Description: Background and Purpose— We aimed to develop a risk score (intracerebral hemorrhage–associated pneumonia score, ICH-APS) for predicting hospital-acquired stroke-associated pneumonia (SAP) after ICH. Methods— The ICH-APS was developed based on the China National Stroke Registry (CNSR), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Variables routinely collected at presentation were used for predicting SAP after ICH. For testing the added value of hematoma volume measure, we separately developed 2 models with (ICH-APS-B) and without (ICH-APS-A) hematoma volume included. Multivariable logistic regression was performed to identify independent predictors. The area under the receiver operating characteristic curve (AUROC), Hosmer–Lemeshow goodness-of-fit test, and integrated discrimination index were used to assess model discrimination, calibration, and reclassification, respectively. Results— The SAP was 16.4% and 17.7% in the overall derivation (n=2998) and validation (n=2000) cohorts, respectively. A 23-point ICH-APS-A was developed based on a set of predictors and showed good discrimination in the overall derivation (AUROC, 0.75; 95% confidence interval, 0.72–0.77) and validation (AUROC, 0.76; 95% confidence interval, 0.71–0.79) cohorts. The ICH-APS-A was more sensitive for patients with length of stay 〉48 hours (AUROC, 0.78; 95% confidence interval, 0.75–0.81) than those with length of stay 〈48 hours (AUROC, 0.64; 95% confidence interval, 0.55–0.73). The ICH-APS-A was well calibrated (Hosmer–Lemeshow test) in the derivation ( P =0.20) and validation ( P =0.66) cohorts. Similarly, a 26-point ICH-APS-B was established. The ICH-APS-A and ICH-APS-B were not significantly different in discrimination and reclassification for SAP after ICH. Conclusion— The ICH-APSs are valid risk scores for predicting SAP after ICH, especially for patients with length of stay 〉48 hours.

Description: Cancers, Vol. 10, Pages 13: Translocation Breakpoints Preferentially Occur in Euchromatin and Acrocentric Chromosomes Cancers doi: 10.3390/cancers10010013 Authors: Cheng-Yu Lin Ankit Shukla John Grady J. Fink Eloise Dray Pascal Duijf Chromosomal translocations drive the development of many hematological and some solid cancers. Several factors have been identified to explain the non-random occurrence of translocation breakpoints in the genome. These include chromatin density, gene density and CCCTC-binding factor (CTCF)/cohesin binding site density. However, such factors are at least partially interdependent. Using 13,844 and 1563 karyotypes from human blood and solid cancers, respectively, our multiple regression analysis only identified chromatin density as the primary statistically significant predictor. Specifically, translocation breakpoints preferentially occur in open chromatin. Also, blood and solid tumors show markedly distinct translocation signatures. Strikingly, translocation breakpoints occur significantly more frequently in acrocentric chromosomes than in non-acrocentric chromosomes. Thus, translocations are probably often generated around nucleoli in the inner nucleoplasm, away from the nuclear envelope. Importantly, our findings remain true both in multivariate analyses and after removal of highly recurrent translocations. Finally, we applied pairwise probabilistic co-occurrence modeling. In addition to well-known highly prevalent translocations, such as those resulting in BCR-ABL1 (BCR-ABL) and RUNX1-RUNX1T1 (AML1-ETO) fusion genes, we identified significantly underrepresented translocations with putative fusion genes, which are probably subject to strong negative selection during tumor evolution. Taken together, our findings provide novel insights into the generation and selection of translocations during cancer development.