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Original Investigation |

Morphometric Age and Mortality After Liver Transplant

Seth A. Waits, MD1; Edward K. Kim, BS1; Michael N. Terjimanian, MS1; Lindsay M. Tishberg2; Calista M. Harbaugh, BS1; Kyle H. Sheetz, BS1; Christopher J. Sonnenday, MD, MPH1; June Sullivan, MBA1; Stewart C. Wang, MD, PhD1; Michael J. Englesbe, MD1
[+] Author Affiliations
1Morphomics Analysis Group, Department of Surgery, University of Michigan Medical School, Ann Arbor
2currently an undergraduate at University of Michigan, Ann Arbor
JAMA Surg. 2014;149(4):335-340. doi:10.1001/jamasurg.2013.4823.
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Importance  Morphometric assessment has emerged as a strong predictor of postoperative morbidity and mortality. However, a gap exists in translating this knowledge to bedside decision making. We introduced a novel measure of patient-centered surgical risk assessment: morphometric age.

Objective  To investigate the relationship between morphometric age and posttransplant survival.

Data Sources  Medical records of recipients of deceased-donor liver transplants (study population) and kidney donors/trauma patients (morphometric age control population).

Study Selection  A retrospective cohort study of 348 liver transplant patients and 3313 control patients. We assessed medical records for validated morphometric characteristics of aging (psoas area, psoas density, and abdominal aortic calcification). We created a model (stratified by sex) for a morphometric age equation, which we then calculated for the control population using multivariate linear regression modeling (covariates). These models were then applied to the study population to determine each patient's morphometric age.

Data Extraction and Synthesis  All analytic steps related to measuring morphometric characteristics were obtained via custom algorithms programmed into commercially available software. An independent observer confirmed all algorithm outputs. Trained assistants performed medical record review to obtain patient characteristics.

Results  Cox proportional hazards regression model showed that morphometric age was a significant independent predictor of overall mortality (hazard ratio, 1.03 per morphometric year [95% CI, 1.02-1.04; P < .001]) after liver transplant. Chronologic age was not a significant covariate for survival (hazard ratio, 1.02 per year [95% CI, 0.99-1.04; P = .21]). Morphometric age stratified patients at high and low risk for mortality. For example, patients in the middle chronologic age tertile who jumped to the oldest morphometric tertile have worse outcomes than those who jumped to the youngest morphometric tertile (74.4% vs 93.2% survival at 1 year [P = .03]; 45.2% vs 75.0% at 5 years [P = .03]).

Conclusions and Relevance  Morphometric age correlated with mortality after liver transplant with better discrimination than chronologic age. Assigning a morphometric age to potential liver transplant recipients could improve prediction of postoperative mortality risk.

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Figure 1.
Effect of Morphometric Age on Survival of Patients Undergoing Liver Transplant

Percentiles are quantified by the covariate adjusted hazard ratio (HR).

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Figure 2.
Covariate-Adjusted Survival Rates for Patients Stratified by Tertiles of Morphometric and Chronologic Ages

A, One-year survival. The adjusted survival rate of the youngest chronologic age tertile was 90.4% compared with 78.9% for the oldest and was 94.0% for the youngest morphometric tertile compared with 74.1% for the oldest. B, Five-year survival. The youngest chronologic age tertile had an adjusted survival rate of 65.4% compared to 55.6% for the oldest, whereas the youngest morphometric tertile had a 74.4% adjusted survival rate compared with 46.5% for the oldest.

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Figure 3.
Survival Rates of Liver Transplant Recipients by Chronologic Middle Age Tertiles

Differences in survival rates between patients who “jumped” from the middle morphometric age tertile to the older and younger tertiles. One-year survival was 74.4% and 93.2% in the respective groups (P = .03); 5-year survival, 45.2% and 75.0% in the respective groups (P = .03).

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