Based on this, we propose that physiological poroelastic mimics of articular cartilage should be developed under a fiber-reinforced poroelastic framework.