Detailed, precise information on a pharmaceutical’s projected therapeutic use is required for horizon scanning. Inferring an estimated indication from trial protocols is a key skill of horizon scanners. The International Horizon Scanning Initiative (IHSI) database utilizes semi-automated data collection. This pilot aimed to verify that the extraction of relevant word sets to generate an estimated indication could be semi-automated.

Ten drugs approved in Europe in 2021 were selected as the pilot test set. The test set included drugs approved for the treatment of rare diseases (n=4), haemato-oncology (n=3), and non-oncology conditions (n=3). Eight of the drugs were approved based on phase III trials. The assessment comprised a review of the pivotal trial that supported product registration for these drugs. We undertook a comparison between a human curator and a natural language processing (NLP) algorithm in generating granular tags relating to key aspects of the drugs’ estimated indication (stage of disease, patient-specific subgroup, and place in treatment).

In 50 percent of cases, the NLP accurately tagged a word or word set related to stage of disease, patient-specific subgroup, or place in treatment, which was also tagged by human curators. In 50 percent of cases, the NLP did not identify words or word sets tagged by human curators. Where relevant, the NLP successfully tagged the same word sets relating to stage of disease for all drugs in the test set. The same word sets relating to patient-specific subgroup were successfully tagged for three drugs in the set. NLP successfully tagged word sets relating to place in treatment for two drugs.

The NLP algorithm is successful in extracting relevant word sets, which can be used to generate an estimated indication in an automated or semi-automated process. The pilot highlighted that further testing is required to advance the sensitivity of the algorithm. Further piloting exploring both unsupervised and supervised modeling approaches (named entity recognition and deep neural networks, respectively) is planned.

Detail on a technology’s projected therapeutic use is required for horizon scanning. The International Horizon Scanning Initiative (IHSI) database will utilize natural language processing (NLP) augmented by human curation to generate an estimated indication for technologies in development. We compared the estimated indication, generated as a test-set for NLP, with health technology developers’ (HTDs) proposed indications identified from Ireland’s horizon scanning system (HSS).

Eight oncology technologies common to both Ireland’s HSS and the IHSI database were analyzed. The analysis included unlicensed technologies in late-stage development that have not submitted a European marketing authorization application. Ireland’s HSS receives data on proposed indications for technologies from HTDs. IHSI database curators extract and convert terms from clinical trials into structured inputs (condition, combination therapy, stage of disease, place in treatment, patient/disease-specific subgroups) to produce an estimated indication for a technology. We sought to identify, by structured input, the degree of alignment between HTDs’ proposed indications with the IHSI database’s estimated indication.

There was 100 percent alignment between the HTD’s proposed indication and the estimated indication generated in the IHSI database for five of the eight included technology records. There was 83 percent alignment for two records and 67 percent alignment for one record. Across all records there was full alignment on condition, combination therapy details, patient-specific subgroup, disease-specific subgroup, and place in treatment. Stage of disease was the only element where data was either not generated for the IHSI database’s estimated indication, not aligned with the HTD’s proposed indication, or reported in an incorrect field.

There is a high degree of alignment between an HTD-proposed indication and the IHSI-estimated indication. The processes for generating an estimated indication will involve both NLP-generation and human co-curation. The current (curator-selected) elements are being used to train the NLP engine. Thereafter, the engine will process clinical trial data to surface tags for human selection to generate the structured inputs.

Knowledge of sex differences in risk factors for posttraumatic stress disorder (PTSD) can contribute to the development of refined preventive interventions. Therefore, the aim of this study was to examine if women and men differ in their vulnerability to risk factors for PTSD.

As part of the longitudinal AURORA study, 2924 patients seeking emergency department (ED) treatment in the acute aftermath of trauma provided self-report assessments of pre- peri- and post-traumatic risk factors, as well as 3-month PTSD severity. We systematically examined sex-dependent effects of 16 risk factors that have previously been hypothesized to show different associations with PTSD severity in women and men.

Women reported higher PTSD severity at 3-months post-trauma. Z-score comparisons indicated that for five of the 16 examined risk factors the association with 3-month PTSD severity was stronger in men than in women. In multivariable models, interaction effects with sex were observed for pre-traumatic anxiety symptoms, and acute dissociative symptoms; both showed stronger associations with PTSD in men than in women. Subgroup analyses suggested trauma type-conditional effects.

Our findings indicate mechanisms to which men might be particularly vulnerable, demonstrating that known PTSD risk factors might behave differently in women and men. Analyses did not identify any risk factors to which women were more vulnerable than men, pointing toward further mechanisms to explain women's higher PTSD risk. Our study illustrates the need for a more systematic examination of sex differences in contributors to PTSD severity after trauma, which may inform refined preventive interventions.

History of traumatic brain injury (TBI) is associated with increased risk of dementia, but few studies have evaluated whether TBI history alters the course of neurocognitive decline, and existing literature on this topic is limited to short follow-up and smaller samples. The primary aim of this study was to evaluate whether a history of TBI (TBI+) influences neurocognitive decline later-in-life among older adults with or without cognitive impairment [i.e., normally aging, Mild Cognitive Impairment (MCI), or dementia].

Participants included individuals from the National Alzheimer’s Coordinating Center (NACC) who were at least 50 years old and with 3 to 6 visits (M number of visits = 4.43). Participants with any self-reported history of TBI (n = 1,467) were matched 1:1 to individuals with no reported history of TBI (TBI-) from a sample of approximately 45,000 participants using case-control matching based on age (+/- 2 years), sex, education, race, ethnicity, cognitive diagnosis [cognitively normal (CN), MCI, or all-cause dementia], etiology of cognitive impairment, functional decline (Clinical Dementia Rating Scale, CDR), number of Apolipoprotein E4 (APOE ε4) alleles, and number of annual visits (3 to 6). Mixed linear models were used to assess longitudinal neuropsychological test composites (using NACC normative data) of executive functioning/attention/speed (EFAS), language, and memory in TBI+ and TBI- participants. Interactions between TBI and demographics, APOE ε4 status, and cognitive diagnosis were also examined.

Following matching procedures, TBI+ (n=1467) and TBI- (n=1467) groups were nearly identical in age (TBI+ M = 71.59, SD = 8.49; TBI- M = 71.63, SD = 8.44), education (TBI+ M = 16.12, SD = 2.59; TBI- M = 16.10, SD = 2.52), sex (both 55% male), race (both 90% White), ethnicity (both 98% non-Hispanic), APOE ε4 alleles (both 0 = 62%, 1 = 33%, 2 = 5%), baseline cognitive diagnoses (both CN = 60%, MCI = 18%, dementia = 12%), and global CDR (TBI+ M = 0.30, SD = 0.38, TBI- M = 0.30, SD = 0.38). At baseline, groups had similar Z-scores of in EFAS (TBI+ Mefas = -0.02, SD = 1.21; TBI- Mefas = -0.04, SD = 1.27), language (TBI+ MLanguage = -0.48, SD = 0.98; TBI- MLanguage = -0.55, SD = 1.05), and memory (TBI+ MMemory = -0.45, SD = 1.28; TBI- MMemory = -0.45, SD =1.28). The course of change in neuropsychological functioning worsened longitudinally, but did not differ between TBI groups (p’s > .110). There were no significant interactions between TBI history and age, sex, education, race/ethnicity, number of APOE ε4 status, or cognitive diagnosis (all p’s > .027).

In this matched case-control design, our findings suggest that a history of TBI, regardless of demographic factors, APOE ε4 status, and cognitive diagnosis, does not significantly alter the course of neurocognitive functioning later-in-life in older adults with and without cognitive impairment. Future clinicopathological longitudinal studies with well characterized TBI histories and the associated clinical course are needed to help clarify the mechanism by which TBI may increase dementia risk for some individuals, without affecting course of decline.

Patients and their families often ask clinicians to estimate when full-time care (FTC) will be needed after Alzheimer's Disease (AD) is diagnosed. Although a few algorithms predictive algorithms for duration to FTC have been created, these have not been widely adopted for clinical use due to questions regarding precision from limited sample sizes and lack of an easy, user friendly prediction model. Our objective was to develop a clinically relevant, data-driven predictive model using machine learning to estimate time to FTC in AD based on information gathered from a) clinical interview alone, and b) clinical interview plus neuropsychological data.

The National Alzheimer's Coordinating Center dataset was used to examine 3,809 participants (M age at AD diagnosis = 76.05, SD = 9.76; 47.10% male; 87.20% Caucasian) with AD dementia who were aged >50 years, had no history of stroke, and not dependent on others for basic activities of daily living at time of diagnosis based on qualitative self or informant report. To develop a predictive model for time until FTC, supervised machine learning algorithms (e.g., gradient descent, gradient boosting) were implemented. In Model 1, 29 variables captured at the time of AD diagnosis and often gathered in a clinical interview, including sociodemographic factors, psychiatric conditions, medical history, and MMSE, were included. In Model 2, additional neuropsychological variables assessing episodic memory, language, attention, executive function, and processing speed were added. To train and test the algorithm(s), data were split into a 70:30 ratio. Prediction optimization was examined via cross validation using 1000 bootstrapped samples. Model evaluation included assessment of confusion matrices and calculation of accuracy and precision.

The average time to requiring FTC after AD diagnosis was 3.32 years (Range = 0.53-14.57 years). For the clinical interview only model (Model 1), younger age of onset, use of cholinesterase inhibitor medication, incontinence, and apathy were among the clinical variables that significantly predicted duration to FTC, with the largest effects shown for living alone, a positive family history of dementia, and lower MMSE score. In Model 2, the clinical predictors remained significant, and lower Boston Naming Test and Digit-Symbol Coding scores showed the largest effects in predicting duration to FTC among the neuropsychological measures. Final prediction models were further tested using five randomly selected cases. The average estimated time to FTC using the clinical interview model was within an average of 5.2 months of the recorded event and within an average of 5.8 months for the model with neuropsychological data.

Predicting when individuals diagnosed with AD will need FTC is important as the transition often carries significant financial costs related to caregiving. Duration to FTC was predicted by clinical and neuropsychological variables that are easily obtained during standard dementia evaluations. Implementation of the model for prediction of FTC in cases showed encouraging prognostic accuracy. The two models show promise as a first step towards creation of a user friendly prediction calculator that could help clinicians better counsel patients on when FTC after AD diagnosis may occur, though the development of separate models for use in more diverse populations will be essential.

Episodic memory functioning is distributed across two brain circuits, one of which courses through the dorsal anterior cingulate cortex (dACC). Thus, delivering non-invasive neuromodulation technology to the dACC may improve episodic memory functioning in patients with memory problems such as in amnestic mild cognitive impairment (aMCI). This preliminary study is a randomized, double-blinded, sham-controlled clinical trial to examine if high definition transcranial direct current stimulation (HD-tDCS) can be a viable treatment in aMCI.

Eleven aMCI participants, of whom 9 had multidomain deficits, were randomized to receive 1 mA HD-tDCS (N=7) or sham (N=4) stimulation. HD-tDCS was applied over ten 20-minute sessions targeting the dACC. Neuropsychological measures of episodic memory, verbal fluency, and executive function were completed at baseline and after the last HD-tDCS session. Changes in composite scores for memory and language/executive function tests were compared between groups (one-tailed t-tests with a = 0.10 for significance). Clinically significant change, defined as > 1 SD improvement on at least one test in the memory and non-memory domains, was compared between active and sham stimulation based on the frequency of participants in each.

No statistical or clinically significant change (N-1 X2; p = 0.62) was seen in episodic memory for the active HD-tDCS (MDiff = 4.4; SD = 17.1) or sham groups (MDiff = -0.5; SD = 9.7). However, the language and executive function composite showed statistically significant improvement (p = 0.04; MDiff = -15.3; SD = 18.4) for the active HD-tDCS group only (Sham MDiff = -5.8; SD = 10.7). Multiple participants (N=4) in the active group had clinically significant enhancement in language and executive functioning tests, while nobody in the sham group did (p = 0.04).

HD-tDCS targeting the dACC had no direct benefit for episodic memory deficits in aMCI based on preliminary findings for this ongoing clinical trial. However, significant improvement in language and executive function skills occurred in response to HD-tDCS, suggesting HD-tDCS in this configuration has promising potential as an intervention for language and executive function deficits in MCI.

Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians.

In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance.

Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12.

Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.