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Adolescent girls and young women in Africa are at high risk of HIV and should be considered a key population for HIV prevention initiatives. Oral Tenofovir/Emtricitabine as pre-exposure prophylaxis (PrEP) has been shown to be effective on an individual and population level among key populations in Europe, Australia, and the US. However, studies in sub-Saharan Africa in a generalised epidemic have been less promising with adherence to daily tablets identified as a major problem. Long-acting PrEP drugs are being developed as a response to this problem. The first of these long-acting agents, injectable Cabotegravir given every two months has shown superiority to oral PreP and has been approved by the US Food and Drug Administration (FDA). Another long-acting PrEP drug in development is Lenacapavir which is an investigational, first-in-class long-acting HIV-1 capsid inhibitor that can be given as a six-monthly injection. These long-acting drugs could be a highly effective public health HIV prevention intervention. If made readily available to a vulnerable population of adolescent young women who are at high risk of HIV they could play an important role in protecting this key population against HIV and potentially reduce the population level risk of HIV.
Assuntos
Fármacos Anti-HIV , Infecções por HIV , Profilaxia Pré-Exposição , Humanos , Profilaxia Pré-Exposição/métodos , Feminino , Infecções por HIV/prevenção & controle , Adolescente , Fármacos Anti-HIV/administração & dosagem , Piridonas/administração & dosagem , Adesão à Medicação , África Subsaariana , Preparações de Ação Retardada , DicetopiperazinasRESUMO
Purpose: Automated machine learning (AutoML) has emerged as a novel tool for medical professionals lacking coding experience, enabling them to develop predictive models for treatment outcomes. This study evaluated the performance of AutoML tools in developing models predicting the success of pneumatic retinopexy (PR) in treatment of rhegmatogenous retinal detachment (RRD). These models were then compared with custom models created by machine learning (ML) experts. Design: Retrospective multicenter study. Participants: Five hundred and thirty nine consecutive patients with primary RRD that underwent PR by a vitreoretinal fellow at 6 training hospitals between 2002 and 2022. Methods: We used 2 AutoML platforms: MATLAB Classification Learner and Google Cloud AutoML. Additional models were developed by computer scientists. We included patient demographics and baseline characteristics, including lens and macula status, RRD size, number and location of breaks, presence of vitreous hemorrhage and lattice degeneration, and physicians' experience. The dataset was split into a training (n = 483) and test set (n = 56). The training set, with a 2:1 success-to-failure ratio, was used to train the MATLAB models. Because Google Cloud AutoML requires a minimum of 1000 samples, the training set was tripled to create a new set with 1449 datapoints. Additionally, balanced datasets with a 1:1 success-to-failure ratio were created using Python. Main Outcome Measures: Single-procedure anatomic success rate, as predicted by the ML models. F2 scores and area under the receiver operating curve (AUROC) were used as primary metrics to compare models. Results: The best performing AutoML model (F2 score: 0.85; AUROC: 0.90; MATLAB), showed comparable performance to the custom model (0.92, 0.86) when trained on the balanced datasets. However, training the AutoML model with imbalanced data yielded misleadingly high AUROC (0.81) despite low F2-score (0.2) and sensitivity (0.17). Conclusions: We demonstrated the feasibility of using AutoML as an accessible tool for medical professionals to develop models from clinical data. Such models can ultimately aid in the clinical decision-making, contributing to better patient outcomes. However, outcomes can be misleading or unreliable if used naively. Limitations exist, particularly if datasets contain missing variables or are highly imbalanced. Proper model selection and data preprocessing can improve the reliability of AutoML tools. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
RESUMO
Background: Drug resistance testing aids in appropriate antiretroviral therapy selection to improve treatment success but may not be readily available. We evaluated the impact of switching to dolutegravir/lamivudine (DTG/3TC) using pooled data from the TANGO and SALSA trials in adults who were virologically suppressed with or without historical resistance results at screening. Methods: Adults who were virologically suppressed (HIV-1 RNA <50 copies/mL for >6 months) with no prior virologic failure were randomized to switch to DTG/3TC (TANGO, n = 369; SALSA, n = 246) or continue their current antiretroviral regimen (CAR; TANGO, n = 372; SALSA, n = 247). Week 48 HIV-1 RNA ≥50 and <50 copies/mL (Snapshot algorithm, Food and Drug Administration; intention-to-treat exposed), CD4+ cell count, and safety were analyzed by availability of historical resistance results. Results: Overall, 294 of 615 (48%) participants in the DTG/3TC group and 277 of 619 (45%) participants in the CAR group had no historical resistance results at screening. At week 48, proportions with Snapshot HIV-1 RNA ≥50 copies/mL were low (≤1.1%) and similar across treatment groups and by historical resistance results availability. High proportions (91%-95%) maintained virologic suppression through week 48, regardless of results availability. Across both subgroups of results availability, greater increases in CD4+ cell count from baseline to week 48 occurred with DTG/3TC vs CAR. No participants taking DTG/3TC had confirmed virologic withdrawal, regardless of historical resistance results availability. One participant undergoing CAR without historical resistance results had confirmed virologic withdrawal; no resistance was detected. Overall, DTG/3TC was well tolerated; few adverse events led to withdrawal. Conclusions: Findings support DTG/3TC as a robust switch option for adults who are virologically suppressed with HIV-1 and no prior virologic failure, regardless of historical resistance results availability. Clinical trial registration: TANGO: NCT03446573, https://clinicaltrials.gov/study/NCT03446573. SALSA: NCT04021290, https://clinicaltrials.gov/study/NCT04021290.
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BACKGROUND: The liver is the most frequent site of metastases in colorectal cancer (CRC). This study aimed to assess the response rate and survival outcomes in metastatic CRC patients with non-liver metastases (NLM) compared to those with liver metastases (LM) across different lines of treatment. METHODS: A total of 17,924 mCRC patients included in 26 trials from the ARCAD CRC database were analyzed. The analysis was conducted based on the presence or absence of LM across different treatment groups: chemotherapy (CT) alone, CT + anti-VEGF, CT + anti-EGFR in KRAS wild-type tumors, within the first-line (1 L) and second-line (2 L), and patients enrolled in third-line (≥3 L) trials treated with trifluridine/tipiracil or regorafenib or placebo. The endpoints were overall survival (OS), progression-free survival (PFS), and overall response rate (ORR). RESULTS: Out of the 17,924 patients, 14,066 had LM (30.6 % with only liver involvement and 69.4 % with liver and other metastatic sites), while 3858 patients had NLM. In the CT alone and CT + anti-VEGF subgroups, NLM patients showed better OS and PFS in the 1 L and 2 L settings. However, in the CT + anti-EGFR 1 L and 2 L subgroups, there was no significant difference in OS and PFS between NLM and LM patients. In the ≥ 3 L subgroups, better OS and PFS were observed in NLM patients. ORRs were higher in LM patients than in NLM patients across all cohorts treated in the 1 L and only in the anti-EGFR cohort in the 2 L. CONCLUSION: LM is a poor prognostic factor for mCRC increasing from 1 L to ≥ 3 L except for patients in 1 L and 2 L receiving CT+anti-EGFR. These data justify using LM as a stratification factor in future trials for patients with unresectable mCRC.