One-year outcome of robotical vs. manual percutaneous coronary intervention.

BACKGROUND: Robotic-assisted percutaneous coronary intervention (R-PCI) is a promising technology for optimizing the treatment of patients with coronary heart disease. For a better understanding of the potential of R-PCI in clinical routine compared to conventional manual PCI (M-PCI) both initial treatment success of the index procedure and long-term outcome have to be analysed. METHODS: Prospective evaluation from the FRiK (DRKS00023868) registry of all R-PCI cases with the CorPath GRX Cardiology by Siemens Healthineers and Corindus in the Freiburg University Heart Center between 04/2022 and 03/2023. Index procedure success and safety, radiation dose of patients and personnel, and 1-year outcome will be reported. Findings will be compared to a prospective control group of M-PCI patients treated by the same team of interventionalists during the same observation period. RESULTS: Seventy patients received R-PCI and were included in the registry. PCI success rate was 100%, with 19% requiring manual assistance. No complications (MACE-major adverse cardiovascular events) occurred. Compared with 70 matched-pair M-PCI patients, there was a higher median procedural time (103 min vs. 67 min, p < 0.001) and fluoroscopy time (18 min vs. 15 min, p = 0.002), and more contrast volume was used (180 ml vs. 160 ml, p = 0.041) in R-PCI vs. M-PCI patients. However, there was no significant difference of the dose-area product (4062 vs. 3242 cGycm2, p = 0.361). One year after the intervention, there was no difference in mortality, rehospitalisation, unscheduled PCI or target vessel failure. Health-related quality of life evaluation 6 and 12 months after the index procedure (NYHA, CCS, SAQ7 and EQ-5D-5L) was similar in both groups. CONCLUSION: R-PCI is feasible and safe. Compared to M-PCI, index procedure success rate is high, safety profile is favourable, and manual assistance was required in only few cases. At 1-year follow-up results for R-PCI vs. M-PCI considering mortality, rehospitalisation, morbidity and target vessel failure were equal.

Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data.

OBJECTIVE: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. MATERIALS AND METHODS: Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. RESULTS: The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. DISCUSSION: We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. CONCLUSIONS: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

The dark matter of the cancer genome: aberrations in regulatory elements, untranslated regions, splice sites, non-coding RNA and synonymous mutations.

Cancer is a disease of the genome caused by oncogene activation and tumor suppressor gene inhibition. Deep sequencing studies including large consortia such as TCGA and ICGC identified numerous tumor-specific mutations not only in protein-coding sequences but also in non-coding sequences. Although 98% of the genome is not translated into proteins, most studies have neglected the information hidden in this "dark matter" of the genome. Malignancy-driving mutations can occur in all genetic elements outside the coding region, namely in enhancer, silencer, insulator, and promoter as well as in 5'-UTR and 3'-UTR Intron or splice site mutations can alter the splicing pattern. Moreover, cancer genomes contain mutations within non-coding RNA, such as microRNA, lncRNA, and lincRNA A synonymous mutation changes the coding region in the DNA and RNA but not the protein sequence. Importantly, oncogenes such as TERT or miR-21 as well as tumor suppressor genes such as TP53/p53, APC, BRCA1, or RB1 can be affected by these alterations. In summary, coding-independent mutations can affect gene regulation from transcription, splicing, mRNA stability to translation, and hence, this largely neglected area needs functional studies to elucidate the mechanisms underlying tumorigenesis. This review will focus on the important role and novel mechanisms of these non-coding or allegedly silent mutations in tumorigenesis.