GENCODE reference annotation for the human and mouse genomes

The accurate identification and description of the genes in the human and mouse genomes is a fundamental requirement for high quality analysis of data informing both genome biology and clinical genomics. Over the last 15 years, the GENCODE consortium has been producing reference quality gene annotations to provide this foundational resource. The GENCODE consortium includes both experimental and computational biology groups who work together to improve and extend the GENCODE gene annotation. Specifically, we generate primary data, create bioinformatics tools and provide analysis to support the work of expert manual gene annotators and automated gene annotation pipelines. In addition, manual and computational annotation workflows use any and all publicly available data and analysis, along with the research literature to identify and characterise gene loci to the highest standard. GENCODE gene annotations are accessible via the Ensembl and UCSC Genome Browsers, the Ensembl FTP site, Ensembl Biomart, Ensembl Perl and REST APIs as well as https://www.gencodegenes.org.National Human Genome Research Institute of the National Institutes of Healt

Validation of the CREST model and comparison with SCAI shock classification for the prediction of circulatory death in resuscitated out-of-hospital cardiac arrest

Aims We validated the CREST model, a 5 variable score for stratifying the risk of circulatory aetiology death (CED) following out-of-hospital cardiac arrest (OHCA) and compared its discrimination with the SCAI shock classification. Circulatory aetiology death occurs in approximately a third of patients admitted after resuscitated OHCA. There is an urgent need for improved stratification of the patient with OHCA on arrival to a cardiac arrest centre to improve patient selection for invasive interventions. Methods and results The CREST model and SCAI shock classification were applied to a dual-centre registry of 723 patients with cardiac aetiology OHCA, both with and without ST-elevation myocardial infarction (STEMI), between May 2012 and December 2020. The primary endpoint was a 30-day CED. Of 509 patients included (62.3 years, 75.4% male), 125 patients had CREST = 0 (24.5%), 162 had CREST = 1 (31.8%), 140 had CREST = 2 (27.5%), 75 had CREST = 3 (14.7%), 7 had a CREST of 4 (1.4%), and no patients had CREST = 5. Circulatory aetiology death was observed in 91 (17.9%) patients at 30 days [STEMI: 51/289 (17.6%); non-STEMI (NSTEMI): 40/220 (18.2%)]. For the total population, and both NSTEMI and STEMI subpopulations, an increasing CREST score was associated with increasing CED (all P &lt; 0.001). The CREST score and SCAI classification had similar discrimination for the total population [area under the receiver operating curve (AUC) = 0.72/calibration slope = 0.95], NSTEMI cohort (AUC = 0.75/calibration slope = 0.940), and STEMI cohort (AUC = 0.69 and calibration slope = 0.925). Area under the receiver operating curve meta-analyses demonstrated no significant differences between the two classifications. Conclusion The CREST model and SCAI shock classification show similar prediction results for the development of CED after OHCA.</p

MIRACLE 2 Score Compared With Downtime and Current Selection Criterion for Invasive Cardiovascular Therapies After OHCA.

BACKGROUND: The MIRACLE 2 score is the only risk score that does not incorporate and can be used for selection of therapies after out-of-hospital cardiac arrest (OHCA). OBJECTIVES: This study sought to compare the discrimination performance of the MIRACLE 2 score, downtime, and current randomized controlled trial (RCT) recruitment criteria in predicting poor neurologic outcome after out-of-hospital cardiac arrest (OHCA). METHODS: We used the EUCAR (European Cardiac Arrest Registry), a retrospective cohort from 6 centers (May 2012-September 2022). The primary outcome was poor neurologic outcome on hospital discharge (cerebral performance category 3-5).RESULTS: A total of 1,259 patients (total downtime = 25 minutes; IQR: 15-36 minutes) were included in the study. Poor outcome occurred in 41.8% with downtime &lt;30 minutes and in 79.3% for those with downtime &gt;30 minutes. In a multivariable logistic regression analysis, MIRACLE 2 had a stronger association with outcome (OR: 2.23; 95% CI: 1.98-2.51; P &lt; 0.0001) than zero flow (OR: 1.07; 95% CI: 1.01-1.13; P = 0.013), low flow (OR: 1.04; 95% CI: 0.99-1.09; P = 0.054), and total downtime (OR: 0.99; 95% CI: 0.95-1.03; P = 0.52). MIRACLE 2 had substantially superior discrimination for the primary endpoint (AUC: 0.877; 95% CI: 0.854-0.897) than zero flow (AUC: 0.610; 95% CI: 0.577-0.642), low flow (AUC: 0.725; 95% CI: 0.695-0.754), and total downtime (AUC: 0.732; 95% CI: 0.701-0.760). For those modeled for exclusion from study recruitment, the positive predictive value of MIRACLE 2 ≥5 for poor outcome was significantly higher (0.92) than the CULPRIT-SHOCK (Culprit lesion only PCI Versus Multivessel PCI in Cardiogenic Shock) (0.80), EUROSHOCK (Testing the value of Novel Strategy and Its Cost Efficacy In Order to Improve the Poor Outcomes in Cardiogenic Shock) (0.74) and ECLS-SHOCK (Extra-corporeal life support in Cardiogenic shock) criteria (0.81) (P &lt; 0.001). CONCLUSIONS: The MIRACLE 2 score has superior prediction of outcome after OHCA than downtime and higher discrimination of poor outcome than the current RCT recruitment criteria. The potential for the MIRACLE 2 score to improve the selection of OHCA patients should be evaluated formally in future RCTs. </p