Identifying and managing patient–ventilator asynchrony: An international survey.

Objective: To describe the main factors associated with proper recognition and management of patient ventilator asynchronies (PVA). Design: Analytical cross-sectional study. Setting: International study conducted in 20 countries through an online survey. Participants: Physicians, respiratory therapists, nurses and physiotherapists that are currently working at the Intensive Care Unit (ICU). Main variables of interest: Univariate and multivariate logistic regression models were used to establish associations between all variables (profession, training in mechanical ventilation, type of training program, years of experience and ICU characteristics) with the ability of HCPs to correctly identify and manage 6 PVA. Results: A total of 431 HCPs answered a validated survey. The main factors associated with the proper recognition of PVA were: specific training program in mechanical ventilation (MV) (OR 2.27; 95% CI 1.14-4.52; p = 0.019), courses with more than 100 hours completed (OR 2.28; 95% CI 1.29-4.03; p = 0.005) and the number of intensive care unit (ICU) beds (OR 1.037; 95% CI 1.01-1.06; p = 0.005). The main factor that influenced PVA management was recognizing 6 PVA correctly (OR 118.98; 95%CI 35.25-401.58; p < 0.001). Conclusion: Identifying and managing PVA using ventilator waveform analysis is influenced by many factors including specific training programs in MV, number of ICU beds and the recognized number of PVA.pre-print169 K

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types