Identification and Prognostication of End-of-Life State Using a Japanese Guideline-Based Diagnostic Method: A Diagnostic Accuracy Study

Abstract

Prognostic uncertainty can be a barrier to providing palliative care. Accurate prognostic estimation for patients at the end of life is challenging. This study aimed to evaluate the accuracy of end-of-life diagnosis using our unique diagnostic method. A retrospective longitudinal observational study was conducted through collaboration among three medical facilities in a rural super-aged community in Japan. In 2007, we established a unique end-of-life diagnostic process comprising (1) physicians' judgement, (2) disclosure to patients, and (3) discussion at an end-of-life case conference (EOL-CC), based on Japanese end-of-life-related guidelines. Research subjects were consecutive patients discussed in EOL-CC between January 1, 2010, and September 30, 2017. The primary outcome was mortality within 6 months after the initial EOL-CC decision. Sensitivity, specificity, and diagnostic odds ratio were calculated using EOL-CC diagnosis (end-of-life or non-end-of-life) as an index test and overall survival (<6 months or ≥6 months) as a reference standard. In total, 315 patients were eligible for survival analysis (median age 89, range 54-107). The study population was limited to patients with severe conditions such as advanced cancer, organ failures, advanced dementia with severe deterioration in functioning. EOL-diagnosis by our methods was associated with much lower survival rate at 6 months after EOL-CC than non-EOL-diagnosis (6.9% vs 43.5%; P < 0.001). Of the patients, 297 were eligible for diagnostic accuracy analysis (median age 89, range 54-107). The EOL-diagnosis showed high sensitivity (0.95; 95% confidence interval [CI] 0.92-0.97) but low specificity (0.35; 95% CI 0.20-0.53) against the outcomes. It also showed a high diagnostic odds ratio (10.32; 95% CI 4.08-26.13). The diagnostic process using the Japanese end-of-life guidelines had tolerable accuracy in identification and prognostication of end of life.