三维及多平面重建CT技术在鼻部整形中的应用价值分析

目的:分析在鼻部整形中三维及多平面重建CT技术的应用价值。方法:选择2018年2月~2019年7月本院收治的60例行鼻部整形的患者进行分析,所有患者均开展三维及多平面重建CT技术检测,结合检查结果,开展相应的隆鼻手术。分析术后不良反应、患者满意度。术后6个月,在CT测量下,检测前后鼻部解剖数据。结果:在鼻部整形中,三维及多平面重建CT技术的应用,可以准确了解鼻部结构,如观察侧鼻骨、鼻部皮肤、鼻根等情况。术后评价,患者对鼻部整形满意度较高,术后6个月,患者对鼻部整形满意度较高,术后与术后6个月患者鼻部满意度相比,无统计学意义,P>0.05。术后60例患者没有出现出血、切口感染等反应,同时也没有出现肋软骨外漏与倾斜等现象。经随访术后6个月发现1例患者的鼻背软骨出现轻度弯曲,但无明显表现,没有做特殊处理;其余患者,鼻部外形均表现为鼻尖挺拔、鼻背曲线流畅。结论:在鼻部整形中,应用三维及多平面重建CT技术,可以辅助提高隆鼻手术准确度,并在CT技术的引导下,准确植入自体肋软骨,可以为手术成功奠定基础,促使鼻唇与周围器官形态的协调性,进一步提高患者满意度。     

Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities

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.     

The History and Future of the “ISPOR Value Flower”: Addressing Limitations of Conventional Cost-Effectiveness Analysis

ObjectivesSince its publication as part of the 2018 ISPOR Special Task Force (STF) on US Value Assessments, the “ISPOR value flower,” with its petals highlighting elements that may be overlooked or underappreciated in conventional drug value assessments, has been discussed and debated. We review the history of the value flower, describe recent developments, and consider implications for future value assessments.MethodsWe discuss various antecedents to the value flower, as well as conceptual and empirical articles published in the past 4 years.ResultsSince the publication of the ISPOR STF report, researchers have provided more rigorous theoretical and mathematical foundations for certain novel value elements (eg, severity of illness, value of insurance, value of hope) through “generalized risk-adjusted cost-effectiveness analysis,” which incorporates risk aversion in people’s preferences and uncertainty in treatment outcomes. Empirical estimates are also emerging to support key elements, such as insurance value, real option value, value of hope, and value of knowing. Although health technology assessment bodies have applied or are considering certain elements (eg, severity modifiers to cost-effectiveness thresholds), other elements have yet to gain traction.ConclusionsFive years after the STF began its work, the development of novel value measures continues to evolve. Although it is encouraging to see supporting empirical studies emerging, more are needed. Additional efforts are also needed to illustrate how the estimates can be used in the deliberative processes that are integral to health technology assessments.     

Development of an EQ-5D Value Set for India Using an Extended Design (DEVINE) Study: The Indian 5-Level Version EQ-5D Value Set

ObjectivesThis study aimed to develop the Indian 5-level version EQ-5D (EQ-5D-5L) value set, which is a key input in health technology assessment for resource allocation in healthcare.MethodsA cross-sectional survey using the EuroQol Group’s Valuation Technology was undertaken in a representative sample of 3548 adult respondents, selected from 5 different states of India using a multistage stratified random sampling technique. The participants were interviewed using a computer-assisted personal interviewing technique. This study adopted a novel extended EuroQol Group’s Valuation Technology design that included 18 blocks of 10 composite time trade-off (c-TTO) tasks, comprising 150 unique health states, and 36 blocks of 7 discrete choice experiment (DCE) tasks, comprising 252 DCE pairs. Different models were explored for their predictive performance. Hybrid modeling approach using both c-TTO and DCE data was used to estimate the value set.ResultsA total of 2409 interviews were included in the analysis. The hybrid heteroscedastic model with censoring at ?1 combining c-TTO and DCE data yielded the most consistent results and was used for the generation of the value set. The predicted values for all 3125 health states ranged from ?0.923 to 1. The preference values were most affected by the pain/discomfort dimension.ConclusionsThis is the largest EQ-5D-5L valuation study conducted so far in the world. The Indian EQ-5D-5L value set will promote the effective conduct of health technology assessment studies in India, thereby generating credible evidence for efficient resource use in healthcare.     

Smoking Cessation and Quality of Life: Insights From Analysis of Longitudinal Australian Data,an Application for Economic Evaluations

ObjectivesA number of studies have shown an association between smoking habit and quality of life, but these have mainly involved cross-sectional data. This study takes advantage of longitudinal panel data to estimate the effect of the transition from “smoker” to “ex-smoker” status (smoking cessation) on health-related quality of life (HRQoL), measured by SF-36, in an Australian general population sample.MethodsPanel data from 13 waves (2001-2013) of a nationally representative longitudinal survey of Household Income and Labour Dynamics of Australia (HILDA) were used; 1858 respondents (5% of total HILDA sample) who experienced only 1 cessation event in their HILDA life were selected. HRQoL trajectories elicited by SF-36 (0-100 scale, worst to best health) were modeled before and after cessation events using a piecewise (segmented) 2-way fixed-effect linear regression, adopted to capture within-person differences. This enabled measurement of changes of regression slopes and intercept while controlling time-invariant characteristics (eg, country of birth, gender) and time-varying changes in health status.ResultsAnnual pre-post intervention improvements were estimated for the following dimensions: role physical 0.65 (95% CI 0.62-1.24), bodily pain 0.48 (95% CI 0.10-0.86), general health 0.55 (95% CI 0.2-0.9), and the physical component summary score 0.22 (95% CI 0.01-0.04). Immediate effects (discontinuity at the time of cessation) of smoking cessation existed for bodily pain –1.5 (95% CI –2.52 to –0.40) and general health 1.82 (95% CI 1.01-2.62). The effects for mental health domains were not significant.ConclusionsAdjusting for all unmeasured time-invariant confounders and controlling the effect of time, this study revealed the varied effects of smoking cessation on HRQoL; it has positive effect on physical and general health but nonsignificant effect on mental aspects. Preference-based utility measures based on SF-6D capture changes that can be measured in several of the domains of the SF-36.     

Toward Better Data Dashboards for US Drug Value Assessments

ObjectivesTo explore the use of data dashboards to convey information about a drug’s value, and reduce the need to collapse dimensions of value to a single measure.MethodsReview of the literature on US Drug Value Assessment Frameworks, and discussion of the value of data dashboards to improve the manner in which information on value is displayed.ResultsThe incremental cost per quality-adjusted life-year ratio is a useful starting point for conversation about a drug’s value, but it cannot reflect all of the elements of value about which different audiences care deeply. Data dashboards for drug value assessments can draw from other contexts. Decision makers should be presented with well-designed value dashboards containing various metrics, including conventional cost per quality-adjusted life-year ratios as well as measures of a drug’s impact on clinical and patient-centric outcomes, and on budgetary and distributional consequences, to convey a drug’s value along different dimensions.ConclusionsThe advent of US drug value frameworks in health care has forced a concomitant effort to develop appropriate information displays. Researchers should formally test different formats and elements.     

脊柱术后感染病原菌及CD64和中性粒细胞百分比的早期预测价值

目的分析脊柱术后医院感染患者病原菌分布特征及中性粒细胞64(CD64)和中性粒细胞百分比(NEU%)表达情况。方法选取2017年6月-2019年6月天津市天津医院收治的脊柱术后医院感染患者51例为感染组,并选取同期于医院就诊的脊柱术后未发生感染患者51例为未感染组。分析感染组患者病原菌情况,检测患者NEU和CD64水平;ROC曲线分析CD64和NEU%指标水平预测感染的诊断价值。结果 51例感染患者共培养分离病原菌78株,其中革兰阴性菌42株(53.85%)、革兰阳性菌33株(42.31%)、真菌3株(3.85%),以铜绿假单胞菌及金黄色葡萄球菌为主。术后7 d,感染组体温、WBC、中性粒细胞计数、CRP、ESR分别为(38.47±0.52)℃、(8.34±2.17)×109/L、(5.98±1.94)×109/L、(54.52±19.93)mg/L、(51.74±21.88)mm/h均高于非感染组(P<0.05)。术后7 d,感染组CD64、NEU%分别为(8.87±2.13)%、(84.93±13.39)高于非感染组(P<0.05)。CD64指数与NEU%水平诊断ROC曲线下面积分别为0.922、0.826。结论脊柱术后医院感染以革兰阴性菌为主,CD64及NEU%水平有助于脊柱术后医院感染的早期诊断。     

超声弹性成像联合BI-RADS分类在乳腺微小癌诊断中的临床价值

目的:探讨在乳腺微小癌诊断中联合应用超声弹性成像及数据系统(BI-RADS)分类的临床价值。方法:选择2018年3月~2019年3月本院接收的疑似乳腺微小癌的96例患者作为研究对象。均进行超声弹性成像检查、BI-RADS分类,以病理检查结果为诊断金标准,比较单项检查与联合检查的准确率、灵敏度、特异度。结果:数据显示,在96例疑似乳腺微小癌患者中,共80例患者经病理检查确诊为乳腺微小癌,同时乳腺钼靶联合超声检查的准确性、灵敏度均高于单项检测,数据之间的差异有统计学意义(P<0.05)。此外,单项与联合检测的特异度对比,差异无统计学意义(P>0.05)。结论:在乳腺微小癌诊断中联合应用超声弹性成像及BI-RADS分类检查,可有效提高诊断准确性及灵敏度,对早期疾病诊断、临床治疗具有重要意义。     

PCT与SDI比值对重症细菌性肺炎患者短期预后的预测价值

 目的 探讨血清降钙素原(PCT)与肺泡灌洗液辛普森菌群多样性指数(SDI)比值对重症监护病房(ICU)内细菌性肺炎患者短期预后的预测价值。方法 回顾性调查扬州大学附属医院ICU 2019年10月—2021年7月选择肺泡灌洗液宏基因组二代测序(mNGS)技术的56例细菌性肺炎患者病历资料,依据其入ICU 24 h内急性生理学与慢性健康状况评分Ⅱ(APACHE-Ⅱ)分为非危重症组21例和危重症组35例。以细菌性肺炎造成死亡为终点事件,记录28天转归,并将患者分为生存组38例和死亡组18例。对各组患者的SDI、PCT、C-反应蛋白(CRP)、PCT/SDI、CRP/SDI进行比较分析。结果 与非危重症组比较,危重症组患者血清PCT/SDI、PCT水平均升高,且呼吸机辅助通气时间更长,28天病死率更高(均P＜0.05);与存活组比较,死亡组患者SDI较低,PCT/SDI、PCT水平均较高(均P＜0.05);SDI与呼吸机辅助通气时间呈负相关(r值为-0.655,P＜0.001),PCT水平、PCT/SDI与呼吸机辅助通气时间呈正相关(r值分别为0.660、0.734,均P＜0.001)。受试者工作特征曲线(ROC曲线)显示,PCT/SDI预测患者28天死亡的ROC曲线下面积(AUC)为0.851,其次为PCT+SDI (0.845)、PCT (0.808)、SDI (0.785)、CRP/SDI (0.731),PCT/SDI的最佳截断值为11.56时预判患者28天死亡的灵敏度为89.5%,特异度为66.7%。Cox回归分析显示,PCT/SDI值高(HR=1.562,95%CI：1.271~1.920,P=0.031)、PCT水平高(HR=1.106,95%CI：1.021~1.198,P=0.024)是ICU细菌性肺炎患者死亡的独立危险因素。结论 PCT/SDI、PCT、PCT+SDI、SDI、CRP/SDI均可作为ICU细菌性肺炎患者短期预后的评估指标。与其他指标相比,PCT/SDI预测患者短期预后更有价值。     

中草药的变态反应

通过大量的文献调查、以鱼腥草和丹参及其制剂引起的变态反应为重点,介绍常用中草药诱发变态反应的临床表现、分型、处理和预防方法。为临床安全、有效使用中草药提供借鉴。