EstroGene2.0: A multi-omic database of response to estrogens, ER-modulators, and resistance to endocrine therapies in breast cancer.

Endocrine therapies targeting the estrogen receptor (ER/ESR1) are the cornerstone to treat ER-positive breast cancers patients, but resistance often limits their effectiveness. Understanding the molecular mechanisms is thus key to optimize the existing drugs and to develop new ER-modulators. Notable progress has been made although the fragmented way data is reported has reduced their potential impact. Here, we introduce EstroGene2.0, an expanded database of its precursor 1.0 version. EstroGene2.0 focusses on response and resistance to endocrine therapies in breast cancer models. Incorporating multi-omic profiling of 361 experiments from 212 studies across 28 cell lines, a user-friendly browser offers comprehensive data visualization and metadata mining capabilities (https://estrogeneii.web.app/). Taking advantage of the harmonized data collection, our follow-up meta-analysis revealed substantial diversity in response to different classes of ER-modulators including SERMs, SERDs, SERCA and LDD/PROTAC. Notably, endocrine resistant models exhibit a spectrum of transcriptomic alterations including a contra-directional shift in ER and interferon signaling, which is recapitulated clinically. Furthermore, dissecting multiple ESR1-mutant cell models revealed the different clinical relevance of genome-edited versus ectopic overexpression model engineering and identified high-confidence mutant-ER targets, such as NPY1R. These examples demonstrate how EstroGene2.0 helps investigate breast cancer's response to endocrine therapies and explore resistance mechanisms.

Construction and validation of a nomogram for predicting the prognosis of breast cancer patients who received adjuvant therapy: an analysis based on the SEER database.

Background: Breast cancer is the most common malignant tumor in women globally. Despite advances in primary treatment, the role of adjuvant therapy in reducing recurrence and improving survival is critical; however, there is a notable lack of tailored prognostic models for patients receiving adjuvant therapy. This study used the Surveillance, Epidemiology, and End Results (SEER) database to develop a prognostic nomogram for breast cancer patients receiving adjuvant therapy. Methods: The data of breast cancer patients who received adjuvant therapy after surgery in 2014-2015 were extracted from the SEER database. Univariate Cox regression identified significant prognostic variables that were further refined by least absolute shrinkage and selection operator (LASSO) regression and cross-validation analyses. These variables were incorporated into a multivariate Cox regression analysis to establish the predictive model. This model was visualized and validated using various statistical measures. Results: A total of 54,960 patients were included in the study, with 38,472 in the training set and 16,488 in the validation set. Age, sex, race, marital status, grade, tumor (T) stage, lymph node (N) stage, subtype, and radiotherapy were found to be significant independent risk factors of 1-, 3-, and 5-year overall survival (OS). The receiver operating characteristic curve area for 1-, 3-, and 5-year OS was >0.76 in both sets. The consistency index values were 0.768 and 0.763 for the training and validation sets, respectively. The calibration curves showed good fit, and the nomogram exhibited substantial clinical utility. Conclusions: Incorporating various significant factors, the constructed nomogram was able to effectively predict the prognosis of breast cancer patients who received adjuvant therapy. This nomogram extends understandings of complex prognosis scenarios. In addition, it could enhance personalized treatment plans and assist in patient counseling.

Establishment of a logistic regression model nomogram for clinicopathological characteristics and risk factors with axillary lymph node metastasis in T1 locally advanced breast cancer: a retrospective study.

Background: Although the research reports on locally advanced breast cancer (LABC) are increasing year by year, there are few reports on T1 LABC axillary lymph node metastasis (ALNM). By establishing a prediction model for T1 LABC ALNM, this study provides a reference value for the probability of ALNM of related patients, which helps clinicians to develop a more effective and individualized treatment plan for LABC. Methods: Cases with pathologically confirmed T1 breast cancer (BC) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were identified. Logistic regression was used to analyze the correlation between LABC lymph node metastasis and every factor, and the odds ratio (OR) and 95% confidence interval (CI) were used to identify any influencing factors. A nomogram was drawn after incorporating meaningful factors identified in multivariate logistic regression into the model. The receiver operating characteristic (ROC) curve of the model was drawn, and the area under the curve (AUC) and its 95% CI were calculated. Hosmer-Lemeshow goodness-of-fit test and clinical decision curve analysis (DCA) were performed. The results were validated in the validation group. Results: A total of 200,933 female T1 BC patients were included in this study. Univariate and multivariate logistic regression analysis of T1 BC showed that progesterone receptor (PR)-negative, race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and larger tumor diameter increased the probability of ALNM in T1 LABC. A predictive nomogram was established using the above predictors, the AUC of the modeling group was 0.739 (95% CI: 0.732-0.747), and when the AUC cut-off value was 0.026, the specificity and sensitivity of the model were 65.78% and 69.99%, respectively. Validation of the model showed that the AUC of the validation group (n=60,280) was 0.741. When all the risk factors were met, the predicted probability of N2-N3 was 50.40%. Conclusions: In this study, it was found that PR-negative, Black race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and tumor diameter increased the probability of large lymph node metastasis in T1 LABC small tumors.

Hepatitis-related adverse events associated with immune checkpoint inhibitors in cancer patients: an observational, retrospective, pharmacovigilance study using the FAERS database.

Background: Immune checkpoint inhibitors (ICIs), including anti-PD-1, anti-PD-L1 and anti-CTLA-4 antibodies, have become a standard treatment for multiple cancer types. However, ICIs can induce immune-related adverse events, with hepatitis-related adverse events (HRAEs) being of particular concern. Our objective is to identify and characterize HRAEs that exhibit a significant association with ICIs using real-world data. Methods: In this observational and retrospective pharmacovigilance study, we extracted real-world adverse events reports from the FDA Adverse Event Reporting System database spanning from the first quarter of 2004 to the first quarter of 2023. We conducted both Frequentist and Bayesian methodologies in the framework of disproportionality analysis, which included the reporting odds ratios (ROR) and information components (IC) to explore the intricate relationship between ICIs and HRAEs. Results: Through disproportionality analysis, we identified three categories of HRAEs as being significantly related with ICIs, including autoimmune hepatitis (634 cases, ROR 19.34 [95% CI 17.80-21.02]; IC025 2.43), immune-mediated hepatitis (546 cases, ROR 217.24 [189.95-248.45]; IC025 4.75), and hepatitis fulminant (80 cases, ROR 4.56 [3.65-5.70]; IC025 0.49). The median age of patients who report ICI-related HRAEs was 63 years (interquartile range [IQR] 53.8-72), with a fatal outcome observed in 24.9% (313/1,260) of these reports. Cases pertaining to skin cancer, lung cancer, and kidney cancer constituted the majority of these occurrences. Patients treated with anti-PD-1 or anti-PD-L1 antibodies exhibited a higher frequency of immune-mediated hepatitis in comparison to those undergoing anti-CTLA-4 monotherapy, with a ROR of 3.59 (95% CI 1.78-6.18). Moreover, the dual ICI therapy demonstrated higher reporting rates of ICI-related HRAEs compared to ICI monotherapy. Conclusion: Our findings confirm that ICI treatment carries a significant risk of severe HRAEs, in particular autoimmune hepatitis, immune-mediated hepatitis, and hepatitis fulminant. Healthcare providers should exercise heightened vigilance regarding these risks when managing patients receiving ICIs.

2024 Update of The Society of Thoracic Surgeons Short-Term Esophagectomy Risk Model: More Inclusive and Improved Calibration.

BACKGROUND: The Society of Thoracic Surgeons General Thoracic Surgery Database (STS-GTSD) previously reported short-term risk models for esophagectomy for esophageal cancer. We sought to update existing models using more inclusive contemporary cohorts, with consideration of additional risk factors based on clinical evidence. METHODS: The study population consisted of adult patients in the STS-GTSD who underwent esophagectomy for esophageal cancer between January 2015 and December 2022. Separate esophagectomy risk models were derived for three primary endpoints: operative mortality, major morbidity, and composite morbidity or mortality. Logistic regression with backward selection was used with predictors retained in models if p<0.10. All derived models were validated using 9-fold cross validation. Model discrimination and calibration were assessed for the overall cohort and specified subgroups. RESULTS: A total of 18,503 patients from 254 centers underwent esophagectomy for esophageal cancer. Operative mortality, morbidity, and composite morbidity or mortality rates were 3.4%, 30.5% and 30.9%, respectively. Novel predictors of short-term outcomes in the updated models included body surface area and insurance payor type. Overall discrimination was similar or superior to previous GTSD models for operative mortality [C-statistic = 0.72] and for composite morbidity or mortality [C-statistic = 0.62], Model discrimination was comparable across procedure- and demographic-specific sub-cohorts. Model calibration was excellent in all patient sub-groups. CONCLUSIONS: The newly derived esophagectomy risk models showed similar or superior performance compared to previous models, with broader applicability and clinical face validity. These models provide robust preoperative risk estimation and can be used for shared decision-making, assessment of provider performance, and quality improvement.

The survival prediction of advanced colorectal cancer received neoadjuvant therapy-a study of SEER database.

PURPOSE: The aim of study was to screen factors associated with the overall survival of colorectal cancer patients with lymph nodes metastasis who received neoadjuvant therapy and construct a nomogram model. METHODS: All enrolled subjects of the SEER database were randomly assigned to the training and testing group in a ratio of 3:2. The patients of Tangdu Hospital were seemed as validation group. Univariate cox regression analysis, lasso regression and random forest survival were used to screen variables related to the survival of advanced CRC patients received neoadjuvant therapy in the training group. Area under curves were adopted to evaluate the 1,3,5-year prediction value of the optimal model in three cohorts. Calibration curves were drawn to observe the prediction accuracy of the nomogram model. Decision curve analysis was used to assess the potential clinical value of the nomogram model. RESULTS: A total of 1833 subjects were enrolled in this study. After random allocation, 1055 cases of the SEER database served as the training group, 704 cases as the testing group and 74 patients from our center as the external validation group. Variables were screened by univariate cox regression used to construct a nomogram survival prediction model, including M, age, chemotherapy, CEA, perineural invasion, tumor size, LODDS, liver metastasis and radiation. The AUCs of the model for predicting 1-year OS in the training group, testing and validation group were 0.765 (0.703,0.827), 0.772 (0.697,0.847) and 0.742 (0.601,0.883), predicting 3-year OS were 0.761 (0.725,0.780), 0.742 (0.699,0.785), 0.733 (0.560,0.905) and 5-year OS were 0.742 (0.711,0.773), 0.746 (0.709,0.783), 0.838 (0.670,0.980), respectively. The calibration curves showed the difference between prediction probability of the model and the actual survival was not significant in three cohorts and the decision curve analysis revealed the practice clinical application value. And the prediction value of model was better for young CRC than older CRC patients. CONCLUSION: A nomogram model including LODDS for the prognosis of advanced CRC received neoadjuvant therapy was constructed and verified based on the SEER database and single center practice. The accuracy and potential clinical application value of the model performed well, and the model had better predictive value for EOCRC than LOCRC.

Predicting surgical outcomes of acute diffuse peritonitis: Updated risk models based on real-world clinical data.

Aim: The existing predictive risk models for the surgical outcome of acute diffused peritonitis (ADP) need renovation by adding relevant variables such as ADP's definition or causative etiology to pursue outstanding data collection reflecting the real world. We aimed to revise the risk models predicting mortality and morbidities of ADP using the latest Japanese Nationwide Clinical Database (NCD) variable set. Methods: Clinical dataset of ADP patients who underwent surgery, and registered in the NCD between 2016 and 2019, were used to develop a risk model for surgical outcomes. The primary outcome was perioperative mortality. Results: After data cleanup, 45 379 surgical cases for ADP were derived for analysis. The perioperative and 30-day mortality were 10.6% and 7.2%, respectively. The prediction models have been created for the mortality and 10 morbidities associated with the mortality. The top five relevant predictors for perioperative mortality were age >80, advanced cancer with multiple metastases, platelet count of <50 000/mL, serum albumin of <2.0 g/dL, and unknown ADP site. The C-indices of perioperative and 30-day mortality were 0.859 and 0.857, respectively. The predicted value calculated with the risk models for mortality was highly fitted with the actual probability from the lower to the higher risk groups. Conclusions: Risk models for postoperative mortality and morbidities with good predictive performance and reliability were revised and validated using the recent real-world clinical dataset. These models help to predict ADP surgical outcomes accurately and are available for clinical settings.

'X' marks the spot! Utilising factor Xa inhibitors to optimise thromboprophylaxis in multiple myeloma.

Venous thromboembolism (VTE) remains a significant cause of morbidity and mortality among multiple myeloma patients. Chang and colleagues' findings indicate that factor Xa inhibitors are as effective as warfarin in preventing VTE without raising the risk of gastrointestinal or intracranial bleeding complications. Commentary on: Chang et al. The comparative efficacy and safety of factor Xa inhibitors and warfarin for primary thromboprophylaxis in multiple myeloma patients undergoing immunomodulatory therapy. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19612.

A DPC Database Study on the Safety of Atezolizumab/Bevacizumab/Carboplatin/Paclitaxel in Non-small Cell Lung Cancer in Japanese Patients.

INTRODUCTION: Atezolizumab, bevacizumab, carboplatin, and paclitaxel (ABCP) combination therapy is a standard of care for advanced non-squamous non-small cell lung cancer (NSQ-NSCLC); however, the lack of safety data limits its clinical application in Japan. METHODS: This study compared the safety of ABCP with that of bevacizumab, carboplatin, and paclitaxel (BCP) combination for the treatment of advanced NSQ-NSCLC in Japanese patients by evaluating the clinical background and incidence of adverse events (AEs) based on data extracted from the Diagnosis Procedure Combination (DPC) database. Incidence rates and restricted mean survival times (RMSTs) for up to 1 year were analyzed for 19 clinically important AEs. Covariates were adjusted using the inverse probability weighting method. RESULTS: A search conducted using the International Statistical Classification of Diseases and Related Health Problems 10th Revision codes identified 350,987 patients, of whom 202 were included in the ABCP cohort and 232 in the BCP cohort. Among the 19 AEs, the incidence of skin disorder and febrile neutropenia (FN) was significantly higher in the ABCP cohort versus the BCP cohort. The adjusted incidence rate ratios were 2.65 [95% confidence interval (CI) 1.43-4.91] for skin disorder and 1.70 (95% CI 1.01-2.85) for FN. The adjusted RMST differences were - 64.2 days (95% CI - 93.0 to - 35.4 days) and - 46.0 days (95% CI - 73.5 to - 18.5 days) for skin disorder and FN, respectively. These results were comparable to those of other pivotal clinical trials. CONCLUSIONS: The findings of this DPC database study highlight the safety of ABCP in Japanese clinical practice, and this methodology may facilitate more efficient research in real-world settings. TRIAL REGISTRATION: UMIN Clinical Trials Registry ID UMIN000041507.

A DPC Database Study on the Safety of Atezolizumab/Carboplatin/Etoposide in Extensive-Disease Small Cell Lung Cancer in Japanese Patients.

INTRODUCTION: Atezolizumab, carboplatin, and etoposide (ACE) therapy is a standard of care for extensive-disease small cell lung cancer (SCLC); however, its safety data are scarce, limiting generalization to the Japanese population. METHODS: This study aimed to compare the safety of ACE versus carboplatin and etoposide (CE) therapies in Japanese patients using the Diagnosis Procedure Combination (DPC) database by comparing the incidence of adverse events (AEs). Retrospective data on clinical background and AEs were extracted from the DPC database. Incidence rates and restricted mean survival times (RMSTs) up to 6 months were analyzed for 19 clinically important AEs. Covariates were adjusted using the inverse probability weighting method. RESULTS: A total of 330,774 patients were identified using the International Statistical Classification of Diseases and Related Health Problems 10th Revision codes, of whom 277 were included in the ACE cohort and 478 in the CE cohort. Among the 19 AEs, the incidence of skin disorder and thyroid dysfunction was significantly higher in the ACE cohort compared with the CE cohort. The adjusted incidence rate ratios were 2.38 (95% confidence interval [CI] 1.04-5.43) for skin disorder and 6.92 (95% CI 2.00-23.89) for thyroid dysfunction. The adjusted RMST differences were - 8.2 days (95% CI - 16.0 to - 0.4 days) for skin disorder and - 8.8 days (95% CI - 15.7 to - 1.9 days) for thyroid dysfunction. CONCLUSIONS: This study provides evidence regarding the safety of ACE combination therapy in Japanese clinical practice using the DPC database, with results comparable to those reported in pivotal clinical trials. TRIAL REGISTRATION: UMIN Clinical Trials Registry ID UMIN000041508.

Making MS Omics Data ML-Ready: SpeCollate Protocols.

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts. The first part describes the data analysis needed for MS-based experiments and a general introduction to our deep learning model SpeCollate-which we will use throughout the chapter for illustration. The second part of the chapter explores the transformation of MS data for inference, providing a step-by-step guide for users to deduce peptides from their MS data. This section aims to bridge the gap between data acquisition and practical applications by detailing the necessary steps for data preparation and interpretation. In the final part, we present a demonstrative example of SpeCollate, a deep learning-based peptide database search engine that overcomes the problems of simplistic simulation of theoretical spectra and heuristic scoring functions for peptide-spectrum matches by generating joint embeddings for spectra and peptides. SpeCollate is a user-friendly tool with an intuitive command-line interface to perform the search, showcasing the effectiveness of the techniques and methodologies discussed in the earlier sections and highlighting the potential of machine learning in the context of mass spectrometry data analysis. By offering a comprehensive overview of data transformation, inference, and ML model applications for mass spectrometry, this chapter aims to empower researchers and practitioners in leveraging the power of machine learning to unlock novel insights and drive innovation in the field of mass spectrometry-based omics.

Unveiling the untreated: development of a database algorithm to identify potential Fabry disease patients in Germany.

BACKGROUND: Fabry disease (FD), an X-linked lysosomal storage disorder, is caused by mutations in the gene encoding α-galactosidase A, resulting in lysosomal accumulation of globotriaosylceramide and other glycosphingolipids. Early detection of FD is challenging, accounting for delayed diagnosis and treatment initiation. This study aimed to develop an algorithm using a logistic regression model to facilitate early identification of patients based on ICD-10-GM coding using a German Sickness Fund Database. METHODS: The logistic regression model was fitted on a binary outcome variable based on either a treated FD cohort or a control cohort (without FD). Comorbidities specific to the involved organs were used as covariates to identify potential FD patients with ICD-10-GM E75.2 diagnosis but without any FD-specific medication. Specificity and sensitivity of the model were optimized to determine a likely threshold. The cut-point with the largest values for the Youden index and concordance probability method and the lowest value for closest to (0,1) was identified as 0.08 for each respective value. The sensitivity and specificity for this cut-point were 80.4% and 79.8%, respectively. Additionally, a sensitivity analysis of the potential FD patients with at least two codes of E75.2 diagnoses was performed. RESULTS: A total of 284 patients were identified in the potential FD cohort using the logistic regression model. Most potential FD patients were < 30 years old and female. The identification and incidence rates of FD in the potential FD cohort were markedly higher than those of the treated FD cohort. CONCLUSIONS: This model serves as a tool to identify potential FD patients using German insurance claims data.

Association of Definitive Radiotherapy for Esophageal Cancer and the Incidence of Secondary Head and Neck Cancers: A SEER Population-Based Study.

Background: Impact of radiotherapy (RT) for esophageal cancer (EC) patients on the development of secondary head and neck cancer (SHNC) remains equivocal. The objective of this study was to investigate the link between definitive RT used for EC treatment and subsequent SHNC. Methods: This study was conducted using the Surveillance, Epidemiology, and End Results (SEER) database to collect the data of primary EC patients. Fine-Gray competing risk regression and standardized incidence ratio (SIR) and propensity score matching (PSM) method were used to match SHNC patients with only primary head and neck cancer (HNC) patients. Overall survival (OS) rates were applied by Kaplan-Meier analysis. Results: In total, 14,158 EC patients from the SEER database were included, of which 9,239 patients (65.3%) received RT and 4,919 patients (34.7%) received no radiation therapy (NRT). After a 12-month latency period, 110 patients (1.2%) in the RT group and 36 patients (0.7%) in the NRT group experienced the development of SHNC. In individuals with primary EC, there was an increased incidence of SHNC compared to the general US population (SIR = 5.95, 95% confidence interval (CI): 5.15 - 6.84). Specifically, the SIR for SHNC was 8.04 (95% CI: 6.78 - 9.47) in the RT group and 3.51 (95% CI: 2.64 - 4.58) in the NRT group. Patients who developed SHNC after RT exhibited significantly lower OS compared to those after NRT. Following PSM, the OS of patients who developed SHNC after RT remained significantly lower than that of matched patients with only primary HNC. Conclusion: An association was discovered between RT for EC and increased long-term risk of SHNC. This work enables radiation oncologists to implement mitigation strategies to reduce the long-term risk of SHNC in patients who have received RT following primary EC.

The Risk of Infection-Caused Mortality in Gastric Adenocarcinoma: A Population-Based Study.

Background: Gastric adenocarcinoma (GAC) is a deadly tumor. Postoperative complications, including infections, worsen its prognosis and may affect overall survival. Little is known about perioperative complications as well as modifiable and non-modifiable risk factors. Early detection and treatment of these risk factors may affect overall survival and mortality. Methods: We extracted GAC patient's data from the Surveillance, Epidemiology, and End Results (SEER) database and analyzed using Pearson's Chi-square, Cox regression, Kaplan-Meier, and binary regression methods in SPSS. Results: At the time of analysis, 59,580 GAC patients were identified, of which 854 died of infection. Overall, mean survival in months was better for younger patients, age < 50 years vs. ≥ 50 years (60.45 vs. 56.75), and in females vs. males (65.23 vs. 53.24). The multivariate analysis showed that the risk of infectious mortality was higher in patients with age ≥ 50 years (hazard ratio (HR): 3.137; 95% confidence interval (CI): 2.178 - 4.517), not treated with chemotherapy (HR: 1.669; 95% CI: 1.356 - 2.056), or surgery (HR: 1.412; 95% CI:1.132 - 1.761) and unstaged patients (HR: 1.699; 95% CI: 1.278 - 2.258). In contrast, the mortality risk was lower in females (HR: 0.658; 95% CI: 0.561 - 0.773) and married patients (HR: 0.627; 95% CI: 0.506 - 0.778). The probability of infection was higher in older patients (odds ratio (OR) of 2.094 in ≥ 50 years), other races in comparison to Whites and Blacks (OR: 1.226), lesser curvature, not other specified (NOS) as a primary site (OR: 1.325), and patients not receiving chemotherapy (OR: 1.258). Conclusion: Older, unmarried males with GAC who are not treated with chemotherapy or surgery are at a higher risk for infection-caused mortality and should be given special attention while receiving treatment.

Substance Use Disorder as Risk Factor for Intubation in Angioedema: A Nationwide Cohort Study.

OBJECTIVES: Individuals with angioedema (AE) are at high risk for airway compromise and often require endotracheal intubation. Patient factors predisposing one to airway compromise are not well described. The objective of this study is to examine whether substance use disorder (SUD) in patients with AE is associated with need for airway intervention. METHODS: This population-based retrospective cohort study compared AE patients with SUD versus propensity-matched control groups. Outcomes were hospitalization, intubation, and tracheotomy. Using the TriNetX National Database, this study included 28,931 patients with SUD and 117,509 patients without SUD who presented with AE. RESULTS: Among patients with AE, those with each subtype of SUD (alcohol, cannabis, cocaine, tobacco, and opioids) were found to have higher risk of severe AE compared to propensity-matched non-SUD cohorts. Rate of hospitalization after cohort matching ranged from 20.4% for tobacco use disorder to 30.4% for cocaine use disorder, all significantly higher than the 8.0% in a population without SUD. Each SUD subtype was associated with a higher rate of intubation compared with matched non-SUD groups, with cannabis use disorder having the highest relative risk (RR) of 3.67 (95% CI: 2.69-5.02). Tobacco (RR = 2.45, 95% CI: 1.79-3.34) and alcohol (RR = 2.82, 95% CI: 1.73-4.58) use disorders were both associated with significantly higher risk of tracheotomy. CONCLUSION: These data suggest that patients with SUD, regardless of subtype, and after propensity matching for demographics and comorbidities are at higher risk for adverse outcomes when presenting with AE. This study highlights clinically relevant predictors of airway compromise. LEVEL OF EVIDENCE: Level 3 Laryngoscope, 2024.

Data mining study on adverse events of tirzepatide based on FAERS database.

BACKGROUND: Tirzepatide is a novel dual gastric inhibitory polypeptide (GIP) and glucagon-like peptide-1 receptor agonist (GLP-1 RA) for type 2 diabetes or obesity. To explore the safety profile of tirzepatide in real-world clinical applications. RESEARCH DESIGN AND METHODS: A retrospective analysis of adverse events (AEs) reports associated with tirzepatide was conducted from the second quarter of 2022 through the fourth quarter of 2023, utilizing the FDA Adverse Event Reporting System (FAERS) database. Signal mining utilized the reporting odds ratio (ROR) method, and onset time was analyzed utilizing the Weibull Shape Parameter (WSP). RESULTS: We identified 25,215 AE reports related to tirzepatide, predominantly in the 65 to 85 age group. Four positive signals were found at the system organ classes level. Additionally,109 AEs at the preferred terms level with positive signals were indicated. Included among these are novel signals, such as the presence of thyroid mass, medullary thyroid carcinoma, and conditions affecting the reproductive system and breast. Most AEs occurred within the first 30 days. The WSP was 0.66, indicating a propensity for early failure type. CONCLUSIONS: This study identified several novel AE signals for tirzepatide, highlighting the need for careful monitoring, especially in the early stages of treatment.

The Cancer Clinical Library Database (CCLD) from the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) Project.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea's cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.