Evaluate how a foundation-supported fellowship employs early health-technology assessment (eHTA) to guide the development and positioning of emerging health innovations.

We reviewed all eHTA reports conducted under the Fellowship from 2018 to 2021 (n = 10), extracting technology class, development stage, economic modeling, and recommendations. In 2023, we conducted thirty-minute structured video interviews with developers of each technology (eleven invitees, ten responses). The interview comprised Likert questions on perceived usefulness and intention to update the model in later stages, and six open-ended questions on perceived advantages, implementation barriers, and downstream actions. Likert data were summarized descriptively; open-ended responses were summarized and discussed within the research team until consensus on key themes.

The eHTA subject technologies were four diagnostics, three therapeutics, two predictive algorithms, and one curative device, all preclinical. Analyses used six Markov or decision-tree frameworks, four hybrid models or simulations, and six value-based-pricing scenarios. Five technologies were potentially cost-effective, three conditionally cost-effective, one unlikely to be cost-effective without stronger evidence, and one cost-effective yet unlikely to break even. Eight developers rated eHTA “useful” or “very useful”; three had already leveraged results in grant or investor materials and two planned to do so when more data emerged. Reported barriers included evidence gaps, funding constraints, and misalignment with pharmaceutical partners on codevelopment strategies; two projects were discontinued.

eHTA supplies developers with early economic insight, but its guidance is most reliable when interpreted alongside budget impact, feasibility, regulatory, and adoption considerations.

The use of explosive weapons in populated areas (EWIPA) has a disproportionate impact on civilians. Many humanitarian organizations utilize varying sheltering guidelines to recommend safe positions for civilians affected by explosive threats. It is not known whether these recommendations are standardized or derived from evidence. This study aimed to identify existing recommendations and potential gaps in literature relevant to sheltering guidelines for civilians during explosive events.

A scoping review was conducted of the literature including indexed databases and grey literature to identify reports that described sheltering guidelines for civilians during explosive events. Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology was followed.

The search identified 3582 peer-reviewed records. After title/abstract and full text screening, only 2 peer-reviewed reports remained eligible. These were combined with 13 gray literature reports obtained from humanitarian organizations and internet searches. The peer-reviewed reports included mine and unexploded ordnance guidelines, not guidelines for EWIPA or aerial bombardments. There is a substantial knowledge gap and heterogeneity in existing sheltering guidelines from explosive events, particularly those appropriate for protection from EWIPA.

Findings from this scoping review demonstrate a need for the creation and standardization of evidence-based civilian sheltering guidelines to mitigate the threat of explosive weapons to civilians in conflict.

Natural language processing (NLP) methods hold promise for improving clinical prediction by utilising information otherwise hidden in the clinical notes of electronic health records. However, clinical practice – as well as the systems and databases in which clinical notes are recorded and stored – change over time. As a consequence, the content of clinical notes may also change over time, which could degrade the performance of prediction models. Despite its importance, the stability of clinical notes over time has rarely been tested.

The lexical stability of clinical notes from the Psychiatric Services of the Central Denmark Region in the period from January 1, 2011, to November 22, 2021 (a total of 14,811,551 clinical notes describing 129,570 patients) was assessed by quantifying sentence length, readability, syntactic complexity and clinical content. Changepoint detection models were used to estimate potential changes in these metrics.

We find lexical stability of the clinical notes over time, with minor deviations during the COVID-19 pandemic. Out of 2988 data points, 17 possible changepoints (corresponding to 0.6%) were detected. The majority of these were related to the discontinuation of a specific note type.

We find lexical and syntactic stability of clinical notes from psychiatric services over time, which bodes well for the use of NLP for predictive modelling in clinical psychiatry.

Recent estimates suggest that 40% of dementia cases could be avoided by treating recognised cardiovascular risk factors such as hypertension, diabetes, smoking and physical inactivity. Whether diet is associated with dementia remains largely unknown. We tested if low adherence to established dietary guidelines is associated with elevated lipids and lipoproteins and with increased risk of Alzheimer's disease and non-Alzheimer's dementia – a dementia subtype with a high frequency of cardiovascular risk factors.

We used the prospective Copenhagen General Population Study including 94 184 individuals with dietary information and free of dementia at baseline. Mean age at study entry was 58 years, and 55% (N = 51 720) were women and 45% (N = 42 464) were men. Adherence to dietary guidelines was grouped into low, intermediate and high adherence based on food frequency questionnaires. Main outcomes were non-Alzheimer's dementia and Alzheimer's disease.

Low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol and plasma triglyceride levels were higher in individuals with intermediate and low adherence to dietary guidelines compared with individuals with high adherence (all p for trends <0.001). Age and sex-adjusted hazard ratios (HRs) for non-Alzheimer's dementia v. individuals with high adherence were 1.19 (95% confidence interval 0.97–1.46) for intermediate adherence, and 1.54 (1.18–2.00) for low adherence. Corresponding HRs in multivariable-adjusted models including APOE genotype were 1.14 (0.92–1.40) and 1.35 (1.03–1.79). These relationships were not observed in individuals on lipid-lowering therapy.

Low adherence to national dietary guidelines is associated with an atherogenic lipid profile and with increased risk of non-Alzheimer's dementia – the subtype of dementia with a high frequency of vascular risk factors. This study suggests that implementation of dietary guidelines associated with an anti-atherogenic lipid profile could be important for prevention of non-Alzheimer's dementia.

Involuntary admissions are increasing in numbers across Europe.1 They can be traumatic for the patients2 and are associated with large societal costs.3 Individuals with psychotic disorder are at particularly elevated risk of involuntary admission.

This study aims to investigate whether machine learning methods including natural language processing can predict involuntary admission among patients with psychotic disorder.

We have obtained a dataset based on electronic health records for all patients having had at least one contact with the psychiatric services in the Central Denmark Region from 2011 to 2021. This dataset covers more than 120,000 patients, of which approximately 10,000 have been diagnosed with a psychotic disorder. The dataset contains both structured data, such as diagnoses, blood tests etc., as well as unstructured data (text). We will train machine learning models, basic logistic regression-models as well as state-of-the-art neural networks, to predict involuntary admission after contacts to the psychiatric services.

As the machine learning models are under development, no results are available at this time. Preliminary results are expected in spring 2022.

If involuntary admission can be predicted among patients with psychotic disorder based on data from electronic health records, it will pave the way for potentially preventive interventions. References: 1. Sheridans-Rains, L et al., 2019 2. Frueh, B.C et al., 2005 3. Smith,S., 2020

No significant relationships.

We characterized antibiotic prescribing patterns and management practices among recurrent urinary tract infection (rUTI) patients, and we identified factors associated with lack of guideline adherence to antibiotic choice, duration of treatment, and urine cultures obtained. We hypothesized that prior resistance to nitrofurantoin or trimethoprim–sulfamethoxazole (TMP-SMX), shorter intervals between rUTIs, and more frequent rUTIs would be associated with fluoroquinolone or β-lactam prescribing, or longer duration of therapy.

This study was a retrospective database study of adult women with International Classification of Diseases, Tenth Revision (ICD-10) cystitis codes meeting American Urological Association rUTI criteria at outpatient clinics within our academic medical center between 2016 and 2018. We excluded patients with ICD-10 codes indicative of complicated UTI or pyelonephritis. Generalized estimating equations were used for risk-factor analysis.

Among 214 patients with 566 visits, 61.5% of prescriptions comprised first-line agents of nitrofurantoin (39.7%) and TMP-SMX (21.5%), followed by second-line choices of fluoroquinolones (27.2%) and β-lactams (11%). Most fluoroquinolone prescriptions (86.7%), TMP-SMX prescriptions (72.2%), and nitrofurantoin prescriptions (60.2%) exceeded the guideline-recommended duration. Approximately half of visits lacked a urine culture. Receiving care through urology via telephone was associated with receiving a β-lactam (adjusted odds ratio [aOR], 6.34; 95% confidence interval [CI], 2.58–15.56) or fluoroquinolone (OR, 2.28; 95% CI, 1.07–4.86). Having >2 rUTIs during the study period and seeking care from a urology practice (RR, 1.28, 95% CI, 1.15–1.44) were associated with longer antibiotic duration.

We found low guideline concordance for antibiotic choice, duration of therapy and cultures obtained among rUTI patients. These factors represent new targets for outpatient antibiotic stewardship interventions.

To propose a set of internationally harmonized procedures and methods for assessing neurocognitive functions, smell, taste, mental, and psychosocial health, and other factors in adults formally diagnosed with COVID-19 (confirmed as SARS-CoV-2 + WHO definition).

We formed an international and cross-disciplinary NeuroCOVID Neuropsychology Taskforce in April 2020. Seven criteria were used to guide the selection of the recommendations’ methods and procedures: (i) Relevance to all COVID-19 illness stages and longitudinal study design; (ii) Standard, cross-culturally valid or widely available instruments; (iii) Coverage of both direct and indirect causes of COVID-19-associated neurological and psychiatric symptoms; (iv) Control of factors specifically pertinent to COVID-19 that may affect neuropsychological performance; (v) Flexibility of administration (telehealth, computerized, remote/online, face to face); (vi) Harmonization for facilitating international research; (vii) Ease of translation to clinical practice.

The three proposed levels of harmonization include a screening strategy with telehealth option, a medium-size computerized assessment with an online/remote option, and a comprehensive evaluation with flexible administration. The context in which each harmonization level might be used is described. Issues of assessment timelines, guidance for home/remote assessment to support data fidelity and telehealth considerations, cross-cultural adequacy, norms, and impairment definitions are also described.

The proposed recommendations provide rationale and methodological guidance for neuropsychological research studies and clinical assessment in adults with COVID-19. We expect that the use of the recommendations will facilitate data harmonization and global research. Research implementing the recommendations will be crucial to determine their acceptability, usability, and validity.

The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalised predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes. To overcome these challenges, we have established the PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort, which will form the basis for extensive studies in the upcoming years.

PSYCOP is a retrospective cohort study that includes all patients with at least one contact with the psychiatric services of the Central Denmark Region in the period from January 1, 2011, to October 28, 2020 (n = 119 291). All data from the electronic health records (EHR) are included, spanning diagnoses, information on treatments, clinical notes, discharge summaries, laboratory tests, etc. Based on these data, machine learning methods will be used to make prediction models for a range of clinical outcomes, such as diagnostic shifts, treatment response, medical comorbidity, and premature mortality, with an explicit focus on clinical feasibility and implementation.

We expect that studies based on the PSYCOP cohort will advance the field of precision psychiatry through the use of state-of-the-art machine learning methods on a large and representative data set. Implementation of prediction models in clinical psychiatry will likely improve treatment and, hopefully, increase the quality of life and lifespan of those with mental illness.

In this study, we examined the relationship between polygenic liability for depression and number of stressful life events (SLEs) as risk factors for early-onset depression treated in inpatient, outpatient or emergency room settings at psychiatric hospitals in Denmark.

Data were drawn from the iPSYCH2012 case-cohort sample, a population-based sample of individuals born in Denmark between 1981 and 2005. The sample included 18 532 individuals who were diagnosed with depression by a psychiatrist by age 31 years, and a comparison group of 20 184 individuals. Information on SLEs was obtained from nationwide registers and operationalized as a time-varying count variable. Hazard ratios and cumulative incidence rates were estimated using Cox regressions.

Risk for depression increased by 35% with each standard deviation increase in polygenic liability (p < 0.0001), and 36% (p < 0.0001) with each additional SLE. There was a small interaction between polygenic liability and SLEs (β = −0.04, p = 0.0009). The probability of being diagnosed with depression in a hospital-based setting between ages 15 and 31 years ranged from 1.5% among males in the lowest quartile of polygenic liability with 0 events by age 15, to 18.8% among females in the highest quartile of polygenic liability with 4+ events by age 15.

These findings suggest that although there is minimal interaction between polygenic liability and SLEs as risk factors for hospital-treated depression, combining information on these two important risk factors could potentially be useful for identifying high-risk individuals.