Reported Challenges in Health Technology Assessment of Complex Health Technologies

ObjectivesWith complex health technologies entering the market, methods for health technology assessment (HTA) may require changes. This study aimed to identify challenges in HTA of complex health technologies.MethodsA survey was sent to European HTA organizations participating in European Network for HTA (EUnetHTA). The survey contained open questions and used predefined potentially complex health technologies and 7 case studies to identify types of complex health technologies and challenges faced during HTA. The survey was validated, tested for reliability by an expert panel, and pilot tested before dissemination.ResultsA total of 22 HTA organizations completed the survey (67%). Advanced therapeutic medicinal products (ATMPs) and histology-independent therapies were considered most challenging based on the predefined complex health technologies and case studies. For the case studies, more than half of the reported challenges were “methodological,” equal in relative effectiveness assessments as in cost-effectiveness assessments. Through the open questions, we found that most of these challenges actually rooted in data unavailability. Data were reported as “absent,” “insufficient,” “immature,” or “low quality” by 18 of 20 organizations (90%), in particular data on quality of life. Policy and organizational challenges and challenges because of societal or political pressure were reported by 8 (40%) and 4 organizations (20%), respectively. Modeling issues were reported least often (n = 2, 4%).ConclusionsMost challenges in HTA of complex health technologies root in data insufficiencies rather than in the complexity of health technologies itself. As the number of complex technologies grows, the urgency for new methods and policies to guide HTA decision making increases.     

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.     

Do Cost-Effectiveness Analyses Account for Drug Genericization? A Literature Review and Assessment of Implications

ObjectivesWe investigated how health technology assessment (HTA) organizations around the world have handled drug genericization (an allowance for future generic drug entry and subsequent drug price declines) in their guidelines for cost-effectiveness analyses (CEAs). We also analyzed a large sample of published CEAs to examine prevailing practices in the field.MethodsWe reviewed 43 HTA guidelines to determine whether and how they addressed drug genericization in their CEAs. We also selected a sample of 270 US-based CEAs from the Tufts Medical Center’s CEA Registry, restricting the sample to studies on pharmaceuticals published from 1991 to 2019 and to analyses taking a lifetime time horizon. We determined whether each CEA examined genericization (and if so, whether in base case or sensitivity analyses), and how inclusion of genericization influenced the estimated incremental cost-effectiveness ratios.ResultsFourteen (33%) of the 43 HTA guidelines mention genericization for CEAs and 4 (9%) recommend that base case analyses include assumptions about future drug price changes due to genericization. Most published CEAs (95%) do not include assumptions about future generic prices for intervention drugs. Only 2% include such assumptions about comparator drugs. Most studies (72%) conduct sensitivity analyses on drug prices unrelated to genericization.ConclusionsThe omission of assumptions about genericization means that CEAs may misrepresent the long run opportunity costs for drugs. The field needs clearer guidance for when CEAs should account for genericization, and for the inclusion of other price dynamics that might influence a drug’s cost-effectiveness.     

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.     

Diagnostics and Treatments of COVID-19: A Living Systematic Review of Economic Evaluations

ObjectivesAs healthcare systems continue to respond to the COVID-19 pandemic, cost-effectiveness evidence will be needed to identify which tests and treatments for COVID-19 offer value for money. We sought to review economic evaluations of diagnostic tests and treatments for COVID-19, critically appraising the methodological approaches used and reporting cost-effectiveness estimates, using a “living” systematic review approach.MethodsKey databases (including MEDLINE, EconLit, Embase) were last searched on July 12, 2021. Gray literature and model repositories were also searched. Only full economic evaluations published in English were included. Studies were quality assessed and data were extracted into standard tables. Results were narratively summarized. The review was completed by 2 reviewers independently, with disagreements resolved through discussion with a senior reviewer.ResultsOverall, 3540 records were identified, with 13 meeting the inclusion criteria. After quality assessment, 6 were excluded because of very severe limitations. Of the 7 studies included, 5 were cost-utility analyses and 2 were cost-effectiveness analyses. All were model-based analyses. A total of 5 evaluated treatments (dexamethasone, remdesivir, hypothetical) and 2 evaluated hypothetical testing strategies. Cost-effectiveness estimates were sensitive to the treatment effect on survival and hospitalization, testing speed and accuracy, disease severity, and price.ConclusionsPresently, there are few economic evaluations for COVID-19 tests and treatments. They suggest treatments that confer a survival benefit and fast diagnostic tests may be cost effective. Nevertheless, studies are subject to major evidence gaps and take inconsistent analytical approaches. The evidence may improve for planned updates of this “living” review.     

基于混合测序策略的两品种当归中类黄酮调控基因差异分析

目的 比较2个不同茎叶色当归品种岷归1号与岷归2号转录水平差异。方法 以2种颜色当归的新鲜叶片（带叶柄）和上端茎为材料,采用混合测序策略,应用全长转录组技术构建当归无参全长转录本文库,利用RNA-seq技术对两品种进行差异基因表达分析,再利用公共数据库对差异基因的生物学功能进行注释和精细分类,筛选调控当归茎叶色差异的主要候选基因。结果 当归转录本的测序结果良好,测序数据质量较高,当归全长转录本的34 528条序列在非冗余蛋白（NR）、京都基因与基因组百科全书（KEGG）、SwissProt及KOG数据库中分别注释到33 947、33 241、29 150和22 601条。对两品种当归差异表达基因（DEGs）进行精细分类,具有生物学功能和分子功能的705个DGEs可分为11类,主要富集在初级代谢（17.87%）、逆境响应（14.47%）、次级代谢（11.49%）等功能上,与颜色有关的差异表达基因主要集中在类黄酮生物合成途径上。结论 两品种当归茎叶颜色差异的主要原因可能与调控类黄酮的生物合成基因的表达差异有关,可为后续进行功能验证,进一步明确与当归主要药效成分之间的联系奠定基础。     

电子鼻技术应用于白及及其近似饮片快速辨识的可行性分析

目的：探讨电子鼻技术应用于白及及其近似饮片快速辨识的可行性。方法：收集134批白及及其近似饮片（白及45批、天麻30批、玉竹30批、黄花白及29批）作为待测样品,使用PEN3型电子鼻采集样品嗅觉感官数据作为自变量X,基于2020年版《中华人民共和国药典》和地方标准的鉴别结果,以及各饮片高效液相色谱法（HPLC）指纹图谱和原始采购信息,获得辨识模型的标杆数据Y,分别采用主成分分析-判别分析（PCA-DA）、偏最小二乘法-判别分析（PLS-DA）、最小二乘法-支持向量机（LS-SVM）及K-最近邻（KNN）4种化学计量学方法建立45批白及与89批非白及的二分类辨识模型和上述4种饮片的四分类辨识模型Y=F(X)。结果：经留一法交互验证,在二分类辨识中,上述4种模型分类正判率分别为97.01%、97.01%、98.51%和97.01%;在四分类辨识中,这4种模型分类正判率分别为97.76%、89.55%、98.51%和97.01%。二分类和四分类辨识模型的最高正判率均可达到98.51%,且均以LS-SVM算法为最优,最优核函数分别选择径向基核函数和线性核函数。最优模型判别结果良好,没有未分类样...     

数字医学技术在儿童腹部实质脏器损伤诊治中的临床应用效果

目的:探讨儿童腹部实质脏器损伤诊治中应用数字医学技术的效果。方法:选取2016年1月至2022年8月贵港市人民医院采用数字医学技术指导期间诊治的100例腹部实质脏器损伤患儿作为观察组,另选取2012年1月至2015年12月未采用数字医学技术指导期间诊治的100例腹部实质脏器损伤患儿作为对照组,比较两组患儿中,不同创伤类型患儿在保守治疗中的输血量、住院时间、保守治疗成功率;以及不同创伤类型的患儿中转手术治疗的术中出血量、手术时间及术后并发症情况。结果:选择保守治疗的患儿中,观察组肝损伤、脾损伤、肾损伤患儿的保守治疗成功率均高于对照组,输血量均少于对照组,住院时间均短于对照组,差异均具有统计学意义(P <0.05);中转手术治疗的患儿中,观察组肝损伤、脾损伤、肾损伤患儿的术中出血量均少于对照组,手术时间均短于对照组,差异均具有统计学意义(P <0.05);两组中不同创伤类型患儿的手术并发症发生率比较,差异均无统计学意义(P> 0.05)。结论:数字医学技术应用在儿童腹部实质脏器损伤诊治中,可为治疗方案的选择提供准确的指导,有助于提高治疗方案的效果,改善患儿临床指标,缩短住...     

3D打印技术在脊柱侧凸矫形中的应用效果分析

目的探讨3D打印技术在脊柱侧凸矫形中的应用效果。方法 76例脊柱侧凸患者随机分为两组各38例。两组均实施后路小切口微创分期术,对照组行常规CT或X线辅助手术治疗,观察组行术前3D打印技术辅助手术治疗。比较两组的手术效果以及并发症。结果观察组的置钉准确率高于对照组,平均置钉时间短于对照组(P <0.05)。术后,观察组的终末融合后侧凸主侧凸冠状位Cobb角低于对照组,初次平均矫正率高于对照组(P <0.05)。两组的并发症发生率无统计学差异(P>0.05)。结论术前应用3D打印技术辅助治疗脊柱侧凸,可有效提升手术效果,更好地恢复脊柱生理弯曲,提高矫形成功率。     

How Assessment-Schedule Matching Limits Bias When Comparing Progression-Free Survival in Single-Arm Studies: An Application in Second-Line Urothelial Carcinoma Treatments

ObjectivesPopulation-adjusted comparisons of progression-free survival (PFS) from single-arm trials of cancer treatments can be derived using matching-adjusted indirect comparisons (MAICs); however, results are still susceptible to bias, particularly if the trials had different tumor assessment schedules. This study aims to assess the effects of assessment-schedule matching (ASM) on the relative effectiveness on the PFS of avelumab versus approved comparator immunotherapies or chemotherapy after population matching in the second-line (2L) setting for metastatic urothelial carcinoma.MethodsThe MAIC used patient-level data for avelumab from the JAVELIN Solid Tumor trial (NCT01772004). PFS was compared with published curves for other treatments to obtain population-adjusted hazard ratios (HRs). The MAIC was repeated after conducting ASM for differences in tumor assessment scheduled first at 6 weeks for avelumab and durvalumab and at 8 or 9 weeks for other treatments.ResultsMAIC adjustment alone altered the HR estimates up to 23%, whereas MAIC plus ASM resulted in up to 32.7% reductions from naive comparisons. Even in cases in which MAIC had little effect, ASM brought an additional change of 11.1% to 15.4%. Overall, the HR range of avelumab versus other treatments changed from 0.83 to 1.25 for naive comparisons to 0.76 to 0.99 after ASM plus MAIC, numerically favoring avelumab.ConclusionsSmall variations in assessment schedules can introduce bias in unanchored indirect treatment comparisons of interval-censored time-to-event outcomes. In this study, adjusted PFS was comparable across second-line urothelial carcinoma treatment options, numerically favoring avelumab versus immunotherapies and chemotherapy agents. Correcting this bias is especially important when HRs are applied in cost-effectiveness models to transition patients between states.