Protocol for the development and validation of a risk score to predict risk of adverse events associated with systemic anti-cancer treatment in late-stage lung cancer: Lung Cancer Improved Decisions (LUCID).

Abstract

BACKGROUND: Less than 20% of patients diagnosed with advanced lung cancer will survive beyond five years and half of these will suffer a serious adverse event (SAE) caused by systemic anticancer therapy (SACT) that will result in a hospital attendance. As multiple different SACT treatments are available for patients, a risk score that predicts the likelihood of a SAE following each type of SACT treatment would improve both communication with the patient and shared decision making with all those involved in delivering care for patients. There are currently no risk scores available for use in those with advanced stage lung cancer. AIM: The overarching aim of this research is to develop and internally validate a risk score that will calculate the individualised risk of SAEs for different SACT treatments for patients with late stage lung cancer. METHODS: Utilising linked cancer registry data (National Cancer Registration and Analysis Service (NCRAS), England) for over 20,000 late stage lung cancer patients, a risk score will be developed using a multivariable logistic regression model to predict the risk of an acute admission within 30 days of SACT administration. Model performance will be summarised using calibration and discrimination. Internal validation will be used to quantify the degree of optimism due to overfitting, using re-sampling bootstrapping. Heterogeneity will be assessed, and the model will be fine-tuned. Fine-tuning and interrogation will be used to evaluate differences in performance between hospitals. The clinical utility will be assessed through calculating the net benefit in preventing SAEs. CONCLUSION: A developed risk score (under each treatment strategy) has real potential to support individualised treatment decisions and optimise management of SACT-induced SAEs for patients and reduce hospital attendances.