Favorable factors for the survival of ST-segment elevation myocardial infarction patients with medium- and high-risk thrombolysis in myocardial infarction scores.

OBJECTIVE: ST-segment myocardial infarction (STEMI) is a time-sensitive emergency. This study screened the favorable factors for the survival of STEMI patients with medium- and high-risk thrombolysis in myocardial infarction (TIMI) scores. METHODS: According to the TIMI scores at admission, 433 STEMI patients were retrospectively and consecutively selected and allocated into low-/medium-/high-risk groups, with their general information/blood routine/biochemical indicators/coagulation indicators documented. The factors influencing the in-hospital survival of STEMI patients were analyzed using univariate and multivariate logistic regression analyses. Moreover, the predictive value of favorable factors was analyzed by receiver operating characteristics (ROC) curve, and patients were assigned into high/low level groups based on the cut-off value of these factors, with their in-hospital survival rates compared. RESULTS: The in-hospital survival rate of the medium-/high-risk groups was lower than that of the low-risk group. Emergency percutaneous coronary intervention (PCI), lymphocyte (LYM), total protein (TP), albumin (ALB), and sodium (Na) were independent favorable factors for in-hospital survival in the medium-/high-risk groups. Besides, LYM > 1.275 × 109/L, TP > 60.25 g/L, ALB > 34.55 g/L, and Na > 137.9 mmo1/L had auxiliary predictive value for the survival of STEMI patients with medium-/high-risk TIMI scores. Patients with high levels of LYM, TP, ALB, and Na exhibited higher in-hospital survival rates than patients with low levels. CONCLUSION: For STEMI patients with medium- and high-risk TIMI scores, accepting emergency PCI and normal levels of LYM, TP, ALB, and Na were more conducive to in-hospital survival.

Inference for covariate-adjusted time-dependent prognostic accuracy measures.

Evaluating the prognostic performance of candidate markers for future disease onset or progression is one of the major goals in medical research. A marker's prognostic performance refers to how well it separates patients at the high or low risk of a future disease state. Often the discriminative performance of a marker is affected by the patient characteristics (covariates). Time-dependent receiver operating characteristic (ROC) curves that ignore the informativeness of the covariates will lead to biased estimates of the accuracy parameters. We propose a time-dependent ROC curve that accounts for the informativeness of the covariates in the case of censored data. We propose inverse probability weighted (IPW) estimators for estimating the proposed accuracy parameters. We investigate the performance of the IPW estimators through simulation studies and real-life data analysis.

The Leucine-Rich α2-Glycoprotein-1 Levels in Patients with Multiple Myeloma.

INTRODUCTION: Angiogenesis is considered important in the pathogenesis of multiple myeloma (MM), as well as in the targeted treatment of the disease. Leucine-rich α2-glycoprotein 1 (LRG1) is a protein that participates in angiogenesis and its effect on solid organ tumors has been investigated recently. This study aimed to investigate the relationship between MM and LRG1. METHODS: The MM patients who admitted to Hatay Mustafa Kemal University Hematology Clinic between September 2021 and October 2022 were included in the study. The study consists of a total of 4 groups: newly diagnosed MM (NDMM), relapsed refractory MM (RRMM), MM in remission (Rem-MM), and control group. Demographic data were retrieved from hospital records. Blood samples of our study groups were centrifuged at 1,500 × g for 10 min and serum was collected. LRG1, IL-6, IL-8, TGF-ß1, HIF-1α, FGF-2, and VEGF levels were analyzed in all groups by ELISA method, and statistical analysis was performed. RESULTS: A total of 112 individuals, including NDMM (n: 27), RRMM (n: 18), Rem-MM (n: 42), and control group (n: 25), were enrolled in the study. Based on the analyses, the NDMM group exhibited significantly elevated levels of LRG1 (p < 0.001), TGF-1 (p < 0.001), and HIF-1α (p = 0.046, p < 0.001, and p = 0.003 compared to the RRMM, Rem-MM, and control groups, respectively) compared to the other groups. LRG1 levels were positively correlated with creatinine (r: 0.363, p = 0.001), calcium (r: 0.344, p = 0.001), total protein (r: 0.473, p < 0.001), erythrocyte sedimentation rate (r: 0.547, p < 0.001), lactate dehydrogenase (r: 0.321, p = 0.003), beta-2-microglobulin (r: 0.312, p = 0.017), IL-6 (r: 0.478, p < 0.001), IL-8 (r: 0.240, p = 0.03), TGF-ß1 (r: 0.521, p < 0.001), and HIF-1α (r: 0.321, p = 0.003) levels and were negatively correlated with hemoglobin (r: -0.512, p < 0.001) and albumin (r: -0.549, p < 0.001) levels. Receiver operating characteristics (ROC) analysis revealed the association of LRG1 with the highest AUC value of 0.959 (95% CI: 0.904-1, p < 0.001) and the optimal cut-off value of 534.95 ng/mL (sensitivity: 93% and specificity: 99%) in the NDMM group compared to the control group. CONCLUSION: In this study, providing data for the first time on LRG1 levels in the setting of MM. LRG1 levels were found to be significantly higher in NDMM patients and in our study discriminate this patient population from RRMM, Rem-MM, and normal controls. Therefore, LRG1 seems to a potential biomarker that should be evaluated in future studies addressing the diagnosis, staging, follow-up, prognosis, and treatment target of MM.

Determining a Cutoff Value for Hand Grip Strength to Predict Favorable Outcomes of Adult Spinal Deformity Surgery.

STUDY DESIGN: Retrospective review. OBJECTIVES: To establish a cutoff value for hand grip strength and predict the favorable outcomes of adult spinal deformity surgery. SUMMARY OF BACKGROUND DATA: Hand grip strength (HGS) has been suggested to predict surgical outcomes in various fields, including adult spinal deformity (ASD). However, to the best of our knowledge, no study has established a cutoff value for HGS in patients with ASD. METHODS: This study included 115 female patients who underwent reconstructive spinal surgery for ASD between September 2016 and September 2020. HGS was measured preoperatively. The Oswestry Disability Index (ODI), EuroQOL-5-dimension (EQ-5D), and visual analog scale (VAS) scores for back pain were all recorded both before and after surgery. Patients were dichotomized either into favorable or unfavorable outcome groups using an ODI cutoff score of 22 at 1 year after surgery. Multivariate logistic regression analysis was done to identify significant factors leading to favorable outcomes. A receiver operating characteristic (ROC) curve was drawn to define the cutoff value of HGS for favorable outcomes. RESULTS: Multivariate logistic regression analysis showed that HGS is significantly associated with favorable surgical outcomes in ASD (P = .031). The ROC curve suggested a cutoff value of 14.20 kg for HGS (area under the curve (AUC) = .678, P = .013) to predict favorable surgical outcomes in ASD. The surgical complications were not significantly affected by HGS. CONCLUSION: The HGS of patients with ASD can be interpreted with a cutoff value of 14.20 kg. Patients with HGS above this cutoff value showed superior surgical outcomes at 1 year after surgery compared to those below this cutoff value.

Machine Learning Risk Prediction for Incident Heart Failure in Patients With Atrial Fibrillation.

Background: Atrial fibrillation (AF) increases the risk of heart failure (HF); however, little focus is placed on the risk stratification for, and prevention of, incident HF in patients with AF. Objectives: This study aimed to construct and validate a machine learning (ML) prediction model for HF hospitalization in patients with AF. Methods: The Fushimi AF Registry is a community-based prospective survey of patients with AF in Fushimi-ku, Kyoto, Japan. We divided the data set of the registry into derivation (n = 2,383) and validation (n = 2,011) cohorts. An ML model was built to predict the incidence of HF hospitalization using the derivation cohort, and predictive ability was examined using the validation cohort. Results: HF hospitalization occurred in 606 patients (14%) during a median follow-up period of 4.4 years in the entire registry. Data of transthoracic echocardiography and biomarkers were frequently nominated as important predictive variables across all 6 ML models. The ML model based on a random forest algorithm using 7 variables (age, history of HF, creatinine clearance, cardiothoracic ratio on x-ray, left ventricular [LV] ejection fraction, LV end-systolic diameter, and LV asynergy) had high prediction performance (area under the receiver operating characteristics curve [AUC]: 0.75) and was significantly superior to the Framingham HF risk model (AUC: 0.67; P < 0.001). Based on Kaplan-Meier curves, the ML model could stratify the risk of HF hospitalization during the follow-up period (log-rank; P < 0.001). Conclusions: The ML model revealed important predictors and helped us to stratify the risk of HF, providing opportunities for the prevention of HF in patients with AF.

A Novel Inflammation-Based Risk Score Predicts Mortality in Acute Type A Aortic Dissection Surgery: The Additive Anti-inflammatory Action for Aortopathy and Arteriopathy Score.

Objective: To develop an inflammation-based risk stratification tool for operative mortality in patients with acute type A aortic dissection. Methods: Between January 1, 2016 and December 31, 2021, 3124 patients from Beijing Anzhen Hospital were included for derivation, 571 patients from the same hospital were included for internal validation, and 1319 patients from other 12 hospitals were included for external validation. The primary outcome was operative mortality according to the Society of Thoracic Surgeons criteria. Least absolute shrinkage and selection operator regression were used to identify clinical risk factors. A model was developed using different machine learning algorithms. The performance of the model was determined using the area under the receiver operating characteristic curve (AUC) for discrimination, calibration curves, and Brier score for calibration. The final model (5A score) was tested with respect to the existing clinical scores. Results: Extreme gradient boosting was selected for model training (5A score) using 12 variables for prediction-the ratio of platelet to leukocyte count, creatinine level, age, hemoglobin level, prior cardiac surgery, extent of dissection extension, cerebral perfusion, aortic regurgitation, sex, pericardial effusion, shock, and coronary perfusion-which yields the highest AUC (0.873 [95% confidence interval (CI) 0.845-0.901]). The AUC of 5A score was 0.875 (95% CI 0.814-0.936), 0.845 (95% CI 0.811-0.878), and 0.852 (95% CI 0.821-0.883) in the internal, external, and total cohort, respectively, which outperformed the best existing risk score (German Registry for Acute Type A Aortic Dissection score AUC 0.709 [95% CI 0.669-0.749]). Conclusion: The 5A score is a novel, internally and externally validated inflammation-based tool for risk stratification of patients before surgical repair, potentially advancing individualized treatment. Trial Registration: clinicaltrials.gov Identifier: NCT04918108.

High leukocyte-to-lymphocyte ratio is associated with acute relapse in multiple sclerosis patients.

BACKGROUND: Multiple sclerosis (MS) is an immune-mediated chronic disease characterized by inflammatory demyelination in the central nervous system (CNS).As there is limited evidence on whether leukocyte-to-lymphocyte ratios (LLRs) are associated with MS, we carried out an investigation on the association between LLRs and MS as favorable markers and aimed to determine the cut-off LLR for the identification of early-stage MS patients. METHODS: A matched case-control study enrolled a total of 120 MS inpatients and 120 age- and sex-matched non-MS inpatients from January 2013 to June 2018. LLRs were tested from peripheral venous blood routinely during hospitalization. Conditional logistic regression analyses were used to explore differences in LLRs between cases and controls. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic ability of LLRs and determine the best cut-off value. Disease disability was assessed using the Expanded Disability Status Scale (EDSS). RESULTS: The LLR was significantly associated with MS in hospitalized patients (OR: 2.372, 95% CI: 1.282 to 4.387, p < 0.001) after adjusting for potential confounders. The area under the curve (AUC) value was 0.793 (95% CI: 0.736 to 0.851). The cut-off value for LLR was 3.18, with sensitivity and specificity values of 62.5% (95% CI: 53.2% to 71.2%) and 88.3% (95% CI: 81.2% to 93.5%), respectively. The EDSS scores of the higher LLR group were significantly higher than the lower group. CONCLUSION: Systemic inflammation measured using LLRs may be an inflammatory marker among MS inpatients. LLRs may serve as favorable inflammatory markers with which to discriminate MS among Chinese subjects.

IgA rheumatoid factor in rheumatoid arthritis.

OBJECTIVES: Rheumatoid factor (RF) is a well-established marker for the diagnosis and classification of rheumatoid arthritis (RA). Most studies evaluated IgM RF or isotype-nonspecific total RF assays. We evaluated the added value of IgA RF in this context. METHODS: An international sample cohort consisting of samples from 398 RA patients and 1073 controls was tested for IgA RF with 3 commercial assays. For all RA patients and 100 controls essential clinical and serological data for ACR/EULAR classification were available. RESULTS: The sensitivity of IgA RF for diagnosing RA was lower than the sensitivity of IgM RF. Differences in numerical values between IgA RF assays were observed. With all assays, the highest IgA RF values were found in patients with primary Sjögren's syndrome. Double positivity for IgM RF and IgA RF had a higher specificity for RA than either IgM RF or IgA RF. The sensitivity of double positivity was lower than the sensitivity of either IgA RF or IgM RF. Single positivity for IgA RF was at least as prevalent in controls than in RA patients. Adding IgA RF to IgM RF and anti-citrullinated protein antibodies (ACPA) did not affect RA classification. However, combined positivity for IgA RF, IgM RF and IgG ACPA had a higher specificity and lower sensitivity for RA classification than positivity for either of the antibodies. CONCLUSIONS: IgA RF showed a lower sensitivity than IgM RF. Combining IgA RF with IgM RF and ACPA did not improve sensitivity of RA classification. Combined positivity (IgA-RF/IgM-RF/ACPA) increased specificity.

Real-Time Continuous Glucose Monitoring Can Predict Severe Hypoglycemia in People with Type 1 Diabetes: Combined Analysis of the HypoDE and DIAMOND Trials.

Objective: We combined data from two landmark trials (DIAMOND and HypoDE) to examine the diagnostic performance of low glucose measurements derived from open and masked continuous glucose monitoring (CGM) to predict the occurrence of future severe hypoglycemia (SH). Methods: We analyzed hypoglycemia parameters (low blood glucose index [LBGI], % <70 mg/dL, 54-69 mg/dL [level 1 hypoglycemia] and <54 mg/dL [level 2 hypoglycemia]) from masked CGM over 14 days during baseline and from open CGM over 14 days after randomization. We used receiver operating characteristics (ROC) curves to evaluate the screening performance of these measures to predict future SH. Positive likelihood ratios were calculated to indicate the overall diagnostic performance of these parameters. Results: Data from 288 individuals with type 1 diabetes (mean age 45.6 ± 12.8 years, diabetes duration 20.7 ± 13.7 years, HbA1c 8.2% ± 1.0%, Hypoglycemia Unawareness Score 3.4 ± 2.1) were analyzed. Area under ROC-curve (AUC) for LBGI and % <70 mg/dL ranged between 0.68 and 0.75, indicating that LBGI and % <70 mg/dL could significantly predict future SH. Significance of AUC regarding % <54 mg/dL were mixed (0.63-0.72). Positive and negative likelihood ratios ranged between 1.82 to 3.40 and 0.56 to 0.32, respectively. Suggested optimal cutoff values were remarkedly lower in open CGM than in masked CGM. Conclusion: These results indicate that CGM-derived hypoglycemic parameters have a good screening performance to significantly predict future clinical hypoglycemia. In addition, this analysis suggests that cutoff values to indicate elevated hypoglycemia risk in the future are substantially lower in open CGM than in masked CGM. ClinicalTrials.gov registration numbers: HypoDE: NCT02671968. DIAMOND: NCT02282397.

Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer.

Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast cancer (BrCa) subtype characterized by high mortality and high frequency of distant metastasis cases. The identification of prognostic biomarker can improve prognosis and personalized treatment regimes. Herein, we collected gene expression datasets representing TNBC and Non-TNBC BrCa. From the complete dataset, a subset reflecting solely known cancer driver genes was also constructed. Recursive Feature Elimination (RFE) was employed to identify top 20, 25, 30, 35, 40, 45, and 50 gene signatures that differentiate TNBC from the other BrCa subtypes. Five machine learning algorithms were employed on these selected features and on the basis of model performance evaluation, it was found that for the complete and driver dataset, XGBoost performs the best for a subset of 25 and 20 genes, respectively. Out of these 45 genes from the two datasets, 34 genes were found to be differentially regulated. The Kaplan-Meier (KM) analysis for Distant Metastasis Free Survival (DMFS) of these 34 differentially regulated genes revealed four genes, out of which two are novel that could be potential prognostic genes (POU2AF1 and S100B). Finally, interactome and pathway enrichment analyses were carried out to investigate the functional role of the identified potential prognostic genes in TNBC. These genes are associated with MAPK, PI3-AkT, Wnt, TGF-ß, and other signal transduction pathways, pivotal in metastasis cascade. These gene signatures can provide novel molecular-level insights into metastasis.

Evaluation of machine learning classifiers for predicting essential genes in Mycobacterium tuberculosis strains.

Accurate investigation and prediction of essential genes from bacterial genome is very important as it might be explored in effective targets for antimicrobial drugs and understanding biological mechanism of a cell. A subset of key features data obtained from 14 genome sequence-based features of 20 strains of Mycobacterium tuberculosis bacteria whose essential gene information was downloaded from ePath and NCBI database for mapping and matching essential genes by using a genome extraction program. The selection of key features was performed by using Genetic Algorithm. For each of three classifiers, 80%, 10% and 10% of subset key features were used for training, validation and testing, respectively. Experimental results (10-f-cv) illustrated that DNN (proposed), DT, and SVM achieved AUC of 0.98, 0.88 and 0.82, respectively. DNN (proposed) outperformed DT and SVM. The higher prediction accuracy of classifiers was observed because of using only key features which also justified better generalizability of classifiers and efficiency of key features related to gene essentiality. Besides, DNN (proposed) also showed best prediction performance while compared with other predictors used in previous studies. The genome extraction program was developed for mapping and matching of essential genes between ePath and NCBI database.

Determining a healthy reference range and factors potentially influencing PRO-C3 - A biomarker of liver fibrosis.

BACKGROUND & AIMS: Progressive fibrosis has been identified as the major predictor of mortality in patients with non-alcoholic fatty liver disease (NAFLD). Several biomarkers are currently being evaluated for their ability to substitute the liver biopsy as the reference standard. Recent clinical studies in NAFLD/NASH patients support the utility of PRO-C3, a marker of type III collagen formation, as a marker for the degree of fibrosis, disease activity, and effect of treatment. Here we establish the healthy reference range, optimal sample handling conditions for both short- and long-term serum storage, and robustness for the PRO-C3 assay. METHODS: PRO-C3 was measured in 269 healthy volunteers and in 222 NAFLD patients. Robustness of the PRO-C3 assay was measured according to Clinical and Laboratory Standards Institute standards and included validation of interference, precision, and reagent stability, whilst sample stability was defined for storage at different temperatures and for 3 freeze-thaw cycles. Fibrosis scoring was based on histological assessments and used as a reference for the diagnostic ability of PRO-C3 to discriminate between patients with different levels of fibrosis. RESULTS: Robustness of the PRO-C3 analysis validated by interference, precision, and reagent stability was found to be within the predefined acceptance criteria. The healthy reference range was determined to be 6.1-14.7 ng/ml. Levels of PRO-C3 were not affected by sex, age, BMI, or ethnicity. Levels of PRO-C3 were able to identify patients with clinically significant fibrosis and advanced fibrosis (AUC = 0.83 (95% CI [0.77-0.88], p <0.0001), and AUC = 0.79 (95% CI [0.73-0.85], p <0.0001), respectively). CONCLUSIONS: The assay proved to be robust and sample stability was found to comply with hospital sample handling requirements. PRO-C3 measured in samples from patients with NAFLD/NASH was diagnostic for significant and advanced liver fibrosis. LAY SUMMARY: We showed that PRO-C3 levels were stable under conditions conforming with hospital sample-handling requirements. We determined a healthy reference range and showed that PRO-C3 levels were not associated with sex, age, BMI, or ethnicity. Finally, we provide further evidence of an association of PRO-C3 with increasing liver fibrosis.

Assessment Tool of Bipolar Disorder for Primary Health Care: The SAEBD.

Mixed states are highly prevalent in patients with bipolar disorder and require comprehensive scales. Considering this, the current study aims to develop a measure to assess the full spectrum of clinical manifestations of bipolar disorder. A sample of 88 patients was evaluated; the Hamilton Depression Scale (HAM-D), Montgomery-Asberg Depression Scale (MADRS), and the Young Mania Rating Scale (YMRS) were applied, together with the preliminary version of the Scale for the Assessment of Episodes in Bipolar Disorder (SAEBD). After analyzing the appropriateness and statistical properties of the items, discriminant analysis and analysis of diagnostic capacity were performed. The discriminant functions correctly classified 100% of the cases in euthymia, predominant depressive symptoms or mixed symptoms, as well as 92.3% of the cases with predominant manic symptoms. Overall, the functions correctly classified 98.9% of the cases. The area under the curve (0.935) showed high capacity to discriminate between clinical and non-clinical cases (i.e., in euthymia). The SAEBD sensitivity was 0.95, specificity was 0.71, the Positive Predictive Value (PPV) was 0.88, the Negative Predictive Value (NPV) was 0.87, the Positive Likelihood Ratio (+LR) was 3.33, and the Negative Likelihood Ratio (-LR) was 0.07. In conclusion, the SAEBD is a promising scale that shows high reliability and validity, as well as diagnostic utility as a screening tool for use in diverse health care settings.

Personalized Prediction of Hospital Mortality in COVID-19-Positive Patients.

OBJECTIVE: To develop predictive models for in-hospital mortality and length of stay (LOS) for coronavirus disease 2019 (COVID-19)-positive patients. PATIENTS AND METHODS: We performed a multicenter retrospective cohort study of hospitalized COVID-19-positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from March 9, 2020, to May 20, 2020, who had reverse transcriptase-polymerase chain reaction-proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7, 14, and 30 days of hospitalization) and in-hospital LOS. Our final cohort was composed of 764 patients admitted to 14 different hospitals within our system. RESULTS: The median LOS was 5 (range, 1-44) days for patients admitted to the regular nursing floor and 10 (range, 1-38) days for patients admitted to the intensive care unit. Patients who died during hospitalization were older, initially admitted to the intensive care unit, and more likely to be white and have worse organ dysfunction compared with patients who survived their hospitalization. Using the 10 most important variables only, the final model's area under the receiver operating characteristics curve was 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality in the validation cohort. CONCLUSION: We developed a decision tool that can provide explainable and patient-specific prediction of in-hospital mortality and LOS for COVID-19-positive patients. The model can aid health care systems in bed allocation and distribution of vital resources.

miRNAs in lung cancer. A systematic review identifies predictive and prognostic miRNA candidates for precision medicine in lung cancer.

Lung cancer (LC) is the leading cause of cancer-related death worldwide and miRNAs play a key role in LC development. To better diagnose LC and to predict drug treatment responses we evaluated 228 articles encompassing 16,697 patients and 12,582 healthy controls. Based on the criteria of ≥3 independent studies and a sensitivity and specificity of >0.8 we found blood-borne miR-20a, miR-10b, miR-150, and miR-223 to be excellent diagnostic biomarkers for non-small cell LC whereas miR-205 is specific for squamous cell carcinoma. The systematic review also revealed 38 commonly regulated miRNAs in tumor tissue and the circulation, thus enabling the prediction of histological subtypes of LC. Moreover, theranostic biomarker candidates with proven responsiveness to checkpoint inhibitor treatments were identified, notably miR-34a, miR-93, miR-106b, miR-181a, miR-193a-3p, and miR-375. Conversely, miR-103a-3p, miR-152, miR-152-3p, miR-15b, miR-16, miR-194, miR-34b, and miR-506 influence programmed cell death-ligand 1 and programmed cell death-1 receptor expression, therefore providing a rationale for the development of molecularly targeted therapies. Furthermore, miR-21, miR-25, miR-27b, miR-19b, miR-125b, miR-146a, and miR-210 predicted response to platinum-based treatments. We also highlight controversial reports on specific miRNAs. In conclusion, we report diagnostic miRNA biomarkers for in-depth clinical evaluation. Furthermore, in an effort to avoid unnecessary toxicity we propose predictive biomarkers. The biomarker candidates support personalized treatment decisions of LC patients and await their confirmation in randomized clinical trials.

Blood Eosinophil and Risk of Exacerbation in Chronic Obstructive Pulmonary Disease Patients: A Retrospective Cohort Analysis.

Purpose: Blood eosinophil is a readily available biomarker to reflect the eosinophilic inflammation in chronic obstructive pulmonary disease (COPD) patients, yet its association with exacerbation is inconclusive. It is uncertain which measurement, eosinophil percentage or absolute eosinophil count, should be used and what is the optimal cutoff for exacerbation prediction. Patients and Methods: A total of 247 COPD patients were included in this retrospective cohort study. Blood eosinophil during stable disease state, baseline demographics, and clinical characteristics in 12 months after the index complete blood count (CBC) were recorded. Exacerbation frequencies were compared between patients with high and low blood eosinophil percentage using 2% as cut-off. Logistic regression and receiver operating characteristics (ROC) curve analyses were conducted. Results: Patients with blood eosinophil ≥2% were associated with more frequent exacerbations than patients with eosinophil <2% in the 12 months after the index CBC (mean exacerbation 1.07 vs 0.34, p < 0.001). Higher blood eosinophil percentage conferred a higher risk of exacerbation. Adjusted odds ratio for exacerbation in 12 months after the index CBC for blood eosinophil ≥2% was 2.98 (95% confidence interval = 1.42-6.25). The area under the ROC curve of eosinophil percentage was significantly higher than that of absolute eosinophil count (0.678 vs 0.640, p = 0.010). The optimal cutoff of blood eosinophil percentage for exacerbation prediction was 2.8%. Conclusion: Blood eosinophilia was associated with higher exacerbation risk in COPD patients. Further studies are required to elucidate the mechanism of eosinophilic inflammation in COPD and determine the optimal treatment strategy to reduce exacerbations.

A Preliminary Study on the Ability of the Trypsin-Like Peptidase Activity Assay Kit to Detect Periodontitis.

This study aimed to explore whether the Trypsin-Like Peptidase Activity Assay Kit (TLP-AA-Kit), which measures the activity of N-benzoyl-dl-arginine peptidase (trypsin-like peptidase), can be used as a reliable tool for periodontitis detection in population-based surveillance. In total, 105 individuals underwent a full-mouth periodontal examination and provided tongue swabs as specimens for further analyses. The results of the TLP-AA-Kit were scored between 1 and 5; higher scores indicated higher trypsin concentrations. Receiver operating characteristic analyses were used to evaluate the predictive validity of the TLP-AA-Kit, where the periodontitis case definition provided by the Centers for Disease Control/American Academy of Periodontology served as the reference. Severe and moderate periodontitis were identified in 4.8% and 16.2% of the study population, respectively. The TLP-AA-Kit showed high diagnostic accuracy for severe periodontitis, with an area under the curve of 0.93 (95% confidence interval = 0.88-0.99). However, the diagnostic accuracy of the TLP-AA-Kit for moderate/severe periodontitis was not reliable. While further studies are necessary to validate our results, the results provided herein highlight the potential of the TLP-AA-Kit as a useful tool for the detection of periodontitis, particularly in severe cases, for population-based surveillance.

Volatomic analysis identifies compounds that can stratify non-alcoholic fatty liver disease.

BACKGROUND & AIMS: Analysis of volatile organic compounds (VOCs) in exhaled breath, 'volatomics', provides opportunities for non-invasive biomarker discovery and novel mechanistic insights into a variety of diseases. The purpose of this pilot study was to compare breath VOCs in an initial cohort of patients with non-alcoholic fatty liver disease (NAFLD) and healthy controls. METHODS: Breath samples were collected from 15 participants with Child-Pugh class A NAFLD cirrhosis, 14 with non-cirrhotic NAFLD, and 14 healthy volunteers. Exhaled breath samples were collected using an established methodology and VOC profiles were analysed by gas chromatography-mass spectrometry. The levels of 19 VOCs previously associated with cirrhosis were assessed. Peaks of the VOCs were confirmed and integrated using Xcalibur® software, normalised to an internal standard. Receiver-operating characteristic (ROC) curves were used to determine the diagnostic accuracy of the candidate VOCs. RESULTS: Terpinene, dimethyl sulfide, and D-limonene provided the highest predictive accuracy to discriminate between study groups. Combining dimethyl sulfide with D-limonene led to even better discrimination of patients with NAFLD cirrhosis from healthy volunteers (AUROC 0.98; 95% CI 0.93-1.00; p <0.001) and patients with NAFLD cirrhosis from those with non-cirrhotic NAFLD (AUROC 0.91; 95% CI 0.82-1.00; p <0.001). Breath terpinene concentrations discriminated between patients with non-cirrhotic NAFLD and healthy volunteers (AUROC 0.84; 95% CI 0.68-0.99; p = 0.002). CONCLUSION: Breath terpinene, dimethyl sulfide, and D-limonene are potentially useful volatomic markers for stratifying NAFLD; in addition, a 2-stage approach enables the differentiation of patients with cirrhosis from those without. However, these observations require validation in a larger NAFLD population. (ClinicalTrials.gov Identifier: NCT02950610). LAY SUMMARY: Breath malodour has been associated with a failing liver since the ancient Greeks. Analytical chemistry has provided us an insight into ubiquitous volatile organic compounds associated with liver (and other) diseases. This has vastly improved our understanding of the mechanistic processes of liver damage. Our study aims to identify volatile organic compounds which are specific to non-alcoholic fatty liver disease and that can be exploited for rapid diagnostics.

Indian National Association for the Study of the Liver Consensus Statement on Acute Liver Failure (Part 1): Epidemiology, Pathogenesis, Presentation and Prognosis.

Acute liver failure (ALF) is an infrequent, unpredictable, potentially fatal complication of acute liver injury (ALI) consequent to varied etiologies. Etiologies of ALF as reported in the literature have regional differences, which affects the clinical presentation and natural course. In this part of the consensus article designed to reflect the clinical practices in India, disease burden, epidemiology, clinical presentation, monitoring, and prognostication have been discussed. In India, viral hepatitis is the most frequent cause of ALF, with drug-induced hepatitis due to antituberculosis drugs being the second most frequent cause. The clinical presentation of ALF is characterized by jaundice, coagulopathy, and encephalopathy. It is important to differentiate ALF from other causes of liver failure, including acute on chronic liver failure, subacute liver failure, as well as certain tropical infections which can mimic this presentation. The disease often has a fulminant clinical course with high short-term mortality. Death is usually attributable to cerebral complications, infections, and resultant multiorgan failure. Timely liver transplantation (LT) can change the outcome, and hence, it is vital to provide intensive care to patients until LT can be arranged. It is equally important to assess prognosis to select patients who are suitable for LT. Several prognostic scores have been proposed, and their comparisons show that indigenously developed dynamic scores have an edge over scores described from the Western world. Management of ALF will be described in part 2 of this document.

The use of uterine artery doppler indices for prediction of pre-eclampsia in Port-Harcourt, Nigeria.

CONTEXT: Pre-eclampsia (PrE), a clinical syndrome characterised by elevated blood pressure arising after 20 weeks of gestation, is a leading cause of maternal death worldwide. We evaluated the role of uterine artery Doppler (UtAD) in screening for PrE among unselected, pregnant women. METHODOLOGY: This was a prospective cohort study of 170 healthy gravid women between 18 and 26 weeks of gestation recruited consecutively from the Antenatal Clinic of Braithwaite Memorial Specialist Hospital, Port-Harcourt, Nigeria, between July 2016 and June 2017. All had UtAD scans with an abnormal result defined as pulsatility index (PI), resistance index or systolic/diastolic (S/D) ratio >95th centile for gestational age or proto-diastolic notching. Outcome was obtained from antenatal records. Data were analysed using Statistical Package for Social Sciences, version 20 at statistical significance level of P < 0.05. RESULTS: The prevalence of PrE was 7.6%. There was significant association between an abnormal PI (χ2 = 16.29, P = 0.00), S/D ratio (χ2 = 8.55, P = 0.00) and the combined result (χ2 = 11.5, P = 0.007) with subsequent PrE. The highest sensitivity (53.8%) was obtained for the combined result with specificity, negative predictive value (NPV) and positive predictive value of 86.6%, 95.8% and 25%, respectively, area under the curve (AUC) of 0.71 (95% confidence interval [CI]: 0.534-0.871). A normal result had a very high NPV for all indices. The accuracy for the prediction of severe PrE was greater for all indices being highest for the combined result AUC of 0.830 (95% CI: 0.624-1.000; P = 0.01). CONCLUSION: Abnormal UtAD indices were associated with PrE and may be used in PrE screening.