Establishing the utility of Recursive Partitioning Analysis for patients with intra-cranial metastases managed in a KwaZulu-Natal state sector Oncology unit

Abstract

Background: Brain metastases are relatively common and carry a poor prognosis. In a resource-constrained environment, the judicious use of radiotherapy must be considered in the context of its benefit. The Recursive Partitioning Analysis (RPA) scoring system is internationally validated to predict median survival in patients with brain metastases. It may be used to guide appropriate management of patients with brain metastases. Aim: To establish the relevance of applying the RPA prognostic scoring system to the local setting. Setting: The Department of Oncology, Greys Hospital, Pietermaritzburg, South Africa. Methods: A retrospective chart review of patients treated for brain metastases for the period 01 January 2014 to 31 December 2019 was performed. Data was collected to determine the RPA class for each patient. Multivariate analysis of potential factors which could impact survival was done, and the actual survival of each patient was calculated. Results: The commonest primary cancer in the study cohort was breast (67%), followed by lung (17%). Survival differences between RPA classes were statistically significant (p 0.001). Actual survival relative to that predicted by the RPA model was 4.5 versus 7–12 months, 3.6 versus 4–7 months, and 0.8 versus 2–4 months, for classes I, II and III, respectively. Conclusion: Results support the use of the RPA classification to risk stratify patients in this setting – and therefore may be used in treatment decision-making. However, it over-predicts the median survival for the local population. Larger studies are warranted in diagnostically homogenous patient groups with brain metastases, to determine survival more accurately.

Keywords

• ain metastases
• recursive partitioning analysis
• whole brain radiotherapy
• prognostic scoring system