Citations

Abstract Introduction During the Coronavirus (COVID‐19) pandemic, healthcare providers have overcome difficult experiences such as workplace violence. Nurses are particularly vulnerable to workplace violence. The objective of this study was to characterize violence and aggression against nurses during the COVID‐19 pandemic in Latin America. Methods An electronic cross‐sectional survey was conducted in 19 Latin American countries to characterize the frequency and type of violent actions against front‐line healthcare providers. Results Of the original 3544 respondents, 16% were nurses ( n = 567). The mean age was 39.7 ± 9.0 years and 79.6% ( n = 2821) were women. In total, 69.8% ( n = 2474) worked in public hospitals and 81.1% ( n = 2874) reported working regularly with COVID‐19 patients. Overall, about 68.6% ( n = 2431) of nurses experienced at least one episode of workplace aggression during the pandemic. Nurses experienced weekly aggressions more frequently than other healthcare providers (45.5% versus 38.1%, p < .007). Nurses showed a trend of lower reporting rates against the acts of aggression suffered ( p = .076). In addition, nurses were more likely to experience negative cognitive symptoms after aggressive acts (33.4% versus 27.8%, p = .028). However, nurses reported considering changing their work tasks less frequently compared to other healthcare providers after an assault event ( p = .005). Conclusion Workplace violence has been a frequent problem for all healthcare providers during COVID‐19 pandemic in Latin America. Nurses were a particularly vulnerable subgroup, with higher rates of aggressions and cognitive symptoms and lower rate of complaints than other healthcare providers who suffered from workplace violence. It is imperative to develop strategies to protect this vulnerable group from aggressions during their tasks.

AbstractIntegration and mining of bioimaging data remains a challenge and lags behind the rapidly expanding digital pathology field. We introduce Hourglass, an open‐access analytical framework that streamlines biology‐driven visualization, interrogation, and statistical assessment of multiparametric datasets. Cognizant of tissue and clinical heterogeneity, Hourglass systematically organizes observations across spatial and global levels and within patient subgroups. Applied to an extensive bioimaging dataset, Hourglass promptly consolidated a breadth of known interleukin‐6 (IL‐6) functions via its downstream effector STAT3 and uncovered a so‐far unknown sexual dimorphism in the IL‐6/STAT3‐linked intratumoral T‐cell response in human pancreatic cancer. As an R package and cross‐platform application, Hourglass facilitates knowledge extraction from multi‐layered bioimaging datasets for users with or without computational proficiency and provides unique and widely accessible analytical means to harness insights hidden within heterogeneous tissues at the sample and patient level. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Abstract Background Distinguishing between different causes of microcytic anemia is essential for management of anemic patients. More than 20 different RBC indices have been used to screen thalassemia trait with inconsistent results. Differentiation between iron deficiency anemia (IDA) and alpha-thalassemia is challenging based on RBC indices alone. Moreover, thalassemia and IDA may coexist. A highly sensitive screening algorithm is needed to select clinical cases for alpha globin gene analysis to reduce unnecessary testing and improve pre-test probabilities. Methods A retrospective study was performed to evaluate the reliability of 22 RBC indices (RBC, MCV, RDW, MCH, Mentzer Index, RDW Index, etc) and formulae in differentiating cases with 1–2 alpha globin gene deletions from negative cases, in a cohort of 90 individuals with confirmatory alpha globin gene analysis results. Results The number of patients with each alpha globin gene deletion pattern were: 37 patients with alpha-thalassemia trait, 21 silent carriers (one gene deletion), 31 patients with negative result and 1 patient with triplicated alpha globin gene. The mean RBC count in each group were 4.79 ± 0.65 (trait), 4.45 ± 0.73 (carrier), and 4.21 ± 0.84 (negative) (million/mm). The mean MCV in each group were 71.1 ± 4.8 (trait), 76.1 ± 5.9 (carrier), and 74.2 ± 9.3 (negative) (fL). The mean RDW in each group were 16.5 ± 3.0 (trait), 17.2 ± 3.4 (carrier), and 18.3 ± 4.9 (negative) (%). The mean MCH in each group were 22.0 ± 1.8 (trait), 23.8 ± 2.3 (carrier), and 23.5 ± 3.7 (negative) (pg). None of the RBC indices showed significant difference between carrier and negative groups, while 20 RBC indices showed significant difference (P &lt; 0.05) between trait and negative groups. The sensitivities of the RBC indices in detecting 1–2 alpha globin gene deletions based on published cut-offs ranged between 3.4% and 81.0%, while the specificities ranged between 41.9% and 100%. A novel scoring algorithm was evaluated based on the combination of 12 selected RBC indices and formulae, with each index or formula contributing to a score of 0 or 1. The average value of the negative cases was used as the score cut-off value for each index or formula. The scoring algorithm gave a score distribution between 0 and 12, with lower scores indicating alpha globin gene deletions. It achieved a sensitivity of 96.6% in detecting 1–2 alpha globin gene deletions, with a negative likelihood ratio of 0.15. The negative likelihood ratio of the scoring algorithm outperformed each of the individual RBC indices at similar levels of sensitivities (&gt;95%). Conclusion The novel algorithm is a highly sensitive tool for screening of alpha globin gene deletions, including one gene deletion. It is more effective at identifying negative cases compared to individual RBC indices. Implementation of the algorithm could efficiently reduce 10.0% (9/90) of the test volume of alpha globin gene analysis. A more stringent version of the algorithm that incorporates standard deviation in the cut-off values could potentially achieve nearly 100% sensitivity and reduce 5.6% (5/90) of the alpha globin gene analysis test volume.

Introduction A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review aims to compile all up-to-date studies addressing such algorithms’ use in clinical practice. Methods We searched PubMed/MEDLINE, EMBASE, CINAHL, and Web of Science using the keywords “machine learning,” “opioid,” and “prediction.” The results were limited to human studies with full-text availability in English. We included all peer-reviewed journal articles that addressed an ML model to predict PPO use by adult patients. Results Fifteen studies were included with a sample size ranging from 381 to 112898, primarily orthopedic-surgery-related. Most authors define a prolonged misuse of opioids if it extends beyond 90 days postoperatively. Input variables ranged from 9 to 23 and were primarily preoperative. Most studies developed and tested at least two algorithms and then enhanced the best-performing model for use retrospectively on electronic medical records. The best-performing models were decision-tree-based boosting algorithms in 5 studies with AUC ranging from .81 to .66 and Brier scores ranging from .073 to .13, followed second by logistic regression classifiers in 5 studies. The topmost contributing variable was preoperative opioid use, followed by depression and antidepressant use, age, and use of instrumentation. Conclusions ML algorithms have demonstrated promising potential as a decision-supportive tool in predicting prolonged opioid use in post-surgical patients. Further validation studies would allow for their confident incorporation into daily clinical practice.

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare’s path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

PURPOSE The Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) is a phase II platform trial that uses response adaptive randomization and genomic profiling to efficiently identify novel therapies for phase III testing. Three initial experimental arms (abemaciclib [a cyclin-dependent kinase [CDK]4/6 inhibitor], neratinib [an epidermal growth factor receptor [EGFR]/human epidermal growth factor receptor 2 inhibitor], and CC-115 [a deoxyribonucleic acid–dependent protein kinase/mammalian target of rapamycin inhibitor]) were simultaneously evaluated against a common control arm. We report the results for each arm and examine the feasibility and conduct of the adaptive platform design. PATIENTS AND METHODS Patients with newly diagnosed O 6 -methylguanine–DNA methyltransferase-unmethylated glioblastoma were eligible if they had tumor genotyping to identify prespecified biomarker subpopulations of dominant glioblastoma signaling pathways (EGFR, phosphatidylinositol 3-kinase, and CDK). Initial random assignment was 1:1:1:1 between control (radiation therapy and temozolomide) and the experimental arms. Subsequent Bayesian adaptive randomization was incorporated on the basis of biomarker-specific progression-free survival (PFS) data. The primary end point was overall survival (OS), and one-sided P values are reported. The trial is registered with ClinicalTrials.gov (identifier: NCT02977780 ). RESULTS Two hundred thirty-seven patients were treated (71 control; 73 abemaciclib; 81 neratinib; 12 CC-115) in years 2017-2021. Abemaciclib and neratinib were well tolerated, but CC-115 was associated with ≥ grade 3 treatment-related toxicity in 58% of patients. PFS was significantly longer with abemaciclib (hazard ratio [HR], 0.72; 95% CI, 0.49 to 1.06; one-sided P = .046) and neratinib (HR, 0.72; 95% CI, 0.50 to 1.02; one-sided P = .033) relative to the control arm but there was no PFS benefit with CC-115 (one-sided P = .523). None of the experimental therapies demonstrated a significant OS benefit ( P > .05). CONCLUSION The INSIGhT design enabled efficient simultaneous testing of three experimental agents using a shared control arm and adaptive randomization. Two investigational arms had superior PFS compared with the control arm, but none demonstrated an OS benefit. The INSIGhT design may promote improved and more efficient therapeutic discovery in glioblastoma. New arms have been added to the trial.

Abstract Background Invasive fungal infections have been described throughout the COVID-19 pandemic. Cryptococcal disease after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been reported in several isolated case reports and 1 larger case series. We sought to describe cryptococcal infections following SARS-CoV-2 through establishing a database to investigate underlying risk factors, disease manifestations, and outcomes. Methods We created a crowdsourced call for cases solicited through the Mycoses Study Group Education and Research Consortium, the Centers for Disease Control and Prevention Emerging Infectious Diseases Network, and infectious diseases Twitter groups. Data were collected in a web-based and secure REDCap survey without personal identifiers. Results Sixty-nine cases were identified and submitted by 29 separate institutional sites. Cryptococcosis was diagnosed a median of 22 days (interquartile range, 9–42 days) after SARS-CoV-2 infection. Mortality among those with available follow-up was 72% (26/36) for the immunocompetent group and 48% (15/31) for the immunocompromised group (likelihood ratio, 4.01; P = .045). We observed a correlation between disease manifestation (central nervous system infection, proven/probable disseminated disease, and respiratory) and mortality (P = .002). Conclusions The mortality rate of 59% for patients with cryptococcosis following SARS-CoV-2 is higher than that of modern Cryptococcus cohorts. There was an association between immunocompromised status and cryptococcal disease manifestations as well as mortality. Moreover, our series emphasizes the need for clinical and laboratory assessment of opportunistic infections beyond 30 days when concerning symptoms develop.

ImportanceLip, oral, and pharyngeal cancers are important contributors to cancer burden worldwide, and a comprehensive evaluation of their burden globally, regionally, and nationally is crucial for effective policy planning.ObjectiveTo analyze the total and risk-attributable burden of lip and oral cavity cancer (LOC) and other pharyngeal cancer (OPC) for 204 countries and territories and by Socio-demographic Index (SDI) using 2019 Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study estimates.Evidence ReviewThe incidence, mortality, and disability-adjusted life years (DALYs) due to LOC and OPC from 1990 to 2019 were estimated using GBD 2019 methods. The GBD 2019 comparative risk assessment framework was used to estimate the proportion of deaths and DALYs for LOC and OPC attributable to smoking, tobacco, and alcohol consumption in 2019.FindingsIn 2019, 370 000 (95% uncertainty interval [UI], 338 000-401 000) cases and 199 000 (95% UI, 181 000-217 000) deaths for LOC and 167 000 (95% UI, 153 000-180 000) cases and 114 000 (95% UI, 103 000-126 000) deaths for OPC were estimated to occur globally, contributing 5.5 million (95% UI, 5.0-6.0 million) and 3.2 million (95% UI, 2.9-3.6 million) DALYs, respectively. From 1990 to 2019, low-middle and low SDI regions consistently showed the highest age-standardized mortality rates due to LOC and OPC, while the high SDI strata exhibited age-standardized incidence rates decreasing for LOC and increasing for OPC. Globally in 2019, smoking had the greatest contribution to risk-attributable OPC deaths for both sexes (55.8% [95% UI, 49.2%-62.0%] of all OPC deaths in male individuals and 17.4% [95% UI, 13.8%-21.2%] of all OPC deaths in female individuals). Smoking and alcohol both contributed to substantial LOC deaths globally among male individuals (42.3% [95% UI, 35.2%-48.6%] and 40.2% [95% UI, 33.3%-46.8%] of all risk-attributable cancer deaths, respectively), while chewing tobacco contributed to the greatest attributable LOC deaths among female individuals (27.6% [95% UI, 21.5%-33.8%]), driven by high risk-attributable burden in South and Southeast Asia.Conclusions and RelevanceIn this systematic analysis, disparities in LOC and OPC burden existed across the SDI spectrum, and a considerable percentage of burden was attributable to tobacco and alcohol use. These estimates can contribute to an understanding of the distribution and disparities in LOC and OPC burden globally and support cancer control planning efforts.