Morphometric age and surgical risk

Abstract

Background: A cornerstone of a surgeon's clinical assessment of suitability for major surgery is best described as the "eyeball test." Preoperative imaging may provide objective measures of this subjective assessment by calculating a patient's morphometric age. Our hypothesis is that morphometric age is a surgical risk factor distinct from chronologic age and comorbidity and correlates with surgical mortality and length of stay.

Study design: This is a retrospective cohort study within a large academic medical center. Using novel analytic morphomic techniques on preoperative CT scans, a morphometric age was assigned to a random sample of patients having inpatient general and vascular abdominal surgery from 2006 to 2011. The primary outcomes for this study were postoperative mortality (1-year) and length of stay (LOS).

Results: The study cohort (n = 1,370) was stratified into tertiles based on morphometric age. The postoperative risk of mortality was significantly higher in the morphometric old age group when compared with the morphometric middle age group (odds ratio 2.42, 95% CI 1.52 to 3.84, p < 0.001). Morphometric old age patients were predicted to have a LOS 4.6 days longer than the morphometric middle age tertile. Similar trends were appreciated when comparing morphometric middle and young age tertiles. Chronologic age correlated poorly with these outcomes. Furthermore, patients in the chronologic middle age tertile found to be of morphometric old age had significantly inferior outcomes (mortality 21.4% and mean LOS 13.8 days) compared with patients in the chronologic middle age tertile found to be of morphometric young age (mortality 4.5% and mean LOS 6.3 days, p < 0.001 for both).

Conclusions: Preoperative imaging can be used to assign a morphometric age to patients, which accurately predicts mortality and length of stay.

Figure 1

Morphometric age as an independent domain of surgical risk. Red circle, the 10% oldest patients by morphometric age; green circle, the 10% oldest patients by chronological age; blue circle, the 10% with the most comorbidities. Note that 57.2% of patients within the group with the oldest morphometric age were not in the oldest chronologic age group. Similarly, 73.0% of patients within the group of the oldest morphometric age were not in the group with the most medical comorbidities.

Figure 2

Covariate-adjusted 1-year mortality rates stratified by tertiles of morphometric and chronologic age. Blue bar, chronologic age; red bar, morphometric age. Mortality was adjusted for the covariates listed in Table 3, determined by logistic regression. Morphometric age was a significant predictor of 1-year mortality, while chronologic age was not. The morphometric youngest had half the risk of 1-year mortality compared to the chronologic youngest, while the morphometric oldest had greater risk than the chronologic oldest.

Figure 3

Covariate-adjusted length of stay stratified by tertiles of morphometric and chronologic age. Blue bar, chronologic age; red bar, morphometric age. Length of stay was adjusted for the covariates listed in Table 4, determined by linear regression. Patients in the youngest morphometric tertile were predicted to have shorter LOS than those in the youngest chronologic tertile, while patients in the oldest morphometric tertile were predicted to have longer LOS than the chronologic oldest patients.

Figure 4

(A) The clinical implications of morphometric age adjustment on patients within the chronologic middle tertile. Patients in the chronologic middle age tertile but who jumped into the old morphometric age group had remarkably inferior outcomes (mortality 21.4%) compared to patients in the chronologic middle age tertile but who jumped into the morphometric young group (mortality 4.5%, p<0.001). Note there was no significant difference in the patients who jumped with respect to chronologic age and case complexity. (B) Patients in the chronologic youngest tertile (mean age 40.6 y) but morphometric oldest tertile had poor surgical outcomes, including a mortality rate of 17.8%. Conversely, patients in the chronologic old tertile (mean age 66.9 y) but the morphometric young tertile had good surgical outcomes, including a mortality of 3.0% (p<0.001).

Figure 4

(A) The clinical implications of morphometric age adjustment on patients within the chronologic middle tertile. Patients in the chronologic middle age tertile but who jumped into the old morphometric age group had remarkably inferior outcomes (mortality 21.4%) compared to patients in the chronologic middle age tertile but who jumped into the morphometric young group (mortality 4.5%, p<0.001). Note there was no significant difference in the patients who jumped with respect to chronologic age and case complexity. (B) Patients in the chronologic youngest tertile (mean age 40.6 y) but morphometric oldest tertile had poor surgical outcomes, including a mortality rate of 17.8%. Conversely, patients in the chronologic old tertile (mean age 66.9 y) but the morphometric young tertile had good surgical outcomes, including a mortality of 3.0% (p<0.001).