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目的探讨品管圈在植入式静脉输液港(IVAP)患者护理中的应用。方法选取本院2018年10月-2018年12月IVAP患者30例作为对照组,2019年1月-2019年3月品管圈活动干预后IVAP患者30例作为观察组。对照组采用常规护理措施,观察组采用品管圈活动干预后的护理措施。比较两组IVAP患者并发症的发生率和对护理工作的满意度。结果观察组导管堵塞1例;并发症发生率为3.33%(1/30)。对照组感染4例,血栓2例,导管堵塞4例;并发症发生率为33.33%(10/30)。观察组并发症发生率低于对照组(P <0.05)。观察组对优质服务、技术水平、关怀、出院指导、护理总满意度评分分别为(42.39±5.27)分、(31.49±4.32)分、(22.25±4.69)分、(20.26±4.91)分、(112.24±10.38)分;对照组分别为(38.65±4.91)分、(28.68±4.26)分、(19.96±4.30)分、(19.94±4.63)分、(103.37±10.12)分;观察组对优质服务、技术水平、关怀、出院指导、护理总满意度评分均高于对照组(P <0.05)。结论通过品管圈活动干预后的护理措施明显降低IVAP患者并发症的发生率,提高患者对护理工作的满意度。 相似文献
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严桂英 《中国卫生标准管理》2020,(8):95-98
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。 相似文献
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Whitney S. Brandt Wanpu Yan Jian Zhou Kay See Tan Joseph Montecalvo Bernard J. Park Prasad S. Adusumilli James Huang Matthew J. Bott Valerie W. Rusch Daniela Molena William D. Travis Mark G. Kris Jamie E. Chaft David R. Jones 《The Journal of thoracic and cardiovascular surgery》2019,157(2):743-753.e3
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.Methods
Patients with cT2-4N0-1M0 non–small cell lung cancer who received platinum-based chemotherapy were retrospectively identified. Exclusion criteria included stage IV disease, induction radiotherapy, and targeted therapy. The primary end point was disease-free survival. Secondary end points were overall survival, chemotherapy tolerance, and ability of Response Evaluation Criteria In Solid Tumors response to predict survival. Survival was estimated using the Kaplan–Meier method, compared using the log-rank test and Cox proportional hazards models, and stratified using matched pairs after propensity score matching.Results
In total, 330 patients met the inclusion criteria (n = 92/group after propensity-score matching; median follow-up, 42 months). Five-year disease-free survival was 49% (95% confidence interval, 39-61) for neoadjuvant chemotherapy versus 48% (95% confidence interval, 38-61) for adjuvant chemotherapy (P = .70). On multivariable analysis, disease-free survival was not associated with neoadjuvant chemotherapy or adjuvant chemotherapy (hazard ratio, 1.1; 95% confidence interval, 0.64-1.90; P = .737), nor was overall survival (hazard ratio, 1.21; 95% confidence interval, 0.63-2.30; P = .572). The neoadjuvant chemotherapy group was more likely to receive full doses and cycles of chemotherapy (P = .014/0.005) and had fewer grade 3 or greater toxicities (P = .001). Response Evaluation Criteria In Solid Tumors response to neoadjuvant chemotherapy was associated with disease-free survival (P = .035); 15% of patients receiving neoadjuvant chemotherapy (14/92) had a major pathologic response.Conclusions
Timing of chemotherapy, before or after surgery, is not associated with an improvement in overall or disease-free survival among patients with cT2-4N0-1M0 non–small cell lung cancer who undergo complete surgical resection. 相似文献9.
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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. 相似文献