Background Recognising dying is difficult and is an ongoing difficulty for doctors. We believe there is a process to dying and animal studies support this theory. We previously demonstrated that a number of volatile organic compounds in urine, change in the last weeks and days of life of patients in a small mixed cancer group. We needed to verify this finding in a suitably powered follow-up study.
Method We prospectively collected urine samples from people with lung cancer. We aimed to compare samples from 25 people in each of the last 3 weeks of life to a control group, 50 people with lung cancer who lived 3 or more months from the time of sampling. The urine samples were analysed for volatile organic compounds by gas chromatography mass spectrometry (GC-MS).
Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression was used to analyse the GC-MS data and create a statistical model.
Results We recruited 162 people in total; 29 in the last week; 28 in the second last week; 30 in the third last week of life; 74 controls i.e. samples taken >3 months from death; 424 urine samples.
A model was created to predict whether a patient would die within 1 week. It has an optimism corrected AUC of 0.851 (95% CI: 0.767, 0.911); sensitivity 78.6% (95% CI: (64.3%, 89.3%)); specificity 83.1% (95% CI: (69.9%, 92.3%)). The model identified a selection of compounds that contributed to the identification of patients who were close to death.
The results confirm that volatile organic compounds can predict when people with lung cancer are in the last week of life.
Our model to predict when a person with lung cancer is in the last week of life is approximately 80% accurate.
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