老年人生命晚期获知疾病信息意向及影响因素CSCD

目的了解老年人生命晚期获知疾病相关信息意向及影响因素。方法2016年10月至2017年6月,采用生命晚期疾病信息意向问卷,利用方便抽样法对福州市中心城区7所养老机构及15个社区的414例年龄≥60岁的老年人进行横断面调查,采用单因素分析、多元线性回归与有序多分类logistic回归分析老年人对疾病相关信息的需求水平、获知程度意向及其影响因素。结果414例老年人疾病相关信息需求得分为(17.1±4.9)分;48.8%(202/414)希望详尽知晓,30.7%(127/414)希望选择性了解,20.5%(85/414)不想知道任何信息;多元线性回归分析显示,年龄、文化程度、是否接受/见过其他生命维持治疗(LSTs)是影响老年人疾病相关信息需求水平的主要因素(标准化回归系数分别为-0.141、0.116、0.115,均P<0.05);有序多分类logistic分析显示,年龄(以60~69岁为参照,70~79岁:OR=0.544,95%CI:0.310~0.957;80~89岁:OR=0.526,95%CI:0.289~0.956)、文化程度(以小学及以下为参照,大专及以上:OR=2.166,95%CI:1.093~4.290)、主要生活费来源(以其他补贴为参照,家人支持:OR=7.303,95%CI:1.157~46.108;退休金:OR=9.288,95%CI:1.502~57.415;公积金/储蓄:OR=15.676,95%CI:2.122~115.793)、是否接受/见过其他LSTs(以是为参照,OR=1.985,95%CI:1.150~3.425)是影响老年人疾病相关信息获知程度意向的主要因素。结论老年人生命晚期获知疾病相关信息的意向程度较高,年龄、文化程度、主要生活费来源、是否接受/见过其他生命维持治疗等是其主要影响因素。     

Inferior Vena Cava Filters: Aligning Practice With Evidence to Improve Patient Outcomes

BackgroundDespite indications for the removal of temporary inferior vena cava (IVC) filters, many filters are unintentionally left in place, predisposing patients to adverse outcomes.ObjectiveThis quality improvement study set out to determine the impact of an IVC filter retrieval protocol on filter retrieval rates and patients lost to follow-up for patients who had undergone placement of a temporary IVC filter.MethodsFollowing a quasi-experimental design, data of all consecutive patients who underwent insertion of a temporary IVC filter for a period of 24-month preprotocol and 12-month postprotocol were compared.ResultsFilter retrieval rates of eligible filters increased from 64.2% to 100%; patients lost to follow-up decreased from 35.9% to 0% (p < .01, both outcomes).ConclusionAdoption of a comprehensive IVC filter protocol by the service that implants these devices can improve filter retrieval rates and decrease patients being lost to follow-up.     

李延应用泽泻汤合温胆汤治疗舒张压高的临床经验

随着社会的发展,人类生活方式的改变,高血压病越来越高发,具有低龄化趋势,代谢综合征常伴随发生,此种情况下的高血压往往是以舒张压升高为主,临床表现常与“亚健康”状态混淆,未能引起患者甚至部分医生的重视,然而事实上越来越多的科学研究证实舒张压高之危害十分明显,不容忽视。尽管现代医学对舒张压高的病因及病理机制有明确的阐述,但是暂时没有特效的药物。李延教授在治疗高血压病方面有着丰富的临床经验,临床中运用泽泻汤合温胆汤加减治疗舒张压高之眩晕,切中病机,加减灵活,屡有良效。文中从中医角度阐述舒张压高的病因病机,介绍李师辨病辨证思路,组方用药特点,附三则典型验案,另加个人心得体会,以期为舒张压高的有效治疗提供思路。     

益气养精法对老年肺癌患者肿瘤标志物、生存期影响研究

目的探究益气养精法对老年肺癌患者肿瘤标志物、生存期影响。方法研究纳入60例老年肺癌患者,均由本院2016年1月-2017年1月收治,采取随机数字表法将患者分为两组,对照组患者(30例)常规化疗治疗,观察组患者(30例)在化疗基础上联合益气养精法治疗,比较两组患者治疗效果、治疗前后癌胚抗原(carcino-embryonic antigen,CEA)及血清癌抗原125(serum oncoantigen 125,CA 125)肿瘤标志物水平、不良反应情况及患者2年生存率。结果观察组患治疗有效率高于对照组,P<0.05;治疗前,两组患者CEA及CA 125水平相当,P>0.05,治疗后均改善,观察组优于对照组,P<0.05;观察组患者不良反应与对照组相当,均较低,P>0.05;观察组患者2年生存率高于对照组,P<0.05。结论益气养精法治疗老年肺癌患者效果患者,患者症状改善,不良反应少,安全可靠,且患者2年生存率较高。     

Outcomes after neoadjuvant or adjuvant chemotherapy for cT2-4N0-1 non–small cell lung cancer: A propensity-matched analysis

Objective

Comparative survival between neoadjuvant chemotherapy and adjuvant chemotherapy for patients with cT2-4N0-1M0 non–small cell lung cancer has not been extensively studied.

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.