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DOI 10.18632/aging.205464

Медицинская библиотека · Aging · 2024

The integration of multi-omics analysis and machine learning for the identification of prognostic assessment and immunotherapy efficacy through aging-associated genes in lung cancer.

Lu Wei, Zhou Yun, Zhao Ruixuan, Liu Qiushi, Yang Wei, Zhu Tianyi

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Аннотация

BACKGROUND AND

OBJECTIVES

Adolescents account for one-fifth of new HIV infections in the United States. Despite Centers for Disease Control and Prevention (CDC) recommendations for universal opt-out HIV screening for individuals aged 13 years or older in all health care settings, many adolescents remain undiagnosed due to limited access to testing. This study aimed to describe HIV testing rates in US pediatric emergency departments (EDs) and identify patient and hospital factors associated with testing.

METHODS

We conducted a cross-sectional study of pediatric ED encounters for patients aged 13 to 18 years at 40 US children's hospitals (January 2019-December 2023). Patient and hospital characteristics were analyzed using multivariable logistic regression analysis to assess associations with HIV testing.

RESULTS

Of 2 134 464 ED encounters, 28 052 (1.3%) included HIV testing. Of these, 75% (n = 20 972) were targeted tests based on ED diagnoses, whereas 25% (n = 7080) were screening tests. Factors associated with HIV testing were female sex (adjusted odds ratio [aOR], 1.46; 95% CI, 1.42-1.51), Black/African American race (aOR, 1.71; 95% CI, 1.65-1.77), 2 or more races documented (aOR, 1.52; 95% CI, 1.38-1.66), public insurance (aOR, 1.69; 95% CI, 1.63-1.75), and living in very low Childhood Opportunity Index neighborhoods (aOR, 1.67; 95% CI, 1.59-1.75). Hispanic/Latino (aOR, 0.84; 95% CI, 0.80-0.87), Asian (aOR, 0.73; 95% CI, 0.64-0.83), and adolescents with complex chronic conditions (aOR, 0.53; 95% CI, 0.49-0.57) were less likely to have HIV testing.

CONCLUSIONS

HIV testing during pediatric ED encounters was rare, demonstrating an opportunity to enhance pediatric HIV screening practices in accordance with recommendations from the CDC.

Для цитирования: Lu Wei, Zhou Yun, Zhao Ruixuan, Liu Qiushi, Yang Wei, Zhu Tianyi. The integration of multi-omics analysis and machine learning for the identification of prognostic assessment and immunotherapy efficacy through aging-associated genes in lung cancer.. Aging. 2024. DOI: 10.18632/aging.205464.

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