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Koulouris, Andreas, Baio, Gabriella, Clark, Allan 
ORCID: https://orcid.org/0000-0003-2965-8941 and Alexandre, Leo
(2023)
Opioid burden in patients with inoperable pancreatic adenocarcinoma and the development of a multivariable risk prediction model for opioid use: A retrospective cohort study.
Pancreatology, 23 (7).
pp. 818-828.
ISSN 1424-3903
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Abstract
Introduction: Risk prediction models to guide patient selection for early pre-emptive endoscopic ultrasound guided coeliac plexus neurolysis are lacking. This study aimed to determine in patients with inoperable pancreatic cancer: (1) opioid burden, (2) the relationship between opioid use and all-cause mortality, (3) risk factors for opioid use, and (4) develop and internally validate a risk prediction model for opioid use at three months. Methods: This was a single-centre retrospective cohort study of patients with confirmed pancreatic cancer. Cox proportional hazard regression estimated the association between opioid use at baseline and all-cause mortality. Logistic regression estimated the associations between clinical and radiological variables with opioid use by three months. Two risk prediction models were developed for opioid use (clinical and clinical-radiological). Model discrimination and calibration was assessed. Results: In total, 383 patients with inoperable pancreatic cancer were included. Prevalence of pain ranged between 37% and 47% at three monthly intervals in the first year of diagnosis. Opioid use at baseline was associated with poorer survival. Age, pain at presentation, performance status, tumour distance from the right ganglion, the anterior-posterior and the latero-lateral tumour dimensions were independent risk factors for the opioid use at three months. The Area Under Curve (AUC) for the clinical and clinical-radiological models was 0.81 and 0.84, respectively. Models were well calibrated. Conclusions: Opioid use is prevalent in patients with pancreatic cancer, associated with poor prognosis, and can be predicted based on clinical and radiological variables. External validation of this predictive model is required.
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