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1.
背景国内外用于评估癌症患者支持性照护需求的量表较多,但有关此类量表质量的标准化评价研究及不同量表间的横向比较研究较为缺乏,也少有研究者对此类量表的测量特性进行系统的整合与评价。目的评价中文版癌症患者支持性照护需求量表的测量学性能及研究的方法学质量。方法2021年4月检索中国知网、万方数据知识服务平台、维普中文科技期刊全文数据库、中国生物医学文献数据库、PubMed、EmBase、Web of Science、CINAHL Complete数据库,获取有关中文版癌症患者支持性照护需求量表测量学性能评价的研究,检索时限均为建库至2021年3月30日。由两位研究者独立筛选文献、提取资料后,采用健康测量工具遴选标准(COSMIN)系统综述指南,在对量表的测量特性及研究的方法学质量进行评价的基础上,综合评定中文版癌症患者支持性照护需求评估量表各测量特性的证据等级,并形成对于量表的最终推荐意见。采用描述分析法对评价结果进行汇总、分析。结果共纳入15项研究,涉及8个中文版癌症患者支持性照护需求评估量表〔癌症患者支持性照护需求简明问卷中文版(SCNS-SF34)、中文版支持性照护需求筛查工具(SCNS-ST9-C)、癌症患者综合需求评估量表(CNAT)、癌症需求简明问卷(CNQ-SF)、中文版癌症患者未满足需求量表(CaSUN-C)、癌症患者未满足需求简明量表(SF-SUNS)、晚期癌症患者需求评估问卷(ACNQ-41)、晚期癌症患者需求评估表简表(ACNQ-29)〕。就量表的测量特性质量而言,除ACNQ-29的内容效度为"未提及"外,其余7个量表的内容效度均为"不确定";除CaSUN-C、SF-SUNS的结构效度为"充分"外,其余6个量表的结构效度均为"不确定";SCNS-SF34、CNQ-SF、CaSUN-C、SF-SUNS的内部一致性为"充分",ACNQ-41的内部一致性为"不充分",其余3个量表的内部一致性为"不确定";CNAT、CNQ-SF、ACNQ-29的假设检验为"未提及",CaSUN-C、SF-SUNS、ACNQ-41的假设检验为"不确定",SCNS-SF34、SCNS-ST9-C的假设检验为"充分";除ACNQ-41的稳定性为"不充分",SCNS-ST9-C、ACNQ-29的稳定性为"未提及"外,其余5个量表的稳定性均为"充分";仅SCNS-SF34的跨文化效度为"充分",其余7个量表的跨文化效度均为"未提及"。8个量表的推荐等级均为B级。结论SCNS-SF34的测量特性得到了最为全面的评价,其具有较好的信效度,且临床应用可行性高,可暂时被推荐使用,但上述结论仍有待更多高质量证据加以支撑。  相似文献   
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目的研究医疗器械上市后风险评估的统计学方法,提高风险管理的科学水平。方法参考欧盟医疗器械新法规中即将实施的趋势报告要求,探索趋势分析方法在医疗器械上市后风险评估中的实践运用,举例说明通过历史数据确定控制限和持续趋势监测的过程,讨论实践中面临的问题。结果趋势分析是科学评估医疗器械上市后风险的方法之一,适于在国内推行。结论企业可运用真实世界风险数据开展趋势分析,提升上市后风险预警能力。  相似文献   
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《Value in health》2022,25(3):331-339
ObjectivesClinical artificial intelligence (AI) is a novel technology, and few economic evaluations have focused on it to date. Before its wider implementation, it is important to highlight the aspects of AI that challenge traditional health technology assessment methods.MethodsWe used an existing broad value framework to assess potential ways AI can provide good value for money. We also developed a rubric of how economic evaluations of AI should vary depending on the case of its use.ResultsWe found that the measurement of core elements of value—health outcomes and cost—are complicated by AI because its generalizability across different populations is often unclear and because its use may necessitate reconfigured clinical processes. Clinicians’ productivity may improve when AI is used. If poorly implemented though, AI may also cause clinicians’ workload to increase. Some AI has been found to exacerbate health disparities. Nevertheless, AI may promote equity by expanding access to medical care and, when properly trained, providing unbiased diagnoses and prognoses. The approach to assessment of AI should vary based on its use case: AI that creates new clinical possibilities can improve outcomes, but regulation and evidence collection may be difficult; AI that extends clinical expertise can reduce disparities and lower costs but may result in overuse; and AI that automates clinicians’ work can improve productivity but may reduce skills.ConclusionsThe potential uses of clinical AI create challenges for health technology assessment methods originally developed for pharmaceuticals and medical devices. Health economists should be prepared to examine data collection and methods used to train AI, as these may impact its future value.  相似文献   
4.
ObjectivesThe purpose of this study was to investigate the prognostic weight of multimorbidity and functional impairment over long-term mortality among older patients discharged from acute care hospitals.DesignA prospective multicenter observational study.Setting and ParticipantsOur series consisted of 1967 adults aged ≥65 years consecutively admitted to acute care wards in Italy, in the context of the Report-AGE project.MethodsAfter signing a written informed consent, all patients underwent comprehensive geriatric assessment by Inter-RAI Minimum Data Set acute care. The primary endpoint of the present study was long-term mortality. Patients were grouped into 3 functional clusters and 3 disease clusters using the K-medians cluster analysis. The association of functional clusters, disease clusters, and Charlson score categories with long-term mortality was investigated through Cox regression analysis and the intercluster classification agreement was further estimated. Finally, the additive effect of either disease clusters or Charlson score on predictive ability of functional clusters was assessed by using changes in Harrell’s C-index and categorical Net Reclassification Index (NRI).ResultsFunctional clusters, disease clusters, and Charlson score were significant predictors of long-term mortality, but the interclassification agreement was poor. Functional clusters predicted mortality with greater accuracy [C-index 0.66, 95% confidence interval (CI) 0.65–0.68] compared with disease clusters (C-index 0.54, 95% CI 0.53–0.56), and Charlson score (C-index 0.58, 95% CI 0.56–0.59). Adding multimorbidity (NRI 0.23, 95% CI 0.14–0.31) or Charlson score (NRI 0.13, 95% CI 0.03–0.20) to functional cluster model slightly improved the accuracy of prediction.Conclusions and ImplicationsFunctional impairment may better predict prognosis compared with multimorbidity, which may be relevant to optimally address individuals’ needs and to design tailored preventive interventions.  相似文献   
5.
ObjectivesThe recently developed Hospital Frailty Risk Score (HFRS) allows ascertainment of frailty from administrative data. We aimed to compare the HFRS against the widely used FRAIL Scale and Frailty Index.DesignPopulation-based cohort study linked to Western Australian Hospital Morbidity Data Collection and Death Registrations.Setting and ParticipantsThe Health in Men Study with frailty determined at Wave 2 (2001/2004), mortality in the 1-year period following Wave 2, and disability at Wave 3 (2008). Participants were 4228 community-based men aged ≥75 years, followed until Wave 3.MeasurementsWe used multivariable regression to determine the association between each frailty measure and outcomes of length of stay (LOS), death, and disability. We also determined if the additional cases of frailty identified by one measure over the other was associated with these outcomes.ResultsOf 4228 men studied, the HFRS (n = 689) identified fewer men as frail than the FRAIL Scale (n = 1648) and Frailty Index (n = 1820). In the fully adjusted models, all 3 frailty measures were associated with longer LOS and mortality, whereas only the FRAIL Scale and Frailty Index were significantly associated with disability. The additional cases of frailty identified by the FRAIL Scale and Frailty Index had longer LOS and greater risks of death and disability. The fully adjusted hazard ratio for death among the additional cases of frailty identified by the FRAIL Scale (compared to being not frail on both HFRS and FRAIL Scale) was 2.14 (95% CI 1.48-3.08).Conclusions and ImplicationsThe HFRS is associated with adverse outcomes. However, it identified approximately 60% fewer men who were frail than the FRAIL Scale and Frailty Index, and the additional cases identified were also at high risks of adverse outcomes. Users of the HFRS should be aware of the differences with other frailty measures.  相似文献   
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目的 为给兽药减量政策的制定、动物源性食品安全的维护提供理论依据。方法 利用每日允许摄入量(ADI)、估算每日摄入量(EDI)及慢性风险熵(CRQ),对中国居民通过摄入肉蛋乳等动物性食品暴露于吉他霉素的耐药性风险进行点评估。结果 随着年龄增长,中国居民的吉他霉素膳食暴露量逐渐降低。2~7岁人群吉他霉素的膳食暴露量最高,男女分别为2.17和2.29 μg/ ( kg·bw·d);>65岁人群膳食暴露量最低,男女分别为0.45和0.46 μg/ ( kg·bw·d)。2~7岁人群通过摄入乳制品的吉他霉素的膳食暴露量最大,男女分别为1.15和1.22 μg/ ( kg·bw·d),8岁以上人群通过摄入肉类食品的吉他霉素的膳食暴露量最大。中国居民通过摄入肉蛋乳等动物性食品暴露于吉他霉素,各年龄段CRQ均小于1。其中2~7岁人群CRQ值最高,风险最大;65岁以上人群CRQ值最低,风险最小。结论 中国居民通过摄入动物性食品的吉他霉素耐药性风险较低,危害程度较小,但儿童通过摄入乳制品的吉他霉素耐药性风险较高,有必要制定乳制品中吉他霉素残留的限量标准。  相似文献   
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目的评估CHA2DS2-VASc评分对急性心肌梗死(AMI)患者院内结局事件的预测价值。方法回顾性分析冠心病医疗结果评价和临床转化研究(China PEACE)回顾性急性心肌梗死研究中23728例AMI患者的病历信息,按CHA2DS2-VASc评分分为低(0~3分)、中(4~6分)、高(7~9分)分值组。院内结局包括主要不良心血管事件、死亡、死亡或放弃治疗、再发心肌梗死、缺血性卒中等。采用多因素Cox回归分析CHA2DS2-VASc评分对AMI患者院内结局的影响。通过受试者工作特征(ROC)曲线,评估CHA2DS2-VASc评分对AMI患者院内死亡与死亡或放弃治疗的预测价值。结果入组患者年龄66(56,75岁)岁,女性占30.7%。CHA2DS2-VASc评分高分值组患者院内结局事件发生率更高,基础疾病更多(P值均<0.001);多因素logistic回归中,院内病死率(OR=6.13,95%CI 4.77~7.87,P<0.001)、院内死亡或放弃治疗率(OR=6.43,95%CI 5.16~8.00,P<0.001)、主要心血管事件发生率(OR=4.94,95%CI 4.06~6.01,P<0.001)明显高于其他两组。ROC曲线分析显示,无论院内病死率,还是死亡或放弃治疗率,CHA2DS2-VASc评分与简化版全球急性冠状动脉事件登记(global registry of acute coronary events,mini-GRACE)评分相比差异无统计学意义(ROC曲线下面积:0.699与0.696,P=0.752;0.708与0.713,P=0.489)。结论CHA2DS2-VASc评分是一种有效预测AMI患者院内风险的评估工具,该评分操作简单,预测价值与mini-GRACE评分相当。  相似文献   
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