Analysis of risk factors and construction of a predictive model for macrosomia in deliveries with gestational diabetes.

BACKGROUND: Gestational diabetes, a frequent pregnancy complication marked by elevated maternal blood glucose, can cause serious adverse effects for both mother and fetus, including increased amniotic fluid and risks of fetal asphyxia, hypoxia, and premature birth. OBJECTIVE: To construct a predictive model to analyze the risk factors for macrosomia in deliveries with gestational diabetes. METHODS: From January 2021 to February 2023, 362 pregnant women with gestational diabetes were selected for the study. They were followed up until delivery. Based on newborn birth weight, the participants were divided into the macrosomia group (birth weight ⩾ 4000 g) and the non-macrosomia group (birth weight < 4000 g). The data of the two groups of pregnant women were compared. ROC curves were plotted to analyze the predictive value of multiple factors for the delivery of macrosomic infants among pregnant women with gestational diabetes. A logistic regression model was constructed to identify the risk factors for delivering macrosomic infants and the model was tested. RESULTS: A total of 362 pregnant women with gestational diabetes were included, of which 58 (16.02%) had babies with macrosomia. The macrosomia group exhibited higher metrics in several areas compared to those without: pre-pregnancy BMI, fasting glucose, 1 h and 2 h OGTT sugar levels, weight gain during pregnancy, and levels of triglycerides, LDL-C, and HDL-C, all with significant differences (P< 0.05). ROC analysis revealed predictive value for macrosomia with AUCs of 0.761 (pre-pregnancy BMI), 0.710 (fasting glucose), 0.671 (1 h OGTT), 0.634 (2 h OGTT), 0.850 (weight gain), 0.837 (triglycerides), 0.742 (LDL-C), and 0.776 (HDL-C), indicating statistical significance (P< 0.05). Logistic regression identified high pre-pregnancy BMI, fasting glucose, weight gain, triglycerides, and LDL-C levels as independent risk factors for macrosomia, with odds ratios of 2.448, 2.730, 1.884, 16.919, and 5.667, respectively, and all were statistically significant (P< 0.05). The model's AUC of 0.980 (P< 0.05) attests to its reliability and stability. CONCLUSION: The delivery of macrosomic infants in gestational diabetes may be related to factors such as body mass index before pregnancy, blood-glucose levels, gain weight during pregnancy, and lipid levels. Clinical interventions targeting these factors should be implemented to reduce the incidence of macrosomia.

Establishment and validation of a predictive nomogram for polyuria during general anesthesia in thoracic surgery.

BACKGROUND: To develop and evaluate a predictive nomogram for polyuria during general anesthesia in thoracic surgery. METHODS: A retrospective study was designed and performed. The whole dataset was used to develop the predictive nomogram and used a stepwise algorithm to screen variables. The stepwise algorithm was based on Akaike's information criterion (AIC). Multivariable logistic regression analysis was used to develop the nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the model's discrimination ability. The Hosmer-Lemeshow (HL) test was performed to check if the model was well calibrated. Decision curve analysis (DCA) was performed to measure the nomogram's clinical usefulness and net benefits. P < 0.05 was considered to indicate statistical significance. RESULTS: The sample included 529 subjects who had undergone thoracic surgery. Fentanyl use, gender, the difference between mean arterial pressure at admission and before the operation, operation type, total amount of fluids and blood products transfused, blood loss, vasopressor, and cisatracurium use were identified as predictors and incorporated into the nomogram. The nomogram showed good discrimination ability on the receiver operating characteristic curve (0.6937) and is well calibrated using the Hosmer-Lemeshow test. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS: Individualized and precise prediction of intraoperative polyuria allows for better anesthesia management and early prevention optimization.

Development of an AI predictive model to categorize and predict online learning behaviors of students in Thailand.

This qualitative study has three objectives: (1) to develop a predictive AI model to categorize the online learning behavior of Thai students who study through a Thai Massive Open Online Course (MOOC); (2) to categorize students' online behavior in a Thai MOOC; and (3) to evaluate the prediction accuracy of the developed predictive AI models. Data were collected from 8000 learners enrolled in the KMUTT015 course on the Thai MOOC platform. The k-means clustering algorithm classified learners enrolled in the Thai MOOC platform based on their online learning behaviors. The decision tree algorithm was used to assess the accuracy of the AI model prediction capability. The study finds the predictive AI model successfully categorizes learners based on their learning behaviors and predicts their future online learning behaviors in the online learning environment. The k-means clustering algorithm yields three groups of learners in the Thai MOOC platform: High Active Participants (HAP), Medium Active Participants (MAP), and Lurking participants. The findings also indicate high predictive accuracy rates for each behavioral group (HAP cluster = 0.98475, Lurking participants cluster = 0.967625, and MAP cluster = 0.955375), indicating the proficiency of the AI predictive model in forecasting learner behavior. The results of this study will benefit the design of online courses that respond to the needs of students with different online learning characteristics and help them achieve a high level of academic performance.

Study on the relationship between body mass index and blood pressure indices in children aged 7-17 during COVID-19.

Background: To explore the relationship between body mass index (BMI), age, sex, and blood pressure (systolic blood pressure, SBP; diastolic blood pressure, DBP) in children during COVID-19, providing reference for the prevention and screening of hypertension in children. Methods: This study adopted a large-scale cross-sectional design to investigate the association between BMI and blood pressure in 7-17-year-old students in City N, China, during COVID-19. Thirty-six primary and secondary schools in City N were sampled using a stratified cluster sampling method. A total of 11,433 students aged 7-17 years in City N, China, were selected for blood pressure (Diastolic blood pressure, DBP, Systolic blood pressure, SBP), height, and weight, Resting heart rate (RHR), chest circumference, measurements, and the study was written using the STROBE checklist. Data analysis was conducted using SPSS 26.0, calculating the mean and standard deviation of BMI and blood pressure for male and female students in different age groups. Regression analysis was employed to explore the impact of BMI, age, and sex on SBP and DBP, and predictive models were established. The model fit was evaluated using the model R2. Results: The study included 11,287 primary and secondary school students, comprising 5,649 boys and 5,638 girls. It was found that with increasing age, BMI and blood pressure of boys and girls generally increased. There were significant differences in blood pressure levels between boys and girls in different age groups. In regression models, LC, Age, BMI, and chest circumference show significant positive linear relationships with SBP and DBP in adolescents, while RHR exhibits a negative linear relationship with SBP. These factors were individually incorporated into a stratified regression model, significantly enhancing the model's explanatory power. After including factors such as Age, Gender, and BMI, the adjusted R2 value showed a significant improvement, with Age and BMI identified as key predictive factors for SBP and DBP. The robustness and predictive accuracy of the model were further examined through K-fold cross-validation and independent sample validation methods. The validation results indicate that the model has a high accuracy and explanatory power in predicting blood pressure in children of different weight levels, especially among obese children, where the prediction accuracy is highest. Conclusion: During COVID-19, age, sex, and BMI significantly influence blood pressure in children aged 7-17 years, and predictive models for SBP and DBP were established. This model helps predict blood pressure in children and reduce the risk of cardiovascular diseases. Confirmation of factors such as sex, age, and BMI provide a basis for personalized health plans for children, especially during large-scale infectious diseases, providing guidance for addressing health challenges and promoting the health and well-being of children.

A nomogram for predicting neurogenic lower urinary tract dysfunction in patients with acute ischemic stroke: A retrospective study.

AIMS: To investigate the risk factors for neurogenic lower urinary tract dysfunction (NLUTD) in patients with acute ischemic stroke (AIS), and develop an internally validated predictive nomogram. The study aims to offer insights for preventing AIS-NLUTD. METHODS: We conducted a retrospective study on AIS patients in a Shenzhen Hospital from June 2021 to February 2023, categorizing them into non-NLUTD and NLUTD groups. The bivariate analysis identified factors for AIS-NLUTD (p < 0.05), integrated into a least absolute shrinkage and selection operator (LASSO) regression model. Significant variables from LASSO were used in a multivariate logistic regression for the predictive model, resulting in a nomogram. Nomogram performance and clinical utility were evaluated through receiver operating characteristic curves, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC). Internal validation used 1000 bootstrap resamplings. RESULTS: A total of 373 participants were included in this study, with an NLUTD incidence rate of 17.7% (66/373). NIHSS score (OR = 1.254), pneumonia (OR = 6.631), GLU (OR = 1.240), HGB (OR = 0.970), and hCRP (OR = 1.021) were used to construct a predictive model for NLUTD in AIS patients. The model exhibited good performance (AUC = 0.899, calibration curve p = 0.953). Internal validation of the model demonstrated strong discrimination and calibration abilities (AUC = 0.898). Results from DCA and CIC curves indicated that the prediction model had high clinical utility. CONCLUSIONS: We developed a predictive model for AIS-NLUTD and created a nomogram with strong predictive capabilities, assisting healthcare professionals in evaluating NLUTD risk among AIS patients and facilitating early intervention.

The association between shift work, shift work sleep disorders and premature ejaculation in male workers.

OBJECTIVE: Shift work and Shift Work Sleep Disorder (SWSD) are known to affect the secretion of several neurotransmitters and hormones associated with premature ejaculation (PE). However, their specific influence on the regulation of male ejaculation remains unclear. This study explores the relationship between shift work, SWSD, and PE. METHODS: From April to October 2023, a cross-sectional survey was conducted across five regions of China to explore the work schedules, sleep quality, and sexual function of male workers. Participants' sleep quality was evaluated using a validated SWSD questionnaire, and their erectile function and ejaculatory control were assessed with the International Inventory of Erectile Function (IIEF-5) scores and Premature Ejaculation Diagnostic Tool (PEDT) scores, respectively. Univariate and multivariate linear regression analyses were employed to identify risk factors associated with PE. Confounders were controlled using multiple regression models, and clinical prediction models were developed to predict PE onset and assess the contribution of risk factors. RESULTS: The study included 1239 eligible participants, comprising 840 non-shift workers and 399 shift workers (148 with SWSD and 251 without SWSD). Compared to non-shift working males, those involved in shift work (ß 1.58, 95% CI 0.75 - 2.42, p < 0.001) and those suffering from SWSD (ß 2.86, 95% CI 1.86 - 3.85, p < 0.001) they had significantly higher PEDT scores. Additionally, we identified daily sleep of less than six hours, depression, anxiety, diabetes, hyperlipidemia, frequent alcohol consumption (more than twice a week), and erectile dysfunction as risk factors for PE. The predictive model for PE demonstrated commendable efficacy. CONCLUSION: Both shift work and SWSD significantly increase the risk of premature ejaculation, with the risk magnifying in tandem with the duration of shift work. This study reveals the potential impact of shift work and SWSD on PE and provides new theoretical foundations for the risk assessment and prevention of this condition.

Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability.

BACKGROUND: Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures. PURPOSE: By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability. METHODS: The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. RESULTS: The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific. CONCLUSION: This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Maternal diseases and congenital anomalies of the kidney and urinary tract in offspring: a cohort study.

BACKGROUND: Congenital anomalies of the kidneys and urinary tract (CAKUT) are the most common cause of prenatally diagnosed developmental malformation. This study aimed to assess the relationship between maternal diseases and CAKUT in offspring. METHODS: This retrospective study enrolled all pregnant women registered from January 2020 to December 2022 at one medical center. Medical information on maternal noncommunicable diseases, including obesity, hypertension, diabetes mellitus, kidney disease, hyperthyroidism, hypothyroidism, psychiatric disease, epilepsy, cancer, and autoimmune disease was collected. Based on the records of ultrasound scanning during the third trimester, the diagnosis was classified as isolated urinary tract dilation (UTD) or kidney anomalies. Multivariate logistic regression was performed to establish models to predict antenatal CAKUT. RESULTS: Among the 19,656 pregnant women, perinatal ultrasound detected suspicious CAKUT in 114 (5.8/1000) fetuses, comprising 89 cases with isolated UTD and 25 cases with kidney anomalies. The risk of antenatal CAKUT was increased in the fetuses of mothers who experienced gestational diabetes, thyroid dysfunction, neuropsychiatric disease, anemia, ovarian and uterine disorders. A prediction model for isolated UTD was developed utilizing four confounding factors, namely gestational diabetes, gestational hypertension, maternal thyroid dysfunction, and hepatic disease. Similarly, a separate prediction model for kidney anomalies was established based on four distinct confounding factors, namely maternal thyroid dysfunction, gestational diabetes, disorders of ovarian/uterine, and kidney disease. CONCLUSIONS: Isolated UTD and kidney anomalies were associated with different maternal diseases. The results may inform the clinical management of pregnancy and highlight potential differences in the genesis of various subtypes of CAKUT.

Development and validation of a nomogram for breast cancer-related lymphedema.

To establish and validate a predictive model for breast cancer-related lymphedema (BCRL) among Chinese patients to facilitate individualized risk assessment. We retrospectively analyzed data from breast cancer patients treated at a major single-center breast hospital in China. From 2020 to 2022, we identified risk factors for BCRL through logistic regression and developed and validated a nomogram using R software (version 4.1.2). Model validation was achieved through the application of receiver operating characteristic curve (ROC), a calibration plot, and decision curve analysis (DCA), with further evaluated by internal validation. Among 1485 patients analyzed, 360 developed lymphedema (24.2%). The nomogram incorporated body mass index, operative time, lymph node count, axillary dissection level, surgical site infection, and radiotherapy as predictors. The AUCs for training (N = 1038) and validation (N = 447) cohorts were 0.779 and 0.724, respectively, indicating good discriminative ability. Calibration and decision curve analysis confirmed the model's clinical utility. Our nomogram provides an accurate tool for predicting BCRL risk, with potential to enhance personalized management in breast cancer survivors. Further prospective validation across multiple centers is warranted.

Construction and Validation of a Predictive Model for Coronary Artery Disease Using Extreme Gradient Boosting.

Purpose: Early recognition of coronary artery disease (CAD) could delay its progress and significantly reduce mortality. Sensitive, specific, cost-efficient and non-invasive indicators for assessing individual CAD risk in community population screening are urgently needed. Patients and Methods: 3112 patients with CAD and 3182 controls were recruited from three clinical centers in China, and differences in baseline and clinical characteristics were compared. For the discovery cohort, the least absolute shrinkage and selection operator (LASSO) regression was used to identify significant features and four machine learning algorithms (logistic regression, support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost)) were applied to construct models for CAD risk assessment, the receiver operating characteristics (ROC) curve and precision-recall (PR) curve were conducted to evaluate their predictive accuracy. The optimal model was interpreted by Shapley additive explanations (SHAP) analysis and assessed by the ROC curve, calibration curve, and decision curve analysis (DCA) and validated by two external cohorts. Results: Using LASSO filtration, all included variables were considered to be statistically significant. Four machine learning models were constructed based on these features and the results of ROC and PR curve implied that the XGBoost model exhibited the highest predictive performance, which yielded a high area of ROC curve (AUC) of 0.988 (95% CI: 0.986-0.991) to distinguish CAD patients from controls with a sensitivity of 94.6% and a specificity of 94.6%. The calibration curve showed that the predicted results were in good agreement with actual observations, and DCA exhibited a better net benefit across a wide range of threshold probabilities. External validation of the model also exhibited favorable discriminatory performance, with an AUC, sensitivity, and specificity of 0.953 (95% CI: 0.945-0.960), 89.9%, and 87.1% in the validation cohort, and 0.935 (95% CI: 0.915-0.955), 82.0%, and 90.3% in the replication cohort. Conclusion: Our model is highly informative for clinical practice and will be conducive to primary prevention and tailoring the precise management for CAD patients.

LASSO-Based Machine Learning Algorithm for Prediction of PICS Associated with Sepsis.

Introduction: This study aims to establish a comprehensive, multi-level approach for tackling tropical diseases by proactively anticipating and managing Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PICS) within the initial 14 days of Intensive Care Unit (ICU) admission. The primary objective is to amalgamate a diverse array of indicators and pathogenic microbial data to pinpoint pivotal predictive variables, enabling effective intervention specifically tailored to the context of tropical diseases. Methods: A focused analysis was conducted on 1733 patients admitted to the ICU between December 2016 and July 2019. Utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression, disease severity and laboratory indices were scrutinized. The identified variables served as the foundation for constructing a predictive model designed to forecast the occurrence of PICS. Results: Among the subjects, 13.79% met the diagnostic criteria for PICS, correlating with a mortality rate of 38.08%. Key variables, including red-cell distribution width coefficient of variation (RDW-CV), hemofiltration (HF), mechanical ventilation (MV), Norepinephrine (NE), lactic acidosis, and multiple-drug resistant bacteria (MDR) infection, were identified through LASSO regression. The resulting predictive model exhibited a robust performance with an Area Under the Curve (AUC) of 0.828, an accuracy of 0.862, and a specificity of 0.977. Subsequent validation in an independent cohort yielded an AUC of 0.848. Discussion: The acquisition of RDW-CV, HF requirement, MV requirement, NE requirement, lactic acidosis, and MDR upon ICU admission emerges as a pivotal factor for prognosticating PICS onset in the context of tropical diseases. This study highlights the potential for significant improvements in clinical outcomes through the implementation of timely and targeted interventions tailored specifically to the challenges posed by tropical diseases.

Development and validation of a predictive model for double-J stent encrustation after upper urinary tract calculi surgery.

The study is aimed to establish a predictive model of double-J stent encrustation after upper urinary tract calculi surgery. We collected the clinical data of 561 patients with indwelling double-J tubes admitted to a hospital in Shandong Province from January 2019 to December 2020 as the modeling group and 241 cases of indwelling double-J tubes from January 2021 to January 2022 as the verification group. Univariate and binary logistic regression analyses were used to explore risk factors, the risk prediction equation was established, and the receiver operating characteristic (ROC) curve analysis model was used for prediction. In this study, 104 of the 561 patients developed double-J stent encrustation, with an incidence rate of 18.5%. We finally screened out BMI (body mass index) > 23.9 (OR = 1.648), preoperative urine routine white blood cell quantification (OR = 1.149), double-J tube insertion time (OR = 1.566), postoperative water consumption did not reach 2000 ml/d (OR = 8.514), a total of four factors build a risk prediction model. From the ROC curve analysis, the area under the curve (AUC) was 0.844, and the maximum Oden index was 0.579. At this time, the sensitivity was 0.735 and the specificity was 0.844. The research established in this study has a high predictive value for the occurrence of double-J stent encrustation in the double-J tube after upper urinary tract stone surgery, which provides a basis for the prevention and treatment of double-J stent encrustation.

Predicting Inadequate Weight Loss After Bariatric Surgery: Derivation and Validation of a Four Factor Model.

INTRODUCTION: Weight loss following bariatric surgery is variable and predicting inadequate weight loss is required to help select patients for bariatric surgery. The aim of the present study was to determine variables associated with inadequate weight loss and to derive and validate a predictive model. METHODS: All patients who underwent laparoscopic sleeve gastrectomy and Roux-en-Y gastrectomy (2008-2022) in a tertiary referral centre were followed up prospectively. Inadequate weight loss was defined as excess weight loss (EWL) < 50% by 24 months. A top-down approach was performed using multivariate logistic regression and then internally validated using bootstrapping. Patients were categorised into risk groups. RESULTS: A total of 280 patients (median age, 49 years; M:F, 69:211) were included (146 LSG; 134 LRYGB). At 24 months, the median total weight loss was 30.9% and 80.0% achieved EWL ≥ 50% by 24 months. Variables associated with inadequate weight loss were T2DM (OR 2.42; p = 0.042), age 51-60 (OR 1.93, p = 0.006), age > 60 (OR 4.93, p < 0.001), starting BMI > 50 kg/m² (OR 1.93, p = 0.037) and pre-operative weight loss (OR 3.51; p = 0.036). The validation C-index was 0.75 (slope = 0.89). Low, medium and high-risk groups had a 4.9%, 16.7% and 44.6% risk of inadequate weight loss, respectively. CONCLUSIONS: Inadequate weight loss can be predicted using a four factor model which could help patients and clinicians in decision-making for bariatric surgery.

ProsTAV, a clinically useful test in prostate cancer: an extension study.

PURPOSE: To assess the clinical performance of ProsTAV®, a blood-based test based on telomere associate variables (TAV) measurement, to support biopsy decision-making when diagnosing suspicious prostate cancer (PCa). METHODS: Preliminary data of a prospective observational pragmatic study of patients with prostate-specific antigen (PSA) levels 3-10 ng/ml and suspicious PCa. Results were combined with other clinical data, and all patients underwent prostate biopsies according to each center's routine clinical practice, while magnetic resonance imaging (MRI) before the prostate biopsy was optional. Sensitivity, specificity, positive and negative predicted values, and subjects where biopsies could have been avoided using ProsTAV were determined. RESULTS: The mean age of the participants (n = 251) was 67.4 years, with a mean PSA of 5.90 ng/ml, a mean free PSA of 18.9%, and a PSA density of 0.14 ng/ml. Digital rectal examination was abnormal in 21.1% of the subjects, and according to biopsy, the prevalence of significant PCa was 47.8%. The area under the ROC curve of ProsTAV was 0.7, with a sensitivity of 0.90 (95% CI, 0.85-0.95) and specificity of 0.27 (95% CI, 0.19-0.34). The positive and negative predictive values were 0.53 (95% CI, 0.46-0.60) and 0.74 (95% CI, 0.62-0.87), respectively. ProsTAV could have reduced the biopsies performed by 27% and showed some initial evidence of a putative benefit in the diagnosis pathway combined with MRI. CONCLUSIONS: ProsTAV increases the prediction capacity of significant PCa in patients with PSA between 3 and 10 ng/ml and could be considered a complementary tool to improve the patient diagnosis pathway.

External validation of the barcelona magnetic resonance imaging predictive model for detecting significant prostate cancer including men receiving 5-alpha reductase inhibitors.

PURPOSE: To validate the Barcelona-magnetic resonance imaging predictive model (BCN-MRI PM) for clinically significant prostate cancer (csPCa) in Catalonia, a Spanish region with 7.9 million inhabitants. Additionally, the BCN-MRI PM is validated in men receiving 5-alpha reductase inhibitors (5-ARI). MATERIALS AND METHODS: A population of 2,212 men with prostate-specific antigen serum level > 3.0 ng/ml and/or a suspicious digital rectal examination who underwent multiparametric MRI and targeted and/or systematic biopsies in the year 2022, at ten participant centers of the Catalonian csPCa early detection program, were selected. 120 individuals (5.7%) were identified as receiving 5-ARI treatment for longer than a year. The risk of csPCa was retrospectively assessed with the Barcelona-risk calculator 2 (BCN-RC 2). Men undergoing 5-ARI treatment for less than a year were excluded. CsPCa was defined when the grade group was ≥ 2. RESULTS: The area under the curve of the BCN-MRI PM in 5-ARI naïve men was 0.824 (95% CI 0.783-0.842) and 0.849 (0.806-0.916) in those receiving 5-ARI treatment, p 0.475. Specificities at 100, 97.5, and 95% sensitivity thresholds were to 2.7, 29.3, and 39% in 5-ARI naïve men, while 43.5, 46.4, and 47.8%, respectively in 5-ARI users. The application of BCN-MRI PM would result in a reduction of 23.8% of prostate biopsies missing 5% of csPCa in 5-ARI naïve men, while reducing 25% of prostate biopsies without missing csPCa in 5-ARI users. CONCLUSIONS: The BCN-MRI PM has achieved successful validation in Catalonia and, notably, for the first time, in men undergoing 5-ARI treatment.

Emergence and Potential Spread of Rust Disease on Wisteria floribunda and Corydalis incisa Influenced by Climate Change in Korea.

Global climate change influences the emergence, spread, and severity of rust diseases that affect crops and forests. In Korea, the rust diseases that affect Wisteria floribunda and its alternate host Corydalis incisa are rapidly spreading northwards. Through morphological, molecular, phylogenetic, and pathogenicity approaches, Neophysopella kraunhiae was identified as the causal agent, alternating between the two host plants to complete its life cycle. Using the maximum entropy model (Maxent) under shared socioeconomic pathways (SSPs), the results of this study suggest that by the 2050s, C. incisa is likely to extend its range into central Korea owing to climate shifts, whereas the distribution of W. floribunda is expected to remain unchanged nationwide. The generalized additive model revealed a significant positive correlation between the presence of C. incisa and the incidence of rust disease, highlighting the role that climate-driven expansion of this alternate host plays in the spread of N. kraunhiae. These findings highlight the profound influence of climate change on both the distribution of a specific plant and the disease a rust fungus causes, raising concerns about the potential emergence and spread of other rust pathogens with similar host dynamics.

Construction of a predictive model for postoperative hospitalization time in colorectal cancer patients based on interpretable machine learning algorithm: a prospective preliminary study.

Objective: This study aims to construct a predictive model based on machine learning algorithms to assess the risk of prolonged hospital stays post-surgery for colorectal cancer patients and to analyze preoperative and postoperative factors associated with extended hospitalization. Methods: We prospectively collected clinical data from 83 colorectal cancer patients. The study included 40 variables (comprising 39 predictor variables and 1 target variable). Important variables were identified through variable selection via the Lasso regression algorithm, and predictive models were constructed using ten machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Light Gradient Boosting Machine, KNN, and Extreme Gradient Boosting, Categorical Boosting, Artificial Neural Network and Deep Forest. The model performance was evaluated using Bootstrap ROC curves and calibration curves, with the optimal model selected and further interpreted using the SHAP explainability algorithm. Results: Ten significantly correlated important variables were identified through Lasso regression, validated by 1000 Bootstrap resamplings, and represented through Bootstrap ROC curves. The Logistic Regression model achieved the highest AUC (AUC=0.99, 95% CI=0.97-0.99). The explainable machine learning algorithm revealed that the distance walked on the third day post-surgery was the most important variable for the LR model. Conclusion: This study successfully constructed a model predicting postoperative hospital stay duration using patients' clinical data. This model promises to provide healthcare professionals with a more precise prediction tool in clinical practice, offering a basis for personalized nursing interventions, thereby improving patient prognosis and quality of life and enhancing the efficiency of medical resource utilization.

Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review.

BACKGROUND: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes. OBJECTIVE: This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA. METHODS: We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases-PubMed, Embase, and IEEE Xplore-and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). RESULTS: A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence. CONCLUSIONS: Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI's impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.

A novel web-based risk calculator for predicting surgical site infection in HIV-positive facture patients: a multicenter cohort study in China.

Background: Surgical site infection (SSI) is a common complication in HIV-positive fracture patients undergoing surgery, leading to increased morbidity, mortality, and healthcare costs. Accurate prediction of SSI risk can help guide clinical decision-making and improve patient outcomes. However, there is a lack of user-friendly, Web-based calculator for predicting SSI risk in this patient population. Objective: This study aimed to develop and validate a novel web-based risk calculator for predicting SSI in HIV-positive fracture patients undergoing surgery in China. Method: A multicenter retrospective cohort study was conducted using data from HIV-positive fracture patients who underwent surgery in three tertiary hospitals in China between May 2011 and September 2023. We used patients from Beijing Ditan Hospital as the training cohort and patients from Chengdu Public Health and Changsha First Hospital as the external validation cohort. Univariate, multivariate logistic regression analyses and SVM-RFE were performed to identify independent risk factors for SSIs. A web-based calculator was developed using the identified risk factors and validated using an external validation cohort. The performance of the nomogram was evaluated using the area under the receiver operating characteristic (AUC) curves, calibration plots, and decision curve analysis (DCA). Results: A total of 338 HIV-positive patients were included in the study, with 216 patients in the training cohort and 122 patients in the validation cohort. The overall SSI incidence was 10.7%. The web-based risk calculator (https://sydtliubo.shinyapps.io/DynNom_for_SSI/) incorporated six risk factors: HBV/HCV co-infection, HIV RNA load, CD4+ T-cell count, Neu and Lym level. The nomogram demonstrated good discrimination, with an AUC of 0.890 in the training cohort and 0.853 in the validation cohort. The calibration plot showed good agreement between predicted and observed SSI probabilities. The DCA indicated that the nomogram had clinical utility across a wide range of threshold probabilities. Conclusion: Our study developed and validated a novel web-based risk calculator for predicting SSI risk in HIV-positive fracture patients undergoing surgery in China. The nomogram demonstrated good discrimination, calibration, and clinical utility, and can serve as a valuable tool for risk stratification and clinical decision-making in this patient population. Future studies should focus on integrating this nomogram into hospital information systems for real-time risk assessment and management.