Objectives: To assess the relationship of variation of blood pressure and neurological deterioration (ND) in ischemic stroke patients. Methods: We recruited patients with the fi rst-ever ischemic stroke at a teaching hospital. The National Institutes of Health Stoke Score (NIHSS) of each patient was monitored for 2 months. ND was defi ned as an increase of ≥ 2 points in NIHSS during the fi rst 7 days after stroke. Blood pressure was measured every 6 hours for fi rst 7 days. We analyzed blood pressure data in the fi rst 36 hours to study the relationship between variation of blood pressure and ND. Successive variation of systolic (svSBP) and diastolic (svDBP) blood pressure was calculated as svSBP= |SBPn+1 – SBPn | and svDBP= |DBPn+1 – DBPn | respectively. The largest svSBP in the fi rst 36 hours of hospitalization or before ND was defi ned as maximum variation of systolic blood pressure (maxvSBP). Then, the mean variation of systolic (mvSBP) and diastolic (mvDBP) blood pressure was calculated as mvSBP= svSBP/N and mvDBP= svDBP/N respectively. Results: A total of 121 patients were included in this study, and 38 of them had ND. The mvSBP was higher in the ND Group (17.9±8.4 mmHg vs. 13.7±4.4 mmHg, p=0.006) but the difference in mvDBP did not reach statistical signifi cance (9.8±3.5mmHg vs. 8.6±3.0 mmHg p=0.06). The ND Group had a larger maxvSBP (35.2±17.2 vs. 27.6±11.6 mmHg, p =0.01), which was more frequently over 30mmHg than that in the stable group (P=0.02). Conclusions: A large svSBP is associated with an increased risk for ND. The study highlights the importance of close monitoring of blood pressure in ischemic stroke patients.

PURPOSE: Epidermal growth factor receptor (EGFR) T790M mutation serves as an important predictor of osimertinib efficacy. However, little is known about how it works among patients with various timings of T790M emergence and treatment. MATERIALS AND METHODS: Advanced EGFR-mutant lung adenocarcinoma patients with positive T790M mutation in tumor were retrospectively enrolled and observed to determine the outcomes of osimertinib treatment. We evaluated the association between patients’ characteristics and the efficacy of osimertinib treatment, particularly with respect to the timing of T790M emergence and osimertinib prescription. RESULTS: A total of 91 patients were enrolled, including 14 (15.4%) with primary and 77 (84.6%) with acquired T790M mutation. The objective response rate and disease controlratewere 60.9% and 85.1%, respectively. The median progression-free survival (PFS) and overall survival were 11.5 months (95% confidence interval [CI], 9.0 to 14.0) and 30.4 months (95% CI, 11.3 to 49.5), respectively. There was no significant difference in response rate and PFS between primary and acquired T790M populations. In the acquired T790M subgroup, patientswho received osimertinib after T790M had been confirmed by rebiopsy had a longer PFS than those with intercalated treatments between rebiopsy and osimertinib prescription (14.0 months [95% CI, 9.0 to 18.9] vs. 7.2 months [95% CI, 3.7 to 10.8]; adjusted hazard ratio, 0.48 [95% CI, 0.24 to 0.98; p=0.043]). Rebiopsy timing did not influence the outcome. CONCLUSION: Osimertinib prescription with intercalated treatment following rebiopsy but not the timing of T790M emergence influenced the treatment outcome. We suggest that it is better to start osimertinib treatment once T790M mutation has been confirmed by biopsy.
Adenocarcinoma ; Biopsy ; Disease-Free Survival* ; Humans ; Lung ; Prescriptions ; Receptor, Epidermal Growth Factor ; Retrospective Studies ; Treatment Outcome

Traditional acute kidney injury (AKI) classifications, which are centered around semi-anatomical lines, can no longer capture the complexity of AKI. By employing strategies to identify predictive and prognostic enrichment targets, experts could gain a deeper comprehension of AKI’s pathophysiology, allowing for the development of treatment-specific targets and enhancing individualized care. Subphenotyping, which is enriched with AKI biomarkers, holds insights into distinct risk profiles and tailored treatment strategies that redefine AKI and contribute to improved clinical management. The utilization of biomarkers such as N-acetyl-β-D-glucosaminidase, tissue inhibitor of metalloprotease-2·insulin-like growth factor-binding protein 7, kidney injury molecule-1, and liver fatty acid-binding protein garnered significant attention as a means to predict subclinical AKI. Novel biomarkers offer promise in predicting persistent AKI, with urinary motif chemokine ligand 14 displaying significant sensitivity and specificity. Furthermore, they serve as predictive markers for weaning patients from acute dialysis and offer valuable insights into distinct AKI subgroups. The proposed management of AKI, which is encapsulated in a structured flowchart, bridges the gap between research and clinical practice. It streamlines the utilization of biomarkers and subphenotyping, promising a future in which AKI is swiftly identified and managed with unprecedented precision. Incorporating kidney biomarkers into strategies for early AKI detection and the initiation of AKI care bundles has proven to be more effective than using care bundles without these novel biomarkers. This comprehensive approach represents a significant stride toward precision medicine, enabling the identification of high-risk subphenotypes in patients with AKI.

Background/Aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

PURPOSE: The main objective of this study was to investigate the relationship among the clinical characteristics and the frequency of T790M mutation in advanced epidermal growth factor receptor (EGFR)–mutant lung adenocarcinoma patients with acquired resistance after firstline EGFR–tyrosine kinase inhibitor (TKI) treatment. MATERIALS AND METHODS: We enrolled EGFR-mutant stage IIIB-IV lung adenocarcinoma patients, who had progressed to prior EGFR-TKI therapy, and evaluated their rebiopsy EGFR mutation status. RESULTS: A total of 205 patients were enrolled for analysis. The overall T790M mutation rate of rebiopsy was 46.3%. The T790M mutation rates among patients with exon 19 deletion mutation, exon 21 L858R point mutation, and other mutations were 55.0%, 37.3%, and 27.3%, respectively. Baseline exon 19 deletion was associated with a significantly higher frequency of T790M mutation (adjusted odds ratio, 2.14; 95% confidence interval [CI], 1.20 to 3.83; p=0.010). In the exon 19 deletion subgroup, there was a greater prevalence of T790M mutation than other exon 19 deletion subtypes in patients with the Del E746-A750 mutation (61.6% vs. 40.6%; odds ratio, 2.35; 95% CI, 1.01 to 5.49; p=0.049). The progression-free survival (PFS) of first-line TKI treatment > 11 months was also associated with a higher T790M mutation rate (54.1% vs. 39.3%; adjusted odds ratio, 1.82; 95% CI, 1.02 to 3.25; p=0.044). Patients who underwent rebiopsy at metastatic sites had more chance to harbor T790M mutation (52.6% vs. 33.8%; adjusted odds ratio, 1.97; 95% CI, 1.06 to 3.67; p=0.032). CONCLUSION: PFS of first-line EGFR-TKI, rebiopsy site, EGFR exon 19 deletion and its subtype Del E746-A750 mutation are associated with the frequency of T790M mutation.
Adenocarcinoma* ; Disease-Free Survival ; Epidermal Growth Factor* ; Exons ; Humans ; Lung Neoplasms ; Lung* ; Mutation Rate ; Odds Ratio ; Phosphotransferases ; Point Mutation ; Prevalence ; Receptor, Epidermal Growth Factor* ; Sequence Deletion

Purpose: The aim of this study was to investigate the efficacy of various epidermal growth factor receptor (EGFR)–tyrosine kinase inhibitors (TKIs) plus bevacizumab in advanced EGFR-mutant lung adenocarcinoma patients. Materials and Methods: From August 2016 to October 2020, we enrolled advanced lung adenocarcinoma patients harboring exon 19 deletion or L858R receiving gefitinib, erlotinib and afatinib plus bevacizumab as the first-line treatment for the purposes of analysis. Results: A total of 36 patients were included in the final analysis. Three patients received gefitinib, 17 received erlotinib, and 16 received afatinib combined with bevacizumab as the first-line treatment. The objective response rate was 77.8%, and disease control rate was 94.4%. The overall median progression-free survival (PFS) was 16.4 months, while the median PFS was 17.1 months in patients with exon 19 deletion, and 16.2 months in patients with L858R mutation (p=0.311). Regarding the use of different EGFR-TKIs, the median PFS was 17.1 months in the erlotinib group and 21.6 months in the afatinib group (p=0.617). In patients with brain metastasis at baseline, the median PFS was 18.9 months in the erlotinib group and 16.4 months in the afatinib group (p=0.747). Amongst patients harboring exon 19 deletion, the median PFS was 16.2 months in the erlotinib group and not-reached in the afatinib group (p=0.141). In patients with L858R mutation, the median PFS was 18.9 months in the erlotinib group and 16.2 months in the afatinib group (p=0.481). Conclusion Our research demonstrates that not only erlotinib combined with bevacizumab, but also afatinib plus bevacizumab as first-line treatment, provides solid clinical efficacy in advanced EGFR-mutant lung adenocarcinoma patients.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.