Characterising differences in red blood cell usage patterns between healthcare sectors in South Africa: 2014-2019.

BACKGROUND: South Africa aims to transition from a two-tiered healthcare system (public and private) to universal health coverage. Data on red blood cell (RBC) product usage reveal disparities between the sectors. Blood transfusion services further need to understand differing disease profiles and transfusion prescribing practices between the sectors to ensure blood security should the transition to a two-tiered health system come to fruition. MATERIALS AND METHODS: Operational data for public and private healthcare RBC requests between 1 January 2014 and 31 March 2019, obtained from the South African National Blood Service (SANBS), were retrospectively analysed. Sector-specific demographic and utilisation trends were compared for the dominant clinical disciplines. Pre-transfusion haemoglobin (Hb) patterns were also delineated for 2018. RESULTS: Between 2014 and 2019, 2,356,411 public and private sector RBC transfusion events resulted in the issue of 4,020,094 RBC units (1,553,159 transfusion events and 2,495,054 units within the public sector versus 803,282 transfusion events and 1,525,040 units in private). The dominant clinical disciplines within the public sector were Medical (32.9%), Gynaecology/Obstetrics (27.3%), General Surgery (13.6%), and Paediatrics (including Paediatric Surgery) (6.5%), compared to Intensive Care Units (33.2%), Medical (28.3%), General Surgery (10.4%), and Haematology/Oncology (8.3%) in the private sector. Median pre-transfusion Hb values for 2018 were lower in the public than in the private sector: 6.9 g/dL public sector versus 8 g/dL private sector. DISCUSSION: Clinical drivers of RBC usage within the public and private healthcare sectors in South Africa differ significantly. Disparate pre-transfusion Hb between the sectors are likely due to differing disease profiles and severity, as well as differences in practice in prescribing transfusions. Implementation of a nationally co-ordinated Patient Blood Management programme may help to address these disparities and help ensure a sustainable blood transfusion system.

Describing the evolution of myeloid-derived leucocytes in treated B-lineage paediatric acute lymphoblastic leukaemia with a data-driven granulocyte-monocyte-blast model.

Acute lymphoblastic leukaemia (ALL) is associated with a compromised myeloid system. Understanding the state of granulopoiesis in a patient during treatment, places the clinician in an advantageous position. Mathematical models are aids able to present the clinician with insight into the behaviour of myeloid-derived leucocytes. The main objective of this investigation was to determine whether a proposed model of ALL during induction therapy would be a usable descriptor of the system. The model assumes the co-occurrence of the independent leukaemic and normal marrow populations. It is comprised of four delay-differential equations, capturing the fundamental characteristics of the blood and bone marrow myeloid leucocytes and B-lineage lymphoblasts. The effect of treatment was presumed to amplify cell loss within both populations. Clinical data was used to inform the construction, calibration and examination of the model. The model is promising-presenting a good foundation for the development of a clinical supportive tool. The predicted parameters and forecasts aligned with clinical expectations. The starting assumptions were also found to be sound. A comparative investigation highlighted the differing responses of similarly diagnosed patients during treatment and further testing on patient data emphasized patient specificity. Model examination recommended the explicit consideration of the suppressive effects of treatment on the normal population production. Additionally, patient-related factors that could have resulted in such different responses between patients need to be considered. The parameter estimates require refinement to incorporate the action of treatment. Furthermore, the myeloid populations require separate consideration. Despite the model providing explanation, incorporating these recommendations would enhance both model usability and predictive capacity.

A proposed fractional-order Gompertz model and its application to tumour growth data.

A fractional-order Gompertz model of orders between 0 and 2 is proposed. The main purpose of this investigation is to determine whether the ordinary or proposed fractional Gompertz model would best fit our experimental dataset. The solutions for the proposed model are obtained using fundamental concepts from fractional calculus. The closed-form equations of both the proposed model and the ordinary Gompertz model are calibrated using an experimental dataset containing tumour growth volumes of a Rhabdomyosarcoma tumour in a mouse. With regard to the proposed model, the order, within the interval mentioned, that resulted in the best fit to the data was used in a further investigation into the prediction capability of the model. This was compared to the prediction capability of the ordinary Gompertz model. The result of the investigation was that a fractional-order Gompertz model of order 0.68 produced a better fit to our experimental dataset than the well-known ordinary Gompertz model.