Background and Objectives: We tested if a novel combination of predictors could improve the accuracy of outcome prediction after transfemoral transcatheter aortic valve implantation (TAVI). Materials and Methods: This prospective study recruited 169 participants (49% female; median age 81 years). The primary endpoint was midterm mortality; secondary endpoints were acute Valve Academic Research Consortium (VARC)-3 complication rate and post-TAVI in-hospital length of stay (LoS). EuroSCORE II (ESII), comorbidities (e.g., coronary artery disease), eGFR (estimated glomerular filtration rate; based on cystatin C), hemoglobin, creatinine, N-Terminal pro-Brain Natriuretic Peptide (NTproBNP) levels and patient-reported outcome measures (PROMs, namely EuroQol-5-Dimension-5-Levels, EQ5D5L; Kansas City Cardiomyopathy Questionnaire, KCCQ; clinical frailty scale, CFS) at baseline were tested as predictors. Regression (uni- and multi-variate Cox; linear; binary logistic) and receiver operating characteristic (ROC)-curve analysis were applied. Results: Within a median follow-up of 439 (318–585) days, 12 participants died (7.1%). Independent predictors of mortality using multivariate Cox regression were baseline eGFR (p = 0.001) and KCCQ (p = 0.037). Based on these predictors, a Linear Prediction Score (LPS1) was calculated. The LPS1-area under the curve (AUC)-value (0.761) was significantly higher than the ESII-AUC value (0.597; p = 0.035). Independent predictors for LoS > 6 days (the median LoS) were eGFR (p = 0.028), NTproBNP (p = 0.034), and EQ5D5L values (p = 0.002); a respective calculated LPS2 provided an AUC value of 0.677 (p < 0.001). Eighty participants (47.3%) experienced complications. Male sex predicted complications only in the univariate analysis. Conclusions: The combination of KCCQ and eGFR can better predict midterm mortality than ES II alone. Combining eGFR, NTproBNP, and EQ5D5L can reliably predict LoS after TAVI. This novel method improves personalized TAVI risk stratification and hence may help reduce post-TAVI risk.

Background: The aim of this study was to identify pre-operative parameters able to predict length of stay (LoS) based on clinical data and patient-reported outcome measures (PROMs) from a scorecard database in patients with significant aortic stenosis who underwent TAVI (transfemoral aortic valve implantation). Methods: 302 participants (51.7% males, age range 78.2–84.2 years.) were prospectively recruited. After computing the median LoS value (=6 days, range = 5–8 days), we implemented a decision tree algorithm by setting dichotomized values at median LoS as the dependent variable and assessed baseline clinical variables and PROMs (Clinical Frailty Scale (CFS), EuroQol-5 Dimension-5 Levels (EQ-5D) and Kansas City Cardiomyopathy Questionnaire (KCCQ)) as potential predictors. Results: Among clinical parameters, only peripheral arterial disease (p = 0.029, HR = 1.826) and glomerular filtration rate (GFR, cut-off < 33 mL/min/1.73 m2, p = 0.003, HR = 2.252) were predictive of LoS. Additionally, two PROMs (CFS; cut-off = 3, p < 0.001, HR = 1.324 and KCCQ; cut-off = 30, p = 0.003, HR = 2.274) were strong predictors. Further, a risk score for LoS (RS_LoS) was calculated based on these predictors. Patients with RS_LoS = 0 had a median LoS of 5 days; patients RS_LoS ≥ 3 had a median LoS of 8 days. Conclusions: based on the pre-operative values of the above four predictors, a personalized prediction of LoS after TAVI can be achieved.

Management options for patients with vestibular schwannoma include observation and active treatment, namely surgical resection, gamma knife stereotactic radiosurgery or fractionated radiation therapy. Although for large tumors microsurgery remains the mainstay of treatment, management of the small-and medium-sized vestibular schwannomas has been the matter of considerable controversy. Computerized clinical decision support systems have been developed to assist clinicians in this demanding task. Nonetheless, the complexity of the problem requires adaptation of the decision strategy to specific circumstances arising within a particular clinical scenario which can not always be modeled with adequate precision or addressed adequately within a mathematical framework. We present a set of clinical, neurophysiologic and radiologic parameters and the respective evidence which may guide carers' decisions. These parameters include the size, growth rate and localization of the tumor, the age, general medical condition, facial nerve function, hearing and hearing deterioration rate, balance, vestibular and trigeminal nerve function of the affected individual, the estimated risk of malignant transformation and radiation-induced tumors, the health-related quality of life measures, the patient's and surgeon's preference and the issue of cost-effectiveness. A complex decision analysis, guided by evidence and tailored to each individual patient is required.