Accuracy of Postoperative Risk Scores for Survival Prediction in Interagency Registry for Mechanically Assisted Circulatory Support Profile 1 Continuous-Flow Left Ventricular Assist Device Recipients.

In this study, we sought to determine the accuracy of several critical care risk scores for predicting survival of Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) Profile 1 patients after continuous-flow left ventricular assist device (CF-LVAD) placement. We retrospectively analyzed the records of 605 patients who underwent CF-LVAD implantation between 2003 and 2016. We calculated the preoperative HeartMate II Risk Score (HMRS) and preoperative Right Ventricular Failure Risk Score (RVFRS) and the following risk scores for postoperative days 1-5: HMRS, RVFRS, Model for End-stage Liver Disease (MELD), MELD-eXcluding International Normalized Ratio, Post Cardiac Surgery (POCAS) risk score, Sequential Organ Failure Assessment (SOFA) risk score, and Acute Physiology and Chronic Health Evaluation III. The preoperative scores and the postoperative day 1, 5-day mean, and 5-day maximum scores were entered into a receiver operating characteristic curve analysis to examine accuracy for predicting 30-day, 90-day, and 1-year survival. The mean POCAS score was the best predictor of 30-day and 90-day survival (area under the curve [AUC] = 0.869 and 0.816). The postoperative mean RVFRS was the best predictor of 1-year survival (AUC = 0.7908). The postoperative maximum and mean RVFRS and HMRS were more accurate than the preoperative scores. Both of these risk score measurements of acuity in the postoperative intensive care unit setting help predict early mortality after LVAD implantation.

MAC: identifying and correcting annotation for multi-nucleotide variations.

BACKGROUND: Next-Generation Sequencing (NGS) technologies have rapidly advanced our understanding of human variation in cancer. To accurately translate the raw sequencing data into practical knowledge, annotation tools, algorithms and pipelines must be developed that keep pace with the rapidly evolving technology. Currently, a challenge exists in accurately annotating multi-nucleotide variants (MNVs). These tandem substitutions, when affecting multiple nucleotides within a single protein codon of a gene, result in a translated amino acid involving all nucleotides in that codon. Most existing variant callers report a MNV as individual single-nucleotide variants (SNVs), often resulting in multiple triplet codon sequences and incorrect amino acid predictions. To correct potentially misannotated MNVs among reported SNVs, a primary challenge resides in haplotype phasing which is to determine whether the neighboring SNVs are co-located on the same chromosome. RESULTS: Here we describe MAC (Multi-Nucleotide Variant Annotation Corrector), an integrative pipeline developed to correct potentially mis-annotated MNVs. MAC was designed as an application that only requires a SNV file and the matching BAM file as data inputs. Using an example data set containing 3024 SNVs and the corresponding whole-genome sequencing BAM files, we show that MAC identified eight potentially mis-annotated SNVs, and accurately updated the amino acid predictions for seven of the variant calls. CONCLUSIONS: MAC can identify and correct amino acid predictions that result from MNVs affecting multiple nucleotides within a single protein codon, which cannot be handled by most existing SNV-based variant pipelines. The MAC software is freely available and represents a useful tool for the accurate translation of genomic sequence to protein function.