Individual Diagnoses Research Articles

A Rare Case Report Of Sigmoid Volvulus – An Important Differential To Consider In Acute Abdomen

Background: Sigmoid volvulus an uncommon source of bowel obstruction in middle-aged individuals.. This perilous condition pervades globally, with a pronounced predilection for elderly population burdened by concurrent health ailments. While both genders can fall victim, males (1) are more frequently affected. Case Report: A middle aged gentleman presented to emergency center reporting pain abdomen, vomiting , obstipation. On palpation generalized tenderness , guarding present, X – ray abdomen showed classic coffee bean (volvulus) immediately underwent emergency laparotomy - found sigmoid colon was distended and gangrenous. Sigmoidectomy with Hartmann’s performed. Conclusion: This case underscores the criticality of swift action in addressing this life-threatening ailment. Its evaluation should be of utmost importance among the potential diagnoses for individuals reporting with abdominal distention and pain, vomiting,obstipation. An abdominal X-ray (3) both erect and supine positions provides excellent diagnostic results

Evaluating Agreement Between Medical Record Diagnoses and Independent Evaluator Diagnoses in a Community-Based Effectiveness Study

ABSTRACT Background Youth anxiety disorders are prevalent but undertreated. Poor diagnostic agreement between clinicians and researchers may contribute to misdiagnosis and mistreatment. However, few studies of diagnostic agreement focus specifically on anxiety disorders. Objective The current study evaluates diagnostic agreement between community clinicians and independent evaluators (IE) associated with a research study. Method IEs administered the Anxiety Disorders Interview Schedule for DSM-5, Child Version, Child and Parent Forms to youth participating in a randomized controlled trial. Diagnostic agreement was assessed using Cohen’s kappas. Youth (N = 103) were about 14.3 years old (SD = 1.6), largely cis-gender female (66%, n = 68), heterosexual (57.3%, n = 59), Hispanic/Latinx (53.4%, n = 55), and White (53.4%, n = 55). Results IEs assigned significantly more diagnoses than community clinicians (t(99)=-12.738, d = 1.66, p < .001). Individual diagnoses were collapsed into diagnostic clusters: anxiety, depression, trauma and stressor, obsessive-compulsive, and behavioral disorders. There was no agreement for anxiety, trauma and stressor, obsessive-compulsive clusters (ks < 0.20). The depression cluster showed minimal agreement (k = 0.28, p < .001), and behavioral disorders showed weak agreement (k = 0.49, p < .001). There was no agreement for four individual anxiety disorder diagnoses (ks < 0.20) except for panic disorder, which had minimal agreement (k = .32, p < .001). Four additional anxiety disorder diagnoses could not be analyzed due to no diagnoses in one or both settings. Discussion This study shows diagnostic agreement is poor between clinicians and researchers, especially for anxiety disorders. Findings suggest that semi-structured interviews can detect diagnoses that are being missed by clinicians. Future work should seek to identify and address barriers to implementation of evidenced-based assessments in community settings.

Chronic headaches after traumatic brain injury: Diagnostic complexity associated with increased cost.

Chronic headache after traumatic brain injury (TBI) is a common, yet disabling, disorder whose diverse clinical characteristics and treatment needs remain poorly defined. To examine diagnostic coding patterns and cost among military Veterans with comorbid chronic headache and TBI. We identified 141,125 post-9/11 era Veterans who served between 2001 and 2019 with a headache disorder diagnosed after TBI. We first identified patterns of Complex Headache Combinations (CHC) and then compared the patterns of healthcare costs in 2022-dollar values in the three years following the TBI diagnosis. Veterans had diverse individual headache and CHC diagnoses with uniformly high cost of care. Post-whiplash and post-TBI CHCs were common and consistently associated with higher costs after TBI than those with other types of headache and CHCs. Post-TBI migraine had the highest unadjusted mean inpatient ($27,698), outpatient ($61,417), and pharmacy ($4,231) costs, which persisted even after adjustment for confounders including demographic, military, and clinical characteristics. Headache diagnoses after TBI, particularly those diagnosed with post-traumatic headache, are complex, and associated with dual high cost and care burdens. More research is needed to examine whether this higher expenditure reflects more intensive treatment and better outcomes or refractory headache with worse outcomes.

THE FIGHT INHERITED RETINAL BLINDNESS! PROJECT: A NEW TREATMENT OUTCOME AND NATURAL HISTORY REGISTRY FOR INHERITED RETINAL DISEASE.

To design and build a new disease registry to track the natural history and outcomes of approved gene therapy in patients with inherited retinal diseases (IRDs). A core committee of 6 members was convened to oversee the construction of the FIRB! module. A further 11 experts formed a steering committee, which discussed disease classification and variables to form minimum datasets via a consensus approach. The web-based FIRB! registry records baseline demographic, clinical and genetic data together with follow-up data. The Human Phenotype Ontology and Monarch Disease Ontology nomenclature were incorporated within the FIRB! architecture to standardise nomenclature. The registry software assigns individual diagnoses to one of 7 broad phenotypic groups, with minimum datasets dependent upon the broad phenotypic group. Additionally, minimum datasets were agreed upon for patients undergoing approved gene therapy with voretigene neparvovec (Luxturna). New patient entries can be completed in 5 minutes, and follow-up data can be entered in 2 minutes. Fight Inherited Retinal Blindness! (FIRB!) is an organized, web-based system that uses observational study methods to collect uniform data from IRD patients to track natural history and (uniquely) treatment outcomes. It is free to Users, who have control over their data.

Otolaryngology Diagnoses at a Student-Run Community Clinic.

Purpose Compared to insured individuals who have established pathways for primary and specialist care, uninsured patients often delay seeking medical attention and are more likely to rely on emergency departments or community clinics for low-cost services. This study aims to identify the most prevalent otolaryngology (ENT) diagnoses at a free, student-run community clinic for uninsured patients. Additionally, it seeks to elucidate their management needs in order to inform strategies for enhancing support and care for this vulnerable population. Methods The electronic medical records for a free, student-run community clinic (St. Vincent's Hope Student Clinic in Galveston, Texas) were reviewed. Patient age, sex, chief complaint, date of medical encounter, and encounter diagnoses from April 2021 to May 2024 were recorded; medical diagnoses were then manually stratified into system categories and diagnostic subgroups. Diagnoses unrelated to ENT were excluded and likely represented secondary encounter diagnoses. Results A total of 38 patients were evaluated across 51 total ENT encounters. The average number of encounters per patient per year was 1.34 (1.05-1.63, 95% CI). The most common ENT category was throat (37%), and the most common individual diagnosis was balance problems (11% of total diagnoses). Conclusion The most common ENT system was the throat, followed by ear, nose, and other. Balance problems, reflux, and allergic rhinitis were the most common individual ENT diagnoses. The analysis of data collected for this study may help direct future care and resource allocation at this clinic.

Statistical Analysis of the Prevalence and Predictors of Asthma and Allergic Rhinitis in Nigeria

The incidence and prevalence of asthma and allergic rhinitis have increased worldwide over the past ten years and it places a burden on already poorly financed healthcare systems. This article explores the prevalence of asthma and allergic rhinitis in Nigeria, utilizing statistical analyses to investigate the prevalence, demographic predictors, and familial associations. Objectives include determining average ages of diagnosis, identifying significant predictors, examining the relationship between asthma and allergic rhinitis, and investigating familial associations. The study employed the use of statistical tools like logistics regression, chi-square test of independence, cross-tabulation, and descriptive statistics. The analysis reveals an average age of 22 for asthma and 28 for allergic rhinitis. Significant predictors influencing asthma include age, body mass index, years of smoking, level of education, and the number of cigarettes smoked per day. The study establishes a positive correlation between asthma and allergic rhinitis, emphasizing their interconnected nature. Importantly, a highly significant relationship is identified between a family history of asthma and individual diagnoses. The prevalence of asthma and allergic rhinitis in Nigeria is influenced by various demographic factors that should be looked into, emphasizing the importance of considering demographic factors and age in developing effective approaches to address and manage asthma in the Nigerian population.

Quantitative sensory testing and chronic pain syndromes: a cross-sectional study from TwinsUK

ObjectiveThe chronic pain syndromes (CPS) include syndromes such as chronic widespread pain (CWP), dry eye disease (DED) and irritable bowel syndrome (IBS). Highly prevalent and lacking pathognomonic biomarkers, the CPS...

Caspase-4 Has Potential Utility as a Colorectal Tissue Biomarker for Dysplasia and Early-Stage Cancer

Background and AimsColorectal cancer (CRC) is the second most deadly cancer globally. The rapidly rising incidence rate of CRC, coupled with increased diagnoses in individuals <50 years, indicates that early detection of CRC, and those at an increased risk of CRC development, is paramount to improve the survival rates of these patients. Here, we profile caspase-4 expression across two distinct CRC development pathways, sporadic CRC (sCRC) and inflammatory bowel disease-associated CRC (IBD-CRC), to examine its utility as a novel biomarker for CRC risk and diagnosis. MethodsTissue samples from patients with CRC, colonic polyps, IBD-associated CRC, and sporadic CRC were assessed by Immunohistochemistry (IHC) for caspase-4 expression in epithelial and stromal compartments. RNAseq expression data for caspase-4 in CRC and normal tissue samples were mined from online databases. ResultsEpithelial caspase-4 expression is selectively elevated in CRC tumour tissue compared to adjacent-normal tissue, where it is not expressed. In the sCRC pathway, caspase-4 is expressed in the epithelial and stromal tissue of all histological subtypes of colonic polyps, with a significant increase in epithelial expression from LGD to HGD progression. For the IBD-CRC pathway, caspase-4 epithelial expression was specifically upregulated in dysplastic and neoplastic tissue of IBD-CRC but was not expressed in normal or inflamed tissue. ConclusionsThis study demonstrates that epithelial caspase-4 is selectively expressed in colon tissue during the development of dysplasia. As such, epithelial caspase-4 represents a promising novel tissue biomarker for CRC risk and diagnosis.

Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study.

Postacute sequelae of COVID-19 (PASC), also known as long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19. These symptoms can occur across a range of biological systems, leading to challenges in determining risk factors for PASC and the causal etiology of this disorder. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts. However, current knowledge regarding PASC risk factors is limited. Using a sample of 55,257 patients (at a ratio of 1 patient with PASC to 4 matched controls) from the National COVID Cohort Collaborative, as part of the National Institutes of Health Long COVID Computational Challenge, we sought to predict individual risk of PASC diagnosis from a curated set of clinically informed covariates. The National COVID Cohort Collaborative includes electronic health records for more than 22 million patients from 84 sites across the United States. We predicted individual PASC status, given covariate information, using Super Learner (an ensemble machine learning algorithm also known as stacking) to learn the optimal combination of gradient boosting and random forest algorithms to maximize the area under the receiver operator curve. We evaluated variable importance (Shapley values) based on 3 levels: individual features, temporal windows, and clinical domains. We externally validated these findings using a holdout set of randomly selected study sites. We were able to predict individual PASC diagnoses accurately (area under the curve 0.874). The individual features of the length of observation period, number of health care interactions during acute COVID-19, and viral lower respiratory infection were the most predictive of subsequent PASC diagnosis. Temporally, we found that baseline characteristics were the most predictive of future PASC diagnosis, compared with characteristics immediately before, during, or after acute COVID-19. We found that the clinical domains of health care use, demographics or anthropometry, and respiratory factors were the most predictive of PASC diagnosis. The methods outlined here provide an open-source, applied example of using Super Learner to predict PASC status using electronic health record data, which can be replicated across a variety of settings. Across individual predictors and clinical domains, we consistently found that factors related to health care use were the strongest predictors of PASC diagnosis. This indicates that any observational studies using PASC diagnosis as a primary outcome must rigorously account for heterogeneous health care use. Our temporal findings support the hypothesis that clinicians may be able to accurately assess the risk of PASC in patients before acute COVID-19 diagnosis, which could improve early interventions and preventive care. Our findings also highlight the importance of respiratory characteristics in PASC risk assessment. RR2-10.1101/2023.07.27.23293272.

Youth mental health care use during the COVID-19 pandemic in Alberta, Canada: an interrupted time series, population-based study

BackgroundDuring the COVID-19 pandemic, youth had rising mental health needs and changes in service accessibility. Our study investigated changes in use of mental health care services for Canadian youth in Alberta before and during the COVID-19 pandemic. We also investigated how youth utilization patterns differed for subgroups based on social factors (i.e., age, gender, socioeconomic status, and geography) previously associated with health care access.MethodsWe used cross-sectional population-based data from Alberta, Canada to understand youth (15–24 year) mental health care use from 2018/19 to 2021/22. We performed interrupted time series design, segmented regression modeling on type of mental health care use (i.e., general physician, psychiatrist, emergency room, and hospitalization) and diagnosis-related use. We also investigated the characteristics of youth who utilized mental health care services and stratified diagnosis-related use patterns by youth subgroups.ResultsThe proportion of youth using mental health care significantly increased from 15.6% in 2018/19 to 18.8% in 2021/22. Mental health care use showed an immediate drop in April 2020 when the COVID-19 pandemic was declared and public health protections were instituted, followed by a steady rise during the next 2 years. An increase was significant for general physician and psychiatrist visits. Most individual diagnoses included in this study showed significant increasing trends during the pandemic (i.e., anxiety, adjustment, ADHD, schizophrenia, and self-harm), with substance use showing an overall decrease. Mortality rates greatly increased for youth being seen for mental health reasons from 71 per 100,000 youth in 2018/19 to 163 per 100,000 in 2021/22. In addition, there were clear shifts over time in the characteristics of youth using mental health care services. Specifically, there was increased utilization for women/girls compared to men/boys and for youth from wealthier neighborhoods. Increases over time in the utilization of services for self-harm were limited to younger youth (15–16 year).ConclusionsThe study provides evidence of shifts in mental health care use during the COVID-19 pandemic. Findings can be used to plan for ongoing mental health needs of youth, future pandemic responses, and other public health emergencies.

Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease

BackgroundDespite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hospital workflows...

Diagnostic Accuracy of a Mobile AI-Based Symptom Checker and a Web-Based Self-Referral Tool in Rheumatology: Multicenter Randomized Controlled Trial.

The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help patients navigate the health care system more efficiently. The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)-based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs. A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists. A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport's disease suggestion and Ada's top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada's D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada's diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs. To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs. German Register of Clinical Trials DRKS00017642; https://drks.de/search/en/trial/DRKS00017642.

Clinical signatures of genetic epilepsies precede diagnosis in electronic medical records of 32,000 individuals

PurposeAn early genetic diagnosis can guide the time-sensitive treatment of individuals with genetic epilepsies. However, most genetic diagnoses occur long after disease onset. We aimed to identify early clinical features suggestive of genetic diagnoses in individuals with epilepsy through large-scale analysis of full-text electronic medical records. MethodsWe extracted 89 million time-stamped standardized clinical annotations using Natural Language Processing from 4,572,783 clinical notes from 32,112 individuals with childhood epilepsy, including 1925 individuals with known or presumed genetic epilepsies. We applied these features to train random forest models to predict SCN1A-related disorders and any genetic diagnosis. ResultsWe identified 47,774 age-dependent associations of clinical features with genetic etiologies a median of 3.6 years before molecular diagnosis. Across all 710 genetic etiologies identified in our cohort, neurodevelopmental differences between 6 to 9 months increased the likelihood of a later molecular diagnosis 5-fold (P < .0001, 95% CI = 3.55-7.42). A later diagnosis of SCN1A-related disorders (area under the curve [AUC] = 0.91) or an overall positive genetic diagnosis (AUC = 0.82) could be reliably predicted using random forest models. ConclusionClinical features predictive of genetic epilepsies precede molecular diagnoses by up to several years in conditions with known precision treatments. An earlier diagnosis facilitated by automated electronic medical records analysis has the potential for earlier targeted therapeutic strategies in the genetic epilepsies.

PATH-02. THE IMPACT OF EVOLVING DIAGNOSTIC METHODOLOGY ON THE CLASSIFICATION AND OUTCOME OF CENTRAL NERVOUS SYSTEM TUMOURS IN CHILDHOOD: A RETROSPECTIVE COHORT STUDY

Abstract BACKGROUND Central nervous system tumours are the second most common childhood tumours and the largest cause of childhood cancer death in Australia. Tumours arise from many cell types within the developing and mature CNS, leading to a wide spectrum of clinical behaviour despite overlapping histological appearances. Emerging molecular testing methods allow more precise determination of cell lineage as well as genetic and epigenetic changes driving tumour development. Particularly since 2016, molecular findings have been incorporated into international classification schema, potentially impacting the frequency of individual diagnoses and their observed prognosis. METHODS We performed a retrospective cohort study including patients with newly diagnosed CNS tumours at Royal Children’s Hospital, Melbourne between 2011 and 2020 (total 473). The first and last 100 eligible patients in the data set were reviewed for diagnosis, survival and recorded use of molecular testing. Reviewed patients were assigned to one of two cohorts, Cohort 1 2011-2013 (pre-2016 WHO revision) or Cohort 2 2018-2020 (post-2016 WHO revision). RESULTS The frequency of diagnoses on biopsy rather than imaging alone was stable (81% Cohort 1 vs 83% Cohort 2). Molecular testing utilization increased markedly over time (10 molecular investigations in Cohort 1 vs 348 in Cohort 2). The incidence of astrocytic and oligodendroglial diagnoses was stable. The incidence of embryonal and neuronal/ mixed glial neuronal diagnoses increased in Cohort 2, and the incidence of ependymal diagnoses decreased (p=0.0122). There were no event free or overall survival differences between the whole of Cohorts 1 and 2, with numbers of individual diagnoses too small for meaningful comparisons over time. CONCLUSIONS Access to molecular testing has impacted on the pattern of reported diagnoses of central nervous system tumours in children. Although outcomes have not changed, it is hoped that over time, improved molecular understanding will lead to more effective therapy selection.

Atopic dermatitis is associated with an increased risk of cardiovascular diseases: a large-scale, propensity-score matched US-based retrospective study.

Atopic dermatitis (AD) is a chronic relapsing inflammatory skin disease characterized by intense itch, and impacting heavily on patients' and caregivers' quality of life. Its clinical presentation is accompanied by a variety of comorbidities associated with type 2 inflammation, such as asthma, hay fever and food allergies. However, current data on cardiovascular comorbidities are inconsistent. To identify the risk of cardiovascular diseases (CVDs) in patients with AD. Data from electronic health records of 1 070 965 patients with AD and equally distributed propensity-score matched controls were retrieved from the US Collaborative Network, part of the federated TriNetX network. Hazard ratios (HRs) for the risk of onset of CVDs with a prevalence of ≥ 1% in both cohorts within 20 years after diagnosis were determined. In total, 55 CVDs belonging to 8 major cardiovascular groups were identified. Of those, 53 diagnoses displayed a significantly increased risk in patients with AD. Different diagnoses of heart failure and heart disease were found most often, followed by valve insufficiencies, arrhythmia, tachycardia, atrial fibrillation and flutter, but also major adverse cardiovascular events and venous thromboembolism. The highest HRs were displayed by the individual diagnoses of venous insufficiency, atherosclerosis of native arteries of the extremities, and unspecified diastolic (congestive) heart failure. AD is associated with an increased risk for multiple CVDs.

Artificial intelligence for SDG 4 of the 2030 agenda: Transforming education to achieve quality, equality, and inclusion

The objective of this article is to discuss the possibility of using generative artificial intelligence (AI) to enhance teaching practices and pedagogical support to improve the quality of education provided to young people in elementary and secondary schooling. This issue is linked to the global perspective of a shortage of teachers, which directly affects the Sustainable Development Goal 4 (SDG 4), concerning the enhancement of education quality as a target for global sustainable development. From this viewpoint, the potential use of AI may also relate to the improvement of educational quality and the reduction of social inequalities, yielding indirect effects on other sustainable development goals. As a method, we intend to conduct an extensive theoretical discussion addressing the challenges for teacher education and work worldwide, utilizing existing data from databases such as UNESCO, the UN, and the OECD, among others. In addition to data on teachers, we plan to analyze the potential for creating an artificial intelligence, based on existing ones but trained for the specific contexts of each country’s educational system. The goal is to examine the potential for formatting artificial intelligence to provide pedagogical support for teachers, such as: grading of objective and discursive assessments, individualized intelligent tutoring, analysis of students’ individual pedagogical development, preparation of individual student diagnoses, suggestions of specific pedagogical actions based on curricula and materials used, and all other pedagogical actions that support teachers in their educational journey. This work was funded by CAPES, CNPq and FAPEMIG.

Particle analysis of surgical lung biopsies from deployed and non-deployed US service members during the Global War on Terrorism.

The role that inhaled particulate matter plays in the development of post-deployment lung disease among US service members deployed to Southwest Asia during the Global War on Terrorism has been difficult to define. There is a persistent gap in data addressing the relationship between relatively short-term (months to a few years) exposures to high levels of particulate matter during deployment and the subsequent development of adverse pulmonary outcomes. Surgical lung biopsies from deployed service members and veterans (DSMs) and non-deployed service members and veterans (NDSMs) who develop lung diseases can be analyzed to potentially identify residual deployment-specific particles and develop associations with pulmonary pathological diagnoses. We examined 52 surgical lung biopsies from 25 DSMs and 27 NDSMs using field emission scanning electron microscopy (FE-SEM) with energy dispersive x-ray spectroscopy (EDS) to identify any between-group differences in the number and composition of retained inorganic particles, then compared the particle analysis results with the original histopathologic diagnoses. We recorded a higher number of total particles in biopsies from DSMs than from NDSMs, and this difference was mainly accounted for by geologic clays (illite, kaolinite), feldspars, quartz/silica, and titanium-rich silicate mixtures. Biopsies from DSMs deployed to other Southwest Asia regions (SWA-Other) had higher particle counts than those from DSMs primarily deployed to Iraq or Afghanistan, due mainly to illite. Distinct deployment-specific particles were not identified. Particles did not qualitatively associate with country of deployment. The individual diagnoses of the DSMs and NDSMs were not associated with elevated levels of total particles, metals, cerium oxide, or titanium dioxide particles. These results support the examination of particle-related lung disease in DSMs in the context of comparison groups, such as NDSMs, to assist in determining the strength of associations between specific pulmonary pathology diagnoses and deployment-specific inorganic particulate matter exposure.

Marion Kenworthy and values in the history of American child psychiatry.

Marion Kenworthy (1891-1980) was a pioneering child psychiatrist, mental hygiene and child guidance leader, and early member of the American Orthopsychiatric Association (now the Global Alliance for Behavioral Health and Social Justice). Throughout her illustrious career, Kenworthy advocated for values in the emerging field of child psychiatry, especially around prevention of mental illness, interdisciplinary collaboration, and social justice. Kenworthy's history provides not only an illustration of the importance of values in the work related to children but also a reminder of perspectives that can get lost in the contemporary focus on individual diagnoses and treatments (especially with pharmaceuticals). The social, cultural, and economic problems encountered by Kenworthy and her contemporaries remain as challenges in the present and the future, ones that require ongoing interdisciplinary collaboration and advocacy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Clinical Frailty Scale for Risk Stratification in Patients With Fibrotic Interstitial Lung Disease

Previous studies have shown the importance of frailty in patients with fibrotic interstitial lung disease (ILD). Is the Clinical Frailty Scale (CFS) a valid tool to improve risk stratification in patients with fibrotic ILD? Patients with fibrotic ILD were included from the prospective multicenter Canadian Registry for Pulmonary Fibrosis. The CFS was assessed using available information from initial ILD clinic visits. Patients were stratified into fit (CFS score 1-3), vulnerable (CFS score 4), and frail (CFS score 5-9) subgroups. Cox proportional hazards and logistic regression models with mixed effects were used to estimate time to death or lung transplantation. A derivation and validation cohort was used to establish prognostic performance. Trajectories of functional tests were compared using joint models. Of the 1,587 patients with fibrotic ILD, 858 (54%) were fit, 400 (25%) were vulnerable, and 329 (21%) were frail. Frailty was a risk factor for early mortality (hazard ratio, 5.58; 95%CI, 3.64-5.76, P< .001) in the entire cohort, in individual ILD diagnoses, and after adjustment for potential confounders. Adding frailty to established risk prediction parameters improved the prognostic performance in derivation and validation cohorts. Patients in the frail subgroup had larger annual declines in FVC %predicted than patients in the fit subgroup (-2.32; 95%CI,-3.39 to-1.17 vs-1.55; 95%CI,-2.04 to-1.15, respectively; P= .02). The simple and practical CFS is associated with pulmonary and physical function decline in patients with fibrotic ILD and provides additional prognostic accuracy in clinical practice.

Infection and Sepsis Trends during United States' Delivery Hospitalizations from 2000 to 2020.

This study aimed to evaluate trends, risk factors, and outcomes associated with infections and sepsis during delivery hospitalizations in the United States. The 2000-2020 National Inpatient Sample was used for this repeated cross-sectional analysis. Delivery hospitalizations of patients aged 15 to 54 with and without infection and sepsis were identified. Common infection diagnoses during delivery hospitalizations analyzed included (i) pyelonephritis, (ii) pneumonia/influenza, (iii) endometritis, (iv) cholecystitis, (v) chorioamnionitis, and (vi) wound infection. Temporal trends in sepsis and infection during delivery hospitalizations were analyzed. The associations between sepsis and infection and common chronic health conditions including asthma, chronic hypertension, pregestational diabetes, and obesity were analyzed. The associations between clinical, demographic, and hospital characteristics, and infection and sepsis were determined with unadjusted and adjusted logistic regression models with unadjusted odds ratio (OR) and adjusted odds ratios with 95% confidence intervals as measures of association. An estimated 80,158,622 delivery hospitalizations were identified and included in the analysis, of which 2,766,947 (3.5%) had an infection diagnosis and 32,614 had a sepsis diagnosis (4.1 per 10,000). The most common infection diagnosis was chorioamnionitis (2.7% of deliveries) followed by endometritis (0.4%), and wound infections (0.3%). Infection and sepsis were more common in the setting of chronic health conditions. Evaluating trends in individual infection diagnoses, endometritis and wound infection decreased over the study period both for patients with and without chronic conditions, while risk for pyelonephritis and pneumonia/influenza increased. Sepsis increased over the study period for deliveries with and without chronic condition diagnoses. Risks for adverse outcomes including mortality, severe maternal morbidity, the critical care composite, and acute renal failure were all significantly increased in the presence of sepsis and infection. Endometritis and wound infections decreased over the study period while risk for sepsis increased. Infection and sepsis were associated with chronic health conditions and accounted for a significant proportion of adverse obstetric outcomes including severe maternal morbidity. · Sepsis increased over the study period for deliveries with and without chronic condition diagnoses.. · Endometritis and wound infection decreased over the study period.. · Infection and sepsis accounted for a significant proportion of adverse obstetric outcomes..