The update of the preventive activities for this year 2022 in the field of infectious diseases is of special relevance due to the importance that prevention has gained and more specifically, vaccination as a tool to control the pandemic caused by the SARS-CoV-2 virus declared on March 11, 2020. The pandemic has focused much of the prevention efforts on its containment, but the importance of maintaining high vaccination coverage of the rest of the recommended vaccines to maintain good control of vaccine-preventable diseases and avoid complications in particularly vulnerable patients should not be forgotten. In this year's review we present a practical document with the aim of providing tools to primary care professionals who work with adults, to make the indication of each vaccine whether it is systematically recommended or if it is because the patient belongs to some risk group due to their condition or underlying pathology. In this way, throughout the document, we will comment on the most innovative aspects of systematic vaccination (flu, pneumococcus, meningococcal vaccines and vaccines against the human papillomavirus [HPV]), the new vaccines (pandemic vaccines against COVID-19, vaccines against herpes zoster of subunits, vaccines against monkeypox) and the recommended vaccines according to risk condition (pregnancy and lactation, travelers, patients with immunosuppression or underlying pathology). 

[Infectious Disease Prevention Group: Update on vaccines, 2022].

Patients with triple‐negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis.We used single‐center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC).We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65–0.76 versus 0.64, 95% CI: 0.57–0.71, P = .004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co‐occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI‐RADS 4 than BI‐RADS 5 compared to non‐TNBC patients (P = .002).A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.

Ultrasound Radiomics Features to Identify Patients With Triple‐Negative Breast Cancer: A Retrospective, Single‐Center Study

Prevention of Post-Mastectomy Pain Syndrome: A Review of Recent Literature on Perioperative Interventions

Background/Objectives: Our objective was to evaluate the clinical feasibility and sustainability of an awake breast conserving surgery (BCS) protocol integrating opioid-free anesthesia, telemedicine, and environmental sustainability compared to standard care. Methods: A prospective, monocentric, non-inferiority randomized controlled trial (133.24 CET2 ptv; NCT06624917) named Breast Green Surgery (BuGS) was planned. Women aged 18–75 years eligible for level I BCS were randomized to either the BuGS group—comprising opioid-free monitored anesthesia care (MAC), a telehealth follow-up, and intraoperative waste reduction—or the control. The primary endpoint was postoperative pain at rest (PPR) and during movement (PPD) both at 24 h, measured with the Numeric Pain Rating Scale (NPRS). Secondary exploratory endpoints included SF-36, PSQ-18, TSQ test, LOS, and KgCO2 equivalent (KgCO2e) from surgical waste, patient transport, and complications. The interim analysis included patients completing a 30-day follow-up by 31 December 2024. Results: A total of 45 patients were enrolled (BuGS n = 18, control n = 27). While disproportionate, no significant differences were observed in PPR/PPD at 24 h (4.75 (3.725–5.875) vs. 4.5 (4.15–5.35); p = 0.626; 4 (3.10–4.75) vs. 4.6 (3.90–5.2); p = 0.130), confirmed using group ANOVA analysis (p = 0.515; p = 0.779, respectively). The BuGS group reported a reduced surgical room occupation (80.03 (64.84–101.87) vs. 133.23 (95.47–144.25) min; p = 0.002) and length of stay (0 (0–1) vs. 1 (1–2); p = 0.0001), without hospital unplanned admissions. Reduced KgCO2e emissions from waste disposal were reported, with no difference in SF-36, PSQ-18, and complication rates. Conclusions: If confirmed after complete accrual, BuGS could potentially promote a clinically equivalent, environmentally sustainable, and hospital efficient surgery without affecting the QoL of our patients. Further multicentric validation is warranted. 

Feasibility of a Green Breast Surgery Protocol to Reduce Carbon Footprint of Care: BuGS Trial Interim Results

Background Breast and gynecological cancer have a high prevalence and a significant impact on public health. It is important to note that the time intervals until diagnosis and treatment influence the prognosis. The objective was to describe the delay in the diagnosis of breast and gynecological cancer and to identify the variables related to the patient, healthcare and the disease that intervene in the time interval until diagnosis and treatment. Methods We conducted a retrospective study (2014–2023) following a cohort of women with breast and gynecological cancer, from the onset of symptoms to the start of treatment. The study included 722 women from 30 general practice clinics in Albacete, Spain, and data were obtained from both primary care and hospital settings. Results Among breast cancer patients, 150 (25.7%) had been diagnosed through screening, and among those diagnosed with cervical cancer, 14 (37.8%), it was not possible to calculate some time intervals. In breast cancer the variables associated with a total time interval (from first symptoms to start of treatment) of more than 90 days were: age over 50 and symptoms other than a breast lump. In gynecological cancer, the related variables were: no family history and having attended the health center for the first consultation. In the diagnostic interval (from first consultation to diagnosis), the variables associated with a duration of more than 30 days were: presenting with fewer than two risk factors in breast cancer and first consultation at the health center in gynecological cancer. Conclusion Most patients with breast and/or gynecological cancer are diagnosed in the early stages of the disease, except in the case of ovarian cancer. Most breast and cervical tumors are not diagnosed through screening. The time interval that most influences the total interval is the diagnostic interval, which includes the primary care interval. The treatment interval is high in most tumors, exceeding the recommended time. The results provide useful information for proposing improvements in access to diagnostic and therapeutic resources, as well as preferential referral circuits to improve early detection and prognosis of the disease. 

pdf/time-intervals-in-the-pathway-to-diagnosis-and-treatment-of-jyxwlvkdbx8h.pdf

Time intervals in the pathway to diagnosis and treatment of patients with breast and gynecological cancer

Abstract Background This study aimed to investigate the effect of the time from diagnosis to breast cancer surgery on breast cancer patients’ prognosis. Methods Of the 1900 patients diagnosed with invasive (stage 1–3) breast cancer who underwent surgery in KUH between 2012 and 2019, 279 patients were enrolled in this study. All patients, including those who received neoadjuvant chemotherapy, were classified as Model 1 subjects, and those who received immediate surgical treatment were classified as Model 2 subjects. We conducted a Cox regression analysis to identify prognostic factors of breast cancer associated with the time from diagnosis to surgery. Results The univariate results indicated a sharp drop in both groups’ survival rates when the time to surgery was delayed for more than 8 weeks (Model 1 p = 0.000; Model 2 p = 0.001). In the multivariate analysis, the hazard ratio (HR) of Model 1increased (HR = 6.84, 95% CI 1.06–44.25) in response to a delay in surgery of more than 8 weeks. Smoking and the American Joint Committee on Cancer (AJCC) staging system had a negative effect on breast cancer prognosis, while hormone therapy had a positive effect. Conclusion For all patients, a delay in breast cancer surgery of more than 8 weeks was inversely associated with survival. 

/pdf/time-to-surgery-and-survival-in-breast-cancer-2br9qid4.pdf

Time to surgery and survival in breast cancer

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Time to surgery and survival in breast cancer