NT-pro-BNP as a Predictor for Recurrence of Atrial Fibrillation after Primary Cryoballoon Pulmonary Vein Isolation

NT-pro-BNP is produced in the cardiac atria and ventricles in response to increased wall stress. It may be a marker of both AF disease progression and co-morbidities that affect success after pulmonary vein isolation (PVI). This single-center retrospective study analyzed the association between pre-procedural NT-pro-BNP serum levels and the long-term outcome after a first-ever PVI in cryo-technique. Patients were followed by searching the hospital information system and conducting structured telephone interviews. Treatment failure was defined as any relapse of atrial fibrillation (AF) occurring 90 days after the index PVI at the earliest. Kaplan–Meier survival curves and Cox proportional hazards models were computed to assess the impact of NT-pro-BNP on AF recurrence. Following 374 patients over a median of 3.8 years (range: 0.25–9.4 years), baseline NT-pro-BNP was associated with the combined endpoint in univariate analysis (HR 1.04 per 100 pg/mL increase, 95% CI: 1.02–1.07, p < 0.001). Results were virtually unchanged in the multivariate model or if the data were log-transformed. Intraprocedural left atrial pressure correlated positively with log NT-pro-BNP. NT-pro-BNP was associated with AF relapse during a long-term follow-up after first-ever cryo-PVI in our cohort of patients with predominantly normal left ventricular function. This lab parameter is easy to obtain and has significant potential to guide treatment decisions

Individualizing Follow-Up Strategies in High-Grade Soft Tissue Sarcoma with Flexible Parametric Competing Risk Regression Models

Currently, patients with extremity soft tissue sarcoma (eSTS) who have undergone curative resection are followed up by a heuristic approach, not covering individual patient risks. The aim of this study was to develop two flexible parametric competing risk regression models (FPCRRMs) for local recurrence (LR) and distant metastasis (DM), aiming at providing guidance on how to individually follow-up patients. Three thousand sixteen patients (1931 test, 1085 validation cohort) with high-grade eSTS were included in this retrospective, multicenter study. Histology (9 categories), grading (time-varying covariate), gender, age, tumor size, margins, (neo)adjuvant radiotherapy (RTX), and neoadjuvant chemotherapy (CTX) were used in the FPCRRMs and performance tested with Harrell-C-index. Median follow-up was 50 months (interquartile range: 23.3–95 months). Two hundred forty-two (12.5%) and 603 (31.2%) of test cohort patients developed LR and DM. Factors significantly associated with LR were gender, size, histology, neo- and adjuvant RTX, and margins. Parameters associated with DM were margins, grading, gender, size, histology, and neoadjuvant RTX. C-statistics was computed for internal (C-index for LR: 0.705, for DM: 0.723) and external cohort (C-index for LR: 0.683, for DM: 0.772). Depending on clinical, pathological, and patient-related parameters, LR- and DM-risks vary. With the present model, implemented in the updated Personalised Sarcoma Care (PERSARC)-app, more individualized prediction of LR/DM-risks is made possible