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Importance Lensectomy with primary intraocular lens (IOL) implantation is often used in the management of nontraumatic pediatric cataract, but long-term data evaluating the association of age and IOL location with the incidence of complications are limited. Objective To describe the incidence of complications and additional eye surgeries through 5 years following pediatric lensectomy with primary IOL implantation and association with age at surgery and IOL location. Design, Setting, and Participants This prospective cohort study used Pediatric Eye Disease Investigator Group cataract registry data from 61 institution- and community-based practices over 3 years (June 2012 to July 2015). Participants were children younger than 13 years without baseline glaucoma who had primary IOL implantation (345 bilateral and 264 unilateral) for nontraumatic cataract. Data analysis was performed between September 2021 and January 2023. Exposures Lensectomy with primary IOL implantation. Main Outcome and Measures Five-year cumulative incidence of complications by age at surgery (<2 years, 2 to <4 years, 4 to <7 years, and 7 to <13 years) and by IOL location (sulcus vs capsular bag) were estimated using Cox proportional hazards models. Results The cohort included 609 eyes from 491 children (mean [SD] age, 5.6 [3.3] years; 261 [53%] male and 230 [47%] female). Following cataract extraction with primary IOL implantation, a frequent complication was surgery for visual axis opacification (VAO) (cumulative incidence, 32%; 95% CI, 27%-36%). Cumulative incidence was lower with anterior vitrectomy at the time of IOL placement (12%; 95% CI, 8%-16%) vs without (58%; 95% CI, 50%-65%), and the risk of undergoing surgery for VAO was associated with not performing anterior vitrectomy (hazard ratio [HR], 6.19; 95% CI, 3.70-10.34; P < .001). After adjusting for anterior vitrectomy at lens surgery, there were no differences in incidence of surgery for VAO by age at surgery (<2 years, HR, 1.35 [95% CI, 0.63-2.87], 2 to <4 years, HR, 0.86 [95% CI, 0.44-1.68], 4 to <7 years, HR, 1.06 [95% CI, 0.72-1.56]; P = .74) or by capsular bag vs sulcus IOL fixation (HR, 1.22; 95% CI, 0.36-4.17; P = .75). Cumulative incidence of glaucoma plus glaucoma suspect by 5 years was 7% (95% CI, 4%-9%), which did not differ by age after controlling for IOL location and laterality. Conclusions and Relevance In this cohort study, a frequent complication following pediatric lensectomy with primary IOL was surgery for VAO, which was associated with primary anterior vitrectomy not being performed but was not associated with age at surgery or IOL location. The risk of glaucoma development across all ages at surgery suggests a need for long-term monitoring.

Abstract Moyamoya disease, a cerebrovascular disease leading to strokes in children and young adults, is characterized by progressive occlusion of the distal internal carotid arteries and the formation of collateral vessels. Altered genes play a prominent role in the aetiology of moyamoya disease, but a causative gene is not identified in the majority of cases. Exome sequencing data from 151 individuals from 84 unsolved families were analysed to identify further genes for moyamoya disease, then candidate genes assessed in additional cases (150 probands). Two families had the same rare variant in ANO1, which encodes a calcium-activated chloride channel, anoctamin-1. Haplotype analyses found the families were related, and ANO1 p.Met658Val segregated with moyamoya disease in the family with an LOD score of 3.3. Six additional ANO1 rare variants were identified in moyamoya disease families. The ANO1 rare variants were assessed using patch-clamp recordings, and the majority of variants, including ANO1 p.Met658Val, displayed increased sensitivity to intracellular Ca2+. Patients harbouring these gain-of-function ANO1 variants had classic features of moyamoya disease, but also had aneurysm, stenosis and/or occlusion in the posterior circulation. Our studies support that ANO1 gain-of-function pathogenic variants predispose to moyamoya disease and are associated with unique involvement of the posterior circulation.

Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area.

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

ImportanceGlaucoma can develop following cataract removal in children.ObjectiveTo assess the cumulative incidence of glaucoma-related adverse events (defined as glaucoma or glaucoma suspect) and factors associated with risk of these adverse events in the first 5 years after lensectomy prior to 13 years of age.Design, Setting, and ParticipantsThis cohort study used longitudinal registry data collected at enrollment and annually for 5 years from 45 institutional and 16 community sites. Participants were children aged 12 years or younger with at least 1 office visit after lensectomy from June 2012 to July 2015. Data were analyzed from February through December 2022.ExposuresUsual clinical care after lensectomy.Main Outcomes and MeasuresThe main outcomes were cumulative incidence of glaucoma-related adverse events and baseline factors associated with risk of these adverse events.ResultsThe study included 810 children (1049 eyes); 443 eyes of 321 children (55% female; mean [SD] age, 0.89 [1.97] years) were aphakic after lensectomy, and 606 eyes of 489 children (53% male; mean [SD] age, 5.65 [3.32] years) were pseudophakic. The 5-year cumulative incidence of glaucoma-related adverse events was 29% (95% CI, 25%-34%) in 443 eyes with aphakia and 7% (95% CI, 5%-9%) in 606 eyes with pseudophakia; 7% (95% CI, 5%-10%) of aphakic eyes and 3% (95% CI, 2%-5%) of pseudophakic eyes were diagnosed as glaucoma suspect. Among aphakic eyes, a higher risk for glaucoma-related adverse events was associated with 4 of 8 factors, including age less than 3 months (vs ≥3 months: adjusted hazard ratio [aHR], 2.88; 99% CI, 1.57-5.23), abnormal anterior segment (vs normal: aHR, 2.88; 99% CI, 1.56-5.30), intraoperative complications at time of lensectomy (vs none; aHR, 2.25; 99% CI, 1.04-4.87), and bilaterality (vs unilaterality: aHR, 1.88; 99% CI, 1.02-3.48). Neither of the 2 factors evaluated for pseudophakic eyes, laterality and anterior vitrectomy, were associated with risk of glaucoma-related adverse events.Conclusions and RelevanceIn this cohort study, glaucoma-related adverse events were common after cataract surgery in children; age less than 3 months at surgery was associated with elevated risk of the adverse events in aphakic eyes. Children with pseudophakia, who were older at surgery, less frequently developed a glaucoma-related adverse event within 5 years of lensectomy. The findings suggest that ongoing monitoring for the development of glaucoma is needed after lensectomy at any age.

Objective: Limited data exist around the receipt of palliative care (PC) in patients hospitalized with common chronic conditions. We studied the independent predictors, temporal trends in rates of PC utilization in patients hospitalized with acute exacerbation of common chronic diseases. Methods: Population-based cohort study of all hospitalizations with an acute exacerbation of heart disease (HD), cerebrovascular accident (CVA), cancer (CA), and chronic lower respiratory disease (CLRD). Patients aged ≥18 years or older between January 1, 2004, and December 31, 2017, referred for inpatient PC were extracted from the National Inpatient Sample. Poisson regression analyses were used to estimate temporal trends. Results: Between 2004 and 2017, of 91,877,531 hospitalizations, 55.2%, 13.9%, 17.2%, and 13.8% hospitalizations were related to HD, CVA, CA, and CLRD, respectively. There was a temporal increase in the uptake of PC across all disease groups. Age-adjusted estimated rates of PC per 100,000 hospitalizations/year were highest for CA (2308 (95% CI 2249–2366) to 10,794 (95% CI 10,652–10,936)), whereas the CLRD cohort had the lowest rates of PC referrals (255 (95% CI 231–278) to 1882 (95% CI 1821–1943)) between 2004 and 2017, respectively. In the subgroup analysis of patients who died during hospitalization, the CVA group had the highest uptake of PC per 100,000 hospitalizations/year (4979 (95% CI 4918–5040)) followed by CA (4241 (95% CI 4189–4292)), HD (3250 (95% CI 3211–3289)) and CLRD (3248 (95% CI 3162–3405)). Conclusion: PC service utilization is increasing but remains disparate, particularly in patients that die during hospital admission from common chronic conditions. These findings highlight the need to develop a multidisciplinary, patient-centered approach to improve access to PC services in these patients.

Background Despite improvements in the management and prevention of stroke, increasing hospitalizations for stroke and stagnant mortality rates have been described in young adults. However, there is a paucity of contemporary national mortality estimates in young adults. Methods and Results Trends in mortality related to stroke in young adults (aged 25–64 years) were assessed using the Centers for Disease Control and Prevention Wide‐Ranging Online Data for Epidemiologic Research database. Age‐adjusted mortality rates per 100 000 people with associated annual percentage change were calculated. Joinpoint regression was used to assess the trends in the overall sample and different demographic (sex, race and ethnicity, and age) and geographical (state, urban‐rural, and regional) subgroups. Between 1999 and 2019, a total of 566 916 stroke‐related deaths occurred among young adults. After the initial decline in mortality in the overall population, age‐adjusted mortality rate increased from 2013 to 2019 with an associated annual percentage change of 1.5 (95% CI, 1.1–2.0). Mortality rates were higher in men versus women and in non‐Hispanic Black people versus individuals of other races and ethnicities. Non‐Hispanic American Indian or Alaskan Native people had a marked increase in stroke‐related mortality (annual percentage change 2010–2019: 3.3). Furthermore, rural (nonmetropolitan) counties experienced the greatest increase in mortality (annual percentage change 2012–2019: 3.1) compared with urban (metropolitan) counties. Conclusions Following the initial decline in stroke‐related mortality, young adults have experienced increasing mortality rates from 2013 to 2019, with considerable differences across demographic groups and regions.

The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to compare for the first time the discriminative ability of two machine learning (ML) algorithms based on real-time feature analysis of arterial waveforms obtained from a non-invasive continuous blood pressure system (Finometer®) signal to predict the onset of decompensated shock: the compensatory reserve index (CRI) and the compensatory reserve metric (CRM). One hundred ninety-one healthy volunteers underwent progressive simulated hemorrhage using lower body negative pressure (LBNP). The least squares means and standard deviations for each measure were assessed by LBNP level and stratified by tolerance status (high vs. low tolerance to central hypovolemia). Generalized Linear Mixed Models were used to perform repeated measures logistic regression analysis by regressing the onset of decompensated shock on CRI and CRM. Sensitivity and specificity were assessed by calculation of receiver-operating characteristic (ROC) area under the curve (AUC) for CRI and CRM. Values for CRI and CRM were not distinguishable across levels of LBNP independent of LBNP tolerance classification, with CRM ROC AUC (0.9268) being statistically similar (p = 0.134) to CRI ROC AUC (0.9164). Both CRI and CRM ML algorithms displayed discriminative ability to predict decompensated shock to include individual subjects with varying levels of tolerance to central hypovolemia. Arterial waveform feature analysis provides a highly sensitive and specific monitoring approach for the detection of ongoing hemorrhage, particularly for those patients at greatest risk for early onset of decompensated shock and requirement for implementation of life-saving interventions.