CDK4/6 inhibitors and endocrine therapy in the treatment of metastatic breast cancer: A real-world and propensity score-adjusted comparison.

INTRODUCTION/BACKGROUND: Hormone Receptor-positive (HR+) and Human Epidermal Growth Factor Receptor 2-negative (HER2-) breast cancer is the most common subtype, predominantly treated with endocrine therapy. The efficacy of CDK4/6 inhibitors combined with endocrine therapy in this context remains to be fully evaluated. MATERIALS (OR PATIENTS) AND METHODS: This study compared the effectiveness of CDK4/6 inhibitors (palbociclib and ribociclib) in combination with an aromatase inhibitor or fulvestrant against endocrine therapy alone in patients with HR+/HER2- advanced breast cancer. The main focus was on progression-free survival (PFS) and overall survival (OS). The study involved a population treated exclusively with endocrine therapy for bone involvement, examining median OS and PFS, and adjusting for variables like stage, visceral metastasis, age, and treatment line. RESULTS: The study found no significant OS difference between treatments with palbociclib, ribociclib, and endocrine therapy alone. However, ribociclib combined with letrozole significantly improved PFS over letrozole alone. Propensity score weighting indicated a potential 50 % reduction in death risk with ribociclib compared to palbociclib, though this was not confirmed by cox regression. CONCLUSION: CDK4/6 inhibitors, particularly ribociclib in combination with letrozole, show promise in improving outcomes for HR+/HER2- breast cancer patients. While palbociclib may not be superior to traditional endocrine therapy, the results underscore the need for further research. These findings could influence future treatment protocols, emphasizing the importance of personalized therapy in this patient group.

Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models.

This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key moments in the obstetric care process, including discharge and various stages of hospitalization. Our analysis: using 6 different machine learning methods like Decision Trees, Bayesian methods, Stochastic Gradient Descent, K-nearest neighbors, AdaBoost, and Multi-layer Perceptron and 19 different variables with various distributions and types, revealed that distributed models were at least equal, and often superior, to centralized versions and local versions. We also describe thoroughly the preprocessing stage in order to help others implement this method in real-world scenarios. The preprocessing steps included cleaning and harmonizing missing values, handling missing data and encoding categorical variables with multisite logic. Even though the type of machine learning model and the distribution of the outcome variable can impact the result, we reached results of 66% being superior to the centralized and local counterpart and 77% being better than the centralized with AdaBoost. Our experiments also shed light in the preprocessing steps required to implement distributed models in a real-world scenario. Our results advocate for distributed learning as a promising tool for applying machine learning in clinical settings, particularly when privacy and data security are paramount, thus offering a robust solution for privacy-concerned clinical applications.

Fast Healthcare Interoperability Resources-Based Support System for Predicting Delivery Type: Model Development and Evaluation Study.

BACKGROUND: The escalating prevalence of cesarean delivery globally poses significant health impacts on mothers and newborns. Despite this trend, the underlying reasons for increased cesarean delivery rates, which have risen to 36.3% in Portugal as of 2020, remain unclear. This study delves into these issues within the Portuguese health care context, where national efforts are underway to reduce cesarean delivery occurrences. OBJECTIVE: This paper aims to introduce a machine learning, algorithm-based support system designed to assist clinical teams in identifying potentially unnecessary cesarean deliveries. Key objectives include developing clinical decision support systems for cesarean deliveries using interoperability standards, identifying predictive factors influencing delivery type, assessing the economic impact of implementing this tool, and comparing system outputs with clinicians' decisions. METHODS: This study used retrospective data collected from 9 public Portuguese hospitals, encompassing maternal and fetal data and delivery methods from 2019 to 2020. We used various machine learning algorithms for model development, with light gradient-boosting machine (LightGBM) selected for deployment due to its efficiency. The model's performance was compared with clinician assessments through questionnaires. Additionally, an economic simulation was conducted to evaluate the financial impact on Portuguese public hospitals. RESULTS: The deployed model, based on LightGBM, achieved an area under the receiver operating characteristic curve of 88%. In the trial deployment phase at a single hospital, 3.8% (123/3231) of cases triggered alarms for potentially unnecessary cesarean deliveries. Financial simulation results indicated potential benefits for 30% (15/48) of Portuguese public hospitals with the implementation of our tool. However, this study acknowledges biases in the model, such as combining different vaginal delivery types and focusing on potentially unwarranted cesarean deliveries. CONCLUSIONS: This study presents a promising system capable of identifying potentially incorrect cesarean delivery decisions, with potentially positive implications for medical practice and health care economics. However, it also highlights the challenges and considerations necessary for real-world application, including further evaluation of clinical decision-making impacts and understanding the diverse reasons behind delivery type choices. This study underscores the need for careful implementation and further robust analysis to realize the full potential and real-world applicability of such clinical support systems.

Health Information Exchange: Understanding the Policy Landscape and Future of Data Interoperability.

OBJECTIVES: To review recent literature on health information exchange (HIE), focusing on the policy approach of five case study nations: the United States of America, the United Kingdom, Germany, Israel, and Portugal, as well as synthesize lessons learned across countries and provide recommendations for future research. METHODS: A narrative review of each nation's HIE policy frameworks, current state, and future HIE strategy. RESULTS: Key themes that emerged include the importance of both central decision-making as well as local innovation, the multiple and complex challenges of broad HIE adoption, and the varying role of HIE across different national health system structures. CONCLUSION: HIE is an increasingly important capability and policy priority as electronic health record (EHR) adoption becomes more common and care delivery is increasingly digitized. While all five case study nations have adopted some level of HIE, there are significant differences across their level of data sharing infrastructure and maturity, and each nation took a different policy approach. While identifying generalizable strategies across disparate international systems is challenging, there are several common themes across successful HIE policy frameworks, such as the importance of central government prioritization of data sharing. Finally, we make several recommendations for future research to expand the breadth and depth of the literature on HIE and guide future decision-making by policymakers and practitioners.

Dataset Comparison Tool: Utility and Privacy.

Synthetic data has been more and more used in the last few years. While its applications are various, measuring its utility and privacy is seldom an easy task. Since there are different methods of evaluating these issues, which are dependent on data types, use cases and purpose, a generic method for evaluating utility and privacy does not exist at the moment. So, we introduced a compilation of the most recent methods for evaluating privacy and utility into a single executable in order to create a report of the similarities and potential privacy breaches between two datasets, whether it is related to synthetic or not. We catalogued 24 different methods, from qualitative to quantitative, column-wise or table-wise evaluations. We hope this resource can help scientists and industries get a better grasp of the synthetic data they have and produce more easily and a better basis to create a new, more broad method for evaluating dataset similarities.