Identification and immunological characterization of genes associated with ferroptosis in Alzheimer's disease and experimental demonstration.

 The incidence of Alzheimer's disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis­related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.

A nomogram for prediction of early mortality in patients undergoing cardiac surgery for infective endocarditis: a retrospective single-center study.

OBJECTIVE: Despite advancements in surgical techniques, operations for infective endocarditis (IE) remain associated with relatively high mortality. The aim of this study was to develop a nomogram model to predict the early postoperative mortality in patients undergoing cardiac surgery for infective endocarditis based on the preoperative clinical features. METHODS: We retrospectively analyzed the clinical data of 357 patients with IE who underwent surgeries at our center between January 2007 and June 2023. Independent risk factors for early postoperative mortality were identified using univariate and multivariate logistic regression models. Based on these factors, a predictive model was developed and presented in a nomogram. The performance of the nomogram was evaluated through the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). Internal validation was performed utilizing the bootstrapping method. RESULTS: The nomogram included nine predictors: age, stroke, pulmonary embolism, albumin level, cardiac function class IV, antibotic use <4weeks, vegetation size ≥1.5 cm, perivalvular abscess and preoperative dialysis. The area under the ROC curve (AUC) of the model was 0.88 (95%CI:0.80-0.96). The calibration plot indicated strong prediction consistency of the nomogram with satisfactory Hosmer-Lemeshow test results (χ2 = 13.490, p = 0.142). Decision curve analysis indicated that the nomogram model provided greater clinical net benefits compared to "operate-all" or "operate-none" strategies. CONCLUSIONS: The innovative nomogram model offers cardiovascular surgeons a tool to predict the risk of early postoperative mortality in patients undergoing IE operations. This model can serve as a valuable reference for preoperative decision-making and can enhance the clinical outcomes of IE patients.

Development and validation of an intraoperative hypothermia nomograph model for patients undergoing video-assisted thoracoscopic lobectomy: a retrospective study.

This study aimed to develop and internally validate a nomogram model for assessing the risk of intraoperative hypothermia in patients undergoing video-assisted thoracoscopic (VATS) lobectomy. This study is a retrospective study. A total of 530 patients who undergoing VATS lobectomy from January 2022 to December 2023 in a tertiary hospital in Wuhan were selected. Patients were divided into hypothermia group (n = 346) and non-hypothermia group (n = 184) according to whether hypothermia occurred during the operation. Lasso regression was used to screen the independent variables. Logistic regression was used to analyze the risk factors of hypothermia during operation, and a nomogram model was established. Bootstrap method was used to internally verify the nomogram model. Receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model. Calibration curve and Hosmer Lemeshow test were used to evaluate the accuracy of the model. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. Intraoperative hypothermia occurred in 346 of 530 patients undergoing VATS lobectomy (65.28%). Logistic regression analysis showed that age, serum total bilirubin, inhaled desflurane, anesthesia duration, intraoperative infusion volume, intraoperative blood loss and body mass index were risk factors for intraoperative hypothermia in patients undergoing VATS lobectomy (P < 0.05). The area under ROC curve was 0.757, 95% CI (0.714-0.799). The optimal cutoff value was 0.635, the sensitivity was 0.717, and the specificity was 0.658. These results suggested that the model was well discriminated. Calibration curve has shown that the actual values are generally in agreement with the predicted values. Hosmer-Lemeshow test showed that χ2 = 5.588, P = 0.693, indicating that the model has a good accuracy. The DCA results confirmed that the model had high clinical utility. The nomogram model constructed in this study showed good discrimination, accuracy and clinical utility in predicting patients with intraoperative hypothermia, which can provide reference for medical staff to screen high-risk of intraoperative hypothermia in patients undergoing VATS lobectomy.

A clinical tool to predict overall survival of elderly patients with soft tissue sarcoma after surgical resection.

With the aging world population, the incidence of soft tissue sarcoma (STS) in the elderly gradually increases and the prognosis is poor. The primary goal of this research was to analyze the relevant risk factors affecting the postoperative overall survival in elderly STS patients and to provide some guidance and assistance in clinical treatment. The study included 2,353 elderly STS patients from the Surveillance, Epidemiology, and End Results database. To find independent predictive variables, we employed the Cox proportional risk regression model. R software was used to develop and validate the nomogram model to predict postoperative overall survival. The performance and practical value of the nomogram were evaluated using calibration curves, the area under the curve, and decision curve analysis. Age, tumor primary site, disease stage, tumor size, tumor grade, N stage, and marital status, are the risk variables of postoperative overall survival, and the prognostic model was constructed on this basis. In the two sets, both calibration curves and receiver operating characteristic curves showed that the nomogram had high predictive accuracy and discriminative power, while decision curve analysis demonstrated that the model had good clinical usefulness. A predictive nomogram was designed and tested to evaluate postoperative overall survival in elderly STS patients. The nomogram allows clinical practitioners to more accurately evaluate the prognosis of individual patients, facilitates the progress of individualized treatment, and provides clinical guidance.

Nomogram to predict radicular grooves in maxillary lateral incisors in preoperative orthodontic population.

OBJECTIVES: This study aimed to develop and validate a predictive nomogram for diagnosing radicular grooves (RG) in maxillary lateral incisors (MLIs), integrating demographic information, anatomical measurements, and Cone Beam Computed Tomography (CBCT) data to diagnose the RG in MLIs based on the clinical observation before resorting to the CBCT scan. MATERIALS AND METHODS: A retrospective cohort of orthodontic patients from the School and Hospital of Stomatology, Wuhan University, was analyzed, including demographic characteristics, photographic anatomical assessments, and CBCT diagnoses. The cohort was divided into development and validation groups. Univariate and multivariate logistic regression analyses identified significant predictors of RG, which informed the development of a nomogram. This nomogram's performance was validated using receiver operating characteristic analysis. RESULTS: The study included 381 patients (64.3% female) and evaluated 760 MLIs, with RG present in 26.25% of MLIs. The nomogram incorporated four significant anatomical predictors of RG presence, demonstrating substantial predictive efficacy with an area under the curve of 0.75 in the development cohort and 0.71 in the validation cohort. CONCLUSIONS: A nomogram for the diagnosis of RG in MLIs was successfully developed. This tool offers a practical checklist of anatomical predictors to improve the diagnostic process in clinical practice. CLINICAL RELEVANCE: The developed nomogram provides a novel, evidence-based tool to enhance the detection and treatment planning of MLIs with RG in diagnostic and therapeutic strategies.

Two novel nomograms predict 30-day mortality after off-pump coronary artery bypass grafting.

Background: With the development of surgical techniques and medical equipment, the mortality rate of off-pump coronary artery bypass grafting (CABG) has been declining year by year, but there is a lack of convenient and accurate predictive models. This study aims to use two nomograms to predict 30-day mortality after off-pump CABG. Methods: Patients with isolated off-pump CABG from January 2016 to January 2021 were consecutively enrolled. Potential predictive factors were first screened by lasso regression, and then predictive models were constructed by multivariate logistic regression. To earlier identify high-risk patients, two nomograms were constructed for predicting mortality risk before and after surgery. Results: A total of 1840 patients met the inclusion and exclusion criteria. The 30-day mortality was 3.97 % (73/1840) in this cohort. Multivariate logistic analysis showed that age, BMI<18.5 kg/m2, surgical time, creatinine, LVEF, history of previous stroke, and major adverse intraoperative events (including conversion to cardiopulmonary bypass or implantation of intra-aortic balloon pump) were independently associated with 30-day mortality. Model 1 contained preoperative and intraoperative variables, and the AUC was 0.836 (p < 0.001). The AUC of the K-fold validation was 0.819. Model 2 was only constructed by preoperative information. The AUC was 0.745 (p < 0.001). The AUC of the K-fold validation was 0.729. The predictive power of Model 1 was significantly higher than the SinoScore (DeLong's test p < 0.001). Conclusions: The two novel nomograms could be conveniently and accurately used to predict the risk of 30-day mortality after isolated off-pump CABG.

Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors.

Objectives: To develop and validate radiomics models utilizing endoscopic ultrasonography (EUS) images to distinguish insulinomas from non-functional pancreatic neuroendocrine tumors (NF-PNETs). Methods: A total of 106 patients, comprising 61 with insulinomas and 45 with NF-PNETs, were included in this study. The patients were randomly assigned to either the training or test cohort. Radiomics features were extracted from both the intratumoral and peritumoral regions, respectively. Six machine learning algorithms were utilized to train intratumoral prediction models, using only the nonzero coefficient features. The researchers identified the most effective intratumoral radiomics model and subsequently employed it to develop peritumoral and combined radiomics models. Finally, a predictive nomogram for insulinomas was constructed and assessed. Results: A total of 107 radiomics features were extracted based on EUS, and only features with nonzero coefficients were retained. Among the six intratumoral radiomics models, the light gradient boosting machine (LightGBM) model demonstrated superior performance. Furthermore, a peritumoral radiomics model was established and evaluated. The combined model, integrating both the intratumoral and peritumoral radiomics features, exhibited a comparable performance in the training cohort (AUC=0.876) and achieved the highest accuracy in predicting outcomes in the test cohorts (AUC=0.835). The Delong test, calibration curves, and decision curve analysis (DCA) were employed to validate these findings. Insulinomas exhibited a significantly smaller diameter compared to NF-PNETs. Finally, the nomogram, incorporating diameter and radiomics signature, was constructed and assessed, which owned superior performance in both the training (AUC=0.929) and test (AUC=0.913) cohorts. Conclusion: A novel and impactful radiomics model and nomogram were developed and validated for the accurate differentiation of NF-PNETs and insulinomas utilizing EUS images.

Development and validation of a nomogram predictive model for cognitive impairment in cerebral small vessel disease: a comprehensive retrospective analysis.

Background: Cerebral small vessel disease (CSVD) is a common neurodegenerative condition in the elderly, closely associated with cognitive impairment. Early identification of individuals with CSVD who are at a higher risk of developing cognitive impairment is crucial for timely intervention and improving patient outcomes. Objective: The aim of this study is to construct a predictive model utilizing LASSO regression and binary logistic regression, with the objective of precisely forecasting the risk of cognitive impairment in patients with CSVD. Methods: The study utilized LASSO regression for feature selection and logistic regression for model construction in a cohort of CSVD patients. The model's validity was assessed through calibration curves and decision curve analysis (DCA). Results: A nomogram was developed to predict cognitive impairment, incorporating hypertension, CSVD burden, apolipoprotein A1 (ApoA1) levels, and age. The model exhibited high accuracy with AUC values of 0.866 and 0.852 for the training and validation sets, respectively. Calibration curves confirmed the model's reliability, and DCA highlighted its clinical utility. The model's sensitivity and specificity were 75.3 and 79.7% for the training set, and 76.9 and 74.0% for the validation set. Conclusion: This study successfully demonstrates the application of machine learning in developing a reliable predictive model for cognitive impairment in CSVD. The model's high accuracy and robust predictive capability provide a crucial tool for the early detection and intervention of cognitive impairment in patients with CSVD, potentially improving outcomes for this specific condition.

Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients.

Objective: To identify independent risk factors of pulmonary infection in intensive care unit (ICU) patients, and to construct a prediction model. Methods: Medical data of 398 patients treated in the ICU of Jiaxing Hospital of Traditional Chinese Medicine from January 2019 to January 2023 were analyzed. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for pulmonary infection in ICU patients. R software was used to construct a nomogram prediction model, and the prediction model was internally validated using computer simulation bootstrap method. Predictive value of the model was analyzed using the receiver operating characteristic (ROC) curve. Results: A total of 97 ICU patients (24.37%) developed pulmonary infection. Age, ICU stay time, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness were all identified as risk factors for pulmonary infection. The calibration curve of the constructed nomogram prediction model showed a good consistency between the predicted value of the model and the actual observed value. ROC curve analysis showed that the area under the curve (AUC) of the model was 0.784 (95% CI: 0.731-0.837), indicating a certain predictive value. Conclusions: Age, length of stay in ICU, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness are risk factors for pulmonary infection in ICU patients. The nomogram prediction model constructed based on the above risk factors has shown a good predictive value.

Prediction model for severe maternal morbidity in pregnant women with hypertensive disorders of pregnancy.

Objectives: To investigate risk factors for severe maternal morbidity (SMM) in pregnant women with hypertensive disorders of pregnancy (HDP) and to develop a risk prediction model. Methods: A prospective observational cohort study was conducted among pregnant women who were hospitalized for hypertensive disorders of pregnancy (HDP) between January 2016 and December 2020 in Fujian College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Province, China (a training set), and a risk predictive model was constructed. Pregnant women with HDP who were hospitalized between January 2021 and December 2021 were selected as a validation set. Concordance index (C-index) and calibration curves were used to test predictive model discrimination and calibration. Results: We included 970 pregnant women (790 in the training set and 180 in the validation set). Least absolute shrinkage and selection operator regression was used to screen for nine related variables such as intra-uterine growth retardation (IUGR), diastolic blood pressure (DBP) and systolic blood pressure (SBP) at suspected diagnosis, total bilirubin, albumin (ALB), uric acid, total cholesterol, serum magnesium, and suspected gestational age. SBP at suspected diagnosis (OR =1.22, 95%CI:1.08-1.42) and total cholesterol (OR = 1.78, 95%CI:1.17-2.80) were independent risk factors of severe maternal morbidity in pregnant women with HDP. A nomogram was constructed, and internal validation of the nomogram model was done using the bootstrap self-sampling method. C-index in the training and the validation set was 0.798 and 0.909, respectively. Conclusion: Our prediction model can be used to determine gestational hypertension severity in pregnant women.

Risk factors and prediction model of delirium in elderly patients after hip arthroplasty.

Objective: To analyze the risk factors of delirium in elderly patients after hip arthroplasty and to construct a prediction model. Methods: Clinical data of 248 elderly patients who underwent hip arthroplasty in the Department of Traumatology and Orthopedics at Wuhan Fourth Hospital were retrospectively collected from November 2021 to February 2023. Logistic regression analysis was used to identify the risk factors of delirium after hip arthroplasty, and a nomogram prediction model was constructed using the RMS package of R4.1.2 software. The accuracy and stability of the model was evaluated based on the Hosmer-Lemeshow goodness-of-fit test and the receiver operating characteristic (ROC) curve. Results: Age, nighttime sleep, anesthesia method, intraoperative blood loss, hypoxemia, and C-reactive protein (CRP) level were all risk factors of delirium after the hip arthroplasty (P<0.05). These factors were used to construct a nomogram prediction model that was internally validated using the Bootstrap method. The prediction model had the area under ROC curve (AUC) of 0.980 (95% CI: 0.964-0.996), indicating that it has certain predictive value for postoperative delirium. When the optimal cut off value was selected, the sensitivity and specificity were 92.7% and 92.3%, respectively, indicating that the prediction model is effective. Conclusions: Age, short nighttime sleep, general anesthesia, high intraoperative blood loss, hypoxemia, and high CRP levels are independent risk factors for delirium after hip arthroplasty. The nomogram prediction model constructed based on these risk factors can effectively predict delirium in elderly patients after hip arthroplasty.

Establishment and validation of a bad outcomes prediction model based on EEG and clinical parameters in prolonged disorder of consciousness.

Objective: This study aimed to explore the electroencephalogram (EEG) indicators and clinical factors that may lead to poor prognosis in patients with prolonged disorder of consciousness (pDOC), and establish and verify a clinical predictive model based on these factors. Methods: This study included 134 patients suffering from prolonged disorder of consciousness enrolled in our department of neurosurgery. We collected the data of sex, age, etiology, coma recovery scales (CRS-R) score, complications, blood routine, liver function, coagulation and other laboratory tests, resting EEG data and follow-up after discharge. These patients were divided into two groups: training set (n = 107) and verification set (n = 27). These patients were divided into a training set of 107 and a validation set of 27 for this study. Univariate and multivariate regression analysis were used to determine the factors affecting the poor prognosis of pDOC and to establish nomogram model. We use the receiver operating characteristic (ROC) and calibration curves to quantitatively test the effectiveness of the training set and the verification set. In order to further verify the clinical practical value of the model, we use decision curve analysis (DCA) to evaluate the model. Result: The results from univariate and multivariate logistic regression analyses suggested that an increased frequency of occurrence microstate A, reduced CRS-R scores at the time of admission, the presence of episodes associated with paroxysmal sympathetic hyperactivity (PSH), and decreased fibrinogen levels all function as independent prognostic factors. These factors were used to construct the nomogram. The training and verification sets had areas under the curve of 0.854 and 0.920, respectively. Calibration curves and DCA demonstrated good model performance and significant clinical benefits in both sets. Conclusion: This study is based on the use of clinically available and low-cost clinical indicators combined with EEG to construct a highly applicable and accurate model for predicting the adverse prognosis of patients with prolonged disorder of consciousness. It provides an objective and reliable tool for clinicians to evaluate the prognosis of prolonged disorder of consciousness, and helps clinicians to provide personalized clinical care and decision-making for patients with prolonged disorder of consciousness and their families.

A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.

Objective: This study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors. Methods: 242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients' tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model. Results: The performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96-0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86-0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility. Conclusion: The fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.

Incidence rate and risk factors for suicide in patients with breast cancer in the USA: A surveillance, epidemiology, and end results analysis (SEER).

PURPOSE: To investigate suicide mortality and the related factors among female breast cancer patients in the United States. METHODS: The SEER database was used to identify 716,422 patients diagnosed with breast cancer between 2010 and 2018 to calculate a standardized mortality rate (SMR). An analysis of risk factors for suicide death was conducted using the univariate and multivariate Cox proportional risk model. An estimation of suicide probability was performed through a nomogram model. RESULTS: Compared with the expected suicide cases (n = 155) in the general population of the United States at the corresponding period (a suicide death rate of 5.71 per 100,000 person-years), the suicide rate among 716,422 breast cancer patients was followed during 2010-2018 and showed a relatively higher rate of 9.02 per 100,000 person-years. The SMR was 1.58 (95%CI: 1.39-1.79). White and other races were nine and seven times more likely to complete suicide than Black race, respectively (aHR = 9.013, 95%CI: 3.335-24.36, P < 0.001; aHR = 7.129, 95%CI: 2.317-21.931, P = 0.001); unmarried or single patients were at higher risk than married patients (aHR = 1.693, 95%CI: 1.206-2.377, P = 0.002). Patients receiving radiotherapy (aHR = 0.731, 95%CI: 0.545-0.980, P = 0.036) were less likely to complete suicide than those who did not. CONCLUSION: Female breast cancer patients in the United States have a higher suicide rate than the general public, and the risk factors consist of non-black ethnicity, being single or unmarried, and not being treated with radiotherapy. As a result of this study, clinicians may be able to identify female breast cancer patients who are at high risk of suicide, thus providing appropriate psychological support at the early stage.

Construction and validation of a predictive model for the risk of contralateral patent processus vaginalis in children with unilateral inguinal hernia or hydrocele.

To develop a nomogram model for predicting contralateral patent processus vaginalis in children with unilateral inguinal hernia or hydrocele. A retrospective analysis was conducted on 259 children with unilateral inguinal hernia or hydrocele who underwent laparoscopic surgery at the Southern Hospital of Southern Medical University from January 2021 to December 2023. The patients were randomly divided into a training set (n = 207) and a validation set (n = 52) in an 8:2 ratio to analyze the characteristics of CPPV. Multivariate logistic regression analysis was used to screen for independent risk factors for CPPV, and a nomogram prediction model was constructed. The predictive ability, calibration, and clinical net benefit of the model were evaluated by plotting receiver operating characteristic (ROC) curves, calibration curves (HL), and clinical decision curves (DCA). Among children under 1 year old, the laparoscopic exploration revealed a CPPV incidence rate of 55.17%. The incidence rates for children aged 2-10 years ranged from 29.03 to 39.13%, and the incidence rate for children aged 11-14 years was 21.21%. Multivariate logistic regression analysis showed that age (OR = 0.9, 95%CI 0.82-0.99, P = 0.035) and female gender (OR = 2.42, 95%CI 1.21-4.83, P = 0.013) were independent risk factors for CPPV, and the incidence of CPPV decreased with age. The area under the ROC curve (AUC) for the training set of the constructed model was 0.632, and the AUC for the validation set was 0.708. The Hosmer-Lemeshow goodness-of-fit test indicated good model fit (training set P = 0.085, validation set P = 0.221), and the DCA curve suggested good clinical benefit.The nomogram model developed in this study demonstrates good clinical value. Children with unilateral inguinal hernia or hydrocele who are younger in age and female gender should undergo careful intraoperative exploration for the presence of CPPV. What is Known: • The probability of developing inguinal hernia in children with CPPV is 11%-25%, and redo surgery can increase surgical risks and financial burden. • The risk factors of unilateral inguinal hernia combined with CPPV are controversial. What is New: • Age and female gender are independent risk factors for CPPV. • A nomogram prediction model was constructed to provide a theoretical basis as well as an assessment tool for preoperative evaluation of whether children with unilateral indirect inguinal hernia are susceptible to CPPV.

A Nomogram Including Sarcopenia for Predicting Progression-Free Survival in Patients with Localized Papillary Renal Cell Carcinoma: A Retrospective Cohort Study.

BACKGROUND: Because of to the removal of subclassification of papillary renal cell carcinoma (pRCC), the survival prognostification of localized pRCC after surgical treatment became inadequate. Sarcopenia was widely evaluated and proved to be a predictive factor for prognosis in RCC patients. Therefore, we comprehensively investigated the survival prediction of the body composition parameters for localized pRCC. METHODS: Patients pathologically diagnosed with pRCC between February 2012 and February 2022 in our center were enrolled. The body composition parameters, including skeletal muscle index (SMI), subcutaneous adipose tissue (SAT), and perirenal adipose tissue (PRAT), were measured by the images of preoperative computed tomography (CT). The primary outcome was set as progression-free survival (PFS), and the cutoff values of body composition parameters were calculated by using the Youden from receiver operating characteristic curve (ROC) curves. Univariate and multivariate Cox proportional regression analyses were performed to explore independent risk factors for survival prediction. Then, significant factors were used to construct a prognostic nomogram. The performance of the nomogram was evaluated by Harrell's C-index, calibration curves and time-dependent ROC curves. RESULTS: A total of 105 patients were enrolled for analysis. With a median follow-up time of 30.48 months, 25 (23.81%) patients experienced cancer progression. The percentage of sarcopenia was 74.29%. Univariate Cox analysis identified that gender, PRAT, SAT, skeletal muscle (SM), sarcopenia, surgical technique, and tumor diameter were associated with progression. Further multivariate analysis showed that sarcopenia (hazard ratio [HR] 0.15, 95% confidence interval [CI] 0.03-0.66), SAT (HR 6.36, 95% CI 2.39-16.93), PRAT (HR 4.66, 95% CI 1.77-12.27), tumor diameter (HR 0.35, 95% CI 0.14-0.86), and surgical technique (HR 2.85, 95% CI 1.06-7.64) were independent risk factors for cancer progression. Then, a prognostic nomogram based on independent risk factors was constructed and the C-index for progression prediction was 0.831 (95% CI 0.761-0.901), representing a reasonable discrimination, the calibration curves, and the time-dependent ROC curves verified the good performance of the nomogram. CONCLUSIONS: A prognostic nomogram, including sarcopenia, SAT, PRAT, tumor diameter, and surgical technique, was constructed to calculate the probability of progression for localized pRCC patients and needs further external validation for clinical use in the future.

Predicting lymph node metastasis in colorectal cancer patients: development and validation of a column chart model.

Lymph node metastasis (LNM) is one of the crucial factors in determining the optimal treatment approach for colorectal cancer. The objective of this study was to establish and validate a column chart for predicting LNM in colon cancer patients. We extracted a total of 83,430 cases of colon cancer from the Surveillance, Epidemiology, and End Results (SEER) database, spanning the years 2010-2017. These cases were divided into a training group and a testing group in a 7:3 ratio. An additional 8545 patients from the years 2018-2019 were used for external validation. Univariate and multivariate logistic regression models were employed in the training set to identify predictive factors. Models were developed using logistic regression, LASSO regression, ridge regression, and elastic net regression algorithms. Model performance was quantified by calculating the area under the ROC curve (AUC) and its corresponding 95% confidence interval. The results demonstrated that tumor location, grade, age, tumor size, T stage, race, and CEA were independent predictors of LNM in CRC patients. The logistic regression model yielded an AUC of 0.708 (0.7038-0.7122), outperforming ridge regression and achieving similar AUC values as LASSO regression and elastic net regression. Based on the logistic regression algorithm, we constructed a column chart for predicting LNM in CRC patients. Further subgroup analysis based on gender, age, and grade indicated that the logistic prediction model exhibited good adaptability across all subgroups. Our column chart displayed excellent predictive capability and serves as a useful tool for clinicians in predicting LNM in colorectal cancer patients.

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

BACKGROUND: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data. AIM: To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients. METHODS: Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model. RESULTS: More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility. CONCLUSION: This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.

Development and validation of a multi-parameter nomogram for venous thromboembolism in gastric cancer patients: a retrospective analysis.

Objective: Gastric cancer (GC), one of the highest venous thromboembolism (VTE) incidence rates in cancer, contributes to considerable morbidity, mortality, and, prominently, extra cost. However, up to now, there is not a high-quality VTE model to steadily predict the risk for VTE in China. Consequently, setting up a prediction model to predict the VTE risk is imperative. Methods: Data from 3,092 patients from December 15, 2017, to December 31, 2022, were retrospectively analyzed. Multiple logistic regression analysis was performed to assess risk factors for GC, and a nomogram was constructed based on screened risk factors. A receiver operating curve (ROC) and calibration plot was created to evaluate the accuracy of the nomogram. Results: The risk factors of suffering from VTE were older age (OR = 1.02, 95% CI [1.00-1.04]), Karnofsky Performance Status (KPS) ≥ 70 (OR = 0.45, 95% CI [0.25-0.83]), Blood transfusion (OR = 2.37, 95% CI [1.47-3.84]), advanced clinical stage (OR = 3.98, 95% CI [1.59-9.99]), central venous catheterization (CVC) (OR = 4.27, 95% CI [2.03-8.99]), operation (OR = 2.72, 95% CI [1.55-4.77]), fibrinogen degradation product (FDP) >5 µg/mL (OR = 1.92, 95% CI [1.13-3.25]), and D-dimer > 0.5 mg/L (OR = 2.50, 95% CI [1.19-5.28]). The area under the ROC curve (AUC) was 0.82 in the training set and 0.85 in the validation set. Conclusion: Our prediction model can accurately predict the risk of the appearance of VTE in gastric cancer patients and can be used as a robust and efficient tool for evaluating the possibility of VTE.

[Establishment and Validation of a Predictive Model for Gallstone Disease in the General Population: A Multicenter Study]. / èƒ†ç»“çŸ³æ‚£ç— é£Žé™©é¢„æµ‹æ¨¡åž‹çš„æž„å»ºå’Œå¤šä¸­å¿ƒéªŒè¯ç ”ç©¶.

Objective: Gallstone disease (GSD) is one of the common digestive tract diseases with a high worldwide prevalence. The effects of GSD on patients include but are not limited to the symptoms of nausea, vomiting, and biliary colic directly caused by GSD. In addition, there is mounting evidence from cohort studies connecting GSD to other conditions, such as cardiovascular diseases, biliary tract cancer, and colorectal cancer. Early identification of patients at a high risk of GSD may help improve the prevention and control of the disease. A series of studies have attempted to establish prediction models for GSD, but these models could not be fully applied in the general population due to incomplete prediction factors, small sample sizes, and limitations in external validation. It is crucial to design a universally applicable GSD risk prediction model for the general population and to take individualized intervention measures to prevent the occurrence of GSD. This study aims to conduct a multicenter investigation involving more than 90000 people to construct and validate a complete and simplified GSD risk prediction model. Methods: A total of 123634 participants were included in the study between January 2015 and December 2020, of whom 43929 were from the First Affiliated Hospital of Chongqing Medical University (Chongqing, China), 11907 were from the First People's Hospital of Jining City (Shandong, China), 1538 were from the Tianjin Medical University Cancer Institute and Hospital (Tianjin, China), and 66260 were from the People's Hospital of Kaizhou District (Chongqing, China). After excluding patients with incomplete clinical medical data, 35976 patients from the First Affiliated Hospital of Chongqing Medical University were divided into a training data set (n=28781, 80%) and a validation data set (n=7195, 20%). Logistic regression analyses were performed to investigate the relevant risk factors of GSD, and a complete risk prediction model was constructed. Factors with high scores, mainly according to the nomograms of the complete model, were retained to simplify the model. In the validation data set, the diagnostic accuracy and clinical performance of these models were validated using the calibration curve, area under the curve (AUC) of the receiver operating characteristic curve, and decision curve analysis (DCA). Moreover, the diagnostic accuracy of these two models was validated in three other hospitals. Finally, we established an online website for using the prediction model (The complete model is accessible at https://wenqianyu.shinyapps.io/Completemodel/, while the simplified model is accessible at https://wenqianyu.shinyapps.io/Simplified/). Results: After excluding patients with incomplete clinical medical data, a total of 96426 participants were finally included in this study (35876 from the First Affiliated Hospital of the Chongqing Medical University, 9289 from the First People's Hospital of Jining City, 1522 from the Tianjin Medical University Cancer Institute, and 49639 from the People's Hospital of Kaizhou District). Female sex, advanced age, higher body mass index, fasting plasma glucose, uric acid, total bilirubin, gamma-glutamyl transpeptidase, and fatty liver disease were positively associated with risks for GSD. Furthermore, gallbladder polyps, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and aspartate aminotransferase were negatively correlated to risks for GSD. According to the nomograms of the complete model, a simplified model including sex, age, body mass index, gallbladder polyps, and fatty liver disease was constructed. All the calibration curves exhibited good consistency between the predicted and observed probabilities. In addition, DCA indicated that both the complete model and the simplified model showed better net benefits than treat-all and treat-none. Based on the calibration plots, DCA, and AUCs of the complete model (AUC in the internal validation data set=74.1% [95% CI: 72.9%-75.3%], AUC in Shandong=71.7% [95% CI: 70.6%-72.8%], AUC in Tianjin=75.3% [95% CI: 72.7%-77.9%], and AUC in Kaizhou=72.9% [95% CI: 72.5%-73.3%]) and the simplified model (AUC in the internal validation data set=73.7% [95% CI: 72.5%-75.0%], AUC in Shandong=71.5% [95% CI: 70.4%-72.5%], AUC in Tianjin=75.4% [95% CI: 72.9%-78.0%], and AUC in Kaizhou=72.4% [95% CI: 72.0%-72.8%]), we concluded that the complete and simplified risk prediction models for GSD exhibited excellent performance. Moreover, we detected no significant differences between the performance of the two models (P>0.05). We also established two online websites based on the results of this study for GSD risk prediction. Conclusions: This study innovatively used the data from 96426 patients from four hospitals to establish a GSD risk prediction model and to perform risk prediction analyses of internal and external validation data sets in four cohorts. A simplified model of GSD risk prediction, which included the variables of sex, age, body mass index, gallbladder polyps, and fatty liver disease, also exhibited good discrimination and clinical performance. Nonetheless, further studies are needed to explore the role of low-density lipoprotein cholesterol and aspartate aminotransferase in gallstone formation. Although the validation results of the complete model were better than those of the simplified model to a certain extent, the difference was not significant even in large samples. Compared with the complete model, the simplified model uses fewer variables and yields similar prediction and clinical impact. Hence, we recommend the application of the simplified model to improve the efficiency of screening high-risk groups in practice. The use of the simplified model is conducive to enhancing the self-awareness of prevention and control in the general population and early intervention for GSD.