Prevalencia de síntomas depresivos y diagnóstico de sospecha de depresión en pacientes con EPOC

ObjetivoConocer la prevalencia y factores predictores de depresión en pacientes diagnosticados de enfermedad pulmonar obstructiva crónica (EPOC) y remitidos desde Atención Primaria a consultas de Neumología, servicios que comparten la atención al proceso EPOC.DiseñoEstudio observacional, multicéntrico, prospectivo con muestreo no probabilístico, transversal.EmplazamientoDos consultas de neumología de dos hospitales de diferente nivel asistencial.ParticipantesSe diagnosticaron 293 pacientes de EPOC en fase estable de la enfermedad.IntervencionesAplicación de cuestionarios clínicos habituales en la EPOC y test HADS (Hospital Anxiety and Depression Scale).VariablesVariables demográficas, clínicas y funcionales de la EPOC y escala de depresión del test HADS (Hospital Anxiety and Depression Scale).ResultadosSe incluyeron 229 hombres (78,16%) y 64 mujeres (21,8%), con una edad media de 68,2 ± 10,3 años, de los que 93 (31,7%) eran fumadores activos y 200 (68,3%) exfumadores. El 19,45% de los pacientes tenía diagnóstico clínico previo de depresión, pero mediante el test HADS se estableció el diagnóstico de sospecha en el 32,6%. Las variables predictoras fueron: ser mujer, vivir solo y variables relacionadas con la gravedad de la enfermedad (volumen espiratorio forzado en 1 segundo [FEV1] postbroncodilatador, ser paciente de riesgo y fenotipo agudizador según criterios de la Guía Española de la EPOC [GesEPOC] y grados C y D de criterios Global Initiative for Chronic Obstructive Lung Disease [GOLD]).ConclusionesLa prevalencia de la depresión en pacientes con EPOC es alta y está infradiagnosticada. El test diagnóstico HADS es útil para establecer el diagnóstico de sospecha en las consultas de Atención Primaria y Neumología. Existen factores personales y clínicos que pueden considerar predictores y servir de orientación para determinar en qué pacientes realizar el test HADS y, en función de los resultados, derivar al paciente a una Unidad de Salud Mental para confirmar o descartar el diagnóstico.Palabras clave: EPOC, Depresión, HADS, Prevalencia, Factores de riesgo     

Blood pressure and obesity exert independent influences on pulse wave velocity in youth

The objective was to analyze pulse wave velocity (PWV) in normotensive, high-normal, and hypertensive youths by using aortic-derived parameters from peripheral recordings. The impact of obesity on vascular phenotypes was also analyzed. A total of 501 whites from 8 to 18 years of age were included. The subjects were divided according to BP criteria: 424 (85%) were normotensive, 56 (11%) high-normal, and 21 (4%) hypertensive. Obesity was present in 284 (56%) and overweight in 138 (28%). Pulse wave analysis using a SphygmoCor device was performed to determine central blood pressure (BP), augmentation index, and measurement of PWV. Among the BP groups, differences appeared in age, sex, and height but not in body mass index. Significant differences in peripheral and central systolic and diastolic BPs and pulse pressures were observed within groups. A graded increase in PWV was present across the BP strata without differences in augmentation index. Using a multiple regression analysis, age, BP groups, and obesity status were independently associated with PWV. Older and hypertensive subjects had the highest PWV, whereas, from normal weight status to obesity, PWV decreased. Likewise, PWV was positively related to peripheral or central systolic BP and negatively related to body mass index z score. For 1 SD of peripheral systolic BP, PWV increased 0.329 m/s, and for 1 SD of body mass index z score PWV decreased 0.129 m/s. In conclusion, PWV is increased in hypertensive and even in high-normal children and adolescents. Furthermore, obesity, the factor most frequently related to essential hypertension in adolescents, blunted the expected increment in PWV of hypertensive and high-normal subjects.     

Tumor detectability and conspicuity comparison of standard b1000 and ultrahigh b2000 diffusion-weighted imaging in rectal cancer

Abdominal Radiology - To compare tumor detectability and conspicuity of standard b = 1000 s/mm2 (b1000) versus ultrahigh b = 2000 s/mm2 (b2000)...     

Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care

The integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes. We propose a set of strategic assertions to examine before, during, and after adoption of these technologies in order to facilitate healthcare equity across all patient population groups. The purpose is to enable generalists to promote engagement with technology companies and co-create, promote, or support innovation and insights that can potentially inform decision-making and health care equity.

Primary care has a critical role to play in ensuring that mission-driven values aimed at eliminating health care disparities are prioritized in the development, selection, clinical implementation, and use of advanced analytics and AI technologies. Because the application of these technologies in primary care is in its infancy, primary care professionals have a unique opportunity to guide the growth of fair, transparent, and ethical AI and analytics applications that embody health equity principles that meet the needs of diverse populations.Today, clinical decision-making in primary care is influenced by the ongoing integration of advanced analytics and AI technologies into the practice of medicine.¹ Examples include patient risk stratification, predictive modeling for disease progression,^2,3 decision-support applications,^4,5 and population health management tools for cancer screenings,^6,7 diabetes,^8,9 cardiovascular disease,^10–12 and other chronic disease conditions.¹³ These and other similar tools may or may not explicitly address the needs of diverse patient populations in primary care. Unless explicit strategies are used to promote equity, advanced analytics may inadvertently perpetuate inequities in primary care delivery, such as the use of algorithms that erroneously treat race categories as biological rather than social attributes in clinical decision making.¹⁴The importance of articulating equity as a specific goal for integrating AI into care is described in the 2019 National Academy of Medicine (NAM) report, Artificial Intelligence in Health Care: The Hope, The Hype, The Promise, The Peril. The report describes a quintuple aim to improve population health, reduce costs, improve the patient experience, promote care team well-being and achieve health care equity.¹⁵ Specifically, the report suggests that embracing health care equity would challenge a siloed approach to health care by addressing the diversity of patient needs using varied sources of data that include social determinants of health and psychosocial risk factors (Fig. ​(Fig.1).1). Equity, integral to the quintuple aim, would also require engaging diverse stakeholders to inform the design of AI applications and to monitor the impact of these technologies. The NAM report underscores the need for explicit strategies to actively embrace health care equity; without such strategies, AI applications are likely to reflect human biases in ways that will widen inequities by race/ethnicity, gender identity, sexual orientation, disability status, age, social class, geography, and other dimensions of social identity.^15,16 Open in a separate windowFigure 1Building on the quintuple aims of equity and inclusion in health and healthcare (National Academy of Medicine).¹⁴Indifference to technology and passive acceptance of biased tools pose risks to health care equity among diverse groups. To prevent this, we must be willing to articulate the priorities for successful AI and advanced analytics implementation and adopt strategies and processes that lead to equitable outcomes. To further these aims, we propose the following series of questions that should be considered before and during the adoption of an AI technology or advanced analytic strategy into practice. First, what needs of diverse patient populations can be better served by applying advanced analytics and AI technology? How can novel and diverse data sources be leveraged to enhance equity in AI implementations? How can patients and community members engage with stakeholders involved in shaping the use of AI in the delivery of health care? And finally, how are principles of diversity and inclusion reflected among those who are involved in the development, selection, and use of technology solutions to enable equitable health care?     

The Astounding Breadth of Health Disparity: Phenome-Wide Effects of Race on Disease Risk

ObjectiveWe conducted a phenotype-wide association study (PheWAS) to compare diagnoses among Blacks with those of Whites in one health center in Tennessee using data from 1,883,369 patients.MethodsWe used our deidentified EHR, the Synthetic Derivative, to assess risk of diagnoses associated with Black as compared with White race using Firth logistic regression with covariates including age, sex, and density of clinical encounters.ResultsThere were anchoring associations in both directions, including the highest increased risk for Blacks of having sickle cell anemia, and strongest decreased risk of basal cell carcinoma. Results included established areas of disparity and many novel associations.ConclusionsPheWAS is a viable tool for calculating risk associated with any biomarker. The current analysis provide a new approach to generating hypotheses and understanding the breadth of health disparities. Future analyses will further explore causality, risk factors, and potential confounders not accounted for here.     

New Pyrenochaeta Species Causing Keratitis

We report a new fungus as an agent of fungal keratitis in a diabetic woman. The fungal etiology was established by classic microbiology and PCR following 3 months of antibacterial therapy. The morphological features of the isolate and sequence analysis of the internal transcribed spacer region indicate a new species of Pyrenochaeta (Coelomycetes).