Exploring Definitions and Predictors of Severe Asthma Clinical Remission Post-Biologic in Adults.

RATIONALE: There is no consensus on criteria to include in an asthma remission definition in real-life. Factors associated with achieving remission post-biologic-initiation remain poorly understood. OBJECTIVES: To quantify the proportion of adults with severe asthma achieving multi-domain-defined remission post-biologic-initiation and identify pre-biologic characteristics associated with achieving remission which may be used to predict it. METHODS: This was a longitudinal cohort study using data from 23 countries from the International Severe Asthma Registry. Four asthma outcome domains were assessed in the 1-year pre- and post-biologic-initiation. A priori-defined remission cut-offs were: 0 exacerbations/year, no long-term oral corticosteroid (LTOCS), partly/well-controlled asthma, and percent predicted forced expiratory volume in one second ≥80%. Remission was defined using 2 (exacerbations + LTOCS), 3 (+control or +lung function) and 4 of these domains. The association between pre-biologic characteristics and post-biologic remission was assessed by multivariable analysis. MEASUREMENTS AND MAIN RESULTS: 50.2%, 33.5%, 25.8% and 20.3% of patients met criteria for 2, 3 (+control), 3 (+lung function) and 4-domain-remission, respectively. The odds of achieving 4-domain remission decreased by 15% for every additional 10-years asthma duration (odds ratio: 0.85; 95% CI: 0.73, 1.00). The odds of remission increased in those with fewer exacerbations/year, lower LTOCS daily dose, better control and better lung function pre-biologic-initiation. CONCLUSIONS: One in 5 patients achieved 4-domain remission within 1-year of biologic-initiation. Patients with less severe impairment and shorter asthma duration at initiation had a greater chance of achieving remission post-biologic, indicating that biologic treatment should not be delayed if remission is the goal. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Association between pre-biologic T2-biomarker combinations and response to biologics in patients with severe asthma.

Background: To date, studies investigating the association between pre-biologic biomarker levels and post-biologic outcomes have been limited to single biomarkers and assessment of biologic efficacy from structured clinical trials. Aim: To elucidate the associations of pre-biologic individual biomarker levels or their combinations with pre-to-post biologic changes in asthma outcomes in real-life. Methods: This was a registry-based, cohort study using data from 23 countries, which shared data with the International Severe Asthma Registry (May 2017-February 2023). The investigated biomarkers (highest pre-biologic levels) were immunoglobulin E (IgE), blood eosinophil count (BEC) and fractional exhaled nitric oxide (FeNO). Pre- to approximately 12-month post-biologic change for each of three asthma outcome domains (i.e. exacerbation rate, symptom control and lung function), and the association of this change with pre-biologic biomarkers was investigated for individual and combined biomarkers. Results: Overall, 3751 patients initiated biologics and were included in the analysis. No association was found between pre-biologic BEC and pre-to-post biologic change in exacerbation rate for any biologic class. However, higher pre-biologic BEC and FeNO were both associated with greater post-biologic improvement in FEV1 for both anti-IgE and anti-IL5/5R, with a trend for anti-IL4Rα. Mean FEV1 improved by 27-178 mL post-anti-IgE as pre-biologic BEC increased (250 to 1000 cells/µL), and by 43-216 mL and 129-250 mL post-anti-IL5/5R and -anti-IL4Rα, respectively along the same BEC gradient. Corresponding improvements along a FeNO gradient (25-100 ppb) were 41-274 mL, 69-207 mL and 148-224 mL for anti-IgE, anti-IL5/5R, and anti-IL4Rα, respectively. Higher baseline BEC was also associated with lower probability of uncontrolled asthma (OR 0.392; p=0.001) post-biologic for anti-IL5/5R. Pre-biologic IgE was a poor predictor of subsequent pre-to-post-biologic change for all outcomes assessed for all biologics. The combination of BEC + FeNO marginally improved the prediction of post-biologic FEV1 increase (adjusted R2: 0.751), compared to BEC (adjusted R2: 0.747) or FeNO alone (adjusted R2: 0.743) (p=0.005 and <0.001, respectively); however, this prediction was not improved by the addition of IgE. Conclusions: The ability of higher baseline BEC, FeNO and their combination to predict biologic-associated lung function improvement may encourage earlier intervention in patients with impaired lung function or at risk of accelerated lung function decline.

Falls, fracture and frailty risk in multiple sclerosis: a Mendelian Randomization study to identify shared genetics.

INTRODUCTION: Patients with multiple sclerosis (MS) commonly present musculoskeletal disorders characterized by lower bone mineral density (BMD) and muscle weakness. However, the underlying etiology remains unclear. Our objective is to identify shared pleiotropic genetic effects and estimate the causal relationship between MS and musculoskeletal disorders. MATERIALS AND METHODS: We conducted linkage disequilibrium score regression (LDSR), colocalization, and Mendelian randomization (MR) analyses using summary statistics from recent large-scale genome-wide association studies (GWAS), encompassing MS, falls, fractures, and frailty. Additional MR analyses explored the causal relationship with musculoskeletal risk factors, such as BMD, lean mass, grip strength, and vitamin D. RESULTS: We observed a moderate genetic correlation between MS and falls (RG = 0.10, P-value = 0.01) but not between MS with fracture or frailty in the LDSR analyses. MR revealed MS had no causal association with fracture and frailty but a moderate association with falls (OR: 1.004, FDR q-value = 0.018). We further performed colocalization analyses using nine SNPs that exhibited significant associations with both MS and falls in MR. Two SNPs (rs7731626 on ANKRD55 and rs701006 on OS9 gene) showed higher posterior probability of colocalization (PP.H4 = 0.927), suggesting potential pleiotropic effects between MS and falls. The nine genes are associated with central nervous system development and inflammation signaling pathways. CONCLUSION: We found potential pleiotropic genetic effects between MS and falls. However, our analysis did not reveal a causal relationship between MS and increased risks of falls, fractures, or frailty. This suggests that the musculoskeletal disorders frequently reported in MS patients in clinical studies are more likely attributed to secondary factors associated with disease progression and treatment, rather than being directly caused by MS itself.

Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system's reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient's reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.

Exploring definitions and predictors of response to biologics for severe asthma.

BACKGROUND: Biologic effectiveness is often assessed as 'response', a term which eludes consistent definition. Identifying those most likely to respond in real-life has proven challenging. OBJECTIVE: To explore definitions of biologic responders in adults with severe asthma and investigate patient characteristics associated with biologic response. METHODS: This was a longitudinal cohort study using data from 21 countries, which shared data with the International Severe Asthma Registry. Changes in 4 asthma outcome domains were assessed in the 1-year period pre- and post-biologic-initiation in patients with predefined level of pre-biologic impairment. Responder cut-offs were: ≥50% reduction in exacerbation rate, ≥50% reduction in long-term oral corticosteroid [LTOCS] daily dose, ≥1 category improvement in asthma control, and ≥100mL improvement in FEV1. Responders were defined using single- and multiple-domains. The association between pre-biologic characteristics and post-biologic-initiation response were examined by multivariable analysis. RESULTS: 2,210 patients were included. Responder rate ranged from 80.7% (n=566/701) for exacerbation-response to 10.6% (n=9/85) for 4-domain-response. Many responders still exhibited significant impairment post-biologic-initiation: 46.7% (n=206/441) of asthma control-responders with uncontrolled asthma pre-biologic still had incompletely-controlled disease post-biologic-initiation. Predictors of response were outcome-dependent. Lung function-responders were more likely to have higher pre-biologic FeNO (OR:1.20 for every 25ppb increase), and shorter asthma duration (OR:0.81, for every 10-year increase in duration). Higher BEC and presence of T2-related comorbidities were positively associated with higher odds of meeting LTOCS-, control- and lung function-responder criteria. CONCLUSION: Our findings underscore the multi-modal nature of 'response', show that many responders experience residual symptoms post-biologic-initiation, and that predictors of response vary according to outcome assessed.

Microbiome signatures associated with clinical stages of gastric Cancer: whole metagenome shotgun sequencing study.

BACKGROUND: Gastric cancer is one of the global health concerns. A series of studies on the stomach have confirmed the role of the microbiome in shaping gastrointestinal diseases. Delineation of microbiome signatures to distinguish chronic gastritis from gastric cancer will provide a non-invasive preventative and treatment strategy. In this study, we performed whole metagenome shotgun sequencing of fecal samples to enhance the detection of rare bacterial species and increase genome sequence coverage. Additionally, we employed multiple bioinformatics approaches to investigate the potential targets of the microbiome as an indicator of differentiating gastric cancer from chronic gastritis. RESULTS: A total of 65 patients were enrolled, comprising 33 individuals with chronic gastritis and 32 with gastric cancer. Within each group, the chronic gastritis group was sub-grouped into intestinal metaplasia (n = 15) and non-intestinal metaplasia (n = 18); the gastric cancer group, early stage (stages 1 and 2, n = 13) and late stage (stages 3 and 4, n = 19) cancer. No significant differences in alpha and beta diversities were detected among the patient groups. However, in a two-group univariate comparison, higher Fusobacteria abundance was identified in phylum; Fusobacteria presented higher abundance in gastric cancer (LDA scored 4.27, q = 0.041 in LEfSe). Age and sex-adjusted MaAsLin and Random Forest variable of importance (VIMP) analysis in species provided meaningful features; Bacteria_caccae was the most contributing species toward gastric cancer and late-stage cancer (beta:2.43, se:0.891, p:0.008, VIMP score:2.543). In contrast, Bifidobacterium_longum significantly contributed to chronic gastritis (beta:-1.8, se:0.699, p:0.009, VIMP score:1.988). Age, sex, and BMI-adjusted MasAsLin on metabolic pathway analysis showed that GLCMANNANAUT-PWY degradation was higher in gastric cancer and one of the contributing species was Fusobacterium_varium. CONCLUSION: Microbiomes belonging to the pathogenic phylum Fusobacteria and species Bacteroides_caccae and Streptococcus_anginosus can be significant targets for monitoring the progression of gastric cancer. Whereas Bifidobacterium_longum and Lachnospiraceae_bacterium_5_1_63FAA might be protection biomarkers against gastric cancer.

Clinical outcome of bevacizumab or ramucirumab combined with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors as the first line therapy in susceptible EGFR-mutated advanced non-small-cell lung.

Combining epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) with an anti- vascular endothelial growth factor (VEGF) agent, bevacizumab or ramucirumab, is indicated for advanced lung adenocarcinoma harboring EGFR mutation. This study aimed to show the real-world data of combination therapy and compare the effectiveness between bevacizumab and ramucirumab in combination with an EGFR-TKI. This retrospective study enrolled 47 patients diagnosed of stage IV lung adenocarcinoma with exon 19 deletion or L858R point mutation, receiving a first-line EGFR-TKI with anti-VEGF agent, including 34 (72%) and 13 (28%) patients receiving bevacizumab and ramucirumab, respectively. The response rate was similar in both groups (p = 0.38). Patients receiving bevacizumab had similar progression free survival (PFS) as those receiving ramucirumab (median PFS: 21.9 vs. 24.2 months, p = 0.4871); similar finding was noted in overall survival (OS) (median OS: 33.5 months vs. not reached, p = 0.4618). Patients receiving ramucirumab experienced a significantly high-grade hypertension compared to those receiving bevacizumab (p = 0.0351). Multivariable Cox regression analysis found independent risk factors for worse PFS included poorer ECOG performance status, multiple (≥3) metastatic sites, brain metastasis, and pleural metastasis/effusion, while the type of anti-VEGF agent was not a risk factor. Pericardial metastasis/effusion was the only one independent risk factor for worse OS. In summary, ramucirumab may have similar effectiveness as bevacizumab in combination with an EGFR-TKI as first line therapy for advanced lung adenocarcinoma harboring susceptible EGFR mutation. Further large-scale registry-based cohort studies may be needed to validate our findings.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction.

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment.

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan.

Purpose: To address the contentious data sharing across hospitals, this study adopted a novel approach, federated learning (FL), to establish an aggregate model for acute kidney injury (AKI) prediction in critically ill patients in Taiwan. Methods: This study used data from the Critical Care Database of Taichung Veterans General Hospital (TCVGH) from 2015 to 2020 and electrical medical records of the intensive care units (ICUs) between 2018 and 2020 of four referral centers in different areas across Taiwan. AKI prediction models were trained and validated thereupon. An FL-based prediction model across hospitals was then established. Results: The study included 16,732 ICU admissions from the TCVGH and 38,424 ICU admissions from the other four hospitals. The complete model with 60 features and the parsimonious model with 21 features demonstrated comparable accuracies using extreme gradient boosting, neural network (NN), and random forest, with an area under the receiver-operating characteristic (AUROC) curve of approximately 0.90. The Shapley Additive Explanations plot demonstrated that the selected features were the key clinical components of AKI for critically ill patients. The AUROC curve of the established parsimonious model for external validation at the four hospitals ranged from 0.760 to 0.865. NN-based FL slightly improved the model performance at the four centers. Conclusion: A reliable prediction model for AKI in ICU patients was developed with a lead time of 24 h, and it performed better when the novel FL platform across hospitals was implemented. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-023-00248-5.

A modified method for measuring the length of peripherally inserted central catheters to reduce the risk of malposition during catheter insertion.

Background: Malposition may occur during peripherally inserted central catheter insertion. Accurately measuring the length of a peripherally inserted central catheter is crucial to preventing malposition, including "long peripherally inserted central catheter placement," in which the tip of a peripherally inserted central catheter is deeper than the target position. The traditional method of measuring peripherally inserted central catheter length involves measuring from the insertion site to the parasternal notch and down to the third or fourth intercostal space, which may result in overestimation because of the thickness of the pectoralis major and anterior chest wall. To avoid this overestimation, the authors developed and tested a modified method for reducing long peripherally inserted central catheter placement. Methods: This study employed a retrospective design. Chest X-rays were used to examine the peripherally inserted central catheter tip positions in 48 patients in the medical intensive care unit who had undergone peripherally inserted central catheter insertion. The traditional and modified measurement methods were used to measure the peripherally inserted central catheter length in 17 and 31 patients, respectively. Fisher's exact test was used to examine between-group differences in the incidence of different types of peripherally inserted central catheter malposition. Results: The peripherally inserted central catheter tip position was near the target position in five patients (29.41%) in the traditional measurement group and 17 patients (54.84%) in the modified measurement group (p = 0.132), whereas long peripherally inserted central catheter placement occurred in six patients (35.29%) in the traditional measurement group and one patient (3.23%) in the modified measurement group (p = 0.006). However, the incidence of other types of peripherally inserted central catheter malposition did not differ significantly between the groups. Conclusions: The results of this study that the proposed modified measurement method may be able to reduce the incidence of long peripherally inserted central catheter placement among medical intensive care unit patients. The method must be further evaluated in prospective studies and studies with larger sample sizes in the future.

Pleiotropic Genetic Effects between Multiple Sclerosis and Musculoskeletal Traits.

Background: Musculoskeletal disorders were commonly reported in patients with multiple sclerosis. However, the underlying etiology linking Multiple Sclerosis (MS) and musculoskeletal disorders is not well studied. With large-scale Genome-Wide Association Studies (GWAS) publicly available, we conducted genetic correlation analysis to identify shared pleiotropic genetic effects between MS and musculoskeletal traits. We also conducted Mendelian Randomization (MR) to estimate the causal relation between MS and increased risks of musculoskeletal disorders. Methods: Linkage Disequilibrium Score Regression (LDSR) analysis was performed to estimate heritability and genetic correlation. Univariable, multivariable, and bidirectional MR analyses were conducted to estimate the causal relation. These analyses were done by utilizing the recent GWAS summary statistics of MS, fracture, frailty, falls, and several musculoskeletal risk factors, including bone mineral density, lean mass, grip strengths, and vitamin D. Results: LDSR analysis showed a moderate genetic correlation of MS with falls (RG=0.10, p=0.01) but not with fracture and frailty. Genetic variants (rs13191659) in LINC00240 gene which is associated with iron status biomarkers was found to be associated with both MS and falls. In MR analyses after excluding outlier SNPs with potential pleiotropic effects and correcting for multiple testing, MS presented no causal association with fracture and frailty but a minimal association with falls. Falls showed causally increased risks of fracture and frailty. Conclusion: Our study suggests a potential genetic correlation with shared pleiotropic genetic effects between MS and falls. However, we didn't find evidence to support the causal relation between MS and increased risks of falls, fracture, and frailty.

Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma.

OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC. METHODS: The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression. RESULTS: HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ±â€…0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis. CONCLUSIONS: Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.

Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction.

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called CancerSig to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. CancerSig established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.27% ± 6.31%, 0.81 ± 0.12, 0.80 ± 0.10, and 0.80 ± 0.06, respectively. The pan-cancer analysis of miRNA signatures suggested that three miRNAs, hsa-let-7i-3p, hsa-miR-362-3p, and hsa-miR-3651, contributed significantly toward stage prediction across 8 cancers, and each of the 67 miRNAs of the panel was a biomarker of stage prediction in more than one cancer. CancerSig may serve as the basis for cancer screening and therapeutic selection..

Pulmonary alveolar proteinosis in Taiwan.

BACKGROUND/PURPOSE: Pulmonary alveolar proteinosis (PAP) is rare disease manifested as alveolar macrophage dysfunction and abnormal accumulation of surfactant protein in the alveoli. In this nationwide, population-based study, we investigated the epidemiology of PAP in Taiwan, and discovered the comorbidities and prognostic factors of PAP. METHODS: From the National Health Insurance Research Database (NHIRD), we obtained comprehensive information about all patients of PAP in Taiwan between 1995 and 2013. The incidence, baseline characteristics comorbidities, and prognostic factors of PAP were investigated. RESULTS: The annual incidence rate of PAP was around 0.79 (range: 0.49-1.17) patients per million people after 2000, and the prevalence rate was 7.96 patients per million people by the end of 2013. In total, 276 patients of PAP were identified, including 177 (64%) and 99 (36%) patients with primary and secondary PAP, respectively. The median age of diagnosis was 53.8 years. The median survival was 9.6 years after the initial PAP diagnosis, and the 5-year survival rate was 65.96%. Twenty (7%) patients received whole lung lavage (WLL) within three months after the diagnosis had significantly better survival compared to the others. Multivariable Cox regression analyses showed that elder age, secondary PAP, and malignancy were associated with poorer survival, while WLL within 3 months of diagnosis might greatly improve the survival. CONCLUSION: We demonstrated the epidemiology of PAP in Taiwan, showing several poor prognostic factors and the potential effectiveness of WLL. Further prospective studies based on registry are warranted to improve the diagnosis and treatment of PAP.

Local Consolidative Therapy May Have Prominent Clinical Efficacy in Patients with EGFR-Mutant Advanced Lung Adenocarcinoma Treated with First-Line Afatinib.

Afatinib is an irreversible tyrosine kinase inhibitor (TKI) targeting the epidermal growth factor receptor (EGFR), which is utilized for the treatment of patients with advanced lung cancer that harbors EGFR mutations. No studies have evaluated the clinical efficacy of LCT in patients treated with first-line afatinib. In this study, we retrospectively enrolled patients with advanced lung adenocarcinomas harboring susceptible EGFR mutations who were diagnosed and treated with first-line afatinib in three hospitals. A total of 254 patients were enrolled, including 30 (12%) patients who received LCT (15 patients received definitive radiotherapy for the primary lung mass and 15 patients received curative surgery). Patients who received LCT had a significantly longer PFS than those who did not (median PFS: 32.8 vs. 14.5 months, p = 0.0008). Patients who received LCT had significantly longer OS than those who did not (median OS: 67.1 vs. 34.5 months, p = 0.0011). Multivariable analysis showed LCT was an independent prognostic factor for improved PFS (adjusted hazard ratio [aHR] [95% confidence interval (CI)]: 0.44 [0.26-0.73], p = 0.0016) and OS (aHR [95% CI]: 0.26 [0.12-0.54], p = 0.0004). The analyses using propensity score-weighting showed consistent results. We conclude that LCT may improve clinical outcomes, in terms of PFS and OS, in patients with advanced EGFR-mutant lung adenocarcinomas who are treated with first-line afatinib.

Real-world treatment pattern and prognostic factors of stage IV lung squamous cell carcinoma patients.

Lung squamous cell carcinoma (LUSC) represents a minor proportion of nonsmall cell lung cancer (NSCLC) harboring a poor prognosis. Herein, retrospective medical record research was performed to investigate real-world treatment patterns and identify the prognostic factors among LUSC patients. A total of 173 patients with a median age of 68 years were enrolled for analysis. Males were predominant (n = 143, 83%) and current or ex-smokers contributed to 78% of the entire cohort. Pleura and lung were the most common metastatic sites, whereas brain metastasis was only 7%. After diagnosis, however, only 107 patients (62%) had received first-line chemotherapy. In the chemotherapy cohort, median progression-free survival (PFS) and overall survival (OS) were 3.9 and 11.1 months, respectively. After multivariable analysis, bone metastasis and the use of first-line single-agent chemotherapy independently predicted shorter PFS. For baseline characteristics, male sex, metastasis to lung, pleura, liver, and bone independently predicted worse OS. Regarding the treatment pattern, patients who had undergone standard first-line doublet therapy and employed targeted therapies after disease progression linked to longer OS. In the real world, even those who underwent chemotherapy still had poor outcome. The findings may help clinicians to orchestrate the treatment strategies for LUSC patients and provide further direction of large-scale studies.

Survival estimation in patients with stomach and esophageal carcinoma using miRNA expression profiles.

Identifying a miRNA signature associated with survival will open a new window for developing miRNA-targeted treatment strategies in stomach and esophageal cancers (STEC). Here, using data from The Cancer Genome Atlas on 516 patients with STEC, we developed a Genetic Algorithm-based Survival Estimation method, GASE, to identify a miRNA signature that could estimate survival in patients with STEC. GASE identified 27 miRNAs as a survival miRNA signature and estimated the survival time with a mean squared correlation coefficient of 0.80 ± 0.01 and a mean absolute error of 0.44 ± 0.25 years between actual and estimated survival times, and showed a good estimation capability on an independent test cohort. The miRNAs of the signature were prioritized and analyzed to explore their roles in STEC. The diagnostic ability of the identified miRNA signature was analyzed, and identified some critical miRNAs in STEC. Further, miRNA-gene target enrichment analysis revealed the involvement of these miRNAs in various pathways, including the somatotrophic axis in mammals that involves the growth hormone and transforming growth factor beta signaling pathways, and gene ontology annotations. The identified miRNA signature provides evidence for survival-related miRNAs and their involvement in STEC, which would aid in developing miRNA-target based therapeutics.

Shared Decision-Making Facilitates Inhaler Choice in Patients with Newly-Diagnosed Chronic Obstructive Pulmonary Disease: A Multicenter Prospective Study.

Purpose: Inadequate inhaler technique and nonadherence to therapy are associated with poorer clinical outcomes in chronic obstructive pulmonary disease (COPD). Shared decision-making (SDM), based on clinical evidence, patient goals and preferences, improves quality of care. This study aims to investigate the initial patients' choices of inhaler devices in patients with newly-diagnosed COPD after an SDM process. Patients and Methods: We conducted a prospective, observational, multi-center study in four hospitals in Taiwan from December 2019 to July 2021. All treatment-naïve patients with newly-diagnosed COPD who were able to use three different inhalers of dual bronchodilators (Respimat®, Ellipta®, and Breezhaler®) in the outpatient setting were enrolled. After an SDM process, every patient was prescribed with one inhaler chosen by him- or herself. Errors of using inhalers were recorded after prescription of the inhaler, and at the follow-up visit a month later. The patients' adherence, satisfaction score, and willingness to keep the initially chosen inhaler were investigated. Results: In 109 enrolled patients, 43, 45, and 21 patients chose Respimat®, Ellipta®, and Breezhaler®, respectively. Patients chose different inhalers had similar rates of critical error on both visits, while the rates greatly decrease on the follow-up visit, no matter which inhaler devices they chose initially. The majority of patients had good adherence (use as the prescription daily, n = 79, 82%), satisfaction (satisfaction score ≥4, n = 70, 73%), and strong willingness to keep the initial inhaler (n = 89, 93%) on the follow-up visit regardless of disease severity and their comorbidities. Conclusion: SDM might facilitate inhaler choosing, reduce inhaler errors (versus baseline) with good adherence, satisfaction and strong willingness to keep the initial inhaler in patients with newly-diagnosed COPD.