Diagnostic Evaluation of Autism Spectrum Disorders, A Health Professional Survey

Background: Since the prevalence and awareness AutismSpectrum Disorders (ASD) is growing day by day, it is crucial to correctly allocate diagnosis of ASD. According to the guidelines, there should be a multi-agency strategy group for diagnosis of ASD. Aim: To find out the diagnostic practices of ASD among different Health Professionals. Place and Duration of Study: Riphah International University, Lahore campus. The study was conducted from October 2017 till March 2018. Methodology: Data was collected from 116 professionals which included Speech-Language Pathologists, Pediatricians, Psychiatrists, Psychologists and Occupational Therapists by using questionnaire. A cross-sectional survey was carried out by using the technique of convenient sampling. Researcher collected the data from Riphah International University in person and some professionals were sent questionnaires online. Results: Majority of professionals provide diagnostic service i.e. 84.4% as a part of multidisciplinary team whereas 15.6% are sole practitioners for giving diagnosis; 51.1% collaborate with other professionals to make a consensus diagnosis and the most frequently used tool by professionals for diagnosing ASD is Childhood Autism Rating Scale CARS (76.7%) and Diagnostic and Statistical Manual of Mental Disorders V/IV (DSM V OR IV) criteria (67.8%). Conclusion: The professionals in the current study are using multidisciplinary approach for diagnosing ASD and a small number are sole practitioners. The most frequently used tool for diagnosing ASD are CARS and DSM V OR IV criteria. However, very few practitioners use the diagnostic tool Autism Diagnostic Observation Schedule ADOS and Autism Diagnostic Interview-Revised ADI-R. The professionals who provide diagnosis of ASD are Speech and language Pathologists, Psychologists, pediatricians, psychiatrists and Occupational therapists.

Integration of transbronchial cryobiopsy into multidisciplinary board decision: a single center analysis of one hundred consecutive patients with interstitial lung disease

Background Transbronchial cryobiopsy in the evaluation of patients with interstitial lung diseases (ILD) is expected to reduce the need for surgical lung biopsy (SLB). Objective To evaluate the diagnostic value of cryobiopsy in combination with bronchoalveolar lavage (BAL), radiologic and clinical data in patients with ILD. Methods Between 08/15 and 01/20 patients with ILD underwent cryobiopsy if they: did not have (i) an usual interstitial pneumonia (UIP)-pattern on CT, (ii) predominant ground-glass opacities suggesting alveolitis, (iii) findings suggestive of sarcoidosis on CT, or if they had (i) a CT showing UIP-pattern, but had findings suggesting alternative diagnosis than idiopathic pulmonary fibrosis (IPF), or (ii) had previous non-diagnostic conventional transbronchial forceps biopsy. Histological findings were integrated into the multidisciplinary team discussion (MDTD) and a diagnostic consensus was sought. Results One hundred patients underwent cryobiopsy. In 88/100 patients, cryobiopsy was representative with diagnostic findings in 45/88 and non-specific histological findings in 43/88 patients. In 25/43 with non-specific findings, a consensus diagnosis was reached after MDTD integrating BAL, radiologic and clinical data; eight of the remaining 18 patients with non-specific findings were referred to SLB. In 12/100 patients cryobiopsy was not representative and three of these patients were also referred to SLB. In 7/11 patients (64%) SLB was diagnostic. Complications of cryobiopsy included pneumothorax (14%) and locally controlled bleeding (24%). Conclusions The diagnostic yield of cryobiopsy was 70%:45% of cryobiopsies were diagnostic based on histology alone and an additional 25% provided non-specific, but valuable findings allowing a consensus diagnosis after MDTD. Our data demonstrate that the diagnostic value of cryobiopsy is high if combined with BAL, radiologic and clinical data.

Data-Driven vs Consensus Diagnosis of MCI: Enhanced Sensitivity for Detection of Clinical, Biomarker, and Neuropathologic Outcomes

Objective:Given prior work demonstrating that mild cognitive impairment (MCI) can be empirically differentiated into meaningful cognitive subtypes, we applied actuarial methods to comprehensive neuropsychological data from the University of California San Diego (UCSD) Alzheimer’s Disease Research Center (ADRC) in order to identify cognitive subgroups within nondemented ADRC participants, and to examine cognitive, biomarker, and neuropathological trajectories.Methods:Cluster analysis was performed on baseline neuropsychological data (n=738; mean age=71.8). Survival analysis examined progression to dementia (mean follow-up=5.9 years). CSF AD biomarker status and neuropathological findings at follow-up were examined in a subset with available data.Results:Five clusters were identified: “optimal” cognitively normal (CN; n=130) with above-average cognition, “typical” CN (n=204) with average cognition, non-amnestic MCI (naMCI; n=104), amnestic MCI (aMCI; n=216), and mixed MCI (mMCI; n=84). Progression to dementia differed across MCI subtypes (mMCI>aMCI>naMCI), with the mMCI group demonstrating the highest rate of CSF biomarker positivity and AD pathology at autopsy. Actuarial methods classified 29.5% more of the sample with MCI and outperformed consensus diagnoses in capturing those who had abnormal biomarkers, progressed to dementia, or had AD pathology at autopsy.Conclusions:We identified subtypes of MCI and CN with differing cognitive profiles, clinical outcomes, CSF AD biomarkers, and neuropathological findings over more than 10 years of follow-up. Results demonstrate that actuarial methods produce reliable cognitive phenotypes, with data from a subset suggesting unique biological and neuropathological signatures. Findings indicate that data-driven algorithms enhance diagnostic sensitivity relative to consensus diagnosis for identifying older adults at risk for cognitive decline.

CONSENSUS: a Shiny application of dementia evaluation and reporting for the KU ADC longitudinal Clinical Cohort database

Background The University of Kansas Alzheimer’s Disease Center (KU ADC) maintains several large databases to track participant recruitment, enrollment, and capture various research-related activities. It is challenging to manage and coordinate all the research-related activities. One of the crucial activities involves generating a consensus diagnosis and communicating with participants and their primary care providers. Process To effectively manage the cohort, the KU ADC utilizes a combination of open-source electronic data capture (EDC) (i.e. REDCap), along with other homegrown data management and analytic systems developed using R-studio and Shiny. Process evaluation In this article, we describe the method and utility of the user-friendly dashboard that was developed for the rapid reporting of dementia evaluations which allows clinical researchers to summarize recruitment metrics, automatically generate letters to both participants and healthcare providers, which ultimately help optimize workflows. Conclusions We believe this general framework would be beneficial to any institution that build reports and summarizing key metrics of their research from longitudinal databases.

Laptop-Administered NIH Toolbox and Cogstate Brief Battery in Community-Dwelling Black Adults: Unexpected Pattern of Cognitive Performance between MCI and Healthy Controls

Objective: Black adults are approximately twice as likely to develop Alzheimer’s disease (AD) than non-Hispanic Whites and access diagnostic services later in their illness. This dictates the need to develop assessments that are cost-effective, easily administered, and sensitive to preclinical stages of AD, such as mild cognitive impairment (MCI). Two computerized cognitive batteries, NIH Toolbox-Cognition and Cogstate Brief Battery, have been developed. However, utility of these measures for clinical characterization remains only partially determined. We sought to determine the convergent validity of these computerized measures in relation to consensus diagnosis in a sample of MCI and healthy controls (HC). Method: Participants were community-dwelling Black adults who completed the neuropsychological battery and other Uniform Data Set (UDS) forms from the AD centers program for consensus diagnosis (HC = 61; MCI = 43) and the NIH Toolbox-Cognition and Cogstate batteries. Discriminant function analysis was used to determine which cognitive tests best differentiated the groups. Results: NIH Toolbox crystallized measures, Oral Reading and Picture Vocabulary, were the most sensitive in identifying MCI apart from HC. Secondarily, deficits in memory and executive subtests were also predictive. UDS neuropsychological test analyses showed the expected pattern of memory and executive functioning tests differentiating MCI from HC. Conclusions: Contrary to expectation, NIH Toolbox crystallized abilities appeared preferentially sensitive to diagnostic group differences. This study highlights the importance of further research into the validity and clinical utility of computerized neuropsychological tests within ethnic minority populations.

Frequency and Predictors of Traumatic Encephalopathy Syndrome in a Prospective Cohort of Retired Professional Athletes

Traumatic encephalopathy syndrome (TES) is proposed to represent the long-term impact of repetitive head-injury exposure and the clinical manifestation of chronic traumatic encephalopathy (CTE). This study aimed to evaluate the frequency of TES in a cohort of retired professional contact sport athletes, compare the frequency of TES to clinical consensus diagnoses, and identify predictors that increase the likelihood of TES diagnosis. Participants were 85 retired professional contact sport athletes from a prospective cohort at the University of Texas Southwestern Medical Center and the University of Texas at Dallas. Participants ranged in age from 23 to 79 (M = 55.95, SD = 13.82) and obtained 7 to 19 years of education (M = 16.08, SD = 1.03). Retirees were either non-Hispanic white (n = 62) or African-American (n = 23). Retired athletes underwent a standard clinical evaluation, which included a clinical interview, neurological exam, neuroimaging, neuropsychological testing, and consensus diagnosis of normal, mild cognitive impairment, or dementia. TES criteria were applied to all 85 athletes, and frequencies of diagnoses were compared. Fourteen predictors of TES diagnosis were evaluated using binary logistic regressions, and included demographic, neuropsychological, depression symptoms, and head-injury exposure variables. A high frequency (56%) of TES was observed among this cohort of retired athletes, but 54% of those meeting criteria for TES were diagnosed as cognitively normal via consensus diagnosis. Games played in the National Football League (OR = 0.993, p = 0.087), number of concussions (OR = 1.020, p = 0.532), number of concussions with loss of consciousness (OR = 1.141 p = 0.188), and years playing professionally (OR = 0.976, p = 0.627) were not associated with TES diagnosis. Degree of depressive symptomatology, as measured by the total score on the Beck Depression Inventory-II, was the only predictor of TES diagnosis (OR = 1.297, p < 0.001). Our results add to previous findings underscoring the risk for false positive diagnosis, highlight the limitations of the TES criteria in clinical and research settings, and question the relationship between TES and head-injury exposure. Future research is needed to examine depression in retired professional athletes.

Learning from Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study (Preprint)

BACKGROUND The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is the first and only nationally representative study on late-life cognition and dementia in India (N=4,096). LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2,528 respondents. OBJECTIVE This study develops a machine learning model that uses data from the clinical consensus diagnosis in LASI-DAD to support the classification of dementia status. METHODS Clinicians were presented with the extensive data collected from LASI-DAD, including sociodemographic information and health history of respondents, results from the screening tests of cognitive status, and information obtained from informant interviews. Based on the Clinical Dementia Rating (CDR®) and using an online platform, clinicians individually evaluated each case and then reached a consensus diagnosis. A two-step procedure was implemented to train several candidate machine learning models, which were evaluated using a separate test set for predictive accuracy measurement, including the area under receiver-operating curve (AUROC), accuracy, sensitivity, specificity, precision, F1 score, and Kappa statistic. The ultimate model was selected based on overall agreement as measured by Kappa. We further examined the overall accuracy and agreement with the final consensus diagnoses between the selected machine learning model and individual clinicians who participated in the clinical consensus diagnostic process. Finally, we applied the selected model to a subgroup of LASI-DAD participants for whom the clinical consensus diagnosis was not obtained to predict their dementia status. RESULTS Among the 2,528 individuals who received clinical consensus diagnosis, 192 (6.7% after adjusting for sampling weight) were diagnosed with dementia. All candidate machine learning models achieved outstanding discriminative ability as indicated by AUROC>0.9 and had similar accuracy and specificity (both around 0.95). The support vector machine model outperformed other models with the highest sensitivity (0.81), F1 score (0.72), and Kappa (0.70, indicating substantial agreement) and the second highest precision (0.65). As a result, the support vector machine was selected as the ultimate model. Further examination revealed that overall accuracy and agreement were similar between the selected model and individual clinicians. Application of the prediction model on 1,568 individuals without clinical consensus diagnosis classified 127 individuals as living with dementia. After applying sampling weight, we can estimate the prevalence of dementia in the population as 7.4%. CONCLUSIONS The selected machine learning model has outstanding discriminative ability and substantial agreement with clinical consensus diagnosis of dementia. The model can serve as a computer model of the clinical knowledge and experience encoded in the clinical consensus diagnostic process and has many potential applications, including predicting missed dementia diagnoses and serving as a clinical decision-support tool or virtual rater to assist diagnosis of dementia.

Eosinophilia/Hypereosinophilia in the Setting of Reactive and Idiopathic Causes, Well-Defined Myeloid or Lymphoid Leukemias, or Germline Disorders

Objectives To report the findings of the 2019 Society for Hematopathology/European Association for Haematopathology Workshop within the categories of reactive eosinophilia, hypereosinophilic syndrome (HES), germline disorders with eosinophilia (GDE), and myeloid and lymphoid neoplasms associated with eosinophilia (excluding entities covered by other studies in this series). Methods The workshop panel reviewed 109 cases, assigned consensus diagnosis, and created diagnosis-specific sessions. Results The most frequent diagnosis was reactive eosinophilia (35), followed by acute leukemia (24). Myeloproliferative neoplasms (MPNs) received 17 submissions, including chronic eosinophilic leukemia, not otherwise specified (CEL, NOS). Myelodysplastic syndrome (MDS), MDS/MPN, and therapy-related myeloid neoplasms received 11, while GDE and HES received 12 and 11 submissions, respectively. Conclusions Hypereosinophilia and HES are defined by specific clinical and laboratory criteria. Eosinophilia is commonly reactive. An acute leukemic onset with eosinophilia may suggest core-binding factor acute myeloid leukemia, blast phase of chronic myeloid leukemia, BCR-ABL1–positive leukemia, or t(5;14) B-lymphoblastic leukemia. Eosinophilia is rare in MDS but common in MDS/MPN. CEL, NOS is a clinically aggressive MPN with eosinophilia as the dominant feature. Bone marrow morphology and cytogenetic and/or molecular clonality may distinguish CEL from HES. Molecular testing helps to better subclassify myeloid neoplasms with eosinophilia and to identify patients for targeted treatments.

Prospective Consensus Reporting by Gynecologic Pathology and Dermatopathology Improves Diagnosis of Vulvar Biopsies

Context.— Vulvar biopsy interpretation and reporting, particularly of vulvar dermatoses, can be challenging in daily practice for both surgical pathologists (SPs) and dermatopathologists (DPs). Objective.— To investigate whether prospective consensus reporting of vulvar biopsies by SPs and DPs would provide value and improve overall diagnostic concordance. Design.— Consecutive vulvar biopsies during a 6-month period were reviewed prospectively by both gynecologic SPs and DPs. Preliminary, independently generated diagnoses were recorded and then shared in consensus review (SPs+DPs). A third pathologist adjudicated cases without consensus. Multiple data elements were collected for each case: division (SP/DP), age, site, clinical history, diagnostic category, preliminary and final (consensus) diagnosis, need for adjudication, ancillary tests, and diagnostic discrepancy. Results.— Eighty-four biopsies (48 SP, 36 DP) from 70 patients were reviewed. Forty-two of 84 cases (50%) were neoplastic, 38 of 84 (45%) were reactive/inflammatory, with the remaining (5%) showing both or other features. Independent diagnoses were discrepant in 22 of 84 cases (26%), but consensus review resulted in an agreed-upon diagnosis in all cases, with adjudication required in 6 cases. Independent diagnostic agreement increased over time with a reduction in major and minor discrepancies between the first and second half of the study period. Conclusions.— Prospective review of vulvar biopsies by both SPs and DPs can improve overall reporting. Consensus review allows pathologists to gain diagnostic confidence in interpretation of inflammatory (for SPs) and neoplastic (for DPs) vulvar biopsies; therefore, intradepartmental consultation is of value, particularly in select cases.