Respiratory severity score and extubation readiness in very low birth weight infants.

BACKGROUND: The respiratory severity score (RSS) is a byproduct of mean airway pressure (MAP) and fraction of inspired oxygen (FiO2). We sought to determine whether RSS could be used as a screening tool to predict extubation readiness in very low birth weight (VLBW) infants. METHODS: In a retrospective cohort study, medical records of all VLBW infants admitted to our unit (6/1/09-2/28/12) were reviewed for infants' demographics, prenatal characteristics, and medication use. Also, records were reviewed for unplanned vs. planned extubation, blood gas, ventilator parameters and signs of severe respiratory failure [RF, defined as partial pressure of carbon dioxide (pCO2) > 65, pH < 7.20, FiO2 > 50%, and MAP > 10 cm] on the day of extubation. RESULTS: During the study period 31% (45/147) failed extubation. Overall, infants who failed extubation had a lower birth weight (BW) and gestational age (GA), and on the day of extubation had a higher RSS and percentage of having one or more signs of severe RF. In a logistic regression model, adjusting for BW, GA, RSS and RF, RSS remained the only risk factor associated with extubation failure [adjusted OR 1.63 (95% CI: 1.10-2.40); p = 0.01]. RSS had a sensitivity of 0.86 (95% CI: 0.72-0.94) at a cutoff of 1.26 and a specificity of 0.88 (95% CI: 0.80-0.94) at a cutoff of 2.5. There was no difference in extubation failure between unplanned vs. planned extubation [41% (9/22) vs. 29% (36/125); p = 0.25]. CONCLUSION: An elevated RSS is associated with extubation failure. Successful unplanned extubation is common in VLBW infants.

Identification of coding and non-coding mutational hotspots in cancer genomes.

BACKGROUND: The identification of mutations that play a causal role in tumour development, so called "driver" mutations, is of critical importance for understanding how cancers form and how they might be treated. Several large cancer sequencing projects have identified genes that are recurrently mutated in cancer patients, suggesting a role in tumourigenesis. While the landscape of coding drivers has been extensively studied and many of the most prominent driver genes are well characterised, comparatively less is known about the role of mutations in the non-coding regions of the genome in cancer development. The continuing fall in genome sequencing costs has resulted in a concomitant increase in the number of cancer whole genome sequences being produced, facilitating systematic interrogation of both the coding and non-coding regions of cancer genomes. RESULTS: To examine the mutational landscapes of tumour genomes we have developed a novel method to identify mutational hotspots in tumour genomes using both mutational data and information on evolutionary conservation. We have applied our methodology to over 1300 whole cancer genomes and show that it identifies prominent coding and non-coding regions that are known or highly suspected to play a role in cancer. Importantly, we applied our method to the entire genome, rather than relying on predefined annotations (e.g. promoter regions) and we highlight recurrently mutated regions that may have resulted from increased exposure to mutational processes rather than selection, some of which have been identified previously as targets of selection. Finally, we implicate several pan-cancer and cancer-specific candidate non-coding regions, which could be involved in tumourigenesis. CONCLUSIONS: We have developed a framework to identify mutational hotspots in cancer genomes, which is applicable to the entire genome. This framework identifies known and novel coding and non-coding mutional hotspots and can be used to differentiate candidate driver regions from likely passenger regions susceptible to somatic mutation.