Influence of age, education, and blast exposure on the Test of Memory Malingering in an active-duty military sample.

Neuropsychologists use performance validity tests (PVT) to detect performance invalidity across various populations. Unexpected scores for normative and clinical populations on PVT performance could invalidate the assessment if the poor performance does not have a reasonable explanation. One of the most well-validated and frequently used PVT is the Test of Memory Malingering, whose usefulness has been analyzed in various populations, including the military. Studies on the influence of demographic variables and blast exposure on the performance of military samples have yielded inconclusive results. The current study investigates the influence of age, education, and blast exposure on the TOMM Trial 2 in a military sample that is representative of their demographics. The total sample size was 872 participants between 18-62 years of age (M = 26.35, SD = 6.63), divided into 832 males and 40 females. All participants were on active duty, and they had been deployed in war zones in Afghanistan and Iraq. They were referred to Carolina Psychological Health Services from the Naval Hospital of Camp LeJeune for psychological and/or neurological complaints, such as cognitive difficulties. The results show that age, education, and blast exposure variations do not affect TOMM performance. Further studies on the relationship between these variables should be pursued to elucidate how they are associated with the normative or clinical cognitive functioning of military populations.

Machine Learning to Optimize the Echocardiographic Follow-Up of Aortic Stenosis.

BACKGROUND: Disease progression in patients with mild-to-moderate aortic stenosis is heterogenous and requires periodic echocardiographic examinations to evaluate severity. OBJECTIVES: This study sought to explore the use of machine learning to optimize aortic stenosis echocardiographic surveillance automatically. METHODS: The study investigators trained, validated, and externally applied a machine learning model to predict whether a patient with mild-to-moderate aortic stenosis will develop severe valvular disease at 1, 2, or 3 years. Demographic and echocardiographic patient data to develop the model were obtained from a tertiary hospital consisting of 4,633 echocardiograms from 1,638 consecutive patients. The external cohort was obtained from an independent tertiary hospital, consisting of 4,531 echocardiograms from 1,533 patients. Echocardiographic surveillance timing results were compared with the European and American guidelines echocardiographic follow-up recommendations. RESULTS: In internal validation, the model discriminated severe from nonsevere aortic stenosis development with an area under the receiver-operating characteristic curve (AUC-ROC) of 0.90, 0.92, and 0.92 for the 1-, 2-, or 3-year interval, respectively. In external application, the model showed an AUC-ROC of 0.85, 0.85, and 0.85, for the 1-, 2-, or 3-year interval. A simulated application of the model in the external validation cohort resulted in savings of 49% and 13% of unnecessary echocardiographic examinations per year compared with European and American guideline recommendations, respectively. CONCLUSIONS: Machine learning provides real-time, automated, personalized timing of next echocardiographic follow-up examination for patients with mild-to-moderate aortic stenosis. Compared with European and American guidelines, the model reduces the number of patient examinations.

Case study: A positive cognitive outcome following an out-of-hospital cardiac arrest.

OBJECTIVE: Time is critical with any out of hospital cardiac arrest (OHCA). The possibility of brain cell death increases, and the likelihood of a "good" outcome decreases with time. The most prominent impairments involve memory and attentional difficulties. Limited research and few cases have shown positive cognitive results following an OHCA to the extent that this case study depicts. METHOD: The current case study presents a right-handed male in his late 40s, with master's and law degrees, and a high-level functioning in the workplace who experienced an OHCA. He was treated for his OHCA and subsequently underwent neuropsychological testing less than 2 months following his hospital discharge. RESULTS: Expected results suggest impairments in key cognitive areas; however, a neuropsychological exam less than 2-months post-incident, testing pre-morbid IQ, overall cognitive ability, processing speed, attention, executive functioning, language, visuospatial abilities, and memory; each showing normal or better results. Additionally, self and collateral report questionnaires examining cognitive and emotional functioning reported no difficulties and no major changes since his cardiac arrest. CONCLUSIONS: We speculate that this patient's exceptional outcome might be due to his cognitive reserve, and the immediateness of his intervention (5-10 min of CPR and return-of-spontaneous-circulation from an AED shock) and use of a saline cooling procedure upon arrival to the hospital. Overall, we highlight a patient with a remarkable cognitive outcome, utilizing data from neuropsychological testing within 2-months post-incident, and propose protective factors in neuropsychological functioning following an OHCA.

Effects of antiepileptic therapy on bone mineral status evaluated by phalangeal quantitative ultrasound in pediatric patients with epilepsy and motor impairment.

BACKGROUND: In epileptic patients with motor disability, it's difficult to disentangle the effects of antiepileptic drugs (AEDs) on bone health from those provoked by impaired mobility. The aim of this study was to evaluate the effects of AEDs on bone mineral status by phalangeal quantitative ultrasound (QUS), a no-radiation and non-invasive method, in pediatric patients with motor impairment and epilepsy. METHODS: We enrolled 56 patients (31 females, 25 males) with epilepsy and motor impairment and 24 children with only motor disability (13 females, 11 males). Patients were stratified by Gross Motor Function Classification System Scale (GMFCS) in 4 groups: group A1 with epilepsy and mild motor impairment (GMFCS levels I-II), group A2 with only mild motor impairment, group B1 with epilepsy and severe motor impairment (GMFCS levels III-V), group B2 with only severe motor impairment. The bone mineral status was evaluated by phalangeal QUS and amplitude-dependent speed of sound (AD-SoS) Z-score was calculated for each patient. RESULTS: The four groups showed no significant differences in age, gender and 25-hydroxyvitamin D levels. The group B1 had a statistically lower amplitude-dependent speed of sound Z-score as compared to group A2 (P<0.05). The multivariate analysis of independent factors revealed a significant correlation between amplitude-dependent speed of sound Z-score and Gross Motor Function Classification System levels (P=0.004). The mean Z-score value decreased by 0.53, increasing the motor impairment. CONCLUSIONS: The bone mineral status measured as AD-SoS strongly correlates with severity of motor disability evaluated by GMFCS as compared to antiepileptic therapy and 25-hydroxyvitamin D levels.

An alternative approach to TOMM cutoff scores using a large sample of military personnel.

The accuracy of neuropsychological assessments relies on participants exhibiting their true abilities during administration. The Test of Memory Malingering (TOMM) is a popular performance validity test used to determine whether an individual is providing honest answers. While the TOMM has proven to be highly sensitive to those who are deliberately exaggerating their symptoms, there is a limited explanation regarding the significance of using 45 as a cutoff score. The present study aims to further investigate this question by examining TOMM scores obtained in a large sample of active-duty military personnel (N = 859, M = 26 years, SD = 6.14, 97.31% males, 72.44% white). Results indicated that no notable discrepancies existed between the frequency of participants who scored a 45 and those who scored slightly below a 45 on the TOMM. The sensitivity and specificity of the TOMM were derived using the forced-choice recognition (FCR) scores obtained by participants on the California Verbal Learning Test, Second Edition (CVLT-II). The sensitivity for each trial of the TOMM was 0.84, 0.55, and 0.63, respectively; the specificity for each trial of the TOMM was 0.69, 0.93, and 0.92, respectively. Because sensitivity and specificity rates are both of importance in this study, balanced accuracy scores were also reported. Results suggested that various alternative cutoff scores produced a more accurate classification compared to the traditional cutoff of 45. Further analyses using Fisher's exact test also indicated that there were no significant performance differences on the FCR of the CVLT-II between individuals who received a 44 and individuals who received a 45 on the TOMM. The current study provides evidence on why the traditional cutoff may not be the most effective score. Future research should consider employing alternative methods which do not rely on a single score.

Predictive Power for Thrombus Detection after Atrial Appendage Closure: Machine Learning vs. Classical Methods.

Device-related thrombus (DRT) after left atrial appendage (LAA) closure is infrequent but correlates with an increased risk of thromboembolism. Therefore, the search for DRT predictors is a topic of interest. In the literature, multivariable methods have been used achieving non-consistent results, and to the best of our knowledge, machine learning techniques have not been used yet for thrombus detection after LAA occlusion. Our aim is to compare both methodologies with respect to predictive power and the search for predictors of DRT. To this end, a multicenter study including 1150 patients who underwent LAA closure was analyzed. Two lines of experiments were performed: with and without resampling. Multivariate and machine learning methodologies were applied to both lines. Predictive power and the extracted predictors for all experiments were gathered. ROC curves of 0.5446 and 0.7974 were obtained for multivariate analysis and machine learning without resampling, respectively. However, the resampling experiment showed no significant difference between them (0.52 vs. 0.53 ROC AUC). A difference between the predictors selected was observed, with the multivariable methodology being more stable. These results question the validity of predictors reported in previous studies and demonstrate their disparity. Furthermore, none of the techniques analyzed is superior to the other for these data.

Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model.

Background: The integrated approach to electrical cardioversion (EC) in atrial fibrillation (AF) is complex; candidates can resolve spontaneously while waiting for EC, and post-cardioversion recurrence is high. Thus, it is especially interesting to avoid the programming of EC in patients who would restore sinus rhythm (SR) spontaneously or present early recurrence. We have analyzed the whole elective EC of the AF process using machine-learning (ML) in order to enable a more realistic and detailed simulation of the patient flow for decision making purposes. Methods: The dataset consisted of electronic health records (EHRs) from 429 consecutive AF patients referred for EC. For analysis of the patient outcome, we considered five pathways according to restoring and maintaining SR: (i) spontaneous SR restoration, (ii) pharmacologic-cardioversion, (iii) direct-current cardioversion, (iv) 6-month AF recurrence, and (v) 6-month rhythm control. We applied ML classifiers for predicting outcomes at each pathway and compared them with the CHA2DS2-VASc and HATCH scores. Results: With the exception of pathway (iii), all ML models achieved improvements in comparison with CHA2DS2-VASc or HATCH scores (p < 0.01). Compared to the most competitive score, the area under the ROC curve (AUC-ROC) was: 0.80 vs. 0.66 for predicting (i); 0.71 vs. 0.55 for (ii); 0.64 vs. 0.52 for (iv); and 0.66 vs. 0.51 for (v). For a threshold considered optimal, the empirical net reclassification index was: +7.8%, +47.2%, +28.2%, and +34.3% in favor of our ML models for predicting outcomes for pathways (i), (ii), (iv), and (v), respectively. As an example tool of generalizability of ML models, we deployed our algorithms in an open-source calculator, where the model would personalize predictions. Conclusions: An ML model improves the accuracy of restoring and maintaining SR predictions over current discriminators. The proposed approach enables a detailed simulation of the patient flow through personalized predictions.

Low-frequency stimulation of a fiber tract in bilateral temporal lobe epilepsy.

OBJECTIVE: Pharmacoresistant bilateral mesial temporal lobe epilepsy often implies poor resective surgical candidacy. Low-frequency stimulation of a fiber tract connected to bilateral hippocampi, the fornicodorsocommissural tract, has been shown to be safe and efficacious in reducing seizures in a previous short-term study. Here, we report a single-blinded, within-subject control, long-term deep-brain stimulation trial of low-frequency stimulation of the fornicodorsocommissural tract in bilateral mesial temporal lobe epilepsy. Outcomes of interest included safety with respect to verbal memory scores and reduction of seizure frequency. METHODS: Our enrollment goal was 16 adult subjects to be randomized to 2-Hz or 5-Hz low-frequency stimulation of the fornicodorsocommissural tract starting at 2 mA. The study design consisted of four two-month blocks of stimulation with a 50%-duty cycle, alternating with two-month blocks of no stimulation. RESULTS: We terminated the study after enrollment of five subjects due to slow accrual. Fornicodorsocommissural tract stimulation elicited bilateral hippocampal evoked responses in all subjects. Three subjects underwent implantation of pulse generators and long-term low-frequency stimulation with mean monthly seizures of 3.14 ±â€¯2.67 (median 3.0 [IQR 1-4.0]) during stimulation-off blocks, compared with 0.96 ±â€¯1.23 (median 1.0 [IQR 0-1.0]) during stimulation-on blocks (p = 0.0005) during the blinded phase. Generalized Estimating Equations showed that low-frequency stimulation reduced monthly seizure-frequency by 0.71 per mA (p < 0.001). Verbal memory scores were stable with no psychiatric complications or other adverse events. SIGNIFICANCE: The results demonstrate feasibility of stimulating both hippocampi using a single deep-brain stimulation electrode in the fornicodorsocommissural tract, efficacy of low-frequency stimulation in reducing seizures, and safety as regards verbal memory.

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

Cross-Cultural Comparison of Rural Healthy Adults: Russian and American Groups.

OBJECTIVE: The Russian-speaking population is among the largest European-born in the U.S., yet Russian-American cross-cultural research is scarce. Two studies compared neuropsychological test performance in Russian and American urban adults. However, rural populations of the two nations have never been compared. Cross-cultural neuropsychological differences in rural populations might present differently than in urban dwellers. The present study provides a cross-sectional comparison of neuropsychological test performance in Russian and American rural adults. METHODS: Neuropsychological test performance of 51 American (67% female) and 52 Russian (60% female) healthy rural adults age 18-89 was compared using t-test with Bonferroni correction for education-adjusted z-scores for the following tests: Rey Complex Figure Test (RCFT), Rey Auditory Verbal Learning Test (RAVLT), Trail Making Test A and B (TMT A&B), Stroop Neuropsychological Screening Test, Benton Judgment of Line Orientation Test (JLO), Brief Visuospatial Memory Test-Revised (BVMT-R), Color Trails Test 1 and 2 (CTT 1&2), WMS-IV Logical Memory Test (LMT), WAIS-IV Digit Span Forward (DSF) and Backward Test (DSB), and Symbol Digit Modalities Test (SDMT). RESULTS: Age and sex distribution did not differ in the two groups, but the Russian group was more highly educated. The American group outperformed the Russian group on TMT B, CTT 2, recognition trials of RCFT, BVMT-R, LMT, and on DSF. CONCLUSIONS: Cultural differences in attitudes to timed activities, experience with timed tests and multiple-choice format, attention to details, and length of digit-words that put differential demand on short-term memory in Russian and in English may mediate observed between-group differences.

Date of Admission during COVID-19 Pandemic Impacted Patient Outcomes in Addition to the Higher Efficacy of Tocilizumab Plus High-Dose Corticosteroid Therapy Compared to Tocilizumab Alone.

BACKGROUND: The evidence for the efficacy of glucocorticoids combined with tocilizumab (TCZ) in COVID-19 comes from observational studies or subgroup analysis. Our aim was to compare outcomes between hospitalized COVID-19 patients who received high-dose corticosteroid pulse therapy and TCZ and those who received TCZ. METHODS: A retrospective single-center study was performed on consecutive hospitalized patients with severe COVID-19 between 1 March and 23 April 2020. Patients treated with either TCZ (400-600 mg, one to two doses) and methylprednisolone pulses (MPD-TCZ group) or TCZ alone were analyzed for the occurrence of a combined endpoint of death and need for invasive mechanical ventilation during admission. The independence of both treatment groups was tested using machine learning classifiers, and relevant variables that were potentially different between the groups were measured through a mean decrease accuracy algorithm. RESULTS: An earlier date of admission was significantly associated with worse outcomes regardless of treatment type. Twenty patients died (27.0%) in the TCZ group, and 33 (44.6%) died or required intubation (n = 74), whereas in the MPD-TCZ group, 15 (11.0%) patients died and 29 (21.3%) patients reached the combined endpoint (n = 136; p = 0.006 and p < 0.001, respectively). Machine learning methodology using a random forest classifier confirmed significant differences between the treatment groups. CONCLUSIONS: MPD and TCZ improved outcomes (death and invasive mechanical ventilation) among hospitalized COVID-19 patients, but confounding variables such as the date of admission during the COVID-19 pandemic should be considered in observational studies.

Coeliac disease is a risk factor for the development of seronegative arthritis in patients with autoimmune thyroid disease.

OBJECTIVE: Hashimoto's thyroiditis is known to cluster with other systemic autoimmune disorders. Rheumatic manifestations, such as a seronegative non-erosive polyarthritis have been described. The aim of this study was to evaluate the characteristics and the prevalence of rheumatic features in thyroiditis patients, and to ascertain whether the association with systemic autoimmune disorders improved the arthritis manifestations. METHODS: In total, 180 thyroiditis patients were enrolled. Major clinical and demographic characteristics have been recorded. Patients underwent a rheumatological clinical assessment and extra-articular manifestations allowing for a differential diagnosis with systemic autoimmune diseases and spondyloarthropathy. Presence of systemic autoimmune diseases was recorded. RESULTS: A total of 8.33% of thyroiditis patients shown a peripheral inflammatory arthritis (P = 0.002). Female gender (P = 0.042) and thyroid peroxidase (TPOAbs) positivity (P = 0.001) were more frequent. In total, 37 patients had systemic autoimmune diseases (P = 0.0003). A significant high prevalence of coeliac disease and Addison disease was found (P = 0.034 and P = 0.049, respectively). In patients with coeliac disease, the articular manifestations were more frequent (21.21%) (P = 0.001) and the risk to develop joint involvement was 2.96. CONCLUSION: Although we found an articular involvement in about one-third of thyroiditis patients, the prevalence of inflammatory arthropathy was only 8.33%. The prevalence of other coexisting autoimmune disorders was 34.26% with a significant prevalence of coeliac disease (7.41%). Thyroiditis patients with coeliac disease have an articular involvement more frequently than those without. In these patients, we have found a high risk of developing arthritis than patients with only thyroiditis, suggesting cumulative autoimmune effects in the developing articular involvement.

May the analysis of 1918 influenza pandemic give hints to imagine the possible magnitude of Corona Virus Disease-2019 (COVID-19)?

BACKGROUND: In 1918 an unknown infectious agent spread around the world infecting over one-third of the general population and killing almost 50 million people. Many countries were at war, the First World War. Since Spain was a neutral country and Spanish press could report about the infection without censorship, this condition is commonly remembered as "Spanish influenza". This review examines several aspects during the 1918 influenza pandemic to bring out evidences which might be useful to imagine the possible magnitude of the present coronavirus disease 2019 (COVID-19). METHODS: In the first part of this review we will examine the origin of the SARS-Coronavirus-2 and 1918 Spanish Influenza Virus and the role played by host and environment in its diffusion. We will also include in our analysis an evaluation of different approaches utilized to restrain the spread of pandemic and to treat infected patients. In the second part, we will try to imagine the magnitude of the present COVID-19 pandemic and the possible measures able to restrain in the present environment its spread. RESULTS: Several factors characterize the outcome in a viral pandemic infection. They include the complete knowledge of the virus, the complete knowledge of the host and of the environment where the host lives and the pandemic develops. CONCLUSION: By comparing the situation seen in 1918 with the current one, we are now in a more favourable position. The experience of the past teaches us that their success is linked to a rapid, constant and lasting application. Then, rather than coercion, awareness of the need to observe such prevention measures works better.

COVID-19: psychological effects on a COVID-19 quarantined population in Bangladesh.

BACKGROUND: It had been seven months since the first confirmed case (8th March, 2020) of COVID-19 in Bangladesh and people have now got a more complete picture of the extent of the pandemic. Therefore, it is time to evaluate the effects of COVID-19 on mental health. The current population-based study aimed to assess the prevalence of depression and PTSD of the quarantined people in Bangladesh during COVID-19 outbreak. METHODS: A total of 5792 individuals comprised the population of this study. Subjects were respondents to an online questionnaire that was administered through social media. The questionnaire included questions on personal information, quarantine related knowledge, items of the Impact of Event Scale-Revised (IES-R) and the Center for Epidemiologic Studies-Depression (CES-D) scale. Data were collected and analyzed by regression utilizing a using IBM SPSS-22 (Statistical Package for Social Science, Chicago, IL, USA). RESULTS: The most post-traumatic stress disorder (PTSD) symptoms had on the male (n = 1392, 75.7%) who had institutional quarantine. The most depression symptoms were on the female (n = 920, 72.8%) whose income was more than 75000 takas in Bangladeshi currency. 81.8% of respondents had PTSD and their scores ≥24 in the IES-R scale. On the other hand, the respondent's income was 40000-74999 takas in Bangladesh currency had more PTSD symptoms and the odd ratio 19.3 (95% CI: 12.5-27.3), adjusted odds ratio 22.9 (95% CI: 15.6-32.4), after adjusting all personal variables. 85.9% respondents scored 16 ≤ in the CES-D scale, meaning they were depressed. The respondents whose education level grade 10 were most depressed and the odd ratio of 3.8 (95% CI: 3.1-4.65), adjusted odds ratio of 13.19 (95% CI: 9.88-17.62) after adjusting all personal variables. CONCLUSIONS: The prevalence of depression and PTSD of the quarantined people higher than that of the affected group during the outbreak of COVID-19 in Bangladesh. If the administration and health care workers become conscious of such results, actions and policies can be taken to improve the consequential sufferings.

Prevalence and correlates of concussion in children: Data from the Adolescent Brain Cognitive Development study.

INTRODUCTION: Concussions are one of the most common causes for emergency room use in the United States (US) among youth and adolescents; however, prevalence data on concussion in this population are inconsistent. A growing body of literature has explored associations of a range of variables with pediatric concussion, but they have not been explored simultaneously in a well-powered sample in the US. The present study aimed to present lifetime concussion prevalence, evaluate demographic, psychological, and cognitive correlates of concussion, and assess for differences across these variables based on age of first concussion in a large sample of US children. METHODS: We analyzed the Adolescent Brain Cognitive Development (ABCD) sample, which monitors biopsychosocial development in 11,875 children at 21 sites across the US between ages 9 and 10. Along with presenting rates of concussion, we also evaluated the association of demographics, sleep disturbance, cognitive functioning, and externalizing and internalizing symptoms with concussion history using backwards binary logistic regression. We further conducted univariate comparisons of all variables between those who experienced their first concussion before and after age 5. Significance was based on α = .02, with Benjamini-Hochberg FDR adjustments for multiple comparisons. RESULTS: We found approximately 4% of the sample had experienced a concussion, and significant correlates of experiencing a concussion were male sex, increased family income, and higher somatic symptoms after FDR correction. Symptoms of ADHD were also noted as nominally significant. No differences based on age of first concussion were found. DISCUSSION: Our analyses provided updated prevalence estimates of pediatric concussion in the US that aligns with many hospital records-based studies. Our findings largely mirrored those in the literature with the exception of somatic symptoms. Limitations of findings and implications of individual findings are discussed.