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

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

External validation of a 2-year all-cause mortality prediction tool developed using machine learning in patients with stage 4-5 chronic kidney disease.

BACKGROUND: Chronic kidney disease (CKD) is associated with increased mortality. Individual mortality prediction could be of interest to improve individual clinical outcomes. Using an independent regional dataset, the aim of the present study was to externally validate the recently published 2-year all-cause mortality prediction tool developed using machine learning. METHODS: A validation dataset of stage 4 or 5 CKD outpatients was used. External validation performance of the prediction tool at the optimal cutoff-point was assessed by the area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and specificity. A survival analysis was then performed using the Kaplan-Meier method. RESULTS: Data of 527 outpatients with stage 4 or 5 CKD were analyzed. During the 2 years of follow-up, 91 patients died and 436 survived. Compared to the learning dataset, patients in the validation dataset were significantly younger, and the ratio of deceased patients in the validation dataset was significantly lower. The performance of the prediction tool at the optimal cutoff-point was: AUC-ROC = 0.72, accuracy = 63.6%, sensitivity = 72.5%, and specificity = 61.7%. The survival curves of the predicted survived and the predicted deceased groups were significantly different (p < 0.001). CONCLUSION: The 2-year all-cause mortality prediction tool for patients with stage 4 or 5 CKD showed satisfactory discriminatory capacity with emphasis on sensitivity. The proposed prediction tool appears to be of clinical interest for further development.

Development and application of an advanced algorithm for environmental risk assessment and safety management of pesticide residues in agricultural soils: Monitoring of currently used pesticide in upland soils.

This study conducted the development of an advanced risk assessment algorithm system and safety management strategies using pesticide residue monitoring data from soils. To understand the status of pesticide residues in agricultural soils, monitoring was performed on 116 types of pesticides currently in use across 300 soil sites. The analysis of the monitoring results, alongside the physicochemical properties of the pesticides, led to the selection of soil half-life as a critical component in residue analysis. The use of Toxicity Exposure Ratio (TER) and Risk Quotient (RQ) for environmental risk assessment, based on monitoring data, presents limitations due to its single-component, conservative approach, which does not align with actual field conditions. Therefore, there is a necessity for a risk assessment process applicable in real-world scenarios. In this research, an efficient and accurate risk assessment algorithm system, along with a safety management model, was developed. Using the physicochemical properties of pesticides (such as soil half-life), monitoring results, and toxicity data, cluster analysis and Principal Component Analysis (PCA) validation identified four pesticides: boscalid, difenoconazole, fluquinconazole, and tebuconazole. The k-mean cluster analysis selected three priority management sites where the contribution of these four pesticides to the RQ was between 94-99 %, showing similar results to the RQ calculated for all pesticides. Predictions made with the developed model for the time required for soil half-life based RQ to drop below 1 at these priority sites showed only a 1-9 day difference between the four pesticides of concern and all pesticides, indicating comparable outcomes. The scenario of replacing high-risk pesticides with those of lower risk demonstrated that the RQ could be consistently maintained at about 50 % level. The results of this study suggest that through monitoring, evaluation, and management, effective and accurate environmental safety management of pesticides in soil can be achieved.

Machine learning thermal comfort prediction models based on occupant demographic characteristics.

This study aims to investigate the predictive occupant demographic characteristics of thermal sensation (TS) and thermal satisfaction (TSa) as well as to find the most effective machine learning (ML) algorithms for predicting TS and TSa. To achieve this, a survey campaign was carried out in three mixed-mode buildings to develop TS and TSa prediction models by using six ML algorithms (Logistic Regression, Naïve Bayes, Decision Tree (DT), Random Forest (RF), K-Nearest Neighborhood (KNN) and Support Vector Machine). The prediction models were developed based on six demographic characteristics (gender, age, thermal history, education level, income, occupation). The results show that gender, age, and thermal history are significant predictors of both TS and TSa. Education level, income, and occupation were not significant predictors of TS, but were significant predictors of TSa. The study also found that RF and KNN are the most effective ML algorithms for predicting TS, while DT and RF are the most effective ML algorithms for predicting TSa. The study found that the accuracy of TS prediction models ranges from 83% to 99%, with neutral being the most correctly classified scale. The accuracy of TSa prediction models ranges from 84% to 97%, with dissatisfaction being the most common misclassification.

Timing and prediction of secondary bacteremia in patients with COVID-19: A retrospective cohort study.

Background: We aimed to aid the appropriate use of antimicrobial agents by determining the timing of secondary bacteremia and validating and updating clinical prediction models for bacteremia in patients with COVID-19. Methods: We performed a retrospective cohort study on all hospitalized patients diagnosed with COVID-19 who underwent blood culture tests from January 1, 2020, and September 30, 2021, at an urban teaching hospital in Japan. The primary outcome measure was secondary bacteremia in patients with COVID-19. Results: Of the 507 patients hospitalized with COVID-19, 169 underwent blood culture tests. Eleven of them had secondary bacteremia. The majority of secondary bacteremia occurred on or later than the 9th day after symptom onset. Positive blood culture samples collected on day 9 or later after disease onset had an odds ratio of 22.4 (95% CI 2.76-181.2, p < 0.001) compared with those collected less than 9 days after onset. The area under the receiver operating characteristic curve of the modified Shapiro rule combined with blood culture collection on or after the 9th day from onset was 0.919 (95% CI, 0.843-0.995), and the net benefit was high according to the decision curve analysis. Conclusions: The timings of symptom onset and hospital admission may be valuable indicators for making a clinical decision to perform blood cultures in patients hospitalized with COVID-19.

Development and validation of a nomogram model of depression and sleep disorders and the risk of disease progression in patients with breast cancer.

BACKGROUND: In this study, we investigated the relationship between the risk of postoperative progressive disease (PD) in breast cancer and depression and sleep disorders in order to develop and validate a suitable risk prevention model. METHODS: A total of 750 postoperative patients with breast cancer were selected from the First People's Hospital of LianYunGang, and the indices of two groups (an event group and a non-event group) were compared to develop and validate a risk prediction model. The relationship between depression, sleep disorders, and PD events was investigated using the follow-up data of the 750 patients. RESULTS: SAS, SDS, and AIS scores differed in the group of patients who experienced postoperative disease progression versus those who did not; the differences were statistically significant and the ability to differentiate prognosis was high. The area under the receiver operating characteristic (ROC) curves (AUC) were: 0.8049 (0.7685-0.8613), 0.768 (0.727-0.809), and 0.7661 (0.724--0.808), with cut-off values of 43.5, 48.5, and 4.5, respectively. Significant variables were screened by single-factor analysis and multi-factor analysis to create model 1, by lasso regression and cross-lasso regression analysis to create model 2, by random forest calculation method to create model 3, by stepwise regression method (backward method) to create model 4, and by including all variables for Cox regression to include significant variables to create model 5. The AUC of model 2 was 0.883 (0.848-0.918) and 0.937 (0.893-0.981) in the training set and validation set, respectively. The clinical efficacy of the model was evaluated using decision curve analysis and clinical impact curve, and then the model 2 variables were transformed into scores, which were validated in two datasets, the training and validation sets, with AUCs of 0.884 (0.848-0.919) and 0.885 (0.818-0.951), respectively. CONCLUSION: We established and verified a model including SAS, SDS and AIS to predict the prognosis of breast cancer patients, and simplified it by scoring, making it convenient for clinical use, providing a theoretical basis for precise intervention in these patients. However, further research is needed to verify the generalization ability of our model.

Corrigendum: A new prognostic model for recurrent pregnancy loss: assessment of thyroid and thromboelastograph parameters.

[This corrects the article DOI: 10.3389/fendo.2024.1415786.].

A risk prediction model of gestational diabetes mellitus based on traditional and genetic factors.

BACKGROUND: Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication during pregnancy. We aimed to evaluate a risk prediction model of GDM based on traditional and genetic factors. METHODS: A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to gather general data. Serum test results were collected from the laboratory information system. Independent risk factors for GDM were identified using univariate and multivariate logistic regression analyses. A GDM risk prediction model was constructed and evaluated with the Hosmer-Lemeshow goodness-of-fit test, goodness-of-fit calibration plot, receiver operating characteristic curve and area under the curve. RESULTS: Among traditional factors, age ≥30 years, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms (SNPs) (rs2779116, rs5215, rs11605924, rs7072268, rs7172432, rs10811661, rs2191349, rs10830963, rs174550, rs13266634 and rs11071657) were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk. CONCLUSIONS: Both genetic and traditional factors contribute to the risk of GDM in women, operating through diverse mechanisms. Strengthening the risk prediction of SNPs for postpartum DM among women with GDM history is crucial for maternal and child health protection.

We aimed to evaluate a risk prediction model of gestational diabetes mellitus (GDM) based on traditional and genetic factors. A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to collect general data. Among traditional factors, age ≥30 years old, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk. Both genetic and traditional factors increase the risk of GDM in women.

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

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

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

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

The Accuracy of PUMA Questionnaire in Combination With Peak Expiratory Flow Rate to Identify At-risk, Undiagnosed COPD Patients.

INTRODUCTION: The English PUMA questionnaire emerges as an effective COPD case-finding tool. We aimed to use the PUMA questionnaire in combination with peak expiratory flow rate (PEFR) to improve case-finding efficacy in Chinese population. METHODS: This cross-sectional, observational study included two stages: translating English to Chinese PUMA (C-PUMA) questionnaire with linguistic validation and psychometric evaluation, followed by clinical validation. Eligible participants (with age ≥40 years, respiratory symptoms, smoking history ≥10 pack-years) were enrolled and subjected to three questionnaires (C-PUMA, COPD assessment test [CAT], and generic health survey [SF-12V2]), PEFR measurement, and confirmatory spirometry. The C-PUMA score and PEFR were incorporated into a PUMA-PEFR prediction model applying binary logistic regression coefficients to estimate the probability of COPD (PCOPD). RESULTS: C-PUMA was finalized through standard forward-backward translation processes and confirmation of good readability, comprehensibility, and reliability. In clinical validation, 240 participants completed the study. 78/240 (32.5%) were diagnosed with COPD. C-PUMA exhibited significant validity (correlated with CAT or physical component scores of SF-12V2, both P<0.05, respectively). PUMA-PEFR model had higher diagnostic accuracy than C-PUMA alone (area under ROC curve, 0.893 vs. 0.749, P<0.05). The best cutoff values of C-PUMA and PUMA-PEFR model (PCOPD) were ≥6 and ≥0.39, accounting for a sensitivity/specificity/numbers needed to screen of 77%/64%/3 and 79%/88%/2, respectively. C-PUMA ≥5 detected more underdiagnosed patients, up to 11.5% (vs. C-PUMA ≥6). CONCLUSION: C-PUMA is well-validated. The PUMA-PEFR model provides more accurate and cost-effective case-finding efficacy than C-PUMA alone in at-risk, undiagnosed COPD patients. These tools can be useful to detect COPD early.

CT radiomics combined with clinical and radiological factors predict hematoma expansion in hypertensive intracerebral hemorrhage.

OBJECTIVES: This study aimed to establish a hematoma expansion (HE) prediction model for hypertensive intracerebral hemorrhage (HICH) patients by combining CT radiomics, clinical information, and conventional imaging signs. METHODS: A retrospective continuous collection of HICH patients from three medical centers was divided into a training set (n = 555), a validation set (n = 239), and a test set (n = 77). Extract radiomics features from baseline CT plain scan images and combine them with clinical information and conventional imaging signs to construct radiomics models, clinical imaging sign models, and hybrid models, respectively. The models will be evaluated using the area under the curve (AUC), clinical decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: In the training, validation, and testing sets, the radiomics model predicts an AUC of HE of 0.885, 0.827, and 0.894, respectively, while the clinical imaging sign model predicts an AUC of HE of 0.759, 0.725, and 0.765, respectively. Glasgow coma scale score at admission, first CT hematoma volume, irregular hematoma shape, and radiomics score were used to construct a hybrid model, with AUCs of 0.901, 0.838, and 0.917, respectively. The DCA shows that the hybrid model had the highest net profit rate. Compared with the radiomics model and the clinical imaging sign model, the hybrid model showed an increase in NRI and IDI. CONCLUSION: The hybrid model based on CT radiomics combined with clinical and radiological factors can effectively individualize the evaluation of the risk of HE in patients with HICH. CLINICAL RELEVANCE STATEMENT: CT radiomics combined with clinical information and conventional imaging signs can identify HICH patients with a high risk of HE and provide a basis for clinical-targeted treatment. KEY POINTS: HE is an important prognostic factor in patients with HICH. The hybrid model predicted HE with training, validation, and test AUCs of 0.901, 0.838, and 0.917, respectively. This model provides a tool for a personalized clinical assessment of early HE risk.

Risk Factors and Predictive Nomogram for Survival in Elderly Patients with Brain Glioma.

OBJECTIVE: To determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram. METHODS: Data from elderly individuals (age ≥65 years) histologically diagnosed with brain glioma were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. The dataset was randomly divided into a training cohort and an internal validation cohort at a 6:4 ratio. Additionally, data obtained from Tangdu Hospital constituted an external validation cohort for the study. The identification of independent prognostic factors was achieved through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, enabling the construction of a nomogram. Model performance was evaluated using C-index, ROC curves, calibration plot and decision curve analysis (DCA). RESULTS: A cohort of 20 483 elderly glioma patients was selected from the SEER database. Five prognostic factors (age, marital status, histological type, stage, and treatment) were found to significantly impact overall survival (OS) and cancer-specific survival (CSS), with tumor location emerging as a sixth variable independently linked to CSS. Subsequently, nomogram models were developed to predict the probabilities of survival at 6, 12, and 24 months. The assessment findings from the validation queue indicate a that the model exhibited strong performance. CONCLUSION: Our nomograms serve as valuable prognostic tools for assessing the survival probability of elderly glioma patients. They can potentially assist in risk stratification and clinical decision-making.

Prediction Models for Sarcopenia in Patients with Maintenance Hemodialysis: A Systematic Review and Meta-Analysis.

BACKGROUND: This systematic review and meta-analysis investigated all prediction models for sarcopenia in Maintenance Hemodialysis (MHD) patients. METHODS: This study used the Systematic Reviews and Meta-Analysis statement (PRISMA) for systematic review. DATA SOURCES: PubMed, Web of Science, Embase, Cochrane Library and Medline databases up to September 2023. DATA ANALYSIS: Risk of bias (ROB) was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Random effect models were calculated due to high heterogeneity identified. RESULTS: Fifteen models from twelve studies were analyzed. All studies had high ROB and three of them posed a high risk in terms of applicability. The pooled AUC, sensitivity, and specificity were 0.715, 0.583 and 0.656 respectively. The diagnostic criteria (P=0.0046), country (P=0.0046), and study design (P=0.0087) were significant sources of the heterogeneity. Analysing purely from the data perspective, grouping by diagnostic criterias, the AUC and specificity [(0.773, 95% CI 0.12-0.99, (0.652, 95% CI 0.641-0.664)] of the Asian Working Group for Sarcopenia (AWGS) group was lower than the European Working Group on Sarcopenia in Older People (EWGSOP) group [(0.859, 95% CI 0.12-1.00), (0.874, 95% CI 0.803-0.926)]. Grouping by styles of research, the AUC, sensitivity, and specificity in development group [(0.890, 95% CI 0.16-1.00), (0.751, 95% CI 0.697-0.800), (0.875, 95% CI 0.854-0.895)] were all higher than validation group [(0.715, 95% CI 0.09-0.98), (0.550, 95% CI 0.524-0.576), (0.617, 95% CI 0.604-0.629)]. CONCLUSIONS: Moving forward, there is a critical need to create low-ROB, high-applicability, and more accurate sarcopenia prediction models for MHD patients, customized for diverse global populations.

Construction and validation of a nomogram for predicting survival in elderly patients with severe acute pancreatitis: a retrospective study from a tertiary center.

PURPOSE: There is a lack of adequate models specifically designed for elderly patients with severe acute pancreatitis (SAP) to predict the risk of death. This study aimed to develop a nomogram for predicting the overall survival of SAP in elderly patients. METHODS: Elderly patients diagnosed with SAP between January 1, 2017 and December 31, 2022 were included in the study. Risk factors were identified through least absolute shrinkage and selection operator regression analysis. Subsequently, a novel nomogram model was developed using multivariable logistic regression analysis. The predictive performance of the nomogram was evaluated using metrics such as the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS: A total of 326 patients were included in the analysis, with 260 in the survival group and 66 in the deceased group. Multivariate logistic regression indicated that age, respiratory rate, arterial pH, total bilirubin, and calcium were independent prognostic factors for the survival of SAP patients. The nomogram demonstrated a performance comparable to sequential organ failure assessment (P = 0.065). Additionally, the calibration curve showed satisfactory predictive accuracy, and the DCA highlighted the clinical application value of the nomogram. CONCLUSION: We have identified key demographic and laboratory parameters that are associated with the survival of elderly patients with SAP. These parameters have been utilized to create a precise and user-friendly nomogram, which could be an effective and valuable clinical tool for clinicians.

A novel amino acid metabolism-related gene signature to predict the overall survival of esophageal squamous cell carcinoma patients.

Background: Esophageal squamous cell carcinoma (ESCC) has a poor early detection rate, prognosis, and survival rate. Effective prognostic markers are urgently needed to assist in the prediction of ESCC treatment outcomes. There is accumulating evidence of a strong relationship between cancer cell growth and amino acid metabolism. This study aims to determine the relationship between amino acid metabolism and ESCC prognosis. Methods: This study comprehensively evaluates the association between amino acid metabolism-related gene (AAMRG) expression profiles and the prognosis of ESCC patients based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the expression of prognosis-related genes. Results: A univariate Cox regression analysis of TCGA data identified 18 prognosis-related AAMRGs. The gene expression profiles of 90 ESCC tumor and normal tissues were obtained from the GSE20347 and GSE67269 datasets. Two differently expressed genes (DEGs) were considered as ESCC prognosis-related genes; and they were branched-chain amino acid transaminase 1 (BCAT1) and methylmalonic aciduria and homocystinuria type C protein (MMACHC). These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients. Both BCAT1 and MMACHC expression were verified by RT-qPCR. A prognostic nomogram that incorporated clinical factors and BCAT1 and MMACHC gene expression was constructed, and the calibration plots showed that it had good prognostic performance. Conclusions: The AAMRG signature established in our study is efficient and could be used in clinical settings to predict the early prognosis of ESCC patients.

Prediction and analysis of albumin-bilirubin score combined with liver function index and carcinoembryonic antigen on liver metastasis of colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a common malignant tumor, and liver metastasis is one of the main recurrence and metastasis modes that seriously affect patients' survival rate and quality of life. Indicators such as albumin bilirubin (ALBI) score, liver function index, and carcinoembryonic antigen (CEA) have shown some potential in the prediction of liver metastasis but have not been fully explored. AIM: To evaluate its predictive value for liver metastasis of CRC by conducting the combined analysis of ALBI, liver function index, and CEA, and to provide a more accurate liver metastasis risk assessment tool for clinical treatment guidance. METHODS: This study retrospectively analyzed the clinical data of patients with CRC who received surgical treatment in our hospital from January 2018 to July 2023 and were followed up for 24 months. According to the follow-up results, the enrolled patients were divided into a liver metastasis group and a nonliver metastasis group and randomly divided into a modeling group and a verification group at a ratio of 2:1. The risk factors for liver metastasis in patients with CRC were analyzed, a prediction model was constructed by least absolute shrinkage and selection operator (LASSO) logistic regression, internal validation was performed by the bootstrap method, the reliability of the prediction model was evaluated by subject-work characteristic curves, calibration curves, and clinical decision curves, and a column graph was drawn to show the prediction results. RESULTS: Of 130 patients were enrolled in the modeling group and 65 patients were enrolled in the verification group out of the 195 patients with CRC who fulfilled the inclusion and exclusion criteria. Through LASSO regression variable screening and logistic regression analysis. The ALBI score, alanine aminotransferase (ALT), and CEA were found to be independent predictors of liver metastases in CRC patients [odds ratio (OR) = 8.062, 95% confidence interval (CI): 2.545-25.540], (OR = 1.037, 95%CI: 1.004-1.071) and (OR = 1.025, 95%CI: 1.008-1.043). The area under the receiver operating characteristic curve (AUC) for the combined prediction of CRLM in the modeling group was 0.921, with a sensitivity of 78.0% and a specificity of 95.0%. The H-index was 0.921, and the H-L fit curve had χ2 = 0.851, a P value of 0.654, and a slope of the calibration curve approaching 1. This indicates that the model is extremely accurate, and the clinical decision curve demonstrates that it can be applied effectively in the real world. We conducted internal verification of one thousand resamplings of the modeling group data using the bootstrap method. The AUC was 0.913, while the accuracy was 0.869 and the kappa consistency was 0.709. The combination prediction of liver metastasis in patients with CRC in the verification group had an AUC of 0.918, sensitivity of 85.0%, specificity of 95.6%, C-index of 0.918, and an H-L fitting curve with χ 2 = 0.586, P = 0.746. CONCLUSION: The ALBI score, ALT level, and CEA level have a certain value in predicting liver metastasis in patients with CRC. These three criteria exhibit a high level of efficacy in forecasting liver metastases in patients diagnosed with CRC. The risk prediction model developed in this work shows great potential for practical application.

Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer.

BACKGROUND: Synchronous liver metastasis (SLM) is a significant contributor to morbidity in colorectal cancer (CRC). There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC. AIM: To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix (GLCM) features collected from magnetic resonance imaging (MRI). METHODS: Our study retrospectively enrolled 392 patients with CRC from Yichang Central People's Hospital from January 2015 to May 2023. Patients were randomly divided into a training and validation group (3:7). The clinical parameters and GLCM features extracted from MRI were included as candidate variables. The prediction model was constructed using a generalized linear regression model, random forest model (RFM), and artificial neural network model. Receiver operating characteristic curves and decision curves were used to evaluate the prediction model. RESULTS: Among the 392 patients, 48 had SLM (12.24%). We obtained fourteen GLCM imaging data for variable screening of SLM prediction models. Inverse difference, mean sum, sum entropy, sum variance, sum of squares, energy, and difference variance were listed as candidate variables, and the prediction efficiency (area under the curve) of the subsequent RFM in the training set and internal validation set was 0.917 [95% confidence interval (95%CI): 0.866-0.968] and 0.09 (95%CI: 0.858-0.960), respectively. CONCLUSION: A predictive model combining GLCM image features with machine learning can predict SLM in CRC. This model can assist clinicians in making timely and personalized clinical decisions.

An artificial intelligence-driven support tool for prediction of urine culture test results.

BACKGROUND AND AIMS: We aimed to develop an easily deployable artificial intelligence (AI)-driven model for rapid prediction of urine culture test results. MATERIAL AND METHODS: We utilized a training dataset (n = 34,584 urine samples) and two separate, unseen test sets (n = 10,083 and 9,289 samples). Various machine learning models were compared for diagnostic performance. Predictive parameters included urinalysis results (dipstick and flow cytometry), patient demographics (age and gender), and sample collection method. RESULTS: Although more complex models achieved the highest AUCs for predicting positive cultures (highest: multilayer perceptron (MLP) with AUC of 0.884, 95% CI 0.878-0.89), multiple logistic regression (MLR) using only flow cytometry parameters achieved a very good AUC (0.858, 95% CI 0.852-0.865). To aid interpretation, prediction results of the MLP and MLR models were categorized based on likelihood ratio (LR) for positivity: highly unlikely (LR 0.1), unlikely (LR 0.3), grey zone (LR 0.9), likely (LR 5.0), and highly likely (LR 40). This resulted in 17%, 28%, 34%, 9%, and 13% of samples falling into each respective category for the MLR model and 20%, 26%, 31%, 7%, and 16% for the MLP model. CONCLUSIONS: In conclusion, this robust model has the potential to assist clinicians in their decision-making process by providing insights prior to the availability of urine culture results in a significant portion of samples (∼2/3rd).