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The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM)

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... Mental health. The diagnosis of depression in the PRODE cohort was made by a psychiatrist or clinical psychologist in accordance with ICD-10 criteria [20]. ...
... Dementia diagnosis. The diagnosis of dementia was made according to ICD-10 criteria [20]. At year 1, dementia diagnosis was performed during a follow-up in-person clinical assessment. ...
... At year 3, cognition assessment comprised of several rating scales and questionnaires administered over the phone: (a) a validated telephone version of the Mini Mental State Examination (MMSE) was performed in all but 18 participants whose health condition precluded the implementation of the test by telephone; for them, a traditional MMSE was administered in the primary health service [31]; (b) the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), which contains 16 questions on cognitive changes over time (for the purposes of this study, the period over the last two years was addressed; [32]); the Clinical Dementia Rating scale was used to evaluate cognition and functioning including memory, orientation, judgement and problem solving, community affairs, home and hobbies, and personal care [33]. Based on available information, three researchers, all psychiatrists with a PHD, classified cognition at follow-up into three categories: no cognitive impairment, mild cognitive impairment according to the Winblad criteria [34], and dementia according to ICD-10 [20]. ...
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Late-life depression (LLD) has been linked to increased likelihood of dementia, although mechanisms responsible for this association remain largely unknown. One feature frequently observed in both LLD and dementia is elevated levels of plasma inflammatory markers. The present study aimed to compare the levels of 12 plasma inflammatory markers between older people with LLD and controls, and to explore whether these markers, along with clinical characteristics, can predict dementia in patients with LLD within 3 years of follow-up. Using multiple linear regression with stepwise adjustment, we compared levels of plasma inflammatory markers (IL-1β, IL-1ra, IL-6, IL-10, IL-17a, IL-18, IL-33, TNFα, CD40L, IFN-γ, CCL-2 and CCL-4) between 136 inpatients with LLD (PRODE cohort) and 103 cognitively healthy non-depressed controls (COGNORM cohort). In the PRODE cohort, follow-up data was available for 139 patients (of them 123 had data on baseline plasma inflammatory markers); 36 (25.9%) developed dementia by Year 3 (n = 31 for those with cytokine data). Using Cox proportional hazards regression, we explored whether inflammatory markers and clinical characteristics of LLD (age of onset, treatment response, number of episodes) predicted progression to dementia during follow-up. Levels of IL-1ra, CCL-2, CCL-4, IFN-γ and IL-17a were significantly higher in LLD patients compared to controls in the majority of models. However, none of the inflammatory markers predicted progression from LLD to dementia in the PRODE cohort. Among clinical features, only poor response to treatment significantly predicted higher risk of progression to dementia.
... T01.0-T02.0, T04.0, and T06.0 [12]. These codes were utilized irrespective of the presence of additional injury diagnoses. ...
... Patients were classified as having iTBI if all diagnostic variables contained ICD-10 codes beginning with "S00-S09," indicating injuries confined solely to the head region, without any associated injuries to other anatomical areas. Conversely, patients were categorized as having niTBI if any diagnostic variable contained an ICD-10 code with a prefix outside of "S00-S09," signifying the presence of additional injuries in regions other than the head [12]. ...
... To further elucidate the impact of specific concomitant injury sites on clinical outcomes within the niTBI group, we conducted a detailed subgroup analysis. Patients in the niTBI group were categorized based on the presence or absence of injuries to specific anatomical regions, as identified by their ICD-10 diagnostic codes: Neck (S10-S19), Thorax (S20-S29), Abdomen, Pelvis, and Lower Back (S30-S39), Shoulder and Upper Arm (S40-S49), Elbow and Forearm (S50-S59), Wrist, Hand, and Fingers (S60-S69), Hip and Thigh (S70-S79), Knee and Lower Leg (S80-S89), Ankle and Foot (S90-S99) [12]. For each anatomical site, we performed multivariate logistic regression analyses to calculate aORs for the primary outcomes (24-hour mortality and 30-day mortality) and secondary outcomes (hospital admission and interhospital transfer). ...
Article
Objective: Traumatic brain injury (TBI) often occurs alongside injuries to other body regions, worsening patient outcomes. This study aimed to evaluate the impact of concomitant injuries on clinical outcomes in patients with isolated versus non-isolated TBI. Method: A retrospective cross-sectional analysis was conducted using data from the Emergency Department-based Injury In-depth Surveillance System (EDIIS), encompassing 180,058 TBI patients admitted to 23 tertiary hospitals from January 1, 2020, to December 31, 2022. Patients were categorized into isolated TBI (iTBI, n = 127,673) and non-isolated TBI (niTBI, n = 52,385) groups based on injury diagnostic codes. Clinical outcomes-including 24-hour and 30-day mortality, hospital admission, and interhospital transfer-were compared. Multivariate logistic regression analyses adjusted for potential confounders were performed. Result: niTBI patients exhibited significantly higher 24-hour mortality (1.5% vs. 0.4%), 30-day mortality (2.6% vs. 1.0%), hospital admissions (24.5% vs. 8.4%), and interhospital transfers (3.6% vs. 1.1%) compared to iTBI patients (all p < 0.001). Concomitant injuries increased the adjusted odds of 24-hour mortality (aOR = 1.456; 95% CI: 1.286-1.648) and 30-day mortality (aOR = 1.111; 95% CI: 1.022-1.208). Thoracic injuries were the most significant predictor of adverse outcomes in niTBI patients, nearly sixfold increasing the odds of 24-hour mortality (aOR = 5.958; 95% CI: 5.057-7.019). Conclusions: Concomitant injuries significantly worsen clinical outcomes in TBI patients, with thoracic injuries being the most critical predictor of mortality. These findings highlight the importance of comprehensive trauma assessment and targeted prevention strategies to improve survival rates and optimize resource allocation for patients with multiple injuries.
... We identi ed adult (>/= 18 years) health plan members with an ED encounter for LGIB between March 1st, 2018, and March 1st, 2020, using International Classi cation of Disease 10 (ICD-10 codes) to identify encounters with a primary diagnosis of LGIB or undifferentiated GI hemorrhage (Appendix, Table 1). 19 To limit misclassi cation of patients with UGIB, we excluded patient encounters with a completed esophagogastroduodenoscopy (EGD) study without a concomitant study to evaluate for LGIB (speci cally, we searched for evidence of colonoscopy, separating therapeutic from diagnostic, sigmoidoscopy, CT angiography, CT colonography, or tagged RBC scan) during hospitalization (Appendix, Table 2). We excluded patients who left against medical advice, eloped prior to ED provider evaluation, or who had incomplete or missing Oakland Score components. ...
... Co-morbid illness categories were taken from the Elixhauser Co-morbidity Index. 19 Anti-platelet medications included: Aspirin, also called acetylsalicylic acid ( ...
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Background: The Oakland Score predicts risk of 30-day adverse events among hospitalized patients with lower gastrointestinal bleeding (LGIB) possibly identifying patients who may be safe for discharge. The Oakland Score has not been studied among emergency department (ED) patients with LGIB. The Oakland Score composite outcome includes re-bleeding, defined as additional blood transfusion requirements and/or a further decrease in hematocrit (Hct) >/= 20% after 24 hours in clinical stability; red blood cell transfusion; therapeutic intervention to control bleeding, including surgery, mesenteric embolization, or endoscopic hemostasis; in-hospital death, all cause; and re-admission with further LGIB within 28 days. Prediction variables include age, sex, previous LGIB admission, systolic blood pressure, heart rate, and hemoglobin concentration, and scores range from 0 to 35 points, with higher scores indicating greater risk. Methods: Retrospective cohort study of adult (≥18 years old) patients with a primary ED diagnosis of LGIB across 21 EDs from March 1st, 2018, through March 21st, 2020. We excluded patients who were more likely to have upper gastrointestinal bleeding (esophago-gastroduodenoscopy without LGIB evaluation), patients who left against medical advice or prior to ED provider evaluation, ED patients without active health plan membership, and patients with incomplete Oakland Score variables. We assessed predictive accuracy by reporting the area under the receiver operator curve (AUROC) and sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios at multiple clinically relevant thresholds. Results: We identified 8,283 patients with LGIB, 52% were female, mean age was 68, 49% were non-white, and 27% had an adverse event. The AUROC for predicting an adverse event was 0.85 (95% CI: 0.84 to 0.86). There were 1,358 patients with an Oakland Score of </=8; 4.9% had an adverse event, and sensitivity of the Oakland Score at this threshold was 97% (95% CI 0.96 to 0.98). Conclusion: The Oakland Score had high predictive accuracy among ED patients with LGIB. Prospective evaluation is needed to understand if the risk score could augment ED decision-making and improve outcomes and resource utilization.
... The presence of VV was defined as having two or more claims with International Classification of Diseases 10th Revision (ICD-10) diagnostic codes: I83.0 (VV of lower extremities with ulcer), I83.1 (VV of lower extremities with inflammation), I83.2 (VV of lower extremities with ulcer and inflammation), and I83. 9 (asymptomatic VV of lower extremities) [23] . In the CEAP classification, clinical signs of chronic venous disorders are graded based on severity from C0 to C6, indicating no visible signs and active venous ulcers, respectively [3] . ...
... I83.1, I83.2, and I83.9), the presence of VV can be classified as C2 or higher. Moreover, ICD-10 codes I83.0, I83.1, and I83.2 can be classified as C5 or higher, while ICD-10 codes I83.9 can be classified between C2 and C4 [3,23] . This definition was based on a previous study that examined the national trends in treating chronic venous diseases, including VV [25] . ...
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Background Varicose veins (VV) were once considered benign and common ailments; however, recent research suggests a potential link between VV and cardiovascular diseases or mortality. VV share common risk factors and pathophysiology with cardiovascular disease, potentially influencing the vascular system. Therefore, we aimed to investigate the association between VV and the incidence risk of atrial fibrillation (AF) using a population-based cohort. Methods Our retrospective cohort study included 2,680,971 individuals who underwent examination through the Korean National Health Screening Service from 2010 to 2011. VV was defined by two or more claims with the International Classification of Diseases 10th Revision diagnostic codes: I83.0, I83.1, I83.2 (VV of lower extremities with ulcer or inflammation, severe VV), and I83.9 (asymptomatic VV of lower extremities, mild VV). The 1:3 propensity score matching (PSM) was used to assess the risk of newly developed AF, identified via insurance claims coded as I48. Results The mean age of all participants was 48.5±14.2 years, with 51.4% being male. Among the population, 24,557 (0.91%) had VV including 3,684 (0.14%) of severe VV and 20,873 (0.77%) of mild VV. During a median follow-up of 10.06 years, 24,557 (0.92%) cases of AF occurred. Participants with VV exhibited an increased incidence risk of AF compared to those without it before (HR:1.13, 95% CI 1.06–1.21, P <0.001) and after PSM (HR:1.17, 95% CI 1.08–1.27, P <0.001). This positive association was consistently observed in severe VV both before (HR:1.19, 95% CI [1.09-1.28], P =0.002) and after PSM (HR:1.20, 95% CI [1.10-1.30], P =0.003) and mild VV also before (HR:1.10, 95% CI [1.04-1.16], P =0.003) and after PSM (HR:1.13, 95% CI [1.03-1. 20], P <0.001). Conclusions These findings suggest that VV may be associated with an increased risk of AF. Hence, the presence of VV should be considered as an association factor for AF occurrence.
... All data were de-identified and stored in Health Insurance Portability and Accountability Act (HIPAA)-secured environments. A list of patients was generated through the electronic medical record (EMR) using a list of ICD 10 codes [8], including E09.10 (drug or chemicalinduced diabetes mellitus with ketoacidosis without coma), E09.11 (drug or chemical-induced diabetes mellitus with ketoacidosis with coma), E13.10 (other diabetes mellitus with ketoacidosis without coma), E13.11 (other diabetes mellitus with ketoacidosis with coma), E10.10 (type 1 diabetes mellitus with ketoacidosis without coma), E10.11 (type 1 diabetes mellitus with ketoacidosis with coma), E11.10 (type 2 diabetes mellitus with ketoacidosis without coma), E11.11 (type 2 diabetes mellitus with ketoacidosis with coma), E08.10 (diabetes due to underlying condition with ketoacidosis without coma), and E08.11 (diabetes due to underlying condition with ketoacidosis with coma). This resulted in a list of 280 patients over the course of a two year time period. ...
... Participants were recruited, screened and diagnosed by uniformly trained professional psychiatrists according to the International Classification of Diseases 10th Edition (ICD-10) [30]. Inclusion criteria were participants with moderate to severe MDD moderate (score of 17-23) to severe MDD (score≥24) based on the Hamilton Depression Rating Scale-17 (HAMD-17) score [31], between the ages of 18 and 60 years, and administration of SSRIs at least 6 week. ...
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New combinations or alternative therapies for major depressive disorder (MDD) are necessary. Intradermal acupuncture (IA) shows promise but requires further investigation regarding its efficacy, safety, and mechanisms. Conducted across 3 centers from November 2022 to January 2024, our randomized controlled trial included 120 participants with moderate to severe MDD, divided into the selective serotonin reuptake inhibitors (SSRIs), SSRIs plus sham IA (SSRIs + SIA), and SSRIs plus active IA (SSRIs + AIA) groups. Acupuncture groups received 10 sessions over 6 weeks at Shenmen (HT7), Neiguan (PC6), Sanyinjiao (SP6) and Taichong (LR3) bilaterally, followed by a 4-week follow-up. The primary outcome was changes in Hamilton Depression Rating Scale-17 (HAMD-17) scores at week 6. Furthermore, healthy controls (HCs) and MDD patients underwent magnetic resonance imaging (MRI) scans for functional connectivity (FC) analysis. After 6 weeks of treatment, the SSRIs + AIA group showed a greater reduction in HAMD-17 score than the SSRIs + SIA group (MD, −4.9 [CI, −7.6 to −2.2], P < 0.001) and SSRIs group (MD, −5.1 [CI, −7.8 to −2.3], P < 0.001). No serious adverse events occurred. SSRIs + AIA resulted in lower incidences of palpitations (vs.SSRIs + SIA: OR, 0.1% [CI, 0.0–1.0%]; vs. SSRIs: OR, 0.1% [CI, 0.0–0.7%]; P < 0.05), somnolence (vs.SSRIs + SIA: OR, 0.1% [CI, 0.0–0.9%]; vs.SSRIs: OR, 0.1% [CI, 0.0–0.7%]; P < 0.05), and nausea (vs.SSRIs + SIA: OR, 0.1% [CI, 0.0–1.0%]; vs. SSRIs: OR, 0.1% [CI, 0.0–0.9%]; P < 0.05). MDD patients showed abnormal FCs, and IA enhanced FCs between striatum and frontal_inf_tri, and striatum and cerebellum in the MRI study. Overall, IA as adjunctive therapy provides clinical efficacy and safety for MDD, and it may exert antidepressant effects by modulating striatal FCs.
... The use of standardized vocabularies has paved the way for performing matching based on terms (or semantic) similarity [12,13]. In this regard, many methods have emerged to compare pheno-clinical data encoded with SNOMED CT [14], OMIM [15], ICD-10 [16] or HPO (Human Phenotype Ontology) [17] terms [4,10,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. The widespread use of HPO in comparison methods is not coincidental, stemming from its foundation as a standardized vocabulary that structures data into a directed acyclic graph (DAG). ...
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Background Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype–phenotype correlation analysis. To support these efforts, the Global Alliance for Genomics and Health (GA4GH) established Phenopackets v2 and Beacon v2 standards for storing, sharing, and discovering genomic and phenotypic data. These standards provide a consistent framework for organizing biological data, simplifying their transformation into computer-friendly formats. However, matching participants using GA4GH-based formats remains challenging, as current methods are not fully compatible, limiting their effectiveness. Results Here, we introduce Pheno-Ranker, an open-source software toolkit for individual-level comparison of phenotypic data. As input, it accepts JSON/YAML data exchange formats from Beacon v2 and Phenopackets v2 data models, as well as any data structure encoded in JSON, YAML, or CSV formats. Internally, the hierarchical data structure is flattened to one dimension and then transformed through one-hot encoding. This allows for efficient pairwise (all-to-all) comparisons within cohorts or for matching of a patient’s profile in cohorts. Users have the flexibility to refine their comparisons by including or excluding terms, applying weights to variables, and obtaining statistical significance through Z-scores and p-values. The output consists of text files, which can be further analyzed using unsupervised learning techniques, such as clustering or multidimensional scaling (MDS), and with graph analytics. Pheno-Ranker’s performance has been validated with simulated and synthetic data, showing its accuracy, robustness, and efficiency across various health data scenarios. A real data use case from the PRECISESADS study highlights its practical utility in clinical research. Conclusions Pheno-Ranker is a user-friendly, lightweight software for semantic similarity analysis of phenotypic data in Beacon v2 and Phenopackets v2 formats, extendable to other data types. It enables the comparison of a wide range of variables beyond HPO or OMIM terms while preserving full context. The software is designed as a command-line tool with additional utilities for CSV import, data simulation, summary statistics plotting, and QR code generation. For interactive analysis, it also includes a web-based user interface built with R Shiny. Links to the online documentation, including a Google Colab tutorial, and the tool’s source code are available on the project home page: https://github.com/CNAG-Biomedical-Informatics/pheno-ranker.
... Adult cohort Psychiatric disorder diagnosis was defined based on the primary or secondary diagnosis of a mental, mood, schizophrenia and neurotic disorders according to participants hospital inpatient records, coded according to the International Classification of Diseases version 10 (ICD-10) 68 (UK Biobank field 41270; ICD10 codes: F10-F19 Mental and behavioural disorders due to psychoactive substance use, F20-F29 Schizophrenia, schizotypal and delusional disorders, F30-F39 Mood [affective] disorders, F40-F48 Neurotic, stress-related and somatoform disorders). Cardiometabolic disorders diagnosis was defined by the ICD-10 codes from chapter IV Endocrine, nutritional and metabolic diseases and chapter IX Diseases of the circulatory system (UK Biobank fields: 41270; ICD10 codes: E11-Non-insulin-dependent diabetes, I70-Atherosclerosis, I63-Cerebral infarction, I20-I25 Ischaemic heart diseases). ...
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Cardiometabolic and psychiatric disorders often co-exist and share common early life risk factors, such as low birth weight. However, the biological pathways linking early adversity to adult cardiometabolic/psychiatric comorbidity remain unknown. Dopamine (DA) neurotransmission in the striatum is sensitive to early adversity and influences the development of both cardiometabolic and psychiatric diseases. Here we show that a co-expression based polygenic score (ePGS) reflecting individual variations in the expression of the striatal dopamine transporter gene (SLC6A3) network significantly interacts with birth weight to predict psychiatric and cardiometabolic comorbidities in both adults (UK Biobank, N = 225,972) and adolescents (ALSPAC, N = 1188). Decreased birth weight is associated with an increased risk for psychiatric and cardiometabolic comorbidities, but the effect is dependent on a striatal SLC6A3 ePGS, that reflects individual variation in gene expression of genes coexpressed with the SLC6A3 gene in the striatum. Neuroanatomical analyses revealed that SNPs from the striatum SLC6A3 ePGS were significantly associated with prefrontal cortex gray matter density, suggesting a neuroanatomical basis for the link between early adversity and psychiatric and cardiometabolic comorbidity. Our study reveals that psychiatric and cardiometabolic diseases share common developmental pathways and underlying neurobiological mechanisms that includes dopamine signaling in the striatum.
... Recently, researchers also applied various graph neural networks along with link prediction or graph alignment objectives to generate embeddings [Li et al., 2022]. Despite variations in algorithms, all the aforementioned methods are based on the codified concepts from different coding systems such as ICD10 [Organization et al., 2004], CUI [Bodenreider, 2004], and HPO [Robinson et al., 2008]. Different coding systems hinder the ability of the methods to generalize across different databases. ...
Preprint
We introduce GENomic Encoding REpresentation with Language Model (GENEREL), a framework designed to bridge genetic and biomedical knowledge bases. What sets GENEREL apart is its ability to fine-tune language models to infuse biological knowledge behind clinical concepts such as diseases and medications. This fine-tuning enables the model to capture complex biomedical relationships more effectively, enriching the understanding of how genomic data connects to clinical outcomes. By constructing a unified embedding space for biomedical concepts and a wide range of common SNPs from sources such as patient-level data, biomedical knowledge graphs, and GWAS summaries, GENEREL aligns the embeddings of SNPs and clinical concepts through multi-task contrastive learning. This allows the model to adapt to diverse natural language representations of biomedical concepts while bypassing the limitations of traditional code mapping systems across different data sources. Our experiments demonstrate GENEREL's ability to effectively capture the nuanced relationships between SNPs and clinical concepts. GENEREL also emerges to discern the degree of relatedness, potentially allowing for a more refined identification of concepts. This pioneering approach in constructing a unified embedding system for both SNPs and biomedical concepts enhances the potential for data integration and discovery in biomedical research.
... Screening for cohorts was performed either by using the eligible diagnosis of delirium by the ICD-9 and ICD-10 code [16,17] or documentation of the positive Confusion Assessment Method (CAM) and the participants who met this screening criteria were included in the study. Patients admitted to inpatient palliative medicine/care units, psychiatry units, and inpatient hospices were excluded from the study. ...
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Introduction Delirium is a significant inpatient healthcare challenge and has a greater incidence among older adults with adverse healthcare outcomes. Yet there are limited established models for risk stratification. The objective is to determine the effectiveness of implementing the Charlson comorbidity index (CCI) score, which is calculated based on the assigned weight for various disease conditions, and to analyze the healthcare outcomes in older hospitalized adults with delirium. Methodology A retrospective cohort study of 214 elderly hospitalized patients between January 1, 2015, and December 31, 2016, with the diagnosis of delirium, was analyzed by grouping based on the severity of diseases as defined in CCI. The primary analysis outcome was to analyze all-cause inpatient mortality, length of hospital stay (in days), 30-day readmissions, and discharge destination in patients with delirium based on CCI scores using regression analysis and nonparametric tests. Secondary analysis included the prevalence and characteristics of delirium patients in different severity levels of CCI. Results Patients with the severe CCI category (with a total score of five and above) spent 10 days longer in the hospital than those who were categorized with mild delirium (p = 0.011). There is a strong association between in-hospital mortality and the severe CCI category (odds ratio (OR), 4.566; 95% CI, 1.17- 1.86 (p = 0.035)). Also, patients with severe CCI scores were 4.6 times more likely to die during hospitalization compared to patients with less severe comorbidities. There were no significant differences found for discharge destination (OR, 0.702; 95% CI, 371- 1.328 (p = 0.277)) and readmission risk (OR, 1.660; 95% CI, 0.664- 4.149 (p = 0.278)) among different CCI groups. Conclusions Length of stay and inpatient mortality were significantly higher among the severe CCI category compared to the mild category. Our study suggests that CCI can help clinicians, patients, and their families in prognostication and better understanding of goals of care conversations.
... Furthermore, clinicians have developed several standards for defining diseases and related health problems, such as International Classification of Diseases, Version 9 -Clinical Modification (ICD9CM) 11 [35], International Classification of Diseases, Version 10 (ICD10) 12 [36], or Medical Dictionary for Regulatory Activities Terminology (MedDRA) 13 [37]. These are international standards well-known in the medical community. ...
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Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability. Database URL https://zenodo.org/records/7886998.
... Case definitions of neurodevelopmental delays are provided by the World Health Organization (WHO). Neurodevelopmental delay was reclassified according to the International Statistical Classification of Diseases and Related Health Problems-10 (ICD-10) criteria (6,7). The term neurodevelopmental delay generally does not contain precise limits. ...
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ÖZET Giriş: Yenidoğan yoğun bakım ünitelerinin (YYBÜ) artan teknolojik donanımı, deneyimli yoğun bakım ekibi ve destekleyici tedavilerin (antenatal kortikosteroid, sürfaktan) kullanılması, prematüre bebeklerin hayatta kalma oranlarını ve dolayısıyla morbidite oranlarını artırdı. Prematüre bebeklerde (≤ 34 hafta) en sık görülen nörolojik problem olan serebral palsi (SP) ve epilepsi sıklığının değerlendirilmesi amaçlandı. Yöntem: Bu çalışmaya 2016-2020 yılları arasında 34 hafta ve altı doğan ve takip edilen 253 prematüre bebek dahil edildi. Hastaların gebelik yaşı, doğum kilosu, mekanik ventilasyonu, nöbet durumu, YYBÜ’de kalış süresi, nörolojik muayeneleri ve Denver gelişimsel tarama testi (DGTT- II) incelendi. Bulgular: Ortalama gebelik yaşı 29,44 (24-34) hafta, doğum ağırlığı ise 1225 (990-1607) gramdı. YYBÜ’de kaldığı süre boyunca 26 hastada (%10,3) nöbet görüldü. Nöbetler en sık 28-31 haftalık prematürelerde görüldü (%57,7; p=0,02). Elli beş (%21,7) hastada anormal DDSTII vardı. Yenidoğan dönemindeki nöbetler ve içselleştirme süresi ile anormal DGTT- II arasında anlamlı fark vardı (sırasıyla p<0,001, p<0,05). Hastaların 46’sında (%15,8) nörogelişimsel gecikme, dokuzunda (%3,6) SP vardı (beşinde hemiparetik SP, ikisinde spastik parapleji ve ikisinde spastik tetraparezi vardı). On altı (%6,3) hastada epilepsi vardı; 9’unda (%3,6) konuşma bozukluğu vardı; 3’ünde (%1,2) hidrosefali vardı; 1’inde (%0,4) mikrosefali vardı; ve 1’inde (%0,4) makrosefali vardı. Yenidoğan nöbeti geçiren hastalarda epilepsi daha sık görüldü (p<0,001). Sonuç: Sürekli gelişimsel izleme ve değerlendirme, erken doğmuş bebeklerde gelişimsel gecikmelerin erken tespitine olanak sağlar. Erken tanı ve rehabilitasyon programlarına yönlendirme sekel düzeyini azaltabilir. En sık nörogelişimsel gecikme, SP, epilepsi ve konuşma bozukluğuyla karşılaştık. Anahtar Kelimeler: serebral palsi, DGTT- II, epilepsi, erken doğmuş bebekler
... Attention-deficit/hyperactivity disorder (ADHD) is a prevalent condition in children characterised by an inappropriate pattern of attention and/or hyperactivityimpulsivity. Key symptoms typically manifest before the age of 7 (DSM IV; ICD-10) or up to age 16 (DSM 5), with observable altered behaviour in at least two distinct settings [1][2][3]. ...
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Purpose This study aimed to assess the incidence of EEG abnormalities (EEG-ab) in children diagnosed with ADHD, investigate the risk of epileptic seizures (SZ) and maintenance on methylphenidate (MPH) over a three-year period. Methods A total of 517 ADHD children aged 6–14 years were included. Baseline assessments included the identification of EEG-ab, ADHD inattentive subtype (ADHD-I), comorbid epilepsy, the use of antiepileptic drugs (AEDs) and the use of MPH. At the 3-year follow-up, assessments included the presence of EEG-ab, maintenance on MPH, AED usage, SZ risk in cases with EEG-epileptiform abnormalities (EEG-epi-ab), compared with control ADHD cases without EEG-epi-ab matched for age and gender. Results EEG-ab were identified in 273 (52.8%) cases. No statistically significant differences were observed between the EEG-ab and EEG-non-ab groups in terms of age, gender, ADHD-I type or initial use of MPH. EEG non-epileptiform abnormalities (EEG-non-epi-ab) were found in 234 out of 478 (49%) cases without EEG-epi-ab. Notably, EEG-non-epi-ab occurred more frequently in the group of 39 cases with EEG-epi-ab (30/39 (76.9%) vs. 9/39, (21.3%), a subset selected for 3-year follow-up. At 3-year-follow-up no statistically significant difference was found in maintenance on MPH in ADHD cases with and without EEG-epi-ab. Nobody of ADHD cases without comorbid epilepsy or with comorbid epilepsy with achieved SZ freedom developed new SZ. Only 3 children with drug resistant epilepsy experienced SZs, without increase in SZ frequency. The disappearance rate of EEG-epi-ab was higher than that EEG-non-epi-ab (71.8% vs. 33.3%). Conclusions Children with and without EEG-ab exhibited similar patterns of MPH use (initial use, positive response, and maintenance on MPH). The presence of comorbid epilepsy and EEG-ab, with or without EEG-epi-ab, was not associated with an increased risk of SZ despite the use of MPH.
... 7,8 There is an intense discussion as to whether timely NICU interventions do play a role in improving mortality in the neonate and long term better prognosis and survivability as a result , especially in VLBW(Very Low Birth Weight) infants. [9][10][11][12] There is a higher reporting frequency of Trisomy's in nonconsanguineous marriages one example of which is Down Syndrome. 13 Currently diagnosis is based on increasing maternal age, sonographic findings, serum markers and amniocentesis followed in many cases by pregnancy termination as in the West. ...
... (3) Trial characteristics: Number of research centres (multiple-centre or single-centre trial), funding resources, trial registration information, intervention, control, studying diseases, sample size, and length of follow-up. In this study, the disease categories were coded according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, International Classification of Diseases (ICD)-10 classification [30], and the categories of major diseases were based on the "Guidelines for the Use of Disease Definitions for Major Diseases Insurance" (2020 revision) issued by the China Insurance Association and Chinese Medical Doctor Association in November 2020 [31]. The categories of rare diseases were based on the "first Batch of Rare Diseases Catalogue", which was issued by the National Health Commission of China in May 2018 [32]. ...
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Background Randomized controlled trials (RCTs) are usually the basis of evidence-based medicine, but whether the results of RCTs can be correctly translated into clinical practice depends on the quality of the literature reported. In this study, we evaluated the general characteristics and quality of paediatric RCTs published in China to provide evidence for the reporting of paediatric RCTs and their application in clinical practice. Methods We conducted a cross-sectional observational study of paediatric RCTs published in paediatric journals in China between January 1, 1999, and December 30, 2022. All RCTs that included children (younger than 18 years old) were retrieved, and the general characteristics of the RCTs were extracted and analysed. The quality of the RCTs was assessed by the Cochrane quality assessment protocol. Results After screening 20 available paediatric journals, 3545 RCTs were included for analysis. The average annual growth rate of the number of published paediatric RCTs from 1999 to 2022 was 7.8% (P = 0.005, R² = 0.311). Most of the studies were carried out in East China [1148 (32.4%]; the centres of the RCTs were mainly single-centre [3453 (97.4%], and the interventions were mainly medication [2442 (68.9%)]. Comparing RCTs published in 2017–2022 with RCTs published in 1999–2004, the quality of RCTs significantly improved in terms of random sequence generation, allocation concealment, blinding participants and personnel, incomplete outcome data and selective outcome reporting. RCTs published in multiple centres from the Chinese Science Citation Database were identified, and the approval of the ethics committee was of better quality for all the analysed risk of bias items. Conclusion The number and quality of paediatric RCTs reported in China have improved in recent years, but the overall quality was relatively low. Special attention should be given to allocation concealment and blinding outcome assessment, and dropouts, adverse effects and sample size calculations should be reported. Promoting government policies, strengthening the standardization of journal publishing and advancing the registration of clinical trials are feasible measures.
... For each emergency admission, we obtained a pseudonymised patient identifier, patient's age (years), gender (male/female), discharge status (alive/dead), admission and discharge date and time, diagnoses codes based on the 10th revision of the International Statistical Classification of Diseases (ICD-10) [11,12] We had developed and externally validated four risk scores: (1) CARM_N for predicting in-hospital mortality based on NEWS [10]; (2) CARM_NB for predicting in-hospital mortality that incorporates routine blood test results [7]; CARS_N for predicting sepsis based on NEWS [9]; CARS_NB for predicting sepsis that incorporates routine blood test results [8] (see Table 1). These four equations are collectively known as computer-aided risk scoring systems (CARSS), calculated using index NEWS and blood test results. ...
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Background In the UK National Health Service (NHS), the patient’s vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic. Methods Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code ‘U071’ which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically). Results The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)). Conclusions The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.
... The database is retrospective and contains all daily primary care visits from the period 2010-2019 (L = 3, 653). Each visit is associated with a diagnosis d i coded according to the medical ontology CIM-10 [48] (at an aggregation level of disease code, N = 1, 846). In total, the database contains information on 6,301,095 patients. ...
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Hybrid forecasting methods have emerged as a solution surpassing the limitations of both statistical and deep learning approaches. While the first emphasize the significance of variables, they often produce worse forecasting results when compared to newer techniques. In contrast, deep learning models remain enigmatic "black boxes" in terms of interpretability, although achieving better results in forecasting. This article introduces the Comprehensive Cross-Correlation and Lagged Linear Regression Deep Learning (CCLR-DL) framework, designed to harness the best of both approaches, enhancing forecasting accuracy while retaining model interpretability through a feature selection process. CCLR-DL blens cross-correlation analysis, lagged multiple linear regression and granger's causality procedures with deep learning architectures based on LSTM. In a practical demonstration, CCLR-DL was applied to a real database of clinical visits associated to diagnoses in Catalonia, Spain (tracking a population of 6.3 million patients during 10 years). Predicting visits enables the healthcare managers to be ready for future demand shifts. Results demonstrate a consistent and substantial improvement over standalone statistical and deep learning methods when predicting healthcare demand. This hybrid approach not only showcases its efficacy but also offers a promising solution to the challenge of balancing predictive accuracy with model explicability. In this context, this work aims to design and validate a method for feature selection and forecasting of multivariate high dimensional time series datasets not only to improve prediction accuracy but also to model transparency by identifying a subset of variables that improve predictions and G-cause the target variable.
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Objectives Interoceptive awareness is crucial in mental health research, yet the psychometric characteristics of the most commonly used tool to measure it, the Multidimensional Assessment of Interoceptive Awareness (MAIA), have been underexplored, as the results of previous studies have been inconclusive. This study aimed to evaluate the psychometric characteristics of the Czech version of the tool (MAIACZ). Method Participants were sampled from seven clinical sites in the Czech Republic and the final sample consisted of 431 Czech clinical patients (75% women, Mage = 39.20 ± 11.04 years), suffering from various psychiatric diagnoses. Patients were measured weekly across 8 weeks. A combination of factor analysis and network models was employed. Results Confirmatory factor analysis (CFA) supported the commonly used eight-factor structure of the MAIA, consistent with the original English version, though subscales Not distracting, Not worrying, and Noticing showed poor internal consistency. Invariance across gender, age, and measurement waves was confirmed using multigroup CFA. Convergence validity was established through correlations between MAIACZ subscales and measures of anxiety, depression, alexithymia, and symptom acceptance. A bootstrapped dynamic network model, conducted over 8 weekly measurement waves, showed the dynamic associations between MAIA subscales and its relation to wellbeing. Conclusions Contrary to the theoretical model, the temporal graph revealed that the main source nodes were related to the subjective mindset towards interoception, such as the tendency not to worry or distract from bodily signals, rather than the neutral perception of interoceptive stimuli. Well-being was also a prerequisite rather than a consequence of interoceptive awareness-related mechanisms. The Trusting subscale, which has received significant support for its predictive effects in previous literature, was found to be related to wellbeing primarily at the between-person level.
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Schizophrenia spectrum disorders (SSD) are associated with accelerated brain aging, reflected in an increased brain age gap. This gap serves as a biomarker, indicating poorer brain health, cognitive deficits, and greater severity in specific symptom domains. Physical exercise holds promise as an adjunct therapy to mitigate these deficits by potentially promoting brain recovery. However, the extent of overall improvements in brain health following exercise, along with their predictors and relationships to symptom clusters, are yet to be determined. This study examined the brain age gap metric as a quantitative indicator of brain recovery in response to physical exercise. To achieve this, we aggregated data from two randomized controlled trials, analyzing baseline (n = 134) and 3- or 6-month post-exercise (n = 46) data from individuals with SSD. Our findings revealed that patients with a higher baseline BMI demonstrated greater brain recovery, as evidenced by a reduced brain age gap post-exercise. Furthermore, changes in the brain age gap were associated with improvements in negative symptoms and cognition, suggesting that reductions in brain-predicted age may reflect symptom relief, particularly in domains beyond positive symptoms. These results underscore the importance of BMI in brain health, support using the brain age gap as a surrogate marker for tracking clinically relevant brain recovery, and highlight the need for stratified interventions and combined lifestyle modifications to enhance outcomes in SSD. Keywords: schizophrenia spectrum disorders, brain age gap, physical exercise, neuroplasticity, brain recovery, treatment response, polygenic risk
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Electronic medical records (EMRs) are essential in clinical practice. Although current medical large language models (LLMs) excel in tasks like US Medical Licensing Examination, they struggle with real-world clinical applications due to insufficient large-scale EMR data in their training, hindering their clinical expertise. To address this limitation, we proposed EMR-LLM, an LLM for clinical practice using EMRs. Firstly, we continually pre-trained a general LLM on medical corpora to enhance its domain knowledge. Then, we designed three categories of instruction tasks using EMRs: structure understanding, numerical understanding, and downstream tasks. Finally, we introduced an ability-boosting instruction-tuning method, which mimics human learning, progressing from simple to complex tasks while introducing a data replay strategy to retain learned knowledge. Experimental results demonstrated that EMR-LLM outperformed strong competitors on six EMR tasks, nine medical benchmarks, and three open-domain benchmarks. Moreover, in discharge summary generation, EMR-LLM achieved performance levels close to those of expert clinicians.
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Challenging behavior is prevalent and persistent in people with intellectual and developmental disabilities and is considered a public health concern. Challenging behavior in general, and aggressive behavior in particular pose problems in behavioral management, which are the main reasons for emergency and hospital admissions and have a significant negative impact on people’s health and quality of life. This chapter reviews the definition, prevalence, and diverse etiology of challenging behavior and its assessment and treatment across the lifespan, including information on service models, direct care staff, and the latest directions and research findings on the topic.
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Background Verbal autopsy (VA), though imperfect, serves as a vital tool to determine cause-of-death, particularly for out-of-facility deaths, but challenges persist in integrating VA into Civil Registration and Vital Statistics systems. Objective To describe the challenges and successes of collecting a national sample of verbal autopsy interviews in South Africa to obtain the cause of death profile in 2017/18. Methods We recruited next of kin from 27 randomly selected sub-districts (10.5%) across South Africa between September 2017 and April 2018. Trained fieldworkers conducted face-to-face interviews using the WHO2016 VA instrument, with physicians certifying underlying causes of death. Feasibility was evaluated based on response rates, participation, and data quality. Results Of the total 36,976 deaths registered, only 26% were identified during recruitment, with a 55% overall response rate for VA interviews. Physician-reviewed VA data were deemed of good quality for assigning underlying causes of death in 83% of cases. By comparing cause-specific mortality fractions, physician-reviewed VA identified 22.3% HIV/AIDS and InterVA-5 identified 18.5%, aligning with burden of disease estimates, while Statistics South Africa reported 4.9% HIV/AIDS. Conclusions The study demonstrated the feasibility of using VA on a national scale, but immense challenges in identifying and recruiting next of kin highlight the importance of formalising VAs within the country’s death notification system.
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Psychiatric clinical diagnostic formulation has evolved over time. The changes alter our understanding and our ability to provide a public health perspective on the epidemiology of mental disorders in large populations. Epidemiology is an important perspective and set of tools to assess prevalence, treated prevalence, untreated prevalence, individual risks for mental disorders, and possible links to the etiology of disorders by following the trails of environmental exposures, biological measures, interpersonal dynamics, and genetic risk factors. As communities develop health-care services to treat individuals with mental disorders, knowledge about their prevalence and treatment requirements is also important. Since severe mental disorders may require institutional care, the diagnostic criteria used may either protect an individual's liability for dangerous behavior (i.e. the insanity defense) or be used to control political and social dissidents. The criteria may also be used to determine evidence-based treatment options and eligibility for disability benefits. In this paper, using my vantage points as a physician scientist and public health officer, with leadership positions in national federal and professional mental health organizations, I address the developments in these areas over the past 50 years that have influenced institutional positions in the U.S. National Institute of Mental Health, the World Health Organization, the American Psychiatric Association, and the Center for the Study of Traumatic Stress. These perspectives may aid the next generation of investigators to advance the epidemiological and mental disorder classification scientific fields.
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Despite extensive research, significant gaps remain in understanding racial disparity among individuals with cardiovascular diseases (CVD). These disparities, influenced by factors such as access to care and comorbid conditions, necessitate further investigation to develop targeted interventions. To evaluate the factors contributing to racial and ethnic disparities in healthcare resource utilization and total healthcare expenditure among individuals with CVD. Using data from the Medical Expenditure Panel Survey spanning 2014–2021, total healthcare expenditure and having a CVD visit were compared among Hispanic, Black, and White adults with CVD. Descriptive analysis, linear regression, and logistic regression models were used to compare the results. Multivariable models were used to evaluate the effect of demographic and socioeconomic factors on total healthcare expenditure and the likelihood of having a CVD visit among different races. With a weighted sample of 17,722,706, the study found that Hispanic and Black cohorts had 23% and 11% lower healthcare expenditures (both p < 0.001). Hispanic and Black cohorts also had lower odds of having a CVD visit (odds ratio [OR] = 0.61, 95% confidence interval [CI]:0.55–0.68; OR = 0.58, 95% CI: 0.52–0.65, respectively) compared to the White cohort. Key predictors included physical and cognitive limitations, insurance status, income, region, and the year of data collection. This study highlights the need for targeted interventions to address healthcare disparities and promote health equity among minority populations with CVD.
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Background Bipolar affective disorder (BPAD) and major depressive disorder (MDD) are two mood disorders whose pathophysiology may involve the purinergic system. Elevated uric acid levels, associated with this system, can impact various behaviors in individuals affected by these conditions. In addition to genetic predisposition, blood uric acid levels can be impacted by various factors, including metabolic syndrome, the consumption of psychoactive medications, and other underlying kidney conditions such as gout. Objective The study aims to investigate the relationship between blood uric acid levels and mental health conditions, specifically BPAD subtypes (manic and depressive) and MDD. The study also examines changes in blood uric acid levels following treatment and evaluates the effectiveness of different treatment approaches in reducing uric acid levels. Methodology To be eligible to participate, individuals must have a confirmed diagnosis of BPAD (manic or depressive type) or MDD, according to the International Classification of Diseases (ICD-10). Blood uric acid levels were measured at both baseline and follow-up assessments. Symptoms were assessed weekly using standardized rating scales (Young Mania Rating Scale (YMRS) and Hamilton Rating Scale for Depression (HAM-D)) until treatment response was achieved, which was defined as a 50% reduction in initial scores on both scales. We used ANOVA to examine the differences among the three patient groups and paired sample t-tests to examine the changes in means before and after treatment conditions. Results A significant positive correlation was found between the severity of illness and serum uric acid levels across all three patient groups: those with BPAD-mania, BPAD-depression, and MDD. Notably, patients with BPAD-mania patients had significantly higher serum uric acid levels (5.2±0.9 mg/dL) compared to those with BPAD-depression (4.8±1.0 mg/dL) and MDD (4.0±1.1 mg/dL). After treatment, all patient groups exhibited a decrease in serum uric acid levels. The reduction in serum uric acid levels was pronounced in all patient groups, with decreases of 3.1±0.8 mg/dL in patients with BPAD-mania, 3.1±0.9 mg/dL in those with BPAD-depression, and 3.5±1.1 mg/dL in those with MDD. The study showed that the reduction in serum uric acid levels was significantly correlated with the severity of illness in patients with BPAD-mania, but not in those with BPAD-depression or MDD. Furthermore, the study found that treatment with lithium carbonate, sodium valproate, or carbamazepine was equally effective in reducing serum uric acid levels, regardless of the mood stabilizer used. Conclusion The study supports that dysfunction in the purine system might play a significant role in the development and progression of BPAD, suggesting that this phenomenon is not solely due to chronicity or medication exposure. This study also introduces a fresh perspective on the underlying biological processes that contribute to the development of BPAD and also sheds light on new treatment regimens targeting uric acid reduction in treating patients with bipolar disorder.
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Background Ethno-racial inequalities are critical determinants of health outcomes. We quantified ethnic-racial inequalities on adverse birth outcomes and early neonatal mortality in Brazil. Methods We conducted a cohort study in Brazil using administrative linked data between 2012 and 2019. Estimated the attributable fractions for the entire population (PAF) and specific groups (AF), as the proportion of each adverse outcome that would have been avoided if all women had the same baseline conditions as White women, both unadjusted and adjusted for socioeconomics and maternal risk factors. AF was also calculated by comparing women from each maternal race/skin colour group in different groups of mothers’ schooling, with White women with 8 or more years of education as the reference group and by year. Findings 21,261,936 newborns were studied. If all women experienced the same rate as White women, 1.7% of preterm births, 7.2% of low birth weight (LBW), 10.8% of small for gestational age (SGA) and 11.8% of early neonatal deaths would have been prevented. Percentages preventable were higher among Indigenous (22.2% of preterm births, 17.9% of LBW, 20.5% of SGA and 19.6% of early neonatal deaths) and Black women (6% of preterm births, 21.4% of LBW, 22.8% of SGA births and 20.1% of early neonatal deaths). AF was higher in groups with fewer years of education among Indigenous, Black and Parda for all outcomes. AF increased over time, especially among Indigenous populations. Interpretation A considerable portion of adverse birth outcomes and neonatal deaths could be avoided if ethnic-racial inequalities were non-existent in Brazil. Acting on the causes of these inequalities must be central in maternal and child health policies. Funding 10.13039/100000865Bill & Melinda Gates Foundation and 10.13039/100010269Wellcome Trust.
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A inclusão da Síndrome de Burnout (SB) na lista oficial de doenças ocupacionais traz avanços significativos para a saúde do trabalhador, permitindo a busca de tutela jurisdicional. Com o reconhecimento da SB como doença ocupacional, a saúde mental dos trabalhadores ganhou maior importância, deixando de ser tratada apenas como um problema individual. O Estado e os empregadores agora têm novas responsabilidades legais, devendo promover e garantir um ambiente de trabalho seguro e saudável. Este estudo teve como objetivo analisar as demandas trabalhistas relacionadas à Síndrome de Burnout em profissionais da Enfermagem no Brasil, no período de 2014 a 2022. Trata-se de um estudo quantitativo e descritivo que utilizou técnica jurimétrica. Dos 199.798 processos trabalhistas demandados pela categoria, identificamos 407 que mencionaram a Síndrome de Burnout nos pedidos apresentados na petição inicial. Esse resultado contrasta com pesquisas que indicam alta prevalência do Burnout na categoria. Indicamos a necessidade de uma abordagem interdisciplinar para encontrar soluções que possam atenuar os prejuízos sofridos pelas trabalhadoras, indo além do ambiente de trabalho, uma vez que afetam também o ambiente social que elas fazem parte como cidadãs.
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The aim of this study was to cross-culturally adapt and validate of the Brazilian Portuguese version of the Quality of Life in Autism Questionnaire (QoLA) among parents of children ASD. The translated version was administered to 91 parents (Male: 4, Female: 85, other: 2) of individuals diagnosed with ASD. Among these, 22 completed the questionnaire twice, providing data for the assessment of test–retest reliability (ICC). The B-QoLA score ranged from 41 to 122, with a mean (SD) of 74.3 ± 18.5 in Part A and ranged from 22 to 94, with a mean (SD) of 61.6 ± 16.4, in Part B. Cronbach’s alpha coefficient was 0.94 for Part A, 0.92 for Part B and 0.94 for total B-QoLA, indicating excellent internal consistency. Test–retest reliability was assessed using the intraclass correlation coefficient, which was 0.96 for the total scale, 0.94 for Part A, and 0.95 for Part B. Part A-X2 (df) = 297, (167), X2/2 = 1.7, CFI = 0.85, TLI = 0.84, GFI = 0.78, AGFI = 0.75, and RMSEA (95%CI) = 0.09 (0.07-0.11); Part B-X2 (df) = 297, (167), X2/2 = 1.7, CFI = 0.85, TLI = 0.84, GFI = 0.78, AGFI = 0.75, and RMSEA (95%CI) = 0.09 (0.07-0.11), thus indicating moderate fit of the model. The Brazilian version of the QoLA shows encouraging psychometric properties on each of the two subscales, showing strong internal consistency and good construct validity.
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Allostatic self-efficacy (ASE) represents a computational theory of fatigue and depression. In brief, it postulates that (i) fatigue is a feeling state triggered by a metacognitive diagnosis of loss of control over bodily states (persistently elevated interoceptive surprise); and that (ii) generalisation of low self-efficacy beliefs beyond bodily control induces depression. Here, we convert ASE theory into a structural causal model (SCM). This allows for identification of empirically testable hypotheses regarding casual relationships between variables of interest. We use conditional independence tests on questionnaire data from healthy volunteers (N=60) to identify contradictions to the proposed SCM. Moreover, we estimate two causal effects proposed by ASE theory using three different methods. Our analyses suggest that, in healthy volunteers, the data are not fully compatible with the proposed SCM. We therefore refine the SCM and present an updated version for future research. Second, we confirm the predicted negative average causal effect from metacognition of allostatic control to fatigue across all three different methods of estimation. Our study represents an initial attempt to refine and formalise ASE theory using methods from causal inference. Our results confirm key predictions from the ASE theory but also suggest revisions which require empirical verification in future studies.
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Background Functional neurological disorder (FND) is a common neuropsychiatric condition with established diagnostic criteria and effective treatments but for which the underlying neuropathophysiological mechanisms remain incompletely understood. Recent neuroimaging studies have revealed FND as a multi-network brain disorder, unveiling alterations across limbic, self-agency, attentional/salience, and sensorimotor networks. However, the relationship between identified brain alterations and disease progression or improvement is less explored. Methods This study included resting-state functional magnetic resonance imaging (fMRI) data from 79 patients with FND and 74 age and sex-matched healthy controls (HC). First, voxel-wise BOLD signal variability was computed for each participant and the group-wise difference was calculated. Second, we investigated the potential of BOLD signal variability to serve as a prognostic biomarker for clinical outcome in 47 patients who attended a follow-up measurement after eight months. Results The results demonstrated higher BOLD signal variability in key networks, including the somatomotor, salience, limbic, and dorsal attention networks, in patients compared to controls. Longitudinal analysis revealed an increase in BOLD signal variability in the supplementary motor area (SMA) in FND patients who had an improved clinical outcome, suggesting SMA variability as a potential state biomarker. Additionally, higher BOLD signal variability in the left insula at baseline predicted a worse clinical outcome. Conclusion This study contributes to the understanding of FND pathophysiology, emphasizing the dynamic nature of neural activity and highlighting the potential of BOLD signal variability as a valuable research tool. The insula and SMA emerge as promising regions for further investigation as prognostic and state markers.
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Aims/hypothesis Low birthweight is a risk factor for type 2 diabetes and CVD. This prospective cohort study investigated whether lower birthweight increases CVD risk after diagnosis of type 2 diabetes. Methods Original midwife records were evaluated for 8417 participants recently diagnosed with type 2 diabetes in the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) cohort. Patients were followed for the first occurrence of a composite CVD endpoint (myocardial infarction, coronary revascularisation, peripheral arterial disease, stroke, unstable angina, heart failure or CVD death), a three-component endpoint comprising major adverse cardiovascular events (MACE), and all-cause mortality. Ten-year risks were estimated using the Aalen–Johansen estimator considering non-CVD death as a competing risk. HRs were determined by Cox regression. Models were controlled for sex, age, calendar year at birth, family history of diabetes and born-at-term status. Results A total of 1187 composite CVD endpoints, 931 MACE, and 1094 deaths occurred during a median follow-up period of 8.5 years. The 10-year standardised composite CVD risk was 19.8% in participants with a birthweight <3000 g compared with 16.9% in participants with a birthweight of 3000–3700 g, yielding a risk difference (RD) of 2.9% (95% CI 0.4, 5.4) and an adjusted HR of 1.20 (95% CI 1.03, 1.40). The 10-year MACE risk for birthweight <3000 g was similarly elevated (RD 2.4%; 95% CI 0.1, 4.7; HR 1.22; 95% CI 1.01, 1.46). The elevated CVD risk was primarily driven by stroke, peripheral arterial disease and CVD death. All-cause mortality showed no substantial difference. Conclusions/interpretation Having a birthweight <3000 g is associated with higher CVD risk among patients with type 2 diabetes, driven primarily by risk of stroke and CVD death. Graphical Abstract
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Background Type 3 cardiorenal syndrome (CRS type 3) triggers acute cardiac injury from acute kidney injury (AKI), raising mortality in AKI patients. We aimed to identify risk factors for CRS type 3 and develop a predictive nomogram. Methods In this retrospective study, 805 AKI patients admitted at the Department of Nephrology, Second Hospital of Shanxi Medical University from 1 January 2017, to 31 December 2021, were categorized into a study cohort (406 patients from 2017.1.1-2021.6.30, with 63 CRS type 3 cases) and a validation cohort (126 patients from 1 July 2021 to 31 Dec 2021, with 22 CRS type 3 cases). Risk factors for CRS type 3, identified by logistic regression, informed the construction of a predictive nomogram. Its performance and accuracy were evaluated by the area under the curve (AUC), calibration curve and decision curve analysis, with further validation through a validation cohort. Results The nomogram included 6 risk factors: age (OR = 1.03; 95%CI = 1.009–1.052; p = 0.006), cardiovascular disease (CVD) history (OR = 2.802; 95%CI = 1.193–6.582; p = 0.018), mean artery pressure (MAP) (OR = 1.033; 95%CI = 1.012–1.054; p = 0.002), hemoglobin (OR = 0.973; 95%CI = 0.96-–0.987; p < 0.001), homocysteine (OR = 1.05; 95%CI = 1.03–1.069; p < 0.001), AKI stage [(stage 1: reference), (stage 2: OR = 5.427; 95%CI = 1.781–16.534; p = 0.003), (stage 3: OR = 5.554; 95%CI = 2.234–13.805; p < 0.001)]. The nomogram exhibited excellent predictive performance with an AUC of 0.907 in the study cohort and 0.892 in the validation cohort. Calibration and decision curve analyses upheld its accuracy and clinical utility. Conclusions We developed a nomogram predicting CRS type 3 in AKI patients, incorporating 6 risk factors: age, CVD history, MAP, hemoglobin, homocysteine, and AKI stage, enhancing early risk identification and patient management.
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Objective Contrast-sparing strategies have been developed for percutaneous coronary intervention (PCI) patients at increased risk of contrast-induced acute kidney injury (CI-AKI), and numerous CI-AKI risk prediction models have been created. However, the potential clinical and economic consequences of using predicted CI-AKI risk thresholds for assigning patients to contrast-sparing regimens have not been evaluated. We estimated the clinical and economic consequences of alternative CI-AKI risk thresholds for assigning Medicare PCI patients to contrast-sparing strategies. Methods Medicare data were used to identify inpatient PCI from January 2017 to June 2021. A prediction model was developed to assign each patient a predicted probability of CI-AKI. Multivariable modeling was used to assign each patient two marginal predicted values for each of several clinical and economic outcomes based on (1) their underlying clinical and procedural characteristics plus their true CI-AKI status in the data and (2) their characteristics plus their counterfactual CI-AKI status. Specifically, CI-AKI patients above the predicted risk threshold for contrast-sparing were reassigned their no CI-AKI (counterfactual) outcomes. Expected event rates, resource use, and costs were estimated before and after those CI-AKI patients were reassigned their counterfactual outcomes. This entailed bootstrapped sampling of the full cohort. Results Of the 542,813 patients in the study cohort, 5,802 (1.1%) had CI-AKI. The area under the receiver operating characteristic curve for the prediction model was 0.81. At a predicted risk threshold for CI-AKI of >2%, approximately 18.0% of PCI patients were assigned to contrast-sparing strategies, resulting in (/100,000 PCI patients) 121 fewer deaths, 58 fewer myocardial infarction readmissions, 4,303 fewer PCI hospital days, 11.3millionPCIcostsavings,and11.3 million PCI cost savings, and 25.8 million total one-year cost savings, versus no contrast-sparing strategies. Limitations Claims data may not fully capture disease burden and are subject to inherent limitations such as coding inaccuracies. Further, the dataset used reflects only individuals with fee-for-service Medicare, and the results may not be generalizable to Medicare Advantage or other patient populations. Conclusions Assignment to contrast-sparing regimens at a predicted risk threshold close to the underlying incidence of CI-AKI is projected to result in significant clinical and economic benefits.
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Background. The severe pregnancy complication hyperemesis gravidarum (HG) requires intravenous fluids, antiemetics, and nutrition to prevent maternal and fetal complications. Several guidelines exist for the treatment of HG within and across countries. The aim of this study was to investigate whether the guideline issued by the Norwegian Society for Obstetrics and Gynecology (NGF) was implemented in clinical practice by comparing department treatment protocols and assessing provided treatment. Methods. Department protocols for the treatment of HG were requested from all Norwegian gynecology departments and compared to the NGF guideline regarding the use of Pregnancy Unique Quantification of Emesis (PUQE-24) score, antiemetics, thiamine, and fluid and nutritional therapy. Additionally, we performed a retrospective chart review of provided treatment during 2017–2019 at four hospitals. Results. In all, 28 of 39 (72%) departments replied, of which 11 reported using the NGF guidelines unaltered. Of the 17 local department protocols in use, 16 closely resembled the NGF guidelines regarding the use of PUQE score, fluid therapy, nutritional treatment, and thiamine. Eight department protocols differed slightly from the NGF guidelines regarding the antiemetic medication treatment pathway, and two recommended antiemetic medication not supported by national or international guidelines. The retrospective chart review of 343 patients at four hospitals showed that the provided care aligned with the guidelines regarding intravenous fluids and the use of PUQE score, and the use of antiemetics mostly aligned with the treatment pathway provided in the NGF guideline. However, the proportion of patients receiving ondansetron varied between 32% and 79% and thiamine from 38 to 86% between hospitals. Overall, few patients were provided with nutritional treatment by partial peripheral nutrition (14%), enteral tube feeding (8%), or total parenteral nutrition (1.5%). Conclusion. The NGF guideline was used unaltered or largely integrated in department protocols. Treatment data suggest that the guideline was implemented in clinical practice, but differences in the provision of ondansetron and thiamine suggest geographical inequality of care. Infrequent use of nutritional treatment by parenteral nutrition or enteral feeding tube could suggest improvements in pharmacological symptom management or undertreatment of malnutrition.
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