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

OBJECTIVE

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 CT-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 (1cm), called mini-segments. ML segmentation methods were trained on radiologist grading with predicted severity 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 cumulative ileum injury metrics and traditional bowel measures, adjusting for laboratory values, medications, and prior surgery at the time of CTE.

RESULTS

In 229 CTEs, 8424 mini-segments underwent analysis. Agreement between ML and radiologists injury grading was strong (κ=0.80, 95%CI 0.79-0.81) and similar to inter-radiologist agreement (κ=0.87, 95%CI 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 (AUC=0.76) outperformed models using conventional bowel measures, laboratory values, and medical history (AUC=0.62) for predicting future surgery in biologic users.

CONCLUSION

Automated cumulative ileal injury scores show promise for improving prediction of outcomes in small bowel CD. Beyond replicating expert judgement, spatial enterography analysis can augment the personalization of bowel assessment in CD.

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.

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.

Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans

INTRODUCTION

Undiagnosed cirrhosis remains a significant problem. In this study, we developed and tested an automated liver segmentation tool to predict the presence of cirrhosis in a population of patients with paired liver biopsy and computed tomography (CT) scans.

METHODS

We used a cohort of 1,590 CT scans within the Morphomics database to train an automated liver segmentation model using 3D-U-Net and Google's DeeplLabv3+. Imaging features were then automatically calculated from an external test cohort of patients with chronic liver disease who had a paired liver biopsy and CT within 6 months of each other in January 2004-2012. Using gradient boosting decision trees, we developed multivariate models to predict the presence of histologic cirrhosis and evaluated with 5-fold cross-validated c-statistic.

RESULTS

Our cohort had 351 patients; 96 patients had cirrhosis. Of the total cohort, 72 were postliver transplant. Both fibrosis (FIB)-4 and liver morphomics alone performed equally well with area under the receiving operating characteristics of 0.76 (95% confidence interval 0.70-0.81) and 0.71 (95% confidence interval 0.65-0.76), respectively ( P = 0.2). However, the combination of liver morphomics with laboratory values or liver morphomics with laboratory and demographic data resulted in significant improved performance with area under the receiving operating characteristics of 0.84 (0.80-0.89) and 0.85 (0.81-0.90), respectively, compared with FIB-4 alone ( P < 0.001). In a subgroup analysis, we also examined performance in patients without liver transplantation and saw similar augmentation of FIB-4.

DISCUSSION

This proof-of-principle study demonstrates that automatically extracted features within CT scans can be combined with classic electronic medical record data to improve the prediction of cirrhosis in patients with liver disease. This tool may be used in both pretransplant and posttransplant patients and has the potential to improve our ability to detect undiagnosed cirrhosis.

Reference distributions of aortic calcification and association with Framingham risk score

Evidence supporting aortic calcification as a leverageable cardiovascular risk factor is rapidly growing. Given aortic calcification's potential as a clinical correlate, we assessed granular vertebral-indexed calcification measurements of the abdominal aorta in a well curated reference population. We evaluated the relationship of aortic calcification measurements with Framingham risk scores. After exclusion, 4073 participants from the Reference Analytic Morphomic Population with varying vertebral levels were included. The percent of the aortic wall calcified was used to assess calcification burden at the L1-L4 levels. Descriptive statistics of participants, sex-specific vertebral indexed calcification measurements, relational plots, and relevant associations are reported. Mean aortic attenuation was higher in female than male participants. Overall, mean aortic calcium was higher with reference to inferior abdominal aortic measurements and demonstrated significant differences across all abdominal levels [L3 Area (mm[Formula: see text]): Females 6.34 (sd 16.60), Males 6.23 (sd 17.21); L3 Volume (mm[Formula: see text]): Females 178.90 (sd 474.19), Males 195.80 (sd 547.36); Wall Calcification (%): Females (L4) 6.97 (sd 16.03), Males (L3) 5.46 (13.80)]. Participants with elevated calcification had significantly higher Framingham risk scores compared to participants with normal calcification scores. Opportunistically measuring aortic calcification may inform further cardiovascular risk assessment and enhance cardiovascular event surveillance efforts.

Validity of Administrative Claims and Electronic Health Registry Data From a Single Practice for Eye Health Surveillance

Importance

Diagnostic information from administrative claims and electronic health record (EHR) data may serve as an important resource for surveillance of vision and eye health, but the accuracy and validity of these sources are unknown.

Objective

To estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review.

Design, Setting, and Participants

This cross-sectional study compared the presence and prevalence of eye disorders based on diagnostic codes in EHR and claims records vs clinical medical record review at University of Washington-affiliated ophthalmology or optometry clinics from May 2018 to April 2020. Patients 16 years and older with an eye examination in the previous 2 years were included, oversampled for diagnosed major eye diseases and visual acuity loss.

Exposures

Patients were assigned to vision and eye health condition categories based on diagnosis codes present in their billing claims history and EHR using the diagnostic case definitions of the US Centers for Disease Control and Prevention Vision and Eye Health Surveillance System (VEHSS) as well as clinical assessment based on retrospective medical record review.

Main Outcome and Measures

Accuracy was measured as area under the receiver operating characteristic curve (AUC) of claims and EHR-based diagnostic coding vs retrospective review of clinical assessments and treatment plans.

Results

Among 669 participants (mean [range] age, 66.1 [16-99] years; 357 [53.4%] female), identification of diseases in billing claims and EHR data using VEHSS case definitions was accurate for diabetic retinopathy (claims AUC, 0.94; 95% CI, 0.91-0.98; EHR AUC, 0.97; 95% CI, 0.95-0.99), glaucoma (claims AUC, 0.90; 95% CI, 0.88-0.93; EHR AUC, 0.93; 95% CI, 0.90-0.95), age-related macular degeneration (claims AUC, 0.87; 95% CI, 0.83-0.92; EHR AUC, 0.96; 95% CI, 0.94-0.98), and cataracts (claims AUC, 0.82; 95% CI, 0.79-0.86; EHR AUC, 0.91; 95% CI, 0.89-0.93). However, several condition categories showed low validity with AUCs below 0.7, including diagnosed disorders of refraction and accommodation (claims AUC, 0.54; 95% CI, 0.49-0.60; EHR AUC, 0.61; 95% CI, 0.56-0.67), diagnosed blindness and low vision (claims AUC, 0.56; 95% CI, 0.53-0.58; EHR AUC, 0.57; 95% CI, 0.54-0.59), and orbital and external diseases (claims AUC, 0.63; 95% CI, 0.57-0.69; EHR AUC, 0.65; 95% CI, 0.59-0.70).

Conclusion and Relevance

In this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate. However, vision loss, refractive error, and other broadly defined or lower-risk disorder categories were less accurately identified by diagnosis codes in claims and EHR data.

Comparing Telephone Survey Responses to Best-Corrected Visual Acuity to Estimate the Accuracy of Identifying Vision Loss: Validation Study

BACKGROUND

Self-reported questions on blindness and vision problems are collected in many national surveys. Recently released surveillance estimates on the prevalence of vision loss used self-reported data to predict variation in the prevalence of objectively measured acuity loss among population groups for whom examination data are not available. However, the validity of self-reported measures to predict prevalence and disparities in visual acuity has not been established.

OBJECTIVE

This study aimed to estimate the diagnostic accuracy of self-reported vision loss measures compared to best-corrected visual acuity (BCVA), inform the design and selection of questions for future data collection, and identify the concordance between self-reported vision and measured acuity at the population level to support ongoing surveillance efforts.

METHODS

We calculated accuracy and correlation between self-reported visual function versus BCVA at the individual and population level among patients from the University of Washington ophthalmology or optometry clinics with a prior eye examination, randomly oversampled for visual acuity loss or diagnosed eye diseases. Self-reported visual function was collected via telephone survey. BCVA was determined based on retrospective chart review. Diagnostic accuracy of questions at the person level was measured based on the area under the receiver operator curve (AUC), whereas population-level accuracy was determined based on correlation.

RESULTS

The survey question, "Are you blind or do you have serious difficulty seeing, even when wearing glasses?" had the highest accuracy for identifying patients with blindness (BCVA ≤20/200; AUC=0.797). The highest accuracy for detecting any vision loss (BCVA <20/40) was achieved by responses of "fair," "poor," or "very poor" to the question, "At the present time, would you say your eyesight, with glasses or contact lenses if you wear them, is excellent, good, fair, poor, or very poor" (AUC=0.716). At the population level, the relative relationship between prevalence based on survey questions and BCVA remained stable for most demographic groups, with the only exceptions being groups with small sample sizes, and these differences were generally not significant.

CONCLUSIONS

Although survey questions are not considered to be sufficiently accurate to be used as a diagnostic test at the individual level, we did find relatively high levels of accuracy for some questions. At the population level, we found that the relative prevalence of the 2 most accurate survey questions were highly correlated with the prevalence of measured visual acuity loss among nearly all demographic groups. The results of this study suggest that self-reported vision questions fielded in national surveys are likely to yield an accurate and stable signal of vision loss across different population groups, although the actual measure of prevalence from these questions is not directly analogous to that of BCVA.

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 (C_max ). 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 C_max  > 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 C_max  > 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.

Metabolic abnormalities, liver and body fat in American versus Chinese patients with non‐alcoholic fatty liver disease

Background and Aim

Non‐alcoholic fatty liver disease (NAFLD) is common in the United States and China. We compared prevalence of metabolic syndrome (MS), hepatic steatosis and fibrosis, and quantity and quality of body fat between American versus Chinese patients with NAFLD.

Methods

NAFLD patients were prospectively recruited from the University of Michigan Health System (UMHS) in the United States and Peking University Health Sciences Center (PUHSC) in China. All patients had baseline computed tomography (CT), laboratory tests and Fibroscan® controlled attenuation parameter (CAP) and liver stiffness measurement (LSM). Comparisons were made for overall cohorts and matched cohorts (matched for sex, age, and body mass index [BMI] category). Logistic regression was performed to identify independent predictors of moderate and severe steatosis and lack of advanced fibrosis.

Results

One‐hundred and one American and One‐hundred and sixty Chinese patients were included. UMHS patients were older, with higher prevalence of MS, had higher LSM and CAP scores, and more fat in liver, visceral, subcutaneous, and muscle compartments than PUHSC patients. Differences in LSM, visceral fat Hounsfield unit, and subcutaneous fat area (SFA) persisted in the matched cohort. NAFLD patients with MS had significantly higher LSM, and more fat in liver, visceral, subcutaneous and muscle compartments than those without. Moderate or severe steatosis was independently associated with MS, visceral fat quality, and SFA, while the absence of advanced fibrosis was associated with Asian race and not having MS.

Conclusion

American patients with NAFLD had more liver fibrosis than Chinese patients despite having better quality visceral fat and after matching for age, sex, and BMI category.

Abstract 186: Anatomically Indexed Distributions Of Aortic Calcification In A Reference Population

Introduction: Cardiovascular disease remains the leading cause of death in the United States. Aortic calcification shows strong promise towards preventative screening efforts for cardiovascular risk in clinical and subclinical populations. While still relatively novel, granular distributions of aortic calcium in reference populations are absent from the literature.

Methods: All participants of the Reference Analytic Morphomics Population, an approximation of vehicle occupants in the United States, with abdominal scans were considered for this analysis (n=4080). Analytic Morphomics was used to obtain vertebral-indexed anatomical measurements of the aorta and calcium characteristics (area, volume, density, and aortic wall obfuscation). Calcification was specifically identified as regions with a given morphology and pixel value three standard deviations above the defined central lumen zone.

Results: Participant mean age was 33 (SD 22.6) and were mostly male (62.6%) due to the prevalence of males involved in trauma. Age and calcification distributions were right skewed among all vertebral levels and highest for the L3-L4 reference levels [~3% wall obfuscation (SD 10.5-11.3)]. Males demonstrated a progressive increase in total calcium volume from the 4 th decade of age, while females had comparatively smaller increases and a dramatic increase in total calcium after the 7 th decade. The largest increases in observed calcium volumes were observed in the 8 th decade for males (~2300 cm 3 ) and 7 th for females (~4500 cm 3 ).

Conclusion: Aortic calcium prevalence was higher with reference to lower vertebral levels. Dramatic differential distributions of calcium were observed by age and sex.

Systematic review: development of a consensus code set to identify cirrhosis in electronic health records

BACKGROUND

Electronic health records (EHRs) collate longitudinal data that can be used to facilitate large-scale research in patients with cirrhosis. However, there is no consensus code set to define the presence of cirrhosis in EHR. This systematic review aims to evaluate the validity of diagnostic coding in cirrhosis and to synthesise a comprehensive set of ICD-10 codes for future EHR research.

METHOD

MEDLINE and EMBASE databases were searched for studies that used EHR to identify cirrhosis and cirrhosis-related complications. Validated code sets were summarised, and the performance characteristics were extracted. Citation analysis was done to inform development of a consensus code set. This was then validated in a cohort of patients.

RESULTS

One thousand six hundred twenty-six records were screened, and 18 studies were identified. The positive predictive value (PPV) was the most frequently reported statistical estimate and was ≥80% in 17/18 studies. Citation analyses showed continued variation in those used in contemporary research practice. Nine codes were identified as those most frequently used in the literature and these formed the consensus code set. This was validated in diverse patient populations from Europe and North America and showed high PPV (83%-89%) and greater sensitivity for the identification of cirrhosis than the most often used code set in the recent literature.

CONCLUSION

There is variation in code sets used to identify cirrhosis in contemporary research practice. A consensus set has been developed and validated, showing improved performance, and is proposed to align EHR study designs in cirrhosis to facilitate international collaboration and comparisons.

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.

Healthy US population reference values for CT visceral fat measurements and the impact of IV contrast, HU range, and spinal levels

Measurements of visceral adipose tissue cross-sectional area and radiation attenuation from computed tomography (CT) scans provide useful information about risk and mortality. However, scan protocols vary, encompassing differing vertebra levels and utilizing differing phases of contrast enhancement. Furthermore, fat measurements have been extracted from CT using different Hounsfield Unit (HU) ranges. To our knowledge, there have been no large studies of healthy cohorts that reported reference values for visceral fat area and radiation attenuation at multiple vertebra levels, for different contrast phases, and using different fat HU ranges. Two-phase CT scans from 1,677 healthy, adult kidney donors (age 18–65) between 1999 and 2017, previously studied to determine healthy reference values for skeletal muscle measures, were utilized. Visceral adipose tissue cross-sectional area (VFA) and radiation attenuation (VFRA) measures were quantified using axial slices at T10 through L4 vertebra levels. T-tests were used to compare males and females, while paired t-tests were conducted to determine the effect (magnitude and direction) of (a) contrast enhancement and (b) different fat HU ranges on each fat measure at each vertebra level. We report the means, standard deviations, and effect sizes of contrast enhancement and fat HU range. Male and female VFA and VFRA were significantly different at all vertebra levels in both contrast and non-contrast scans. Peak VFA was observed at L4 in females and L2 in males, while peak VFRA was observed at L1 in both females and males. In general, non-contrast scans showed significantly greater VFA and VFRA compared to contrast scans. The average paired difference due to contrast ranged from 1.6 to − 8% (VFA) and 3.2 to − 3.0% (VFRA) of the non-contrast value. HU range showed much greater differences in VFA and VFRA than contrast. The average paired differences due to HU range ranged from − 5.3 to 22.2% (VFA) and − 5.9 to 13.6% (VFRA) in non-contrast scans, and − 4.4 to 20.2% (VFA) and − 4.1 to 12.6% (VFRA) in contrast scans. The − 190 to − 30 HU range showed the largest differences in both VFA (10.8% to 22.2%) and VFRA (7.6% to 13.6%) compared to the reference range (− 205 to − 51 HU). Incidentally, we found that differences in lung inflation result in very large differences in visceral fat measures, particularly in the thoracic region. We assessed the independent effects of contrast presence and fat HU ranges on visceral fat cross-sectional area and mean radiation attenuation, finding significant differences particularly between different fat HU ranges. These results demonstrate that CT measurements of visceral fat area and radiation attenuation are strongly dependent upon contrast presence, fat HU range, sex, breath cycle, and vertebra level of measurement. We quantified contrast and non-contrast reference values separately for males and females, using different fat HU ranges, for lumbar and thoracic CT visceral fat measures at multiple vertebra levels in a healthy adult US population.

[ ≥ ]

Objective: To investigate the safety and efficacy of retroperitoneal laparoscopic selective renal artery branch occlusion with nephron sparing surgery in patients with renal carcinoma of stage ≥ T1b. Methods: From July 2016 to September 2020, 35 patients with renal cancer ≥T1b underwent retroperitoneoscopic nephron sparing surgery in the First Affiliated Hospital of Shenzhen University. The surgical methods were retroperitoneoscopic nephron sparing surgery with total renal artery occlusion (group A) or selective renal artery branch occlusion (group B). Operation time, heat ischemia time, blood transfusion rate, positive margin rate, intraoperative blood loss, postoperative complications and length of hospital stay were compared between the two groups, and the total glomerular filtration rate (GFR) and the single-nephron glomerular filtration rate (sGFR) of the offected kidneys were compared between the two groups before, 3 months after and 12 months after surgery. Results: Among the 35 patients, 19 were male and 16 were female, aged (55.7±8.4) years and the body mass index is (24.6±3.1) kg/m². The tumor diameter was (54.7±10.3) mm. The difference was statistically significant of operative time between group A and B [(103.5±14.3) vs (123.2±14.1) min,P=0.003]. There were no significant differences in thermal ischemia time, blood transfusion rate, positive margin, intraoperative blood loss, incidence of postoperative complications and length of hospital stay between the two groups (all P>0.05). The decrease of renal sGFR in the group A was significantly higher than group B at 3 months and 12 months after surgery [(23.1±3.6) vs (29.1±7.1) ml/min;(25.9±4.7) vs (30.7±7.2),both P<0.05]. Conclusion: Retroperitoneal laparoscopic selective renal artery branch occlusion and neon-sparing surgery for patients with ≥ T1b stage renal carcinoma is a safe and effective surgical method, which can well protect the renal function of patients in the early postoperative stage without increasing intraoperative blood loss and postoperative complications.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01711-7.

A correction score to compare aortic calcification in contrast enhanced and non-contrast measurements from computed tomography scans

BACKGROUND

Aortic wall calcification shows strong promise as a cardiovascular risk factor. While useful for visual enhancement of vascular tissue, enhancement creates heterogeneity between scans with and without contrast. We evaluated the relationship between aortic calcification in routine abdominal computed tomography scans (CT) with and without contrast.

METHODS

Inclusion was limited to those with abdominal CT-scans with and without contrast enhancement within 120 days. Analytic Morphomics, a semi-automated computational image processing system, was used to provide standardized, granular, anatomically indexed measurements of aortic wall calcification from abdominal CT-scans. Aortic calcification area (ACA) and aortic wall calcification percent (ACP) and were the outcomes of interest. Multiple linear regression was used to evaluate the relationship of aortic measurements. Models were further controlled for age and sex. Stratification of measurements by vertebral level was also performed.

RESULTS

A positive association was observed for non-contrast calcification in ACP β 0.74 (95% CI 0.72, 0.76) and ACA β 0.44 (95% 0.43, 0.45). Stratified results demonstrated the highest coefficient of determination at L2 for percent and L3 for area models [R² 0.91 (ACP) 0.74 (ACA)]. Adjusted lumber-level associations between non-contrast and contrast measurements ranged from (β 0.69-0.82) in ACP and (β 0.37-0.54) in ACA.

CONCLUSION

A straightforward correction score for comparison of abdominal aortic calcification measurements in contrast-enhanced and non-contrast scans is discussed. Correction of aortic calcification from CT scans can reduce scan heterogeneity and will be instrumental in creating larger cardiovascular cohorts as well as cardiovascular risk surveillance programs.

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

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.

Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma

BACKGROUND

Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology.

AIMS

To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation.

METHODS

We performed a systematic review of all full-text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies.

RESULTS

A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c-statistics 0.66-0.95), and to predict microvascular invasion (c-statistic 0.76-0.92), early recurrence after hepatectomy (c-statistics 0.71-0.86), and prognosis after locoregional or systemic therapies (c-statistics 0.74-0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard.

CONCLUSIONS

Quantitative image analysis demonstrates promise as a non-invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.

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.

Does a "Cushion Effect" Really Exist? A Morphomic Analysis of Vulnerable Road Users with Serious Blunt Abdominal Injury

Introduction: The severity of injury from motor vehicle crashes (MVCs) depends on complex biomechanical factors, and the bodily features of the injured person account for some of these factors. By assuming that vulnerable road users (VRUs) have limited protection resulting from vehicles and safety equipment, the current study analyzed the characteristics of fat distribution measured by computed tomography (CT) imaging and investigated the existence of a "cushion effect" in VRUs. Materials and Methods: This retrospective study enrolled 592 VRUs involved in MVCs who underwent CT scans. Visceral fat area and subcutaneous fat cross-sectional area were measured and adjusted according to total body area (TBA) and are presented as the visceral fat ratio and the subQ fat ratio (subcutaneous fat ratio). Risk factors for serious abdominal injury (maximum abbreviated injury scale (MAIS_abd ≥ 3)) resulting from MVCs were determined by univariate and multivariate analysis. Results: MAIS_abd ≥ 3 was observed in 104 (17.6%) of the patients. The subQ fat ratio at the L4 vertebral level was significantly lower in the MAIS_abd ≥ 3 group than in the MAIS_abd < 3 group (24.9 ± 12.0 vs. 28.1 ± 11.9%; p = 0.015). A decreased L4 subQ fat ratio was associated with a higher risk for MAIS_abd ≥ 3 in multivariate analysis (odds ratio 0.063; 95% CI 0.008-0.509; p = 0.009). Conclusion: The current study supported the "cushion effect" theory, and protection was apparently provided by subcutaneous fat tissue. This concept may further improve vehicle and safety designation in the future.

The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease

BACKGROUND

Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routinely collected laboratory studies to predict surgical outcomes in U.S. Veterans with CD.

METHODS

Adults with CD from a Veterans Health Administration, Veterans Integrated Service Networks (VISN) 10 cohort examined between 2001 and 2015 were used for analysis. Patient demographics, medication use, and longitudinal laboratory values were used to model future surgical outcomes within 1 year. Specifically, data at the time of prediction combined with historical laboratory data characteristics, described as slope, distribution statistics, fluctuation, and linear trend of laboratory values, were considered and principal component analysis transformations were performed to reduce the dimensionality. Lasso regularized logistic regression was used to select features and construct prediction models, with performance assessed by area under the receiver operating characteristic using 10-fold cross-validation.

RESULTS

We included 4950 observations from 2809 unique patients, among whom 256 had surgery, for modeling. Our optimized model achieved a mean area under the receiver operating characteristic of 0.78 (SD, 0.002). Anti-tumor necrosis factor use was associated with a lower probability of surgery within 1 year and was the most influential predictor in the model, and corticosteroid use was associated with a higher probability of surgery. Among the laboratory variables, high platelet counts, high mean cell hemoglobin concentrations, low albumin levels, and low blood urea nitrogen values were identified as having an elevated influence and association with future surgery.

CONCLUSIONS

Using machine learning methods that incorporate current and historical data can predict the future risk of CD surgery.

Optimal body size adjustment of L3 CT skeletal muscle area for sarcopenia assessment

Measurements of skeletal muscle cross-sectional area (SMA) at the level of the third lumbar (L3) vertebra derived from clinical computed tomography (CT) scans are commonly used in assessments of sarcopenia, the loss of skeletal muscle mass and function associated with aging. As SMA is correlated with height and Body Mass Index (BMI), body size adjustment is necessary to fairly assess sarcopenic low muscle mass in individuals of different height and BMI. The skeletal muscle index, a widely used measure, adjusts for height as [Formula: see text] but uses no BMI adjustment. There is no agreed upon standard for body size adjustment. We extracted L3 SMA using non-contrast-enhanced CT scans from healthy adults, split into 'Under-40' and 'Over-40' cohorts. Sex-specific allometric analysis showed that height to the power of one was the optimal integer coefficient for height adjusted SMA in both males and females. We computed two height-adjusted measures [Formula: see text] and [Formula: see text], comparing their Pearson correlations versus age, height, weight, and BMI separately by sex and cohort. Finally, in the 'Under-40' cohort, we used linear regression to convert each height-adjusted measure into a z-score ([Formula: see text], [Formula: see text]) adjusted for BMI. [Formula: see text] was less correlated with height in both males and females ([Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text]) than [Formula: see text] ([Formula: see text] and [Formula: see text], [Formula: see text]). [Formula: see text] was uncorrelated with BMI and weight, and minimally correlated with height in males and females ([Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text]). The final [Formula: see text] equation was: [Formula: see text], where [Formula: see text], [Formula: see text], [Formula: see text], and sex = 1 if male, 0 if female. We propose [Formula: see text] for optimal height adjustment and the [Formula: see text] score for optimal height and BMI adjustment. By minimizing correlations with height and BMI, the [Formula: see text] score produces unbiased assessments of relative L3 skeletal muscle area across the full range of body sizes.

Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis

IMPORTANCE

Deep learning, a family of machine learning models that use artificial neural networks, has achieved great success at predicting outcomes in nonmedical domains.

OBJECTIVE

To examine whether deep learning recurrent neural network (RNN) models that use raw longitudinal data extracted directly from electronic health records outperform conventional regression models in predicting the risk of developing hepatocellular carcinoma (HCC).

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study included 48 151 patients with hepatitis C virus (HCV)-related cirrhosis in the national Veterans Health Administration who had at least 3 years of follow-up after the diagnosis of cirrhosis. Patients were identified by having at least 1 positive HCV RNA test between January 1, 2000, to January 1, 2016, and were followed up from the diagnosis of cirrhosis to January 1, 2019, for the development of incident HCC. A total of 3 models predicting HCC during a 3-year period were developed and compared, as follows: (1) logistic regression (LR) with cross-sectional inputs (cross-sectional LR); (2) LR with longitudinal inputs (longitudinal LR); and (3) RNN with longitudinal inputs. Data analysis was conducted from April 2018 to August 2020.

EXPOSURES

Development of HCC.

MAIN OUTCOMES AND MEASURES

Area under the receiver operating characteristic curve, area under the precision-recall curve, and Brier score.

RESULTS

During a mean (SD) follow-up of 11.6 (5.0) years, 10 741 of 48 151 patients (22.3%) developed HCC (annual incidence, 3.1%), and a total of 52 983 samples (51 948 [98.0%] from men) were collected. Patients who developed HCC within 3 years were older than patients who did not (mean [SD] age, 58.2 [6.6] years vs 56.9 [6.9] years). RNN models had superior mean (SD) area under the receiver operating characteristic curve (0.759 [0.009]) and mean (SD) Brier score (0.136 [0.003]) than cross-sectional LR (0.689 [0.009] and 0.149 [0.003], respectively) and longitudinal LR (0.682 [0.007] and 0.150 [0.003], respectively) models. Using the RNN model, the samples with the mean (SD) highest 51% (1.5%) of HCC risk, in which 80% of all HCCs occurred, or the mean (SD) highest 66% (1.2%) of HCC risk, in which 90% of all HCCs occurred, could potentially be targeted. Among samples from patients who achieved sustained virologic response, the performance of the RNN models was even better (mean [SD] area under receiver operating characteristic curve, 0.806 [0.025]; mean [SD] Brier score, 0.117 [0.007]).

CONCLUSIONS AND RELEVANCE

In this study, deep learning RNN models outperformed conventional LR models, suggesting that RNN models could be used to identify patients with HCV-related cirrhosis with a high risk of developing HCC for risk-based HCC outreach and surveillance strategies.

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.

Morphomic calcification score from clinical CT scans: A proxy for coronary artery calcium

BACKGROUND

Screening of cardiovascular risk is essential in preventing cardiac events and quantifying asymptomatic risk. Coronary artery calcium (CAC) scores are a well-established in predicting cardiovascular risk, but require specialized computed tomography (CT) scans. Given the relationship of aortic calcification with cardiovascular risk, we sought to determine whether aortic calcification measures from incidental CT scans may approximate CAC.

STUDY DESIGN

Retrospective CT scans and corresponding volumetric CAC scores were obtained from patients at the University of Michigan. Aortic calcifications were measured in 166 scans. Correlations between a novel morphomic calcium (MC) percent score and CAC score were evaluated using Kendall's correlation coefficients. Comparison of receiver operating characteristic (ROC) curves based on MC at different vertebral levels showed the highest predictive values for measures taken at L4.

RESULTS

MC at L4 shows promise in predicting CAC (AUC 0.90 in non-contrast scans, 0.70 in post-contrast scans). Proposed MC threshold are (4.21% for best sensitivity, B 12.93% for balance, C = 19.26% for specificity) in scans without contrast enhancement and (D = 7.31 for sensitivity, E 8.06 for specificity) in scans with contrast enhancement.

CONCLUSION

The MC score demonstrates promising potential in approximating CAC, particularly at the L4 level. The utilization of MC from incidental CT scans may be useful for assessment of cardiovascular risk. The ability to extract MC from contrast scans makes it especially valuable to patients receiving additional medical or surgical care. Recognition of high-risk patients would allow the use of indicated preventative strategies to avoid hard cardiovascular events in at risk patients.

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.

Assessing Small Bowel Stricturing and Morphology in Crohn's Disease Using Semi-automated Image Analysis

BACKGROUND

Evaluating structural damage using imaging is essential for the evaluation of small intestinal Crohn's disease (CD), but it is limited by potential interobserver variation. We compared the agreement of enterography-based bowel damage measurements collected by experienced radiologists and a semi-automated image analysis system.

METHODS

Patients with small bowel CD undergoing a CT-enterography (CTE) between 2011 and 2017 in a tertiary care setting were retrospectively reviewed. CT-enterography studies were reviewed by 2 experienced radiologists and separately underwent automated computer image analysis using bowel measurement software. Measurements included maximum bowel wall thickness (BWT-max), maximum bowel dilation (DIL-max), minimum lumen diameter (LUM-min), and the presence of a stricture. Measurement correlation coefficients and paired t tests were used to compare individual operator measurements. Multivariate regression was used to model identification of strictures using semi-automated measures.

RESULTS

In 138 studies, the correlation between radiologists and semi-automated measures were similar for BWT-max (r = 0.724, 0.702), DIL-max (r = 0.812, 0.748), and LUM-min (r = 0.428, 0.381), respectively. Mean absolute measurement difference between semi-automated and radiologist measures were no different from the mean difference between paired radiologists for BWT-max (1.26 mm vs 1.12 mm, P = 0.857), DIL-max (2.78 mm vs 2.67 mm, P = 0.557), and LUM-min (0.54 mm vs 0.41 mm, P = 0.596). Finally, models of radiologist-defined intestinal strictures using automatically acquired measurements had an accuracy of 87.6%.

CONCLUSION

Structural bowel damage measurements collected by semi-automated approaches are comparable to those of experienced radiologists. Radiomic measures of CD will become an important new data source powering clinical decision-making, patient-phenotyping, and assisting radiologists in reporting objective measures of disease status.

Body composition predicts mortality and decompensation in compensated cirrhosis patients: A prospective cohort study

Background & Aims

Body composition, particularly sarcopenia, is associated with mortality in patients with decompensated cirrhosis undergoing transplant evaluation. Similar data are limited for non-transplant eligible or compensated patients.

Methods

A total of 274 patients with cirrhosis were followed prospectively for ≤5 years after a CT scan. We utilized Analytic Morphomics® to measure body composition (fat, muscle, and bone) which was rendered into relative values (percentiles) in relation to a reference population. The model for end-stage liver disease (MELD) score was used as a reference model for survival prediction. We validated our models in a separate cohort.

Results

Our cohort had a mean Child-Pugh score of 7.0 and a mean MELD of 11.3. The median follow-up time was 5.05 years. The proportion of patients alive at 1, 3 and 5 years was 86.5%, 68.0%, and 54.3%; 13 (4.6%) underwent liver transplantation. Child-Pugh B/C (vs. A) cirrhosis was associated with decreased muscle, subcutaneous, and visceral fat area but increased subcutaneous/visceral fat density. Decreased normal density muscle mass was associated with mortality (hazard ratio [HR] 0.984, p <0.001) as well as visceral and subcutaneous fat density (HR 1.013 and 1.014, respectively, p <0.001). Models utilizing these features outperformed MELD alone for mortality discrimination in both the derivation and validation cohort, particularly for those with compensated cirrhosis (C-statistics of 0.74 vs. 0.58). Using competing risk analysis, we found that subcutaneous fat density was most predictive of decompensation (subdistribution HR 1.018, p = 0.0001).

Conclusion

The addition of body composition features to predictive models improves the prospective determination of prognosis in patients with cirrhosis, particularly those with compensated disease. Fat density, a novel feature, is associated with the risk of decompensation.

Lay summary

Am I at high risk of getting sicker and dying? This is the key question on the mind of patients with cirrhosis. The problem is that we have very few tools to help guide our patients, particularly if they have early cirrhosis (without symptoms like confusion or fluid in the belly). We found that how much muscle and fat the patient has and what that muscle or fat looks like on a CT scan provide helpful information. This is important because many patients have CT scans and this information is hiding in plain sight.

•

Features of body composition can predict clinical outcomes in patients with cirrhosis awaiting liver transplantation.

•

Data are lacking regarding long-term outcomes among patients with compensated disease.

•

We show that features of muscle and fat are associated with decompensation and risk of death across the spectrum of cirrhosis.

•

CT scans obtained for unrelated clinical purposes can be analyzed as a digital risk biomarker for patients with compensated cirrhosis.

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.

Fat Accumulation, Liver Fibrosis, and Metabolic Abnormalities in Chinese Patients With Moderate/Severe Versus Mild Hepatic Steatosis

Several drugs in development for nonalcoholic fatty liver disease (NAFLD) aim to decrease the amount of fat in the liver. We compared quantity and quality of fat in subcutaneous, visceral and muscle compartments, liver fibrosis, and prevalence of metabolic abnormalities between Chinese patients with moderate/severe hepatic steatosis versus those with mild hepatic steatosis. NAFLD patients were prospectively recruited from Peking University People's Hospital in Beijing, China. All patients had baseline body composition measurements using computed tomography and analytic morphomics, clinical evaluation, labs and Fibroscan® controlled attenuation parameter and liver stiffness measurement. Moderate/severe hepatic steatosis was defined as computed tomography liver attenuation of 40 Hounsfield units or less. Calorie intake and physical activity were based on self‐report. A total of 160 NAFLD patients were included (46% men, median age 47 years): 50% had normal body mass index (BMI), 24% were diabetic, and 56% had metabolic syndrome (MS). Fifty‐three (33%) had moderate/severe steatosis, of whom 19 (35.8%) had normal BMI, and the rest had mild steatosis. Patients who had moderate/severe steatosis had significantly higher BMI, waist circumference, aminotransferases, controlled attenuation parameter, liver stiffness measurement, and prevalence of MS compared to those with mild steatosis. They also had larger visceral fat area, subcutaneous fat area, and low density dorsal muscle area. In addition, their calorie intake was higher and time spent on recreation activities was shorter. Conclusion: NAFLD patients with moderate/severe steatosis, including those with normal BMI, had higher prevalence of MS and more fat in visceral, subcutaneous, and muscle compartments than those with mild steatosis. They also had more advanced liver disease. Strategies to decrease hepatic fat may benefit both liver and metabolic diseases.

Circumscribed choroidal hemangioma: Clinical features and outcomes by age category in 458 cases

Purpose

To investigate features and outcomes of circumscribed choroidal hemangioma by patient age.

Methods

Retrospective review of circumscribed choroidal hemangioma from 3/29/1967–6/4/2018 based on age at presentation (≤20 vs. >20–50 vs. >50 years).

Results

There were 458 circumscribed choroidal hemangiomas diagnosed at mean age (13 vs. 41 vs. 64 years, p < 0.001). The youngest age group had worse presenting visual acuity (20/400 vs. 20/150 vs. 20/100, p < 0.001), larger tumor basal diameter (13.5 vs. 6.6 vs. 6.2, p < 0.001), greater tumor thickness (5.8 vs. 3.1 vs. 2.9, p < 0.001), closer distance to foveola (0.5 vs. 1.4 vs. 1.2, p = 0.03), and greater extent of subretinal fluid (4 quadrants, 26% vs. 8% vs. 2%, p < 0.001). The youngest patients were less likely to be treated with primary observation (39% vs. 39% vs. 56%) or photodynamic therapy (10% vs. 27% vs. 22%) and more likely to be treated with plaque radiotherapy (26% vs. 6% vs. 3%) or external beam radiotherapy (13% vs. 1% vs. 0%) (p < 0.001). The youngest patients required greater total number of treatments (mean 4 vs. 2 vs. 1, p < 0.001). At mean follow-up (44 vs. 68 vs. 60 months, p = 0.37), the youngest patients had worse visual acuity (20/400 vs. 20/200 vs. 20/100, p = 0.03), but no difference in visual acuity loss of 3 or more Snellen lines (27% vs. 13% vs. 16%, p = 0.55).

Conclusion

Younger patients (≤20 years) with circumscribed choroidal hemangioma present with worse visual acuity and larger, more posterior tumors. Future studies are needed to improve early detection and treatment for this subgroup of patients.

Retinoblastoma regression following intra-arterial chemotherapy to the contralateral eye

Intra-arterial chemotherapy (IAC) has assumed a major role in the management of retinoblastoma. This targeted therapy involves the delivery of chemotherapy directly into the ophthalmic artery, minimizing systemic absorption. We report a case of retinoblastoma regression in the untreated eye following IAC to the contralateral eye.

Comorbidities and Nonalcoholic Fatty Liver Disease: The Chicken, the Egg, or Both?

Improvement in NAFLD may lead to improvement of metabolic syndrome, cardiovascular disease, and malignancy and vice versa.

Machine learning models to predict disease progression among veterans with hepatitis C virus

Background

Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. Clinical risk prediction models in chronic hepatitis C virus (CHC) can be challenging due to non-linear nature of disease progression. We developed and compared two ML algorithms to predict cirrhosis development in a large CHC-infected cohort using longitudinal data.

Methods and findings

We used national Veterans Health Administration (VHA) data to identify CHC patients in care between 2000–2016. The primary outcome was cirrhosis development ascertained by two consecutive aspartate aminotransferase (AST)-to-platelet ratio indexes (APRIs) > 2 after time zero given the infrequency of liver biopsy in clinical practice and that APRI is a validated non-invasive biomarker of fibrosis in CHC. We excluded those with initial APRI > 2 or pre-existing diagnosis of cirrhosis, hepatocellular carcinoma or hepatic decompensation. Enrollment was defined as the date of the first APRI. Time zero was defined as 2 years after enrollment. Cross-sectional (CS) models used predictors at or closest before time zero as a comparison. Longitudinal models used CS predictors plus longitudinal summary variables (maximum, minimum, maximum of slope, minimum of slope and total variation) between enrollment and time zero. Covariates included demographics, labs, and body mass index. Model performance was evaluated using concordance and area under the receiver operating curve (AuROC). A total of 72,683 individuals with CHC were analyzed with the cohort having a mean age of 52.8, 96.8% male and 53% white. There are 11,616 individuals (16%) who met the primary outcome over a mean follow-up of 7 years. We found superior predictive performance for the longitudinal Cox model compared to the CS Cox model (concordance 0.764 vs 0.746), and for the longitudinal boosted-survival-tree model compared to the linear Cox model (concordance 0.774 vs 0.764). The accuracy of the longitudinal models at 1,3,5 years after time zero also showed superior performance compared to the CS model, based on AuROC.

Conclusions

Boosted-survival-tree based models using longitudinal information are statistically superior to cross-sectional or linear models for predicting development of cirrhosis in CHC, though all four models were highly accurate. Similar statistical methods could be applied to predict outcomes in other non-linear chronic disease states.

Identifying and Treating Nonalcoholic Fatty Liver Disease

NAFLD improves with 7% or greater weight loss.

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.

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).

Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population

Measurements of skeletal muscle cross-sectional area, index, and radiation attenuation utilizing clinical computed tomography (CT) scans are used in assessments of sarcopenia, the loss of skeletal muscle mass and function associated with aging. To classify individuals as sarcopenic, sex-specific cutoffs for 'low' values are used. Conventionally, cutoffs for skeletal muscle measurements at the level of the third lumbar (L3) vertebra are used, however L3 is not included in several clinical CT protocols. Non-contrast-enhanced CT scans from healthy kidney donor candidates (age 18-40) at Michigan Medicine were utilized. Skeletal muscle area (SMA), index (SMI), and mean attenuation (SMRA) were measured at each vertebral level between the tenth thoracic (T10) and the fifth lumbar (L5) vertebra. Sex-specific means, standard deviations (s.d.), and sarcopenia cutoffs (mean-2 s.d.) at each vertebral level were computed. Associations between vertebral levels were assessed using Pearson correlations and Tukey's difference test. Classification agreement between different vertebral level cutoffs was assessed using overall accuracy, specificity, and sensitivity. SMA, SMI, and SMRA L3 cutoffs for sarcopenia were 92.2 cm2, 34.4 cm2/m2, and 34.3 HU in females, and 144.3 cm2, 45.4 cm2/m2, and 38.5 HU in males, consistent with previously reported cutoffs. Correlations between all level pairs were statistically significant and high, ranging from 0.65 to 0.95 (SMA), 0.64 to 0.95 (SMI), and 0.63 to 0.95 (SMRA). SMA peaks at L3, supporting its use as the primary site for CT sarcopenia measurements. However, when L3 is not available alternative levels (in order of preference) are L2, L4, L5, L1, T12, T11, and T10. Healthy reference values reported here enable sarcopenia assessment and sex-specific standardization of SMA, SMI, and SMRA in clinical populations, including those whose CT protocols do not include L3.

Body Composition Predicts Survival in Patients with Hepatocellular Carcinoma Treated with Transarterial Chemoembolization

PURPOSE

The prognosis of patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE) is often uncertain. We aimed to utilize analytic morphomics, a high-throughput imaging analysis, to assess if body composition is predictive of post-TACE survival.

MATERIALS AND METHODS

We included patients from a single center (Ann Arbor VA)who had TACE as the primary treatment forHCC and had a pre-treatment computed tomography scans. Univariate analysis and multivariate conditional inference tree analysis were utilized to identify the morphomic characteristics predictive of 1-year survival. Results were validated in an external cohort(University of MichiganHealth System) ofHCC patientswho underwent TACE as their primary treatment.

RESULTS

In the 75 patients in the derivation cohort, median survival was 439 (interquartile range, 377 to 685) days from receipt of TACE, with 1-year survival of 61%. Visceral fat density (VFD) was the only morphomic factor predictive of overall and 1-year survival (p < 0.001). Patients with VFD above the 56th percentile had a 1-year survival of 39% versus 78% for those below the 56th percentile. VFD also correlated with 1-year survival in the external validation cohort (44% vs. 72%, p < 0.001). In a secondary analysis, patients with higher VFD were significantly more likely to experience hepatic decompensation after TACE (p < 0.001).

CONCLUSION

VFD served as an objective predictor of mortality in patients undergoing TACE, possibly through its ability to predict hepatic decompensation. VFD may serve as a radiographic biomarker in predicting TACE outcomes.

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%.

Disparities in delivery of ophthalmic care; An exploration of public Medicare data

Purpose

Cataract is a major cause of age-related eye diseases in the United States, and cataract extraction is the most commonly performed surgery on Medicare beneficiaries. Analyzing the pattern in delivery of cataract care at the national level can highlight areas of disparities. We evaluated geographic disparities seen in cataract surgery delivery to Medicare beneficiaries in the United States.

Setting

Cataract extractions across the United States in 2012.

Design

Cross-sectional study examining distance to provider and observed versus expected number of cataract extractions.

Methods

Cataract extraction current procedural terminology codes were used to sum the total observed number of cataract extractions per cataract surgeon. Epidemiology data on expected number of cataract surgeries in one year by decade of life were extrapolated via a Gaussian Process model. A linear regression model was used to compare differences in delivery of care between US economic regions.

Results

2.2 million patients underwent cataract surgery in the Medicare dataset in 2012. The average distance to the nearest provider was 9.846 miles (standard deviation: 11.410 miles). This distance was statistically significant (p < 2.0 x 10⁻²²) in the New England (5.935 mi), Mideast (6.356 mi), Great Lakes (8.733 mi), Far West (9.038 mi), Southeast (9.793 mi), Southwest (12.711 mi), Plains (16.047 mi), and Rocky Mountain (17.934 mi) regions. The total number of expected cataract surgeries greater than 100 miles to the nearest cataract surgeon was 1,901, where Montana, South Dakota, and Texas each had over 200 of these expected distances.

Conclusions

A large discrepancy exists in cataract delivery to the Medicare population based on geographic factors. Patients who live in rural areas travel farther on average to see ophthalmologists, resulting in a lower observed than expected rate of cataract surgery. Our results have implications in future allocation of resources and ophthalmologists.