Comparative Assessment of Weighting Comorbidities in the Hematopoietic Cell Transplantation (HCT) Specific Comorbidity Index (HCT-CI)
Authors: DeFor Todd E , Majhail Navneet S. , Brunstein Claudio , Weisdorf Daniel J. , Le Chap T 
Published: October 2019

Abstract
An increasing number of HCT recipients have pre-existing comorbidities at the time of transplantation. Sorror et al (Blood 2005) have proposed the HCT-CI, which evaluates various comorbidities as a combined categorical weighted score that independently predicts non-relapse mortality (NRM) and overall survival (OS) after HCT. In a recent study that included 373 adult allogeneic HCT recipients at our center, we have shown that the sensitivity and generalizability of the HCT-CI needs to be improved further prior to its routine universal use (Majhail et al, Biology of Blood and Marrow Transplantation 2008). The median age of the original cohort was 47 (range, 18–69) years and included 150 myeloablative and 223 non-myeloablative HCT recipients. The HCT-CI score was zero (low-risk) in 16%, 1–2 (intermediate-risk) in 32%, and ≥3 (high-risk) in 52% patients. We hypothesized that the HCT-CI loses some power in the early and unnecessary step of converting the adjusted hazard ratios (HR) of the comorbidities for NRM to the categorical weights of 0 to 3 prior to summation of the final score. We propose a revised HCT-CI based on a pure multiplicative model that assigns weights to comorbidities more efficiently and increases the discriminating and predictive power of the original HCT-CI for NRM and OS. In our analysis, which included the same study cohort (N=373), we calculated the HR for each comorbidity from a regression analysis on NRM after adjusting for all other comorbidities as well as age, donor type, disease risk and conditioning regimen intensity. Instead of converting the adjusted HR to categorical weights and then summing these weights, we directly calculated a risk index (multiplicative HCT-CI [MHCT-CI]) by exponentiating the sum of all parameter coefficients from the regression analysis. The revised index score is: MHCT-CI = exponent [0.82*(binary indicator (bi) for cardiac disorders) + 0.20*(bi for peptic ulcer) + 0.60*(bi for diabetes) – 0.43*(bi for obesity) + 0.30*(bi for psychiatric disturbance) + 0.34*(bi for Moderate/Severe hepatic function) + 0.63*(bi for infection) + 0.73*(bi for renal insufficiency) + 0.63*(bi for moderate pulmonary abnormalities) + 0.90*(bi for severe pulmonary abnormalities) + 0.22*(bi for prior solid tumor)]. Comorbidities not appearing did not have influence on NRM. The distribution of the MHCT-CI score was ≤1 (low-risk) in 20%, 1–2.5 (intermediate-risk) in 41% and ≥2.5 (high-risk) in 40% patients. MHCT-CI was more predictive for both NRM and OS compared to the original HCT-CI. The HR for intermediate and high risk categories increased (>43% for NRM and >19% for OS). The adjusted likelihood ratio, showing model fit, increased from 13.2 to 22.1 for NRM and increased from 18.8 to 34.1 for OS after substituting MHCT-CI for HCT-CI. There are no statistical tests for this statistic but an increase shows better prediction of the endpoint. The c statistic, which is the proportion of all pairs of patients in the study in which the patient with the higher index score has a worse outcome, increased from 0.641 to 0.752 for NRM (P=0.03) and increased from 0.611 to 0.713 for OS (P=0.01). In conclusion, the MHCT-CI showed higher discriminating and predictive power for post-HCT NRM and OS among our study population. Given that an increasing number of HCT recipients are being transplanted with pre-existing comorbidities, the greater discrimination of assigning patient comorbidity will better inform future studies among HCT recipients by better adjusting for these important risk factors.