Real-World Use of Fedratinib for Myelofibrosis Following Prior Ruxolitinib Failure: Patient Characteristics, Treatment Patterns, and Clinical Outcomes.

BACKGROUND: There is a lack of established clinical outcomes for patients with myelofibrosis (MF) receiving fedratinib following ruxolitinib failure. This study examined real-world patient characteristics, treatment patterns, and clinical outcomes of patients with MF treated with fedratinib following ruxolitinib failure in US clinical practice. PATIENTS AND METHODS: This retrospective patient chart review included adults with a physician-reported diagnosis of MF, who initiated fedratinib after discontinuing ruxolitinib. Descriptive analyses characterized patient characteristics, clinical outcomes, and treatment patterns from MF diagnosis through ruxolitinib and fedratinib treatment. RESULTS: Twenty-four physicians abstracted data for 150 eligible patients. Approximately 55.3% of the patients were male, 68.0% were White, and median age at MF diagnosis was 68 (range, 35-84) years. Median duration of ruxolitinib therapy was 7.6 (range, 0.7-65.5) months. At initiation of fedratinib, 88.0% of patients had palpable spleen and a mean spleen size of 16.0 (standard deviation [SD], 5.9) cm. Spleen size decreased by 19.4% to 13.2 (SD, 7.9) cm at month 3 (P = .0001) and by 53.4% to 7.2 (SD, 7.4) cm at month 6 (P = .01) of fedratinib treatment, respectively. Almost one-third (26.8%) of patients had achieved ≥ 50% spleen reduction by month 6. Mean number of symptoms also decreased significantly at month 3 (P < .0001) and month 6 (P = .01). CONCLUSION: Fedratinib appears to deliver spleen and symptom benefits in real-world patients with MF previously treated with ruxolitinib.

Cervical Cancer Care Continuum in South India: Evidence from a Community-based Screening Program.

In India, cervical cancer screening is conducted at various levels; however, after screening, the adherence to the cancer care continuum is barely understood. This study evaluated a community-based cancer screening program conducted in a rural setting (Tirunelveli and Tuticorin districts) in South India and reviewed the completion of care continuum. In this longitudinal descriptive study involving secondary data collection, data from the case records of 2192 women who were consecutively screened between March 2015 and May 2016 were included. All women underwent conventional cytology-based screening (Pap smear) and Visual Inspection with Acetic Acid (VIA). Those for whom either test was positive were referred for histopathological confirmation. Patients with confirmed precancerous conditions and unsatisfactory Pap smears were referred for further management. In total, 2192 women were screened [age range, 17-69 years; mean (standard deviation), 39.2 (8.5)]. Common symptom and sign were white discharge per vaginum (34.9%) and cervical erosion (34.4%), respectively. The VIA was positive for 24% (523/2178; 14 women did not cooperate for VIA) and 113 (5.1%) had epithelial cell abnormality in the Pap smear test. Per histopathology findings, one woman had non-keratinizing squamous cell carcinoma. Seven, three, and four had cervical intraepithelial neoplasia I, II and III, respectively. Of 2192, 807 were eligible for referral (597 had positive results on either Pap or VIA). Among the 807 women referred, only 74 (9.2%) women visited the referral center. The follow-up rate was very poor accounting to fragmentation of care continuum. The success of the screening program depends on the completion of the care continuum.

Analysis of metabolomic data using support vector machines.

Metabolomics is an emerging field providing insight into physiological processes. It is an effective tool to investigate disease diagnosis or conduct toxicological studies by observing changes in metabolite concentrations in various biofluids. Multivariate statistical analysis is generally employed with nuclear magnetic resonance (NMR) or mass spectrometry (MS) data to determine differences between groups (for instance diseased vs healthy). Characteristic predictive models may be built based on a set of training data, and these models are subsequently used to predict whether new test data falls under a specific class. In this study, metabolomic data is obtained by doing a (1)H NMR spectroscopy on urine samples obtained from healthy subjects (male and female) and patients suffering from Streptococcus pneumoniae. We compare the performance of traditional PLS-DA multivariate analysis to support vector machines (SVMs), a technique widely used in genome studies on two case studies: (1) a case where nearly complete distinction may be seen (healthy versus pneumonia) and (2) a case where distinction is more ambiguous (male versus female). We show that SVMs are superior to PLS-DA in both cases in terms of predictive accuracy with the least number of features. With fewer number of features, SVMs are able to give better predictive model when compared to that of PLS-DA.