Morphometric Age and Mortality After Liver Transplant

Waits, Seth A.; Kim, Edward K.; Terjimanian, Michael N.; Tishberg, L.M.; Harbaugh, Calista M.; Sheetz, Kyle H.; Sonnenday, Christopher J.; Sullivan, J. A.; Wang, Stewart C.; Englesbe, Michael J.

doi:10.1001/jamasurg.2013.4823

Cited by 58 publications

(56 citation statements)

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“…From these 28 records, eight studies assessed muscle mass with means other than CT and one study was performed in a population other than patients awaiting or undergoing liver transplantation. The remaining 19 studies, including 3803 patients in partly overlapping cohorts, were included in this systematic review (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Cross-referencing yielded no additional records.…”

Section: Resultsmentioning

confidence: 99%

“…Nine studies reported the cross-sectional muscle area with corresponding skeletal muscle index (12)(13)(14)17,(22)(23)(24)(25)30), whereas the psoas area was reported in eight studies (15,16,18,19,21,(26)(27)(28) and the dorsal muscle group area was reported in one study (20). One study calculated the morphometric age (calculated with total psoas area, psoas density, and abdominal aortic calcifications) (29). The mean skeletal muscle index ranged from 43.0 to 54.3 cm²/m² (13,30).…”

Section: Resultsmentioning

confidence: 99%

“…One study described the association between morphometric age, but the association between the individual skeletal muscle mass components (i.e. psoas area and psoas density) was not described (29). In addition to multiple definitions for sarcopenia, multiple methods to perform skeletal muscle mass measurements (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Second, selective publication of data could have led to an underestimation of negative results. Third, four research groups from Canada, (22)(23)(24)(25), Ann Arbor, MI, USA (16,19,20,29), Pittsburgh, PA, USA (12)(13)(14), and Japan (21,26) published multiple original articles including patient populations from their center that were all included in the current review. These four centers contributed to 13 of the 19 studies.…”

Section: Discussionmentioning

confidence: 99%

“…In the 11 studies that investigated the association between skeletal muscle mass and posttransplantation survival, seven described an association (13,14,16,18,20,21,29), and three gave no association (17,24,27,28). All details about median survival times, yearly survival rates, and the association between skeletal muscle mass and overall survival are summarized in Table 4.…”

Section: Posttransplantation Survivalmentioning

confidence: 99%

See 4 more Smart Citations

Systematic Review and Meta-Analysis of the Impact of Computed Tomography–Assessed Skeletal Muscle Mass on Outcome in Patients Awaiting or Undergoing Liver Transplantation

Vugt

Levolger

Bruin

et al. 2016

American Journal of Transplantation

290

233

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Posttransplantation Survivalmentioning

confidence: 99%

See 3 more Smart Citations

Systematic Review and Meta-Analysis of the Impact of Computed Tomography–Assessed Skeletal Muscle Mass on Outcome in Patients Awaiting or Undergoing Liver Transplantation

Vugt

Levolger

Bruin

et al. 2016

American Journal of Transplantation

290

233

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Validating the predictors of outcomes after radical cystectomy for bladder cancer

Woldu

Şanlı

Clinton

et al. 2018

Cancer

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Background An assessment of surgical risk is essential for patient counseling and decision making, and it can provide rationale adjustment for patient populations as health care moves from a fee‐for‐service to a value‐based reimbursement model. The modified Frailty Index (mFI) has been proposed as a risk‐stratification tool for radical cystectomy (RC), and the objective of the current study was to validate this potential use of the mFI using an institutional cohort. Methods A retrospective review of all patients who underwent RC for bladder cancer was conducted at the authors’ institution from 2012 to 2016. In addition to detailed clinicopathologic and treatment parameters, patients were categorized according to the mFI, the Charlson Comorbidity Index (CCI), and the American Society of Anesthesiologists (ASA) classification. Covariates were analyzed to determine associations with 1‐month complication rates (according to the Clavien‐Dindo system), 3‐month readmission rates, hospitalization length, and hospitalization costs. Results In total, 346 patients were included in the analysis. The overall complication rate was 56.6%, the major (Clavien grade ≥3) complication rate was 19.4%, and the readmission rate was 27.9%. Receiver operating curve analysis demonstrated a weak association of all indices with major complications after RC: the area under the curve was 0.535 (95% confidence interval [CI], 0.460‐0.611) for the ASA classification; 0.565 (95% CI, 0.485‐0.645) for the CCI score; and 0.551 (95% CI, 0.471‐0.631) for the mFI. There were no significant differences in the rate of major complications when stratifying the results according to the mFI, CCI, or ASA class. Length of hospitalization and associated costs were correlated with mFI. Conclusions Frailty was not associated with postoperative complications and provided little additional predictive ability over the ASA classification and the CCI score. Further research is required to identify patients who are likely to suffer significant complications after RC.

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Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis

et al. 2021

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Body composition measures derived from already available electronic medical records (computed tomography [CT]scans) can have significant value, but automation of measurements is needed for clinical implementation. We sought to use artificial intelligence to develop an automated method to measure body composition and test the algorithm on a clinical cohort to predict mortality. We constructed a deep learning algorithm using Google's DeepLabv3+ on a cohort of de-identified CT scans (n = 12,067). To test for the accuracy and clinical usefulness of the algorithm, we used a unique cohort of prospectively followed patients with cirrhosis (n = 238) who had CT scans performed. To assess model performance, we used the confusion matrix and calculated the mean accuracy of 0.977 ± 0.02 (0.975 ± 0.018 for the training and test sets, respectively). To assess for spatial overlap, we measured the mean intersection over union and mean boundary contour scores and found excellent overlap between the manual and automated methods with mean scores of 0.954 ± 0.030, 0.987 ± 0.009, and 0.948 ± 0.039 (0.983 ± 0.013 for the training and test set, respectively). Using these automated measurements, we found that body composition features were predictive of mortality in patients with cirrhosis. On multivariate analysis, the addition of body composition measures significantly improved prediction of mortality for patients with cirrhosis over Model for End-Stage Liver Disease alone (P < 0.001). Conclusion:The measurement of body composition can be automated using artificial intelligence and add significant value for incidental CTs performed for other clinical indications. This is proof of concept that this methodology could allow for wider implementation into the clinical arena. (Hepatology Communications 2021;0:1-10).

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Morphometric Age and Mortality After Liver Transplant

Abstract: 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.

Cited by 58 publications

References 14 publications

Systematic Review and Meta-Analysis of the Impact of Computed Tomography–Assessed Skeletal Muscle Mass on Outcome in Patients Awaiting or Undergoing Liver Transplantation

Systematic Review and Meta-Analysis of the Impact of Computed Tomography–Assessed Skeletal Muscle Mass on Outcome in Patients Awaiting or Undergoing Liver Transplantation

Validating the predictors of outcomes after radical cystectomy for bladder cancer

Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis

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