An empirical approach for life expectancy estimation based on survival analysis among a post-acute myocardial infarction population

Abstract

BackgroundPractical communication of prognosis is pertinent in the clinical setting. Survival analysis techniques are standardly used in cohort studies; however, their results are not straightforward for interpretation as compared to the graspable notion of life expectancy (LE). The present study empirically examines the relationship between Cox regression coefficients (HRs), which reflect the relative risk of the investigated risk factors for mortality, and years of potential life lost (YPLL) values after acute myocardial infarction (AMI). MethodsThis retrospective population-based study included patients aged 40–80 years, who survived AMI hospitalization from January 1, 2002, to October 25, 2017. A survival analysis approach assessed relationships between variables and the risk for all-cause mortality in an up to 21-year follow-up period. The total score was calculated for each patient as the summation of the Cox regression coefficients (AdjHRs) values. Individual LE and YPLL were calculated. YPLL was assessed as a function of the total score. ResultsThe cohort (n = 6316, age 63.0 ± 10.5 years, 73.4 % males) was randomly split into training (n = 4243) and validation (n = 2073) datasets. Sixteen main clinical risk factors for mortality were explored (total score of 0–14.2 points). After adjustment for age, sex and nationality, a one-point increase in the total score was associated with YPLL of ∼one year. A goodness-of-fit of the prediction model found 0.624 and 0.585 for the training and validation datasets respectively. ConclusionsThis functional derivation for converting coefficients of survival analysis into the comprehensible form of YPLL/LE allows for practical prognostic calculation and communication.