Extent and nature of unlicensed and off-label medicine use in hospitalised children in Palestine

Objective of the study: To determine the extent and nature of unlicensed/off-label prescribing patterns in hospitalised children in Palestine. Setting: Four paediatric wards in two public health system hospitals in Palestine [Caritas children’s hospital (Medical and neonatal intensive care units) and Rafidia general hospital (Medical and surgical units)]. Method: A prospective survey of drugs administered to infants and children <18 years old was carried out over a five-week period in the four paediatric wards. Main outcome measure: Drug-licensing status of all prescriptions was determined according to the Palestinian Registered Product List and the Physician’s Desk Reference. Results: Overall, 917 drug prescriptions were administered to 387 children. Of all drug prescriptions, 528 (57.5%) were licensed for use in children; 65 (7.1%) were unlicensed; and 324 (35.3%) were used off-label. Of all children, 49.6% received off-label prescriptions, 10.1% received unlicensed medications and 8.2% received both. Seventy-two percent of off-label drugs and 66% of unlicensed drugs were prescribed for children <2 years. Multivariate analysis showed that patients who were admitted to the neonatal intensive care unit and infants aged 0–1 years were most likely to receive a greater number of off-label or unlicensed medications (OR 1.80; 95% CI 1.03–3.59 and OR 1.99; 95% CI 0.88–3.73, respectively). Conclusion: The present findings confirmed the elevated prevalence of unlicensed and off-label paediatric drugs use in Palestine and strongly support the need to perform well designed clinical studies in children.

Robust automated tumour segmentation on histological and immunohistochemical tissue images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.