Parenthood in survivors of Hodgkin lymphoma: an EORTC-GELA general population case-control study.

Contains fulltext : 108966.pdf (publisher's version ) (Open Access)PURPOSE: We investigated the impact of Hodgkin lymphoma (HL) on parenthood, including factors influencing parenthood probability, by comparing long-term HL survivors with matched general population controls. PATIENTS AND METHODS: A Life Situation Questionnaire was sent to 3,604 survivors treated from 1964 to 2004 in successive clinical trials. Responders were matched with controls (1:3 or 4) for sex, country, education, and year of birth (10-year groups). Controls were given an artificial date of start of treatment equal to that of their matched case. The main end point was presence of biologic children after treatment, which was evaluated by using conditional logistic regression analysis. Logistic regression analysis was used to analyze factors influencing spontaneous post-treatment parenthood. RESULTS: In all, 1,654 French and Dutch survivors were matched with 6,414 controls. Median follow-up was 14 years (range, 5 to 44 years). After treatment, the odds ratio (OR) for having children was 0.77 (95% CI, 0.68 to 0.87; P < .001) for survivors compared with controls. Of 898 survivors who were childless before treatment, 46.7% achieved post-treatment parenthood compared with 49.3% of 3,196 childless controls (OR, 0.87; P = .08). Among 756 survivors with children before treatment, 12.4% became parents after HL treatment compared with 22.2% of 3,218 controls with children before treatment (OR, 0.49; P < .001). Treatment with alkylating agents, second-line therapy, and age older than 35 years at treatment appeared to reduce the chances of spontaneous post-treatment parenthood. CONCLUSION: Survivors of HL had slightly but significantly fewer children after treatment than matched general population controls. The difference concerned only survivors who had children before treatment and appears to have more personal than biologic reasons. The chance of successful post-treatment parenthood was 76%

Premature ovarian failure and fertility in long-term survivors of Hodgkin's lymphoma: a European Organisation for Research and Treatment of Cancer Lymphoma Group and Groupe d'Etude des Lymphomes de l'Adulte Cohort Study.

Contains fulltext : 109132.pdf (publisher's version ) (Open Access)PURPOSE: In this large cohort of Hodgkin's lymphoma survivors with long follow-up, we estimated the impact of treatment regimens on premature ovarian failure (POF) occurrence and motherhood, including safety of nonalkylating chemotherapy and dose-response relationships for alkylating chemotherapy and age at treatment. PATIENTS AND METHODS: The Life Situation Questionnaire was sent to 1,700 women treated in European Organisation for Research and Treatment of Cancer and Groupe d'Etude des Lymphomes de l'Adulte trials between 1964 and 2004. Women treated between ages 15 and 40 years and currently not using hormonal contraceptives (n = 460) were selected to assess occurrence of POF. Cumulative POF risk was estimated using the life-table method. Predictive factors were assessed by Cox regression analysis. RESULTS: Median follow-up was 16 years (range, 5 to 45 years). Cumulative risk of POF after alkylating chemotherapy was 60% (95% CI, 41% to 79%) and only 3% (95% CI, 1% to 7%) after nonalkylating chemotherapy (doxorubicin, bleomycin, vinblastine, and dacarbazine; epirubicin, bleomycin, vinblastine, and prednisone). Dose relationship between alkylating chemotherapy and POF occurrence was linear. POF risk increased by 23% per year of age at treatment. In women treated without alkylating chemotherapy at age younger than 32 years and age 32 years or older, cumulative POF risks were 3% (95% CI, 1% to 16%) and 9% (95% CI, 4% to 18%), respectively. If menstruation returned after treatment, cumulative POF risk was independent of age at treatment. Among women who ultimately developed POF, 22% had one or more children after treatment, compared with 41% of women without POF. CONCLUSION: Nonalkylating chemotherapy carries little to no excess risk of POF. Dose-response relationships for alkylating chemotherapy and age at treatment are both linear. Timely family planning is important for women at risk of POF

Analysis of biomarker expression in severe endometriosis and determination of possibilities for targeted intraoperative imaging

<p>Objective: To evaluate the expression of biomarkers in endometriotic tissue in order to determine the most promising molecules for targeted intraoperative imaging. Methods: Tissue samples were obtained from 18 patients with endometriosis. The intensity and pattern of expression of the following biomarkers were assessed by immunohistochemistry: C-X-C chemokine receptor type 4 (CXCR4), epithelial cell adhesion molecule (EpCAM), estrogen receptor (ER), folate receptor alpha (FR-alpha), hypoxia-inducible factor 1-alpha (HIF-1 alpha), progesterone receptor (PR), and vascular endothelial growth factor A (VEGF-A). The Target Selection Criteria scoring system was used to select the most promising biomarkers for intraoperative imaging. Results: Expression of OCCR4, EpCAM, ER, PR, and VEGF-A was scored as strong in endometriotic epithelium. Expression of FR-alpha was detected in 94.4% of samples, whereas HIF-1 alpha was expressed in just 5.6% of samples. Of note, CXCR4, ER, and VEGF-A were also expressed in surrounding healthy tissue, thus reducing the target-to-background ratio. Conclusion: Of the 7 biomarkets assessed in the present study, EpCAM, FR-alpha, and VEGF-A seem the most promising for targeted intraoperative imaging of endometriosis. (C) 2012 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.</p>

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes