RNA profiles reveal signatures of future health and disease in pregnancy.

Maternal morbidity and mortality continue to rise, and pre-eclampsia is a major driver of this burden1. Yet the ability to assess underlying pathophysiology before clinical presentation to enable identification of pregnancies at risk remains elusive. Here we demonstrate the ability of plasma cell-free RNA (cfRNA) to reveal patterns of normal pregnancy progression and determine the risk of developing pre-eclampsia months before clinical presentation. Our results centre on comprehensive transcriptome data from eight independent prospectively collected cohorts comprising 1,840 racially diverse pregnancies and retrospective analysis of 2,539 banked plasma samples. The pre-eclampsia data include 524 samples (72 cases and 452 non-cases) from two diverse independent cohorts collected 14.5 weeks (s.d., 4.5 weeks) before delivery. We show that cfRNA signatures from a single blood draw can track pregnancy progression at the placental, maternal and fetal levels and can robustly predict pre-eclampsia, with a sensitivity of 75% and a positive predictive value of 32.3% (s.d., 3%), which is superior to the state-of-the-art method2. cfRNA signatures of normal pregnancy progression and pre-eclampsia are independent of clinical factors, such as maternal age, body mass index and race, which cumulatively account for less than 1% of model variance. Further, the cfRNA signature for pre-eclampsia contains gene features linked to biological processes implicated in the underlying pathophysiology of pre-eclampsia.

Muscle Toxicity of Drugs: When Drugs Turn Physiology into Pathophysiology.

Drugs are prescribed to manage or prevent symptoms and diseases, but may sometimes cause unexpected toxicity to muscles. The symptomatology and clinical manifestations of the myotoxic reaction can vary significantly between drugs and between patients on the same drug. This poses a challenge on how to recognize and prevent the occurrence of drug-induced muscle toxicity. The key to appropriate management of myotoxicity is prompt recognition that symptoms of patients may be drug related and to be aware that inter-individual differences in susceptibility to drug-induced toxicity exist. The most prevalent and well-documented drug class with unintended myotoxicity are the statins, but even today new classes of drugs with unintended myotoxicity are being discovered. This review will start off by explaining the principles of drug-induced myotoxicity and the different terminologies used to distinguish between grades of toxicity. The main part of the review will focus on the most important pathogenic mechanisms by which drugs can cause muscle toxicity, which will be exemplified by drugs with high risk of muscle toxicity. This will be done by providing information on key clinical and laboratory aspects, muscle electromyography patterns and biopsy results, and pathological mechanism and management for a specific drug from each pathogenic classification. In addition, rather new classes of drugs with unintended myotoxicity will be highlighted. Furthermore, we will explain why it is so difficult to diagnose drug-induced myotoxicity, and which tests can be used as a diagnostic aid. Lastly, a brief description will be given of how to manage and treat drug-induced myotoxicity.

Predicting Out-of-Hospital Cardiac Arrest in the General Population Using Electronic Health Records.

BACKGROUND: The majority of out-of-hospital cardiac arrests (OHCAs) occur among individuals in the general population, for whom there is no established strategy to identify risk. In this study, we assess the use of electronic health record (EHR) data to identify OHCA in the general population and define salient factors contributing to OHCA risk. METHODS: The analytical cohort included 2366 individuals with OHCA and 23 660 age- and sex-matched controls receiving health care at the University of Washington. Comorbidities, electrocardiographic measures, vital signs, and medication prescription were abstracted from the EHR. The primary outcome was OHCA. Secondary outcomes included shockable and nonshockable OHCA. Model performance including area under the receiver operating characteristic curve and positive predictive value were assessed and adjusted for observed rate of OHCA across the health system. RESULTS: There were significant differences in demographic characteristics, vital signs, electrocardiographic measures, comorbidities, and medication distribution between individuals with OHCA and controls. In external validation, discrimination in machine learning models (area under the receiver operating characteristic curve 0.80-0.85) was superior to a baseline model with conventional cardiovascular risk factors (area under the receiver operating characteristic curve 0.66). At a specificity threshold of 99%, correcting for baseline OHCA incidence across the health system, positive predictive value was 2.5% to 3.1% in machine learning models compared with 0.8% for the baseline model. Longer corrected QT interval, substance abuse disorder, fluid and electrolyte disorder, alcohol abuse, and higher heart rate were identified as salient predictors of OHCA risk across all machine learning models. Established cardiovascular risk factors retained predictive importance for shockable OHCA, but demographic characteristics (minority race, single marital status) and noncardiovascular comorbidities (substance abuse disorder) also contributed to risk prediction. For nonshockable OHCA, a range of salient predictors, including comorbidities, habits, vital signs, demographic characteristics, and electrocardiographic measures, were identified. CONCLUSIONS: In a population-based case-control study, machine learning models incorporating readily available EHR data showed reasonable discrimination and risk enrichment for OHCA in the general population. Salient factors associated with OCHA risk were myriad across the cardiovascular and noncardiovascular spectrum. Public health and tailored strategies for OHCA prediction and prevention will require incorporation of this complexity.

Opportunities for Improving Biopsy and Non-Biopsy-Based Diagnosis of Celiac Disease.

The accumulating data regarding a non-biopsy diagnosis of celiac disease has led to its adoption in certain scenarios, although debate on whether and when to use non-biopsy criteria in clinical practice is ongoing. Despite the growing popularity and evidence basis for a biopsy-free approach to diagnosis in the context of highly elevated serologies, there will continue to be a role for a biopsy in some groups. This review summarizes the current evidence supporting a non-biopsy approach and arguments supporting continued reliance on biopsy, and focuses on opportunities to improve both approaches.

Colonoscopy vs the Fecal Immunochemical Test: Which is Best?

Although there is no debate around the effectiveness of colorectal cancer screening in reducing disease burden, there remains a question regarding the most effective and cost-effective screening modality. Current United States guidelines present a panel of options that include the 2 most commonly used modalities, colonoscopy and stool testing with the fecal immunochemical test (FIT). Large-scale comparative effectiveness trials comparing colonoscopy and FIT for colorectal cancer outcomes are underway, but results are not yet available. This review will separately state the "best case" for FIT and colonoscopy as the screening tool of first choice. In addition, the review will examine these modalities from a health economics perspective to provide the reader further context about the relative advantages of these commonly used tests.

Diagnosis and Management of Pancreatic Cysts.

As pancreatic cyst incidence rises, likely due to the ubiquitous increase in cross-sectional imaging, their management presents multiple challenges for both the practitioner and patient. It is critical that all pancreatic cysts are appropriately characterized, as treatment decisions depend on an accurate diagnosis. Diagnostic modalities such as cytology, biopsy, and cyst fluid biomarkers allow for definitive diagnosis of virtually all lesions. Some cysts, such as intraductal papillary mucinous neoplasms, mucinous cystic neoplasms, and cystic pancreatic endocrine neoplasms, have malignant potential and must be surveyed. Other cysts, such as serous cystadenomas and pancreatic fluid collections, do not have malignant potential. Surveillance strategies vary widely depending on cyst type and size and while multiple medical societies advocate surveillance, their published surveillance guidelines are heterogenous. Cysts with high-risk stigmata or worrisome features are usually resected, depending on the patient's surgical fitness. In patients unfit for resection, newer endoscopic ablative techniques are advocated. Controversial aspects regarding cyst management include whether surveillance can be stopped, how surveillance should be performed, and the extensive financial burden cyst management places on the health care system. Further study into the natural history of cystic lesions, including definitive determination of the rate of malignant transformation for each cyst type, is essential.

Past, Present, and Future of Noninvasive Tests to Assess Gluten Exposure, Celiac Disease Activity, and End-Organ Damage.

Although many biomarkers have been proposed, and several are in widespread clinical use, there is no single readout or combination of readouts that correlates tightly with gluten exposure, disease activity, or end-organ damage in treated patients with celiac disease. Challenges to developing and evaluating better biomarkers include significant interindividual variability-related to immune amplification of gluten exposure and how effects of immune activation are manifest. Furthermore, the current "gold standard" for assessment of end-organ damage, small intestinal biopsy, is itself highly imperfect, such that a marker that is a better reflection of the "ground truth" may indeed appear to perform poorly. The goal of this review was to analyze past and present efforts to establish robust noninvasive tools for monitoring treated patients with celiac disease and to highlight emerging tools that may prove to be useful in clinical practice.

Rapid Endoscopic Diagnosis of Benign Ulcerative Colorectal Diseases With an Artificial Intelligence Contextual Framework.

BACKGROUND & AIMS: Benign ulcerative colorectal diseases (UCDs) such as ulcerative colitis, Crohn's disease, ischemic colitis, and intestinal tuberculosis share similar phenotypes with different etiologies and treatment strategies. To accurately diagnose closely related diseases like UCDs, we hypothesize that contextual learning is critical in enhancing the ability of the artificial intelligence models to differentiate the subtle differences in lesions amidst the vastly divergent spatial contexts. METHODS: White-light colonoscopy datasets of patients with confirmed UCDs and healthy controls were retrospectively collected. We developed a Multiclass Contextual Classification (MCC) model that can differentiate among the mentioned UCDs and healthy controls by incorporating the tissue object contexts surrounding the individual lesion region in a scene and spatial information from other endoscopic frames (video-level) into a unified framework. Internal and external datasets were used to validate the model's performance. RESULTS: Training datasets included 762 patients, and the internal and external testing cohorts included 257 patients and 293 patients, respectively. Our MCC model provided a rapid reference diagnosis on internal test sets with a high averaged area under the receiver operating characteristic curve (image-level: 0.950 and video-level: 0.973) and balanced accuracy (image-level: 76.1% and video-level: 80.8%), which was superior to junior endoscopists (accuracy: 71.8%, P < .0001) and similar to experts (accuracy: 79.7%, P = .732). The MCC model achieved an area under the receiver operating characteristic curve of 0.988 and balanced accuracy of 85.8% using external testing datasets. CONCLUSIONS: These results enable this model to fit in the routine endoscopic workflow, and the contextual framework to be adopted for diagnosing other closely related diseases.

Autonomous Artificial Intelligence vs Artificial Intelligence-Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial.

BACKGROUND & AIMS: Artificial intelligence (AI)-based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies. However, CADx systems have not yet been validated for autonomous performance. Therefore, we conducted a trial comparing autonomous AI to AI-assisted human (AI-H) optical diagnosis. METHODS: We performed a randomized noninferiority trial of patients undergoing elective colonoscopies at 1 academic institution. Patients were randomized into (1) autonomous AI-based CADx optical diagnosis of diminutive polyps without human input or (2) diagnosis by endoscopists who performed optical diagnosis of diminutive polyps after seeing the real-time CADx diagnosis. The primary outcome was accuracy in optical diagnosis in both arms using pathology as the gold standard. Secondary outcomes included agreement with pathology for surveillance intervals. RESULTS: A total of 467 patients were randomized (238 patients/158 polyps in the autonomous AI group and 229 patients/179 polyps in the AI-H group). Accuracy for optical diagnosis was 77.2% (95% confidence interval [CI], 69.7-84.7) in the autonomous AI group and 72.1% (95% CI, 65.5-78.6) in the AI-H group (P = .86). For high-confidence diagnoses, accuracy for optical diagnosis was 77.2% (95% CI, 69.7-84.7) in the autonomous AI group and 75.5% (95% CI, 67.9-82.0) in the AI-H group. Autonomous AI had statistically significantly higher agreement with pathology-based surveillance intervals compared to AI-H (91.5% [95% CI, 86.9-96.1] vs 82.1% [95% CI, 76.5-87.7]; P = .016). CONCLUSIONS: Autonomous AI-based optical diagnosis exhibits noninferior accuracy to endoscopist-based diagnosis. Both autonomous AI and AI-H exhibited relatively low accuracy for optical diagnosis; however, autonomous AI achieved higher agreement with pathology-based surveillance intervals. (ClinicalTrials.gov, Number NCT05236790).

Effectiveness and Cost-Effectiveness of Colorectal Cancer Screening With a Blood Test That Meets the Centers for Medicare & Medicaid Services Coverage Decision.

BACKGROUND & AIMS: A blood-based colorectal cancer (CRC) screening test may increase screening participation. However, blood tests may be less effective than current guideline-endorsed options. The Centers for Medicare & Medicaid Services (CMS) covers blood tests with sensitivity of at least 74% for detection of CRC and specificity of at least 90%. In this study, we investigate whether a blood test that meets these criteria is cost-effective. METHODS: Three microsimulation models for CRC (MISCAN-Colon, CRC-SPIN, and SimCRC) were used to estimate the effectiveness and cost-effectiveness of triennial blood-based screening (from ages 45 to 75 years) compared to no screening, annual fecal immunochemical testing (FIT), triennial stool DNA testing combined with an FIT assay, and colonoscopy screening every 10 years. The CMS coverage criteria were used as performance characteristics of the hypothetical blood test. We varied screening ages, test performance characteristics, and screening uptake in a sensitivity analysis. RESULTS: Without screening, the models predicted 77-88 CRC cases and 32-36 CRC deaths per 1000 individuals, costing $5.3-$5.8 million. Compared to no screening, blood-based screening was cost-effective, with an additional cost of $25,600-$43,700 per quality-adjusted life-year gained (QALYG). However, compared to FIT, triennial stool DNA testing combined with FIT, and colonoscopy, blood-based screening was not cost-effective, with both a decrease in QALYG and an increase in costs. FIT remained more effective (+5-24 QALYG) and less costly (-$3.2 to -$3.5 million) than blood-based screening even when uptake of blood-based screening was 20 percentage points higher than uptake of FIT. CONCLUSION: Even with higher screening uptake, triennial blood-based screening, with the CMS-specified minimum performance sensitivity of 74% and specificity of 90%, was not projected to be cost-effective compared with established strategies for colorectal cancer screening.

Diagnosis of Seronegative and Ultrashort Celiac Disease.

In its conventional form, celiac disease (CeD) is characterized by both positive serology and flat villi in the duodenum, and is well known by gastroenterologists and general practitioners. The aim of this review was to shed light on 2 neglected and not yet well-defined celiac phenotypes, that is, seronegative and ultrashort CeD. Seronegative CeD can be suspected in the presence of flat villi, positive HLA-DQ2 and/or HLA-DQ8, and the absence of CeD antibodies. After ruling out other seronegative enteropathies, the diagnosis can be confirmed by both clinical and histologic improvements after 1 year of a gluten-free diet. Ultrashort CeD is characterized by the finding of flat villi in the duodenal bulb in the absence of mucosal damage in the distal duodenum and with serologic positivity. Data on the prevalence, clinical manifestations, histologic lesions, genetic features, and outcome of seronegative and ultrashort CeD are inconclusive due to the few studies available and the small number of patients diagnosed. Some additional diagnostic tools have been developed recently, such as assessing intestinal transglutaminase 2 deposits, flow cytometry technique, microRNA detection, or proteomic analysis, and they seem to be useful in the identification of complex cases. Further cooperative studies are highly desirable to improve the knowledge of these 2 still-obscure variants of CeD.

Biopsy Proteome Scoring to Determine Mucosal Remodeling in Celiac Disease.

BACKGROUND & AIMS: Histologic evaluation of gut biopsies is a cornerstone for diagnosis and management of celiac disease (CeD). Despite its wide use, the method depends on proper biopsy orientation, and it suffers from interobserver variability. Biopsy proteome measurement reporting on the tissue state can be obtained by mass spectrometry analysis of formalin-fixed paraffin-embedded tissue. Here we aimed to transform biopsy proteome data into numerical scores that give observer-independent measures of mucosal remodeling in CeD. METHODS: A pipeline using glass-mounted formalin-fixed paraffin-embedded sections for mass spectrometry-based proteome analysis was established. Proteome data were converted to numerical scores using 2 complementary approaches: a rank-based enrichment score and a score based on machine learning using logistic regression. The 2 scoring approaches were compared with each other and with histology analyzing 18 patients with CeD with biopsies collected before and after treatment with a gluten-free diet as well as biopsies from patients with CeD with varying degree of remission (n = 22). Biopsies from individuals without CeD (n = 32) were also analyzed. RESULTS: The method yielded reliable proteome scoring of both unstained and H&E-stained glass-mounted sections. The scores of the 2 approaches were highly correlated, reflecting that both approaches pick up proteome changes in the same biological pathways. The proteome scores correlated with villus height-to-crypt depth ratio. Thus, the method is able to score biopsies with poor orientation. CONCLUSIONS: Biopsy proteome scores give reliable observer and orientation-independent measures of mucosal remodeling in CeD. The proteomic method can readily be implemented by nonexpert laboratories in parallel to histology assessment and easily scaled for clinical trial settings.

Comparative Effectiveness and Cost-Effectiveness of Colorectal Cancer Screening With Blood-Based Biomarkers (Liquid Biopsy) vs Fecal Tests or Colonoscopy.

BACKGROUND & AIMS: Colorectal cancer (CRC) screening is highly effective but underused. Blood-based biomarkers (liquid biopsy) could improve screening participation. METHODS: Using our established Markov model, screening every 3 years with a blood-based test that meets minimum Centers for Medicare & Medicaid Services' thresholds (CMSmin) (CRC sensitivity 74%, specificity 90%) was compared with established alternatives. Test attributes were varied in sensitivity analyses. RESULTS: CMSmin reduced CRC incidence by 40% and CRC mortality by 52% vs no screening. These reductions were less profound than the 68%-79% and 73%-81%, respectively, achieved with multi-target stool DNA (Cologuard; Exact Sciences) every 3 years, annual fecal immunochemical testing (FIT), or colonoscopy every 10 years. Assuming the same cost as multi-target stool DNA, CMSmin cost $28,500/quality-adjusted life-year gained vs no screening, but FIT, colonoscopy, and multi-target stool DNA were less costly and more effective. CMSmin would match FIT's clinical outcomes if it achieved 1.4- to 1.8-fold FIT's participation rate. Advanced precancerous lesion (APL) sensitivity was a key determinant of a test's effectiveness. A paradigm-changing blood-based test (sensitivity >90% for CRC and 80% for APL; 90% specificity; cost ≤$120-$140) would be cost-effective vs FIT at comparable participation. CONCLUSIONS: CMSmin could contribute to CRC control by achieving screening in those who will not use established methods. Substituting blood-based testing for established effective CRC screening methods will require higher CRC and APL sensitivities that deliver programmatic benefits matching those of FIT. High APL sensitivity, which can result in CRC prevention, should be a top priority for screening test developers. APL detection should not be penalized by a definition of test specificity that focuses on CRC only.

Metabolic Dysfunction-Associated Fibrosis 5 (MAF-5) Score Predicts Liver Fibrosis Risk and Outcome in the General Population With Metabolic Dysfunction.

BACKGROUND & AIMS: There is an unmet need for noninvasive tests to improve case-finding and aid primary care professionals in referring patients at high risk of liver disease. METHODS: A metabolic dysfunction-associated fibrosis (MAF-5) score was developed and externally validated in a total of 21,797 individuals with metabolic dysfunction in population-based (National Health and Nutrition Examination Survey 2017-2020, National Health and Nutrition Examination Survey III, and Rotterdam Study) and hospital-based (from Antwerp and Bogota) cohorts. Fibrosis was defined as liver stiffness ≥8.0 kPa. Diagnostic accuracy was compared with FIB-4, nonalcoholic fatty liver disease fibrosis score (NFS), LiverRisk score and steatosis-associated fibrosis estimator (SAFE). MAF-5 was externally validated with liver stiffness measurement ≥8.0 kPa, with shear-wave elastography ≥7.5 kPa, and biopsy-proven steatotic liver disease according to Metavir and Nonalcoholic Steatohepatitis Clinical Research Network scores, and was tested for prognostic performance (all-cause mortality). RESULTS: The MAF-5 score comprised waist circumference, body mass index (calculated as kg / m2), diabetes, aspartate aminotransferase, and platelets. With this score, 60.9% was predicted at low, 14.1% at intermediate, and 24.9% at high risk of fibrosis. The observed prevalence was 3.3%, 7.9%, and 28.1%, respectively. The area under the receiver operator curve of MAF-5 (0.81) was significantly higher than FIB-4 (0.61), and outperformed the FIB-4 among young people (negative predictive value [NPV], 99%; area under the curve [AUC], 0.86 vs NPV, 94%; AUC, 0.51) and older adults (NPV, 94%; AUC, 0.75 vs NPV, 88%; AUC, 0.55). MAF-5 showed excellent performance to detect liver stiffness measurement ≥12 kPa (AUC, 0.86 training; AUC, 0.85 validation) and good performance in detecting liver stiffness and biopsy-proven liver fibrosis among the external validation cohorts. MAF-5 score >1 was associated with increased risk of all-cause mortality in (un)adjusted models (adjusted hazard ratio, 1.59; 95% CI, 1.47-1.73). CONCLUSIONS: The MAF-5 score is a validated, age-independent, inexpensive referral tool to identify individuals at high risk of liver fibrosis and all-cause mortality in primary care populations, using simple variables.

Transcriptomic-Based Microenvironment Classification Reveals Precision Medicine Strategies for Pancreatic Ductal Adenocarcinoma.

BACKGROUND & AIMS: The complex tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) has hindered the development of reliable predictive biomarkers for targeted therapy and immunomodulatory strategies. A comprehensive characterization of the TME is necessary to advance precision therapeutics in PDAC. METHODS: A transcriptomic profiling platform for TME classification based on functional gene signatures was applied to 14 publicly available PDAC datasets (n = 1657) and validated in a clinically annotated independent cohort of patients with PDAC (n = 79). Four distinct subtypes were identified using unsupervised clustering and assessed to evaluate predictive and prognostic utility. RESULTS: TME classification using transcriptomic profiling identified 4 biologically distinct subtypes based on their TME immune composition: immune enriched (IE); immune enriched, fibrotic (IE/F); fibrotic (F); and immune depleted (D). The IE and IE/F subtypes demonstrated a more favorable prognosis and potential for response to immunotherapy compared with the F and D subtypes. Most lung metastases and liver metastases were subtypes IE and D, respectively, indicating the role of clonal phenotype and immune milieu in developing personalized therapeutic strategies. In addition, distinct TMEs with potential therapeutic implications were identified in treatment-naive primary tumors compared with tumors that underwent neoadjuvant therapy. CONCLUSIONS: This novel approach defines a distinct subgroup of PADC patients that may benefit from immunotherapeutic strategies based on their TME subtype and provides a framework to select patients for prospective clinical trials investigating precision immunotherapy in PDAC. Further, the predictive utility and real-world clinical applicability espoused by this transcriptomic-based TME classification approach will accelerate the advancement of precision medicine in PDAC.

Serum identification of at-risk MASH: The metabolomics-advanced steatohepatitis fibrosis score (MASEF).

BACKGROUND: Early identification of those with NAFLD activity score ≥ 4 and significant fibrosis (≥F2) or at-risk metabolic dysfunction-associated steatohepatitis (MASH) is a priority as these patients are at increased risk for disease progression and may benefit from therapies. We developed and validated a highly specific metabolomics-driven score to identify at-risk MASH. METHODS: We included derivation (n = 790) and validation (n = 565) cohorts from international tertiary centers. Patients underwent laboratory assessment and liver biopsy for metabolic dysfunction-associated steatotic liver disease. Based on 12 lipids, body mass index, aspartate aminotransferase, and alanine aminotransferase, the MASEF score was developed to identify at-risk MASH and compared to the FibroScan-AST (FAST) score. We further compared the performance of a FIB-4 + MASEF algorithm to that of FIB-4 + liver stiffness measurements (LSM) by vibration-controlled transient elastography (VCTE). RESULTS: The diagnostic performance of the MASEF score showed an area under the receiver-operating characteristic curve, sensitivity, specificity, and positive and negative predictive values of 0.76 (95% CI 0.72-0.79), 0.69, 0.74, 0.53, and 0.85 in the derivation cohort, and 0.79 (95% CI 0.75-0.83), 0.78, 0.65, 0.48, and 0.88 in the validation cohort, while FibroScan-AST performance in the validation cohort was 0.74 (95% CI 0.68-0.79; p = 0.064), 0.58, 0.79, 0.67, and 0.73, respectively. FIB-4+MASEF showed similar overall performance compared with FIB-4 + LSM by VCTE ( p = 0.69) to identify at-risk MASH. CONCLUSION: MASEF is a promising diagnostic tool for the assessment of at-risk MASH. It could be used alternatively to LSM by VCTE in the algorithm that is currently recommended by several guidance publications.

Comparison of 6 tests for diagnosing minimal hepatic encephalopathy and predicting clinical outcome: A prospective, observational study.

BACKGROUND AND AIMS: Current guidelines recommend the assessment for minimal HE in patients with liver cirrhosis. Various efforts were made to find tools that simplify the diagnosis. Here, we compare the 6 most frequently used tests for their validity and their predictive value for overt hepatic encephalopathy (oHE), rehospitalization, and death. APPROACH AND RESULTS: One hundred thirty-two patients with cirrhosis underwent the Portosystemic Encephalopathy-Syndrome-Test yielding the psychometric hepatic encephalopathy score (PHES), Animal Naming Test (ANT), Critical Flicker Frequency (CFF), Inhibitory Control Test (ICT), EncephalApp (Stroop), and Continuous Reaction Time Test (CRT). Patients were monitored for 365 days regarding oHE development, rehospitalization, and death. Twenty-three patients showed clinical signs of HE grade 1-2 at baseline. Of the remaining 109 neurologically unimpaired patients, 35.8% had abnormal PHES and 44% abnormal CRT. Percentage of abnormal Stroop (79.8% vs. 52.3%), ANT (19.3% vs. 51.4%), ICT (28.4% vs. 36.7%), and CFF results (18.3% vs. 25.7%) changed significantly when adjusted norms were used for evaluation instead of fixed cutoffs. All test results correlated significantly with each other ( p <0.05), except for CFF. During follow-up, 24 patients developed oHE, 58 were readmitted to the hospital, and 20 died. Abnormal PHES results were linked to oHE development in the multivariable model. No other adjusted test demonstrated predictive value for any of the investigated endpoints. CONCLUSIONS: Where applicable, the diagnosis of minimal HE should be made based on adjusted norm values for the tests, exclusively. The minimal HE tests cannot be equated with one another and have an overall limited value in predicting clinical outcomes.

Combined use of the CLivD score and FIB-4 for prediction of liver-related outcomes in the population.

BACKGROUND AND AIMS: A need exists for effective and practical tools to identify individuals at increased risk of liver-related outcomes (LROs) within the general population. APPROACH AND RESULTS: We externally validated the chronic liver disease (CLivD) score for LROs in the UK Biobank cohort. We also investigated the sequential combined use of CLivD and fibrosis-4 (FIB-4) scores. Our analysis included 369,832 adults without baseline liver disease and with available data for CLivD and FIB-4 computation. LROs reflecting compensated or decompensated liver cirrhosis or HCC were ascertained through linkages with electronic health care registries. Discriminatory performance and cumulative incidence were evaluated with competing-risk methodologies. Over a 10-year follow-up, time-dependent AUC values for LRO prediction were 0.80 for CLivD lab (including gamma-glutamyltransferase), 0.72 for CLivD non-lab (excluding laboratory values), and 0.75 for FIB-4. CLivD lab demonstrated AUC values exceeding 0.85 for liver-related death and severe alcohol-associated liver outcomes. The predictive performance of FIB-4 increased with rising CLivD scores; 10-year FIB-4 AUC values ranged from 0.60 within the minimal-risk CLivD subgroup to 0.81 within the high-risk CLivD subgroup. Moreover, in the minimal-risk CLivD subgroup, the cumulative incidence of LRO varied from 0.05% to 0.3% across low-to-high FIB-4 strata. In contrast, within the high-risk CLivD subgroup, the corresponding incidence ranged from 1.7% to 21.1% (up to 33% in individuals with FIB-4 >3.25). CONCLUSIONS: The CLivD score is a valid tool for LRO risk assessment and improves the predictive performance of FIB-4. The combined use of CLivD and FIB-4 identified a subgroup where 1 in 3 individuals developed LROs within 10 years.

Cerebral Glucose Metabolism Is a Valuable Predictor of Survival in Patients with Lewy Body Diseases.

OBJECTIVE: Patients with Lewy body diseases have an increased risk of dementia, which is a significant predictor for survival. Posterior cortical hypometabolism on [18F]fluorodeoxyglucose positron emission tomography (PET) precedes the development of dementia by years. We therefore examined the prognostic value of cerebral glucose metabolism for survival. METHODS: We enrolled patients diagnosed with Parkinson's disease (PD), Parkinson's disease with dementia, or dementia with Lewy bodies who underwent [18F]fluorodeoxyglucose PET. Regional cerebral metabolism of each patient was analyzed by determining the expression of the PD-related cognitive pattern (Z-score) and by visual PET rating. We analyzed the predictive value of PET for overall survival using Cox regression analyses (age- and sex-corrected) and calculated prognostic indices for the best model. RESULTS: Glucose metabolism was a significant predictor of survival in 259 included patients (n = 118 events; hazard ratio: 1.4 [1.2-1.6] per Z-score; hazard ratio: 1.8 [1.5-2.2] per visual PET rating score; both p < 0.0001). Risk stratification with visual PET rating scores yielded a median survival of 4.8, 6.8, and 12.9 years for patients with severe, moderate, and mild posterior cortical hypometabolism (median survival not reached for normal cortical metabolism). Stratification into 5 groups based on the prognostic index revealed 10-year survival rates of 94.1%, 78.3%, 34.7%, 0.0%, and 0.0%. INTERPRETATION: Regional cerebral glucose metabolism is a significant predictor of survival in Lewy body diseases and may allow an earlier survival prediction than the clinical milestone "dementia." Thus, [18F]fluorodeoxyglucose PET may improve the basis for therapy decisions, especially for invasive therapeutic procedures like deep brain stimulation in Parkinson's disease. ANN NEUROL 2024;96:539-550.

Integrative Analyses of Colorectal Cancer Show Immunoscore Is a Stronger Predictor of Patient Survival Than Microsatellite Instability.

Microsatellite instability in colorectal cancer predicts favorable outcomes. However, the mechanistic relationship between microsatellite instability, tumor-infiltrating immune cells, Immunoscore, and their impact on patient survival remains to be elucidated. We found significant differences in mutational patterns, chromosomal instability, and gene expression that correlated with patient microsatellite instability status. A prominent immune gene expression was observed in microsatellite-instable (MSI) tumors, as well as in a subgroup of microsatellite-stable (MSS) tumors. MSI tumors had increased frameshift mutations, showed genetic evidence of immunoediting, had higher densities of Th1, effector-memory T cells, in situ proliferating T cells, and inhibitory PD1-PDL1 cells, had high Immunoscores, and were infiltrated with mutation-specific cytotoxic T cells. Multivariate analysis revealed that Immunoscore was superior to microsatellite instability in predicting patients' disease-specific recurrence and survival. These findings indicate that assessment of the immune status via Immunoscore provides a potent indicator of tumor recurrence beyond microsatellite-instability staging that could be an important guide for immunotherapy strategies.