McCleery Jenny, Laverty Julian, Quinn Terry J
Oxford Health NHS Foundation Trust, Banbury, UK.
Oxford Health NHS Foundation Trust, Oxford, UK.
Cochrane Database Syst Rev. 2021 Jul 20;7(7):CD013786. doi: 10.1002/14651858.CD013786.pub2.
Many millions of people living with dementia around the world are not diagnosed, which has a negative impact both on their access to care and treatment and on rational service planning. Telehealth - the use of information and communication technology (ICT) to provide health services at a distance - may be a way to increase access to specialist assessment for people with suspected dementia, especially those living in remote or rural areas. It has also been much used during the COVID-19 pandemic. It is important to know whether diagnoses made using telehealth assessment are as accurate as those made in conventional, face-to-face clinical settings.
Primary objective: to assess the diagnostic accuracy of telehealth assessment for dementia and mild cognitive impairment. Secondary objectives: to identify the quality and quantity of the relevant research evidence; to identify sources of heterogeneity in the test accuracy data; to identify and synthesise any data on patient or clinician satisfaction, resource use, costs or feasibility of the telehealth assessment models in the included studies.
We searched multiple databases and clinical trial registers on 4 November 2020 for published and 'grey' literature and registered trials. We applied no search filters and no language restrictions. We screened the retrieved citations in duplicate and assessed in duplicate the full texts of papers considered potentially relevant.
We included in the review cross-sectional studies with 10 or more participants who had been referred to a specialist service for assessment of a suspected cognitive disorder. Within a period of one month or less, each participant had to undergo two clinical assessments designed to diagnose dementia or mild cognitive impairment (MCI): a telehealth assessment (the index test) and a conventional face-to-face assessment (the reference standard). The telehealth assessment could be informed by some data collected face-to-face, e.g. by nurses working in primary care, but all contact between the patient and the specialist clinician responsible for synthesising the information and making the diagnosis had to take place remotely using ICT.
Two review authors independently extracted data from included studies. Data extracted covered study design, setting, participants, details of index test and reference standard, and results in the form of numbers of participants given diagnoses of dementia or MCI. Data were also sought on dementia subtype diagnoses and on quantitative measures of patient or clinician satisfaction, resource use, costs and feasibility. We assessed risk of bias and applicability of each included study using QUADAS-2. We entered the results into 2x2 tables in order to calculate the sensitivity and specificity of telehealth assessment for the diagnosis of all-cause dementia, MCI, and any cognitive syndrome (combining dementia and MCI). We presented the results of included studies narratively because there were too few studies to derive summary estimates of sensitivity and specificity.
Three studies with 136 participants were eligible for inclusion. Two studies (20 and 100 participants) took place in community settings in Australia and one study (16 participants) was conducted in veterans' homes in the USA. Participants were referred from primary care with undiagnosed cognitive symptoms or were identified as being at high risk of having dementia on a screening test in the care homes. Dementia and MCI were target conditions in the larger study; the other studies targeted dementia diagnosis only. Only one small study used a 'pure' telehealth model, i.e. not involving any elements of face-to-face assessment. The studies were generally well-conducted. We considered two studies to be at high risk of incorporation bias because a substantial amount of information collected face-to-face by nurses was used to inform both index test and reference standard assessments. One study was at unclear risk of selection bias. For the diagnosis of all-cause dementia, sensitivity of telehealth assessment ranged from 0.80 to 1.00 and specificity from 0.80 to 1.00. We considered this to be very low-certainty evidence due to imprecision, inconsistency between studies and risk of bias. For the diagnosis of MCI, data were available from only one study (100 participants) giving a sensitivity of 0.71 (95% CI 0.54 to 0.84) and a specificity of 0.73 (95% CI 0.60 to 0.84). We considered this to be low-certainty evidence due to imprecision and risk of bias. For diagnosis of any cognitive syndrome (dementia or MCI), data from the same study gave a sensitivity of 0.97 (95% CI 0.91 to 0.99) and a specificity of 0.22 (95% CI 0.03 to 0.60). The majority of diagnostic disagreements concerned the distinction between MCI and dementia, occurring approximately equally in either direction. There was also a tendency for patients identified as cognitively healthy at face-to-face assessment to be diagnosed with MCI at telehealth assessment (but numbers were small). There were insufficient data to make any assessment of the accuracy of dementia subtype diagnosis. One study provided a small amount of data indicating a good level of clinician and especially patient satisfaction with the telehealth model. There were no data on resource use, costs or feasibility.
AUTHORS' CONCLUSIONS: We found only very few eligible studies with a small number of participants. An important difference between the studies providing data for the analyses was whether the target condition was dementia only (two studies) or dementia and MCI (one study). The data suggest that telehealth assessment may be highly sensitive and specific for the diagnosis of all-cause dementia when assessed against a reference standard of conventional face-to-face assessment, but the estimates are imprecise due to small sample sizes and between-study heterogeneity, and may apply mainly to telehealth models which incorporate a considerable amount of face-to-face contact with healthcare professionals other than the doctor responsible for making the diagnosis. For the diagnosis of MCI by telehealth assessment, best estimates of both sensitivity and specificity were somewhat lower, but were based on a single study. Errors occurred at the cognitively healthy/MCI and the MCI/dementia boundaries. However, there is no evidence that diagnostic disagreements were more frequent than would be expected due to the known variation between clinicians' opinions when assigning a dementia diagnosis.
全球数以百万计的痴呆症患者未被诊断出来,这对他们获得护理和治疗以及合理的服务规划都产生了负面影响。远程医疗——利用信息通信技术(ICT)远程提供医疗服务——可能是一种增加疑似痴呆症患者,尤其是居住在偏远或农村地区的患者获得专科评估机会的方式。在新冠疫情期间,远程医疗也得到了广泛应用。了解通过远程医疗评估做出的诊断是否与传统面对面临床环境中的诊断一样准确非常重要。
主要目的:评估远程医疗评估对痴呆症和轻度认知障碍的诊断准确性。次要目的:确定相关研究证据的质量和数量;确定测试准确性数据中的异质性来源;确定并综合纳入研究中关于患者或临床医生满意度、资源使用、成本或远程医疗评估模型可行性的任何数据。
我们于2020年11月4日在多个数据库和临床试验注册库中检索已发表文献、“灰色”文献和注册试验。我们未应用任何检索过滤器,也没有语言限制。我们对检索到的文献进行了重复筛选,并对被认为可能相关的文献全文进行了重复评估。
我们纳入了横断面研究,研究对象为10名或更多被转介至专科服务机构以评估疑似认知障碍的参与者。在一个月或更短的时间内,每位参与者必须接受两项旨在诊断痴呆症或轻度认知障碍(MCI)的临床评估:一次远程医疗评估(索引测试)和一次传统的面对面评估(参考标准)。远程医疗评估可以参考一些面对面收集的数据,例如初级保健护士收集的数据,但患者与负责综合信息并做出诊断的专科临床医生之间的所有联系都必须使用ICT远程进行。
两位综述作者独立从纳入研究中提取数据。提取的数据涵盖研究设计、背景、参与者、索引测试和参考标准的详细信息,以及被诊断为痴呆症或MCI的参与者数量形式的结果。我们还收集了关于痴呆症亚型诊断以及患者或临床医生满意度、资源使用、成本和可行性的定量测量数据。我们使用QUADAS-2评估每个纳入研究的偏倚风险和适用性。我们将结果录入2x2表格,以计算远程医疗评估对全因痴呆症、MCI和任何认知综合征(合并痴呆症和MCI)诊断的敏感性和特异性。由于研究数量太少,无法得出敏感性和特异性的汇总估计值,我们以叙述形式呈现了纳入研究的结果。
三项共136名参与者的研究符合纳入标准。两项研究(分别有20名和100名参与者)在澳大利亚的社区环境中进行,一项研究(16名参与者)在美国的退伍军人之家进行。参与者从初级保健机构被转介,伴有未确诊的认知症状,或者在养老院的筛查测试中被确定为患有痴呆症的高风险人群。在较大规模的研究中,痴呆症和MCI是目标疾病;其他研究仅以痴呆症诊断为目标。只有一项小型研究使用了“纯”远程医疗模式,即不涉及任何面对面评估的元素。这些研究总体上开展得很好。我们认为两项研究存在较高的纳入偏倚风险,因为护士面对面收集的大量信息被用于索引测试和参考标准评估。一项研究的选择偏倚风险尚不清楚。对于全因痴呆症的诊断,远程医疗评估的敏感性范围为0.80至1.00,特异性范围为0.80至1.00。由于不精确性、研究之间的不一致性和偏倚风险,我们认为这是非常低确定性的证据。对于MCI的诊断,仅有一项研究(100名参与者)提供了数据,敏感性为0.71(95%CI 0.54至0.84),特异性为0.73(95%CI 0.60至0.84)。由于不精确性和偏倚风险,我们认为这是低确定性的证据。对于任何认知综合征(痴呆症或MCI)的诊断,同一研究的数据显示敏感性为0.97(95%CI 0.91至0.99),特异性为0.22(95%CI 0.03至0.60)。大多数诊断分歧涉及MCI和痴呆症之间的区分,两个方向的发生频率大致相同。在面对面评估中被确定为认知健康的患者在远程医疗评估中被诊断为MCI的情况也有出现趋势(但数量较少)。没有足够的数据对痴呆症亚型诊断的准确性进行任何评估。一项研究提供了少量数据,表明临床医生尤其是患者对远程医疗模式的满意度较高。没有关于资源使用、成本或可行性的数据。
我们仅发现了极少数符合条件且参与者数量较少的研究。为分析提供数据的研究之间的一个重要差异在于目标疾病是仅为痴呆症(两项研究)还是痴呆症和MCI(一项研究)。数据表明,与传统面对面评估的参考标准相比,远程医疗评估对全因痴呆症的诊断可能具有较高的敏感性和特异性,但由于样本量小和研究间的异质性,估计值并不精确,并且可能主要适用于包含与负责诊断的医生以外的医疗专业人员进行大量面对面接触的远程医疗模式。对于通过远程医疗评估诊断MCI,敏感性和特异性的最佳估计值略低,但仅基于一项研究。在认知健康/MCI和MCI/痴呆症边界处出现了错误。然而,没有证据表明诊断分歧比在分配痴呆症诊断时临床医生意见的已知差异所预期的更频繁。