Construction and evaluation of a liver cancer risk prediction model based on machine learning.

BACKGROUND: Liver cancer is one of the most prevalent malignant tumors worldwide, and its early detection and treatment are crucial for enhancing patient survival rates and quality of life. However, the early symptoms of liver cancer are often not obvious, resulting in a late-stage diagnosis in many patients, which significantly reduces the effectiveness of treatment. Developing a highly targeted, widely applicable, and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals. AIM: To develop a liver cancer risk prediction model by employing machine learning techniques, and subsequently assess its performance. METHODS: In this study, a total of 550 patients were enrolled, with 190 hepatocellular carcinoma (HCC) and 195 cirrhosis patients serving as the training cohort, and 83 HCC and 82 cirrhosis patients forming the validation cohort. Logistic regression (LR), support vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO) regression models were developed in the training cohort. Model performance was assessed in the validation cohort. Additionally, this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) to determine the optimal predictive model for assessing liver cancer risk. RESULTS: Six variables including age, white blood cell, red blood cell, platelet counts, alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR, SVM, RF, and LASSO regression models. The RF model exhibited superior discrimination, and the area under curve of the training and validation sets was 0.969 and 0.858, respectively. These values significantly surpassed those of the LR (0.850 and 0.827), SVM (0.860 and 0.803), LASSO regression (0.845 and 0.831), and ASAP (0.866 and 0.813) models. Furthermore, calibration and DCA indicated that the RF model exhibited robust calibration and clinical validity. CONCLUSION: The RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.

A nomogram with Nottingham prognostic index for predicting locoregional recurrence in breast cancer patients.

Background: The Nottingham prognostic index (NPI) has been shown to negatively impact survival in breast cancer (BC). However, its ability to predict the locoregional recurrence (LRR) of BC remains still unclear. This study aims to determine whether a higher NPI serves as a significant predictor of LRR in BC. Methods: In total, 238 patients with BC were included in this analysis, and relevant clinicopathological features were collected. Correlation analysis was performed between NPI scores and clinicopathological characteristics. The optimal nomogram model was determined by Akaike information criterion. The accuracy of the model's predictions was evaluated using receiver operating characteristic curves (ROC curves), calibration curves and goodness of fit tests. The clinical application value was assessed through decision curve analysis. Results: Six significant variables were identified, including age, body mass index (BMI), TNM stage, NPI, vascular invasion, perineural invasion (P<0.05). Two prediction models, namely a TNM-stage-based model and an NPI-based model, were constructed. The area under the curve (AUC) for the TNM-stage- and NPI-based models were 0.843 (0.785,0.901) and 0.830 (0.766,0.893) in training set and 0.649 (0.520,0.778) and 0.728 (0.610,0.846) in validation set, respectively. Both models exhibited good calibration and goodness of fit. The F-measures were 0.761vs 0.756 and 0.556 vs 0.696, respectively. Clinical decision curve analysis showed that both models provided clinical benefits in evaluating risk judgments based on the nomogram model. Conclusions: a higher NPI is an independent risk factor for predicting LRR in BC. The nomogram model based on NPI demonstrates good discrimination and calibration, offering potential clinical benefits. Therefore, it merits widespread adoption and application.

Establishment and evaluation of prediction model of recurrence after laparoscopic choledocholithotomy.

BACKGROUND: Choledocholithiasis is a common clinical bile duct disease, laparoscopic choledocholithotomy is the main clinical treatment method for choledocholithiasis. However, the recurrence of postoperative stones is a big challenge for patients and doctors. AIM: To explore the related risk factors of gallstone recurrence after laparoscopic choledocholithotomy, establish and evaluate a clinical prediction model. METHODS: A total of 254 patients who underwent laparoscopic choledocholithotomy in the First Affiliated Hospital of Ningbo University from December 2017 to December 2020 were selected as the research subjects. Clinical data of the patients were collected, and the recurrence of gallstones was recorded based on the postoperative follow-up. The results were analyzed and a clinical prediction model was established. RESULTS: Postoperative stone recurrence rate was 10.23% (26 patients). Multivariate Logistic regression analysis showed that cholangitis, the diameter of the common bile duct, the diameter of the stone, number of stones, lithotripsy, preoperative total bilirubin, and T tube were risk factors associated with postoperative recurrence (P < 0.05). The clinical prediction model was ln (p/1-p) = -6.853 + 1.347 × cholangitis + 1.535 × choledochal diameter + 2.176 × stone diameter + 1.784 × stone number + 2.242 × lithotripsy + 0.021 × preoperative total bilirubin + 2.185 × T tube. CONCLUSION: Cholangitis, the diameter of the common bile duct, the diameter of the stone, number of stones, lithotripsy, preoperative total bilirubin, and T tube are the associated risk factors for postoperative recurrence of gallstone. The prediction model in this study has a good prediction effect, which has a certain reference value for recurrence of gallstone after laparoscopic choledocholithotomy.

Predicting the resolution of hypertension following adrenalectomy in primary aldosteronism: Controversies and unresolved issues a narrative review.

BACKGROUND: Hypertension resolution following adrenalectomy in patients with primary aldosteronism (PA) remains a critical clinical challenge. Identifying preoperatively which patients will become normotensive is both a priority and a point of contention. In this narrative review, we explore the controversies and unresolved issues surrounding the prediction of hypertension resolution after adrenalectomy in PA. METHODS: A comprehensive literature review was conducted, focusing on studies published between 1954 and 2024 that evaluated all studies that discussed predictive models for hypertension resolution post-adrenalectomy in PA patients. Databases searched included MEDLINE®, Ovid Embase, and Web of Science databases. RESULTS: The review identified several predictors and predictive models of hypertension resolution, including female sex, duration of hypertension, antihypertensive medication, and BMI. However, inconsistencies in study designs and patient populations led to varied conclusions. CONCLUSIONS: Although certain predictors and predictive models of hypertension resolution post-adrenalectomy in PA patients are supported by evidence, significant controversies and unresolved issues remain. While the current predictive models provide valuable insights, there is a clear need for further research in this area. Future studies should focus on validating and refining these models.

Development of a novel prediction model for carriage of BRCA1/2 pathogenic variant in Japanese patients with breast cancer based on Japanese organization of hereditary breast and ovarian cancer registry data.

PURPOSE: With the increasing demand for BRCA genetic testing, most existing prediction models were developed using data from individuals of European descent. This study aimed to identify clinicopathological factors of hereditary breast and ovarian cancer (HBOC) syndrome and develop the first Japanese-specific prediction model for BRCA pathogenic variant carriers in Japan. METHODS: We utilized data from 3072 Japanese patients with breast cancer aggregated by the Japanese Organization of Hereditary Breast and Ovarian Cancer registry. Prediction models were developed using 70% of the overall dataset and validated using the remaining 30%. Factors associated with the BRCA pathogenic variant status were identified using logistic univariate analysis, and significant factors were further analyzed using logistic multivariate analysis to develop prediction models for BRCA1/2 (BRCA1 and/or BRCA2), BRCA1, and BRCA2 pathogenic variants. RESULTS: BRCA1 showed associations with aggressive clinicopathological factors such as triple-negative breast cancer and nuclear grade 3. Moreover, the prediction model showed a high area under the curve (AUC) of 0.879. By contrast, BRCA2 exhibited fewer characteristic associated factors, and the AUC of the model was 0.669. Common factors shared by BRCA1/2, BRCA1, and BRCA2 were the age at diagnosis of breast cancer and the youngest age of relatives with breast cancer. Consistent with previous research, early-onset breast cancer appeared to be strongly associated with HBOC. CONCLUSION: We successfully developed prediction models for BRCA1/2, BRCA1, and BRCA2 pathogenic variants. By accurately stratifying patients' risk and guiding targeted screening and preventative interventions, these models will contribute to improved management and outcomes of HBOC.

Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice.

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep-learning-based prediction model (DLIF) that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F]Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice.

Machine learning in female urinary incontinence: A scoping review.

Introduction and Hypothesis: The aim was to conduct a scoping review of the literature on the use of machine learning (ML) in female urinary incontinence (UI) over the last decade. Methods: A systematic search was performed among the Medline, Google Scholar, PubMed, and Web of Science databases using the following keywords: [Urinary incontinence] and [(Machine learning) or (Predict) or (Prediction model)]. Eligible studies were considered to have applied ML model to explore different management processes of female UI. Data analyzed included the field of application, type of ML, input variables, and results of model validation. Results: A total of 798 papers were identified while 23 finally met the inclusion criteria. The vast majority of studies applied logistic regression to establish models (91.3%, 21/23). Most frequently ML was applied to predict postpartum UI (39.1%, 9/23), followed by de novo incontinence after pelvic floor surgery (34.8%, 8/23).There are also three papers using ML models to predict treatment outcomes and three papers using ML models to assist in diagnosis. Variables for modeling included demographic characteristics, clinical data, pelvic floor ultrasound, and urodynamic parameters. The area under receiver operating characteristic curve of these models fluctuated from 0.56 to 0.95, and only 11 studies reported sensitivity and specificity, with sensitivity ranging from 20% to 96.2% and specificity from 59.8% to 94.5%. Conclusion: Machine learning modeling demonstrated good predictive and diagnostic abilities in some aspects of female UI, showing its promising prospects in near future. However, the lack of standardization and transparency in the validation and evaluation of the models, and the insufficient external validation greatly diminished the applicability and reproducibility, thus a focus on filling this gap is strongly recommended for future research.

Scoring and validation of a simple model for predicting diabetic retinopathy in patients with type 2 diabetes based on a meta-analysis approach of 21 cohorts.

AIM: To develop and validate a model for predicting diabetic retinopathy (DR) in patients with type 2 diabetes. METHODS: All risk factors with statistical significance in the DR prediction model were scored by their weights. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve, Kaplan-Meier curve, calibration curve and decision curve analysis. The prediction model was externally validated using a validation cohort from a Chinese hospital. RESULTS: In this meta-analysis, 21 cohorts involving 184,737 patients with type 2 diabetes were examined. Sex, smoking, diabetes mellitus (DM) duration, albuminuria, glycated haemoglobin (HbA1c), systolic blood pressure (SBP) and TG were identified to be statistically significant. Thus, they were all included in the model and scored according to their weights (maximum score: 35.0). The model was validated using an external cohort with median follow-up time of 32 months. At a critical value of 16.0, the AUC value, sensitivity and specificity of the validation cohort are 0.772 ((95% confidence interval (95%CI): 0.740-0.803), p < .01), 0.715 and 0.775, respectively. The calibration curve lied close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated that the model had notably higher net benefits. The external validation results proved the reliability of the risk prediction model. CONCLUSIONS: The simple DR prediction model developed has good overall calibration and discrimination performance. It can be used as a simple tool to detect patients at high risk of DR.

Development and validation of a clinical prediction model for osteonecrosis of the jaw in patients receiving zoledronic acid using FAERS and canadian databases.

Background: Osteonecrosis of the jaw (ONJ) stands as a severe complication linked to the use of bisphosphonates, particularly zoledronic acid, which is widely prescribed for managing conditions like osteoporosis and bone metastasis. This study is geared towards the development and validation of a clinical prediction model for ONJ in patients undergoing zoledronic acid treatment. Methods: We harnessed data from the FDA Adverse Event Reporting System (FAERS) as our training dataset, while the Canada Vigilance Adverse Reaction (CVAR) database served as the testing dataset. The study encompassed patients treated with zoledronic acid and subsequently diagnosed with ONJ. We analysed a range of predictive factors, including breast cancer, bone metastasis, osteoporosis, vitamin D and calcium levels, comorbidities, the number of concomitant medications, dosage, age, weight, and gender. Logistic regression and nomogram analysis were the chosen methodologies for constructing the predictive model. To evaluate the model's performance, we utilized receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: The study encompassed a total of 2,126 patients in the training cohort, 911 patients in the internal test cohort from the FAERS database, and 121 patients in the external test cohort from the CVAR database. Notable predictors for ONJ included bone metastasis (OR: 1.65, 95% CI: 1.22-2.24), osteoporosis (OR: 0.33, 95% CI: 0.21-0.52), the number of concomitant medications (OR: 1.07, 95% CI: 1.05-1.09), and the dosage of zoledronic acid (OR: 1.24, 95% CI: 1.10-1.39). The nomogram exhibited robust discriminatory power, evidenced by an area under the curve (AUC) of 0.77 in the training cohort, 0.76 in the internal test cohort, and 0.90 in the external test cohort. Calibration plots demonstrated a strong alignment between observed and predicted probabilities. Furthermore, DCA highlighted the prediction model's significant net benefit across various threshold probabilities. Conclusion: By leveraging data from both the FAERS and Canadian databases, this study has successfully developed and validated a clinical prediction model for ONJ in patients receiving zoledronic acid. This model stands as a valuable tool for clinicians, enabling them to pinpoint high-risk patients and make evidence-based treatment decisions to minimize the risk of ONJ.

Analysis of Influencing Factors and Construction of Predictive Model for Persistent Cough After Lung Cancer Resection Under Thoracoscopy.

Objective: This study aims to explore the influencing factors of cough after pulmonary resection (CAP) after thoracoscopic lung resection in lung cancer patients and to develop a predictive model. Methods: A total of 374 lung cancer patients who underwent lung resection in our hospital from March 2020 to October 2023 were randomly divided into a modeling group (n=262) and a validation group (n=112). Based on the occurrence of CAP in the modeling group, the patients were divided into a CAP group (n=85) and a non-CAP group (n=177). Multivariate Logistic regression analysis was used to identify the influencing factors of CAP in lung cancer patients. A nomogram model for predicting the risk of CAP was constructed using R4.3.1. The consistency of the model's predictions was evaluated, and a clinical decision curve (DCA) was drawn to assess the clinical utility of the nomogram. The predictive performance of the model was evaluated using ROC curves and the Hosmer-Lemeshow test. Results: Multivariate Logistic regression analysis showed that smoking history (OR=6.285, 95% CI: 3.031-13.036), preoperative respiratory function training (OR=20.293, 95% CI: 7.518-54.779), surgical scope (OR=20.667, 95% CI: 7.734-55.228), and peribronchial lymph node dissection (OR=5.883, 95% CI: 2.829-12.235) were significant influencing factors of CAP in lung cancer patients (P<0.05). ROC curves indicated good discriminatory power of the model, and the Hosmer-Lemeshow test showed a high degree of agreement between predicted and actual probabilities. The DCA curve revealed that the nomogram model had high clinical value when the high-risk threshold was between 0.08 and 0.98. Conclusion: The nomogram model based on smoking history, preoperative respiratory function training, surgical scope, and peribronchial lymph node dissection has high predictive performance for CAP in lung cancer patients. It is useful for clinical prediction, guiding preoperative preparation, and postoperative care.

Predicting nosocomial pneumonia risk in level-1 trauma patients: An external validation study using the trauma quality improvement program.

BACKGROUND: Early identification of patients at risk of nosocomial pneumonia enables the opportunity for preventative measures, which may improve survival and reduce costs. Therefore, this study aimed to externally validate an existing prediction model (issued by Croce et al.) to predict nosocomial pneumonia in patients admitted to US level-1 trauma centers. METHODS: A retrospective cohort study including patients admitted to level-1 trauma centers and registered in the TQIP, a US nationwide trauma registry, admitted between 2013-2015 and 2017-2019. The main outcome was total nosocomial pneumonia for the first period and ventilator-associated pneumonia (VAP) for the second. Model discrimination and calibration were assessed before and after recalibration. RESULTS: The study comprised 902,231 trauma patients (N2013-2015 â€‹= â€‹180,601; N2017-2019 â€‹= â€‹721,630), with a median age of 52 in both periods, 64-65 â€‹% male, and approximately 90 â€‹% sustaining blunt traumatic injury. The median Injury Severity Scores were 13 (2013-2015) versus 9 (2017-2019); median Glasgow Coma Scale scores were 15. Nosocomial pneumonia incidence was 4.4 â€‹%, VAP incidence was 0.7 â€‹%. The original model demonstrated good to excellent discrimination for both periods (c-statistic2013-2015 0.84, 95%CI 0.83-0.84; c-statistic2017-2019 0.92, 95%CI 0.91-0.92). After recalibration, discriminatory capacity and calibration for the lower predicted probabilities improved. CONCLUSIONS: The Croce model can identify patients admitted to US level-1 trauma centers at risk of total nosocomial pneumonia and VAP. Implementing (modified) Croce models in route trauma clinical practice could guide judicious use of preventative measures and prescription of additional non-invasive preventative measures (e.g., increased monitoring, pulmonary physiotherapy) to decrease the occurrence of nosocomial pneumonia in at-risk patients.

A comprehensive prediction model for central lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis: BRAF may not be a valuable predictor.

Purpose: Papillary thyroid carcinoma (PTC) frequently coexists with Hashimoto's thyroiditis (HT), which poses challenges in detecting central lymph node metastasis (CLNM) and determining optimal surgical management. Our study aimed to identify the independent predictors for CLNM in PTC patients with HT and develop a comprehensive prediction model for individualized clinical decision-making. Patients and methods: In this retrospective study, a total of 242 consecutive PTC patients who underwent thyroid surgery and central lymph node dissection between February 2019 and December 2021 were included. 129 patients with HT were enrolled as the case group and 113 patients without HT as control. The results of patients' general information, laboratory examination, ultrasound features, pathological evaluation, and BRAF mutation were collected. Multivariate logistic regression analysis was used to identify independent predictors, and the prediction model and nomogram were developed for PTC patients with HT. The performance of the model was assessed using the receiver operating characteristic curve, calibration curve, decision curve analysis, and clinical impact curve. In addition, the impact of the factor BRAF mutation was further evaluated. Results: Multivariate analysis revealed that gender (OR = 8.341, P = 0.013, 95% CI: 1.572, 44.266), maximum diameter (OR = 0.316, P = 0.029, 95% CI: 0.113, 0.888), multifocality (OR = 3.238, P = 0.010, 95% CI: 1.319, 7.948), margin (OR = 2.750, P = 0.046, 95% CI: 1.020, 7.416), and thyrotropin receptor antibody (TR-Ab) (OR = 0.054, P = 0.003, 95% CI: 0.008, 0.374) were identified as independent predictors for CLNM in PTC patients with HT. The area under the curve of the model was 0.82, with accuracy, sensitivity, and specificity of 77.5%, 80.3% and 75.0%, respectively. Meanwhile, the model showed satisfactory performance in the internal validation. Moreover, the results revealed that BRAF mutation cannot further improve the efficacy of the prediction model. Conclusion: Male, maximum diameter > 10mm, multifocal tumors, irregular margin, and lower TR-Ab level have significant predictive value for CLNM in PTC patients with HT. Meanwhile, BRAF mutation may not have a valuable predictive role for CLNM in these cases. The nomogram constructed offers a convenient and valuable tool for clinicians to determine surgical decision and prognostication for patients.

A Nomogram Based on a Non-Invasive Method to Distinguish Between Gram-Positive and Gram-Negative Bacterial Infections of Liver Abscess.

Purpose: The diagnosis of liver abscess (LA) caused by Gram-positive bacteria (GPB) and Gram-negative bacteria (GNB) depends on ultrasonography, but it is difficult to distinguish the overlapping features. Valuable ultrasonic (US) features were extracted to distinguish GPB-LA and GNB-LA and establish the relevant prediction model. Materials and Methods: We retrospectively analyzed seven clinical features, three laboratory indicators and 11 US features of consecutive patients with LA from April 2013 to December 2023. Patients with LA were randomly divided into training group (n=262) and validation group (n=174) according to a ratio of 6:4. Univariate logistic regression and LASSO regression were used to establish prediction models. The performance of the model was evaluated using area under the curve(AUC), calibration curves, and decision curve analysis (DCA), and subsequently validated in the validation group. Results: A total of 436 participants (median age: 55 years; range: 42-68 years; 144 women) were evaluated, including 369 participants with GNB-LA and 67 with GPB-LA, respectively. A total of 11 predictors by LASSO regression analysis, which included gender, age, the liver background, internal gas bubble, echogenic debris, wall thickening, whether the inner wall is worm-eaten, temperature, diabetes mellitus, hepatobiliary surgery and neutrophil(NEUT). The performance of the Nomogram prediction model distinguished between GNB-LA and GPB-LA was 0.80, 95% confidence interval [CI] (0.73-0.87). In the validation group, the AUC of GNB was 0.79, 95% CI (0.69-0.89). Conclusion: A model for predicting the risk of GPB-LA was established to help diagnose pathogenic organism of LA earlier, which could help select sensitive antibiotics before the results of drug-sensitive culture available, thereby shorten the treatment time of patients.

Analyzing risk factors and constructing a predictive model for superficial esophageal carcinoma with submucosal infiltration exceeding 200 micrometers.

OBJECTIVE: Submucosal infiltration of less than 200 µm is considered an indication for endoscopic surgery in cases of superficial esophageal cancer and precancerous lesions. This study aims to identify the risk factors associated with submucosal infiltration exceeding 200 micrometers in early esophageal cancer and precancerous lesions, as well as to establish and validate an accompanying predictive model. METHODS: Risk factors were identified through least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Various machine learning (ML) classification models were tested to develop and evaluate the most effective predictive model, with Shapley Additive Explanations (SHAP) employed for model visualization. RESULTS: Predictive factors for early esophageal invasion into the submucosa included endoscopic ultrasonography or magnifying endoscopy> SM1(P<0.001,OR = 3.972,95%CI 2.161-7.478), esophageal wall thickening(P<0.001,OR = 12.924,95%CI,5.299-33.96), intake of pickled foods(P=0.04,OR = 1.837,95%CI,1.03-3.307), platelet-lymphocyte ratio(P<0.001,OR = 0.284,95%CI,0.137-0.556), tumor size(P<0.027,OR = 2.369,95%CI,1.128-5.267), the percentage of circumferential mucosal defect(P<0.001,OR = 5.286,95%CI,2.671-10.723), and preoperative pathological type(P<0.001,OR = 4.079,95%CI,2.254-7.476). The logistic regression model constructed from the identified risk factors was found to be the optimal model, demonstrating high efficacy with an area under the curve (AUC) of 0.922 in the training set, 0.899 in the validation set, and 0.850 in the test set. CONCLUSION: A logistic regression model complemented by SHAP visualizations effectively identifies early esophageal cancer reaching 200 micrometers into the submucosa.

Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study.

Central venous access devices (CVADs) are integral to cancer treatment. However, catheter-related thrombosis (CRT) poses a considerable risk to patient safety. It interrupts treatment; delays therapy; prolongs hospitalisation; and increases the physical, psychological and financial burden of patients. Our study aims to construct and validate a predictive model for CRT risk in patients with cancer. It offers the possibility to identify independent risk factors for CRT and prevent CRT in patients with cancer. We prospectively followed patients with cancer and CVAD at Xiangya Hospital of Central South University from January 2021 to December 2022 until catheter removal. Patients with CRT who met the criteria were taken as the case group. Two patients with cancer but without CRT diagnosed in the same month that a patient with cancer and CRT was diagnosed were selected by using a random number table to form a control group. Data from patients with CVAD placement in Qinghai University Affiliated Hospital and Hainan Provincial People's Hospital (January 2023 to June 2023) were used for the external validation of the optimal model. The incidence rate of CRT in patients with cancer was 5.02% (539/10 736). Amongst different malignant tumour types, head and neck (9.66%), haematological (6.97%) and respiratory (6.58%) tumours had the highest risks. Amongst catheter types, haemodialysis (13.91%), central venous (8.39%) and peripherally inserted central (4.68%) catheters were associated with the highest risks. A total of 500 patients with CRT and 1000 without CRT participated in model construction and were randomly assigned to the training (n = 1050) or testing (n = 450) groups. We identified 11 independent risk factors, including age, catheterisation method, catheter valve, catheter material, infection, insertion history, D-dimer concentration, operation history, anaemia, diabetes and targeted drugs. The logistic regression model had the best discriminative ability amongst the three models. It had an area under the curve (AUC) of 0.868 (0.846-0.890) for the training group. The external validation AUC was 0.708 (0.618-0.797). The calibration curve of the nomogram model was consistent with the ideal curve. Moreover, the Hosmer-Lemeshow test showed a good fit (P > 0.05) and high net benefit value for the clinical decision curve. The nomogram model constructed in this study can predict the risk of CRT in patients with cancer. It can help in the early identification and screening of patients at high risk of cancer CRT.

Risk prediction models for disability in older adults: a systematic review and critical appraisal.

BACKGROUND: The amount of prediction models for disability in older adults is increasing but the prediction performance of different models varies greatly, and the quality of prediction models is still unclear. OBJECTIVES: To systematically review and critically appraise the studies on risk prediction models for disability in older adults. METHODS: A systematic literature search was conducted on PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and Wanfang Database, published up until June 30, 2023. Data were extracted according to the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the included studies. In addition, all included studies were evaluated for clinical value. RESULTS: A total of 5722 articles were initially retrieved from databases, 16 studies and 17 prediction models were finally included after screening. The sample sizes of studies ranged from 420 to 90,889. Model development methods mainly included logistic regression analysis, Cox proportional hazards regression, and machine learning methods. The C statistic or area under the curve (AUC) of models ranged from 0.650 to 0.853, and nine models had C statistic/AUC higher than 0.75. Age, chronic disease, gender, self-rated health, body mass index (BMI), drinking, smoking and education level were the most common predictors. According to the PROBAST, all included studies were at high risk of bias, and 10 studies were at high concerns for applicability. Only two studies reported following the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. After evaluation, only two models reached the standard of clinical value. CONCLUSION: Although most of the included prediction models had acceptable discrimination, the overall quality and clinical value of the current studies were poor. In the future, researchers should follow the TRIPOD statement and PROBAST checklist to develop prediction models with larger sample sizes, more reasonable study designs, and more scientific analysis methods, to improve the predictive performance and application value. TRIAL REGISTRATION: The review protocol was registered in PROSPERO (registration ID: CRD42023446657).

Creation of a Prediction Model of Local Tumor Recurrence After a Successful Conventional Transcatheter Arterial Chemoembolization Using Cone-Beam Computed Tomography Based-Radiomics.

PURPOSE: To create and evaluate prediction models of local tumor recurrence after successful conventional transcatheter arterial chemoembolization (c-TACE) via radiomics analysis of lipiodol deposition using cone-beam computed tomography (CBCT) images obtained at the completion of TACE. MATERIALS AND METHODS: A total of 103 hepatocellular carcinoma nodules in 71 patients, who achieved a complete response (CR) based on the modified Response Evaluation Criteria in Solid Tumors 1 month after TACE, were categorized into two groups: prolonged CR and recurrence groups. Three types of areas were segmented on CBCT: whole segment (WS), tumor segment (TS), and peritumor segment (PS). From each segment, 105 radiomic features were extracted. The nodules were randomly divided into training and test datasets at a ratio of 7:3. Following feature reduction for each segment, three models (clinical, radiomics, and clinical-radiomics models) were developed to predict recurrence based on logistic regression. RESULTS: The clinical-radiomics model of WS showed the best performance, with the area under the curve values of 0.853 (95% confidence interval: 0.765-0.941) in training and 0.752 (0.580-0.924) in test dataset. In the analysis of radiomic feature importance of all models, among all radiomic features, glcm_MaximumProbability, shape_MeshVolume and shape_MajorAxisLength had negative coefficients. In contrast, shape_SurfaceVolumeRatio, shape_Elongation, glszm_SizeZoneNonUniformityNormalized, and gldm_GrayLevelNonUniformity had positive coefficients. CONCLUSION: In this study, a machine-learning model based on cone-beam CT images obtained at the completion of c-TACE was able to predict local tumor recurrence after successful c-TACE. Nonuniform lipiodol deposition and irregular shapes may increase the likelihood of recurrence.

Nomogram to Predict Nodal Recurrence-Free Survival in Early Oral Squamous Cell Carcinoma.

OBJECTIVE: This study aimed to develop and internally validate a prognostic nomogram for predicting nodal recurrence-free survival (NRFS) in patients with early-stage oral squamous cell carcinoma (OSCC) with clinically negative neck lymph nodes. MATERIALS AND METHODS: The management of early-stage oral cancer patients with clinically negative neck lymph nodes (cN0) remains controversial, especially concerning the need for elective neck dissection. Data from a single institution spanning 2010 to 2020 were utilized to develop and evaluate the nomogram. The nomogram was constructed using multivariable Cox regression and LASSO regression analyses to identify independent risk factors for lymph node metastasis. Internal validation was performed using bootstrap resampling to assess the nomogram's predictive accuracy. RESULTS: A total of 930 cN0 patients with T1 and T2 stage OSCC were randomly divided into training and validation cohorts (8:2 ratio). Independent risk factors for lymph node metastasis included tumor pathological grade (well: reference, moderate/poor: OR 1.69), cT (cT1: reference, cT2: OR 2.01), history of drinking (never: reference, current/former: OR 1.72), and depth of invasion (0 mm < DOI ≤ 5 mm: reference, 5 mm < DOI ≤ 10 mm: OR 1.31). The nomogram, incorporating these variables, demonstrated good predictive accuracy with a C-index of 0.67 (95% CI: 0.58-0.76) in the validation set. In both training and validation groups, the nomogram effectively stratified patients into low-risk and high-risk groups for occult cervical nodal metastases (p < 0.05). CONCLUSIONS: The nomogram enables risk stratification and improved identification of occult cervical nodal metastases in clinically node-negative OSCC patients by incorporating tumor-specific and patient-specific risk factors.

Impact of Mitophagy-Related Genes on the Diagnosis and Development of Esophageal Squamous Cell Carcinoma via Single-Cell RNA-seq Analysis and Machine Learning Algorithms.

As a treatment for esophageal squamous cell carcinoma (ESCC), which is common and fatal, mitophagy is a conserved cellular mechanism that selectively removes damaged mitochondria and is crucial for cellular homeostasis. While tumor development and resistance to anticancer therapies are related to ESCC, their role in ESCC remains unclear. Here, we investigated the relationship between mitophagy-related genes (MRGs) and ESCC to provide novel insights into the role of mitophagy in ESCC prognosis and diagnosis prediction. First, we identified MRGs from the GeneCards database and examined them at both the single-cell and transcriptome levels. Key genes were selected and a prognostic model was constructed using least absolute shrinkage and selection operator analysis. External validation was performed using the GSE53624 dataset and Kaplan-Meier survival analysis was performed to identify PYCARD as a gene significantly associated with survival in ESCC. We then examined the effect of PYCARD on ESCC cell proliferation and migration and identified 169 MRGs at the single-cell and transcriptome levels, as well as the high-risk groups associated with cancer-related pathways. Thirteen key genes were selected for model construction via multiple machine learning algorithms. PYCARD, which is upregulated in patients with ESCC, was negatively correlated with prognosis and its knockdown inhibited ESCC cell proliferation and migration. Our ESCC prediction model based on mitophagy-related genes demonstrated promising results and provides more options for the management and clinical treatment of ESCC patients. Moreover, targeting or regulating PYCARD levels might offer new therapeutic strategies for ESCC patients in clinical settings.

Clinical Characteristics and Risk Factors of Sepsis in Patients with Liver Abscess.

Aims/Background Liver abscess (LA) is a serious medical condition that predisposes patients to sepsis. However, predicting sepsis in LA patients has rarely been explored. This study employed univariate and multivariate logistic regression analyses to identify independent risk factors for sepsis, which would provide guidance for clinical diagnosis and treatment. Methods A total of 122 patients with LA treated in Peking University People's Hospital from 1 January 2016 to 31 October 2022 were recruited. Among the cases, 35 patients had sepsis (sepsis group) while the remaining 87 did not have sepsis (non-sepsis group). Clinical data were collected for all enrolled cases. Univariate analysis was performed to identify potential predictors, which were tested in multivariable logistic analysis to pinpoint the independent risk factors for sepsis in LA patients; these findings were utilized to develop a prediction model. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the prediction model. Informed consent to participate was obtained from the patients or their relatives. Results The incidence of shivering in the sepsis group was significantly higher than that in the non-sepsis group (p < 0.05). Through the univariate analysis, it was found that the reduction in platelet count and prothrombin time activity and the elevation of glycosylated hemoglobin (HbAlc) and procalcitonin (PCT) were more significant in the sepsis group than in the non-sepsis group (p < 0.05). Multivariate logistic regression analysis revealed that PCT and HbAlc were independent risk predictors of sepsis in LA patients within the derivation cohort (p < 0.05). Conclusion Elevated levels of HbAlc and PCT were independent risk factors for sepsis associated with LA. Patients with LA exhibiting elevated PCT levels demonstrated a 21% increased susceptibility to sepsis, and those with elevated HbAlc levels showed a 38% heightened risk for sepsis.