Angiotensin Receptor Blockers and the Risk of Suspected Drug-Induced Liver Injury: A Retrospective Cohort Study Using Electronic Health Record-Based Common Data Model in South Korea

INTRODUCTION

Angiotensin receptor blockers are widely used antihypertensive drugs in South Korea. In 2021, the Korea Ministry of Food and Drug Safety acknowledged the need for national compensation for a drug-induced liver injury (DILI) after azilsartan use. However, little is known regarding the association between angiotensin receptor blockers and DILI.

OBJECTIVE

We conducted a retrospective cohort study in incident users of angiotensin receptor blockers from a common data model database (1 January, 2017-31 December, 2021) to compare the risk of DILI among specific angiotensin receptor blockers against valsartan.

METHODS

Patients were assigned to treatment groups at cohort entry based on prescribed angiotensin receptor blockers. Drug-induced liver injury was operationally defined using the International DILI Expert Working Group criteria. Cox regression analyses were conducted to derive hazard ratios and the inverse probability of treatment weighting method was applied. All analyses were performed using R.

RESULTS

In total, 229,881 angiotensin receptor blocker users from 20 university hospitals were included. Crude DILI incidence ranged from 15.6 to 82.8 per 1000 person-years in treatment groups, most were cholestatic and of mild severity. Overall, the risk of DILI was significantly lower in olmesartan users than in valsartan users (hazard ratio: 0.73 [95% confidence interval 0.55-0.96]). In monotherapy patients, the risk was significantly higher in azilsartan users than in valsartan users (hazard ratio: 6.55 [95% confidence interval 5.28-8.12]).

CONCLUSIONS

We found a significantly higher risk of suspected DILI in patients receiving azilsartan monotherapy compared with valsartan monotherapy. Our findings emphasize the utility of real-world evidence in advancing our understanding of adverse drug reactions in clinical practice.

A Review of Current Digital Mental Health Care Applications for Anxiety Symptoms and Future Prospects

OBJECTIVE

Since the impact of the coronavirus disease-2019 pandemic, the need for efficiency in medical services has become more urgent than ever. The digital treatment market is rapidly growing worldwide and digital therapeutics (DTx), a major part of the digital medical services, is also emerging as a new paradigm for treatment, with its industry growing rapidly as well. Increasing research is done on the effectiveness of mobile DTx in improving mental health conditions such as insomnia, panic, and depression.

METHODS

This review paper investigates 1) the functions and characteristics of mobile digital mental health care applications for the treatment of anxiety symptoms, 2) extracts common attributes of the applications, and 3) compares them with existing traditional treatment mechanisms.

RESULTS

Among the 20,000 mental health management applications that have been developed so far, 8 applications that are relatively widely used were selected and reviewed. Check-in, self-help tips, quick relief, journal, courses for practice are common features of the digital mental health care applications for anxiety and are also widely used feature in the cognitive behavioral therapy.

CONCLUSION

Based on this review, we have proposed the essential elements and directions for the development of a Korean digital mental health care applications for anxiety disorders.

The E592K variant of SF3B1 creates unique RNA missplicing and associates with high-risk MDS without ring sideroblasts

Among the most common genetic alterations in the myelodysplastic syndromes (MDS) are mutations in the spliceosome gene SF3B1. Such mutations induce specific RNA missplicing events, directly promote ring sideroblast (RS) formation, and generally associate with more favorable prognosis. However, not all SF3B1 mutations are the same, and little is known about how distinct hotspots influence disease. Here we report that the E592K variant of SF3B1 associates with high-risk disease features in MDS, including a lack of RS, increased myeloblasts, a distinct co-mutation pattern, and a lack of the favorable survival seen with other SF3B1 mutations. Moreover, compared to other hotspot SF3B1 mutations, E592K induces a unique RNA missplicing pattern, retains an interaction with the splicing factor SUGP1, and preserves normal RNA splicing of the sideroblastic anemia genes TMEM14C and ABCB7. These data have implications for our understanding of the functional diversity of spliceosome mutations, as well as the pathobiology, classification, prognosis, and management of SF3B1-mutant MDS.

Long-Term Prognostic Value of Infarct Transmurality Determined by Contrast-Enhanced Cardiac Magnetic Resonance after ST-Segment Elevation Myocardial Infarction

The long-term prognostic significance of maximal infarct transmurality evaluated by contrast-enhanced cardiac magnetic resonance (CE-CMR) in ST-segment elevation myocardial infarction (STEMI) patients has yet to be determined. This study aimed to see if maximal infarct transmurality has any additional long-term prognostic value over other CE-CMR predictors in STEMI patients, such as microvascular obstruction (MVO) and intramyocardial hemorrhage (IMH). The study included 112 consecutive patients who underwent CE-CMR after STEMI to assess established parameters of myocardial injury as well as the maximal infarct transmurality. The primary clinical endpoint was the occurrence of major adverse cardiac events (MACE), which included all-cause death, non-fatal reinfarction, and new heart failure hospitalization. The MACE occurred in 10 patients over a median follow-up of 7.9 years (IQR, 5.8 to 9.2 years) (2 deaths, 3 nonfatal MI, and 5 heart failure hospitalization). Patients with MACE had significantly higher rates of transmural extent of infarction, infarct size >5.4 percent, MVO, and IMH compared to patients without MACE. In stepwise multivariable Cox regression analysis, the transmural extent of infarction defined as 75 percent or more of infarct transmurality was an independent predictor of the MACE after correction for MVO and IMH (hazard ratio 8.7, 95% confidence intervals [CIs] 1.1-71; p=0.043). In revascularized STEMI patients, post-infarction CE-CMR-based maximal infarct transmurality is an independent long-term prognosticator. Adding maximal infarct transmurality to CE-CMR parameters like MVO and IMH could thus identify patients at high risk of long-term adverse outcomes in STEMI.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications

Background

Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa.

Methods

In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy.

Results

A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05).

Conclusions

DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

A Study to Evaluate the Effectiveness and Safety of Prephase Steroid Treatment before Remission Induction Chemotherapy in Patients with Pediatric Acute Lymphoblastic Leukemia Using Common Data Model-Based Real-World Data: A Retrospective Observational Study

Background

Rapid reduction of leukemic cells in the bone marrow during remission induction chemotherapy (RIC) can lead to significant complications such as tumor lysis syndrome (TLS). We investigated whether prephase steroid treatment before RIC could decrease TLS incidence and improve overall survival in pediatric patients with acute lymphoblastic leukemia (ALL).

Methods

Data were extracted from the Common Data Model databases in two tertiary-care hospitals in Seoul, South Korea. Patients were classified into the treated or untreated group if they had received RIC with prephase steroid treatment ≥7 days before RIC in 2012-2021 or not, respectively. Stabilized Inverse Probability of Treatment Weighting (sIPTW) was applied to ensure compatibility between the treated and untreated groups. The incidence of TLS within 14 days of starting RIC, overall survival (OS), and the incidence of adverse events of special interest were the primary endpoints. Multiple sensitivity analyses were performed.

Results

Baseline characteristics were effectively balanced between the treated (n=308.4) and untreated (n=246.6) groups after sIPTW. Prephase steroid treatment was associated with a significant 88% reduction in the risk of TLS (OR 0.12, 95% CI: 0.03-0.41). OS was numerically greater in the treated group than in the untreated group although the difference was not statistically significant (HR 0.64, 95% CI 0.25-1.64). The treated group experienced significantly elevated risks for hyperbilirubinemia and hyperglycemia. The reduction in TLS risk by prephase steroid treatment was maintained in all of the sensitivity analyses.

Conclusion

Prephase steroid treatment for ≥7 days before RIC in pediatric patients with ALL reduces the risk of TLS, while careful monitoring for toxicities is necessary. If adequately analyzed, real-world data can provide crucial effectiveness and safety information for proper management of pediatric patients with ALL, for whom prospective randomized studies may be difficult to perform for ethical and practical reasons.

Pacing-induced cardiomyopathy in patients with preserved ejection fraction undergoing permanent cardiac pacemaker placement

BACKGROUND

Chronic right-ventricular (RV) pacing can worsen heart failure in patients with a low ejection fraction (EF), but little is known about pacing-induced cardiomyopathy (PICM) in patients with preserved EF. We aimed to investigate risk factors of PICM in these patients during long-term follow-up.

METHODS

The prospective registry at Chosun University Hospital, South Korea, included de novo patients with preserved EF undergoing transvenous permanent pacemaker (PPM) implantation for atrioventricular blockage from 2017 to 2021. Patients with EF ≥ 50% and expected ventricular pacing ≥ 40% were included. Composite outcomes were cardiac death (pump failure), hospitalization because of heart failure, PICM, and biventricular pacing (BVP) upgrade.

RESULTS

A total of 168 patients (69 men, 76.3 ± 10.4 years) were included. During three years of follow-up, one patient died, 14 were hospitalized, 16 suffered PICM, and two underwent BVP upgrade. PICM were associated with reduced global longitudinal strain (GLS), prolonged paced QRS duration (pQRSd) and diastolic variables (E/e', LAVI). Cox regression analysis identified pQRSd (hazard ratio [HR], 1.111; 95% confidence interval [CI], 1.011-1.222; P = 0.03) and reduced GLS (HR, 1.569; 95% CI, 1.163-2.118; P = 0.003) as independent predictors of PICM. GLS showed high predictive accuracy for PICM, with an area under the curve of 0.84 (95% CI 0.779-0.894; P < 0.001) [GLS -12.0, 62.5% sensitivity, and 86.1% specificity].

CONCLUSION

RV pacing increased the risk of PICM in patients with preserved EF. Reduced GLS and prolonged pQRSd could help identify individuals at high risk of PICM even with preserved EF.

Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model

The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.

Developing a Cost-Effectiveness Model of Digital Therapeutics for Smoking Cessation

The purpose of this research was to construct a Markov model of digital therapeutics to predict the lifetime costs and consequences that would be incurred by a hypothetical group of adult smokers in Korea who only made a single attempt to stop smoking. To determine the efficacy of DTx, we created an annual cycle Markov model. The result shows that the NRT strategy is determined as the dominant strategy. Digital therapeutics acts as a complement to pharmacotherapy and is a low-cost option.

Development of depression detection algorithm using text scripts of routine psychiatric interview

Background

A psychiatric interview is one of the important procedures in diagnosing psychiatric disorders. Through this interview, psychiatrists listen to the patient's medical history and major complaints, check their emotional state, and obtain clues for clinical diagnosis. Although there have been attempts to diagnose a specific mental disorder from a short doctor-patient conversation, there has been no attempt to classify the patient's emotional state based on the text scripts from a formal interview of more than 30 min and use it to diagnose depression. This study aimed to utilize the existing machine learning algorithm in diagnosing depression using the transcripts of one-on-one interviews between psychiatrists and depressed patients.

Methods

Seventy-seven clinical patients [with depression (n = 60); without depression (n = 17)] with a prior psychiatric diagnosis history participated in this study. The study was conducted with 24 male and 53 female subjects with the mean age of 33.8 (± 3.0). Psychiatrists conducted a conversational interview with each patient that lasted at least 30 min. All interviews with the subjects between August 2021 and November 2022 were recorded and transcribed into text scripts, and a text emotion recognition module was used to indicate the subject's representative emotions of each sentence. A machine learning algorithm discriminates patients with depression and those without depression based on text scripts.

Results

A machine learning model classified text scripts from depressive patients with non-depressive ones with an acceptable accuracy rate (AUC of 0.85). The distribution of emotions (surprise, fear, anger, love, sadness, disgust, neutral, and happiness) was significantly different between patients with depression and those without depression (p < 0.001), and the most contributing emotion in classifying the two groups was disgust (p < 0.001).

Conclusion

This is a qualitative and retrospective study to develop a tool to detect depression against patients without depression based on the text scripts of psychiatric interview, suggesting a novel and practical approach to understand the emotional characteristics of depression patients and to use them to detect the diagnosis of depression based on machine learning methods. This model could assist psychiatrists in clinical settings who conduct routine conversations with patients using text transcripts of the interviews.

Investigation of hepatic adverse events due to quetiapine by using the common data model

PURPOSE

Quetiapine is a drug used to treat schizophrenia, bipolar disorder, and major depressive disorder. However, it can cause mild or severe hepatic adverse events and rarely fatal liver damage. This study was aimed at investigating hepatic toxicity caused by quetiapine use by analyzing the information captured from hospital electronic health records by using the Observational Medical Outcomes Partnership common data model (CDM).

METHODS

This was a retrospective observational study involving a nested case-control method. A CDM based on an electronic health record database from five hospitals between January 2009 and May 2020 was used. We analyzed the status of quetiapine use, adverse events, and hepatic impairment.

RESULTS

The numbers of patients with non-serious and severe hepatic adverse reactions were 2566 (5.05%) and 835 (1.64%) out of 50 766 patients, respectively. After adjusting for covariates, the odds ratio of hepatic adverse events was 2.35 (95% CI: 2.03-2.72), and the odds ratio of severe hepatic adverse events was 1.76 (95% CI: 1.16-2.66).

CONCLUSION

Our findings suggest that quetiapine should be cautiously used, and hepatic function should be monitored in patients using quetiapine because it can cause mild or severe hepatic adverse events, complications, and in rare cases, fatal liver damage.

Healthcare data quality assessment for improving the quality of the Korea Biobank Network

Numerous studies make extensive use of healthcare data, including human materials and clinical information, and acknowledge its significance. However, limitations in data collection methods can impact the quality of healthcare data obtained from multiple institutions. In order to secure high-quality data related to human materials, research focused on data quality is necessary. This study validated the quality of data collected in 2020 from 16 institutions constituting the Korea Biobank Network using 104 validation rules. The validation rules were developed based on the DQ4HEALTH model and were divided into four dimensions: completeness, validity, accuracy, and uniqueness. Korea Biobank Network collects and manages human materials and clinical information from multiple biobanks, and is in the process of developing a common data model for data integration. The results of the data quality verification revealed an error rate of 0.74%. Furthermore, an analysis of the data from each institution was performed to examine the relationship between the institution's characteristics and error count. The results from a chi-square test indicated that there was an independent correlation between each institution and its error count. To confirm this correlation between error counts and the characteristics of each institution, a correlation analysis was conducted. The results, shown in a graph, revealed the relationship between factors that had high correlation coefficients and the error count. The findings suggest that the data quality was impacted by biases in the evaluation system, including the institution's IT environment, infrastructure, and the number of collected samples. These results highlight the need to consider the scalability of research quality when evaluating clinical epidemiological information linked to human materials in future validation studies of data quality.

Bioluminescent monitoring of recombinant lactic acid bacteria and their products

IMPORTANCE

Lactic acid bacteria constitute a genetically diverse group of microorganisms with significant roles in the food industry, biotechnology, agriculture, and medicine. A core understanding of bacterial physiology in diverse environments is crucial to select and develop bacteria for industrial and medical applications. However, there is a lack of versatile tools to track (recombinant) protein production in lactic acid bacteria. In this study, we adapted a peptide-based bioluminescent tagging system that is functional across multiple genera and species. This system enables tracking of tagged proteins both in vitro and in situ, while it also can be used to enumerate recombinant bacteria from the mouse gastrointestinal tract with accuracy comparable to that of conventional plate counts. Our work expands the lactic acid bacteria genetic toolbox and will facilitate researchers in industry and academia with opportunities to monitor microbes and proteins under different physiologically relevant conditions.

Association between HbA1c and hearing loss: a tertiary care center-based study

The purpose of this study was to investigate the correlation between glycated hemoglobin (HbA1c) levels and hearing loss (HL) using data from a tertiary hospital. Our hypothesis regarding the relationship between HL and HbA1c levels was that elevated HbA1c levels are associated with an increased risk of HL. We retrospectively reviewed the medical charts of patients diagnosed with sensorineural HL or diabetes between 2006 and 2021 at the Catholic Medical Center (CMC). Data were collected from the CMC's Clinical Data Warehouse. Participants were selected from patients who were prescribed pure-tone audiometry and an HbA1c blood test. The survey was completed for 5287 participants. The better ear pure-tone audiometry (PTA) for air conduction thresholds at 500, 1000, 2000, and 4000 Hz was calculated. Sensorineural HL was defined as a better ear PTA of 25 dB or higher. We used the HbA1c level as a diagnostic criterion for diabetes. The following criteria were used to define the HbA1c level: normal, HbA1c level below 5.6%; prediabetes, level between 5.6 and 6.4%; and diabetes, level of 6.5% or more. Among 5287 participants, 1129 were categorized as normal, 2119 as prediabetic, and 2039 as diabetic. The diabetic group was significantly older (p < 0.05). The PTA also significantly deteriorated in the diabetes group (p < 0.05). We analyzed the effects of age, sex, and HbA1c level on frequency-specific hearing using multiple regression. The hearing thresholds at all frequencies deteriorated significantly with increasing age and HbA1c level (p < 0.05). A case-control study was also performed to facilitate a comprehensive comparison between distinct groups. The participants were categorized into two groups: a case (PTA > 25 dB) and control group (PTA ≤ 25 dB), based on their PTA threshold of four frequencies. After adjusting for age and sex, we found no significant odds ratio (OR) of HL between the prediabetes group and the normal group. Notably, the OR of HL was significantly higher in the diabetes group with each PTA threshold and frequency. The 6.3% HbA1c level cutoff value was determined by analyzing the receiver operating characteristic curve for predicting hearing impairment > 25 dB. Diabetes was associated with hearing loss in all frequency ranges, particularly at high frequencies. Screening for HL is strongly recommended for patients with elevated HbA1c levels.

Peripheral Neuron-Organoid Interaction Induces Colonic Epithelial Differentiation via Non-Synaptic Substance P Secretion

Background and Objectives

The colonic epithelial layer is a complex structure consisting of multiple cell types that regulate various aspects of colonic physiology, yet the mechanisms underlying epithelial cell differentiation during development remain unclear. Organoids have emerged as a promising model for investigating organogenesis, but achieving organ-like cell configurations within colonic organoids is challenging. Here, we investigated the biological significance of peripheral neurons in the formation of colonic organoids.

Methods and Results

Colonic organoids were co-cultured with human embryonic stem cell (hESC)-derived peripheral neurons, resulting in the morphological maturation of columnar epithelial cells, as well as the presence of enterochromaffin cells. Substance P released from immature peripheral neurons played a critical role in the development of colonic epithelial cells. These findings highlight the vital role of inter-organ interactions in organoid development and provide insights into colonic epithelial cell differentiation mechanisms.

Conclusions

Our results suggest that the peripheral nervous system may have a significant role in the development of colonic epithelial cells, which could have important implications for future studies of organogenesis and disease modeling.

Development and Application of an Active Pharmacovigilance Framework Based on Electronic Healthcare Records from Multiple Centers in Korea

INTRODUCTION

With the availability of retrospective pharmacovigilance data, the common data model (CDM) has been identified as an efficient approach towards anonymized multicenter analysis; however, the establishment of a suitable model for individual medical systems and applications supporting their analysis is a challenge.

OBJECTIVE

The aim of this study was to construct a specialized Korean CDM (K-CDM) for pharmacovigilance systems based on a clinical scenario to detect adverse drug reactions (ADRs).

METHODS

De-identified patient records (n = 5,402,129) from 13 institutions were converted to the K-CDM. From 2005 to 2017, 37,698,535 visits, 39,910,849 conditions, 259,594,727 drug exposures, and 30,176,929 procedures were recorded. The K-CDM, which comprises three layers, is compatible with existing models and is potentially adaptable to extended clinical research. Local codes for electronic medical records (EMRs), including diagnosis, drug prescriptions, and procedures, were mapped using standard vocabulary. Distributed queries based on clinical scenarios were developed and applied to K-CDM through decentralized or distributed networks.

RESULTS

Meta-analysis of drug relative risk ratios from ten institutions revealed that non-steroidal anti-inflammatory drugs (NSAIDs) increased the risk of gastrointestinal hemorrhage by twofold compared with aspirin, and non-vitamin K anticoagulants decreased cerebrovascular bleeding risk by 0.18-fold compared with warfarin.

CONCLUSION

These results are similar to those from previous studies and are conducive for new research, thereby demonstrating the feasibility of K-CDM for pharmacovigilance. However, the low quality of original EMR data, incomplete mapping, and heterogeneity between institutions reduced the validity of the analysis, thus necessitating continuous calibration among researchers, clinicians, and the government.

Comparison between single and serial computed tomography images in classification of acute appendicitis, acute right-sided diverticulitis, and normal appendix using EfficientNet

This study aimed to develop a convolutional neural network (CNN) using the EfficientNet algorithm for the automated classification of acute appendicitis, acute diverticulitis, and normal appendix and to evaluate its diagnostic performance. We retrospectively enrolled 715 patients who underwent contrast-enhanced abdominopelvic computed tomography (CT). Of these, 246 patients had acute appendicitis, 254 had acute diverticulitis, and 215 had normal appendix. Training, validation, and test data were obtained from 4,078 CT images (1,959 acute appendicitis, 823 acute diverticulitis, and 1,296 normal appendix cases) using both single and serial (RGB [red, green, blue]) image methods. We augmented the training dataset to avoid training disturbances caused by unbalanced CT datasets. For classification of the normal appendix, the RGB serial image method showed a slightly higher sensitivity (89.66 vs. 87.89%; p = 0.244), accuracy (93.62% vs. 92.35%), and specificity (95.47% vs. 94.43%) than did the single image method. For the classification of acute diverticulitis, the RGB serial image method also yielded a slightly higher sensitivity (83.35 vs. 80.44%; p = 0.019), accuracy (93.48% vs. 92.15%), and specificity (96.04% vs. 95.12%) than the single image method. Moreover, the mean areas under the receiver operating characteristic curve (AUCs) were significantly higher for acute appendicitis (0.951 vs. 0.937; p < 0.0001), acute diverticulitis (0.972 vs. 0.963; p = 0.0025), and normal appendix (0.979 vs. 0.972; p = 0.0101) with the RGB serial image method than those obtained by the single method for each condition. Thus, acute appendicitis, acute diverticulitis, and normal appendix could be accurately distinguished on CT images by our model, particularly when using the RGB serial image method.

Data Quality Assessment for Observational Medical Outcomes Partnership Common Data Model of Multi-Center

The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.

The E592K variant of SF3B1 creates unique RNA missplicing and associates with high-risk MDS without ring sideroblasts

Among the most common genetic alterations in the myelodysplastic syndromes (MDS) are mutations in the spliceosome gene SF3B1. Such mutations induce specific RNA missplicing events, directly promote ring sideroblast (RS) formation, generally associate with more favorable prognosis, and serve as a predictive biomarker of response to luspatercept. However, not all SF3B1 mutations are the same, and here we report that the E592K variant of SF3B1 associates with high-risk disease features in MDS, including a lack of RS, increased myeloblasts, a distinct co-mutation pattern, and decreased survival. Moreover, in contrast to canonical SF3B1 mutations, E592K induces a unique RNA missplicing pattern, retains an interaction with the splicing factor SUGP1, and preserves normal RNA splicing of the sideroblastic anemia genes TMEM14C and ABCB7. These data expand our knowledge of the functional diversity of spliceosome mutations, and they suggest that patients with E592K should be approached differently from low-risk, luspatercept-responsive MDS patients with ring sideroblasts and canonical SF3B1 mutations.

Abstract 1831: A long-acting and CD122-enhanced IL-2 analog, HM16390, synergizes with immune checkpoint inhibitor by remodeling an immune cell profile in tumor microenvironment

Immune checkpoint inhibitors (CPI) are widely used for cancer immunotherapy. However, the response to CPIs is dependent on phenotype of tumor microenvironment (TME). A cold tumor known as immune-excluded or -desert has shown poor response to CPIs due to an absence of effector T cells in TME. IL-2 analog, which is immune stimulator and able to recruit cancer-fighting cells into TME, may be promising therapeutic partner for overcoming a limitation of CPIs. Previously, HM16390, a long-acting CD-122-enhanced IL-2 analog, exerted dose-dependent anti-tumor activity in low immunogenic B16F10 melanoma mice. Here, we further investigated the immune cells composition in TME following HM16390 treatment and synergistic anti-tumor effect after combination with CPI. B16F10 mice were sacrificed at days 1, 3, and 8 following single subcutaneous administration of HM16390 or 5 consecutive daily administrations of aldesleukin. According to the result, HM16390 transiently increased peripheral cytokines (IFNγ and TNFα) more than aldesleukin. In line with this, we observed that the tumor-infiltrating NK/Treg ratio was significantly increased approximately 19 by treatment of HM16390 while showing 4.2 in aldesleukin treated group at Day 3. Furthermore, tumor-infiltrating CD8+/Treg ratio was also upregulated approximately 74 by treatment of HM16390 while only showing 6.1 in aldesleukin treated group at Day 8. Significantly increased pro-inflammatory molecules such as GrzB and IFNγ were also observed in HM16390 treated group compared to aldesleukin (p&lt;0.01 and p&lt;0.001, respectively). Next, we investigated synergistic anti-tumor effects in combination with CPI. B16F10 mice were repeatedly given HM16390 once a week or aldesleukin 5 consecutive days per week with or without mouse anti-PD1. After four weeks treatment, 25% of B16F10 mice showed complete response by treatment of HM16390, and significantly increased up to 88% by combination with anti-PD1. However, none of the mice showed complete response by treatment of aldesleukin/anti-PD1 combo. In conclusion, HM16390 effectively induced tumor growth inhibition through the activation and tumor infiltration of cytotoxic lymphocytes. This favorable immune alteration in tumor elicited a TME remodeling in which CPI could sufficiently respond.

Citation Format: Jaehyuk Choi, Jinyoung Kim, Yu Yon Kim, Seongju Jeong, Sungmin Bae, Daejin Kim, In Young Choi. A long-acting and CD122-enhanced IL-2 analog, HM16390, synergizes with immune checkpoint inhibitor by remodeling an immune cell profile in tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1831.

Abstract 1814: A long-acting and CD122-enhanced IL-2 analog, HM16390, shows a potent and durable anti-tumor effect in both syngeneic B16F10 or CT26 mouse models

Although recombinant IL-2 (aldesleukin) was approved for the treatment of metastatic renal cell carcinoma and melanoma, its suboptimal ligand binding affinity required high-dose administrations, resulting in dose-limiting toxicity such as vascular leak syndrome. To overcome the limitation of the current IL-2 therapy, a number of pharmaceutical companies have developed CD25 binding attenuated IL-2 muteins via various platform technology. These physical changes, however, accompanied a decrease in CD122-mediated signaling which was associated with cytotoxic lymphocytes expansion, leading to unsatisfied clinical consequences. In this context, we developed HM16390, a long-acting IL-2 analog with enhanced CD122 binding affinity to elicit potent anti-tumor efficacy. Furthermore, optimal binding affinity to CD25 was incorporated to prevent from unwanted toxicity. The pharmacokinetics in mice after single subcutaneous administration of HM16390 supported an extended duration of action (t1/2= 24.5 hr, bioavailability= 43.0%). To further investigate anti-tumor effect on various immunological conditions, syngeneic tumor mouse models, which are representing highly immunogenic colon cancer (CT26) and poorly immunogenic melanoma (B1F10), were chosen. After two weeks treatment in CT26 mice, complete response (CR) was observed in 89% and 100% of the mice by once weekly treatment of HM16390 at 2.1 and 8.5 mg/kg respectively, while only 22% of the mice showed CR by 5 consecutive day treatment per week of aldesleukin at 3.0 mg/kg. The cured mice had no evidence of relapse after tumor re-challenges at days 50 and 152. This immunological memory response against the CT26 tumor was demonstrated by significant increase in tumor-specific memory T cells in lymphoid organ and blood. Next, after four weeks treatment in B16F10 mice, once weekly treatment of HM16390 was well tolerated up to 42 mg/kg, and led to significant and dose-dependent retardation in tumor growth (vs syngeneic tumor vehicle) from 62.9% to 96.4% as well as dose-dependently prolonged median overall survival from 15 to 38 days (14 days in syngeneic tumor vehicle) at the dose range of 0.34 to 42 mg/kg. Of note, at doses ≥17 mg/kg, CR was observed in up to 22% of the mice. To sum up with the results, HM16390 has a dose-dependent and potent anti-tumor efficacy in murine models with the broad range of the immunogenic condition via CD122-enhanced IL-2 agonism.

Citation Format: Jinyoung Kim, Jaehyuk Choi, Yu Yon Kim, Seongju Jeong, Sang Hyun Park, Sungmin Bae, Daejin Kim, In Young Choi. A long-acting and CD122-enhanced IL-2 analog, HM16390, shows a potent and durable anti-tumor effect in both syngeneic B16F10 or CT26 mouse models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1814.

Quality improvement of outpatient clinical chemistry tests through a novel middleware-laboratory information system solution

OBJECTIVES

Rapid and accurate laboratory tests are essential to support clinical decision-making. Despite the various efforts to control quality in the laboratory, our outpatient chemistry turnaround time (TAT) has deteriorated since 2018. Moreover, these difficulties have accelerated further due to the COVID-19 pandemic. Therefore, we aimed to improve laboratory work efficiency by identifying and eliminating the causes of reduced laboratory work efficiency.

DESIGN & METHODS

We surveyed to identify tasks that reduce work efficiency. Based on our survey, a new-concept of work assistance middleware linked to laboratory information system (LIS) was developed. The middleware supports test end-time prediction, automatic real-time TAT monitoring, and urgent test requests so that medical technologists can focus on their chemistry tests. The developed middleware was used for 6 months in laboratory and outpatient clinics, and its effectiveness was evaluated.

RESULTS

The median TAT for outpatient chemistry tests was reduced by 6.6 min, from 72.4 min to 65.8 min. And not only did the maximum TAT for the sample decrease from 353 min to 214 min, but the proportion of samples exceeding the TAT target (120 min) also decreased by 77%; from 2.00% in 2010 (1,905 out of 94,989 samples) to 0.46% in 2021 (453 out of 98,117 samples). 2,199 samples were urgently requested through middleware, and they were processed about 15% faster than other samples, effectively performing urgent tests. The test end-time prediction showed an error of 8.6 min in the evaluation using the MAE (Mean Absolute Error) index.

CONCLUSIONS

Through this study, the quality and efficiency of the laboratory were improved, and while reducing the workload of medical staff, it contributed to enhancing patient safety and satisfaction.