Dataset Information

Predictive Algorithm for Hepatic Steatosis Detection Using Elastography Data in the Veterans Affairs Electronic Health Records.

ABSTRACT:

Background and aims

Nonalcoholic fatty liver disease (NAFLD) has reached pandemic proportions. Early detection can identify at-risk patients who can be linked to hepatology care. The vibration-controlled transient elastography (VCTE) controlled attenuation parameter (CAP) is biopsy validated to diagnose hepatic steatosis (HS). We aimed to develop a novel clinical predictive algorithm for HS using the CAP score at a Veterans' Affairs hospital.

Methods

We identified 403 patients in the Greater Los Angeles VA Healthcare System with valid VCTEs during 1/2018-6/2020. Patients with alcohol-associated liver disease, genotype 3 hepatitis C, any malignancies, or liver transplantation were excluded. Linear regression was used to identify predictors of NAFLD. To identify a CAP threshold for HS detection, receiver operating characteristic analysis was applied using liver biopsy, MRI, and ultrasound as the gold standards.

Results

The cohort was racially/ethnically diverse (26% Black/African American; 20% Hispanic). Significant positive predictors of elevated CAP score included diabetes, cholesterol, triglycerides, BMI, and self-identifying as Hispanic. Our predictions of CAP scores using this model strongly correlated (r = 0.61, p < 0.001) with actual CAP scores. The NAFLD model was validated in an independent Veteran cohort and yielded a sensitivity of 82% and specificity 83% (p < 0.001, 95% CI 0.46-0.81%). The estimated optimal CAP for our population cut-off was 273.5 dB/m, resulting in AUC = 75.5% (95% CI 70.7-80.3%).

Conclusion

Our HS predictive algorithm can identify at-risk Veterans for NAFLD to further risk stratify them by non-invasive tests and link them to sub-specialty care. Given the biased referral pattern for VCTEs, future work will need to address its applicability in non-specialty clinics. Proposed clinical algorithm to identify patients at-risk for NAFLD prior to fibrosis staging in Veteran.

SUBMITTER: Bangaru S

PROVIDER: S-EPMC10635943 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Predictive Algorithm for Hepatic Steatosis Detection Using Elastography Data in the Veterans Affairs Electronic Health Records.

Bangaru Saroja S Sundaresh Ram R Lee Anna A Prause Nicole N Hao Frank F Dong Tien S TS Tincopa Monica M Cholankeril George G Rich Nicole E NE Kawamoto Jenna J Bhattacharya Debika D Han Steven B SB Patel Arpan A AA Shaheen Magda M Benhammou Jihane N JN

Digestive diseases and sciences 20231021 12

<h4>Background and aims</h4>Nonalcoholic fatty liver disease (NAFLD) has reached pandemic proportions. Early detection can identify at-risk patients who can be linked to hepatology care. The vibration-controlled transient elastography (VCTE) controlled attenuation parameter (CAP) is biopsy validated to diagnose hepatic steatosis (HS). We aimed to develop a novel clinical predictive algorithm for HS using the CAP score at a Veterans' Affairs hospital.<h4>Methods</h4>We identified 403 patients in ...[more]

PMID: 37864738

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Project description:ImportanceMany veterans enrolled in the Veterans Affairs (VA) health care system have access to non-VA care through insurance and VA-purchased community care. Prior comparisons of VA and non-VA hospital outcomes have been limited to subpopulations.ObjectiveTo compare outcomes for 6 acute conditions in VA and non-VA hospitals for younger and older veterans using VA and all-payer discharge data.Design, setting, and participantsThis cohort study used a repeated cross-sectional analysis of hospitalization records for acute myocardial infarction (AMI), coronary artery bypass graft (CABG), gastrointestinal (GI) hemorrhage, heart failure (HF), pneumonia, and stroke. Participants included VA enrollees from 11 states at VA and non-VA hospitals from 2012 to 2017. Analysis was conducted from July 1, 2022, to October 18, 2023.ExposuresTreatment in VA or non-VA hospital.Main outcome and measuresThirty-day mortality, 30-day readmission, length of stay (LOS), and costs. Average treatment outcomes of VA hospitals were estimated using inverse probability weighted regression adjustment to account for selection into hospitals. Models were stratified by veterans' age (aged less than 65 years and aged 65 years and older).ResultsThere was a total of 593 578 hospitalizations and 414 861 patients with mean (SD) age 75 (12) years, 405 602 males (98%), 442 297 hospitalizations of non-Hispanic White individuals (75%) and 73 155 hospitalizations of non-Hispanic Black individuals (12%) overall. VA hospitalizations had a lower probability of 30-day mortality for HF (age ≥65 years, -0.02 [95% CI, -0.03 to -0.01]) and stroke (age <65 years, -0.03 [95% CI, -0.05 to -0.02]; age ≥65 years, -0.05 [95% CI, -0.07 to -0.03]). VA hospitalizations had a lower probability of 30-day readmission for CABG (age <65 years, -0.04 [95% CI, -0.06 to -0.01]; age ≥65 years, -0.05 [95% CI, -0.07 to -0.02]), GI hemorrhage (age <65 years, -0.04 [95% CI, -0.06 to -0.03]), HF (age <65 years, -0.05 [95% CI, -0.07 to -0.03]), pneumonia (age <65 years, -0.04 [95% CI, -0.06 to -0.03]; age ≥65 years, -0.03 [95% CI, -0.04 to -0.02]), and stroke (age <65 years, -0.11 [95% CI, -0.13 to -0.09]; age ≥65 years, -0.13 [95% CI, -0.16 to -0.10]) but higher probability of readmission for AMI (age <65 years, 0.04 [95% CI, 0.01 to 0.06]). VA hospitalizations had a longer mean LOS and higher costs for all conditions, except AMI and stroke in younger patients.Conclusions and relevanceIn this cohort study of veterans, VA hospitalizations had lower mortality for HF and stroke and lower readmissions, longer LOS, and higher costs for most conditions compared with non-VA hospitalizations with differences by condition and age group. There were tradeoffs between better outcomes and higher resource use in VA hospitals for some conditions.

Project description:BackgroundElectronic frailty indices (eFIs) can expand measurement of frailty in research and practice and have demonstrated predictive validity in associations with clinical outcomes. However, their construct validity is less well studied. We aimed to assess the construct validity of the VA-FI, an eFI developed for use in the U.S. Veterans Affairs Healthcare System.MethodsVeterans who underwent comprehensive geriatric assessments between January 31, 2019 and June 6, 2022 at VA Boston and had sufficient data documented for a comprehensive geriatric assessment-frailty index (CGA-FI) were included. The VA-FI, based on diagnostic and procedural codes, and the CGA-FI, based on geriatrician-measured deficits, were calculated for each patient. Geriatricians also assessed the Clinical Frailty Scale (CFS), functional status (ADLs and IADLs), and 4-meter gait speed (4MGS).ResultsA total of 132 veterans were included, with median age 81.4 years (IQR 75.8-88.7). Across increasing levels of VA-FI (<0.2; 0.2-0.4; >0.4), mean CGA-FI increased (0.24; 0.30; 0.40). The VA-FI was moderately correlated with the CGA-FI (r 0.45, p < 0.001). Every 0.1-unit increase in the VA-FI was associated with an increase in the CGA-FI (linear regression beta 0.05; 95% confidence interval [CI] 0.03-0.06), higher CFS category (ordinal regression OR 1.69; 95% CI 1.24-2.30), higher odds of ADL dependency (logistic regression OR 1.59; 95% CI 1.20-2.11), IADL dependency (logistic regression OR 1.68; 95% CI 1.23-2.30), and a decrease in 4MGS (linear regression beta -0.07, 95% CI -0.12 to -0.02). All models were adjusted for age and race, and associations held after further adjustment for the Charlson Comorbidity Index.ConclusionOur results demonstrate the construct validity of the VA-FI through its associations with clinical measures of frailty, including summary frailty measures, functional status, and objective physical performance. Our findings complement others' in showing that eFIs can capture functional and mobility domains of frailty beyond just comorbidity and may be useful to measure frailty among populations and individuals.

Project description:ObjectivesTo evaluate the relationship between biopsy-assessed hepatic steatosis, magnetic resonance imaging (MRI)-assessed proton density fat fraction (PDFF), and magnetic resonance elastography (MRE)-assessed liver stiffness measurement (LSM), in patients with or at risk for nonalcoholic fatty liver disease (NAFLD).MethodsA retrospective study was performed, encompassing 256 patients who had a liver biopsy and MRI/MRE examination performed within 1 year. Clinical and laboratory data were retrieved from the electronic medical record. Hepatic steatosis and fibrosis were assessed by histopathological grading/staging. First, we analyzed the diagnostic performance of PDFF for distinguishing hepatic steatosis with the receiver operating characteristic analyses. Second, variables influencing LSM were screened with univariant analyses, then identified with multivariable linear regression. Finally, the potential relationship between PDFF and LSM was assessed with linear regression after adjustment for other influencing factors, in patients with diagnosed steatosis (PDFF ≥ 5%).ResultsThe diagnostic accuracy of PDFF in distinguishing steatosis grades (S0-3) was above 0.82. No significant difference in LSM was found between patients with S1, S2, and S3 steatosis and between all steatosis grades after patients were grouped according to fibrosis stage. No statistically significant relationship was found between the LSM and PDFF (estimate = - 0.02, p = 0.065) after adjustment for fibrosis stage and age in patients with diagnosed steatosis (PDFF ≥ 5%).ConclusionsIn patients with NAFLD, the severity of hepatic steatosis has no significant influence on the liver stiffness measurement with magnetic resonance elastography.Key points• The MRI-based proton density fat fraction provides a quantitative assessment of hepatic steatosis with high accuracy. • No significant effect of hepatic steatosis on MRE-based liver stiffness measurement was found in patients with S1, S2, and S3 steatosis and between all steatosis grades after patients were grouped according to fibrosis stage. • After adjusting for fibrosis stage and age, there was no statistically significant relationship between liver stiffness and proton density fat fraction in patients with hepatic steatosis (p = 0.065).

Project description:BackgroundElectronic health record (EHR) prediction models may be easier to use in busy clinical settings since EHR data can be auto-populated into models. This study assessed whether adding functional status and/or Medicare claims data (which are often not available in EHRs) improves the accuracy of a previously developed Veterans Affairs (VA) EHR-based mortality index.MethodsThis was a retrospective cohort study of veterans aged 75 years and older enrolled in VA primary care clinics followed from January 2014 to April 2020 (n = 62,014). We randomly split participants into development (n = 49,612) and validation (n = 12,402) cohorts. The primary outcome was all-cause mortality. We performed logistic regression with backward stepwise selection to develop a 100-predictor base model using 854 EHR candidate variables, including demographics, laboratory values, medications, healthcare utilization, diagnosis codes, and vitals. We incorporated functional measures in a base + function model by adding activities of daily living (range 0-5) and instrumental activities of daily living (range 0-7) scores. Medicare data, including healthcare utilization (e.g., emergency department visits, hospitalizations) and diagnosis codes, were incorporated in a base + Medicare model. A base + function + Medicare model included all data elements. We assessed model performance with the c-statistic, reclassification metrics, fraction of new information provided, and calibration plots.ResultsIn the overall cohort, mean age was 82.6 years and 98.6% were male. At the end of follow-up, 30,263 participants (48.8%) had died. The base model c-statistic was 0.809 (95% CI 0.805-0.812) in the development cohort and 0.804 (95% CI 0.796-0.812) in the validation cohort. Validation cohort c-statistics for the base + function, base + Medicare, and base + function + Medicare models were 0.809 (95% CI 0.801-0.816), 0.811 (95% CI 0.803-0.818), and 0.814 (95% CI 0.807-0.822), respectively. Adding functional status and Medicare data resulted in similarly small improvements among other model performance measures. All models showed excellent calibration.ConclusionsIncorporation of functional status and Medicare data into a VA EHR-based mortality index led to small but likely clinically insignificant improvements in model performance.

Project description:ImportanceRecent legislation facilitates veterans' ability to receive non-Veterans Affairs (VA) surgical care. Although veterans are predominantly male, the number of women receiving care within the VA has nearly doubled to 10% over the past decade and recent data comparing the surgical care of women in VA and non-VA care settings are lacking.ObjectiveTo compare postoperative outcomes among women treated in VA hospitals vs private-sector hospitals.Design, setting, and participantsThis coarsened exact-matched cohort study across 9 noncardiac specialties in the Veterans Affairs Surgical Quality Improvement Program (VASQIP) and American College of Surgeons National Surgical Quality Improvement Program (NSQIP) took place from January 1, 2016, to December 31, 2019. Multivariable Poisson models with robust standard errors were used to evaluate the association between VA vs private-sector care settings and 30-day mortality. Hospitals participating in American College of Surgeons NSQIP and VASQIP were included. Data analysis was performed in January 2023. Participants included female patients 18 years old or older.ExposuresSurgical care in VA or private-sector hospitals.Main outcomes and measuresPostoperative 30-day mortality and failure to rescue (FTR).ResultsAmong 1 913 033 procedures analyzed, patients in VASQIP were younger (VASQIP: mean age, 49.8 [SD, 13.0] years; NSQIP: mean age, 55.9 [SD, 16.9] years; P < .001) and although most patients in both groups identified as White, there were significantly more Black women in VASQIP compared with NSQIP (29.6% vs 12.7%; P < .001). The mean risk analysis index score was lower in VASQIP (13.9 [SD, 6.4]) compared with NSQIP (16.3 [SD, 7.8]) (P < .001 for both). Patients in the VA were more likely to have a preoperative acute serious condition (2.4% vs 1.8%: P < .001), but cases in NSQIP were more frequently emergent (6.9% vs 2.6%; P < .001). The 30-day mortality, complications, and FTR were 0.2%, 3.2%, and 0.1% in VASQIP (n = 36 762 procedures) as compared with 0.8%, 5.0%, and 0.5% in NSQIP (n = 1 876 271 procedures), respectively (all P < .001). Among 1 763 540 matched women (n = 36 478 procedures in VASQIP; n = 1 727 062 procedures in NSQIP), these rates were 0.3%, 3.7%, and 0.2% in NSQIP and 0.1%, 3.4%, and 0.1% in VASQIP (all P < .01). Relative to private-sector care, VA surgical care was associated with a lower risk of death (adjusted risk ratio [aRR], 0.41; 95% CI, 0.23-0.76). This finding was robust among women undergoing gynecologic surgery, inpatient surgery, and low-physiologic stress procedures. VA surgical care was also associated with lower risk of FTR (aRR, 0.41; 95% CI, 0.18-0.92) for frail or Black women and inpatient and low-physiologic stress procedures.Conclusions and relevanceAlthough women comprise the minority of veterans receiving care within the VA, in this study, VA surgical care for women was associated with half the risk of postoperative death and FTR. The VA appears better equipped to meet the unique surgical needs and risk profiles of veterans, regardless of sex and health policy decisions, including funding, should reflect these important outcome differences.

Dataset Information

Predictive Algorithm for Hepatic Steatosis Detection Using Elastography Data in the Veterans Affairs Electronic Health Records.

Background and aims

Methods

Results

Conclusion

Publications

Predictive Algorithm for Hepatic Steatosis Detection Using Elastography Data in the Veterans Affairs Electronic Health Records.

Similar Datasets

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