Derivation and validation of a multivariate model to predict mortality from pulmonary embolism with cancer: The POMPE-C tool

BackgroundClinical guidelines recommend risk stratification of patients with acute pulmonary embolism (PE). Active cancer increases risk of PE and worsens prognosis, but also causes incidental PE that may be discovered during cancer staging. No quantitative decision instrument has been derived specifically for patients with active cancer and PE. Methods Classification and regression technique was used to reduce 25 variables prospectively collected from 408 patients with AC and PE. Selected variables were transformed into a logistic regression model, termed POMPE-C, and compared with the pulmonary embolism severity index (PESI) score to predict the outcome variable of death within 30 days. Validation was performed in an independent sample of 182 patients with active cancer and PE. Results POMPE-C included eight predictors: body mass, heart rate > 100, respiratory rate, SaO2%, respiratory distress, altered mental status, do not resuscitate status, and unilateral limb swelling. In the derivation set, the area under the ROC curve for POMPE-C was 0.84 (95% CI: 0.82-0.87), significantly greater than PESI (0.68, 0.60-0.76). In the validation sample, POMPE-C had an AUC of 0.86 (0.78-0.93). No patient with POMPE-C estimate ≤ 5% died within 30 days (0/50, 0-7%), whereas 10/13 (77%, 46-95%) with POMPE-C estimate > 50% died within 30 days. Conclusion In patients with active cancer and PE, POMPE-C demonstrated good prognostic accuracy for 30 day mortality and better performance than PESI. If validated in a large sample, POMPE-C may provide a quantitative basis to decide treatment options for PE discovered during cancer staging and with advanced cancer

Mapping and characterization of structural variation in 17,795 human genomes

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline1 to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0–11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing

The Cholecystectomy As A Day Case (CAAD) Score: A Validated Score of Preoperative Predictors of Successful Day-Case Cholecystectomy Using the CholeS Data Set

Background Day-case surgery is associated with significant patient and cost benefits. However, only 43% of cholecystectomy patients are discharged home the same day. One hypothesis is day-case cholecystectomy rates, defined as patients discharged the same day as their operation, may be improved by better assessment of patients using standard preoperative variables. Methods Data were extracted from a prospectively collected data set of cholecystectomy patients from 166 UK and Irish hospitals (CholeS). Cholecystectomies performed as elective procedures were divided into main (75%) and validation (25%) data sets. Preoperative predictors were identified, and a risk score of failed day case was devised using multivariate logistic regression. Receiver operating curve analysis was used to validate the score in the validation data set. Results Of the 7426 elective cholecystectomies performed, 49% of these were discharged home the same day. Same-day discharge following cholecystectomy was less likely with older patients (OR 0.18, 95% CI 0.15–0.23), higher ASA scores (OR 0.19, 95% CI 0.15–0.23), complicated cholelithiasis (OR 0.38, 95% CI 0.31 to 0.48), male gender (OR 0.66, 95% CI 0.58–0.74), previous acute gallstone-related admissions (OR 0.54, 95% CI 0.48–0.60) and preoperative endoscopic intervention (OR 0.40, 95% CI 0.34–0.47). The CAAD score was developed using these variables. When applied to the validation subgroup, a CAAD score of ≤5 was associated with 80.8% successful day-case cholecystectomy compared with 19.2% associated with a CAAD score >5 (p < 0.001). Conclusions The CAAD score which utilises data readily available from clinic letters and electronic sources can predict same-day discharges following cholecystectomy