Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.

INTRODUCTION: Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is difficult. We aimed to develop machine learning (ML) methodologies for automated estimation of cumulative ileal injury on computed tomography-enterography (CTE) to help predict future bowel surgery. METHODS: Adults with ileal CD using biologic therapy at a tertiary care center underwent ML analysis of CTE scans. Two fellowship-trained radiologists graded bowel injury severity at granular spatial increments along the ileum (1 cm), called mini-segments. ML segmentation methods were trained on radiologist grading with predicted severity and then spatially mapped to the ileum. Cumulative injury was calculated as the sum (S-CIDSS) and mean of severity grades along the ileum. Multivariate models of future small bowel resection were compared with cumulative ileum injury metrics and traditional bowel measures, adjusting for laboratory values, medications, and prior surgery at the time of CTE. RESULTS: In 229 CTE scans, 8,424 mini-segments underwent analysis. Agreement between ML and radiologists injury grading was strong (κ = 0.80, 95% confidence interval 0.79-0.81) and similar to inter-radiologist agreement (κ = 0.87, 95% confidence interval 0.85-0.88). S-CIDSS (46.6 vs 30.4, P = 0.0007) and mean cumulative injury grade scores (1.80 vs 1.42, P < 0.0001) were greater in CD biologic users that went to future surgery. Models using cumulative spatial metrics (area under the curve = 0.76) outperformed models using conventional bowel measures, laboratory values, and medical history (area under the curve = 0.62) for predicting future surgery in biologic users. DISCUSSION: Automated cumulative ileal injury scores show promise for improving prediction of outcomes in small bowel CD. Beyond replicating expert judgment, spatial enterography analysis can augment the personalization of bowel assessment in CD.

Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.

BACKGROUND AND AIMS: Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenotypic data which have significant prognostic value. However, data extractions have not traditionally been applied to imaging data. In this study, we used artificial intelligence to automate biomarker extraction from CT scans and examined the value of these features in improving risk prediction in patients with liver disease. APPROACH AND RESULTS: Using a regional liver disease cohort from the Veterans Health System, we retrieved administrative, laboratory, and clinical data for Veterans who had CT scans performed for any clinical indication between 2008 and 2014. Imaging biomarkers were automatically derived using the analytic morphomics platform. In all, 4614 patients were included. We found that the eCTP Score had a Concordance index of 0.64 for the prediction of overall mortality while the imaging-based model alone or with eCTP Score performed significantly better [Concordance index of 0.72 and 0.73 ( p <0.001)]. For the subset of patients without hepatic decompensation at baseline (n=4452), the Concordance index for predicting future decompensation was 0.67, 0.79, and 0.80 for eCTP Score, imaging alone, or combined, respectively. CONCLUSIONS: This proof of concept demonstrates that the potential of utilizing automated extraction of imaging features within CT scans either alone or in conjunction with classic health data can improve risk prediction in patients with chronic liver disease.

Independent Associations of Aortic Calcification with Cirrhosis and Liver Related Mortality in Veterans with Chronic Liver Disease.

INTRODUCTION: Abdominal aortic calcifications (AAC) are incidentally found on medical imaging and useful cardiovascular burden approximations. The Morphomic Aortic Calcification Score (MAC) leverages automated deep learning methods to quantify and score AACs. While associations of AAC and non-alcoholic fatty liver disease (NAFLD) have been described, relationships of AAC with other liver diseases and clinical outcome are sparse. This study's purpose was to evaluate AAC and liver-related death in a cohort of Veterans with chronic liver disease (CLD). METHODS: We utilized the VISN 10 CLD cohort, a regional cohort of Veterans with the three forms of CLD: NAFLD, hepatitis C (HCV), alcohol-associated (ETOH), seen between 2008 and 2014, with abdominal CT scans (n = 3604). Associations between MAC and cirrhosis development, liver decompensation, liver-related death, and overall death were evaluated with Cox proportional hazard models. RESULTS: The full cohort demonstrated strong associations of MAC and cirrhosis after adjustment: HR 2.13 (95% CI 1.63, 2.78), decompensation HR 2.19 (95% CI 1.60, 3.02), liver-related death HR 2.13 (95% CI 1.46, 3.11), and overall death HR 1.47 (95% CI 1.27, 1.71). These associations seemed to be driven by the non-NAFLD groups for decompensation and liver-related death [HR 2.80 (95% CI 1.52, 5.17; HR 2.34 (95% CI 1.14, 4.83), respectively]. DISCUSSION: MAC was strongly and independently associated with cirrhosis, liver decompensation, liver-related death, and overall death. Surprisingly, stratification results demonstrated comparable or stronger associations among those with non-NAFLD etiology. These findings suggest abdominal aortic calcification may predict liver disease severity and clinical outcomes in patients with CLD.

AGA Clinical Practice Guideline on Systemic Therapy for Hepatocellular Carcinoma.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC), the most common primary liver cancer, remains a deadly cancer, with an incidence that has tripled in the United States since 1980. In recent years, new systemic therapies for HCC have been approved and a critical assessment of the existing data is necessary to balance benefits and harms and inform the development of evidence-based guidelines. METHODS: The American Gastroenterological Association formed a multidisciplinary group consisting of a Technical Review Panel and a Guideline Panel. The Technical Review Panel prioritized clinical questions and outcomes according to their importance for clinicians and patients and conducted an evidence review of systemic therapies in patients with advanced-stage HCC. The Grading of Recommendations Assessment, Development and Evaluation framework was used to assess evidence. The Guideline Panel reviewed the evidence and used the Evidence-to-Decision Framework to develop recommendations. RESULTS: The Panel reviewed the evidence, summarized in the Technical Review, for the following medications approved by the US Food and Drug Administration for HCC: first-line therapies: bevacizumab+atezolizumab, sorafenib, and lenvatinib; second-line therapies: cabozantinib, pembrolizumab, ramucirumab, and regorafenib; and other agents: bevacizumab, nivolumab, and nivolumab+ipilimumab. CONCLUSIONS: The Panel agreed on 11 recommendations focused on systemic therapy for HCC in patients who are not eligible for locoregional therapy or resection, those with metastatic disease and preserved liver function, those with poor liver function, and those on systemic therapy as adjuvant therapy.

Effect of sarcopenia on survival in patients with cirrhosis: A meta-analysis.

BACKGROUND & AIMS: The association between sarcopenia and prognosis in patients with cirrhosis remains to be determined. In this study, we aimed to quantify the association between sarcopenia and the risk of mortality in patients with cirrhosis, stratified by sex, underlying liver disease etiology, and severity of hepatic dysfunction. METHODS: PubMed, Web of Science, EMBASE, and major scientific conference sessions were searched without language restriction through 13 January 2021 with an additional manual search of bibliographies of relevant articles. Cohort studies of ≥100 patients with cirrhosis and ≥12 months of follow-up that evaluated the association between sarcopenia, muscle mass and the risk of mortality were included. RESULTS: Twenty-two studies involving 6,965 patients with cirrhosis were included. The pooled prevalence of sarcopenia in patients with cirrhosis was 37.5% overall (95% CI 32.4%-42.8%), and was higher in male patients, those with alcohol-associated liver disease, those with Child-Pugh grade C cirrhosis, and when sarcopenia was defined by L3-SMI (third lumbar-skeletal muscle index). Sarcopenia was associated with an increased risk of mortality in patients with cirrhosis (adjusted hazard ratio [aHR] 2.30, 95% CI 2.01-2.63), with similar findings in a sensitivity analysis of patients with cirrhosis without hepatocellular carcinoma (aHR 2.35, 95% CI 1.95-2.83) and in subgroups stratified by sex, liver disease etiology, and severity of hepatic dysfunction. The association between quantitative muscle mass index and mortality further supports the association between sarcopenia and poor prognosis (aHR 0.95, 95% CI 0.93-0.98). There was no significant heterogeneity in any of our analyses. CONCLUSIONS: Sarcopenia was highly and independently associated with higher risk of mortality in patients with cirrhosis. LAY SUMMARY: The prevalence of sarcopenia and its association with death in patients with cirrhosis remain unclear. This meta-analysis indicated that sarcopenia affected about one-third of patients with cirrhosis and up to 50% of patients with alcohol-related liver disease or Child-Pugh class C cirrhosis. Sarcopenia was independently associated with an ∼2-fold higher risk of mortality in patients with cirrhosis. The mortality rate increased with greater severity or longer durations of sarcopenia. Increasing awareness about the importance of sarcopenia in patients with cirrhosis among stakeholders must be prioritized.

Muscle mass affects paclitaxel systemic exposure and may inform personalized paclitaxel dosing.

AIMS: Patients with low muscle mass have increased risk of paclitaxel-induced peripheral neuropathy, which is dependent on systemic paclitaxel exposure. Dose optimization may be feasible through the secondary use of radiologic data for body composition. The objective of this study was to interrogate morphomic parameters as predictors of paclitaxel pharmacokinetics to identify alternative dosing strategies that may improve treatment outcomes. METHODS: This was a secondary analysis of female patients with breast cancer scheduled to receive 80 mg/m2 weekly paclitaxel infusions. Paclitaxel was measured at the end of initial infusion to estimate maximum concentration (Cmax ). Computed tomography (CT) scans were used to measure 29 body composition features for inclusion in pharmacokinetic modelling. Monte Carlo simulations were performed to identify infusion durations that limit the probability of exceeding Cmax > 2885 ng/mL, which was selected based on prior work linking this to an unacceptable risk of peripheral neuropathy. RESULTS: Thirty-nine patients were included in the analysis. The optimal model was a two-compartment pharmacokinetic model with T11 skeletal muscle area as a covariate of paclitaxel volume of distribution (Vd). Simulations suggest that extending infusion of the standard paclitaxel dose from 1 hour to 2 and 3 hours in patients who have skeletal muscle area 4907-7080 mm2 and <4907 mm2 , respectively, would limit risk of Cmax > 2885 ng/mL to <50%, consequently reducing neuropathy, while marginally increasing overall systemic paclitaxel exposure. CONCLUSION: Extending paclitaxel infusion duration in ~25% of patients who have low skeletal muscle area is predicted to reduce peripheral neuropathy while maintaining systemic exposure, suggesting that personalizing paclitaxel dosing based on body composition may improve treatment outcomes.

Predicting outcomes of abdominal surgical emergencies in the elderly population using a CT muscle gauge.

BACKGROUND: Frailty has been shown to be an independent negative predictor of surgical outcomes in geriatric patients. Traditional measurements of muscle strength and mass are impractical in emergency settings, and computed tomography (CT)-measured skeletal muscle mass has been proposed as an alternative. However, the cutoff values for low muscle mass are still unknown, and their impact on abdominal emergencies in the elderly population is unclear. METHODS: A total of 462 young trauma patients aged 18-40 years were analyzed to establish sex-specific reference cutoff values for the CT-measured muscle index (MI) and muscle gauge (MG) values. The impacts of low MI and MG values were investigated in 1192 elderly patients (aged ≥ 65 years) undergoing abdominal surgery. RESULTS: The sex-specific cutoff values for MI and MG were determined by adopting European Working Group on Sarcopenia in Older People 2 guidelines. The correlation between MG and aging was significantly stronger than that between MI and ageing. With regard to the MG, the L4 psoas muscle gauge (L4 PMG) was further investigated in an elderly cohort owing to its high predictive value and ease of use in the clinical setting. A low L4 PMG value was an independent risk factor for overall complications and mortality in elderly patients with abdominal emergencies. CONCLUSION: The current study was the largest study investigating the correlations between MG values and aging in the Asian population. A low L4 PMG value may help surgeons during preoperative decision making regarding geriatric patients with abdominal emergencies.

Adapted time-varying covariates Cox model for predicting future cirrhosis development performs well in a large hepatitis C cohort.

BACKGROUND: Patients with hepatitis C virus (HCV) frequently remain at risk for cirrhosis after sustained virologic response (SVR). Existing cirrhosis predictive models for HCV do not account for dynamic antiviral treatment status and are limited by fixed laboratory covariates and short follow up time. Advanced fibrosis assessment modalities, such as transient elastography, remain inaccessible in many settings. Improved cirrhosis predictive models are needed. METHODS: We developed a laboratory-based model to predict progression of liver disease after SVR. This prediction model used a time-varying covariates Cox model adapted to utilize longitudinal laboratory data and to account for antiretroviral treatment. Individuals were included if they had a history of detectable HCV RNA and at least 2 AST-to-platelet ratio index (APRI) scores available in the national Veterans Health Administration from 2000 to 2015, Observation time extended through January 2019. We excluded individuals with preexisting cirrhosis. Covariates included baseline patient characteristics and 16 time-varying laboratory predictors. SVR, defined as permanently undetectable HCV RNA after antiviral treatment, was modeled as a step function of time. Cirrhosis development was defined as two consecutive APRI scores > 2. We predicted cirrhosis development at 1-, 3-, and 5-years follow-up. RESULTS: In a national sample of HCV patients (n = 182,772) with a mean follow-up of 6.32 years, 42% (n = 76,854) achieved SVR before 2016 and 16.2% (n = 29,566) subsequently developed cirrhosis. The model demonstrated good discrimination for predicting cirrhosis across all combinations of laboratory data windows and cirrhosis prediction intervals. AUROCs ranged from 0.781 to 0.815, with moderate sensitivity 0.703-0.749 and specificity 0.723-0.767. CONCLUSION: A novel adaptation of time-varying covariates Cox modeling technique using longitudinal laboratory values and dynamic antiviral treatment status accurately predicts cirrhosis development at 1-, 3-, and 5-years among patients with HCV, with and without SVR. It improves upon earlier cirrhosis predictive models and has many potential population-based applications, especially in settings without transient elastography available.

Automated Measurements of Muscle Mass Using Deep Learning Can Predict Clinical Outcomes in Patients With Liver Disease.

INTRODUCTION: There is increasing recognition of the central role of muscle mass in predicting clinical outcomes in patients with liver disease. Muscle size can be extracted from computed tomography (CT) scans, but clinical implementation will require increased automation. We hypothesize that we can achieve this by using artificial intelligence. METHODS: Using deep convolutional neural networks, we trained an algorithm on the Reference Analytic Morphomics Population (n = 5,268) and validated the automated methodology in an external cohort of adult kidney donors with a noncontrast CT scan (n = 1,655). To test the clinical usefulness, we examined its ability to predict clinical outcomes in a prospectively followed cohort of patients with clinically diagnosed cirrhosis (n = 254). RESULTS: Between the manual and automated methodologies, we found excellent inter-rater agreement with an intraclass correlation coefficient of 0.957 (confidence interval 0.953-0.961, P < 0.0001) in the adult kidney donor cohort. The calculated dice similarity coefficient was 0.932 ± 0.042, suggesting excellent spatial overlap between manual and automated methodologies. To assess the clinical usefulness, we examined its ability to predict clinical outcomes in a cirrhosis cohort and found that automated psoas muscle index was independently associated with mortality after adjusting for age, gender, and child's classification (P < 0.001). DISCUSSION: We demonstrated that deep learning techniques can allow for automation of muscle measurements on clinical CT scans in a diseased cohort. These automated psoas size measurements were predictive of mortality in patients with cirrhosis showing proof of principal that this methodology may allow for wider implementation in the clinical arena.

Morphomic Signatures Derived from Computed Tomography Predict Hepatocellular Carcinoma Occurrence in Cirrhotic Patients.

BACKGROUND AND AIMS: Computed tomography (CT) provides scans of the human body from which digitized features can be extracted. The aim of this study was to examine the role of these digital biomarkers for predicting subsequent occurrence of hepatocellular carcinoma (HCC) in cirrhotic patients. METHODS: A cohort of 269 patients with cirrhosis were recruited and prospectively followed for the occurrence of HCC in Taiwan. CT scans were retrospectively retrieved and computationally processed using analytic morphomics. A predictive score was constructed using Cox regression and the generalized iterative modeling method, maximizing the log likelihood of the time to HCC development. An independent cohort of 274 patients from University of Michigan was utilized to examine the predictive validity of this score in a Western population. RESULTS: Of the 27 digitized features at the 12th thoracic vertebral level, six features were significantly associated with HCC occurrence. Two digitized features (fascia eccentricity and the bone mineral density) were able to stratify patients into high- and low-risk groups with distinct cumulative incidence of HCC in both the training and validation cohorts (P = 0.015 and 0.044, respectively). When the two digitized features were tested in the Michigan cohort, only bone mineral density remained an effective predictor. CONCLUSION: Digitized features derived from the CT were effective in predicting subsequent occurrence of HCC in cirrhosis patients. The bone mineral density measured on CT was an effective predictor for patients in both Taiwan and USA.

Eliciting patient views on the allocation of limited healthcare resources: a deliberation on hepatitis C treatment in the Veterans Health Administration.

BACKGROUND: In response to the development of highly effective but expensive new medications, policymakers, payors, and health systems are considering novel and pragmatic ways to provide these medications to patients. One approach is to target these treatments to those most likely to benefit. However, to maximize the fairness of these policies, and the acceptance of their implementation, the values and beliefs of patients should be considered. The provision of treatments for chronic hepatitis C (CHC) in the resource-constrained context of the Veterans Health Administration (VHA) offered a real-world example of this situation, providing the opportunity to test the value of using Democratic Deliberation (DD) methods to solicit the informed opinions of laypeople on this complex issue. METHODS: We recruited Veterans (n = 30) from the VHA to attend a DD session. Following educational presentations from content experts, participants engaged in facilitated small group discussions to: 1) identify strategies to overcome CHC treatment barriers and 2) evaluate, vote on, and modify/improve two CHC treatment policies - "first come, first served" (FCFS) and "sickest first" (SF). We used transcripts and facilitators' notes to identify key themes from the small group discussions. Additionally, participants completed pre- and post-DD surveys. RESULTS: Most participants endorsed the SF policy over the FCFS policy, emphasizing the ethical and medical appropriateness of treating the sickest first. Concerns about SF centered on the difficulty of implementation (e.g., how is "sickest" determined?) and unfairness to other Veterans. Proposed modifications focused on: 1) the need to consider additional health factors, 2) taking behavior and lifestyle into account, 3) offering education and support, 4) improving access, and 5) facilitating better decision-making. CONCLUSIONS: DD offered a robust and useful method for addressing the allocation of the scarce resource of CHC treatment. Participants were able to develop a modified version of the SF policy and offered diverse recommendations to promote fairness and improve quality of care for Veterans. DD is an effective approach for incorporating patient preferences and gaining valuable insights for critical healthcare policy decisions in resource-limited environments.

A risk score to predict the development of hepatic encephalopathy in a population-based cohort of patients with cirrhosis.

Over 40% of patients with cirrhosis will develop hepatic encephalopathy (HE). HE is associated with decreased survival, falls, motor vehicle accidents, and frequent hospitalization. Accordingly, we aimed to develop a tool to risk-stratify patients for HE development. We studied a population-based cohort of all patients with cirrhosis without baseline HE (n = 1,979) from the Veterans Administration from Michigan, Indiana, and Ohio (January 1, 2005-December 31, 2010) using demographic, clinical, laboratory, and pharmacy data. The primary outcome was the development of HE. Risk scores were constructed with both baseline and longitudinal data (annually updated parameters) and validated using bootstrapping. The cohort had a mean age of 58.0 ± 8.3 years, 36% had hepatitis C, and 17% had ascites. Opiates, benzodiazepines, statins, and nonselective beta-blockers were taken at baseline by 24%, 13%, 17%, and 12%, respectively. Overall, 863 (43.7%) developed HE within 5 years. In multivariable models, risk factors (hazard ratio, 95% confidence interval) for HE included higher bilirubin (1.07, 1.05-1.09) and nonselective beta-blocker use (1.34, 1.09-1.64), while higher albumin (0.54, 0.48-0.59) and statin use (0.80, 0.65-0.98) were protective. Other clinical factors, including opiate and benzodiazepine use, were not predictive. The areas under the receiver operating characteristics curve for HE using the four significant variables in baseline and longitudinal models were 0.68 (0.66-0.70) and 0.73 (0.71-0.75), respectively. Model effects were validated and converted into a risk score. A score ≤0 in our longitudinal model assigns a 6% 1-year probability of HE, while a score >20 assigns a 38% 1-year risk. CONCLUSION: Patients with cirrhosis can be stratified by a simple risk score for HE that accounts for changing clinical data; our data also highlight a role for statins in reducing cirrhosis complications including HE. (Hepatology 2017).

Virtual Consultations Through the Veterans Administration SCAN-ECHO Project Improves Survival for Veterans With Liver Disease.

Access to specialty care has been associated with improved survival in patients with liver disease but universal access is not always feasible. Methods of care delivery using virtual modalities including the SCAN-ECHO (Specialty Access Network-Extension of Community Healthcare Outcome) program were implemented by the Veterans Health Administration (VHA) to address this need but limited data are available on patient outcomes. We sought to evaluate the efficacy of a SCAN-ECHO visit within the context of a regional cohort of patients with liver disease in the VHA (n = 62,237) following implementation in the Ann Arbor SCAN-ECHO Liver Clinic from June 1, 2011, to March 31, 2015. The effect of a SCAN-ECHO visit on all-cause mortality was compared with patients with no liver clinic visit. To adjust for the differences among patients who had a SCAN-ECHO visit versus those with no visit, propensity score matching was performed on condition factors that affect the likelihood of a SCAN-ECHO visit: demographics, geographic location, liver disease diagnosis, severity, and comorbidities. During the study period, 513 patients who had a liver SCAN-ECHO visit were found within the cohort. Patients who had completed a virtual SCAN-ECHO visit were more likely younger, rural, with more significant liver disease, and evidence for cirrhosis. Propensity-adjusted mortality rates using the Cox Proportional Hazard Model showed that a SCAN-ECHO visit was associated with a hazard ratio of 0.54 (95% confidence interval 0.36-0.81, P = 0.003) compared with no visit. Conclusion: Improved survival in patients using SCAN-ECHO suggests that this approach may be an effective method to improve access for selected patients with liver disease, particularly in rural and underserved populations where access to specialty care is limited.

Bedside Measures of Frailty and Cognitive Function Correlate with Sarcopenia in Patients with Cirrhosis.

BACKGROUND: Frailty and sarcopenia are associated with mortality and poor outcomes among patients with cirrhosis. Frailty is multifactorial but due in part to sarcopenia and cognitive dysfunction. Data are limited regarding the correlation of bedside frailty and cognitive function measures with sarcopenia. AIMS: To evaluate the correlations between frailty measures and muscle indices from computed tomography (CT). METHODS: We prospectively enrolled 106 patients with clinically compensated cirrhosis (and no prior hepatic encephalopathy). All patients underwent CT scan and cognitive testing (via inhibitory control test, ICT), and were subject to hand grip, 30-s chair stands, mid-arm muscle area (MAMA), and a four-question algorithm based on the Sickness Impact Profile (SIP) predictive of minimal HE. We evaluated Spearman correlations between all measures as well as the sensitivity and specificity of each measure for falls. RESULTS: In total, 106 (35.3%) patients (55 men) had CT scans to measure skeletal muscle area and quality. Hand grip correlated strongly with skeletal muscle area (correlation coefficient 0.64, p < 0.001) and mildly with ICT performance (0.34, p = 0.002). However, for women, the strongest correlation with hand grip was ICT performance (0.60, p < 0.001). Chair stand performance correlated best with SIP (correlation coefficient - 0.35, p < 0.001). MAMA was not correlated with CT-based muscle indices among women but was for men. Poor chair stand performance (< 10/30-s) had a sensitivity/specificity for falls of 73%/54%; low muscle radiation attenuation (density) was 40%/80% sensitive/specific. CONCLUSION: Bedside measures of physical function, muscle bulk, and cognitive performance are correlated with CT-based muscle measures. Bedside measures of frailty may provide an advantage over sarcopenia for outcome assessment that should be confirmed prospectively.

Measurement of Skeletal Muscle Area Improves Estimation of Aminoglycoside Clearance across Body Size.

A consistent approach to the dosing of aminoglycosides across the modern body size distribution has been elusive. We evaluated whether radiologically derived measures of body composition could explain more of the interpatient variability in aminoglycoside pharmacokinetics (PK) than standard body size metrics. This retrospective study included adult patients treated with gentamicin or tobramycin with at least three drug concentrations and computed tomography (CT) imaging available. Aminoglycoside volume and clearance (CL) estimates were computed using a two-compartment model by Bayesian analysis. Morphomic data were extracted from CT images using a custom algorithm. Bivariable and multivariable linear regression were used to assess relationships between PK parameters and covariates. A total of 335 patients were included with a median (minimum, maximum) of 4 (3, 16) aminoglycoside concentrations per patient. The median (minimum, maximum) age, height, and weight of included patients were 57 (21, 93) years, 170 (145, 203) centimeters, and 81 (42, 187) kilograms. Both standard and morphomic measures poorly explained variability in volume (R2 < 0.06). Skeletal muscle area and volume explained more of the interpatient variability in CL than weight or sex. Higher precision was observed using a modified Cockcroft-Gault equation with skeletal muscle area at L3 (R2= 0.38) or L4 (R2= 0.37) than the standard Cockcroft-Gault equation using lean (R2= 0.23), adjusted (R2= 0.23), or total (R2= 0.22) body weights. These results highlight that skeletal muscle measurements from CT images obtained in the course of care can improve the precision of aminoglycoside CL estimation over current body size scalars.

Relationships of Vancomycin Pharmacokinetics to Body Size and Composition Using a Novel Pharmacomorphomic Approach Based on Medical Imaging.

Antibiotics such as vancomycin are empirically dosed on the basis of body weight, which may not be optimal across the expanding adult body size distribution. Our aim was to compare the relationships between morphomic parameters generated from computed tomography images to conventional body size metrics as predictors of vancomycin pharmacokinetics (PK). This single-center retrospective study included 300 patients with 1,622 vancomycin concentration (52% trough) measurements. Bayesian estimation was used to compute individual vancomycin volume of distribution of the central compartment (Vc) and clearance (CL). Approximately 45% of patients were obese with an overall median (5th, 95th percentile) weight and body mass index of 87.2 (54.7, 123) kg and 28.8 (18.9, 43.7) kg/m2, respectively. Morphomic parameters of body size such as body depth, total body area, and torso volume of the twelfth thoracic through fourth lumbar vertebrae (T12 to L4) correlated with Vc. The relationship of vancomycin Vc was poorly predicted by body size but was stronger with T12-to-L4 torso volume (coefficient of determination [R2] = 0.11) than weight (R2 = 0.04). No relationships between vancomycin CL and traditional body size metrics could be discerned; however, relationships with skeletal muscle volume and total psoas area were found. Vancomycin CL independently correlated with total psoas area and inversely correlated with age. Thus, vancomycin CL was significantly related to total psoas area over age (R2 = 0.23, P < 0.0001). This proof-of-concept study suggests a potential role for translation of radiographic information into parameters predictive of drug pharmacokinetics. Prediction of individual antimicrobial pharmacokinetic parameters using analytic morphomics has the potential to improve antimicrobial dose selection and outcomes of obese patients.

Specialty Care Access Network-Extension of Community Healthcare Outcomes Model Program for Liver Disease Improves Specialty Care Access.

BACKGROUND AND AIMS: To improve subspecialty access, VA Ann Arbor Healthcare System (VAAAHS) implemented the first Specialty Care Access Network (SCAN)-Extension of Community Healthcare Outcomes (ECHO) in chronic liver disease. SCAN-ECHO Liver links primary care providers (PCPs) to hepatologists via secure video-teleconferencing. We aim to describe characteristics of participants (PCPs) and patients (clinical question and diagnosis) in SCAN-ECHO Liver. METHODS: This is a prospective study of the VAAAHS SCAN-ECHO Liver (June 10, 2011-March 31, 2015). This evaluation was carried out as a non-research activity under the guidance furnished by VHA Handbook 1058.05. It was approved through the Medicine Service at VAAAHS as noted in the attestation document which serves as documentation of approved non-research, quality improvement activities in VHA. RESULTS: In total, 106 PCPs from 23 sites participated. A total of 155 SCAN-ECHO sessions discussed 519 new and 49 return patients. 29.4% of Liver Clinic requests were completed in SCAN-ECHO Liver. SCAN-ECHO Liver consults were completed an average of 10 days sooner than in conventional clinic. Potential travel saving was 250 miles round-trip (median 255 (IQR 142-316) per patient. CONCLUSION: SCAN-ECHO Liver provided specialty care with increased efficiency and convenience for chronic liver disease patients. One of three of Liver Clinic consults was diverted to SCAN-ECHO Liver, reducing consult completion time by 20%.