Anxiety in pregnancy and after giving birth (the perinatal period) is highly prevalent but under-recognised. Robust methods of assessing perinatal anxiety are essential for services to identify and treat women appropriately.

To determine which assessment measures are most psychometrically robust and effective at identifying women with perinatal anxiety (primary objective) and depression (secondary objective).

We conducted a prospective longitudinal cohort study of 2243 women who completed five measures of anxiety and depression (Generalized Anxiety Disorder scale (GAD) two- and seven-item versions; Whooley questions; Clinical Outcomes in Routine Evaluation (CORE-10); and Stirling Antenatal Anxiety Scale (SAAS)) during pregnancy (15 weeks, 22 weeks and 31 weeks) and after birth (6 weeks). To assess diagnostic accuracy a sample of 403 participants completed modules of the Mini-International Neuropsychiatric Interview (MINI).

The best diagnostic accuracy for anxiety was shown by the CORE-10 and SAAS. The best diagnostic accuracy for depression was shown by the CORE-10, SAAS and Whooley questions, although the SAAS had lower specificity. The same cut-off scores for each measure were optimal for identifying anxiety or depression (SAAS ≥9; CORE-10 ≥9; Whooley ≥1). All measures were psychometrically robust, with good internal consistency, convergent validity and unidimensional factor structure.

This study identified robust and effective methods of assessing perinatal anxiety and depression. We recommend using the CORE-10 or SAAS to assess perinatal anxiety and the CORE-10 or Whooley questions to assess depression. The GAD-2 and GAD-7 did not perform as well as other measures and optimal cut-offs were lower than currently recommended.

Early detection of ST-segment elevation myocardial infarction (STEMI) on the prehospital electrocardiogram (ECG) improves patient outcomes. Current software algorithms optimize sensitivity but have a high false-positive rate. The authors propose an algorithm to improve the specificity of STEMI diagnosis in the prehospital setting.

A dataset of prehospital ECGs with verified outcomes was used to validate an algorithm to identify true and false-positive software interpretations of STEMI. Four criteria implicated in prior research to differentiate STEMI true positives were applied: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. The test characteristics were calculated and regression analysis was used to examine the association between the number of criteria included and test characteristics.

There were 44,611 cases available. Of these, 1,193 were identified as STEMI by the software interpretation. Applying all four criteria had the highest positive likelihood ratio of 353 (95% CI, 201-595) and specificity of 99.96% (95% CI, 99.93-99.98), but the lowest sensitivity (14%; 95% CI, 11-17) and worst negative likelihood ratio (0.86; 95% CI, 0.84-0.89). There was a strong correlation between increased positive likelihood ratio (r2 = 0.90) and specificity (r2 = 0.85) with increasing number of criteria.

Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.

This study aimed to compare Greek Australian and English language normative data with regard to impairment rates yielded within a healthy Greek Australian older adult sample. We also examined whether optimal cut scores could be identified and capable of sensitively and specifically distinguishing between healthy Greek Australians from those with a diagnosis of Alzheimer’s disease (AD).

Ninety healthy Greek Australian older adults and 20 demographically matched individuals with a diagnosis of AD completed a range of neuropsychological measures, including the Wechsler Adult Intelligence Scale-Fourth Edition, Greek Adaptation (WAIS-IV GR), verbal and visual memory, language and naming, and executive functions. Impairment rates derived from the use of either Greek Australian or English language normative data were calculated and compared, using a 1.5 standard deviation criterion to denote impairment. Receiver operating characteristics curve analysis was used to investigate the sensitivity and specificity of alternate cut scores.

Impairment rates derived from the Greek Australian normative data showed that rates of impairment generally fell within the expected 7% range. In contrast, impairment rates for all tests derived using English language normative data were significantly higher and ranged from 11%–66%. Comparisons between healthy and AD participants with moderate dementia showed significant differences across all measures. Area under the curve results ranged from .721 to .999 across all measures, with most tests displaying excellent sensitivity and specificity.

English language normative data were found to be inappropriate for use with Greek Australian elders, potentially leading to erroneous diagnostic outcomes. The use of minority group specific normative data and associated cut points appear to partially ameliorate this issue. Clinical implications are discussed alongside future research directions.

Primary health care (PHC) professionals may play a crucial role in improving early diagnosis of depressive disorders. However, only 50% of cases are detected in PHC. The most widely used screening instrument for major depression is the Patient Health Questionnaire (PHQ), including the two-, eight- and nine-item versions. Surprisingly, there is neither enough evidence about the validity of PHQ in PHC patients in Spain nor indications about how to interpret the total scores. This study aimed to gather validity evidence to support the use of the three PHQ versions to screen for major depression in PHC in Spain. Additionally, the present study provided information for helping professionals to choose the best PHQ version according to the context.

The sample was composed of 2579 participants from 22 Spanish PHC centers participating in the EIRA-3 study. The reliability and validity of the three PHQ versions for Spanish PHC patients were assessed based on responses to the questionnaire.

The PHQ-8 and PHQ-9 showed high internal consistency. The results obtained confirm the theoretically expected relationship between PHQ results and anxiety, social support and health-related QoL. A single-factor solution was confirmed. Regarding to the level of agreement with the CIDI interview (used as the criterion), our results indicate that the PHQ has a good discrimination power. The optimal cut-off values were: ⩾2 for PHQ-2, ⩾7 for PHQ-8 and ⩾8 for PHQ-9.

PHQ is a good and valuable tool for detecting major depression in PHC patients in Spain.

No significant relationships.

Evidence-based diagnostic methods have clinical and research applications in neuropsychology. A flexible Bayesian model was developed to yield diagnostic posttest probabilities from a single person’s neuropsychological score profile by utilizing sample descriptive statistics of the test battery across diagnostic populations of interest.

Three studies examined the model’s performance. One simulation examined estimation accuracy of true z-scores. A diagnostic accuracy simulation utilized descriptive statistics from two popular neuropsychological tests, the Wechsler Adult Intelligence Scale–IV (WAIS-IV) and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The final simulation examined posterior predictive accuracy of scores to those reported in the WAIS manual.

The model produced minimally biased z-score estimates (root mean square errors: .02–.18) with appropriate credible intervals (95% credible interval empirical coverage rates: .94–1.00). The model correctly classified 80.87% of simulated normal, mild cognitive impairment, and Alzheimer’s disease cases using a four subtest WAIS-IV and the RBANS compared to accuracies of 60.67–65.60% from alternative methods. The posterior predictions of raw scores closely aligned to percentile estimates published in the WAIS-IV manual.

This model permits estimation of posttest probabilities for various combinations of neuropsychological tests across any number of clinical populations with the principal limitation being the accessibility of applicable reference samples. The model produced minimally biased estimates of true z-scores, high diagnostic classification rates, and accurate predictions of multiple reported percentiles while using only simple descriptive statistics from reference samples. Future nonsimulation research on clinical data is needed to fully explore the utility of such diagnostic prediction models.

Depression is common in persons experiencing mild cognitive impairment (MCI), with 32% (95% Cl 27, 37) overall experiencing depression. Persons with MCI who have depression have more cognitive changes compared to those without depression. To understand how we can detect depressive symptoms in persons with MCI, we undertook a systematic review to identify tools that were validated compared with a reference standard.

We searched MEDLINE, EMBASE, PsycINFO, and Cochrane from inception to April 25, 2021, and conducted a gray literature search. Title/abstract and full-text screening were completed in duplicate. Demographic information, reference standards, prevalence, and diagnostic accuracy measures were then extracted from included articles (PROSPERO CRD: CRD42016052120).

Across databases, 8,748 abstracts were generated after removing duplicates. Six hundred and sixty-five records underwent full-text screening, with six articles included for data extraction. Nine tools were identified compared to a reference standard, with multiple demonstrating a sensitivity of 100% (Brief Assessment Schedule Depression Cards, Beck Depression Inventory-II, Cornell Scale for Depression in Dementia, Zung Self-Rated Depression Scale, and the Neuropsychiatric Inventory). The second highest sensitivity reported was 89% (Patient Health Questionnaire-9). Too few studies were available for a meta-analysis.

Multiple depression detection tools have been examined amongst MCI outpatients, with several showing high sensitivity. However, this evidence is only present in single studies, with little demonstration of how differing MCI types affect accuracy. More research is needed to confirm the accuracy of these tools amongst persons with MCI. At this time, several tools could be suitable for use in cognitive clinics.

Clinical neuropsychology has been slow in adopting novelties in psychometrics, statistics, and technology. Researchers have indicated that the stationary nature of clinical neuropsychology endangers its evidence-based character. In addition to a technological crisis, there may be a statistical crisis affecting clinical neuropsychology. That is, the frequentist null hypothesis significance testing framework remains the dominant approach in clinical practice, despite a recent surge in critique on this framework. While the Bayesian framework has been put forward as a viable alternative in psychology in general, the possibilities it offers to clinical neuropsychology have not received much attention.

In the current position paper, we discuss and reflect on the value of Bayesian methods for the advancement of evidence-based clinical neuropsychology.

We aim to familiarize clinical neuropsychologists and neuropsychological researchers to Bayesian methods of inference and provide a clear rationale for why these methods are valuable for clinical neuropsychology.

We argue that Bayesian methods allow for a more intuitive answer to our diagnostic questions and form a more solid foundation for sequential and adaptive diagnostic testing, representing uncertainty about patients’ observed test scores and cognitive modeling of test results.

Ictal semiology interpretation for differentiating psychogenic nonepileptic seizures (PNESs) and epileptic seizures (ESs) is important for the institution of appropriate treatment. Our objective was to assess the ability of different health care professionals (HCPs) or students to distinguish PNES from ES based on video-recorded seizure semiology.

This study was designed following the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidelines. We showed in a random mix 36 videos of PNES or ES (18 each) and asked 558 participants to classify each seizure. The diagnostic accuracy of various groups of HCPs or students for PNES versus ES was assessed, as well as the effect of patient age and sex. Measures of diagnostic accuracy included sensitivity, specificity, and area under the curve (AUC).

The descending order of diagnostic accuracy (AUC) was the following (p ≤ 0.001): (1) neurologists and epileptologists; (2) neurology residents; (3) other specialists and nurses with experience in epilepsy; and (4) undergraduate medical students. Although there was a strong trend toward statistical difference, with AUC 95% confidence intervals (CIs) that were not overlapping, between epileptologists (95% CI 93, 97) compared to neurologists (95% CI 88, 91), and neurologists compared to electroencephalography technicians (95% CI 82, 87), multiple pairwise comparisons with the conservative Tukey–Kramer honest significant difference test revealed no statistical difference (p = 0.25 and 0.1, respectively). Patient age and sex did not have an effect on diagnostic accuracy in neurology specialists.

Visual recognition of PNES by HCPs or students varies overall proportionately with the level of expertise in the field of neurology/epilepsy.

Dopaminergic imaging is an established biomarker for dementia with Lewy bodies, but its diagnostic accuracy at the mild cognitive impairment (MCI) stage remains uncertain.

To provide robust prospective evidence of the diagnostic accuracy of dopaminergic imaging at the MCI stage to either support or refute its inclusion as a biomarker for the diagnosis of MCI with Lewy bodies.

We conducted a prospective diagnostic accuracy study of baseline dopaminergic imaging with [123I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane single-photon emission computerised tomography (123I-FP-CIT SPECT) in 144 patients with MCI. Images were rated as normal or abnormal by a panel of experts with access to striatal binding ratio results. Follow-up consensus diagnosis based on the presence of core features of Lewy body disease was used as the reference standard.

At latest assessment (mean 2 years) 61 patients had probable MCI with Lewy bodies, 26 possible MCI with Lewy bodies and 57 MCI due to Alzheimer's disease. The sensitivity of baseline FP-CIT visual rating for probable MCI with Lewy bodies was 66% (95% CI 52–77%), specificity 88% (76–95%) and accuracy 76% (68–84%), with positive likelihood ratio 5.3.

It is over five times as likely for an abnormal scan to be found in probable MCI with Lewy bodies than MCI due to Alzheimer's disease. Dopaminergic imaging appears to be useful at the MCI stage in cases where Lewy body disease is suspected clinically.

Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9.

We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy.

16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (−0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).

PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.

Acute aortic dissection (AAD) is a time sensitive, difficult to diagnose, aortic emergency. We sought to explore the quality of history taking in AAD and assess its impact on misdiagnosis.

We studied a retrospective cohort of patients >18 years old who presented to two tertiary care emergency departments from January 1st 2004 – December 31st 2012 and were diagnosed with an acute aortic dissection (AAD) on CT, MRI or TEE. Trained reviewers’ extracted data using a standardized data collection form. The definitions of 5 pain characteristics – character, onset, duration, quality, and radiation were defined a priori.

Data were collected for 194 cases of acute aortic dissection with a mean age of 65(SD 14.1) and 66.7% male, 34(17.6%) missed on initial presentation. Only 20(14.8%) patients were asked all 5 questions. The most common initial incorrect diagnosis were acute coronary syndrome (16, 47%), pulmonary embolism (5, 14.7%) and stroke (4, 11.7%). If <2 questions were asked 1 in 5 cases were missed, 4 times greater than if >2 were asked (P < 0.01).

Clinicians should ask and document the character, onset, duration, radiation and severity of pain in any patient presenting with chest, abdominal or flank pain. A focused history still remains the keystone to reducing misdiagnosis.