Risultati per: Screening del cancro al seno nelle donne

Objective
To understand what leads to the non-adherence to the cervical cancer screening (CCS) recommendations during a consultation.

Design
For this qualitative study, in-depth semistructured interviews were carried out with French healthcare professionals. An interview guide was developed and tested. It included the following themes: CCS recommendations, patients’ profiles, relationship with patients, emotional work, over-screening and under-screening. Interviews were carried out until data saturation (no new data, theoretical diversity reached). The grounded theory was used for data analysis.

Participants
Gynaecologists, midwives and general practitioners (GPs). The sample diversity was achieved using the following criteria: place of work, type of healthcare profession, type of patients, private-sector or hospital professional.

Setting
Interviews were conducted between July and December 2022 in six regions in France.

Results
In-depth semistructured interviews were carried out with 15 midwives, 24 GPs and 11 gynaecologists from six French regions. Their analysis highlighted that the following factors contributed to the non-adherence to the CCS recommendation: burden of caring for family members for some women, adhesion to the principle of yearly screening by healthcare professionals and patients, need of negotiating the respect of the CCS recommendations, use of emotions, and arbitration to prioritise what is needed for good health maintenance. The search for mutual emotional comfort led some healthcare professionals to adopt attitudes towards the CCS that avoid positioning conflicts, even if this means departing from the recommendations.

Conclusion
CCS can be correctly performed if healthcare professionals and patients agree on the need of actively taking care of their health, which is difficult for women from lower sociocultural backgrounds. During the one-to-one meeting with their patients, healthcare professionals may find difficult to apply the CCS recommendations, although they know and agree with them.

The OvaMTA system based on ultrasound imaging is a simple and practical auxiliary tool for screening for ovarian cancer, with a diagnostic performance comparable to that of senior radiologists. This provides a potential tool for screening ovarian cancer.

In Reply Our study adjusted the FIT cutoff to yield the same overall positivity rate as reported for the mt-sRNA test (17%) to enhance comparability of diagnostic performance of both tests. Below we address each of the 3 points made by Drs Yang and Ma.

This randomized clinical trial evaluates the effects of Helicobacter pylori stool antigen assessment plus fecal immunochemical testing (FIT), vs FIT alone, on gastric cancer incidence and mortality among adults in Taiwan.

To the Editor We read with interest the recent study that compared the fecal immunochemical test (FIT) positivity threshold vs multitarget stool RNA (mt-sRNA) testing for colorectal cancer screening. However, we have concerns regarding the study methodology and the capability of FIT to fully replace the mt-sRNA panel.

Circulation, Volume 150, Issue 21, Page 1741-1744, November 19, 2024.

Circulation, Volume 150, Issue 21, Page 1656-1658, November 19, 2024.

La Presidenza del Consiglio aderisce a ‘Illumina novembre’

Cherubini (Siedp) “evitabili in oltre 450 bimbi ogni anno”

The absolute number of annual cases of gastric cancer in Europe is rising. The Council of the European Union has recommended implementation of gastric cancer screening for countries or regions with a high gastric cancer incidence and death rates. However, as of 2024 no organised gastric cancer screening programme has been launched in Europe.
There are several ways to decrease gastric cancer burden, but the screen and treat strategy for Helicobacter pylori (H. pylori) seems to be the most appropriate for Europe. It has to be noted that increased use of antibiotics would be associated with this strategy.
Only organised population-based cancer screening is recommended in the European Union, therefore gastric cancer screening also is expected to fulfil the criteria of an organised screening programme. In this respect, several aspects of screening organisation need to be considered before full implementation of gastric cancer prevention in Europe; the age range of the target group, test types, H. pylori eradication regimens and surveillance strategies are among them. Currently, ongoing projects (GISTAR, EUROHELICAN, TOGAS and EUCanScreen) are expected to provide the missing evidence. Feedback from the decision-makers and the potential target groups, including vulnerable populations, will be important to planning the programme.
This paper provides an overview of the recent decisions of the European authorities, the progress towards gastric cancer implementation in Europe and expected challenges. Finally, a potential algorithm for gastric cancer screening in Europe is proposed.

Hepatocellular carcinoma (HCC) is a significant global health problem, and its incidence is expected to exceed 1 million new HCC annually by 2025.1 The reported 3-year survival rate for advanced-stage HCC is less than 17%, while 70% of patients diagnosed with early-stage HCC can achieve 5-year survival.2 Despite well-established guidelines and the clear benefits of early detection, the meta-analysis results (29 papers, 1 18 799 patients) showed that only 24% of individuals at risk for developing HCC were screened.3 Efforts to surmount barriers at patient, provider and healthcare levels have shown a minimal screening rate increase over time.3 4 One of the reasons for the disappointing results might be the fact that authors focused on individual barriers, rather than considering the screening failure the result of the interplay of different factors. Additionally, the published studies have the following limitations, detailed reasons for…

Background
Hepatocellular carcinoma (HCC) is plagued by failures across the cancer care continuum, leading to frequent late-stage diagnoses and high mortality. We evaluated the effectiveness of mailed outreach invitations plus patient navigation to promote HCC screening process completion in patients with cirrhosis.

Methods
Between April 2018 and September 2021, we conducted a multicentre pragmatic randomised clinical trial comparing mailed outreach plus patient navigation for HCC screening (n=1436) versus usual care with visit-based screening (n=1436) among patients with cirrhosis at three US health systems. Our primary outcome was screening process completion over a 36-month period, and our secondary outcome was the proportion of time covered (PTC) by screening. All patients were included in intention-to-screen analyses.

Results
All 2872 participants (median age 61.3 years; 32.3% women) were included in intention-to-screen analyses. Screening process completion was observed in 6.6% (95% CI: 5.3% to 7.9%) of patients randomised to outreach and 3.3% (95% CI: 2.4% to 4.3%) of those randomised to usual care (OR 2.05, 95% CI: 1.44 to 2.92). The intervention increased HCC screening process completion across most subgroups including age, sex, race and ethnicity, Child-Turcotte-Pugh class and health system. PTC was also significantly higher in the outreach arm than usual care (mean 37.5% vs 28.2%; RR 1.33, 95% CI: 1.31 to 1.35). Despite screening underuse, most HCC in both arms were detected at an early stage.

Conclusion
Mailed outreach plus navigation significantly increased HCC screening process completion versus usual care in patients with cirrhosis, with a consistent effect across most examined subgroups. However, screening completion remained suboptimal in both arms, underscoring a need for more intensive interventions.

Trial registration number
NCT02582918.

Circulation, Volume 150, Issue Suppl_1, Page A4145826-A4145826, November 12, 2024. Background:Previous studies have shown that AF screening in at-risk populations can reduce stroke incidence. However, non-targeted screening approaches often result in high false positive rates, placing an unnecessary burden on the healthcare system. In contrast, artificial intelligence-guided screening has been demonstrated to increase diagnostic yield in large prospective clinical trials. This approach, however, requires recording an ECG and a large-scale dataset for model training. Heart rate variability (HRV) analysis has proven effective in deciphering key heart dynamics. By analyzing HRV as interrelated dynamic systems, it may be possible to facilitate targeted AF screening using wearable devices that measure heart rate.The Koopman operator, used for data-driven modeling of interrelated dynamic systems, has been shown to accurately predict complex phenomena in chaotic systems such as climate forecasting and drug adverse reaction prediction. This is achieved by utilizing common characteristics of the systems for most model parameters, with only a small fraction of the parameters being specific to a certain system.Methods:Long ( >10 hour) records from 361 individuals (AFDB, LTAFDB) and healthy individuals’ datasets from PhysioNet and THEW were analyzed for inter-beat intervals. The unified dataset was then split into 94 training, 17 validation, and 250 test set patients. Recordings from the training set were used to train both the common and specific parts of the interrelated dynamic systems model for each patient, along with a shared small neural network classifying patients into low and high risk for AF based on the unique (not shared between patients) singular values of the dynamic system model. Patient-specific dynamic system models were then fitted for the validation and test sets to calculate the patient dynamic singular values, which were used to classify patients into low and high risk for AF groups.Results:Atrial fibrillation occurred in 48 of 202 (23%) patients classified as low risk and 35 of 48 (72.9%) patients classified as high risk (odds ratio 8.63, 95% CI 4.23-17.64), yielding 72.9% sensitivity with 76.2% specificity.Conclusion:In this retrospective analysis, classification of the dynamic system model singular values identified patients at high risk for atrial fibrillation from sinus rhythm period.

Circulation, Volume 150, Issue Suppl_1, Page A4139194-A4139194, November 12, 2024. Background:In the U.S., about 8% of adults never received cholesterol screening. Although machine learning (ML) has been used to develop decision tools for Atherosclerotic Cardiovascular Disease (ASCVD) risk prediction, its application in behavioral forecasting has not yet been explored in the context of cholesterol screening behaviors. This study aimed to examine the performance and accuracy of ML algorithms in forecasting cholesterol screening behaviors in adults after age 50.Methods:This analysis used deidentified data from the Health and Retirement Study (HRS) 2004-2018. HRS is a longitudinal survey among 23,000 households in the U.S. Participants were excluded from the current analysis if they passed away by 2019, ever had ASCVD or stroke, were under age 50 at baseline, or had missing data in self-reported cholesterol screening. In total, 7176 participants (mean age [SD]=62 [8]) met the inclusion criteria; participants were randomly split into a training set (80%) and a testing set (20%). The synthetic minority oversampling technique was used to solve the imbalance distribution of the rare event. Five ML algorithms were used: random forest, gradient boosting machine (GBM), XGBoost, Support Vector Machine (SVM), and logistic regression. Accuracy, AUROC, and positive predictive value (PPV) were used to compare model performance. The average gain was evaluated for feature importance in the demographic and health domains.Results:In total, 232 (3.2%) respondents did not receive any cholesterol screening from 2008 to 2018. Experiments with five ML algorithms suggested that XGBoost with deeper trees and learning rate performed better in classifying those who did not screen for cholesterol levels over 10 years. Adding prior cholesterol screening history (2004-2006) into the model significantly improved model performance. Hypertension, self-rated health, and smoking were the major health features, while insurance, poverty, and work status were the major demographic features in the predictive model (accuracy=0.97; AUROC=0.88; PPV=0.42).Conclusion:Findings underscore the potential utility of ML models in predicting cholesterol screening behaviors after age 50. This could be the basis for developing decision tools for clinicians to identify those with a lower chance of cholesterol screening or make reminders accordingly. The low-cost predictive model might improve the uptake of preventive screening behaviors in middle-aged and older adults.