What is the relationship between the local population change and cancer incidence in patients with dyslipidemia: Evidence of the impact of local extinction in Korea.

BACKGROUND: Changes in the local population are intricately linked to healthcare infrastructure, which subsequently impacts the healthcare sector. A decreasing local population can result in lagging health infrastructure, potentially leading to adverse health outcomes as patients may be at risk of not receiving optimal care and treatment. While some studies have explored the relationship between chronic diseases and local population decline, evidence regarding cancer is insufficient. In this study, we focused on how deteriorating management of chronic diseases such as dyslipidemia could influence the risk of cancer. We investigated the relationship between changes in the local population and cancer incidence among patients with dyslipidemia. METHODS: This cohort study was conducted using claims data. Data from adult patients with dyslipidemia from the National Health Insurance Service-National Sample Cohort conducted between 2002 and 2015 were included. Population changes in each region were obtained from the Korean Statistical Information Service and were used to link each individual's regional code. Cancer risk was the dependent variable, and Cox proportional hazards regression was used to estimate the target associations. RESULTS: Data from 336,883 patients with dyslipidemia were analyzed. Individuals who resided in areas with a decreasing population had a higher risk of cancer than those living in areas with an increasing population (decrease: hazard ratio (HR) = 1.06, 95% CI = 1.03-1.10; normal: HR = 1.05, 95% CI = 1.02-1.09). Participants living in regions with a low number of hospitals had a higher risk of cancer than those in regions with a higher number of hospitals (HR = 1.20, 95% CI = 1.12-1.29). CONCLUSION: Patients in regions where the population has declined are at a higher risk of cancer, highlighting the importance of managing medical problems caused by regional extinction. This could provide evidence for and useful insights into official policies on population decline and cancer risk.

The impact of physical disability on the risk of gastric cancer incidence in elderly patients with diabetes: a focus on regional disparity.

PURPOSE: Elderly patients with type 2 diabetes mellitus (T2DM) may have a higher risk of physical disability. This study investigated the incidence of gastric cancer according to physical disability status in elderly patients with T2DM. METHODS: The National Health Insurance Service claims data were used. A total of 76,162 participants aged 60 years or above, diagnosed with T2DM, were included. The association between physical disability status and gastric cancer incidence was evaluated using the Cox regression analysis. Additionally, subgroup analysis was performed according to region. RESULTS: A total of 9,154 (12.0%) individuals had physical disability. Gastric cancer incidence was more common in participants with physical disability (3.3%) than those without (2.4%). A higher risk of gastric cancer incidence was found in elderly T2DM patients with physical disability (Hazard Ratio (HR) 1.18, 95% Confidence Interval (95% CI) 1.04-1.34). Such tendencies were maintained regardless of region, although the effect of physical disability status on gastric cancer incidence was particularly significant in individuals residing in non-metropolitan areas (HR: 1.19, 95% CI: 1.01-1.40). CONCLUSION: Elderly patients with T2DM who had physical disability showed a higher risk of gastric cancer incidence. The findings suggest a need to monitor elderly T2DM patients with disability as they may be susceptible to difficulties in accessing cancer-related healthcare.

Risk factors for hyperglycemia in COVID-19 patients treated with remdesivir.

The primary objective of this study was to investigate the factors contributing to hyperglycemic adverse events (AEs) associated with the administration of remdesivir in hospitalized patients diagnosed with coronavirus disease 2019 (COVID-19). Furthermore, the study aimed to develop a risk score model employing various machine learning approaches. A total of 1262 patients were enrolled in this investigation. The relationship between covariates and hyperglycemic AEs was assessed through logistic regression analysis. Diverse machine learning algorithms were employed for the purpose of forecasting hyperglycemia-related complications. After adjusting for covariates, individuals with a body mass index ≥23 kg/m2 , those using proton pump inhibitors, cholinergic medications, or individuals with cardiovascular diseases exhibited approximately 2.41-, 2.73-, 2.65-, and 1.97-fold higher risks of experiencing hyperglycemic AEs (95% CI 1.271-4.577, 1.223-6.081, 1.168-5.989, and 1.119-3.472, respectively). Multivariate logistic regression, elastic net, and random forest models displayed area under the receiver operating characteristic curve values of 0.65, 0.66, and 0.60, respectively (95% CI 0.572-0.719, 0.640-0.671, and 0.583-0.611, respectively). This study comprehensively explored factors associated with hyperglycemic complications arising from remdesivir administration and, concurrently, leveraged a range of machine learning methodologies to construct a risk scoring model, thereby facilitating the tailoring of individualized remdesivir treatment regimens for patients with COVID-19.

The effect of the introduction of the national lung cancer screening program on short-term mortality in Korea.

BACKGROUND: South Korea introduced the National Lung Cancer Screening Program (NLCSP) in 2019. This study investigated the effect of the NLCSP on one-year mortality in individuals with a history of lung cancer. METHODS: This study used the 2018-2020 National Health Insurance Service claims data. The difference-in-differences approach was used to investigate the effect of participating in the NLCSP between the case and control groups before and after the intervention period. The case group included individuals aged between 54 and 74 years with a smoking history of ≥ 30 pack-years and the control group those aged between 54 and 74 years with a history of smoking of <30 pack-years and non-smokers. The pre-intervention period was from January 2018 to June 2019 and the post-intervention period from July 2019 to December 2020. RESULTS: The introduction of the NLCSP was related to an overall decrease in one-year mortality (-3.21 % points, 95 % Confidence Interval (CI) -4.84 to -1.58). Specifically, this reduction was significant for lung cancer related mortality (lung cancer: -2.69 % points, 95 % CI -4.24 to -1.13). Furthermore, stronger associations were found in individuals of older age, residing in non-metropolitan areas, and who visited healthcare institutions in non-metropolitan areas. CONCLUSION: The findings confirm a relationship between implementation of the NLCSP and one-year mortality in eligible individuals with a history of lung cancer, which is noteworthy considering that Korea is one of the first countries to include lung cancer into the national cancer screening program.

Machine Learning Approaches for Assessing Risk Factors of Adrenal Insufficiency in Patients Undergoing Immune Checkpoint Inhibitor Therapy.

Adrenal insufficiency is a rare, yet life-threatening immune-related adverse event of immune checkpoint inhibitors (ICIs). This study aimed to establish a risk scoring system for adrenal insufficiency in patients receiving anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. Moreover, several machine learning methods were utilized to predict such complications. This study included 209 ICI-treated patients from July 2015 to February 2021, excluding those with prior adrenal insufficiency, previous steroid therapy, or incomplete data to ensure data integrity. Patients were continuously followed up at Gyeongsang National University Hospital, with morning blood samples taken for basal cortisol level measurements, facilitating a comprehensive analysis of their adrenal insufficiency risk. Using a chi-squared test and logistic regression model, we derived the odds ratio and adjusted odds ratio (AOR) through univariate and multivariable analyses. This study utilized machine learning algorithms, such as decision trees, random forests, support vector machines (SVM), and logistic regression to predict adrenal insufficiency in patients treated with ICIs. The performance of each algorithm was evaluated using metrics like accuracy, sensitivity, specificity, precision, and the area under the receiver operating characteristic curve (AUROC), ensuring rigorous assessment and reproducibility. A risk scoring system was developed from the multivariable and machine learning analyses. In a multivariable analysis, proton pump inhibitors (PPIs) (AOR 4.5), and α-blockers (AOR 6.0) were significant risk factors for adrenal insufficiency after adjusting for confounders. Among the machine learning models, logistic regression and elastic net showed good predictions, with AUROC values of 0.75 (0.61-0.90) and 0.76 (0.64-0.89), respectively. Based on multivariable and machine learning analyses, females (1 point), age ≥ 65 (1 point), PPIs (1 point), α-blockers (2 points), and antipsychotics (3 points) were integrated into the risk scoring system. From the logistic regression curve, patients with 0, 1, 2, 4, 5, and 6 points showed approximately 1.1%, 2.8%, 7.3%, 17.6%, 36.8%, 61.3%, and 81.2% risk for adrenal insufficiency, respectively. The application of our scoring system could prove beneficial in patient assessment and clinical decision-making while administering PD-1/PD-L1 inhibitors.

The impact of the incidence of diabetic complications on mortality in colorectal cancer patients with diabetes: A nationwide study.

PURPOSE: The prevalence of diabetes is higher in patients with colorectal cancer, which is important because diabetes is recognized as a risk factor for increased mortality. This study investigated the impact of incident diabetes-related complications on all-cause five-year mortality in older aged colorectal cancer patients with diabetes. METHODS: The 2008 to 2019 National Health Insurance Service data on the elderly were used to identify patients with colorectal cancer aged 60 years or above diagnosed with type 2 diabetes mellitus. The outcome measure was all-cause five-year mortality. The main independent variable was incident status of diabetes-related complications using the Diabetes Complications Severity Index (DCSI). Survival analyses were performed using the Cox proportional hazards model, in addition to the calculation of risk differences. Subgroup analysis was conducted based on the type of complication and DCSI scores. RESULTS: Among 1,312 individuals, 319 (24.3%) died within five years after one year of a cancer diagnosis. The risk of mortality was higher in patients with diabetes and cancer having incident diabetes-related complications (Hazard Ratio 1.29, 95% Confidence Interval 1.03-1.63). These tendencies were generally maintained regardless of the type of complication and DCSI scores. CONCLUSION: The incidence of diabetes-related complications after cancer diagnosis was associated with an increased risk of all-cause five-year mortality in older patients with colorectal cancer and preexisting diabetes.

Changes in cervical dysplasia, carcinoma in situ, and cervical cancer after expanding the National Cancer Screening Program to younger women in Korea.

The National Cancer Screening Program of South Korea for cervical cancer was expanded from women aged ≥30 years to women aged ≥20 years in 2016. This study investigated the effect of this policy on occurrence rates of cervical dysplasia, carcinoma in situ, and cervical cancer in women in their twenties. The National Health Information Database for the years 2012-19 was used. The outcome measures were monthly occurrence rates of cervical dysplasia, cervical carcinoma in situ, and cervical cancer. An interrupted time series analysis was performed to investigate whether the number of occurrences changed after policy implementation. For cervical dysplasia, a pre-intervention decreasing trend of 0.3243 per month (P-value < .0001) was found. The post-intervention trend did not differ significantly, although the slope increased at a rate of 0.4622 per month (P-value < .0001). For carcinoma in situ, a trend of increase at 0.0128 per month (P-value = .0099) was seen before policy implementation. The post-intervention trend did not escalate, but the slope showed an increasing trend of 0.0217 per month (P-value < .0001). For cervical cancer, no significant trend was present before intervention. Occurrences of cervical cancer escalated at a rate of 0.0406 per month (P-value < .0001) after policy implementation, and the slope also showed an increasing trend at a rate of 0.0394 per month (P-value < .0001). Expanding the target population for cervical cancer screening increased detection rates for cervical cancer in women aged between 20 and 29 years.

Association between SYVN1 and SEL1 genetic polymorphisms and remission in rheumatoid arthritis patients treated with TNF-α inhibitors: a machine learning approach.

Rheumatoid arthritis (RA) is a severe chronic inflammatory condition that affects joint synovium. Suppressor/enhancer of lin-12-like (SEL1L)-Synoviolin 1 (SYVN1)-mediated endoplasmic reticulum-associated degradation (ERAD) is highly associated with RA development. Although targeting SEL1L-SYVN1-mediated ERAD can be beneficial, studies that evaluate the association between polymorphisms in their genes and remission from the disease in RA patients taking tumor necrosis factor (TNF)-α inhibitors have yet to be carried out. Hence, the purpose of this study was to investigate the association between SYVN1 and SEL1L polymorphisms and TNF-α inhibitor response using various machine learning models. A total of 12 single-nucleotide polymorphisms (SNPs), including 5 SNPs in SYVN1 and 7 SNPs of SEL1L were investigated. Logistic regression analysis was used to examine the relationship between genetic polymorphisms and response to treatment. Various machine learning methods were employed to evaluate factors associated with remission in patients receiving TNF-α inhibitors. After adjusting for covariates, we found that sulfasalazine and rs2025214 in SEL1L increase the remission rates by approximately 3.3 and 2.8 times, respectively (95% confidence intervals 1.126-9.695 and 1.074-7.358, respectively). Machine learning approaches showed acceptable prediction estimates for remission in RA patients receiving TNF-α inhibitors, with the area under the receiver-operating curve (AUROC) values ranging from 0.60 to 0.65. A polymorphism of the SEL1L gene (rs2025214) and sulfasalazine were found to be associated with treatment response in RA patients receiving TNF-α inhibitors. These preliminary data could be used to tailor treatment for RA patients using TNF-α inhibitors.

Impact of Disability Status on Mortality in Patients with Gastric Cancer: A Nationwide Study Focusing on Regional Disparities.

BACKGROUND: Disparities in mortality according to disability status require investment, as individuals with disabilities form the largest subset of the vulnerable population. This study aimed to investigate the association between mortality and disability status in patients with gastric cancer as well as how regional disparities modify this relationship. METHODS: Data were obtained from the National Health Insurance claims database in South Korea for the period of 2006-2019. The outcome measures were all-cause 1-year, 5-year, and overall mortality. The main variable of interest was disability status, categorized into "no disability", "mild disability", and "severe disability". A survival analysis based on the Cox proportional hazards model was conducted to analyze the association between mortality and disability status. Subgroup analysis was conducted according to region. RESULTS: Of the 200,566 study participants, 19,297 (9.6%) had mild disabilities, and 3243 (1.6%) had severe disabilities. Patients with mild disabilities had higher 5- and overall mortality risks, and those with severe disabilities had higher 1-year, 5-year, and overall mortality risks than those without disabilities. These tendencies were generally maintained regardless of the region, but the magnitude of the differences in the mortality rates according to disability status was higher in the group residing in non-capital regions than in the group living in the capital city. CONCLUSION: Disability status was associated with all-cause mortality in patients with gastric cancer. The degree of the differences in mortality rates among those with "no disability", "mild disability", and "severe disability" was augmented in the group residing in non-capital regions.

Disparities in All-Cause Mortality in Older Patients with Colorectal Cancer According to Disability Status: A Nationwide Analysis.

BACKGROUND: Although investigating patterns of cancer mortality is important in understanding the effect of cancer on population health, knowledge regarding mortality in cancer patients with disability is scarce. This study examined the association between disability status and all-cause mortality in older patients with colorectal cancer. METHODS: Data were obtained from the 2008-2019 National Health Insurance Service claims data. The study population included patients with colorectal cancer aged 60 years or above. The outcome measure was all-cause 5-year and overall mortality. A survival analysis was performed using the Cox proportional hazards model to analyze the association between all-cause mortality and disability status. Subgroup analysis was conducted based on disability severity. RESULTS: The study population consisted of 6340 patients, and disability was reported in 15.8% of the included individuals. Participants with disability had a higher risk of both all-cause 5-year (hazard ratio (HR) 1.21, 95% confidence interval (95% CI) 1.07-1.37) and overall mortality (HR 1.15, 95% CI 1.03-1.28). These findings were particularly significant in individuals with severe rather than mild disability. CONCLUSION: Older colorectal cancer patients with disabilities showed a higher risk of overall and 5-year all-cause mortality, which was evident in individuals with severe disabilities. The findings indicated disparities in mortality according to disability status. Further, we suggest that policies that can mediate such disparities must be strengthened.

Impact of Cardiovascular Diseases on Mortality in Gastric Cancer Patients with Preexisting Chronic Disease.

PURPOSE: Chronic diseases and cardiovascular diseases (CVD) have been independently linked to poorer cancer outcomes. This study investigated whether gastric cancer patients with hypertension, diabetes, or dyslipidemia have higher mortality if diagnosed with CVD in the past year before cancer diagnosis. MATERIALS AND METHODS: Data were obtained from the National Health Insurance database for 2002 to 2019. The study population consisted of gastric cancer patients with hypertension, diabetes, or dyslipidemia. The outcome measure was 5-year mortality in relation to incident status of CVD within 1 year before cancer diagnosis. A survival analysis was conducted using the Cox proportional hazards model. Subgroup analysis was conducted according to age, economic status, and type of hospital first visited for cancer treatment. RESULTS: Of a total of 6458 individuals, 2123 (32.7%) were diagnosed with CVDs in the past year before cancer diagnosis. Compared to participants without a history of CVD, those who were diagnosed with CVD showed a higher risk of 5-year mortality (hazard ratio 1.259, 95% confidence interval 1.138-1.394). The extent to which the mortality risk differed between those with and without CVD was greater for individuals of low economic status and in those receiving their initial cancer treatment in a general hospital. CONCLUSION: Patients with gastric cancer and hypertension, diabetes, or dyslipidemia diagnosed with CVD within 1 year before their cancer diagnosis had a higher mortality risk, emphasizing the importance of managing cancer patients with chronic disease and subsequent incidence of CVDs.

Effects of RETN polymorphisms on treatment response in rheumatoid arthritis patients receiving TNF-α inhibitors and utilization of machine-learning algorithms.

This study was designed to investigate the effects of polymorphisms in RETN on remission in RA patients receiving TNF-α inhibitors. In addition, machine learning algorithms were trained to predict remission. Ten single-nucleotide polymorphisms were investigated. Univariate and multivariable analyses were performed to evaluate associations between genetic polymorphisms and the efficacy of TNF-α inhibitors. A random forest-based classification approach was used to assess the importance of different variables associated with the efficacy of TNF-α inhibitors. Various machine learning methods were used for finding vital factors and prediction of remission. The eight most significant features included in the multivariable analysis were sex, age, hypertension, sulfasalazine, rs1862513, rs3219178, rs3219177, and rs3745369. T-allele carriers of rs3219177 and males showed approximately 6.0- and 3.6-fold higher remission rates compared to those with the CC genotype and females, respectively. The elastic net algorithm was the best machine-learning method for predicting remission of patients with RA treated with TNF-α inhibitors. On the basis of the results of this study, it may be possible to design individually tailored treatment regimens to predict the efficacy of TNF-α inhibitors.

Association between Genetic Polymorphism of GSTP1 and Toxicities in Patients Receiving Platinum-Based Chemotherapy: A Systematic Review and Meta-Analysis.

Platinum-based chemotherapy regimens have been proven to be effective in various cancers; however, considerable toxicities may develop and can even lead to treatment discontinuation. Diverse factors may influence adverse treatment events, with pharmacogenetic variations being one prime example. Polymorphisms within the glutathione S-transferase pi 1 (GSTP1) gene may especially alter enzyme activity and, consequently, various toxicities in patients receiving platinum-based chemotherapy. Due to a lack of consistency in the degree of elevated complication risk, we performed a systematic literature review and meta-analysis to determine the level of platinum-associated toxicity in patients with the GSTP1 rs1695 polymorphism. We conducted a systematic search for eligible studies published before January 2022 from PubMed, Web of Science, and EMBASE based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to evaluate the strength of the association between the rs1695 polymorphism and various toxicities. Ten eligible studies met the inclusion criteria. The pooled ORs for hematological toxicity and neutropenia in the patients with the variant (G) allele were 1.7- and 2.6-times higher than those with the AA genotype (95% CI 1.06-2.73 and 1.07-6.35), respectively. In contrast, the rs1695 polymorphism resulted in a 44% reduced gastrointestinal toxicity compared to wild-type homozygotes. Our study found that the GSTP1 rs1695 polymorphism was significantly correlated with platinum-induced toxicities. The study also revealed that rs1695 expression exhibited tissue-specific patterns and thus yielded opposite effects in different tissues. A personalized chemotherapy treatment based on these polymorphisms may be considered for cancer patients in the future.

Disparities in healthcare expenditures according to economic status in cancer patients undergoing end-of-life care.

BACKGROUNDS: A desire for better outcome influences cancer patients' willingness to pay. Whilst cancer-related costs are known to have a u-shaped distribution, the actual level of healthcare utilized by patients may vary depending on income and ability to pay. This study examined patterns of healthcare expenditures in the last year of life in patients with gastric, colorectal, lung, and liver cancer and analyzed whether differences exist in the level of end-of-life costs for cancer care according to economic status. METHODS: This study is a retrospective cohort study which used data from the Korean National Elderly Sampled Cohort, 2002 to 2015. End-of-life was defined as 1 year before death. Economic status was classified into three categorical variables according to the level of insurance premium (quantiles). The relationship between the dependent and independent variables were analyzed using multiple gamma regression based on the generalized estimated equation (GEE) model. RESULTS: This study included 3083 cancer patients, in which total healthcare expenditure was highest in the high-income group. End-of-life costs increased the most in the last 3 months of life. Compared to individuals in the 'middle' economic status group, those in the 'high' economic status group (RR 1.095, 95% CI 1.044-1.149) were likely to spend higher amounts. The percentage of individuals visiting a general hospital was highest in the 'high' economic status group, followed by the 'middle' and 'low' economic status groups. CONCLUSION: Healthcare costs for cancer care increased at end-of-life in Korea. Patients of higher economic status tended to spender higher amounts of end-of-life costs for cancer care. Further in-depth studies are needed considering that end-of-life medical costs constitute a large proportion of overall expenditures. This study offers insight by showing that expenditures for cancer care tend to increase noticeably in the last 3 months of life and that differences exist in the amount spent according economic status.

How Do Patients Value the Benefit of Minimally Invasive Surgery in Cancer Treatment?

OBJECTIVES: Due to the increasing cost of cancer treatment, the demand for value-based healthcare is increasing. Although several value frameworks have been developed recently in the field of oncology, the nononcological benefits of minimally invasive surgery have not been addressed. This study aimed to estimate how patients value nononcological benefits in minimally invasive cancer surgery. METHODS: The value that patients placed on various benefits of cancer surgery was termed throughout the study as patient value (PV). To quantize PVs for the benefits of cancer surgery, a one-tiered analytic hierarchy process model was constructed. The model includes 6 well-known surgical outcomes, including nononcological benefits. The study participants included 303 patients with cancer and family caregivers who participated in a questionnaire survey. RESULTS: The PVs for "decreased operation time," "reduced length of hospital stay," and "improved cosmetic results" were 0.050, 0.044, and 0.045, respectively, whereas the PVs for "increased survival," "prevention of disease recurrence," and "avoidance of complications" were 0.366, 0.292, and 0.203, respectively. The PV placed on nononcological benefits from minimally invasive surgery was one-tenth (10.2%) of the total value. CONCLUSIONS: Nononcological benefits arising from minimally invasive surgery were relatively small but nonnegligible. This value should be considered in the process of developing a value framework for cancer surgery and shared decision making.

Health-related quality of life among cancer patients and survivors and its relationship with current employment status.

INTRODUCTION: Health-related quality of life (HRQoL) is an important end point to measure in cancer patients and survivors. This study investigated whether differences in HRQOL exist between cancer patients, cancer survivors, and the general population, in addition to how employment status interplays in this relationship. METHODS: Data were from the 2019 Korea National Health & Nutrition Examination. HRQoL was measured using the HRQoL Instrument with 8 Items (HINT-8) index, which encompasses physical, mental, social, and health-related aspects. All variables were entered simultaneously into the fully adjusted model. Multiple regression analysis was used to evaluate the association between HRQoL in cancer patients and cancer survivors compared to the general population. An interaction analysis was conducted based on current employment status. RESULTS: A total of 3805 cancer patients, 109 cancer survivors, and 3609 individuals of the general population were included in this study. The HRQoL scores of cancer patients (ß: - 0.0221, p-value: 0.0218) were poorer compared to the general population with statistical significance. In contrast, the HRQoL scores of cancer survivors did not show statistically significant differences. The interaction term between cancer status and economic activity status was statistically significant for cancer patients * unemployed (ß: - 0.0557, p-value: 0.0020). CONCLUSION: Cancer patients had lower HRQoL than the general population. Additionally, the interaction analysis reveals that unemployed cancer patients have poorer HRQoL scores than the employed general population. The results reveal that cancer patients are vulnerable to decreases in HRQoL, in particular those who are currently unemployed.

Association between nuclear factor of activated T cells C2 polymorphisms and treatment response in rheumatoid arthritis patients receiving tumor necrosis factor-alpha inhibitors.

OBJECTIVES: Nuclear factor of activated T cells C2 (NFATC2) is known as a member of the transcription family and enhances tumor necrosis factor-alpha (TNF-α) synthesis in human T cells at the gene transcription level. Although NFATC2 has a potential role in rheumatoid arthritis (RA) progression and treatment, no study has investigated the association between NFATC2 gene polymorphisms and response status in RA patients receiving TNF-α inhibitors. This study aimed to examine the effects of polymorphisms in NFATC2, a TNF-α transcription factor, on response to TNF-α inhibitors. METHODS: This prospective observational study was performed in two centers. Seven single nucleotide polymorphisms (SNPs) were investigated. Good responders were defined as patients with disease activity score (DAS)28 ≤3.2 after 6 months of treatment. Logistic regression analyses were used to investigate the association between genetic polymorphisms and response to the treatment. To test the model's goodness of fit, a Hosmer-Lemeshow test was performed. RESULTS: This study included 98 patients, among whom 46 showed favorable responses to the treatment. Patients with hypertension revealed an approximately three-fold lower response to TNF-α inhibitors compared to those without hypertension (23.5 vs. 76.5%; P = 0.049). After adjusting for covariates, C allele carriers of NFATC2 rs3787186 exhibited approximately three-fold lower rates of treatment response compared to those with TT genotype (P = 0.037). The Hosmer-Lemeshow test showed that the fitness of the multivariable analysis model was satisfactory (χ2 = 9.745; 8 degrees of freedom; P = 0.283). CONCLUSION: This study suggested an association between the C allele of rs3787186 and treatment response in RA patients receiving TNF-α inhibitors.

Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models.

Targets of immune checkpoint inhibitors (ICIs) regulate immune homeostasis and prevent autoimmunity by downregulating immune responses and by inhibiting T cell activation. Although ICIs are widely used in immunotherapy because of their good clinical efficacy, they can also induce autoimmune-related adverse events. Thyroid-related adverse events are frequently associated with anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. The present study aims to investigate the factors associated with thyroid dysfunction in patients receiving PD-1 or PD-L1 inhibitors and to develop various machine learning approaches to predict complications. A total of 187 patients were enrolled in this study. Logistic regression analysis was conducted to investigate the association between such factors and adverse events. Various machine learning methods were used to predict thyroid-related complications. After adjusting for covariates, we found that smoking history and hypertension increase the risk of thyroid dysfunction by approximately 3.7 and 4.1 times, respectively (95% confidence intervals (CIs) 1.338-10.496 and 1.478-11.332, p = 0.012 and 0.007). In contrast, patients taking opioids showed an approximately 4.0-fold lower risk of thyroid-related complications than those not taking them (95% CI 1.464-11.111, p = 0.007). Among the machine learning models, random forest showed the best prediction, with an area under the receiver operating characteristic of 0.770 (95% CI 0.648-0.883) and an area under the precision-recall of 0.510 (95%CI 0.357-0.666). Hence, this study utilized various machine learning models for prediction and showed that factors such as smoking history, hypertension, and opioids are associated with thyroid-related adverse events in cancer patients receiving PD-1/PD-L1 inhibitors.

Association of TLR 9 gene polymorphisms with remission in patients with rheumatoid arthritis receiving TNF-α inhibitors and development of machine learning models.

Toll-like receptor (TLR)-4 and TLR9 are known to play important roles in the immune system, and several studies have shown their association with the development of rheumatoid arthritis (RA) and regulation of tumor necrosis factor alpha (TNF-α). However, studies that investigate the association between TLR4 or TLR9 gene polymorphisms and remission of the disease in RA patients taking TNF-α inhibitors have yet to be conducted. In this context, this study was designed to investigate the effects of polymorphisms in TLR4 and TLR9 on response to TNF-α inhibitors and to train various models using machine learning approaches to predict remission. A total of six single nucleotide polymorphisms (SNPs) were investigated. Logistic regression analysis was used to investigate the association between genetic polymorphisms and response to treatment. Various machine learning methods were utilized for prediction of remission. After adjusting for covariates, the rate of remission of T-allele carriers of TLR9 rs352139 was about 5 times that of the CC-genotype carriers (95% confidence interval (CI) 1.325-19.231, p = 0.018). Among machine learning algorithms, multivariate logistic regression and elastic net showed the best prediction with the area under the receiver-operating curve (AUROC) value of 0.71 (95% CI 0.597-0.823 for both models). This study showed an association between a TLR9 polymorphism (rs352139) and treatment response in RA patients receiving TNF-α inhibitors. Moreover, this study utilized various machine learning methods for prediction, among which the elastic net provided the best model for remission prediction.

Do Patients Residing in Provincial Areas Transport and Spend More on Cancer Treatment in Korea?

BACKGROUND: With the increasing burden of cancer worldwide, a need exists to investigate patterns of healthcare utilization and costs. This study aimed to investigate whether the area of residence is associated with the likelihood of a patient receiving treatment at an institution located outside their residing region. This study also analyzed whether medical travel was related to levels of healthcare utilization and costs. METHODS: This study used the 2007 to 2015 National Health Insurance (NHI) claims data. The residing area was categorized into capital area, metropolitan cities, and provincial area. Healthcare utilization was measured based on days of care and costs based on direct, covered medical costs. Chi-square test and analysis of variance (ANOVA) was conducted to investigate the general characteristics of the study population. The relationship between the dependent and independent variables were analyzed using the generalized estimating equation (GEE) model. RESULTS: Of the 64,505 participants included in this study, 19,975 (31.0%) visited medical institutions located outside their residing area. Compared to individuals residing in the capital area, those living in provincial regions (OR 2.202, 95% CI 2.068-2.344) were more likely to visit medical institutions outside their residing area. Healthcare costs were higher in individuals receiving treatment at hospitals located elsewhere (RR 1.054, 95% CI 1.017-1.093). CONCLUSION: Cancer patients residing in provincial areas were likely to visit institutions located outside their residing area for treatment. Medical travel was associated with higher levels of spent healthcare costs. Policies should focus on preventing possible related regional cancer disparity and promoting optimal configuration of cancer services.