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Introduction
The objective of this study was to report outcome measures with third-generation pre-attached scrotal port adjustable transobturator male system (ATOMS) for male stress urinary incontinence (SUI) after radical prostatectomy.Methods
A prospective open study was conducted on consecutive patients. Evaluation included cough test, urethroscopy, filling and voiding cystometry, 24-h pad count and pad test, patient-reported outcomes (ICIQ-SF, IIQ-7, PGI, GRA, and VAS), complications according to the Clavien–Dindo system, operative results, number of adjustments, and filling of the system.Results
Thirty-four patients with median pad test 510 (170–1225) ml were operated on. Preoperative SUI was mild (5.9%), moderate (17.6%), and severe (76.5%). At median 18.5 (12–26) months follow-up distribution of SUI was none (85.3%), mild (8.8%), and moderate (5.9%). Median intraoperative filling was 14 (8–17) ml, number of adjustments 1 (0–5), and total filling 17.5 (11–33.5) ml. At 3 months, median ICIQ-SF (p = 0.0001) and IIQ-7 (p < 0.0001) decreased. At 12 months, 24-h pad count and pad test decreased (both p < 0.0001), residual volume slightly increased (p = 0.018), PGI-I was 1 (1–3), GRA 6 (3–6), and 97% were satisfied with treatment. Continence (p = 0.016) and satisfaction (p = 0.09) were worse in irradiated patients. Median operative time was 67 (35–120) min, hospital stay 1 (1–3) days, and VAS for pain on postoperative day 1 was 0 (0–2). Complications presented in 14.7% (8.8% grade I and 5.9% grade III).Conclusion
Treatment of severe male SUI after radical prostatectomy with pre-attached scrotal port ATOMS is safe and very effective in the short term. A positive cough test before implant and intraoperative overfilling of the system may optimize patient selection and results.Areas covered: The current review summarizes prevailing knowledge on the role of lipids in atheromathosis in CKD patients, including an overview of lipoprotein metabolism highlighting the CKD-induced alterations. Moreover, to obtain information beyond traditional lipid parameters, new state-of-the-art technologies such as lipoprotein subfraction profiling and lipidomics are also reviewed. Finally, we analyse the potential of new lipoprotein subclasses as therapeutic targets in CKD.
Expert opinion: The CKD-induced lipid profile has specific features distinct from the general population. Besides quantitative alterations, renal patients have a plethora of qualitative lipid alterations that cannot be detected by routine determinations and are responsible for the excess of cardiovascular risk. New parameters, such as lipoprotein particle number and size, together with new biomarkers obtained by lipidomics will personalize the management of these patients. Therefore, nephrologists need to be aware of new insights into lipoprotein metabolism to improve cardiovascular risk assessment. 相似文献
Methods: Cross-sectional study in 395 non-diabetic individuals (209 CKD patients and 186 controls) without statin therapy. Conventional lipid determinations were combined with advanced lipoprotein profiling by nuclear magnetic resonance, and their discrimination ability was assessed by machine learning.
Results: CKD patients showed an increase of very-low-density (VLDL) particles and a reduction of LDL particle size. Cholesterol and triglyceride content of VLDLs and intermediate-density (IDL) particles increased. However, low-density (LDL) and high-density (HDL) lipoproteins gained triglycerides and lost cholesterol. Total-Cholesterol, HDL-Cholesterol, LDL-Cholesterol, non-HDL-Cholesterol and Proprotein convertase subtilisin-kexin type (PCSK9) were negatively associated with CKD stages, whereas triglycerides, lipoprotein(a), remnant cholesterol, and the PCSK9/LDL-Cholesterol ratio were positively associated. PCSK9 was positively associated with total-Cholesterol, LDL-Cholesterol, LDL-triglycerides, LDL particle number, IDL-Cholesterol, and remnant cholesterol. Machine learning analysis by random forest revealed that new parameters have a higher discrimination ability to classify patients into the CKD group, compared to traditional parameters alone: area under the ROC curve (95% CI), .789 (.711, .853) vs .687 (.611, .755).
Conclusions: non-diabetic CKD patients have a hidden proatherogenic lipoprotein profile. 相似文献