Hospital Diagnosis Research Articles

Impact of Immunosuppressive Therapy, Vaccination, and Monoclonal Antibody Use With Outcomes in Liver and Kidney Transplant Recipients With COVID-19: A Retrospective Study.

Patients who have undergone solid organ transplantation are at an elevated risk of severe coronavirus disease (COVID-19) because of post-transplantation immunosuppressive therapy. However, optimization of vaccination, modification of immunosuppression, and implementation of monoclonal antibody (mAb) therapy in transplant recipients with COVID-19 is uncertain. A retrospective cross-sectional study was conducted on patients who underwent liver or kidney transplants and were diagnosed with COVID-19. The association of several vaccine doses, mycophenolate therapy, and mAB therapy with mortality outcomes after COVID-19 diagnosis (3 and 6 months), hospitalization, and length of hospital stay were assessed. This study included 255 patients with a median age of 59 (23-89) were included. Many COVID-19 vaccine doses were not associated with any outcome; however, patients with a liver transplanted with mycophenolate had higher 3-month (19% vs. 0%; p = 0.02) and 6-month (21% vs. 0%; p = 0.01) mortality rates than those who did not. In addition, transplant recipients who received mAb therapy for COVID-19 were less likely to be hospitalized (37% vs. 68%; p < 0.001). For organ transplant recipients with COVID-19, vaccination alone may not be an optimal strategy for preventing serious outcomes. Rather, the types of organ transplant, immunosuppressive therapy (particularly mycophenolate), and COVID-19 treatment strategy should be synergistically considered to promote an optimal therapeutic dynamic for a vulnerable population.

Rapid inference of antibiotic susceptibility phenotype of uropathogens using metagenomic sequencing with neighbor typing.

Urinary tract infections (UTIs) are a common diagnosis in hospitals and are often treated empirically with broad-spectrum antibiotics. These broad-spectrum agents can select for resistance in these bacteria and co-colonizing organisms. The use of narrow-spectrum agents is desirable as an antibiotic stewardship measure; however, it is counterbalanced by the need for adequate therapy. Identification of causative organisms and their antibiotic susceptibility can help direct treatment; however, conventional testing requires days to produce actionable results. Methods to quickly and accurately predict susceptibility phenotypes for pathogens causing UTI could thus improve both patient outcomes and antibiotic stewardship. Here, expanding on previous work showing accurate prediction for certain Gram-positive pathogens, we demonstrate how the use of RASE from metagenomic sequencing can provide informative and rapid phenotype prediction results for common Gram-negative pathogens in UTI, highlighting the future potential of this method to be used in clinical settings to guide empiric antibiotic selection.

Patients with Osteoporotic Vertebral Fractures in Denmark 2010-2018: A National Registry Analysis.

Register-based cohort study based on linked data from multiple national registries. To describe the sociodemographic and health characteristics of individuals in Denmark with osteoporotic vertebral fractures (OVF) and analyze time trends. The study compares labor market participation and comorbidities between OVF patients and a matched control group without these fractures. The incidence and characteristics of OVF patients in Denmark are not well-documented. Multiple national health and economic registries enable linking of individual patient data, providing new insights. We identified individuals with hospital-diagnosed vertebral compression fractures from 2010 to 2018 using the Danish National Patient Register. A control group was matched by sex, age, and municipality. Work status prior to OVF was categorized as employed, on paid sickness benefit, in early retirement, or on old-age pension. Comorbidities were analyzed based on hospital diagnoses within 10 years prior to the fracture. During 2010 to 2018, 20.262 individuals had a hospital contact with an OVF. Sixty-four percent were women and the mean age was 73.6 (SD 12.4) years. OVF incidence increased from 86 in 2010 to 142 per 100,000 persons in 2018. In 2018, the regional incidence ranged from 120 to 154 per 100,000. More than a fifth of the patients were working prior to the fracture, while 77% were in early retirement or old-age pension. The population with OVF had more comorbidities than the matched comparison group. The incidence of hospital-diagnosed OVFs has risen over the last decade, with regional differences. Notably, over 20% of OVF patients were still employed, highlighting the socioeconomic impact of this condition.

Numerical Cincinnati Stroke Scale versus Stroke Severity Screening Tools for the Prehospital Determination of Large Vessel Occlusion

ABSTRACT OBJECTIVES Previous research demonstrated that the numerical Cincinnati Prehospital Stroke Scale (CPSS) identifies large vessel occlusion (LVO) at similar rates compared to dedicated LVO screening tools. We aimed to compare numerical CPSS to additional stroke scales using a national Emergency Medical Services (EMS) database. METHODS Using the ESO Data Collaborative, the largest EMS database with linked hospital data, we retrospectively analyzed prehospital patient records from 2022. Each EMS record was linked to corresponding emergency department (ED) and inpatient records through a data exchange platform. Prehospital CPSS was compared to the Cincinnati Stroke Triage Assessment Tool (C-STAT), the Field Assessment Stroke Triage for Emergency Destination (FAST-ED), and the Balance Eyes Face Arm Speech Time (BE-FAST). The optimal prediction cut points for LVO screening were determined by intersecting the sensitivity and specificity curves for each scale. To compare the discriminative abilities of each scale among those diagnosed with LVO, we used the area under the receiver operating curve (AUROC). RESULTS We identified 17,442 prehospital records from 754 EMS agencies with ≥ 1 documented stroke scale of interest: 30.3% (n = 5,278) had a hospital diagnosis of stroke, of which 71.6% (n = 3,781) were ischemic; of those, 21.6% (n = 817) were diagnosed with LVO. CPSS score ≥ 2 was found to be predictive of LVO with 76.9% sensitivity, 68.0% specificity, and AUROC 0.787 (95% CI 0.722-0.801). All other tools had similar predictive abilities, with sensitivity/specificity/AUROC of: C-STAT 62.5%/76.5%/0.727 (0.555-0.899); FAST-ED 61.4%/76.1%/0.780 (0.725-0.836); BE-FAST 70.4%/67.1%/0.739 (0.697-0.788). CONCLUSIONS The less complex CPSS exhibited comparable performance to three frequently employed LVO detection tools. The EMS leadership, medical directors, and stroke system directors should weigh the complexity of stroke severity instruments and the challenges of ensuring consistent and accurate use when choosing which tool to implement. The straightforward and widely adopted CPSS may improve compliance while maintaining accuracy in LVO detection.

Barriers Experienced by Psychiatric Nurses to Facilitate Therapeutic Relationships With Mental Healthcare Users With Dual Diagnosis in a Psychiatric Hospital in South Africa

ABSTRACTBackgroundPsychiatric nurses are in a unique position to build therapeutic relationships with mental healthcare users with dual diagnoses to foster trust and recovery. However, a dual diagnosis poses barriers to establishing and maintaining a therapeutic nurse–patient relationship.AimThe overall aim of this study was to explore and describe barriers experienced by psychiatric nurses to facilitate therapeutic relationships with mental healthcare users with dual diagnosis in a psychiatric hospital in Limpopo province, South Africa.DesignA qualitative, explorative, descriptive and contextual design was followed.MethodSemi‐structured interviews were conducted with 12 purposively selected participants who cared for mental healthcare users with dual diagnoses. Tech's method of open coding was used to analyse the data.FindingsDisruptive and disrespectful behaviour and mental healthcare users' substance use during hospitalisation resulted in nurses harbouring ambivalent feelings that impaired therapeutic nurse–patient relationships.ConclusionThe barriers affecting therapeutic relationships with mental healthcare users with dual diagnosis should be addressed to enhance recovery and treatment compliance.RecommendationsAn integrated approach with the involvement of the interprofessional team, debriefing and training for nurses may help to foster therapeutic nurse–patient relationships, empower nurses and enhance the recovery of mental healthcare users with dual diagnosis.

Abstract 4139199: Quantifying the Burden of High Polygenic Risk for Cardiometabolic Disease in a US-based Healthcare System Biobank

Background: Healthcare systems are increasingly collecting genotype data on patients. Polygenic risk scores (PRS), which quantify inherited risk of a given disease, have the potential to leverage this data to identify high risk individuals. While PRS for single traits have often been evaluated, the same data can be used to calculate PRSs across traits and evaluate the overall burden of high cardiometabolic polygenic risk using a single test. Methods: We examined the association of cardiometabolic PRS from the Polygenic Score Catalog among 236,393 All of Us (AoU) participants for 6 target traits of interest, using all available PRS for coronary artery disease (CAD; 57 PRS), atrial fibrillation (AF; 30 PRS), heart failure (HF; 9 PRS), type 2 diabetes mellitus (T2DM; 133 PRS), venous thromboembolism (VTE; 9 PRS), and thoracic aortic aneurysm (TAA; 2 PRS). PRSMix is a tool that incorporates information across several PRS for a given trait to improve prediction accuracy for a target population. We generated a PRSMix for each trait and externally benchmarked its performance against individual PRS in the Mass General Brigham Biobank (MGBB). Phenotypes were ascertained using hospital diagnosis and procedural codes. Logistic regression was performed, adjusting for age, sex, and the first 10 principal components of inferred genetic similarity. Results: Of the 53,306 genotyped MGBB participants, 55.6% are female, mean enrollment age is 53 ± 17 years. Each trait-specific PRSMix demonstrated generally stronger associations with the target trait compared to any individual contributing score. This included trait-specific PRSMix associations (OR/SD, [95%CI]) of 2.07 [1.99-2.15] for CAD, 1.88 [1.82-1.95] for AF, 1.47 [1.43-1.51] for HF, 2.07 [2.00-2.14] for T2DM, 1.43 [1.39-1.47] for VTE, and 1.40 [1.34-1.46] for TAA. Findings were consistent across inferred genetically similar ancestry groups, with stronger PRSMix associations for CAD detected in the EUR group (2.14 [2.05-2.22]) compared to the AFR group (1.43 [1.21-1.70]). Approximately 37% of people in MGBB have 3-fold greater risk of at least one cardiometabolic disease, with 22% at risk for CAD, 14% for T2DM, 13% for Afib, 3% for VTE, 2% for HF and &lt;1% for TAA. Conclusion: We developed PRSMix scores for cardiometabolic traits in AoU and validated them in MGBB. We identified 37% of biobank participants with at least 3-fold greater disease risk associated with high polygenic scores for at least one cardiometabolic outcome.

Diabetes distress and depression in type 2 diabetes. A cross-sectional study in 18,000 individuals in the Central Denmark region.

Type 2 diabetes is linked to psychological distress and a doubled risk of depression. This study aims to characterize individuals with type 2 diabetes experiencing diabetes distress and/or depression in relation to lifestyle and metabolic outcomes. A population-based survey in 2020 targeted individuals with type 2 diabetes (aged 18-75 years) in the Central Denmark Region. This cross-sectional study assessed diabetes distress (using Problem-Area-in-Diabetes-scale) and depression (via hospital diagnosis and prescribed medication) as exposures. Logistic regression, adjusting for potential confounders, compared exposed and non-exposed groups on lifestyle habits, metabolic factors and medication usage related to cardio-metabolic risks. Of 18,222 respondents with type 2 diabetes (46% response rate), 11% had depression, 14% had diabetes distress and 4% had both. Compared to those with neither condition, those with depression were more often smokers (OR: 2.0, 95% CI: 1.8; 2.3) and sedentary in leisure time (OR: 2.0, 95% CI: 1.8; 2.2). Diabetes distress was associated with elevated HbA1c (OR: 1.8, 95% CI: 1.5; 2.0) and treatment with insulin (OR: 1.8, 95% CI: 1.6; 2.0). Half with diabetes distress displayed stable blood glucose levels. Those with both conditions had a higher risk of sedentary behaviour (OR: 2.7, 95% CI: 2.3; 3.2), clinical insomnia (OR: 6.5, 95% CI: 5.5; 7.7) and low self-rated health (OR: 7.5, 95% CI: 6.3; 9.0) than those with either psychological condition in isolation. This study emphasizes the importance of recognizing distinct features and risk factors associated with diabetes distress and depression in individuals with type 2 diabetes. Tailored care strategies for comorbid mental health issues are crucial for comprehensive management.

Current situation and capacity of cancer diagnosis and treatment in county hospitals in China: a multicenter cross-sectional survey

PurposeCounty-level hospitals in China constitute a vital component of the healthcare system, delivering essential medical services, emergency care, and specialized treatments, particularly in oncology. This study aimed to comprehensively evaluate the current status of cancer diagnosis and treatment in county hospitals and assess their overall capacity.MethodsA nationwide cross-sectional survey by the China Anti-cancer Association (CACA) from February to May 2023 assessed cancer diagnosis and treatment in mainland China's county hospitals. Using a multi-center approach, a fifty-five-question questionnaire was administered to oncology professionals and management staffs, focusing on hospital facilities, oncology specialists, cancer cases, treatment capabilities, and healthcare collaboration. Questionnaire reliability and validity were established in a 2022 national survey. Participants, aged 18 or older, were recruited through purposive sampling, receiving self-administered questionnaires for confidentiality. SAS 9.4 software facilitated statistical analysis.ResultsThe study encompassed 76 county-level medical institutions, with 82.9% of them featuring established oncology departments providing comprehensive cancer care, including early screening, diagnosis, treatment, and long-term follow-up. Notably, a total of 56.6% of the institutions surveyed implemented programs for cancer screening and early diagnosis. A significant proportion of advanced cancer patients received treatment and follow-up at the county level. Persistent disparities in medical insurance policies at the county level were identified as a continuing challenge. Furthermore, 82.9% of hospitals had cultivated collaborative relationships with higher-level institutions to enhance patient care quality and treatment capacity. The study revealed a 56.6% shortage of remote collaboration platforms in these hospitals. The need for a patient-centered approach in county hospitals was underscored.ConclusionWhile county-level hospitals face challenges, such as a shortage of specialized oncologists and advanced resources, they exhibit progress in standardizing cancer care to enhance diagnosis and treatment in rural areas.

External Validation of the PeRSonal Gestational Diabetes Model for Predicting Adverse Pregnancy Outcomes in Women with Gestational Diabetes: A Retrospective Cohort Study in a Tertiary Hospital in Thailand

Objective: This study aimed to validate the PeRSonal Gestational Diabetes (GDM) model with two-step glucose tolerance diagnostic criteria.Material and Methods: A retrospective cohort study was conducted on participants having delivered with GDM diagnosis in a tertiary hospital; from October 1, 2020, until September 30, 2022. The main outcome was a composite of maternal and perinatal adverse pregnancy complications. Model validation evaluated the predictors and calculated risk by using a two-step glucose tolerance test in the PeRSonal model formula. Model performance was analyzed for discrimination, calibration, and overall performance. Results: This study analyzed 685 from the initial 764 participants with GDM, with 218 (31.8%) developing adverse pregnancy outcomes. The most frequent adverse outcomes were hypertensive disorders in pregnancy 132 (19.3%) and neonatal hypoglycemia 91 (13.3%). This validation achieved an area under the curve (AUC) of 0.70 (95% confidence interval (CI) 0.65 to 0.74), calibration-in-the-large of 0.17, a calibration slope of 1.34, and a Brier score of 0.20, respectively. The cut-off clinical risk probability of 27.5% can predict adverse outcomes with a sensitivity of 67.3%, specificity of 63.8%, a positive predictive value (PPV) of 46.7%, and a negative predictive value (NPV) of 80.5%. Conclusion: The PeRSonal model maintains its predictive effectiveness in two-step glucose tolerance diagnostic criteria.

Educational outcomes of children with major congenital anomalies: Study protocol for a population-based cohort study using linked hospital and education data from England

Background Major congenital anomalies (CAs) affect around 2% of live births and are a primary cause of infant mortality, childhood morbidity and long-term disability, often requiring hospitalisation and/or surgery. Children with CAs are at greater risk of lower educational attainment compared with their peers, which could be due to learning disabilities, higher rates of ill-health and school absences, or lack of adequate educational support. Our study will compare the educational attainment of children with CAs to those of their peers up to age 11 in England, using linked administrative health and education data. Methods We will analyse data from the ECHILD (Education and Child Health Insights from Linked Data) database. Children born in NHS-funded hospitals from 1st September 2003 to 31st August 2008 whose hospital records were linked to their educational records at three Key Stages (ages 4/5, 6/7 and 10/11 years) will be included. Children with different CAs, indicated by recorded hospital diagnosis codes, will be compared to children without CAs. We will compare the proportions of enrolled children who take the assessment, the proportions who reached national expected levels of attainment, and the mean standardised attainment scores for Maths and English at each Key Stage. We will describe variations in outcome by sex, ethnic minority background, region, and neighbourhood deprivation, and perform regression modelling to compare the attainment trajectories of children with and without CAs, controlling for sociodemographic factors. Ethics and dissemination Ethics approval has been obtained for the analyses of the ECHILD database. Our findings will provide information for parents regarding their children’s expected academic potential, and also enable the development of interventions to support those at risk of not doing well. We will disseminate our findings to academics, policy makers, service users and providers through seminars, peer-reviewed publications, conference abstracts and other media (lay summaries and infographics).

Riboflavin transporter deficiency, the search for the undiagnosed: a retrospective data mining study

BackgroundRiboflavin transporter deficiency (RTD) is an inborn error of riboflavin transport causing progressive neurological symptoms if left untreated. While infants with symptomatic RTD rapidly deteriorate, presentation later in childhood or in adulthood is more gradual. Symptoms overlap with more common diseases, carrying a risk of misdiagnosis, and given the relatively recent discovery of the genetic basis of RTD in 2010 it is likely that older patients have not been tested. Treatment with oral riboflavin (vitamin B2) halts disease progression and can be lifesaving. We hypothesized that patients may have been left unrecognized at the time of presentation and therefore we performed a datamining study to detect undiagnosed RTD patients in a tertiary referral hospital.MethodsA systematic search in Electronic Health Records (EHR) of all patients visiting the Amsterdam University Medical Centers between January 2004 and July 2021 was performed by a medical data text-mining tool. Pseudonymized patient records, matching pre-defined search terms (hearing loss or auditory neuropathy spectrum disorders combined with key clinical symptoms or riboflavin) were screened and included if no definitive alternative diagnosis for symptoms indicating possible RTD was found. Included patients were offered genetic testing. We documented total number of patients with possible RTD, number of patients that underwent genetic testing for RTD and results of genetic testing.ResultsEHR of 2.288.901 patients were automatically screened. Thirteen patients with possible RTD were identified and offered genetic testing. Seven patients chose not to participate. Genetic testing was performed in 6 patients and was negative. The datamining did detect all previously known RTD patients in the hospital.ConclusionsBy screening a large cohort of patients of all ages in a tertiary referral hospital in a period spanning 17 years, no new RTD patients were found. Although not all suspected patients underwent genetic testing, our findings suggest that the prevalence of RTD is low and the chance of having missed this diagnosis in a tertiary referral hospital is limited.

Epidemiology of valvular heart disease in France

BackgroundDemographic changes and improvements in the diagnosis and treatment of valvular heart diseases (VHDs) have led to changes in its epidemiological profile. AimsTo describe the epidemiology of VHD in France in 2022. MethodsAdults hospitalized due to VHD in 2022 were identified from the French National Health Data System and categorized by type of VHD on the basis of hospital diagnoses and interventions. Incidence and prevalence rates were calculated using national French demographic data. ResultsIn 2022, 51,894 adults (60.1% men) were hospitalized for VHD (97.0/100,000 inhabitants). The most frequently observed hospitalized VHDs were AS (61.6%) and MR (23.2%). The mean age at hospitalization was 74.0years, and this was higher for AS than MR (77.3 vs 71.2years). Infectious endocarditis was managed during the index hospitalization in 13.3% of patients. During the index hospitalization and the following 6months, 75.0% of patients underwent valve repair or replacement. Among hospitalized patients with AS, 56.9% had transcatheter aortic valve implantation and 24.9% had surgical aortic valve replacement. Among patients hospitalized for MR, 27.1% underwent surgical mitral valve repair, 12.7% transcatheter mitral valve repair and 19.1% mitral valve replacement. The all-cause death rate 1year after hospitalization for VHD was 13.7%. Overall, in France, on 1 January 2023, 1.90% of the adult population had VHD (2.08% of men and 1.72% of women). Overall, 363,574 had aortic stenosis (AS) and 409,570 had mitral regurgitation (MR). ConclusionVHDs are a major burden in France, particularly degenerative valve diseases of the left heart in older adults.

Association between temperature and air pollutants with atrial fibrillation presentations to emergency departments.

Understanding the impact of environmental exposures on disease incidence is important for environmental guidelines, health services management, and advising patients. We aimed to assess the relationship between daily mean temperature and common pollutants with atrial fibrillation (AF) presentations to emergency departments (EDs). The study included consecutive adult patients presenting with AF to EDs from 1/1/2014 to 31/12/2020 with linkage to hospital and emergency discharge diagnosis data. A time series quasi-Poisson regression with a distributed lag nonlinear model was fitted to assess the association between AF with mean air temperature and five common pollutants, overall and according to sex and region, with adjustment for season, day of the week, long-term trend, and co-pollutants. In 82 575 AF presentations to EDs during the study period, mean (standard deviations [SD]) (SD) age was 69.6 (SD 14.7) years and 50.7% were female. AF presentations were associated with elevated levels of Particulate Matter (PM) 2.5 (≥57.7 µg/m3) and nitrous dioxide (NO2) (≥16.5 parts per billion), but not mean air temperature or other pollutants (carbon monoxide, sulphur dioxide, or ozone). The attributable fraction of AF presentations relating to above optimal NO2 and PM 2.5 levels was 7.24% and 3.81% resulting in 854 and 450 annual excess AF presentations, respectively. High levels of NO2 and PM 2.5 are associated with increased risk of AF presentations to EDs. These findings have important implications for environmental policies and advice to patients susceptible to AF presentations to EDs, especially in regions with higher baseline levels of PM 2.5 or NO2.

Diagnoses of children living with HIV before and during the COVID-19 pandemic.

There is limited data on diagnoses during hospital stay among children living with HIV(CLHIV) in the antiretroviral and coronavirus disease 2019 (COVID-19) era. The aim of this study was to describe hospital diagnoses and clinical characteristics of CLHIV before and during the COVID-19 pandemic. A retrospective descriptive cross-sectional study was performed. Clinical and laboratory data were retrieved by reviewing folders and discharge summaries from January 2019 to December 2021. Period A (pre-COVID-19) was defined as the period from January 2019 to March 2020. Period B (During COVID-19) was defined as being from April 2020 to December 2021. Ninety-six children contributed 215 diagnoses over the study period. The five most common diagnoses were unspecified HIV disease (47/215, 21.9%), tuberculosis (TB) (42/215, 19.5%), pneumonia (13/215, 6.0%), encephalopathy (11/215, 5.1%) and malnutrition (11/215, 5.1%). Median CD4 count was 377 cells/mm (IQR 126, 726) and 8.0% of the children were virally suppressed. Ninety-five per cent of the children had WHO Stage 3 and 4 (95%) disease and 12.5% of children required ICU admission. No child was diagnosed with COVID-19 despite universal screening. Moreover, 81.7% of the children had a social worker referral documented. Advanced HIV disease (AHD) remains prevalent with TB being the most common diagnosis. There were no cases of COVID-19 recorded in CLHIV. The findings provide a description of the diagnoses of CLHIV in the South African setting prior to and during the COVID-19 pandemic. It highlights the need for more specific documentation of diagnoses to inform better prevention of AHD in children.

Comparing Italian hospital discharge and mortality data: implications for epidemiological research

Abstract Background In Italy, regional governments maintain data warehouses of linked administrative health data (AHD) linked to demographic information including mortality. Legal reporting pathways for death certificate detail bypass the Region, resulting in significant amounts of missing data on cause and location of death in regional AHD warehouses and limiting epidemiological research on mortality and incidence. In this study, we determine for the first time in Italy the degree of agreement between last hospital diagnosis and primary cause of death, and how that agreement changes over time. Previous studies have found agreement ranging from 40% to 60%. Methods The COV-CVD cohort is comprised of 7.3 million adults aged 30+ from the Lombardy region of northwest Italy, representing all Lombardy residents registered with the health system as of 31 December 2019. Concordance was assessed using the first three characters of the reported cause of death (ICD-10 codes) and primary diagnosis at last hospitalization (converted from ICD-9CM). Results In the COV-CVD cohort there were 401,085 deaths between 1 January 2020 and 23 June 2023. Cause and location of death is missing for 50% and 54% of these deaths, respectively. 60% of deceased individuals had a hospitalization in the year before their death. Agreement between primary diagnosis at last hospitalization and cause of death was 27% overall and 40.5% for deaths in hospital. Missing location of death was reduced from 54% to 26%. Conclusions When using AHD from Italy, primary diagnosis at last hospitalization prior to death is not an appropriate proxy for cause of death, even when the death occurred in hospital. Different coding systems (ICD-10, ICD-9CM) further complicate comparisons and definitions in observational studies. Key messages • In Italy, primary diagnosis at last hospitalization before death is not an appropriate proxy for cause of death. • Poor linkage of mortality and health data limits epidemiological research.

Prescription time trends in patients with high-impact chronic pain: A National Patient Registry Study.

High-impact chronic pain (HICP), defined as chronic pain with a significant impact on daily function, affects approximately 8% of the Western population. In Denmark, HICP still remains to be described at the population level. Some patients with HICP are referred to the Danish pain centres, where they are registered with a procedural code. We conducted a nationwide registry-based study of all Danish patients registered with a visit to a pain centre from January 2005 to March 2022, to explore time trends in the prescription of analgesics and sedatives in this HICP subpopulation. Furthermore, data on socioeconomics and hospital diagnoses are reported. We used data from the Central Person Registry, the Danish National Patient Registry, the Danish National Prescription Registry, combined with data on socioeconomic information from Statistics Denmark. Data was collected on prescriptions 3 months before the first visit to a pain centre. Prescriptions were stratified into five calendar periods: before 2011, 2011-2013, 2014-2016, 2017-2019, and 2020-2022. We identified 66,577 patients referred to a pain centre. Over 15 years, prescriptions before referral declined for opioids (from 53.2% to 31.7%), NSAIDs (from 28.3% to 23.5%), antidepressants (from 20.5% to 16.8%), anxiolytics (from 12.3% to 3.2%), and sleep medication (from 15.8% to 7.6%). In contrast, prescriptions increased for paracetamol (from 31.1% to 48.9%) and gabapentinoids (from 19.2% to 27.7%). In patients with HICP visiting Danish pain centres, prescriptions before referral decreased for opioids over 15 years, with a simultaneously increased prescription of gabapentinoids (gabapentin and pregabalin). This nationwide study of 66,577 Danish patients with high-impact chronic pain reveals a significant decrease in filled opioid prescriptions over the past 15 years, with a simultaneous rise in gabapentinoid use before referral to pain centres. These findings suggest a shift in clinical practice towards alternative pain management strategies. The study underscores the need for continued research into the long-term effects of these changes and their impact on patient outcomes.

Ischaemic cardiotoxicity of aromatase inhibitors in postmenopausal patients with early breast cancer in Denmark: a cohort study of real-world data

For aromatase inhibitor treatment (AIT) in breast cancer, there is an unresolved concern about ischaemic cardiotoxicity. We investigated the association between AIT and ischaemic cardiotoxicity in a prospective cohort of female patients with early breast cancer who received contemporary treatment in Denmark. In this prospective cohort study in Denmark, we identified postmenopausal patients of any age diagnosed with breast cancer as recorded in the nationwide Danish Breast Cancer Cooperative Group (DBCG) clinical database between Jan 1, 2009, and Dec 31, 2020, and linked them to other nationwide registries. Exclusion criteria included having a history of other primary cancer, less than 2 years of residency in Denmark, and no inclusion in a treatment protocol according to the DBCG database, including for metastatic or locally advanced breast cancer. Information on demography, hospital diagnoses, filled prescriptions, laboratory testing, and socioeconomic status were recorded. We stratified the patient cohort according to history (yes vs no) of selected cardiovascular disease defined as ischaemic heart disease, ischaemic stroke, and heart failure, and defined the primary outcome as two-point major adverse cardiovascular events (MACE; acute myocardial infarction or ischaemic stroke). We estimated cause-specific hazard ratios (HRs) according to allocation to AIT versus not in an intention-to-treat analysis using a Cox proportional hazards regression model with age as the underlying time scale, adjusting for demographic characteristics, tumour characteristics, and other anti-cancer treatments. 43 440 postmenopausal patients diagnosed with breast cancer were identified, of whom 32 635 were followed up and included in analyses. Of 29 118 postmenopausal patients with no history of selected cardiovascular disease, we observed 510 two-point MACEs among 22 135 patients allocated to AIT (incidence rate 4·3/1000 person-years of follow-up) and 170 two-point MACEs among 6983 patients not allocated to AIT (4·1/1000 person-years). The adjusted HR was 0·91 (95% CI 0·73-1·14) for patients allocated to AIT versus patients not allocated to AIT. Among 3517 patients with a history of selected cardiovascular disease, we observed 158 two-point MACEs among 2661 patients allocated to AIT (incidence rate 12·4/1000 person-years) and 50 two-point MACEs (12·1/1000 person-years) among 856 patients not allocated to AIT (adjusted HR 0·81 [95% CI 0·58-1·15]). Our findings do not support a clinically relevant ischaemic cardiotoxic potential of AIT in patients with early breast cancer and do not support avoiding AIT prescription in patients with early breast cancer. Bispebjerg and Frederiksberg Hospital, Kræftens Bekæmpelse, Fonden til Lægevidenskabens Fremme, Aase og Ejnar Danielsens Fond, Helsefonden, and Læge Sofus Carl Emil Friis og Hustru Olga Doris Friis' Legat.

Importance and limits of cardiovascular risk factors on the prediction of SCD using machine learning approach

Abstract Background SCD is mainly due to lethal ventricular arrhythmias and occurs often among patients with ischemic heart disease. SCD and ischemic heart disease share a similar pattern of cardiovascular risk factors (CVRF). Commonly developed risk scores have been focused on cardiac-related risk factors. We aimed to investigate whether non-CVRF variables could enhance predictive performance beyond standard CVRF of SCD. Method We compared 2 different strategies for variable inclusion. First, we built a prediction model containing EHR data restricted to cardiovascular diseases and risk factors (CVD MR model) that occurred up to 5 years before SCD. We selected all available main cardiovascular variables as surrogate markers for coronary artery disease, stroke, diabetes, hypertension, smoking status, obesity, lipid disorders and chronic renal failure. On the other hand, our global approach (ALL MR model) includes all medical records codes, without any prior selection. To estimate the risk of SCD over three months, we trained a machine learning model on EHR data representing 8,566,229 drug prescriptions and 801,352 hospital diagnoses up to five years prior to SCD. The data were obtained from a cohort of 12,338 SCD in France and 12,338 controls from 2011 to 2015. We then validated the results on two external cohorts: one temporal in the same area between 2016 and 2020 with 11,620 SCD and 11,620 controls and one geographical from the USA with 892 SCD and 892 controls from 2013 to 2021. Results The CVD MR + ML model (CatBoost algorithm) yielded moderate performances with an AUC of 0.68 (95%CI 0.65-0.68), a sensitivity of 45%.(95%CI 42-47) and a specificity of 82% (95%CI 80-84). We found that the EHR model with all medical records (All MR + ML model) offered better performances. In the derivation cohort, this model achieved an AUC of 0.80 (95% CI: 0.78–0.82) with a sensitivity of 67% (95% CI 64-69), a specificity of 80% (95% CI 78-82) (Figure 1). The logistic regression (All MR + LR) applied on all medical records performed better than the CatBoost algorithm when restricted to CVD medical records (Figure 1). Conclusion The inclusion of all potential variables beyond the usual CVRF significantly improves the performances of SCD prediction models, independently of the methods (AI and logistic regression). Figure 1 : Receiver operating characteristic curves of the different models (ALL MR + ML, ALL MR + LR, CVD MR + ML and CVD MR + LR) in the derivation cohort and two validation cohorts (temporal and geographical)Figure 1.ROC curves

Youth mental health care use during COVID-19: Evidence from administrative data in Alberta, Canada

Abstract Background The COVID-19 pandemic affected health care service delivery and accessibility. Our study examined how Canadian youth used mental health services before and during the COVID-19 pandemic, and whether youth access and use patterns differed for subgroups based on social determinants previously associated with health care access. Methods Cross-sectional population-based administrative health care data from the province of Alberta in Canada was used to examine youth (ages 15-24) mental health care use from 2018/19 to 2021/22. Data was analyzed using an interrupted time series design and segmented regression modeling on type of mental health care use. Outcome measures were mental health care use for: general physician visits, psychiatrist visits, emergency department, hospitalization, and different mental health diagnoses. We stratified by factors related to social determinants, including gender and socioeconomic status. Results The analysis indicates an overall increase in the proportion of youth that used mental health care from 2018/19 to 2021/22 (14.4% vs 17.5%, respectively). There was an immediate drop in use with the onset of the COVID-19 pandemic in Canada, followed by a steady rise during the pandemic years, which was largely due to an increase in general physician visits. This rise was seen for most mental health diagnoses of study (i.e., anxiety, adjustment, attention-deficit/hyperactivity disorder, schizophrenia, and self-harm disorders). Service use increased for young women/girls compared to young men/boys and for youth from wealthier neighborhoods. Conclusions This study provides evidence to support health services planning and delivery to respond to the mental health needs of youth and to prepare for future public health emergencies. Key messages • Our study findings indicate changes in health care use for mental health among Canadian youth (ages 15-24) during the COVID-19 pandemic, with use patterns differing for youth subgroups. • Administrative health data can provide evidence to support health services planning and delivery to respond to the mental health needs of youth and to prepare for future public health emergencies.

Personalized prediction model specific for sudden cardiac death in the population: a machine learning study based on EHR

Abstract Background Predicting sudden cardiac death (SCD) in the general population remains a significant challenge, particularly at the individual level. SCD and myocardial infarction (MI) share similar pattern of characteristics and risk factors, making their specific prediction highly difficult. As a result, the current prediction models exhibit low specificity. Methods To estimate the specific risk of SCD over three months, we trained a machine learning model on electronic health record (EHR) data representing 8,566,229 drug prescriptions and 801,352 of hospital diagnoses up to five years prior to SCD. The data were obtained from a French cohort of 12,338 SCD and from a cohort of 12,338 controls from 2011 to 2015. We then validated the results on two external cohorts: one temporal cohort in the same area between 2016 and 2020 with 11,620 SCD cases and 11,620 controls and one geographical cohort from the USA with 892 SCD cases and 892 controls from 2013 to 2021. In order to address the specificity of our prediction model for SCD, SCD cases collected between 2016 and 2020 were also matched with a group of 35 000 controls who had myocardial infarction, with no SCD, following the same protocol as for the controls issued from the general population. Findings: The analysis reveals three distinct patterns of distribution for SCD cases, controls, and MI cases. We observed a semi linear increase of SCD cases over the deciles of predicted risk, and our model detected 24% (2,908) of SCD cases with a predicted risk exceeding 90% (highest decile). We observed the opposite among the controls that decreased linearly with increasing deciles of predicted risk: most of the controls were accurately identified in the lowest deciles, and there were only 214 controls (2%) in the highest decile. Interestingly, we observed a relatively stable distribution of MI cases across the deciles, with 10% of MI cases in the highest decile (Figure 1). Interpretation: We developed and validated a personalized prediction model that is able to identify subjects at high-risk for SCD specifically. Figure 1: Histogram of predicted risks for sudden cardiac death cases, controls and myocardial infarction cases in the validation cohort.Figure 1