The spectrum effect in tests for risk prediction, screening, and diagnosis

Usher‐Smith, Juliet A.; Sharp, Stephen J.; Griffin, Simon J.

doi:10.1136/bmj.i3139

Cited by 185 publications

(155 citation statements)

References 16 publications

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“…As studies were markedly heterogenous, individual‐level raw data were not requested. However, comparing and pooling heterogenous data is not appropriate for diagnostic test accuracy studies, particularly given the variations and uncertainty in criterion (cut‐off) threshold test values to define conditions, many of which were not adequately reported. Further, we excluded studies with less than 10% prevalence of VCI.…”

Section: Discussionmentioning

confidence: 99%

Cognitive screening instruments to identify vascular cognitive impairment: A systematic review

Ghafar

Miptah

Ó’Caoimh

2019

Int J Geriat Psychiatry

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Introduction Vascular cognitive impairment (VCI) is common and important to detect as controlling risk factors, particularly hypertension, may slow onset and progression. There is no consensus as to which cognitive screening instrument (CSI) is most suitable for VCI. We systematically reviewed the psychometric properties of brief CSIs for vascular mild cognitive impairment (VMCI) and vascular dementia (VaD). Methods Literature searches were performed using scholarly databases from inception until 31 May 2018. Studies were eligible if participants were aged 18 or older, interviewed face‐to‐face, and standard diagnostic criteria for VCI were applied, excluding those specifically identifying post‐stroke dementia. Risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool. Results Fifteen studies were identified including eight types of CSIs (27 subtests/variants) and 4575 participants (1015 with VCI), mean age range: 51.6 to 75.5 years. Most studies compared more than one instrument. Five papers examined clock‐drawing; four, the Montreal Cognitive Assessment (MoCA) and Mini‐Mental State Examination (MMSE); and three used the Brief Memory and Executive Test (BMET). The MoCA (AUC > 0.90) and MMSE (AUC: 0.86‐0.99) had excellent accuracy in differentiating VaD from controls; the MoCA had good internal consistency (Cronbach's α: .83‐.88). The MoCA (AUC: 0.87‐0.93) and BMET (AUC: 0.94) had the greatest accuracy in separating VMCI from controls. Most studies had low to moderate risk of bias in all domains of the QUIPS. Data were heterogeneous, precluding a meta‐analysis. Conclusions Although few studies were available and further research is required, data suggests that the MoCA is accurate and reliable for differentiating VaD and VMCI from controls.

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Section: Discussionmentioning

confidence: 99%

Cognitive screening instruments to identify vascular cognitive impairment: A systematic review

Ghafar

Miptah

Ó’Caoimh

2019

Int J Geriat Psychiatry

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“…11 Transferring testing strategies from settings with higher (secondary care) or lower (screening) cancer prevalence should be avoided as this leads to inaccurate predictions of test performance. 12 In cancer screening, for example, testing is calibrated to minimise referrals for false positive results rather than minimising false negatives. Continued analysis of primary care data should focus on identifying clusters of symptomatic and at-risk patients for whom a cancer rule-out strategy could be confidently employed.…”

Section: Ruling Out Cancer Not Ruling In Cancermentioning

confidence: 99%

The elusive diagnosis of cancer: testing times

2018

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“…Possible clinical mechanisms include “case‐mix variation”, which is a special type of the “spectrum effect”; the latter describes the variation in sensitivity, specificity and accuracy among different populations and subgroups, while case‐mix variation refers to differences between development and validation studies in clinical settings, subject characteristics, age of outcome assessment, outcome prevalence and/or incidence . In particular, the larger the difference between the characteristics of the original and external validation populations, the higher the discrepancy in predictive performance and the poorer the generalizability of findings.…”

Section: Discussionmentioning

confidence: 99%

Validation of childhood asthma predictive tools: A systematic review

Colicino

Munblit

Minelli

et al. 2019

Clin Experimental Allergy

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Background: There is uncertainty about the clinical usefulness of currently available asthma predictive tools. Validation of predictive tools in different populations and clinical settings is an essential requirement for the assessment of their predictive performance, reproducibility and generalizability. We aimed to critically appraise asthma predictive tools which have been validated in external studies. Methods:We searched MEDLINE and EMBASE (1946-2017) for all available childhood asthma prediction models and focused on externally validated predictive tools alongside the studies in which they were originally developed. We excluded non-English and nonoriginal studies. PROSPERO registration number is CRD42016035727.Results: From 946 screened papers, eight were included in the review. Statistical approaches for creation of prediction tools included chi-square tests, logistic regression models and the least absolute shrinkage and selection operator. Predictive models were developed and validated in general and high-risk populations. Only three prediction tools were externally validated: the Asthma Predictive Index, the PIAMA and the Leicester asthma prediction tool. A variety of predictors has been tested, but no studies examined the same combination. There was heterogeneity in definition of the primary outcome among development and validation studies, and no objective measurements were used for asthma diagnosis. The performance of tools varied at different ages of outcome assessment. We observed a discrepancy between the development and validation studies in the tools' predictive performance in terms of sensitivity and positive predictive values.Conclusions: Validated asthma predictive tools, reviewed in this paper, provided poor predictive accuracy with performance variation in sensitivity and positive predictive value.

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The spectrum effect in tests for risk prediction, screening, and diagnosis

Cited by 185 publications

References 16 publications

Cognitive screening instruments to identify vascular cognitive impairment: A systematic review

Cognitive screening instruments to identify vascular cognitive impairment: A systematic review

The elusive diagnosis of cancer: testing times

Validation of childhood asthma predictive tools: A systematic review

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