Dataset Information

Liver Stiffness Measurement Can Reflect the Active Liver Necroinflammation in Population with Chronic Liver Disease: A Real-world Evidence Study.

ABSTRACT: Background and Aims: Non-invasive evaluation of liver necroinflammation in patients with chronic liver disease is an unmet need in clinical practice. The diagnostic accuracy of transient elastography-based liver stiffness measurement (LSM) for liver fibrosis could be affected by liver necroinflammation, the latter of which could intensify stiffness of the liver. Such results have prompted us to explore the diagnosis potential of LSM for liver inflammation. Methods: Three cross-sectional cohorts of liver biopsy-proven chronic liver disease patients were enrolled, including 1417 chronic hepatitis B (CHB) patients from 10 different medical centers, 106 non-alcoholic steatohepatitis patients, and 143 patients with autoimmune-related liver diseases. Another longitudinal cohort of 14 entecavir treatment patients was also included. The receiver operating characteristic (ROC) curve was employed to explore the diagnostic value of LSM. Results: In CHB patients, LSM value ascended with the increased severity of liver necroinflammation in patients with the same fibrosis stage. Such positive correlation between LSM and liver necroinflammation was also found in non-alcoholic steatohepatitis and autoimmune-related liver diseases populations. Furthermore, the ROC curve exhibited that LSM could identify moderate and severe inflammation in CHB patients (area under the ROC curve as 0.779 and 0.838) and in non-alcoholic steatohepatitis patients (area under the ROC curve as 0.826 and 0.871), respectively. Such moderate diagnostic value was also found in autoimmune-related liver diseases patients. In addition, in the longitudinal entecavir treated CHB cohort, a decline of LSM values was observed in parallel with the control of inflammatory activity in liver. Conclusions: Our study implicates a diagnostic potential of LSM to evaluate the severity of liver necroinflammation in chronic liver disease patients.

SUBMITTER: Wang L

PROVIDER: S-EPMC6943212 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Liver Stiffness Measurement Can Reflect the Active Liver Necroinflammation in Population with Chronic Liver Disease: A Real-world Evidence Study.

Wang Leijie L Zhu Mingyu M Cao Lihua L Yao Mingjie M Lu Yiwei Y Wen Xiajie X Zhang Ying Y Ning Jing J Long Huiling H Zhu Yueyong Y Hu Guoxin G Dang Shuangsuo S Fu Qingchun Q Chen Liang L Zhang Xinxin X Zhao Jingmin J Gao Zhiliang Z Nan Yuemin Y Lu Fengmin F

Journal of clinical and translational hepatology 20191211 4

Background and Aims: Non-invasive evaluation of liver necroinflammation in patients with chronic liver disease is an unmet need in clinical practice. The diagnostic accuracy of transient elastography-based liver stiffness measurement (LSM) for liver fibrosis could be affected by liver necroinflammation, the latter of which could intensify stiffness of the liver. Such results have prompted us to explore the diagnosis potential of LSM for liver inflammation. Methods: Th ...[more]

PMID: 31915600

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Project description:BackgroundProstate cancer is the most common cancer in men and second leading cause of cancer-related deaths. Changes in screening guidelines, adoption of active surveillance (AS), and implementation of high-cost technologies have changed treatment costs. Traditional cost-effectiveness studies rely on clinical trial protocols unlikely to capture actual practice behavior, and existing studies use data predating new technologies. Real-world evidence reflecting these changes is lacking.ObjectiveTo assess real-world costs of first-line prostate cancer management.Design setting and participantsWe used clinical electronic health records for 2008-2018 linked with the California Cancer Registry and the Medicare Fee Schedule to assess costs over 24 or 60 mo following diagnosis. We identified surgery or radiation treatments with structured methods, while we used both structured data and natural language processing to identify AS.Outcome measurements and statistical analysisOur results are risk-stratified calculated cost per day (CCPD) for first-line management, which are independent of treatment duration. We used the Kruskal-Wallis test to compare unadjusted CCPD while analysis of covariance log-linear models adjusted estimates for age and Charlson comorbidity.Results and limitationsIn 3433 patients, surgery (54.6%) was more common than radiation (22.3%) or AS (23.0%). Two years following diagnosis, AS ($2.97/d) was cheaper than surgery ($5.67/d) or radiation ($9.34/d) in favorable disease, while surgery ($7.17/d) was cheaper than radiation ($16.34/d) for unfavorable disease. At 5 yr, AS ($2.71/d) remained slightly cheaper than surgery ($2.87/d) and radiation ($4.36/d) in favorable disease, while for unfavorable disease surgery ($4.15/d) remained cheaper than radiation ($10.32/d). Study limitations include information derived from a single healthcare system and costs based on benchmark Medicare estimates rather than actual payment exchanges.Patient summaryActive surveillance was cheaper than surgery (-47.6%) and radiation (-68.2%) at 2 yr for favorable-risk disease, which decreased by 5 yr (-5.6% and -37.8%, respectively). Surgery was less costly than radiation for unfavorable risk for both intervals (-56.1% and -59.8%, respectively).

Project description:Introduction Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer. Methods Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available artificial intelligence engine, DARWEN (Pentavere, Ontario, Canada). For comparison, 100 of 333 patients that received systemic therapy were randomly selected and clinical data manually extracted by two trained abstractors using the same accepted gold standard feature definitions, including patient, disease characteristics, and treatment data. All cases were re-reviewed by an expert adjudicator. Accuracy and concordance between automated and manual methods are reported. Results Automated extraction required considerably less time (<1 day) than manual extraction (∼225 person-hr). The collection of demographic data (age, sex, diagnosis) was highly accurate and concordant with both methods (96%–100%). Accuracy (for either extraction approach) and concordance were lower for unstructured data elements in EHR, such as performance status, date of diagnosis, and smoking status (NLP accuracy: 88%–94%; Manual accuracy: 78%–94%; concordance: 71%–82%). Concurrent medications (86%–100%) and comorbid conditions (96%–100%), were reported with high accuracy and concordance. Treatment details were also accurately captured with both methods (84%–100%) and highly concordant (83%–99%). Detection of whether biomarker testing was performed was highly accurate and concordant (96%–98%), although detection of biomarker test results was more variable (accuracy 84%–100%, concordance 84%–99%). Features with syntactic or semantic variation requiring clinical interpretation were extracted with slightly lower accuracy by both NLP and manual review. For example, metastatic sites were more accurately identified through NLP extraction (NLP: 88%–99%; manual: 71%–100%; concordance: 70%–99%) with the exception of lung and lymph node metastases (NLP: 66%–71%; manual: 87%–92%; concordance: 58%) owing to analogous terms used in radiology reports not being included in the accepted gold standard definition. Conclusions Automated data abstraction from EHR is highly accurate and faster than manual abstraction. Key challenges include poorly structured EHR and the use of analogous terms beyond the accepted gold standard definition. The application of NLP can facilitate real-world evidence studies at a greater scale than could be achieved with manual data extraction

Dataset Information

Liver Stiffness Measurement Can Reflect the Active Liver Necroinflammation in Population with Chronic Liver Disease: A Real-world Evidence Study.

Publications

Liver Stiffness Measurement Can Reflect the Active Liver Necroinflammation in Population with Chronic Liver Disease: A Real-world Evidence Study.

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