Fragmentation of care as a barrier to optimal ESKD management

Caring for patients with end-stage kidney disease (ESKD) in the United States is challenging, due in part to the complex epidemiology of the disease's progression as well as the ways in which care is delivered. As CKD progresses toward ESKD, the number of comorbidities increases and care involves multiple healthcare providers from multiple subspecialties. This occurs in the context of a fragmented US healthcare delivery system that is traditionally siloed by provider specialty, organization, as well as systems of payment and administration. This article describes the role of care fragmentation in the delivery of optimal ESKD care and identifies research gaps in the evidence across the continuum of care. We then consider the impact of care fragmentation on ESKD care from the patient and health system perspectives and explore opportunities for system-level interventions aimed at improving care for patients with ESKD.     

经后颅窝乙状窦后入路三叉神经微血管减压术的快速康复外科护理

目的 探讨快速康复外科 （FTS）理念在经后颅窝乙状窦后入路三叉神经微血管减压术围术期患者护理中的应用效果。方法 选择2018年1月~2019年1月我院收治并行经后颅窝乙状窦后入路三叉神经微血管减压患者100例，随机分成观察组和对照组，每组50例。对照组应用常规方法进行围术期护理，观察组应用FTS理念进行围术期护理。比较两组焦虑情况、术后并发症发生率、平均住院日及平均住院费用。结果 观察组轻度焦虑多于对照组，中、重度焦虑少于对照组（P＜0.05）；观察组术后并发症发生率低于对照组[恶心呕吐（4.00% vs 16.00%）、尿路感染（4.00% vs 18.00%）、颅内感染（0 vs 8.00%）、颅内血肿（2.00% vs 14.00%）和术后应激性疼痛（16.00% vs 34.00%）]，差异有统计学意义（P＜0.05）；观察组平均住院日和平均住院费用均低于对照组[（9.13±1.14）d vs（12.44±0.89）d]；[（2.15±0.66）万元 vs （3.05±0.61）万元]，差异有统计学意义（P＜0.05）。结论 应用FTS理念能缓解经后颅窝乙状窦后入路三叉神经微血管减压术患者术前紧张恐惧的心理，减少术后并发症，缩短患者住院日，降低住院费用，护理效果较好。     

健康相关生命质量高分组和低分组生活习惯和中医体质量表转换分比较

目的 探索健康相关生命质量高分组和低分组的生活习惯和中医体质转换分的差异。方法 从1 114例健康体检人群数据库中，分别抽取简明健康状况调查问卷（the MOS 36-item short form health survey, SF-36）的生理领域得分、心理领域得分的前27%（301例）和后27%（301例）作为高分组和低分组，比较两组的生活习惯和中医体质量表转换分。结果 SF-36生理领域和心理领域高分组和低分组的吸烟偏好比较，差异无统计学意义（P>0.05）；两组饮酒偏好、睡眠时间、中医体质量表转换分比较，差异具有统计学意义（P<0.05），其中高分组好饮酒者较多，睡眠时间为7～8 h者较多，平和质者较多，平和质转换分较高，8种偏颇体质转换分较低；心理领域高分组有运动习惯者较多（P<0.05），而生理领域高分组和低分组的运动习惯差异无统计学意义（P>0.05）。结论 SF-36高分组和低分组的生活习惯和中医体质因素存在一定差异，通过改善睡眠时间、运动习惯及偏颇体质可能会提高健康相关生命质量。     

Simeprevir added to peginterferon and ribavirin lessens time with fatigue,depressive symptoms and functional limitations in patients with chronic hepatitis C compared with peginterferon and ribavirin: results from 1161 patients in the QUEST‐1, QUEST‐2 and PROMISE studies

The value of adding simeprevir (SMV) vs placebo (PBO) to peginterferon and ribavirin (PR) for treatment of chronic hepatitis C virus infection was examined using patient‐reported outcomes (PROs); further, concordance of PROs with virology endpoints and adverse events (AEs) was explored. Patients (n = 768 SMV/PR, n = 393 PBO/PR) rated fatigue (FSS), depressive symptoms (CES‐D) and functional impairment (WPAI: Hepatitis C Productivity, Daily Activity and Absenteeism) at baseline and throughout treatment in three randomised, double‐blind trials comparing the addition of SMV or PBO during initial 12 weeks of PR. PR was administered for 48 weeks (PBO group) and 24/48 weeks (SMV group) using a response‐guided therapy (RGT) approach. Mean PRO scores (except Absenteeism) worsened from baseline to Week 4 to the same extent in both groups but reverted after Week 24 for SMV/PR and only after Week 48 for PBO/PR. Accordingly, there was a significantly lower area under the curve (baseline–Week 60, AUC₆₀) and fewer weeks with clinically important worsening of scores in the SMV/PR group at any time point. Incidences of patients with fatigue and anaemia AEs were similar in both groups, but FSS scores showed that clinically important increases in fatigue lasted a mean of 6.9 weeks longer with PBO/PR (P < 0.001). PRO score subgroup analysis indicated better outcomes for patients who met the criteria for RGT or achieved sustained virological response 12 weeks post‐treatment (SVR12); differences in mean PRO scores associated with fibrosis level were only observed with PBO/PR. Greater efficacy of SMV/PR enabled reduced treatment duration and reduced time with PR‐related AEs without adding to AE severity.     

Prostatic Artery Embolization—Anatomic Predictors of Technical Outcomes

PurposeTo determine if cone-beam CT and digital subtraction angiography analysis of pelvic arterial anatomy has predictive value for radiation exposure and technical success of prostatic artery embolization (PAE).Materials and MethodsThis prospective, nonrandomized, single-center study included 104 consecutive patients with lower urinary tract symptoms secondary to benign prostatic hyperplasia. Cone-beam CT was performed in 160/208 (76.9%) hemipelves to determine prostatic artery (PA) origin. Classification of pelvic arterial tortuosity was possible in 73/104 (70.2%) patients. Learning curves of 2 interventionalists who performed 86.5% of PAEs were analyzed.ResultsTortuosity of pelvic arteries was classified as mild in 25 (34.2%) patients median age 64 years, moderate in 40 (54.8%) patients median age 69 years, and severe in 8 (11.0%) patients median age 70 years (mild vs moderate, P = .002; mild vs severe, P = .019); median fluoroscopy times were 24, 36, and 46 minutes (P = .008, P = .023); median contrast volumes were 105, 122.5, and 142 mL (P = .029, P = .064); and bilateral PAE rates were 84.0%, 77.5%, and 62.5% (P = .437), respectively. PA origin from superior vesical artery was most frequent (27.5%) and showed higher dose area product (median 402.4 vs 218 Gy ∙ cm², P = .033) and fluoroscopy time (median 42.5 vs 27 min, P = .01) compared with PA origin from obturator artery, which was least frequent. Interventionalist experience revealed significant impact on procedure times (median 159 vs 130 min, P = .006).ConclusionsTortuosity of pelvic arteries was more frequent in older patients and predicted worse technical outcomes of PAE. PA origin from obturator artery was associated with lower dose area product and fluoroscopy time, especially compared with PA origin from superior vesical artery. Interventionalist experience showed significant influence on technical outcome.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.