Digging in real-word electronic database for assessing CDK 4/6 inhibitors adherence in breast cancer patients from Romania.

Introduction: It is imperative for patients to respect the prescribed treatments to achieve the anticipated clinical outcomes, including the outpatients receiving oral anti-cancer drugs such as selective cyclin-dependent kinase 4/6 inhibitors (CDK 4/6i). With the introduction of three CDK 4/6i drugs in the Romanian pharmaceutical market in 2018, our study aimed to evaluate medication adherence and the influencing factors among patients undergoing treatment with palbociclib, ribociclib, or abemaciclib for advanced or metastatic breast cancer. Methods: Medication adherence was assessed using the Proportion of Days Covered (PDC) method, and Spearman correlation analysis was conducted to explore the relationships between adherence, age, gender, and follow-up duration. Results: The study enrolled 330 breast cancer patients, with an average follow-up period of 14.6 ± 12.5 months for palbociclib, 10.6 ± 7.1 months for ribociclib, and 8.6 ± 6.4 months for abemaciclib-treated patients. A small proportion of patients demonstrated non-adherence: 12.8% for palbociclib, 14.6% for ribociclib, and 14.7% for abemaciclib. Among patients receiving palbociclib, there was no significant correlation between adherence, age (rho = 0.07, p = 0.35), or gender (rho = -0.144, p = 0.054). However, a significant correlation was found with the duration of follow-up (rho = -0.304, p < 0.0001). Similar results were observed for patients receiving ribociclib or abemaciclib. Most patients received combination therapy with letrozole (46%) and exemestane (13%) for palbociclib, letrozole (48%) and fulvestrant (19%) for ribociclib, and fulvestrant (39%) and letrozole (27%) for abemaciclib, Discussion: High adherence rates were observed among patients treated with CDK 4/6i drugs, with no significant differences noted among the three drugs in this class. However, the collected patient data was limited, lacking information on adverse reactions that could potentially lead to treatment discontinuation, as determined by the oncologist's decision not to prescribe. Consequently, a comprehensive understanding of all factors contributing to the low adherence levels is hindered.

Predictive Value of Pulmonary Involvement in Stroke Patients Co-Infected with COVID-19.

In response to the intricate clinical challenges posed by the intersection of COVID-19 and acute ischemic stroke, the Neuropsychiatry Hospital of Craiova, Romania, initiated a comprehensive study. This research aims to unravel the impacts of pulmonary complications on ischemic stroke outcomes, comparing patients with concurrent SARS-CoV-2 infection to those without. The study integrates pulmonary assessments, acknowledging the significant role respiratory involvement plays in the progression and prognosis of stroke patients during the pandemic. By systematically examining individuals with both acute ischemic stroke and COVID-19, the study seeks to shed light on the complex interplay between cerebral and pulmonary health. The findings are expected to enhance patient care by informing clinical decisions and leading to more effective management approaches for stroke patients in the COVID-19 era.

Diagnostic Accuracy of Machine-Learning Models on Predicting Chemo-Brain in Breast Cancer Survivors Previously Treated with Chemotherapy: A Meta-Analysis.

We performed a meta-analysis of chemo-brain diagnostic, pooling sensitivities, and specificities in order to assess the accuracy of a machine-learning (ML) algorithm in breast cancer survivors previously treated with chemotherapy. We searched PubMed, Web of Science, and Scopus for eligible articles before 30 September 2022. We identified three eligible studies from which we extracted seven ML algorithms. For our data, the χ2 tests demonstrated the homogeneity of the sensitivity's models (χ2 = 7.6987, df = 6, p-value = 0.261) and the specificities of the ML models (χ2 = 3.0151, df = 6, p-value = 0.807). The pooled area under the curve (AUC) for the overall ML models in this study was 0.914 (95%CI: 0.891-0.939) and partial AUC (restricted to observed false positive rates and normalized) was 0.844 (95%CI: 0.80-0.889). Additionally, the pooled sensitivity and pooled specificity values were 0.81 (95% CI: 0.75-0.86) and 0.82 (95% CI: 0.76-0.86), respectively. From all included ML models, support vector machine demonstrated the best test performance. ML models represent a promising, reliable modality for chemo-brain prediction in breast cancer survivors previously treated with chemotherapy, demonstrating high accuracy.

The Influence of Covid-19 Infection on Neurocognitive Disorders.

Neurocognitive disorders are a group of disorders characterized by an impaired cognition which has not been present since birth or very early life and represents a decline from a previous attained level of functioning. The case we studied is M.E., a 62 years female, married, from rural area, working as a tailor, without any psychiatric history. She develops psychiatric symptoms during Covid-19 infection and treatment, in November 2020. The patient has been admitted in psychiatric care, in that time, for about one month, for a persistent confusion state during and after this event. These symptoms diminish the patient's level of functioning and seemed to be related with the Covid-19 infection or treatment. Psychological investigation underlines a MMSE 14, severe impairment in attention, short-term and long-term memory. CT evaluation presents normal relation except a moderate general atrophy, according with patient's age. Differential diagnosis will be discussed. The treatment has proven its effectiveness, the patient regaining her ability to orientate, could do housework, good improvement in attention and short-term memory. We emphasize that there is correlation between the Covid-19 infection and confusive state and delirium in patients, as a comorbidity, followed in many cases by chronic progressive neurocognitive disorder, especially in elderly.