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Multicollinearity, characterized by significant co-expression patterns among genes, often occurs in high-throughput expression data, potentially impacting the predictive model's reliability. This study examined multicollinearity among closely related genes, particularly in RNA-Seq data obtained from embryoid bodies (EB) exposed to 5-fluorouracil perturbation to identify genes associated with embryotoxicity. Six genes-Dppa5a, Gdf3, Zfp42, Meis1, Hoxa2, and Hoxb1-emerged as candidates based on domain knowledge and were validated using qPCR in EBs perturbed by 39 test substances. We conducted correlation studies and utilized the variance inflation factor (VIF) to examine the existence of multicollinearity among the genes. Recursive feature elimination with cross-validation (RFECV) ranked Zfp42 and Hoxb1 as the top two among the seven features considered, identifying them as potential early embryotoxicity assessment biomarkers. As a result, a t test assessing the statistical significance of this two-feature prediction model yielded a p value of 0.0044, confirming the successful reduction of redundancies and multicollinearity through RFECV. Our study presents a systematic methodology for using machine learning techniques in transcriptomics data analysis, enhancing the discovery of potential reporter gene candidates for embryotoxicity screening research, and improving the predictive model's predictive accuracy and feasibility while reducing financial and time constraints.

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Platinum resistance in ovarian cancer poses a significant challenge, substantially impacting patient outcomes. Developing an accurate predictive model is crucial for improving clinical decision-making and guiding treatment strategies. Proteomic data from 217 high-grade serous ovarian cancer (HGSOC) biospecimens obtained from JHU, PNNL, and PTRC were used to construct a prediction model for identifying individuals who are resistant to platinum-based chemotherapy. A total of 6437 common proteins were detected across all data sets, with 26 proteins overlapping between the development cohorts JHU and PNNL. Using LASSO and logistic regression analysis, a six-protein model (P31323_PRKAR2B, Q13309_SKP2, Q14997_PSME4, Q6ZRP7_QSOX2, Q7LGA3_HS2ST1, and Q7Z2Z2_EFL1) was developed, which accurately predicted platinum resistance, with an AUC of 0.964 (95% CI, 0.929-0.999). Internal validation by resampling resulted in a C-index of 0.972 (95% CI 0.894-0.988). External validation performed on the PTRC cohort achieved an AUC of 0.855 (95% CI 0.748-0.963). Calibration curves showed good consistency, and DCA indicated superior clinical utility. The model also performed well in predicting PFS and OS at various time points. Based on these proteins, our predictive model can precisely predict platinum response and survival outcomes in HGSOC patients, which can assist clinicians in promptly identifying potentially platinum-resistant individuals.

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PURPOSE: This study aims to use a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram to predict preoperative peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer. METHODS: We included a total of 68 PNI patients and 80 LVI patients who underwent surgical resection and pathological confirmation of rectal cancer. According to the PNI/LVI status, patients were divided into PNI positive group (n = 39), the PNI negative group (n = 29), LVI positive group (n = 48), and the LVI negative group (n = 32). External validation included a total of 42 patients with nerve and vascular invasion in patients with surgically resected and pathologically confirmed rectal cancer at another healthcare facility, with a PNI positive group (n = 32) and a PNI-negative group (n = 10) as well as an LVI positive group (n = 35) and LVI-negative group (n = 7). All patients underwent 3.0T magnetic resonance T1WI enhanced scanning. We use Firevoxel software to delineate the region of interest (ROI), extract histogram parameters, and perform univariate analysis, LASSO regression, and multivariate logistic regression analysis in sequence to screen for the best predictive factors. Then, we constructed a clinical prediction model and plotted it into a column chart for personalized prediction. Finally, we evaluate the performance and clinical practicality of the model based on the area under curve (AUC), calibration curve, and decision curve. RESULTS: Multivariate logistic regression analysis found that variance and the 75th percentile were independent risk factors for PNI, while maximum and variance were independent risk factors for LVI. The clinical prediction model constructed based on the above factors has an AUC of 0.734 (95% CI: 0.591-0.878) for PNI in the training set and 0.731 (95% CI: 0.509-0.952) in the validation set; The training set AUC of LVI is 0.701 (95% CI: 0.561-0.841), and the validation set AUC is 0.685 (95% CI: 0.439-0.932). External validation showed an AUC of 0.722 (95% CI: 0.565-0.878) for PNI; and an AUC of 0.706 (95% CI: 0.481-0.931) for LVI. CONCLUSIONS: This study indicates that the combination of enhanced T1WI full volume histogram and clinical prediction model can be used to predict the perineural and lymphovascular invasion status of rectal cancer before surgery, providing valuable reference information for clinical diagnosis.

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Objectives: Randomized controlled trials (RCTs) have shown that attention-deficit/hyperactivity disorder (ADHD) medications significantly reduce symptomatology at a group level, but individual response to ADHD medication is variable. Thus, developing prediction models to stratify treatment according to individual baseline clinicodemographic characteristics is crucial to support clinical practice. A potential valuable source of data to develop accurate prediction models is real-world clinical data extracted from electronic healthcare records (EHRs). Yet, systematic information regarding EHR data on ADHD is lacking. Methods: We conducted a comprehensive review of studies that included EHR reporting data regarding individuals with ADHD, with a specific focus on treatment-related data. Relevant studies were identified from PubMed, Ovid, and Web of Science databases up to February 24, 2024. Results: We identified 103 studies reporting EHR data for individuals with ADHD. Among these, 83 studies provided information on the type of prescribed medication. However, dosage, duration of treatment, and ADHD symptom ratings before and after treatment initiation were only reported by a minority of studies. Conclusion: This review supports the potential use of EHRs to develop treatment response prediction models but emphasizes the need for more comprehensive reporting of treatment-related data, such as changes in ADHD symptom ratings and other possible baseline clinical predictors of treatment response.

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Objectives: This study aims to introduce a prediction model based on a machine learning approach as an efficient solution for prediction purposes to better prognosis and increase CRC survival. Methods: In the current retrospective study, we used the data of 1062 CRC cases to analyse and establish a prediction model for the 5-year CRC survival. The machine learning algorithms were used to develop prediction models, including random Forest, XG-Boost, bagging, logistic regression, support vector machine, artificial neural network, decision tree, and K-nearest neighbours. Results: The current study revealed that the XG-Boost with AU-ROC of 0.906 and 0.813 for internal and external conditions gave us better insight into predictability and generalizability than other algorithms. Conclusion: XG-Boost can be utilised as a knowledge source for implementing intelligent systems as an assistive tool for clinical decision-making in healthcare settings to improve prognosis and increase CRC survival through various clinical solutions that doctors can achieve.

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BACKGROUND: Postoperative recurrence is a vital reason for poor 5-year overall survival in hepatocellular carcinoma (HCC) patients. The ADV score is considered a parameter that can quantify HCC aggressiveness. This study aimed to identify HCC patients at high-risk of recurrence early using the ADV score. METHODS: The medical data of consecutive HCC patients undergoing hepatectomy from The First Affiliated Hospital of Nanjing Medical University (TFAHNJMU) and Nanjing Drum Tower Hospital (NJDTH) were retrospectively reviewed. Based on the status of microvascular invasion and the Edmondson-Steiner grade, HCC patients were divided into three groups: low-risk group (group 1: no risk factor exists), medium-risk group (group 2: one risk factor exists), and high-risk group (group 3: coexistence of two risk factors). In the training cohort (TFAHNJMU), the R package nnet was used to establish a multi-categorical unordered logistic regression model based on the ADV score to predict three risk groups. The Welch's T-test was used to compare differences in clinical variables in three predicted risk groups. NJDTH served as an external validation center. At last, the confusion matrix was developed using the R package caret to evaluate the diagnostic performance of the model. RESULTS: 350 and 405 patients from TFAHNJMU and NJDTH were included. HCC patients in different risk groups had significantly different liver function and inflammation levels. Density maps demonstrated that the ADV score could best differentiate between the three risk groups. The probability curve was plotted according to the predicted results of the multi-categorical unordered logistic regression model, and the best cut-off values of the ADV score were as follows: low-risk ≤ 3.4 log, 3.4 log < medium-risk ≤ 5.7 log, and high-risk > 5.7 log. The sensitivities of the ADV score predicting the high-risk group (group 3) were 70.2% (99/141) and 78.8% (63/80) in the training and external validation cohort, respectively. CONCLUSION: The ADV score might become a valuable marker for screening patients at high-risk of HCC recurrence with a cut-off value of 5.7 log, which might help surgeons, pathologists, and HCC patients make appropriate clinical decisions.

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PURPOSE: Primary immune thrombocytopenia (ITP) is an autoimmune bleeding disorder characterized by isolated thrombocytopenia that is often misdiagnosed due to the lack of a gold standard for diagnosis and currently relies on exclusionary approaches. This project combines several laboratory parameters to construct a clinical prediction model for adult ITP patients. METHODS: A total of 428 patients with thrombocytopenia who visited the West China Hospital of Sichuan University between January 2021 and March 2023 were enrolled. Based on the diagnostic criteria, we divided those patients into an ITP group and a non-ITP group. A total of 34 laboratory parameters were analyzed via univariate analysis and correlation analysis, and the least absolute shrinkage and selection operator regression analysis was used to establish the model. The training and validation sets were divided at a ratio of 7:3, and we used a fivefold cross-validation method to construct the model. RESULTS: The model included the following variables: red blood cell, mean corpuscular hemoglobin concentration, red blood cell distribution width-standard deviation, platelet variability index score, immature platelet fraction, lymphocyte absolute value. The prediction model exhibited good performance, with a sensitivity of 0.89 and a specificity of 0.83 in the training set and a sensitivity of 0.90 and a specificity of 0.87 in the validation set. CONCLUSION: The clinical prediction model can assess the probability of ITP in thrombocytopenic patients and has good predictive accuracy for the diagnosis of ITP.

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Aim: This study aimed to evaluate the mean post-test probability (PTP) of the Maturity-onset diabetes of the young (MODY) calculator in a multiethnic cohort of patients previously diagnosed with type 1 diabetes (T1DM). Materials and methods: The MODY probability calculator proposed by Shields and colleagues (2012) was applied to 117 patients from a T1DM outpatient clinic at a tertiary hospital in Brazil. Additionally, two exons of the HNF1A gene were sequenced in eight patients who hadn't received insulin treatment within six months after the diagnosis. Results: 17.1 % of patients achieved PTP >10 %; 11.1 % achieved PTP >25 % (and all patients >30 %), and 7.7 % achieved PTP >40 %. Among the patients who were selected for genetic sequencing, 100 % presented PTP >30 %, with 66.6 % achieving PTP >40 % and 41.6 % achieving PTP >75 %. These cutoffs are as suggested for the Brazilian population, according to previous investigations. No mutation was observed in the sequenced exons. Conclusion: Considering that only around 10 % of the evaluated cases achieved PTP >30 %, it is highly probable that the most suitable cutoff to select patients for genetic sequencing in a Brazilian cohort of T1DM is higher than the cutoff used in Caucasian populations.

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Lupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF) to analyze single-cell datasets from kidney biopsies, aiming to provide a comprehensive profile of LN. Through this analysis, we identified various immune cell populations and their roles in LN progression and constructed 102 machine learning-based immune-related gene (IRG) predictive models. The most effective models demonstrated high predictive accuracy, evidenced by Area Under the Curve (AUC) values, and were further validated in external cohorts. These models highlight six hub IRGs (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) as key diagnostic markers for LN, showing remarkable diagnostic performance in both renal and peripheral blood cohorts, thus offering a novel approach for noninvasive LN diagnosis. Further clinical correlation analysis revealed that expressions of IFNGR1, PLAUR, and CYBB were negatively correlated with the glomerular filtration rate (GFR), while CYBB also positively correlated with proteinuria and serum creatinine levels, highlighting their roles in LN pathophysiology. Additionally, protein-protein interaction (PPI) analysis revealed significant networks involving hub IRGs, emphasizing the importance of the interleukin family and chemokines in LN pathogenesis. This study highlights the potential of integrating advanced genomic tools and machine learning algorithms to improve diagnosis and personalize management of complex autoimmune diseases like LN.

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BACKGROUND: Intraoperative conversion to open surgery is an adverse event during minimally invasive distal pancreatectomy (MIDP), associated with poor postoperative outcomes. The aim of this study was to develop a model capable of predicting conversion in patients undergoing MIDP. METHODS: A total of 352 patients who underwent MIPD were included in this retrospective analysis and randomly assigned to training and validation cohorts. Potential risk factors related to open conversion were identified through a literature review, and data on these factors in our cohort was collected accordingly. In the training cohort, multivariate logistic regression analysis was performed to adjust the impact of confounding factors to identify independent risk factors for model building. The constructed model was evaluated using the receiver operating characteristics curve, decision curve analysis (DCA), and calibration curves. RESULTS: Following an extensive literature review, a total of ten preoperative risk factors were identified, including sex, BMI, albumin, smoker, size of lesion, tumor close to major vessels, type of pancreatic resection, surgical approach, MIDP experience, and suspicion of malignancy. Multivariate analysis revealed that sex, tumor close to major vessels, suspicion of malignancy, type of pancreatic resection (subtotal pancreatectomy or left pancreatectomy), and MIDP experience persisted as significant predictors for conversion to open surgery during MIDP. The constructed model offered superior discrimination ability compared to the existing model (area under the curve, training cohort: 0.921 vs. 0.757, P < 0.001; validation cohort: 0.834 vs. 0.716, P = 0.018). The DCA and the calibration curves revealed the clinical usefulness of the nomogram and a good consistency between the predicted and observed values. CONCLUSION: The evidence-based prediction model developed in this study outperformed the previous model in predicting conversions of MIDP. This model could contribute to decision-making processes surrounding the selection of surgical approaches and facilitate patient counseling on the conversion risk of MIDP.

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Background: Patients with heart failure (HF) with preserved ejection fraction (HFpEF) are more prone to atrial fibrillation (AF) compared to those with heart failure with reduced ejection fraction (HFrEF). Nevertheless, a risk prediction model for new-onset atrial fibrillation (NOAF) in HFpEF patients remains a notable gap, especially with respect to imaging indicators. Methods: We retrospectively analyzed 402 HFpEF subjects reviewed at the Affiliated Hospital of Qingdao University from 2017 to 2023. Cox regression analysis was performed to screen predictors of NOAF. A nomogram was constructed based on these factors and internally validated through the bootstrap resampling method. A performance comparison between the nomogram and the mC2HEST score was performed. Results: Out of the 402 participants, 62 (15%) developed atrial fibrillation. The risk factors for NOAF were finally screened out to include age, chronic obstructive pulmonary disease (COPD), hyperthyroidism, renal dysfunction, left atrial anterior-posterior diameter (LAD), and pulmonary artery systolic pressure (PASP), all of which were identified to create the nomogram. We calculated the bootstrap-corrected C-index (0.819, 95% CI: 0.762-0.870) and drew receiver operator characteristic (ROC) curves [3-year areas under curves (AUC) = 0.827, 5-year AUC = 0.825], calibration curves, and clinical decision curves to evaluate the discrimination, calibration, and clinical adaptability of the six-factor nomogram. Based on two cutoff values calculated by X-tile software, the moderate- and high-risk groups had more NOAF cases than the low-risk group (P < 0.0001). Our nomogram showed better 3- and 5-year NOAF predictive performance than the mC2HEST score estimated by the Integrated Discriminant Improvement Index (IDI) and the Net Reclassification Index (NRI) (P < 0.05). Conclusions: The nomogram combining clinical features with echocardiographic indices helps predict NOAF among HFpEF patients.

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Objective To identify the risk factors of patients with frequent acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and construct a prediction model based on the clinical data,providing a theoretical basis for the clinical prevention and treatment. Methods A total of 25 638 COPD patients admitted to the Department of Respiratory and Critical Care Medicine,the Third People's Hospital of Chengdu from January 1,2013 to May 1,2023 were selected.Among them,11 315 patients were included according to the inclusion and exclusion criteria,and their clinical characteristics were analyzed.Multivariate Logistic regression was carried out to identify the risk factors for frequent AECOPD.A nomogram model was utilized to quantify the risk of acute exacerbation,and the performance of the prediction model was assessed based on the area under the receiver operating characteristic (ROC) curve. Results In the patients with frequent AECOPD,male percentage (P<0.001),age (P<0.001),urban residence (P<0.001),smoking (P<0.001),length of stay (P<0.001),total cost (P<0.001),antibiotic cost (P<0.001),diabetes (P=0.003),respiratory failure (P<0.001),heart disease (P<0.001),application of systemic glucocorticoids (P<0.001),white blood cell count (P<0.001),neutrophil percentage (P<0.001),C-reactive protein (P<0.001),total cholesterol (P<0.001),and brain natriuretic peptide (BNP) (P<0.001) were all higher than those in the patients with infrequent AECOPD.Multivariate Logistic regression analysis revealed that age,urban residence,smoking,diabetes,heart disease,Pseudomonas aeruginosa infection,application of systemic glucocorticoids,antibiotics,respiratory failure,and elevated white blood cell count,total cholesterol,and BNP were independent risk factors for hospitalization due to frequent AECOPD.A nomogram model of hospitalization due to frequent AECOPD was constructed according to risk factors.The ROC curve was established to evaluate the performance of the model,which showed the area under the ROC curve of 0.899 (95%CI=0.892-0.905),the sensitivity of 85.30%,and the specificity of 79.80%. Conclusion Frequent AECOPD is associated with smoking,heart disease,application of systemic glucocorticoids,Pseudomonas aeruginosa infection,age,low body mass index,and elevated BNP.Predicting the risks of hospitalization due to frequent AECOPD by the established model can provide theoretical support for the treatment and risk factor management of the patients.

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Objective: To explore the risk factors of neck work-related musculoskeletal disorders (WMSDs) among automobile manufacturing enterprise workers, and construct the risk prediction model. Methods: In May 2022, a cluster convenience sampling method was used to selet all front-line workers from an automobile manufacturing factory in Xiangyang City as the research objects. And a questionnaire survey was conducted using the modified Musculoskeletal Disorders Questionnaire to analyze the occurrence and exposure to risk factors of neck WMSDs. Logistic regression was used to analyze the influencing factors of workers' neck WMSDs symptoms, and Nomogram column charts was used to construct the risk prediction model. The accuracy of the model was evaluated by the receiver operating characteristic (ROC) curve, the Bootstrap resampling method was used to verify the model, Hosmer-Lemeshow goodness of fit test was used to evaluate the model, and the Calibration curve was drawn. Results: A total of 1783 workers were surveyed, and the incidence of neck WMSDs symptoms was 24.8% (442/1783). Univariate logistic regression showed that age, female, smoking, working in uncomfortable postures, repetitive head movement, feeling constantly stressed at work, and completing conflicting tasks in work could increase the risk of neck WMSDs symptoms in automobile manufacturing enterprise workers (OR=1.37, 95%CI: 1.16-1.62; OR=2.85, 95%CI: 1.56-5.20; OR=1.50, 95%CI: 1.18-1.91; OR=1.18, 95%CI: 1.02-1.37; OR=1.34, 95%CI: 1.04-1.72; OR=1.62, 95%CI: 1.21-2.17; OR=1.48, 95%CI: 1.13-1.92; P<0.05). While adequate rest time could reduce the risk of neck WMSDs symptoms (OR=0.56, 95%CI: 0.52-0.86, P<0.05). The risk prediction model of neck WMSDs of workers in automobile manutacturing factory had good prediction efficiency, and the area under the ROC curve was 0.72 (95%CI: 0.70-0.75, P<0.001) . Conclusion: The occurrence of neck WMSDs symptoms of workers in automobile manufacturing factory is relatively high. The risk prediction model constructed in this study can play a certain auxiliary role in predicting neck WMSDs symptoms of workers in automobile manufacturing enterprise workers.

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Background: Diagnosing sternal wound infection (SWI) following median sternotomy remains laborious and troublesome, resulting in high mortality rates and great harm to patients. Early intervention and prevention are critical and challenging. This study aimed to develop a simple risk prediction model to identify high-risk populations of SWI and to guide examination programs and intervention strategies. Methods: A retrospective analysis was conducted on the clinical data obtained from 6715 patients who underwent median sternotomy between January 2016 and December 2020. The least absolute shrink and selection operator (LASSO) regression method selected the optimal subset of predictors, and multivariate logistic regression helped screen the significant factors. The nomogram model was built based on all significant factors. Area under the curve (AUC), calibration curve and decision curve analysis (DCA) were used to assess the model's performance. Results: LASSO regression analysis selected an optimal subset containing nine predictors that were all statistically significant in multivariate logistic regression analysis. Independent risk factors of SWI included female [odds ratio (OR) = 3.405, 95% confidence interval (CI) = 2.535-4.573], chronic obstructive pulmonary disease (OR = 4.679, 95% CI = 2.916-7.508), drinking (OR = 2.025, 95% CI = 1.437-2.855), smoking (OR = 7.059, 95% CI = 5.034-9.898), re-operation (OR = 3.235, 95% CI = 1.087-9.623), heart failure (OR = 1.555, 95% CI = 1.200-2.016) and repeated endotracheal intubation (OR = 1.975, 95% CI = 1.405-2.774). Protective factors included bone wax (OR = 0.674, 95% CI = 0.538-0.843) and chest physiotherapy (OR = 0.446, 95% CI = 0.248-0.802). The AUC of the nomogram was 0.770 (95% CI = 0.745-0.795) with relatively good sensitivity (0.798) and accuracy (0.620), exhibiting moderately good discernment. The model also showed an excellent fitting degree on the calibration curve. Finally, the DCA presented a remarkable net benefit. Conclusions: A visual and convenient nomogram-based risk calculator built on disease-associated predictors might help clinicians with the early identification of high-risk patients of SWI and timely intervention.

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Objective: Intellectual disability (ID) is a prevalent comorbidity in children with cerebral palsy (CP), presenting significant challenges to individuals, families and society. This study aims to develop a predictive model to assess the risk of ID in children with CP. Methods: We analyzed data from 885 children diagnosed with CP, among whom 377 had ID. Using least absolute shrinkage and selection operator regression, along with univariate and multivariate logistic regression, we identified key predictors for ID. Model performance was evaluated through receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA). Bootstrapping validation was also employed. Results: The predictive nomogram included variables such as preterm birth, CP subtypes, Gross Motor Function Classification System level, MRI classification category, epilepsy status and hearing loss. The model demonstrated strong discrimination with an area under the receiver operating characteristic curve (AUC) of 0.781 (95% CI: 0.7504-0.8116) and a bootstrapped AUC of 0.7624 (95% CI: 0.7216-0.8032). Calibration plots and the Hosmer-Lemeshow test indicated a good fit (χ2= 7.9061, p = 0.4427). DCA confirmed the model's clinical utility. The cases were randomly divided into test group and validation group at a 7:3 ratio, demonstrating strong discrimination, good fit and clinical utility; similar results were found when stratified by sex. Conclusions: This predictive model effectively identifies children with CP at a high risk for ID, facilitating early intervention strategies. Stratified risk categories provide precise guidance for clinical management, aiming to optimize outcomes for children with CP by leveraging neuroplasticity during early childhood.

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INTRODUCTION: In the current study, we screened for highly sensitive and specific predictors of premature birth, with the aim to establish an sPTB prediction model that is suitable for women in China and easy to operate and popularize, as well as to establish a sPTB prediction scoring system for early, intuitive, and effective assessment of premature birth risk. METHODS: A total of 685 pregnant women with a single pregnancy during the second trimester (16-26 weeks) were divided into premature and non-premature delivery groups based on their delivery outcomes. Clinical and ultrasound information were collected for both groups, and risk factors that could lead to sPTB in pregnant women were screened and analyzed using a cut-off value. A nomogram was developed to establish a prediction model and scoring system for sPTB. In addition, 119 pregnant women who met the inclusion criteria for the modeling cohort were included in the external validation of the model. The accuracy and consistency of the model were evaluated using the area under the receiver operating characteristic (ROC) and C-calibration curves. RESULTS: Multivariate logistic regression analysis showed a significant correlation (P < 0.05) between the number of miscarriages in pregnant women, history of miscarriages in the first week of pregnancy, history of preterm birth, CL of pregnant women, open and continuous cervical opening, and the occurrence of sPTB in pregnant women. We drew a nomogram column chart based on the six risk factors mentioned above, obtained a predictive model for sPTB, and established a scoring system to divide premature birth into three risk groups: low, medium, and high. After validating the model, the Hosmer Lemeshow test indicated a good fit (p = 0.997). The modeling queue C calibration curve was close to diagonal (C index = 0.856), confirming that the queue C calibration curve was also close to diagonal (C index = 0.854). The AUCs of the modeling and validation queues were 0.850 and 0.881, respectively. CONCLUSION: Our predictive model is consistent with China's national conditions, as well as being intuitive and easy to operate, with wide applicability, thus representing a helpful tool to assist with early detection of sPTB in clinical practice, as well as for clinical management in assessing low, medium, and high risks of sPTB.

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Background: Rebound hyperbilirubinemia (HBB) is still present in as high as 10% of newborn babies. However, the applicability of established prediction models for rebound HBB to Chinese newborns is unclear. This study aimed to establish a model to predict HBB rebound after phototherapy among Chinese neonates. Methods: A retrospective cohort study was conducted on 1,035 HBB infants receiving phototherapy. Rebound HBB was defined as total serum bilirubin (TSB) returning to or above the American Academy of Pediatrics (AAP) phototherapy threshold within 72 hours after the end of phototherapy. The predictive effects of previously published two- and three-variable scores were verified. Neonates were randomly assigned in a 6:4 ratio to the training (n=621) group and the testing (n=414) group. All variables in the training set were used to select predictors by least absolute shrinkage and selection operator (LASSO) regression analysis. The internal validation of the prediction model was performed using the testing set. The model's predictive performance was evaluated by area under the curve (AUC), accuracy, sensitivity, and specificity, each with 95% confidence intervals (CIs). Receiver operating characteristic (ROC) and calibration curves were constructed to evaluate the discrimination ability and fitting effect of the prediction model, respectively. Results: Rebound HBB was observed in 210 patients (20.3%). The AUC for the two- and three-variable scores were 0.498 (95% CI: 0.455-0.540) and 0.498 (95% CI: 0.457-0.540), respectively. Predictive factors for the risk of rebound HBB included formula feeding (>3 times/day), standard phototherapy irradiation time, TSB levels and age at termination of phototherapy, neonatal weight, and differences between TSB levels at the phototherapy termination and phototherapy threshold. The prediction model's AUC was 0.935 (95% CI: 0.911-0.958), the sensitivity was 0.880 (95% CI: 0.809-0.950), the specificity was 0.831 (95% CI: 0.790-0.871), and the accuracy was 0.841 (95% CI: 0.805-0.876). Conclusions: The established model performed well in predicting rebound risk among Chinese infants with HBB, which may be beneficial in treating and managing HBB in infants.

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The prognosis of early gastric cancer (EGC) patients is associated with lymph node metastasis (LNM). Considering the relatively high rate of LNM in T1b EGC patients, it is crucial to determine the factors associated with LNM. In this study, we constructed and validated predictive models based on machine learning (ML) algorithms for LNM in patients with T1b EGC. Data from patients with T1b gastric cancer were extracted from the Korean Gastric Cancer Association database. ML algorithms such as logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were applied for model construction utilizing five-fold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical applicability. Moreover, external validation of XGBoost models was performed using the T1b gastric cancer database of The Catholic University Medical Center. In total, 3,468 T1b EGC patients were included in the analysis, whom 550 (15.9%) had LNM. Eleven variables were selected to construct the models. The LR, RF, XGBoost, and SVM models were established, revealing area under the receiver operating characteristic curve values of 0.8284, 0.7921, 0.8776, and 0.8323, respectively. Among the models, the XGBoost model exhibited the best predictive performance in terms of discrimination, calibration, and clinical applicability. ML models are reliable for predicting LNM in T1b EGC patients. The XGBoost model exhibited the best predictive performance and can be used by surgeons for the identification of EGC patients with a high-risk of LNM, thereby facilitating treatment selection.

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Introduction: The morbidity and mortality rates of coronary heart disease are significant, with PCI being the primary treatment. The high incidence of ISR following PCI poses a challenge to its effectiveness. Currently, there are numerous studies on ISR risk prediction models after PCI, but the quality varies and there is still a lack of systematic evaluation and analysis. Methods: To systematically retrieve and evaluate the risk prediction models for ISR after PCI. A comprehensive search was conducted across 9 databases from inception to March 1, 2024. The screening of literature and extraction of data were independently carried out by two investigators, utilizing the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS). Additionally, the risk of bias and applicability were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results: A total of 17 studies with 29 models were included, with a sample size of 175-10,004 cases, and the incidence of outcome events was 5.79%-58.86%. The area under the receiver operating characteristic curve was 0.530-0.953. The top 5 predictors with high frequency were diabetes, number of diseased vessels, age, LDL-C and stent diameter. Bias risk assessment into the research of the risk of higher bias the applicability of the four study better. Discussion: The overall risk of bias in the current ISR risk prediction model post-PCI is deemed high. Moving forward, it is imperative to enhance study design and specify the reporting process, optimize and validate the model, and enhance its performance.