Diagnostic Test Accuracy Protocol

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The accuracy of 18FDG-PET in the early diagnosis of Alzheimer’s disease dementia and other dementias in people with MCI

  1. Marco Vacante1,*,
  2. Nadja Smailagic2,
  3. Christos Sachpekidis3,
  4. Chris Hyde4,
  5. Steven Martin2,
  6. Obioha Ukoumunne5

Editorial Group: Cochrane Dementia and Cognitive Improvement Group

Published Online: 5 JUL 2013

DOI: 10.1002/14651858.CD010632


How to Cite

Vacante M, Smailagic N, Sachpekidis C, Hyde C, Martin S, Ukoumunne O. The accuracy of 18FDG-PET in the early diagnosis of Alzheimer’s disease dementia and other dementias in people with MCI (Protocol). Cochrane Database of Systematic Reviews 2013, Issue 7. Art. No.: CD010632. DOI: 10.1002/14651858.CD010632.

Author Information

  1. 1

    University of Oxford, John Radcliffe Hospital, Nuffield Department of Medicine - OPTIMA, Oxford, Oxfordshire, UK

  2. 2

    University of Cambridge, Institute of Public Health, Cambridge, UK

  3. 3

    German Cancer Research Center, Medical PET Group - Biological Imaging, Clinical Cooperation Unit Nuclear Medicine, Heidelberg, Germany

  4. 4

    University of Exeter Medical School, University of Exeter, Peninsula Technology Assessment Group (PenTAG), Exeter, UK

  5. 5

    University of Exeter Medical School, University of Exeter, PenCLAHRC, Exeter, Devon, UK

*Marco Vacante, Nuffield Department of Medicine - OPTIMA, University of Oxford, John Radcliffe Hospital, Headly Way, Headington, Oxford, Oxfordshire, OX3 9DU, UK. marcovacante@yahoo.it.

Publication History

  1. Publication Status: New
  2. Published Online: 5 JUL 2013

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Background

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Appendices
  6. Contributions of authors
  7. Declarations of interest

The most common cause of dementia in the general population is Alzheimer’s disease (AD). It is useful to distinguish the term Alzheimer's disease, which refers to underlying pathology, and Alzheimer's disease dementia (ADD), which is the final stage of a clinical syndrome associated with the pathology.

Alzheimer’s disease dementia afflicts 5% of men and 6% of women over the age of 60 worldwide (WHO 2010). Its prevalence increases exponentially with age as Alzheimer’s dementia affects fewer than 1% of people 60 to 64 years old, but 24% to 33% in those over 85 (Ferri 2005). The earliest symptoms of Alzheimer's disease dementia include short-term memory loss, a gradual decline in other cognitive abilities and behavioural changes. Cortical intracellular neurofibrillary tangles (NFT) and extracellular β-amyloid (Aβ) plaques (Braak 1991) represent the neuropathological features of Alzheimer's disease dementia and are responsible for synapse dysfunction, neuronal cell loss and consequent brain atrophy (Ballard 2011). According to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria, definite Alzheimer's diseases dementia can only be diagnosed following neuropathological examination of brain tissue, obtained by biopsy or autopsy.

Mild cognitive impairment (MCI) represents a possible intermediary condition between normal cognition and dementia (Morris 2001; Petersen 2009). Currently, 16 different classifications are used to define MCI (Matthews 2008). The different definitions of MCI are based on general criteria that include a cognitive complaint (self-reported and/or informant), preserved basic activities of daily living, cognitive impairment (not normal for age and education) or decline in cognition evidenced by performance on objective cognitive tasks, absence of dementia (Petersen 2004; Winblad 2004). In this protocol MCI refers to the clinical criteria defined by Petersen and Winbald (Petersen 1999; Petersen 2001; Petersen 2004; Winblad 2004) or Cognitive Dementia Rating (clinical dementia rating (CDR) = 0.5) scale (Morris 1993) or any of the 16 descriptions of MCI reported by Matthews (Matthews 2008).

There are four outcomes for those within an MCI population: progression to Alzheimer's disease dementia, progression to another dementia, maintaining stable MCI or recovery. An early identification of those subjects who would convert from MCI to Alzheimer’s disease dementia and other forms of dementia may improve the opportunities for early intervention and might help their carers to plan the future. However, current data in the medical literature are still not adequate to guide clinicians and researchers in understanding the progression of dementia. There is no clinical method to predict the possible conversion of subjects with MCI to Alzheimer's disease dementia or other dementias. Studies (Bruscoli 2004; Mattsson 2009; Petersen 1999; Petersen 2009) indicate that an annual average of 10% to 15% of MCI patients progress to Alzheimer's disease dementia. This all depends on clinical profile, settings and investigation for vascular disease.

Thus, the improvement of diagnostic accuracy is critical for management and treatment of Alzheimer’s disease dementia and other dementias. Research suggests that measurable change in positron emission tomography (PET), magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) biomarkers occurs years in advance of the onset of clinical symptoms (Beckett 2010).

This protocol focuses on the relation between the 18F-2-fluoro-2-deoxy-D-glucose (18F-FDG)-PET biomarker results, the brain tissue glucose metabolism at baseline, and i) ‘conversion from MCI to Alzheimer’s disease dementia’ or ii) ‘conversion from MCI to other forms of dementia’ at follow-up.

 

Target condition being diagnosed

The primary target condition is Alzheimer's disease dementia. The diagnosis is based on the exclusion of other causes of dementia through clinical, paraclinical and neuropsychological investigations criteria as indicated in the NINCDS-ADRDA guidelines (McKhann 1984). Exclusion of other diseases such as depression, hypothyroidism, and non-AD brain lesions is a fundamental part of the diagnostic process (McKhann 1984). A standard diagnostic practice is based on clinical examinations and neurological and mental status examination of the patient. Moreover, the standard diagnostic practice includes caregiver or family member interviews, focusing on progressive cognitive impairments and behavioural changes associated with the disease.

The secondary target condition is any other forms of dementia, including all-cause dementia (APA 1987; APA 1994), vascular dementia (Román 1993), dementia with Lewy bodies (McKeith 2006) and frontotemporal dementia (Lund Manchester 1994; Neary 1998).

 

Index test(s)

PET represents a unique diagnostic nuclear medicine modality of well-documented accuracy. It assesses pathophysiologic and chemical processes by using radiopharmaceuticals that mimic endogenous molecules. Depending on the distribution of the radiotracer in the human body, images are produced and diagnostic information acquired. Kinetic information may also be available. 18F-FDG is the most common molecular imaging biomarker used in PET. In particular,18F-FDG is a radiolabeled glucose analogue and thus by entering the glucose metabolic pathway provides information about tissue metabolism. In other words,18F-FDG is an indicator of intracellular glucose metabolism. It has a wide variety of applications in neurosciences, oncology, and cardiology.

18F-FDG uptake by brain tissue as measured by PET is a well-established method for evaluation of brain function and it has been used in the study of dementia for more than three decades. 18F-FDG PET evaluates the regional cerebral metabolic rate for glucose (CMRgl), thus giving information about the entity of neuronal loss or synapse dysfunction The key finding is the reduced brain glucose metabolism that is associated with neurodegenerative diseases. Glucose metabolism imaging with 18F-FDG is the most sensitive and specific imaging modality available today for the diagnosis of ADD (Lucignani 2006). Hypometabolism/hypoperfusion in the temporo-parietal lobe represents the typical pattern found in Alzhemer's disease dementia (Herholz 2002; Nitrini 2000). Moreover, it has been demonstrated that progression of neurodegenerative changes in subjects with ADD and other dementias is associated with both more cognitive impairment and larger PET metabolic deficits (Duara 1986; Haxby 1986).

The FDG-PET pattern for MCI is not so consistent, which is unsurprising, due to the variable physical history of the disorder. However, MCI patients usually present on PET with mild global and regional hypometabolism (Mosconi 2009). FDG-PET studies have found characteristic and progressive cerebral metabolic rate for glucose (CMRgl) reductions in posterior cingulate, precuneus, parietal, temporal and frontal regions in both ADD and MCI patients, with the findings being more pronounced in MCI patients who eventually converted to ADD (Chen 2010; Morbelli 2010; Patterson 2010). Moreover, a growing body of FDG-PET studies have been carried out specifically in order to evaluate the correlation between glucose metabolism impairment and the progression from MCI to ADD and other dementias. These studies suggest that certain findings on brain PET scans can potentially predict the decline of MCI to ADD. In agreement with this, a recent meta-analysis pointed out that MCI-converter patients, in comparison with subjects who did not convert to ADD, showed hypometabolism/hypoperfusion in the parietal lobe (Schroeter 2009).

The development and utilisation in recent years of new software tools for image analysis have helped in the direction of carrying out many brain FDG-PET studies. These software applications have enabled the quantification of brain PET scans, achieving objective evaluation and thus increasing the physicians’ interpretative confidence. Although subjective (visual) interpretation of the brain scan is usually the standard in clinical practice, the addition of quantitative information is crucial in such studies since it improves the diagnostic accuracy (Patterson 2010).

 

Clinical pathway

Dementia develops over a trajectory of several years. There is a presumed period when people are asymptomatic, and when pathology is accumulating. Individuals or their relatives may then notice subtle impairments of recent memory. Gradually, more cognitive domains become involved, and difficulty planning complex tasks becomes increasingly apparent. In the UK, people usually present to their general practitioner, who may administer some neuropsychological tests, and will potentially refer them to a hospital memory clinic. However many people with dementia do not present until much later in the disorder and will follow a different pathway to diagnosis, for example being identified during an admission to general hospital for a physical illness. Thus the pathway influences the accuracy of the diagnostic test. The accuracy of the test will vary with the experience of the administrator and the accuracy of the subsequent diagnosis will vary with the history of referrals to the particular healthcare setting. Diagnostic assessment pathways may vary in other countries and diagnoses may be made by a variety of specialists including neurologists and geriatricians. 

 

Alternative test(s)

We will not include alternative tests in this review because there are currently no standard practice tests available for the diagnosis of dementia. 

The Cochrane Dementia and Cognitive Improvement Group (CDCIG) is in the process of conducting a series of diagnostic test accuracy reviews of biomarkers and scales (see list below). Although we are conducting reviews on individual tests compared to a reference standard, we plan to compare our results in an overview.

  • 11 C-PIB-PET(Positron emission tomography Pittsburg Compound-B)
  • CSF (Cerebrospinal fluid analysis of abeta and tau)
  • sMRI (structural magnetic resonance imaging)
  • Neuropsychological tests (MMSE; MiniCOG; MoCA)
  • Informant interviews (IQCODE; AD8)
  • APOE e4
  • FP-CIT SPECT (Fluoropropil-Carbomethoxy-lodophenil-Tropane Single-photon emission tomography)

 

Rationale

According to the latest revised NINCDS-ADRA diagnostic criteria for Alzheimer's disease dementia of the National Institute on Aging and Alzheimer Association (Albert 2011; Dubois 2010; McKhann 2011; Sperling 2011), the confidence in diagnosing MCI due to Alzheimer's disease dementia is raised with the application of biomarkers based on imaging or CSF measures. These tests, added to core clinical criteria, might increase the sensitivity or specificity of a testing strategy. However, it is crucial that each of these biomarkers is assessed for their diagnostic accuracy before they are adopted as routine add-on tests in clinical practice.

The 18F-FDG-PET biomarker, as the extra diagnostic criterion, might facilitate accurate identification of those patients with MCI who would convert to Alzheimer's disease dementia or other forms of dementia. At the present time there is no 'cure' for dementia, but there are some treatments which can slow cognitive and functional decline, or reduce the associated behavioural and psychiatric symptoms of dementia (Birks 2006; McShane 2006). In addition, the accurate early diagnosis of dementia may improve opportunities for the use of newly evolving interventions designed to delay or prevent progression to more debilitating stages of dementia (Oddo 2004). Coupled with appropriate contingency planning, proper recognition of the disease may also help to prevent inappropriate and potentially harmful admissions to hospital or institutional care (Bourne 2007).

 

Objectives

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Appendices
  6. Contributions of authors
  7. Declarations of interest

  • To determine the diagnostic accuracy of the 18F-FDG-PET index test for detecting people with MCI at baseline who would clinically convert to Alzheimer’s disease or other forms of dementia at follow-up.

 

Secondary objectives

  • To investigate heterogeneity of test accuracy in the included studies.

We expect that heterogeneity will be likely and that it will be an important component of the review. The potential sources of heterogeneity, which will be used as a framework for the investigation of heterogeneity, include target population, index test, target disorder and study quality and are detailed in the analysis section.

 

Methods

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Appendices
  6. Contributions of authors
  7. Declarations of interest
 

Criteria for considering studies for this review

 

Types of studies

We will consider longitudinal cohort studies in which index test results are obtained at baseline and the reference standard results at follow-up (see below for detail about the nature of the index test and reference standard). These studies necessarily employ delayed verification of conversion to dementia and are sometimes labelled as ‘delayed verification cross-sectional studies’ (Bossuyt 2004; Bossuyt 2008; Knottnerus 2002). 

We will include case-control studies if they incorporate a delayed verification design. We believe this can only occur in the context of a cohort study, so these studies are invariably diagnostic nested case-control studies.

 

Participants

Participants recruited and clinically classified as those with mild cognitive impairment (MCI) at baseline will be eligible for this review. Studies using the Petersen or revised Petersen criteria (Petersen 1999; Petersen 2004; Winblad 2004) or the Cognitive Dementia Raiting (CDR = 0.5) scale (Morris 1993) or any of the 16 different classifications of MCI described by Matthews 2008 will be included. The diagnostic criteria for MCI are presented in the Additional tables ( Table 1 and  Table 2).

We will exclude those studies that involved people with MCI possibly caused by: i) a current or history of alcohol/drug abuse; ii) Central Nervous System trauma (e.g. subdural haematoma), tumour or infection; iii) other neurological conditions e.g. Parkinson’s or Huntington’s diseases.

 

Index tests

18F-FDG-PET biomarker test.

There are currently no generally accepted standards for FDG positivity threshold, and therefore we will use the criteria which were applied in each included primary study to classify participants as either 18F-FDG positive or 18F-FDG negative, according to the brain tissue glucose metabolism. Some studies apply qualitative assessment of PET scans, while some apply both qualitative and quantitative. Moreover, different thresholds are used in quantitative studies. This could possibly lead to heterogeneity, but it should be noted that the addition of quantitative analysis in the interpretation of the FDG-PET brain scan in clinical practice is done in order to support the visual (qualitative) reading of the scan by the physician.

A range of thresholds have been used in primary research, for instance: i) 'the regional cerebral glucose metabolism ratio (rCGM-r) is lower than 80% of whole brain mean of control subjects' (Chételat 2003); ii) 'the rCGM-r of temporoparietal and posterior cingulate < 1.3 - 8' (Anchisi 2005).

The use of any image analysis technique, FDG injection dose, the time between FDG injection and PET acquisition, and FDG reduction regions (e.g. parietal, temporal, frontal lobes, posterior cingulated, precuneus) will be included. The exact administered FDG activity does not affect the PET examination (as long as it ranges between the accepted limits for acquiring proper images) as this can be compensated by the duration of the scan; the number of counts detected by the scanner is the key finding.

The accepted limits of administered activity are defined by guidelines published by the Nuclear Medicine Societies. The two major ones are the Society of Nuclear Medicine (SNM, USA) (Waxman 2009) and the European Association of Nuclear Medicine (EANM, Europe) (Varrone 2009). According to SNM, the recommended FDG activity in adults for brain PET is 185 - 740 MBq (or 5 - 20 mCi). According to EANM, the recommended administered activity for adults is 300 – 600 MBq (typically 370 MBq) in 2-D mode and 125 – 250 MBq (typically 150 MBq) in 3-D mode.

The differences in exact timing of image acquisition also do not influence the study, as long as the acquisition does not start earlier than 30 minutes after FDG injection. It is recommended, however, that each department follow a standard protocol with a fixed time for starting the acquisition (e.g. 30 or 60 minutes after injection) (Varrone 2009; Waxman 2009). The aim of the acquisition is the good contrast between grey and white matter.

A comparator test will not be included because there are currently no standard practice tests available for the diagnosis of dementia. We will compare the index tests with a reference standard.

 

Target conditions

There are two target conditions in this review:

1. Alzheimer’s disease dementia (conversion from MCI to Alzheimer’s disease dementia);

2. Other forms of dementia (conversion from MCI to other forms of dementia, i.e. vascular dementia and/or dementia with Lewy bodies and /or frontotemporal dementia).

 

Reference standards

For the purpose of this review, several definitions of Alzheimer’s disease dementia are acceptable. Included studies may apply probable or possible NINCDS-ADRDA criteria (McKhann 1984). The Diagnostic and Statistical manual of Mental Disorders (DSM) (APA 1987; APA 1994) and International Classification of Diseases (ICD) (ICD 10) definitions for Alzheimer’s disease dementia will also be acceptable.

Similarly, differing clinical definitions of other dementias are acceptable. For Lewy Body Dementia the reference standard is the McKeith criteria (McKeith 1996; McKeith 2006). For frontotemporal dementia the reference standards are the Lund criteria (Lund Manchester 1994), Neary 1998, Boxer 2005, DSM-III (APA 1987), DSM-IV (APA 1994), ICD-9 (WHO 2006), ICD-10 (WHO 2010). For vascular dementia the reference standards are the NINDS-ARIEN criteria (Román 1993), DSM-III (APA 1987), DSM-IV (APA 1994), ICD-9 (WHO 2006) and ICD-10 (WHO 2010).

The time scale over which progression from MCI to Alzheimer’s disease dementia or other forms of dementia happen is also important. The minimum period of delay in the verification of the diagnosis (i.e. the time between the assessment at which a diagnosis of MCI is made and the assessment at which the diagnosis of dementia is made) is one year. Where a mean duration is specified, we will exclude the study if the mean minus one standard deviation is less than one year, which will ensure that no more than 16% of participants were followed up for less than one year if the times are normally distributed.

If possible, we will segment analyses into separate follow-up mean periods for the delay in verification: one year to two years; two to four years; and more than four years. In this event we will clearly note where the same included studies contribute to the analysis for more than one reference standard.

 

Search methods for identification of studies

We will use a variety of information sources to ensure all relevant studies are included. The Trials Search Co-ordinator of the Cochrane Dementia and Cognitive Improvement Group will devise search strategies for electronic database searching.

 

Electronic searches

We will search MEDLINE (OvidSP), EMBASE (OvidSP), Science Citation Index (ISI Web of Knowledge), PsycINFO (OvidSP), BIOSIS previews (ISI Web of Knowledge) and LILACS (Bireme). See Appendix 1 for a proposed draft strategy to be run in MEDLINE (OvidSP). We will design similarly structured search strategies using search terms and syntax appropriate for each database listed above. We will request a search of the Cochrane Register of Diagnostic Test Accuracy Studies (maintained by the Cochrane Renal Group).

There will be no restrictions based on the language of the study reports, and we will use translation services as necessary.

A single review author with extensive experience of systematic reviewing will conduct the initial searches.

 

Searching other resources

Grey literaturechosen electronic databases will include assessments of conference proceedings.

Handsearchingwe will not perform handsearching as there is little published evidence of the benefits of handsearching for reports of diagnostic test accuracy (DTA) studies (Glanville 2010).

Reference lists: we will scan reference lists of all eligible studies and reviews in the field for further possible titles, and will repeat the process until no new titles are found (Greenhalgh 2005).

Correspondence: we will contact research groups who have published or are conducting work on FDG-PET tests for dementia diagnosis.

 

Data collection and analysis

 

Selection of studies

One review author will screen all titles and abstracts generated by electronic database searches for relevance. 

Two review authors will independently assess the remaining abstracts of selected titles, and will select all potentially eligible studies for full paper review. Two review authors will independently assess full manuscripts against the inclusion criteria. Where necessary, a third arbitrator will resolve disagreements that the two review authors cannot resolve through discussion.

Where a study may include usable data but these are not presented in the published manuscript, we will contact the authors directly to request further information. If the same data set is presented in more than one paper we will include the primary paper.

We will detail the numbers of studies selected at each point, using a PRISMA flow diagram.

 

Data extraction and management

We will extract the following data on study characteristics:

Bibliographic details of primary paper:

  • Author, title of study, year and journal

Basic clinical and demographic details:

  • Number of subjects
  • MCI clinical criteria
  • Age
  • Gender
  • Referral centre(s)
  • Participant recruitment
  • Sampling procedures

Details of the index test:

  • Method of the 18F-FDG-PET index test administration, including who administered the test
  • Thresholds used to define positive and negative tests
  • Other technical aspects as seems relevant to the review, e.g. brain areas

Details of the reference standard:

  • Definition of Alzheimer's disease dementia and other dementias used in reference standard
  • Duration of follow-up from time of index test used to define ADD and other dementias in reference standard: 1 to < 2 years; 2 to < 4 years; and > 4 years; if participants have been followed for varied amounts of time we will record a mean follow-up period for each included studies; if possible, we will group those data into minimum, maximum and median follow-up periods; these may then become the subject of subgroup analyses
  • Prevalence or proportion of population developing Alzheimer's disease dementia and other dementias, with severity, if described

The results of the two-by-two tables cross-relating index test results of the reference standards

Table 1: Conversion from MCI to Alzheimer’s disease dementia


Index test informationReferences standard information

ADD presentADD absent

Index test positive 18F-FDG-PET+ who convert to ADD (TP)18F-FDG-PET+  who remain MCI (FP) & 18F-FDG-PET+ who convert to non-AD(FP)

Index test negative18F-FDG-PET- who convert to ADD (FN)18F-FDG-PET-  who remain MCI (TN) & 18F-FDG-PET-who convert to non-AD (TN)



Table 2: Conversion from MCI to non-Alzheimer’s disease dementia


Index test informationReferences standard information

Non-ADD presentNon-ADD absent

Index test positive18F-FDG-PET+ who convert to non-ADD (TP)18F-FDG-PET+  who remain MCI (FP) & 18F-FDG-PET+ who convert to ADD (FP)

Index test negative18F-FDG-PET-  who convert to non-ADD (FN)18F-FDG-PET-  who remain MCI (TN) & 18F-FDG-PET-  who convert to ADD (TN)



Table 3: Conversion from MCI to any forms of dementia


Index test informationReferences standard information

Dementia present (any form of dementia)Dementia absent

Index test positive18F-FDG-PET+ who convert to any forms of dementia (TP)18F-FDG-PET+  who remain MCI (FP)

Index test negative18F-FDG-PET- who convert to any forms of dementia (FN)18F-FDG-PET-  who remain MCI (TN)



The numbers of lost-to-follow-up

We will also extract data necessary for the assessment of quality as defined below.

In general the data extraction proforma will be piloted against two included papers. Two review authors will extract the data independently. Where necessary, a third arbitrator will resolve disagreements about data extraction that the two review authors cannot resolve through discussion.

 

Assessment of methodological quality

We will assess the methodological quality of each study using QUADAS-2 (Whiting 2011) as recommended by The Cochrane Collaboration. The tool is made up of four domains: patient selection; index test; reference standard; patient flow. Each domain is assessed in terms of risk of bias, with the first three domains also considered in terms of applicability (Appendix 2). The components of each domain and a rubric which details how judgements concerning risk of bias are made are detailed in Appendix 3. Key areas important to quality assessment are participant selection, blinding and missing data.

We will pilot a QUADAS-2 assessment on two studies. If agreement is poor, we will refine the signalling questions. We will not use QUADAS-2 data to form a summary quality score, but will produce a narrative summary describing numbers of studies that found high/low/unclear risk of bias as well as concerns regarding applicability.

 

Statistical analysis and data synthesis

We will apply the DTA framework for the analysis of a single test and extract the data from a study into a two-by-two table, showing the binary test results cross-classified with the binary reference standard and ignoring any censoring that might have occurred. We acknowledge that such a reduction in the data may represent a significant oversimplification. We will therefore also adopt an Intention-to-diagnose (ITD) approach. If possible, we will present what the result would be if all drop-outs had or had not developed dementia. We may also need to assume that the proportion of positive and negative test results is the same in the unknown as in the known participants in order to do this.

We will use data from the two-by-two tables abstracted from the included studies (TP, FN, FP, TN) and entered into Review Manager 5 to calculate the sensitivities, specificities and their 95% confidence intervals. We will also present individual study results graphically by plotting estimates of sensitivities and specificities in both a forest plot and a receiver operating characteristic (ROC) space. If more than one threshold is published in primary studies we will report accuracy estimates for all thresholds.

If there are sufficient data we will meta-analyse the pairs of sensitivity and specificity. The preferred approach would be the hierarchical summary ROC curve (HSROC) method proposed by Rutter 2001 and Macaskill 2010, because implicit thresholds are expected in primary studies. We will conduct these analyses in SAS software with support from the UK DTA Support Unit. Particularly if there are common thresholds across included studies, we will also consider the bivariate random-effects approach (Reitsma 2005). When a primary study reports more than one threshold result, we will only select the threshold nearer to the upper left point of the ROC curve for the meta-analysis. We are aware that this data-driven method for threshold selection could lead to an overestimate of diagnostic accuracy (Leeflang 2008). However, there are no accepted thresholds to define positive 18F-FDG-PET test and therefore the accuracy estimates reported in primary studies are likely to be based on data-driven threshold selection.

We will explore the implications of any credible summary accuracy estimates emerging by considering the numbers of false positives and false negatives in populations with different prevalence of dementia subtypes, and by presenting the results as natural frequencies and using alternative metrics such as likelihood ratios and predictive values.

We will prepare a Summary of Results table.

 

Investigations of heterogeneity

The framework for the investigation includes the following factors:

Target population

  • Sociodemographic characteristics. For age, we will examine any studies that include 30% or more patients below the age of 65 years separately.
  • Different clinical criteria of MCI: Petersen criteria versus revised Petersen criteria versus CDR = 0.5 criteria versus different MCI classification (Matthews 2008).
  • Other characteristics (e.g. ApoE status, Mini-Mental State Examination (MMSE)
  • Different referral centres (primary care versus memory clinic versus hospital): Although the 18F-FDG-PET test is carried out only in tertiary care, sources of referrals might differ in this setting. We will investigate the potential influence of different referral centre practices on diagnostic performance of the index test.

 Index test

  • Threshold: if different thresholds used in included studies.
  • Technical features (including different versions of the test): time between 18F-FDG injection and PET acquisition less than 30 minutes after FDG injection.
  • 18F-FDG reduction regions: not prespecified (e.g. parietal, temporal, frontal lobes, posterior cingulated, precuneus).
  • Image analysis: variety of image analysis techniques.
  • Operator characteristics e.g. training.

Target disorder

  • Reference standard/s used: DSM definition, ICD definition, NINDS-ARDRA, or other classification, including pathological definitions; and operationalisation of these classifications (e.g. individual clinician/algorithm/consensus group).
  • Spectrum of target disorder (Alzheimer’s disease dementia and any other dementia subtypes).

Study quality

  • Types of studies: longitudinal cohort studies or diagnostic nested case-control studies.
  • Blinding.  Prior clinical information will increase accuracy of the index test.
  • Duration of follow-up: If possible, we will segment analyses into separate follow-up mean periods for the delay in verification: one to two years versus two to four years versus more than four years. In this case we will clearly note where the same included studies contribute to the analysis for more than one reference standard. Where a mean of duration is specified, we will exclude the study if the mean minus one standard deviation is less than one year, which will ensure that no more than 16% of participants were followed up for less than one year if the times are normally distributed.
  • Loss to follow-up: we will consider separately those studies that have more than 20% drop-outs.

We will investigate heterogeneity in the first instance (informally) through visual examination of forest plots of sensitivities and specificities and through visual examination of the ROC plot of the raw data. Depending on the number of studies available, we will include as many covariates in the regression analyses as possible, up to 10 studies per covariate. We recognise that it is likely that power will be insufficient to allow formal investigation of all possible sources of heterogeneity. However, if we identify further likely sources of heterogeneity, we will investigate these by subgroup analyses and, if data allow, will include them as covariates in the regression model.

 

Sensitivity analyses

If not already explored as part of the investigation of heterogeneity above, we will perform a sensitivity analysis, for example in order to investigate the influence of limiting permitted time between index test and dementia diagnosis on overall diagnostic accuracy of the FDG-PET biomarker.

We will perform a sensitivity analysis with and without the intention-to-diagnose approach.

 

Assessment of reporting bias

We will not investigate reporting bias because of current uncertainty about how it operates in test accuracy studies and the interpretation of existing analytical tools such as funnel plots.

 

Appendices

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Appendices
  6. Contributions of authors
  7. Declarations of interest
 

Appendix 1. Appendix: Search strategy for use with Medline electronic database

1 exp Dementia/
2 Cognition Disorders/
3 Mild Cognitive Impairment/
4 (alzheimer$ or dement$).ti,ab.
5 ((cognit$ or memory or cerebr$ or mental$) adj3 (declin$ or impair$ or los$ or deteriorat$ or degenerat$ or complain$ or disturb$ or disorder$)).ti,ab.
6 (forgetful$ or confused or confusion).ti,ab.
7 MCI.ti,ab.
8 ACMI.ti,ab.
9 ARCD.ti,ab.
10 SMC.ti,ab.
11 CIND.ti,ab.
12 BSF.ti,ab.
13 AAMI.ti,ab.
14 LCD.ti,ab.
15 AACD.ti,ab.
16 MNCD.ti,ab.
17 MCD.ti,ab.
18 or/1-17
19 "Positron emission tomography".ti,ab.
20 exp Tomography, Emission-Computed/
21 PET.ti,ab.
22 tomograph*.ti,ab.
23 or/19-21
24 FDG.ti,ab.
25 ("18f-fdg" or 18fdg or fdg18).ti,ab.
26 Fluorodeoxyglucose.ti,ab.
27 Fluorodeoxyglucose F18/
28 Glucose/
29 glucose metabol*.ti,ab.
30 cerebral metabolic rate.ti,ab.
31 (CMRgl or rCMRGlu).ti,ab.
32 or/24-31
33 18 and 23 and 32

34 exp Dementia/di

35 34 AND 32

36 33 OR 35

 

Appendix 2. Appedix: Assessment of methodological quality table QUADAS-2 tool


DOMAINPATIENT SELECTION  INDEX TEST REFERENCE STANDARDFLOW AND TIMING 

DescriptionDescribe methods of patient selection: Describe included patients (prior testing, presentation, intended use of index test and setting): Describe the index test and how it was conducted and interpretedDescribe the reference standard and how it was conducted and interpretedDescribe any patients who did not receive the index test(s) and/or reference standard or who were excluded from the 2x2 table (refer to flow diagram): Describe the time interval and any interventions between index test(s) and reference standard

Signalling questions

(yes/no/unclear)
Was a consecutive or random sample of patients enrolled?Were the index test results interpreted without knowledge of the results of the reference standard?Is the reference standard likely to correctly classify the target condition?Was there an appropriate interval between index test(s) and reference standard?

Was a case-control design avoided?If a threshold was used, was it pre-specified?Were the reference standard results interpreted without knowledge of the results of the index test?Did all patients receive a reference standard?

Did the study avoid inappropriate exclusions?Did all patients receive the same reference standard?

Were all patients included in the analysis?

Risk of bias: High/low/ unclearCould the selection of patients have introduced bias?Could the conduct or interpretation of the index test have introduced bias?      Could the reference standard, its conduct, or its interpretation have introduced bias?Could the patient flow have introduced bias? 

Concerns regarding applicability: High/low/ unclearAre there concerns that the included patients do not match the review question?Are there concerns that the index test, its conduct, or interpretation differ from the review question?Are there concerns that the target condition as defined by the reference standard does not match the review question? 



 

Appendix 3. Appendix: Anchoring statements for quality assessment of 18F-FDG-PET biomarker diagnostic studies

Table 1: Review question and inclusion criteria


CategoryReview QuestionInclusion Criteria

PatientsParticipants with mild cognitive impairment, no dementiaParticipants fulfilling the criteria for the clinical diagnosis of

MCI at baseline

Index Test18F-FDG-PET biomarker18F-FDG-PET biomarker

Target ConditionAlzheimer’s disease dementia (conversion from MCI to Alzheimer’s disease dementia)

 

Any other forms of dementia (conversion from MCI to any other forms of dementia)
Alzheimer’s disease dementia (conversion from MCI to Alzheimer’s disease dementia)

 

Any other forms of dementia (conversion from MCI to any other forms of dementia)

Reference StandardNINCDS-ADRDA; DSM; ICD; McKeith criteria; Lund criteria; NINDS-ARIEN criteriaNINCDS-ADRDA; DSM; ICD; McKeith criteria; The Lund criteria; NINDS-ARIEN criteria

OutcomeN/AData to construct 2X2 table

Study DesignN/ALongitudinal cohort studies and

Nested case-control studies if they incorporate a delayed

verification design (case-control nested in cohort studies)



Anchoring statements for quality assessment of 18F-FDG-PET biomarker studies

We provide some core anchoring statements for quality assessment of diagnostic test accuracy review of 18F-FDG-PET biomarker in dementia. These statements are designed for use with the QUADAS-2 tool and are based on the guidance for quality assessment of diagnostic test accuracy reviews of IQCODE in dementia (Quinn 2012). 

During the two-day, multidisciplinary focus group and the piloting/validation of the  guidance, it was clear that certain issues were key to assessing quality, while other issues were important to record but less important for assessing overall quality. To assist, we describe a 'weighting' system. Where an item is weighted 'high risk' then that section of the QUADAS-2 results table is likely to be scored as at high risk of bias. For example in dementia diagnostic test accuracy studies, ensuring that clinicians performing dementia assessment are blinded to results of index test is fundamental. If this blinding was not present then the item on reference standard should be scored 'high risk of bias', regardless of the other contributory elements.

In assessing individual items, the score of 'Unclear' should only be given if there is genuine uncertainty. In these situations review authors will contact the relevant study teams for additional information.

Table 2: Anchoring statements to assist with assessment for risk of bias


QuestionResponse and weightingExplanation

Patient Selection

Was the sampling method appropriate?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
Where sampling is used, the designs least likely to cause bias are consecutive sampling or random sampling. Sampling that is based on volunteers or selecting subjects from a clinic or research resource is prone to bias.

Was a case-control or similar design avoided?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
Designs similar to case-control that may introduce bias are those designs where the study team deliberately increase or decrease the proportion of subjects with the target condition, which may not be representative. Some case-control methods may already be excluded if they mix subjects from various settings.

Are exclusion criteria described and appropriate?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
Study will be automatically graded unclear if exclusions are not detailed (pending contact with study authors). Where exclusions are detailed, the study will be graded as “low risk” if exclusions are felt to be appropriate by the review authors. Certain exclusions common to many studies of dementia are: medical instability; terminal disease; alcohol/substance misuse; concomitant psychiatric diagnosis; other neurodegenerative condition. Exclusions are not felt to be appropriate if ‘difficult to diagnose’ patients are excluded. Post hoc and inappropriate exclusions will be labelled “high risk” of bias.

Index Test

Was 18F-FDG-PET biomarker assessment/interpretation performed without knowledge of clinical dementia diagnosis?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
Terms such as “blinded” or “independently and without knowledge of” are sufficient and full details of the blinding procedure are not required. Interpretation of the results of the index test may be influenced by knowledge of the results of reference standard. If the index test is always interpreted prior to the reference standard then the person interpreting the index test cannot be aware of the results of the reference standard and so this item could be rated as ‘yes’.

For certain index tests the result is objective and knowledge of reference standard should not influence result, for example level of protein in cerebrospinal fluid, in this instance the quality assessment may be “low risk” even if blinding was not achieved.

Were 18F-FDG-PET biomarker thresholds prespecified?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
For scales and biomarkers there is often a reference point (in units or categories) above which subjects are classified as “test positive”; this may be referred to as threshold; clinical cut-off or dichotomisation point. A study is classified high risk of bias if the authors define the optimal cut-off post hoc based on their own study data because selecting the threshold to maximise sensitivity and specificity may lead to overoptimistic measures of test performance.

Certain papers may use an alternative methodology for analysis that does not use thresholds and these papers should be classified as not applicable.

Reference Standard

Is the assessment used for clinical diagnosis of dementia acceptable?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
Commonly used international criteria to assist with clinical diagnosis of dementia include those detailed in DSM-IV and ICD-10. Criteria specific to dementia subtypes include but are not limited to NINCDS-ADRDA criteria for Alzheimer’s dementia; McKeith criteria for Lewy Body dementia; Lund criteria for frontotemporal dementia; and the NINDS-AIREN criteria for vascular dementia. Where the criteria used for assessment is not familiar to the review authors or the Cochrane Dementia and Cognitive Improvement group (‘unclear’) this item should be classified as “high risk of bias”.

Was clinical assessment for dementia performed without knowledge of the 18F-FDG-PET biomarker?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
Terms such as “blinded” or “independently and without knowledge of” are sufficient and full details of the blinding procedure are not required. Interpretation of the results of the reference standard may be influenced by knowledge of the results of index test.

Patient flow

Was there an appropriate interval between 18F-FDG-PET biomarker and clinical dementia assessment?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
As we test the accuracy of the 18F-FDG-PET biomarker for MCI conversion to dementia, there will always be a delay between the index test and the reference standard assessments. The time between reference standard and index test will influence the accuracy (Geslani 2005; Okello 2009; Visser 2006), and therefore we will note time as a separate variable (both within and between studies) and will test its influence on the diagnostic accuracy. We have set a minimum mean time to follow-up assessment of 1 year. If more than 16% of subjects of subjects have assessment for MCI conversion before nine months this item will score ‘no’.

Did all subjects get the same assessment for dementia regardless of 18F-FDG-PET biomarker?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
There may be scenarios where subjects who score “test positive” on index test have a more detailed assessment. Where dementia assessment differs between subjects this should be classified as high risk of bias.

Were all patients who received 18F-FDG-PET biomarker assessment included in the final analysis?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
If the number of patients enrolled differs from the number of patients included in the 2X2 table then there is the potential for bias. If patients lost to drop-outs differ systematically from those who remain, then estimates of test performance may differ.

If drop-outs these should be accounted for; a maximum proportion of drop-outs to remain low risk of bias has been specified as 20%.

Were missing 18F-FDG-PET biomarker results or uninterpretable 18F-FDG-PET biomarker results reported?No = high risk of bias

Yes = low risk of bias

Unclear = unclear risk of bias
Where missing or uninterpretable results are reported, and if there is substantial attrition (we have set an arbitrary value of 50% missing data), this should be scored as ‘no’. If those results are not reported, this should be scored as ‘unclear’ and authors will be contacted.

Anchoring statements to assist with assessment for applicability

QuestionExplanation


Were included patients representative of the general population of interest?The included patients should match the intended population as described in the review question. The review authors should consider population in terms of symptoms; pre-testing; potential disease prevalence; setting

If there is a clear ground for suspecting an unrepresentative spectrum the item should be rated poor applicability.


Index test

Were sufficient data on 18F-FDG-PET biomarker application given for the test to be repeated in an independent study?Variation in technology, test execution, and test interpretation may affect estimate of accuracy. In addition, the background, and training/expertise of the assessor should be reported and taken into consideration. If 18F-FDG-PET biomarker was not performed consistently this item should be rated poor applicability.


Reference Standard

Was clinical diagnosis of dementia made in a manner similar to current clinical practice?For many reviews, inclusion criteria and assessment for risk of bias will already have assessed the dementia diagnosis. For certain reviews an applicability statement relating to reference standard may not be applicable. There is the possibility that a form of dementia assessment, although valid, may diagnose a far larger proportion of subjects with disease than usual clinical practice. In this instance the item should be rated poor applicability.



 

Contributions of authors

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Appendices
  6. Contributions of authors
  7. Declarations of interest

All authors contributed to the drafting of the protocol.

 

Declarations of interest

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Appendices
  6. Contributions of authors
  7. Declarations of interest

None

References

Additional references

  1. Top of page
  2. Abstract
  3. Background
  4. Objectives
  5. Methods
  6. Appendices
  7. Contributions of authors
  8. Declarations of interest
  9. Additional references
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