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Poster presentation: Purpose of the study The aim of the Rainbow Cohort is to assess the tolerability and efficacy of initiating treatment with, or switching treatment to the saquinavir (SQV) 500 mg film-coated tablet formulation. We present the final 48-week subgroup analysis of PI-experienced, but SQV-naïve patients. ...
Purpose: To test the effect of anatomic variants of the prostatic apex overlapping the membranous urethra (Lee type classification), as well as median urethral sphincter length (USL) in preoperative multiparametric magnetic resonance imaging (mpMRI) on the very early continence in open (ORP) and robotic-assisted radical prostatectomy (RARP) patients. Methods: In 128 consecutive patients (01/2018–12/2019), USL and the prostatic apex classified according to Lee types A–D in mpMRI prior to ORP or RARP were retrospectively analyzed. Uni- and multivariable logistic regression models were used to identify anatomic characteristics for very early continence rates, defined as urine loss of ≤ 1 g in the PAD-test. Results: Of 128 patients with mpMRI prior to surgery, 76 (59.4%) underwent RARP vs. 52 (40.6%) ORP. In total, median USL was 15, 15 and 10 mm in the sagittal, coronal and axial dimensions. After stratification according to very early continence in the PAD-test (≤ 1 g vs. > 1 g), continent patients had significantly more frequently Lee type D (71.4 vs. 54.4%) and C (14.3 vs. 7.6%, p = 0.03). In multivariable logistic regression models, the sagittal median USL (odds ratio [OR] 1.03) and Lee type C (OR: 7.0) and D (OR: 4.9) were independent predictors for achieving very early continence in the PAD-test. Conclusion: Patients’ individual anatomical characteristics in mpMRI prior to radical prostatectomy can be used to predict very early continence. Lee type C and D suggest being the most favorable anatomical characteristics. Moreover, longer sagittal median USL in mpMRI seems to improve very early continence rates.
Background: The combination of stavudine (d4T), 3TC and NVP was the WHO recommended first-line regimen for the treatment of naïve HIV-1 infected patients in resource-limited settings. But peripheral polyneuropathy, lipoatrophy and symptomatic hyperlactatemia are frequent and treatment-limiting adverse events associated with stavudine, especially when combined with antituberculous drugs. Tenofovir combined with lamivudine and efavirenz has proven excellent efficacy, but there is little experience when given with NVP.
Methods: Retrospective analysis of all patients receiving TDF, NVP and 3TC or FTC as first-line treatment in the Frankfurt HIV cohort.
Summary of results: 70 patients (15 female) with a median CD4 cell count of 210/μl (47–949) and HIV-RNA PCR of 140,000 copies/ml (2,500–2,000,000) at baseline received TDF, NVP and 3TC/FTC, and were treated for a median of 68 weeks (16–278). CD4 cells rose up to cells/μl 322 (119–1075) and 75% of the patients remained on treatment. All patients on treatment at week 48 were <50 c/ml, even those starting with CD4 cells of <200 cells/μl or a HIV-RNA PCR >100,000 c/m. Reasons for discontinuation (24%) were mainly adverse events (13%), with rash (7%) and liver toxicity (6%) being the two most common, whereas virologic failure, drug interaction and non-adherence were all relatively rare (each 3%).
Conclusion: The combination of NVP, TDF and 3TC or FTC is effective and well tolerated in previously naïve HIV-1 infected patients even when started with low CD4 cell counts (<200/ml) and high viral loads (>100,000 c/ml). In the latest amendment of the WHO guidelines TDF, instead of d4T, is the recommended first-line treatment in resource-limited settings.
Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.