The effect of fine grits and fine debris concentrations on the MLVSS/MLSS ratio of an activated sludge system.

This study embarks on an explorative investigation into the effects of typical concentrations and varying particle sizes of fine grits (FG, the involatile portion of suspended solids) and fine debris (FD, the volatile yet unbiodegradable fraction of suspended solids) within the influent on the mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio of an activated sludge system. Through meticulous experimentation, it was discerned that the addition of FG or FD, the particle size of FG, and the concentration of FD bore no substantial impact on the pollutant removal efficiency (denoted by the removal rate of COD and ammonia nitrogen) under constant operational conditions. However, a notable decrease in the MLVSS/MLSS ratio was observed with a typical FG concentration of 20 mg/L, with smaller FG particle sizes exacerbating this reduction. Additionally, variations in FD concentrations influenced both MLSS and MLVSS/MLSS ratios; a higher FD concentration led to an increased MLSS and a reduced MLVSS/MLSS ratio, indicating FD accumulation in the system. A predictive model for MLVSS/MLSS was constructed based on quality balance calculations, offering a tool for foreseeing the MLVSS/MLSS ratio under stable long-term influent conditions of FG and FD. This model, validated using data from the BXH wastewater treatment plant (WWTP), showcased remarkable accuracy.

Factors predicting the return of spontaneous circulation rate of cardiopulmonary resuscitation in China: Development and evaluation of predictive nomogram.

Background: This study aimed to construct and internally validate a probability of the return of spontaneous circulation (ROSC) rate nomogram in a Chinese population of patients with cardiac arrest (CA). Methods: Patients with CA receiving standard cardiopulmonary resuscitation (CPR) were studied retrospectively. The minor absolute shrinkage and selection operator (LASSO) regression analysis and multivariable logistic regression evaluated various demographic and clinicopathological characteristics. A predictive nomogram was constructed and evaluated for accuracy and reliability using C-index, the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis (DCA). Results: A cohort of 508 patients who had experienced CA and received standard CPR was randomly divided into training (70 %, n = 356) and validation groups (30 %, n = 152) for the study. LASSO regression analysis and multivariable logistic regression revealed that thirteen variables, such as age, CPR start time, Electric defibrillation, Epinephrine, Sodium bicarbonate (NaHCO3), CPR Compression duration, The postoperative prothrombin (PT) time, Lactate (Lac), Cardiac troponin (cTn), Potassium (K+), D-dimer, Hypertension (HBP), and Diabetes mellitus (DM), were found to be independent predictors of the ROSC rate of CPR. The nomogram model showed exceptional discrimination, with a C-index of 0.933 (95 % confidence interval: 0.882-0.984). Even in the internal validation, a remarkable C-index value of 0.926 (95 % confidence interval: 0.875-0.977) was still obtained. The accuracy and reliability of the model were also verified by the AUC of 0.923 in the training group and 0.926 in the validation group. The calibration curve showed the model agreed with the actual results. DCA suggested that the predictive nomogram had clinical utility. Conclusions: A predictive nomogram model was successfully established and proved to identify the influencing factors of the ROSC rate in patients with CA. During cardiopulmonary resuscitation, adjusting the emergency treatment based on the influence factors on ROSC rate is suggested to improve the treatment rate of patients with CA.

Prediction model establishment and validation for enteral nutrition aspiration during hospitalization in patients with acute pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is a disease caused by abnormal activation of pancreatic enzymes and can lead to self-digestion of pancreatic tissues and dysfunction of other organs. Enteral nutrition plays a vital role in the treatment of AP because it can meet the nutritional needs of patients, promote the recovery of intestinal function, and maintain the barrier and immune functions of the intestine. However, the risk of aspiration during enteral nutrition is high; once aspiration occurs, it may cause serious complications, such as aspiration pneumonia, and suffocation, posing a threat to the patient's life. This study aims to establish and validate a prediction model for enteral nutrition aspiration during hospitalization in patients with AP. AIM: To establish and validate a predictive model for enteral nutrition aspiration during hospitalization in patients with AP. METHODS: A retrospective review was conducted on 200 patients with AP admitted to Chengdu Shangjin Nanfu Hospital, West China Hospital of Sichuan University from January 2020 to February 2024. Clinical data were collected from the electronic medical record system. Patients were randomly divided into a validation group (n = 40) and a modeling group (n = 160) in a 1:4 ratio, matched with 200 patients from the same time period. The modeling group was further categorized into an aspiration group (n = 25) and a non-aspiration group (n = 175) based on the occurrence of enteral nutrition aspiration during hospitalization. Univariate and multivariate logistic regression analyses were performed to identify factors influencing enteral nutrition aspiration in patients with AP during hospitalization. A prediction model for enteral nutrition aspiration during hospitalization was constructed, and calibration curves were used for validation. Receiver operating characteristic curve analysis was conducted to evaluate the predictive value of the model. RESULTS: There was no statistically significant difference in general data between the validation and modeling groups (P > 0.05). The comparison of age, gender, body mass index, smoking history, hypertension history, and diabetes history showed no statistically significant difference between the two groups (P > 0.05). However, patient position, consciousness status, nutritional risk, Acute Physiology and Chronic Health Evaluation (APACHE-II) score, and length of nasogastric tube placement showed statistically significant differences (P < 0.05) between the two groups. Multivariate logistic regression analysis showed that patient position, consciousness status, nutritional risk, APACHE-II score, and length of nasogastric tube placement were independent factors influencing enteral nutrition aspiration in patients with AP during hospitalization (P < 0.05). These factors were incorporated into the prediction model, which showed good consistency between the predicted and actual risks, as indicated by calibration curves with slopes close to 1 in the training and validation sets. Receiver operating characteristic analysis revealed an area under the curve (AUC) of 0.926 (95%CI: 0.8889-0.9675) in the training set. The optimal cutoff value is 0.73, with a sensitivity of 88.4 and specificity of 85.2. In the validation set, the AUC of the model for predicting enteral nutrition aspiration in patients with AP patients during hospitalization was 0.902, with a standard error of 0.040 (95%CI: 0.8284-0.9858), and the best cutoff value was 0.73, with a sensitivity of 91.9 and specificity of 81.8. CONCLUSION: A prediction model for enteral nutrition aspiration during hospitalization in patients with AP was established and demonstrated high predictive value. Further clinical application of the model is warranted.

Establishment of predictive models and determinants of preoperative gastric retention in endoscopic retrograde cholangiopancreatography.

BACKGROUND: Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography (ERCP) background: With the wide application of ERCP, the risk of preoperative gastric retention affects the smooth progress of the operation. The study found that female, biliary and pancreatic malignant tumor, digestive tract obstruction and other factors are closely related to gastric retention, so the establishment of predictive model is very important to reduce the risk of operation. AIM: To analyze the factors influencing preoperative gastric retention in ERCP and establish a predictive model. METHODS: A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024. Patient baseline clinical data were collected using an electronic medical record system. Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group (n = 38) and a modeling group (n = 152). Patients in the modeling group were divided into the gastric retention group (n = 52) and non-gastric retention group (n = 100) based on whether gastric retention occurred preoperatively. General data of patients in the validation group and modeling group were compared. Univariate and multivariate logistic regression analyses were performed to identify factors influencing preoperative gastric retention in ERCP patients. A predictive model for preoperative gastric retention in ERCP patients was constructed, and calibration curves were used for validation. The receiver operating characteristic (ROC) curve was analyzed to evaluate the predictive value of the model. RESULTS: We found no statistically significant difference in general data between the validation group and modeling group (P > 0.05). The comparison of age, body mass index, hypertension, and diabetes between the two groups showed no statistically significant difference (P > 0.05). However, we noted statistically significant differences in gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction between the two groups (P < 0.05). Multivariate logistic regression analysis showed that gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients (P < 0.05). The results of logistic regression analysis revealed that gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients. The calibration curves in the training set and validation set showed a slope close to 1, indicating good consistency between the predicted risk and actual risk. The ROC analysis results showed that the area under the curve (AUC) of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023 (95%CI: 0.8264-0.9567), and the optimal cutoff value was 0.71, with a sensitivity of 87.5 and specificity of 84.2. In the validation set, the AUC of the predictive model was 0.842 with a standard error of 0.013 (95%CI: 0.8061-0.9216), and the optimal cutoff value was 0.56, with a sensitivity of 56.2 and specificity of 100.0. CONCLUSION: Gender, primary disease, jaundice, opioid use, and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients. A predictive model established based on these factors has high predictive value.

Development and validation of a predictive model for in-hospital mortality from perioperative bacteremia in gastrointestinal surgery.

PURPOSE: Prognostic scores require fluctuating values, such as respiratory rate, which are unsuitable for retrospective auditing. Therefore, this study aimed to develop and validate a predictive model for in-hospital mortality associated with gastrointestinal surgery for retrospective auditing. METHODS: Data from patients with bacteremia related to gastrointestinal surgery performed at Shizuoka General Hospital between July 2006 and December 2021 were extracted from a prospectively maintained database. Patients suspected of having a positive blood culture with contaminating bacteria or missing laboratory data were excluded. The remaining patients were randomly assigned in a 2:1 ratio to the deviation and validation cohorts. A logistic regression model estimated the odds ratios (ORs) and created a predictive model for in-hospital mortality. The model was evaluated using receiver operating characteristic (ROC) curves and calibration plots. RESULTS: Of 20,637 gastrointestinal surgeries, 398 resulted in bacteremia. The median age of patients with bacteremia was 72 years, and 66.1% were male. The most common pathogens were Staphylococcus (13.9%), followed by Bacteroides (12.4%) and Escherichia (11.4%). Multivariable logistic regression showed that creatinine abnormality (P < 0.001, OR = 3.39), decreased prognostic nutritional index (P < 0.001, OR = 0.90/unit), and age ≥ 75 years (P = 0.026, OR = 2.89) were independent prognostic factors for in-hospital mortality. The area under the ROC curve of the predictive model was 0.711 in the validation cohort. The calibration plot revealed that the model slightly overestimated mortality in the validation cohort. CONCLUSIONS: Using age, creatinine level, albumin level, and lymphocyte count, the model accurately predicted in-hospital mortality after bacteremia infection related to gastrointestinal surgery, demonstrating its suitability for retrospective audits.

PROFUGO study protocol: Predictive model for the early diagnosis of anastomotic leak after esophagectomy and gastrectomy.

In esophagogastric surgery, the appearance of an anastomotic leak is the most feared complication. Early diagnosis is important for optimal management and successful resolution. For this reason, different studies have investigated the value of the use of markers to predict possible postoperative complications. Because of this, research and the creation of predictive models that identify patients at high risk of developing complications are mandatory in order to obtain an early diagnosis. The PROFUGO study (PRedictivO Model for Early Diagnosis of anastomotic LEAK after esophagectomy and gastrectomy) is proposed as a prospective and multicenter national study that aims to develop, with the help of artificial intelligence methods, a predictive model that allows for the identification of high-risk cases. of anastomotic leakage and/or major complications by analyzing different clinical and analytical variables collected during the postoperative period of patients undergoing esophagectomy or gastrectomy.

An overview of statistical methods for biomarkers relevant to early clinical development of cancer immunotherapies.

Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies. The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies.

An Advanced Machine Learning Model for a Web-Based Artificial Intelligence-Based Clinical Decision Support System Application: Model Development and Validation Study.

BACKGROUND: Breast cancer is a leading global health concern, necessitating advancements in recurrence prediction and management. The development of an artificial intelligence (AI)-based clinical decision support system (AI-CDSS) using ChatGPT addresses this need with the aim of enhancing both prediction accuracy and user accessibility. OBJECTIVE: This study aims to develop and validate an advanced machine learning model for a web-based AI-CDSS application, leveraging the question-and-answer guidance capabilities of ChatGPT to enhance data preprocessing and model development, thereby improving the prediction of breast cancer recurrence. METHODS: This study focused on developing an advanced machine learning model by leveraging data from the Tri-Service General Hospital breast cancer registry of 3577 patients (2004-2016). As a tertiary medical center, it accepts referrals from four branches-3 branches in the northern region and 1 branch on an offshore island in our country-that manage chronic diseases but refer complex surgical cases, including breast cancer, to the main center, enriching our study population's diversity. Model training used patient data from 2004 to 2012, with subsequent validation using data from 2013 to 2016, ensuring comprehensive assessment and robustness of our predictive models. ChatGPT is integral to preprocessing and model development, aiding in hormone receptor categorization, age binning, and one-hot encoding. Techniques such as the synthetic minority oversampling technique address the imbalance of data sets. Various algorithms, including light gradient-boosting machine, gradient boosting, and extreme gradient boosting, were used, and their performance was evaluated using metrics such as the area under the curve, accuracy, sensitivity, and F1-score. RESULTS: The light gradient-boosting machine model demonstrated superior performance, with an area under the curve of 0.80, followed closely by the gradient boosting and extreme gradient boosting models. The web interface of the AI-CDSS tool was effectively tested in clinical decision-making scenarios, proving its use in personalized treatment planning and patient involvement. CONCLUSIONS: The AI-CDSS tool, enhanced by ChatGPT, marks a significant advancement in breast cancer recurrence prediction, offering a more individualized and accessible approach for clinicians and patients. Although promising, further validation in diverse clinical settings is recommended to confirm its efficacy and expand its use.

Establishing a nomogram for predicting necrotizing enterocolitis in premature infants: A retrospective multicenter cohort study.

BACKGROUND: To construct a nomogram for predicting necrotizing enterocolitis (NEC) in preterm infants. METHODS: A total of 4,724 preterm infants who were admitted into 8 hospitals between April 2019 and September 2020 were initially enrolled this retrospective multicenter cohort study. Finally, 1,092 eligible cases were divided into training set and test set based on a 7:3 ratio. A univariate logistic regression analysis was performed to compare the variables between the two groups. Stepwise backward regression, LASSO regression, and Boruta feature selection were utilized in the multivariate analysis to identify independent risk factors. Then a nomogram model was constructed based on the identified risk factors. RESULTS: Risk factors for NEC included gestational diabetes mellitus, gestational age, small for gestational age, patent ductus arteriosus, septicemia, red blood cell transfusion, intravenous immunoglobulin, severe feeding intolerance, and absence of breastfeeding. The nomogram model developed based on these factors showed well discriminative ability. Calibration and decision curve analysis curves confirmed the good consistency and clinical utility of the model. CONCLUSIONS: We developed a nomogram model with strong discriminative ability, consistency, and clinical utility for predicting NEC. This model could be valuable for the early prediction of preterm infants at risk of developing NEC.

Machine learning-based prediction model for the efficacy and safety of statins.

Objective: The appropriate use of statins plays a vital role in reducing the risk of atherosclerotic cardiovascular disease (ASCVD). However, due to changes in diet and lifestyle, there has been a significant increase in the number of individuals with high cholesterol levels. Therefore, it is crucial to ensure the rational use of statins. Adverse reactions associated with statins, including liver enzyme abnormalities and statin-associated muscle symptoms (SAMS), have impacted their widespread utilization. In this study, we aimed to develop a predictive model for statin efficacy and safety based on real-world clinical data using machine learning techniques. Methods: We employed various data preprocessing techniques, such as improved random forest imputation and Borderline SMOTE oversampling, to handle the dataset. Boruta method was utilized for feature selection, and the dataset was divided into training and testing sets in a 7:3 ratio. Five algorithms, including logistic regression, naive Bayes, decision tree, random forest, and gradient boosting decision tree, were used to construct the predictive models. Ten-fold cross-validation and bootstrapping sampling were performed for internal and external validation. Additionally, SHAP (SHapley Additive exPlanations) was employed for feature interpretability. Ultimately, an accessible web-based platform for predicting statin efficacy and safety was established based on the optimal predictive model. Results: The random forest algorithm exhibited the best performance among the five algorithms. The predictive models for LDL-C target attainment (AUC = 0.883, Accuracy = 0.868, Precision = 0.858, Recall = 0.863, F1 = 0.860, AUPRC = 0.906, MCC = 0.761), liver enzyme abnormalities (AUC = 0.964, Accuracy = 0.964, Precision = 0.967, Recall = 0.963, F1 = 0.965, AUPRC = 0.978, MCC = 0.938), and muscle pain/Creatine kinase (CK) abnormalities (AUC = 0.981, Accuracy = 0.980, Precision = 0.987, Recall = 0.975, F1 = 0.981, AUPRC = 0.987, MCC = 0.965) demonstrated favorable performance. The most important features of LDL-C target attainment prediction model was cerebral infarction, TG, PLT and HDL. The most important features of liver enzyme abnormalities model was CRP, CK and number of oral medications. Similarly, AST, ALT, PLT and number of oral medications were found to be important features for muscle pain/CK abnormalities. Based on the best-performing predictive model, a user-friendly web application was designed and implemented. Conclusion: This study presented a machine learning-based predictive model for statin efficacy and safety. The platform developed can assist in guiding statin therapy decisions and optimizing treatment strategies. Further research and application of the model are warranted to improve the utilization of statin therapy.

Naples Prognostic Score: A Novel Predictor of Survival in Patients with Triple-Negative Breast Cancer.

Purpose: This study investigated the correlation between the Naples prognostic score (NPS), clinicopathological traits, and the postoperative prognoses of patients with triple-negative breast cancer (TNBC). Based on NPS, a predictive nomogram was developed to estimate the long-term survival probabilities of patients with TNBC post-surgery. Patients and Methods: We retrospectively examined the clinical records of 223 women with TNBC treated at Ningbo Medical Center, Lihuili Hospital between January 1, 2016 and December 31, 2020. Blood tests and biochemical analyses were conducted before surgery. The prognostic nutritional index (PNI), controlling nutritional status (CONUT), neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and NPS were determined based on blood-related markers. A Kaplan-Meier survival analysis assessed the association between NPS, PNI, CONUT score, overall survival (OS), and breast cancer-specific survival (BCSS). Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) and C index. The patients were randomly divided into the training and the validation group (6:4 ratio). A nomogram prediction model was developed and evaluated using the R Software for Statistical Computing (RMS) package. Results: NPS outperformed other scores in predicting inflammation outcomes. Patients with an elevated NPS had a poorer prognosis (P<0.001). Lymph node ratio (LNR), surgical method, postoperative chemotherapy, and NPS independently predicted OS, whereas M stage, LNR, and NPS independently predicted BCSS outcome. The OS and BCSS predicted by the nomogram model aligned well with the actual OS and BCSS. The decision curve analysis showed significant clinical utility for the nomogram model. Conclusion: In this study, NPS was an important prognostic indicator for patients with TNBC. The nomogram prognostic model based on NPS outperformed other prognostic scores for predicting patient prognosis. The model demonstrated a clear stratification ability for patient prognosis, which emphasized the potential benefits of early intervention for high-risk patients.

In this study, we aimed to understand how the Naples prognostic score (NPS) scoring system could predict the prognosis for patients with triple-negative breast cancer (TNBC). TNBC is a type of breast cancer that can be difficult to treat. Medical records of 223 women with TNBC were retrospectively analyzed. These women had their blood tested before surgery to check for certain markers related to nutrition and inflammation. NPS was used along with other scores to determine their accuracy in predicting survival. NPS was better at predicting outcomes than the other scores. The patients with higher NPS scores tended to have poorer outcomes. We also created a visual tool called a nomogram to help doctors predict patient outcomes based on the NPS scores. NPS can be a valuable tool for doctors treating patients with TNBC because it can help them predict how well a patient might do after surgery. This information could be used to tailor treatment plans for these patients. The nomogram provides a user-friendly way for doctors to use NPS in their practice. Overall, this study showed that NPS is a powerful tool for predicting outcomes for patients with TNBC, which could lead to better treatment decisions and improved outcomes for these patients.

A machine learning-based predictive model for biliary stricture attributable to malignant tumors: a dual-center retrospective study.

Background: Biliary stricture caused by malignant tumors is known as Malignant Biliary Stricture (MBS). MBS is challenging to differentiate clinically, and accurate diagnosis is crucial for patient prognosis and treatment. This study aims to identify the risk factors for malignancy in all patients diagnosed with biliary stricture by Endoscopic Retrograde Cholangiopancreatography (ERCP), and to develop an effective clinical predictive model to enhance diagnostic outcomes. Methodology: Through a retrospective study, data from 398 patients diagnosed with biliary stricture using ERCP between January 2019 and January 2023 at two institutions: the First People's Hospital affiliated with Jiangsu University and the Second People's Hospital affiliated with Soochow University. The study began with a preliminary screening of risk factors using univariate regression. Lasso regression was then applied for feature selection. The dataset was divided into a training set and a validation set in an 8:2 ratio. We analyzed the selected features using seven machine learning algorithms. The best model was selected based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUROC) and other evaluation indicators. We further evaluated the model's accuracy using calibration curves and confusion matrices. Additionally, we used the SHAP method for interpretability and visualization of the model's predictions. Results: RF model is the best model, achieved an AUROC of 0.988. Shap result indicate that age, stricture location, stricture length, carbohydrate antigen 199 (CA199), total bilirubin (TBil), alkaline phosphatase (ALP), (Direct Bilirubin) DBil/TBil, and CA199/C-Reactive Protein (CRP) were risk factors for MBS, and the CRP is a protective factor. Conclusion: The model's effectiveness and stability were confirmed, accurately identifying high-risk patients to guide clinical decisions and improve patient prognosis.

Next generation sequencing reveals the mutation landscape of Chinese MDS patients and the association between mutations and AML transformations.

BACKGROUND/OBJECTIVE: Approximately 30% of patients with MDS eventually develop to acute myeloid leukemia (AML). Our study aimed to investigate the mutation landscape of Chinese MDS patients and identify the mutated genes which are closely implicated in the transformation of MDS to AML. METHODS: In total, 412 sequencing data collected from 313 patients were used for analysis. Mutation frequencies between different groups were compared by Fisher's exact. A predictive model for risk of transformation/death of newly diagnosed patients was constructed by logistic regression. RESULTS: The most frequently mutated genes in newly diagnosed patients were TP53, TET2, RUNX1, PIGA, and BCOR and mutations of RUNX1, TP53, BCORL1, TET2, and BCOR genes were more common in the treated MDS patients. Besides, we found that the mutation frequencies of IDH2, TET2, and EZH2 were significantly higher in MDS patients aged over 60 years. Moreover, two mutation sites, KRASG12A and TP53H140N were detected only at transformation in one patient, while not detected at diagnosis. In addition, the mutation frequencies of EZH2 V704F and TET2 I1873N were stable from diagnosis to transformation in two patients. Finally, we constructed a predictive model for risk of transformation/death of newly diagnosed patients combing detected data of 10 genes and the number of to leukocyte, with a sensitivity of 63.3% and a specificity of 84.6% in distinguishing individuals with and without risk of transformation/death. CONCLUSION: In summary, our study found several mutations associated with the transformation from MDS to AML, and constructed a predictive model for risk of transformation/death of MDS patients.

Predictive model for novel subtypes of patients undergoing lower extremity amputation for peripheral artery disease: An unsupervised machine learning study.

Background: Peripheral artery disease (PAD) represents the frequently seen circulatory condition related to a risk of critical limb ischemia and amputation. Critical lower extremity ischemia may require amputation, and the outcomes vary. In this study, we developed an artificial intelligence (AI)-driven predictive model for PAD subtypes to assess risk among patients more precisely and accurately to predict disease progression. Methods: The present retrospective study examined clinical data in PAD patents undergoing lower extremity amputation. The data were analyzed using an unsupervised machine learning algorithm (UMLA) for subgroup identification and risk stratification. The clustering result accuracy was validated by analyzing the follow-up data of clusters. Finally, we built the prediction model with binary logistic regression. Results: In total, we enrolled 507 cases into this work. Two distinct subgroups, consisting of Clusters 1 and 2, were identified by UMLA; those from Cluster 1 showed markedly poorer conditions and prognostic outcomes compared with those from Cluster 2. With regard to the new PAD subtype, we established a nomogram with eight predictive factors, including gender, age, smoking history, diabetes and coronary heart disease history, albumin levels, endovascular intervention, and amputation level. The nomogram could accurately categorize patients into two identified clusters, and the area under receiver operating characteristic curve was 0.861 (95 % confidence interval: 0.830-0.893). Conclusion: In this study, UMLA was used to identify new phenotypic subgroups among PAD cases who showed different risks of amputation. Our constructed AI-driven predictive model for PAD subtypes showed that it can be used for risk stratification and clinical management with high accuracy and reliability.

Prediction of PSA Response after Dexamethasone Switch during Abiraterone Acetate + Prednisolone Treatment of Metastatic Castration-Resistant Prostate Cancer Patients.

BACKGROUND: The aim was to elaborate a predictive model to find responders for the corticosteroid switch (from prednisolone to dexamethasone) at the first prostate-specific antigen (PSA) progression (≥25% increase) during abiraterone acetate (AA) treatment of metastatic castration-resistant prostate cancer (mCRPC) patients. METHODS: If PSA has decreased (≥25%) after switch, patients were considered responders. Logistic regression of 19 dichotomized parameters from routine laboratory and patients' history was used to find the best model in a cohort of 67 patients. The model was validated in another cohort of 42 patients. RESULTS: The model provided 92.5% and 90.5% accuracy in the testing and the validation cohorts, respectively. Overall the accuracy was 91.7%. The AUC of ROC curve was 0.92 (95% CI 0.85-0.96). After a median follow-up of 27.9 (26.3-84) months, the median AA+dexamethasone treatment duration (TD) in non-responders and responders was 4.7 (3.1-6.5) and 11.1 (8.5-12.9) months and the median overall survival (OS) was 23.2 (15.6-25.8) and 33.5 (26.1-38) months, respectively. Multivariate Cox regression revealed that responsiveness was an independent marker of TD and OS. CONCLUSIONS: A high accuracy model was developed for mCRPC patients in predicting cases which might benefit from the switch. For non-responders, induction of the next systemic treatment is indicated.

Predictive Value of the Nomogram Model Based on Multimodal Ultrasound Features for Benign and Malignant Thyroid Nodules of C-TIRADS Category 4.

To explore the predictive value of the nomogram model based on multimodal ultrasound features for benign and malignant thyroid nodules of C-TIRADS category 4. A retrospective analysis was conducted on the general conditions and ultrasound features of patients who underwent thyroid ultrasound examination and fine needle aspiration biopsy (FNA) or thyroidectomy at the Affiliated Hospital of Zunyi Medical University from April 2020 to April 2023. Predictive signs for benign and malignant nodules of thyroid C-TIRADS category 4 were screened through LASSO regression and multivariate logistic regression analysis to construct a nomogram prediction model. The predictive efficiency and accuracy of the model were assessed through ROC curves and calibration curves. Seven independent risk factors in the predictive model for benign and malignant thyroid nodules of C-TIRADS category 4 were growth pattern, morphology, microcalcifications, SR, arterial phase enhancement intensity, initial perfusion time, and PE [%]. Based on these features, the area under the curve (AUC) of the constructed prediction model was 0.971 (p ＜ .001, 95% CI: 0.952-0.989), with a prediction accuracy of 93.1%. Internal validation showed that the nomogram calibration curve was consistent with reality, and the decision curve analysis indicated that the model has high clinical application value. The nomogram prediction model constructed based on the multimodal ultrasound features of thyroid nodules of C-TIRADS category 4 has high clinical application value.

Heart failure with improved versus persistently reduced left ventricular ejection fraction: A comparison of the BIOSTAT-CHF (European) study with the ASIAN-HF registry.

AIMS: We investigated the prevalence, clinical characteristics, and prognosis of patients with heart failure (HF) with improved ejection fraction (HFimpEF). METHODS AND RESULTS: We used data from BIOSTAT-CHF including patients with a left ventricular ejection fraction (LVEF) ≤40% at baseline who had LVEF re-assessed at 9 months. HFimpEF was defined as a LVEF >40% and a LVEF ≥10% increase from baseline at 9 months. We validated findings in the ASIAN-HF registry. The primary outcome was a composite of time to HF rehospitalization or all-cause mortality. In BIOSTAT-CHF, about 20% of patients developed HFimpEF, that was associated with a lower primary event rate of all-cause mortality (hazard ratio [HR] 0.52, 95% confidence interval [CI] 0.28-0.97, p = 0.040) and the composite endpoint (HR 0.46, 95% CI 0.30-0.70, p < 0.001) compared with patients who remained in persistent HF with reduced ejection fraction (HFrEF). The findings were similar in the ASIAN-HF (HR 0.40, 95% CI 0.18-0.89, p = 0.024, and HR 0.29, 95% CI 0.17-0.48, p < 0.001). Five independently common predictors for HFimpEF in both BIOSTAT-CHF and ASIAN-HF were female sex, absence of ischaemic heart disease, higher LVEF, smaller left ventricular end-diastolic and end-systolic diameter at baseline. A predictive model combining only five predictors (absence of ischaemic heart disease and left bundle branch block, smaller left ventricular end-systolic and left atrial diameter, and higher platelet count) for HFimpEF in the BIOSTAT-CHF achieved an area under the curve of 0.772 and 0.688 in the ASIAN-HF (due to missing left atrial diameter and platelet count). CONCLUSIONS: Approximately 20-30% of patients with HFrEF improved to HFimpEF within 1 year with better clinical outcomes. In addition, the predictive model with clinical predictors could more accurately predict HFimpEF in patients with HFrEF.

Prediction of lateral lymph node metastasis in rectal cancer patients based on MRI using clinical, deep transfer learning, radiomic, and fusion models.

Introduction: Lateral lymph node (LLN) metastasis in rectal cancer significantly affects patient treatment and prognosis. This study aimed to comprehensively compare the performance of various predictive models in predicting LLN metastasis. Methods: In this retrospective study, data from 152 rectal cancer patients who underwent lateral lymph node (LLN) dissection were collected. The cohort was divided into a training set (n=86) from Tianjin Union Medical Center (TUMC), and two testing cohorts: testing cohort (TUMC) (n=37) and testing cohort from Gansu Provincial Hospital (GSPH) (n=29). A clinical model was established using clinical data; deep transfer learning models and radiomics models were developed using MRI images of the primary tumor (PT) and largest short-axis LLN (LLLN), visible LLN (VLLN) areas, along with a fusion model that integrates features from both deep transfer learning and radiomics. The diagnostic value of these models for LLN metastasis was analyzed based on postoperative LLN pathology. Results: Models based on LLLN image information generally outperformed those based on PT image information. Rradiomics models based on LLLN demonstrated improved robustness on external testing cohorts compared to those based on VLLN. Specifically, the radiomics model based on LLLN imaging achieved an AUC of 0.741 in the testing cohort (TUMC) and 0.713 in the testing cohort (GSPH) with the extra trees algorithm. Conclusion: Data from LLLN is a more reliable basis for predicting LLN metastasis in rectal cancer patients with suspicious LLN metastasis than data from PT. Among models performing adequately on the internal test set, all showed declines on the external test set, with LLLN_Rad_Models being less affected by scanning parameters and data sources.

A novel nomogram to predict psoriatic arthritis in patients with plaque psoriasis.

OBJECTIVE: To construct a predictive model for Psoriatic Arthritis (PsA) based on clinical and ultrasonic characteristics in patients with plaque psoriasis (PsP). PATIENTS AND METHODS: Demographic, clinical, and ultrasound data were collected from patients with PsP and PsA between May 2019 and December 2022. RESULTS: A total of 212 patients with PsP and 123 with PsA in the training cohort, whereas the validation cohort comprised 91 patients with PsP and 49 with PsA. The multivariate logistic regression identified nail psoriasis (odds ratio [OR] 1.88, 95% CI: 1.07-3.29), synovitis (OR 18.23, 95% CI: 4.04-82.33), enthesitis (OR 3.71, 95% CI: 1.05-13.14), and bone erosion (OR 11.39, 95% CI: 3.05-42.63) as effective predictors for PsA. The area under the curve was 0.750 (95% CI, 0.691-0.806) and 0.804 (95% CI, 0.723-0.886) for the training and validation cohorts, respectively. The Hosmer-Lemeshow goodness-of-fit test showed good consistency for both the training cohort (p  =  0.970) and the validation cohort (p  =  0.967). Calibration curves also indicated good calibration for both cohorts. The DCA revealed that the predictive model had good clinical utility. CONCLUSIONS: We have developed a quantitative, intuitive, and convenient predictive model based on nail psoriasis, synovitis, enthesitis, and bone erosion to assess the risk of PsA in patients with plaque psoriasis.

A risk prediction model based on machine learning algorithm for parastomal hernia after permanent colostomy.

OBJECTIVE: To develop a machine learning-based risk prediction model for postoperative parastomal hernia (PSH) in colorectal cancer patients undergoing permanent colostomy, assisting nurses in identifying high-risk groups and devising preventive care strategies. METHODS: A case-control study was conducted on 495 colorectal cancer patients who underwent permanent colostomy at the Second Affiliated Hospital of Anhui Medical University from June 2017 to June 2023, with a 1-year follow-up period. Patients were categorized into PSH and non-PSH groups based on PSH occurrence within 1-year post-operation. Data were split into training (70%) and testing (30%) sets. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and binary classification prediction models were established using Logistic Regression (LR), Support Vector Classification (SVC), K Nearest Neighbor (KNN), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XgBoost). The binary classification label denoted 1 for PSH occurrence and 0 for no PSH occurrence. Parameters were optimized via 5-fold cross-validation. Model performance was evaluated using Area Under Curve (AUC), specificity, sensitivity, accuracy, positive predictive value, negative predictive value, and F1-score. Clinical utility was evaluated using decision curve analysis (DCA), model explanation was enhanced using shapley additive explanation (SHAP), and model visualization was achieved using a nomogram. RESULTS: The incidence of PSH within 1 year was 29.1% (144 patients). Among the models tested, the RF model demonstrated the highest discrimination capability with an AUC of 0.888 (95% CI: 0.881-0.935), along with superior specificity, accuracy, sensitivity, and F1 score. It also showed the highest clinical net benefit on the DCA curve. SHAP analysis identified the top 10 influential variables associated with PSH risk: body mass index (BMI), operation duration, history and status of chronic obstructive pulmonary disease (COPD), prealbumin, tumor node metastasis (TNM) staging, stoma site, thickness of rectus abdominis muscle (TRAM), C-reactive protein CRP, american society of anesthesiologists physical status classification (ASA), and stoma diameter. These insights from SHAP plots illustrated how these factors influence individual PSH outcomes. The nomogram was used for model visualization. CONCLUSION: The Random Forest model demonstrated robust predictive performance and clinical relevance in forecasting colonic PSH. This model aids in early identification of high-risk patients and guides preventive care.