Background: Obesity is a well-known risk factor for chronic kidney disease and its progression. However, the impact of obesity on the renal function of the elderly population is uncertain. We investigated the association between obesity and renal outcomes in the elderly. Methods: We analyzed 130,504 participants from the Korean National Health Insurance Service-Senior cohort. Obesity was classified according to body mass index (BMI), sex-specific waist circumference (WC), and the presence of metabolic syndrome. The primary outcome was renal function decline, defined as a decline in the estimated glomerular filtration rate (eGFR) of at least 50% from baseline or new-onset end-stage renal disease. Results: During a follow-up period of 559,531.1 person-years (median, 4.3 years), 2,486 participants (19.0%; incidence rate of 4.44 per 1,000 person-years) showed renal function decline. A multivariate Cox proportional hazards model revealed that BMI/WC was not associated with renal function decline. However, the group with metabolic syndrome had a significantly increased risk of renal function decline compared to the group without metabolic syndrome (adjusted hazard ratio [HR], 1.24; 95% confidence interval [CI], 1.13–1.36). Compared with the non-metabolic syndrome group, the adjusted HRs (95% CI) for participants with one through five components were 0.96 (0.84–1.11), 1.10 (0.96–1.27), 1.24 (1.06–1.45), 1.37 (1.12–1.66), and 1.99 (1.42–2.79), respectively (p for trend < 0.001). Conclusion In elderly Korean adults, metabolic syndrome and the number of its components were associated with a higher risk of renal function decline, but BMI or WC was not significant.

Early mortality following hemodialysis initiation hinders survival improvement in older patients. This study aimed to develop a clinical risk model for predicting 6-month mortality after dialysis initiation in older Korean hemodialysis patients. Methods: We analyzed data from incident hemodialysis patients aged >70 years from the Korean Society of Geriatric Nephrology (KSGN) database. A prediction model was developed using multivariate logistic regression analysis and externally validated with independent datasets. Results: Among 1,751 incident hemodialysis patients, the 6-month mortality rate was 15.5%. Using multivariate logistic analysis, we constructed the KSGN score as an independent risk factor for 6-month mortality, and its components and score are as follows: old age at dialysis initiation (≥85 years, score 2); hypertension and renovascular disease as a primary etiology of end-stage kidney disease (ESKD) (score 1); malignancy history (yes, score 1); low serum albumin (<3.5 g/dL, score 1); hypertension treatment (yes, score –1); prepared vascular access on maintenance dialysis (arteriovenous fistula/arteriovenous graft, score –3). In the development cohort, the area under the curve (AUC) for the KSGN score was significantly higher than the Alberta Wick’s score (0.707 vs. 0.683, p = 0.001). In the validation cohort, the KSGN score’s performance was comparable to existing models. Conclusion: The KSGN score may be a valuable tool for predicting early mortality after dialysis initiation in older patients with ESKD, aiding in decision-making and management regarding dialysis initiation.

Background: Bulky or multiple lymph node (LN) metastases are associated with poor prognosis in cervical cancer, and the size or number of LN metastases is not yet reflected in the staging system and therapeutic strategy. Although the therapeutic effects of surgical resection of bulky LNs before standard treatment have been reported in several retrospective studies, wellplanned randomized clinical studies are lacking. Therefore, the aim of the Korean Gynecologic Oncology Group (KGOG) 1047/DEBULK trial is to investigate whether the debulking surgery of bulky or multiple LNs prior to concurrent chemoradiation therapy (CCRT) improves the survival rate of patients with cervical cancer IIICr diagnosed by imaging tests. Methods The KGOG 1047/DEBULK trial is a phase III, multicenter, randomized clinical trial involving patients with bulky or multiple LN metastases in cervical cancer IIICr. This study will include patients with a short-axis diameter of a pelvic or para-aortic LN ≥2 cm or ≥3 LNs with a short-axis diameter ≥1 cm and for whom CCRT is planned. The treatment arms will be randomly allocated in a 1:1 ratio to either receive CCRT (control arm) or undergo surgical debulking of bulky or multiple LNs before CCRT (experimental arm). CCRT consists of extended-field external beam radiotherapy/pelvic radiotherapy, brachytherapy and LN boost, and weekly chemotherapy with cisplatin (40 mg/m 2 ), 4–6 times administered intravenously.The primary endpoint will be 3-year progression-free survival rate. The secondary endpoints will be 3-year overall survival rate, treatment-related complications, and accuracy of radiological diagnosis of bulky or multiple LNs.

Background: Bulky or multiple lymph node (LN) metastases are associated with poor prognosis in cervical cancer, and the size or number of LN metastases is not yet reflected in the staging system and therapeutic strategy. Although the therapeutic effects of surgical resection of bulky LNs before standard treatment have been reported in several retrospective studies, wellplanned randomized clinical studies are lacking. Therefore, the aim of the Korean Gynecologic Oncology Group (KGOG) 1047/DEBULK trial is to investigate whether the debulking surgery of bulky or multiple LNs prior to concurrent chemoradiation therapy (CCRT) improves the survival rate of patients with cervical cancer IIICr diagnosed by imaging tests. Methods The KGOG 1047/DEBULK trial is a phase III, multicenter, randomized clinical trial involving patients with bulky or multiple LN metastases in cervical cancer IIICr. This study will include patients with a short-axis diameter of a pelvic or para-aortic LN ≥2 cm or ≥3 LNs with a short-axis diameter ≥1 cm and for whom CCRT is planned. The treatment arms will be randomly allocated in a 1:1 ratio to either receive CCRT (control arm) or undergo surgical debulking of bulky or multiple LNs before CCRT (experimental arm). CCRT consists of extended-field external beam radiotherapy/pelvic radiotherapy, brachytherapy and LN boost, and weekly chemotherapy with cisplatin (40 mg/m 2 ), 4–6 times administered intravenously.The primary endpoint will be 3-year progression-free survival rate. The secondary endpoints will be 3-year overall survival rate, treatment-related complications, and accuracy of radiological diagnosis of bulky or multiple LNs.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Background: Bulky or multiple lymph node (LN) metastases are associated with poor prognosis in cervical cancer, and the size or number of LN metastases is not yet reflected in the staging system and therapeutic strategy. Although the therapeutic effects of surgical resection of bulky LNs before standard treatment have been reported in several retrospective studies, wellplanned randomized clinical studies are lacking. Therefore, the aim of the Korean Gynecologic Oncology Group (KGOG) 1047/DEBULK trial is to investigate whether the debulking surgery of bulky or multiple LNs prior to concurrent chemoradiation therapy (CCRT) improves the survival rate of patients with cervical cancer IIICr diagnosed by imaging tests. Methods The KGOG 1047/DEBULK trial is a phase III, multicenter, randomized clinical trial involving patients with bulky or multiple LN metastases in cervical cancer IIICr. This study will include patients with a short-axis diameter of a pelvic or para-aortic LN ≥2 cm or ≥3 LNs with a short-axis diameter ≥1 cm and for whom CCRT is planned. The treatment arms will be randomly allocated in a 1:1 ratio to either receive CCRT (control arm) or undergo surgical debulking of bulky or multiple LNs before CCRT (experimental arm). CCRT consists of extended-field external beam radiotherapy/pelvic radiotherapy, brachytherapy and LN boost, and weekly chemotherapy with cisplatin (40 mg/m 2 ), 4–6 times administered intravenously.The primary endpoint will be 3-year progression-free survival rate. The secondary endpoints will be 3-year overall survival rate, treatment-related complications, and accuracy of radiological diagnosis of bulky or multiple LNs.

Tables and figures are commonly adopted methods for presenting specific data or statistical analysis results. Figures can be used to display characteristics and distributions of data, allowing for intuitive understanding through visualization and thus making it easier to interpret the statistical results. To maximize the positive aspects of figure presentation and increase the accuracy of the content, in this article, the authors will describe how to choose an appropriate figure type and the necessary components to include. Additionally, this article includes examples of figures that are commonly used in research and their essential components using virtual data.

Purpose: This study aimed to confirm the decision-making patterns for life-sustaining treatment (LST) and analyze medical service utilization changes after enforcement of the Life-Sustaining Treatment Decision-Making Act. Materials and Methods: Of 1,237 patients who completed legal forms for life-sustaining treatment (hereafter called the LST form) at three academic hospitals and died at the same institutions, 1,018 cancer patients were included. Medical service utilization and costs were analyzed using claims data. Results: The median time to death from completion of the LST form was three days (range, 0 to 248 days). Of these, 517 people died within two days of completing the document, and 36.1% of all patients prepared the LST form themselves. The frequency of use of the intensive care unit, continuous renal replacement therapy, and mechanical ventilation was significantly higher when the families filled out the form without knowing the patient’s intention. In the top 10% of the medical expense groups, the decision-makers for LST were family members rather than patients (28% patients vs. 32% family members who knew and 40% family members who did not know the patient’s intention). Conclusion The cancer patient’s own decision-making rather than the family’s decision was associated with earlier decision-making, less use of some critical treatments (except chemotherapy) and expensive evaluations, and a trend toward lower medical costs.

This article introduces a crossover design that is often used in clinical studies, with the advantage of comparing treatment effects within one study subject. In particular, the advantages and disadvantages of the two-period, two-sequence crossover design (2 × 2 or AB/BA crossover design), which is widely used in clinical practice, are identified, and the elements necessary for analysis are introduced. This article explains the carryover effect, period effect, sequence effect, and period-by-treatment interaction in a crossover design and examines the analysis commands of SAS along with example data. After confirming the carryover effect using a general linear model, the treatment effect is analyzed using a linear mixed effect model.

Background: There is a debate regarding the safety of etomidate. We evaluated the effects of etomidate on mortality in a large cohort of critical care patients. Methods: This retrospective matched-cohort study was performed using the Medical Information Mart for Intensive Care version 3 (MIMIC-III) database. Among 12,526 adult patients who were prescribed etomidate or propofol on the first day of mechanical ventilation, 625 patients administered etomidate were statistically matched with 6,250 patients administered propofol. The primary outcome measures were all-cause in-hospital mortality, 48-hour survival, cardiovascular morbidity, and infectious morbidity. Logistic regression analysis with stepwise selection of variables was performed to examine the dose–mortality relationship of etomidate. Results: All-cause in-hospital mortality was 1.84 times higher in the etomidate cohort (OR, 1.84; 98.75% CI, 1.42, 2.37). Compared to the propofol cohort, the etomidate cohort showed 57% lower odds of 48-hour survival (0.43 [0.27, 0.73]), no difference in odds of cardiovascular morbidity (0.86 [0.66, 1.12]), and 1.77 times higher odds of infectious morbidity (1.77 [1.35, 2.31]). Additionally, the odds of mortality increased by 1.36 times per 0.1 mg/kg of etomidate (1.36 [95% CI: 1.23, 1.49]). Conclusions Etomidate is a poor choice as a hypnotic drug on the first day of mechanical ventilation, as it is associated with a dose-dependent increase in all-cause mortality, and does not improve survival for the first 48 h.