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Predicting Dying: Metabolomic Changes, a Model and the Dying Process in Patients with Lung Cancer
26 Pages Posted: 22 May 2023
More...Abstract
Background: Accurately recognising that a person may be dying is central for improving their experience of care. Yet recognising dying is difficult and predicting dying frequently inaccurate.
Methods: Urine samples from patients (n=112 and 49, training and validation cohorts, respectively)) with lung cancer were analysed using high resolution mass spectrometry. Cox lasso regression was engaged to develop a multivariable model predicting the probability of survival within the last 30 days of life. ANOVA and volcano plot analysis demonstrated metabolites that changed in the last weeks of life. Further analysis identified potential biological pathways affected.
Findings: A model predicting time to death using 7 metabolites had excellent accuracy in the training cohort (AUC = 0×85, 0×85, 0×88 and 0×86 on days 5, 10, 20 and 30) and validation cohort (AUC = 0×86, 0×83, 0×90, 0×86 on days 5, 10, 20 and 30). The model enabled classification of patients at low, medium and high risk of dying on a Kaplan-Meier survival curve. 124 metabolites changed. ANOVA analysis identified 93 metabolites and volcano plot analysis 85 metabolites. 53 metabolites changed using both approaches. Pathways altered in the last weeks included those associated with decreased oral intake, muscle loss, decreased RNA and protein synthesis, mitochondrial dysfunction, disrupted β-oxidation and one carbon metabolism. Epinephrine and cortisol increased in the last 2 weeks and week respectively.
Interpretation: Metabolomic analysis identified metabolites and their associated pathways that change in the last days of life in patients with lung cancer. Prognostic tests, based on the metabolites identified in this study, could aid clinicians in the early recognition of people who may be dying, and have the potential to influence clinical practice and improve the care of dying patients.
Funding: This research received a Wellcome Trust Seed award for Science (202022/Z/16/Z), North West Cancer Research award (SI2018.11), University of Liverpool Enterprise Investment Fund award and Novo Nordisk Foundation (grant NNF20CC0035580).
Declaration of Interest: None.
Ethical Approval: Ethical approval was provided by North Wales (West) Research Ethics Committee (REC reference 15/WA/0464).
Keywords: lung cancer, dying, urine, biomarkers, palliative, metabolomics
Suggested Citation: Suggested Citation