BACKGROUND: Globally, over 39% of individuals are obese. Metabolic syndrome, usually accompanied by obesity, is regarded as a major contributor to noncommunicable diseases. Given this relationship, the concepts of metabolically healthy and unhealthy obesity, considering metabolic status, have been evolving. Attention is being directed to metabolically healthy people with obesity who have relatively low transition rates to noncommunicable diseases. As obesity rates continue to rise and unhealthy behaviors prevail among young adults, there is a growing need for obesity management that considers these metabolic statuses. A nomogram can be used as an effective tool to predict the risk of transitioning to metabolically unhealthy obesity from a metabolically healthy status.
OBJECTIVE: The study aimed to identify demographic factors, health behaviors, and 5 metabolic statuses related to the transition from metabolically healthy obesity to unhealthy obesity among people aged between 20 and 44 years and to develop a screening tool to predict this transition.
METHODS: This secondary analysis study used national health data from the National Health Insurance System in South Korea. We analyzed the customized data using SAS (SAS Institute Inc) and conducted logistic regression to identify factors related to the transition from metabolically healthy to unhealthy obesity. A nomogram was developed to predict the transition using the identified factors.
RESULTS: Among 3,351,989 people, there was a significant association between the transition from metabolically healthy to unhealthy obesity and general characteristics, health behaviors, and metabolic components. Male participants showed a 1.30 higher odds ratio for transitioning to metabolically unhealthy obesity than female participants, and people in the lowest economic status were also at risk for the transition (odds ratio 1.08, 95% CI 1.05-1.1). Smoking status, consuming >30 g of alcohol, and insufficient regular exercise were negatively associated with the transition. Each relevant variable was assigned a point value. When the nomogram total points reached 295, the shift from metabolically healthy to unhealthy obesity had a prediction rate of >50%.
CONCLUSIONS: This study identified key factors for young adults transitioning from healthy to unhealthy obesity, creating a predictive nomogram. This nomogram, including triglycerides, waist circumference, high-density lipoprotein-cholesterol, blood pressure, and fasting glucose, allows easy assessment of obesity risk even for the general population. This tool simplifies predictions amid rising obesity rates and interventions.

This study aimed to establish an effective predictive model for postoperative delirium (POD) risk assessment after total knee arthroplasty (TKA) in older patients. The clinical data of 446 older patients undergoing TKA in the Orthopedics Department of our University from January to December 2022 were retrospectively analyzed, and the POD risk prediction model of older patients after TKA was established. Finally, 446 patients were included, which were divided into training group (n = 313) and verification group (n = 133). Logistic regression method was used to select meaningful predictors. The prediction model was constructed with nomographs, and the model was evaluated with correction curve and receiver operating characteristic curve. The logistic regression analysis showed that age, educational level, American Society of Anesthesiologists grade, accompaniment of chronic obstructive pulmonary disease, accompaniment of cerebral stroke, postoperative hypoxemia, long operation time, and postoperative pain were independent risk factors for POD after TKA (P < .05). The nomogram prediction model established. The area under receiver operating characteristic curve of the model group and the validation group were 0.954 and 0.931, respectively. The calibration curve of the prediction model has a high consistency between the 2 groups. The occurrence of POD was associated with age, educational level, American Society of Anesthesiologists grade, accompaniment of chronic obstructive pulmonary disease, accompaniment of cerebral stroke, postoperative hypoxemia, long operation time, and postoperative pain in TKA patients.

BACKGROUND: Currently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules.
METHODS: The current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People\'s Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model\'s performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).
RESULTS: A cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804-0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application.
CONCLUSIONS: In summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.

OBJECTIVE: Although many prognostic factors in neonates with congenital diaphragmatic hernia (CDH) have been described, no consensus thus far has been reached on which and how many factors are involved. The aim of this study is to analyze the association of multiple prenatal and postnatal factors with 1-month mortality of neonates with CDH and to construct a nomogram prediction model based on significant factors.
METHODS: A retrospective analysis of neonates with CDH at our center from 2013 to 2022 was conducted. The primary outcome was 1-month mortality. All study variables were obtained either prenatally or on the first day of life. Risk for 1-month mortality of CDH was quantified by odds ratio (OR) with 95% confidence interval (CI) in multivariable logistic regression models.
RESULTS: After graded multivariable adjustment, six factors were found to be independently and consistently associated with the significant risk of 1-month mortality in neonates with CDH, including gestational age of prenatal diagnosis (OR, 95% CI, P value: 0.845, 0.772 to 0.925, < 0.001), observed-to-expected lung-to-head ratio (0.907, 0.873 to 0.943, < 0.001), liver herniation (3.226, 1.361 to 7.648, 0.008), severity of pulmonary hypertension (6.170, 2.678 to 14.217, < 0.001), diameter of defect (1.560, 1.084 to 2.245, 0.017), and oxygen index (6.298, 3.383 to 11.724, < 0.001). Based on six significant factors identified, a nomogram model was constructed to predict the risk for 1-month mortality in neonates with CDH, and this model had decent prediction accuracy as reflected by the C-index of 94.42%.
CONCLUSIONS: Our findings provide evidence for the association of six preoperational and intraoperative factors with the risk of 1-month mortality in neonates with CDH, and this association was reinforced in a nomogram model.

The diagnosis of brain metastases (BMs) in patients with lung cancer (LC) predominantly relies on magnetic resonance imaging (MRI), a method that is constrained by high costs and limited accessibility. This study explores the potential of serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) as screening biomarkers for BMs in LC patients. We conducted a retrospective analysis of 700 LC cases at the National Cancer Center, Korea, from July 2020 to June 2022, measuring sNfL and sGFAP levels at initial LC diagnosis. The likelihood of BM was evaluated using multivariate analysis and a predictive nomogram. Additionally, we prospectively monitored 177 samples from 46 LC patients initially without BM. Patients with BMs (n= 135) had significantly higher median sNfL (52.5 pg/mL) and sGFAP (239.2 pg/mL) levels compared to those without BMs (n = 565), with medians of 17.8 pg/mL and 141.1 pg/mL, respectively (p < 0.001 for both). The nomogram, incorporating age, sNfL, and sGFAP, predicted BM with an area under the curve (AUC) of 0.877 (95% CI 0.84-0.914), showing 74.8% sensitivity and 83.5% specificity. Over nine months, 93% of samples from patients without BM remained below the cutoff, while all patients developing BMs showed increased levels at detection. A nomogram incorporating age, sNfL, and sGFAP provides a valuable tool for identifying LC patients at high risk for BM, thereby enabling targeted MRI screenings and enhancing diagnostic efficiency.

OBJECTIVE: To analyze the factors affecting overall survival (OS) of adult patients with core-binding factor acute myeloid leukemia (CBF-AML) and establish a prediction model.
METHODS: A total of 216 newly diagnosed patients with CBF-AML in the First Affiliated Hospital of Zhengzhou University from May 2015 to July 2021 were retrospectively analyzed. The 216 CBF-AML patients were divided into the training and the validation cohort at 7∶3 ratio. The Cox regression model was used to analyze the clinical factors affecting OS. Stepwise regression was used to establish the optimal model and the nomogram. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the model performance.
RESULTS: Age(≥55 years old), peripheral blood blast(≥80%), fusion gene (AML1-ETO), KIT mutations were identified as independent adverse factors for OS. The area under the ROC curve at 3-year was 0.772 and 0.722 in the training cohort and validation cohort, respectively. The predicted value of the calibration curve is in good agreement with the measured value. DCA shows that this model performs better than a single factor.
CONCLUSIONS: This prediction model is simple and feasible, and can effectively predict the OS of CBF-AML, and provide a basis for treatment decision.
UNASSIGNED: 核心结合因子相关成人急性髓系白血病患者总生存临床预测模型的建立.
UNASSIGNED: 分析影响核心结合因子相关成人急性髓系白血病（CBF-AML）患者总生存(OS)的因素，并建立预测模型。.
UNASSIGNED: 回顾性分析2015年5月至2021年7月在郑州大学第一附属医院新诊断的216例CBF-AML的临床资料。将患者按照7∶3随机分成训练集和验证集。采用Cox回归模型对影响OS的临床因素进行分析。采用逐步回归建立最优模型，画出列线图。使用受试者工作特征(ROC)曲线、校准曲线和决策曲线分析(DCA)评估模型性能。.
UNASSIGNED: 年龄≥55 岁、外周血原始幼稚≥80%、AML1-ETO、KIT突变被确定为OS的独立预后不良因素。训练集和验证集3年ROC下面积分别为0.772、0.722；校正曲线预测值与实测值具有较好一致性。DCA表明此模型性能优于单一因素。.
UNASSIGNED: 该预测模型简便易行，可有效预测CBF-AML的OS，为治疗决策提供依据。.

OBJECTIVE: To develop and validate a nomogram for predicting recurrence-free survival (RFS) for clinical T1/2 (cT1/2) clear cell renal cell carcinoma (ccRCC) patients after nephrectomy.
METHODS: Clinicopathological and survival data from 1289 cT1/2 ccRCC patients treated at the Second Hospital of Tianjin Medical University between 2017 and 2020 were included. Cox regression analysis was used to identify independent risk factors in 902 and 387 ccRCC patients in the training and validation cohorts, respectively, and construct the nomogram. The performance of the nomogram was assessed through calibration plots, time-dependent receiver operating characteristic (ROC) curves, C-index (concordance-index), and decision curve analysis (DCA). Kaplan-Meier curves were used to evaluate the probability of RFS in patients with different recurrence risks.
RESULTS: Age, tumor size, surgical approach, Fuhrman grade, and pT3a upstage were identified as independent predictors of RFS. The area under the curve (AUC) for the 3-year and 5-year RFS ROC curves were 0.791 and 0.835 in the training cohort, and 0.860 and 0.880 in the validation cohort. The DCA and calibration plots demonstrated the optimal application and excellent accuracy of the nomogram for predicting 3-year and 5-year RFS. Kaplan-Meier curves revealed significant differences in RFS among the three risk groups in both the training and validation cohorts. Clinically, the developed nomogram provides a more precise tool for risk stratification, enabling tailored postoperative management and surveillance strategies, ultimately aiming to improve patient outcomes.
CONCLUSIONS: We developed a nomogram for predicting RFS in cT1/2 ccRCC patients after nephrectomy with high accuracy. The clinical implementation of this nomogram can significantly enhance clinical decision-making, leading to improved patient outcomes and optimized resource utilization in the management of ccRCC.

BACKGROUND: This study aims to construct a model predicting the probability of RF in AECOPD patients upon hospital admission.
METHODS: This study retrospectively extracted data from MIMIC-IV database, ultimately including 3776 AECOPD patients. The patients were randomly divided into a training set (n = 2643) and a validation set (n = 1133) in a 7:3 ratio. First, LASSO regression analysis was used to optimize variable selection by running a tenfold k-cyclic coordinate descent. Subsequently, a multifactorial Cox regression analysis was employed to establish a predictive model. Thirdly, the model was validated using ROC curves, Harrell\'s C-index, calibration plots, DCA, and K-M curve.
RESULTS: Eight predictive indicators were selected, including blood urea nitrogen, prothrombin time, white blood cell count, heart rate, the presence of comorbid interstitial lung disease, heart failure, and the use of antibiotics and bronchodilators. The model constructed with these 8 predictors demonstrated good predictive capabilities, with ROC curve areas under the curve (AUC) of 0.858 (0.836-0.881), 0.773 (0.746-0.799), 0.736 (0.701-0.771) within 3, 7, and 14 days in the training set, respectively and the C-index was 0.743 (0.723-0.763). Additionally, calibration plots indicated strong consistency between predicted and observed values. DCA analysis demonstrated favorable clinical utility. The K-M curve indicated the model\'s good reliability, revealed a significantly higher RF occurrence probability in the high-risk group than that in the low-risk group (P < 0.0001).
CONCLUSIONS: The nomogram can provide valuable guidance for clinical practitioners to early predict the probability of RF occurrence in AECOPD patients, take relevant measures, prevent RF, and improve patient outcomes.

BACKGROUND: Skin cutaneous melanoma (SKCM) is an aggressive form of malignant melanoma with poor prognosis and high mortality rates. Disulfidptosis is a newly discovered cell death regulatory mechanism caused by the abnormal accumulation of disulfides. This unique pathway is guiding significant new research to understand cancer progression for targeted treatment. However, the correlation between disulfidptosis with long non-coding RNAs (lncRNAs) in SKCM remains unknown at present.
METHODS: The Cancer Genome Atlas database furnished lncRNA expression data and clinical information for SKCM patients. Pearson correlation and Cox regression analyses identified disulfidptosis-related lncRNAs associated with SKCM prognosis. ROC curves and a nomogram validated the model. TME, immune infiltration, GSEA analysis, immune checkpoint gene expression profiling, and drug sensitivity were assessed in high and low-risk groups. Consistent clustering categorized SKCM patients for personalized clinical treatment guidance.
RESULTS: A total of twelve disulfidptosis-related lncRNAs were identified for the development of prognosis prediction models. The area under the curve (AUC) values of the ROC curve and the nomogram provided reliable discrimination to evaluate the prognostic potential for SKCM patients. The TME played a crucial role in tumorigenesis, progression and prognosis, and the risk scores were closely related to immune cell infiltration. Meanwhile, the combination of chemotherapy, targeted therapy, and immunotherapy was recommended for low-risk patients based on drug sensitivity and immune efficacy analyses.
CONCLUSIONS: We identified a risk model of twelve disulfidptosis-related lncRNAs that could be used to predict the prognosis of SKCM patients and help guide immunotherapy and chemotherapy for personalized treatment plans.

Despite remarkable advancements in the treatment of multiple myeloma (MM), relapse remains a challenge. However, the mechanisms underlying this disease remain unclear. This study aimed to identify potential biomarkers that could open new avenues for MM treatment. Microarray data and clinical characteristics of patients with MM were obtained from the Gene Expression Omnibus database. Differential expression analysis and protein-protein interaction (PPI) network construction were used to identify hub genes associated with MM. Predictive performance was further assessed using receiver operating characteristic curves and nomogram construction. Functional enrichment analysis was conducted to investigate possible mechanisms. Mendelian randomization (MR) was used to evaluate the causal relationship between the crucial gene and MM risk. Topological analysis of the PPI network revealed five hub genes associated with MM, with myeloperoxidase (MPO) being the key gene owing to its highest degree and area under the curve values. MPO showed significant differences between patients with MM and controls across all datasets. Functional enrichment analysis revealed a strong association between MPO and immune-related pathways in MM. MR analysis confirmed a causal relationship between MPO and the risk of MM. By integrating microarray analysis and MR, we successfully identified and validated MPO as a promising biomarker for MM that is potentially implicated in MM pathogenesis and progression through immune-related pathways.