Insights into the performance of PREDICT tool in a large Mainland Chinese breast cancer cohort: a comparative analysis of versions 3.0 and 2.2.

BACKGROUND: PREDICT is a web-based tool for forecasting breast cancer outcomes. PREDICT version 3.0 was recently released. This study aimed to validate this tool for a large population in mainland China and compare v3.0 with v2.2. METHODS: Women who underwent surgery for nonmetastatic primary invasive breast cancer between 2010 and 2020 from the First Affiliated Hospital of Wenzhou Medical University were selected. Predicted and observed 5-year overall survival (OS) for both v3.0 and v2.2 were compared. Discrimination was compared using receiver-operator curves and DeLong test. Calibration was evaluated using calibration plots and chi-squared test. A difference greater than 5% was deemed clinically relevant. RESULTS: A total of 5424 patients were included, with median follow-up time of 58 months (IQR 38-89 months). Compared to v2.2, v3.0 did not show improved discriminatory accuracy for 5-year OS (AUC: 0.756 vs 0.771), same as ER-positive and ER-negative patients. However, calibration was significantly improved in v3.0, with predicted 5-year OS deviated from observed by -2.0% for the entire cohort, -2.9% for ER-positive and -0.0% for ER-negative patients, compared to -7.3%, -4.7% and -13.7% in v2.2. In v3.0, 5-year OS was underestimated by 9.0% for patients older than 75 years, and 5.8% for patients with micrometastases. Patients with distant metastases postdiagnosis was overestimated by 10.6%. CONCLUSIONS: PREDICT v3.0 reliably predicts 5-year OS for the majority of Chinese patients with breast cancer. PREDICT v3.0 significantly improved the predictive accuracy for ER-negative groups. Furthermore, caution is advised when interpreting 5-year OS for patients aged over 70, those with micrometastases or metastases postdiagnosis.

An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors.

BACKGROUND: Kidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function (DGF), a common post-operative complication, may negatively impact the long-term outcomes of both the graft and the pediatric recipient. However, there is limited research on DGF in pediatric kidney transplant recipients. This study aims to develop a predictive model for the risk of DGF occurrence after pediatric kidney transplantation by integrating donor and recipient characteristics and utilizing machine learning algorithms, ultimately providing guidance for clinical decision-making. METHODS: This single-center retrospective cohort study includes all recipients under 18 years of age who underwent single-donor kidney transplantation at our hospital between 2016 and 2023, along with their corresponding donors. Demographic, clinical, and laboratory examination data were collected from both donors and recipients. Univariate logistic regression models and differential analysis were employed to identify features associated with DGF. Subsequently, a risk score for predicting DGF occurrence (DGF-RS) was constructed based on machine learning combinations. Model performance was evaluated using the receiver operating characteristic curves, decision curve analysis (DCA), and other methods. RESULTS: The study included a total of 140 pediatric kidney transplant recipients, among whom 37 (26.4%) developed DGF. Univariate analysis revealed that high-density lipoprotein cholesterol (HDLC), donor after circulatory death (DCD), warm ischemia time (WIT), cold ischemia time (CIT), gender match, and donor creatinine were significantly associated with DGF (P < 0.05). Based on these six features, the random forest model (mtry = 5, 75%p) exhibited the best predictive performance among 97 machine learning models, with the area under the curve values reaching 0.983, 1, and 0.905 for the entire cohort, training set, and validation set, respectively. This model significantly outperformed single indicators. The DCA curve confirmed the clinical utility of this model. CONCLUSIONS: In this study, we developed a machine learning-based predictive model for DGF following pediatric kidney transplantation, termed DGF-RS, which integrates both donor and recipient characteristics. The model demonstrated excellent predictive accuracy and provides essential guidance for clinical decision-making. These findings contribute to our understanding of the pathogenesis of DGF.

Developing a Machine Learning Model to Predict 180-day Readmission for Elderly Patients with Angina.

Background: Readmission of elderly angina patients has become a serious problem, with a dearth of available prediction tools for readmission assessment. The objective of this study was to develop a machine learning (ML) model that can predict 180-day all-cause readmission for elderly angina patients. Methods: The clinical data for elderly angina patients was retrospectively collected. Five ML algorithms were used to develop prediction models. Area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), and the Brier score were applied to assess predictive performance. Analysis by Shapley additive explanations (SHAP) was performed to evaluate the contribution of each variable. Results: A total of 1502 elderly angina patients (45.74% female) were enrolled in the study. The extreme gradient boosting (XGB) model showed good predictive performance for 180-day readmission (AUROC = 0.89; AUPRC = 0.91; Brier score = 0.21). SHAP analysis revealed that the number of medications, hematocrit, and chronic obstructive pulmonary disease were important variables associated with 180-day readmission. Conclusions: An ML model can accurately identify elderly angina patients with a high risk of 180-day readmission. The model used to identify individual risk factors can also serve to remind clinicians of appropriate interventions that may help to prevent the readmission of patients.

PD-1+ T lymphocyte proportions and hospitalized exacerbation of COPD: a prospective cohort study.

OBJECTIVE: To evaluate the predictive value of PD-1 expression in T lymphocytes for rehospitalization due to acute exacerbations of COPD (AECOPD) in discharged patients. METHODS: 115 participants hospitalized with COPD (average age 71.8 ± 6.0 years) were recruited at Fujian Provincial Hospital. PD1+T lymphocytes proportions (PD1+T%), baseline demographics and clinical data were recorded at hospital discharge. AECOPD re-admission were collected at 1-year follow-up. Kaplan-Meier analysis compared the time to AECOPD readmissions among groups stratified by PD1+T%. Multivariable Cox proportional hazards regression and stratified analysis determined the correlation between PD1+T%, potential confounders, and AECOPD re-admission. ROC and DCA evaluated PD1+T% in enhancing the clinical predictive values of Cox models, BODE and CODEX. RESULTS: 68 participants (59.1%) were AECOPD readmitted, those with AECOPD readmission exhibited significantly elevated baseline PD-1+CD4+T/CD4+T% and PD-1+CD8 + T/CD8 + T% compared to non-readmitted counterparts. PD1+ T lymphocyte levels statistically correlated with BODE and CODEX indices. Kaplan-Meier analysis demonstrated that those in Higher PD1+ T lymphocyte proportions had reduced time to AECOPD readmission (logRank p < 0.05). Cox analysis identified high PD1+CD4+T and PD1+CD8+T ratios as risk factors of AECOPD readmission, with hazard ratios of 1.384(95%CI [1.043-1.725]) and 1.401(95%CI [1.013-1.789]), respectively. Notably, in patients aged < 70 years and with fewer than twice AECOPD episodes in the previous year, high PD1+T lymphocyte counts significantly increased risk for AECOPD readmission(p < 0.05). The AECOPD readmission predictive model, incorporating PD1+T% exhibited superior discrimination to the Cox model, BODE index and CODEX index, AUC of ROC were 0.763(95%CI [0.633-0.893]) and 0.734(95%CI [0.570-0.899]) (DeLong's test p < 0.05).The DCA illustrates that integrating PD1+T% into models significantly enhances the utility in aiding clinical decision-making. CONCLUSION: Evaluation of PD1+ lymphocyte proportions offer a novel perspective for identifying high-risk COPD patients, potentially providing insights for COPD management. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR, URL: www.chictr.org.cn/ ), Registration number: ChiCTR2200055611 Date of Registration: 2022-01-14.

A novel real-time model for predicting acute kidney injury in critically ill patients within 12 hours.

BACKGROUND: A major challenge in prevention and early treatment of acute kidney injury (AKI) is the lack of high-performance predictors in critically ill patients. Therefore, we innovatively constructed U-AKIpredTM for predicting AKI in critically ill patients within 12 h of panel measurement. METHODS: The prospective cohort study included 680 patients in the training set and 249 patients in the validation set. After performing inclusion and exclusion criteria, 417 patients were enrolled in the training set and 164 patients were enrolled in the validation set finally. AKI was diagnosed by Kidney Disease Improving Global Outcomes (KDIGO) criteria. RESULTS: Twelve urinary kidney injury biomarkers (mALB, IgG, TRF, α1MG, NAG, NGAL, KIM-1, L-FABP, TIMP2, IGFBP7, CAF22 and IL-18) exhibited good predictive performance for AKI within 12 h in critically ill patients. U-AKIpredTM, combined with three crucial biomarkers (α1MG, L-FABP and IGFBP7) by multivariate logistic regression analysis, exhibited better predictive performance for AKI in critically ill patients within 12 h than the other twelve kidney injury biomarkers. The area under the curve (AUC) of the U-AKIpredTM, as a predictor of AKI within 12 h, was 0.802 (95% CI: 0.771-0.833, P < 0.001) in the training set and 0.844 (95% CI: 0.792-0.896, P < 0.001) in validation cohort. A nomogram based on the results of the training and validation sets of U-AKIpredTM was developed which showed optimal predictive performance for AKI. The fitting effect and prediction accuracy of U-AKIpredTM was evaluated by multiple statistical indicators. To provide a more flexible predictive tool, the dynamic nomogram (https://www.xsmartanalysis.com/model/U-AKIpredTM) was constructed using a web-calculator. Decision curve analysis (DCA) and a clinical impact curve were used to reveal that U-AKIpredTM with the three crucial biomarkers had a higher net benefit than these twelve kidney injury biomarkers respectively. The net reclassification index (NRI) and integrated discrimination index (IDI) were used to improve the significant risk reclassification of AKI compared with the 12 kidney injury biomarkers. The predictive efficiency of U-AKIpredTM was better than the NephroCheck® when testing for AKI and severe AKI. CONCLUSION: U-AKIpredTM is an excellent predictive model of AKI in critically ill patients within 12 h and would assist clinicians in identifying those at high risk of AKI.

Scoring model based on cardiac CT and clinical factors to predict early good mitral valve repair in rheumatic mitral disease.

OBJECTIVE: We aimed to evaluate the mitral valve calcification and mitral structure detected by cardiac computed tomography (cardiac CT) and establish a scoring model based on cardiac CT and clinical factors to predict early good mitral valve repair (EGMR) and guide surgical strategy in rheumatic mitral disease (RMD). MATERIALS AND METHODS: This is a retrospective bi-center cohort study. Based on cardiac CT, mitral valve calcification and mitral structure in RMD were quantified and evaluated. The primary outcome was EGMR. A logical regression algorithm was applied to the scoring model. RESULTS: A total of 579 patients were enrolled in our study from January 1, 2019, to August 31, 2022. Of these, 443 had baseline cardiac CT scans of adequate quality. The calcification quality score, calcification and thinnest part of the anterior leaflet clean zone, and papillary muscle symmetry were the independent CT factors of EGMR. Coronary artery disease and pulmonary artery pressure were the independent clinical factors of EGMR. Based on the above six factors, a scoring model was established. Sensitivity = 95% and specificity = 95% were presented with a cutoff value of 0.85 and 0.30 respectively. The area under the receiver operating characteristic of external validation set was 0.84 (95% confidence interval [CI] 0.73-0.93). CONCLUSIONS: Mitral valve repair is recommended when the scoring model value > 0.85 and mitral valve replacement is prior when the scoring model value < 0.30. This model could assist in guiding surgical strategies for RMD. CLINICAL RELEVANCE STATEMENT: The model established in this study can serve as a reference indicator for surgical repair in rheumatic mitral valve disease. KEY POINTS: • Cardiac CT can reflect the mitral structure in detail, especially for valve calcification. • A model based on cardiac CT and clinical factors for predicting early good mitral valve repair was established. • The developed model can help cardiac surgeons formulate appropriate surgical strategies.

Prediction of symptomatic anastomotic leak after rectal cancer surgery: A machine learning approach.

INTRODUCTION: Anastomotic leakage (AL) remains the most dreaded and unpredictable major complication after low anterior resection for mid-low rectal cancer. The aim of this study is to identify patients with high risk for AL based on the machine learning method. METHODS: Patients with mid-low rectal cancer undergoing low anterior resection were enrolled from West China Hospital between January 2008 and October 2019 and were split by time into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) method and stepwise method were applied for variable selection and predictive model building in the training cohort. The area under the receiver operating characteristic curve (AUC) and calibration curves were used to evaluate the performance of the models. RESULTS: The rate of AL was 5.8% (38/652) and 7.2% (15/208) in the training cohort and validation cohort, respectively. The LASSO-logistic model selected almost the same variables (hypertension, operating time, cT4, tumor location, intraoperative blood loss) compared to the stepwise logistic model except for tumor size (the LASSO-logistic model) and American Society of Anesthesiologists score (the stepwise logistic model). The predictive performance of the LASSO-logistics model was better than the stepwise-logistics model (AUC: 0.790 vs. 0.759). Calibration curves showed mean absolute error of 0.006 and 0.013 for the LASSO-logistics model and stepwise-logistics model, respectively. CONCLUSION: Our study developed a feasible predictive model with a machine-learning algorithm to classify patients with a high risk of AL, which would assist surgical decision-making and reduce unnecessary stoma diversion. The involved machine learning algorithms provide clinicians with an innovative alternative to enhance clinical management.

Tumor budding is an optimal indictor of occult cervical metastasis in clinical early-stage buccal mucosa squamous cell carcinoma.

BACKGROUND: Buccal mucosa squamous cell carcinoma (BMSCC) is an aggressive disease. This study investigated the clinicopathological significance of tumor budding (TB), depth of invasion (DOI), and mode of invasion (MOI) on occult cervical metastasis (CM) of BMSCC. METHODS: Seventy-one cT1-2N0 BMSCC patients were included in this retrospective study. TB, DOI, MOI, and other clinicopathological features were reviewed. Risk factors for occult CM, locoregional recurrence-free survival (LRRFS), and overall survival (OS) were analyzed using logistic regression and Cox's proportional hazard models, respectively. RESULTS: Multivariate analysis with the logistic regression model revealed that MOI, DOI, and TB were significantly associated with occult CM in early-stage BMSCC after adjusting for variates. However, multivariate analysis with the Cox's proportional hazard model found only TB to be a prognostic factor for LRRFS (hazard ratio 15.03, 95% confidence interval [CI] 1.94-116.66; p = 0.01; trend test p = 0.03). No significant association was found between MOI, DOI, or TB and OS. CONCLUSIONS: The optimal predictor of occult CM and prognosis of early-stage BMSCC is TB, which may assist clinicians in identifying patients at high risk of cervical metastasis.

Predictive Value of Jugulo-omohyoid Lymph Nodes in Lateral Lymph Node Metastasis of Papillary Thyroid Cancer.

BACKGROUND: Jugulo-omohyoid lymph nodes (JOHLN) metastasis has proven to be associated with lateral lymph node metastasis (LLNM). This study aimed to reveal the clinical features and evaluate the predictive value of JOHLN in PTC to guide the extent of surgery. METHODS: A total of 550 patients pathologically diagnosed with PTC between October 2015 and January 2020, all of whom underwent thyroidectomy and lateral lymph node dissection, were included in this study. RESULTS: Thyroiditis, tumor location, tumor size, extra-thyroidal extension, extra-nodal extension, central lymph node metastasis (CLNM), and LLMM were associated with JOHLN. Male, upper lobe tumor, multifocality, extra-nodal extension, CLNM, and JOHLN metastasis were independent risk factors from LLNM. A nomogram based on predictors performed well. Nerve invasion contributed the most to the prediction model, followed by JOHLN metastasis. The area under the curve (AUC) was 0.855, and the p-value of the Hosmer-Lemeshow goodness of fit test was 0.18. Decision curve analysis showed that the nomogram was clinically helpful. CONCLUSION: JOLHN metastasis could be a clinically sensitive predictor of further LLM. A high-performance nomogram was established, which can provide an individual risk assessment of LNM and guide treatment decisions for patients.

Three new discovery effector proteins from Candidatus Liberibacter asiaticus psy62 inhibit plant defense through interaction with AtCAT3 and AtGAPA.

KEY MESSAGE: We identify three SDEs that inhibiting host defence from Candidatus Liberibacter asiaticus psy62, which is an important supplement to the pathogenesis of HLB. Candidatus Liberibacter asiaticus (CLas) is the main pathogen of citrus Huanglongbing (HLB). 38 new possible sec-dependent effectors (SDEs) of CLas psy62 were predicted by updated predictor SignalP 5.0, which 12 new SDEs were found using alkaline phosphate assay. Among them, SDE4310, SDE4435 and SDE4955 inhibited hypersensitivity reactions (HR) in Arabidopsis thaliana (Arabidopsis, At) and Nicotiana benthamiana leaves induced by pathogens, which lead to a decrease in cell death and reactive oxygen species (ROS) accumulation. And the expression levels of SDE4310, SDE4435, and SDE4955 genes elevated significantly in mild symptom citrus leaves. When SDE4310, SDE4435 and SDE4955 were overexpressed in Arabidopsis, HR pathway key genes pathogenesis-related 2 (PR2), PR5, nonexpressor of pathogenesis-related 1 (NPR1) and isochorismate synthase 1 (ICS1) expression significantly decreased and the growth of pathogen was greatly increased relative to control with Pst DC3000/AvrRps4 treatment. Our findings also indicated that SDE4310, SDE4435 and SDE4955 interacted with AtCAT3 (catalase 3) and AtGAPA (glyceraldehyde-3-phosphate dehydrogenase A). In conclusion, our results suggest that SDE4310, SDE4435 and SDE4955 are CLas psy62 effector proteins that may have redundant functions. They inhibit ROS burst and cell death by interacting with AtCAT3 and AtGAPA to negatively regulate host defense.

Artificial Intelligence Algorithm Can Predict Lymph Node Malignancy from Endobronchial Ultrasound Transbronchial Needle Aspiration Images for Non-Small Cell Lung Cancer.

INTRODUCTION: Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) for lung cancer staging is operator dependent, resulting in high rates of non-diagnostic lymph node (LN) samples. We hypothesized that an artificial intelligence (AI) algorithm can consistently and reliably predict nodal metastases from the ultrasound images of LNs when compared to pathology. METHODS: In this analysis of prospectively recorded B-mode images of mediastinal LNs during EBUS-TBNA, we used transfer learning to build an end-to-end ensemble of three deep neural networks (ResNet152V2, InceptionV3, and DenseNet201). Model hyperparameters were tuned, and the optimal version(s) of each model was trained using 80% of the images. A learned ensemble (multi-layer perceptron) of the optimal versions was applied to the remaining 20% of the images (Test Set). All predictions were compared to the final pathology from nodal biopsies and/or surgical specimen. RESULTS: A total of 2,569 LN images from 773 patients were used. The Training Set included 2,048 LNs, of which 70.02% were benign and 29.98% were malignant on pathology. The Testing Set included 521 LNs, of which 70.06% were benign and 29.94% were malignant on pathology. The final ensemble model had an overall accuracy of 80.63% (95% confidence interval [CI]: 76.93-83.97%), 43.23% sensitivity (95% CI: 35.30-51.41%), 96.91% specificity (95% CI: 94.54-98.45%), 85.90% positive predictive value (95% CI: 76.81-91.80%), 79.68% negative predictive value (95% CI: 77.34-81.83%), and AUC of 0.701 (95% CI: 0.646-0.755) for malignancy. CONCLUSION: There now exists an AI algorithm which can identify nodal metastases based only on ultrasound images with good overall accuracy, specificity, and positive predictive value. Further optimization with larger sample sizes would be beneficial prior to clinical application.

Variables that predict hospital stay and the outcome of Fournier gangrene at King Abdulaziz University Hospital: a retrospective study.

BACKGROUND: The aggressive nature of Fournier gangrene and the associated health issues can result in a more complex clinical course and potentially a longer hospital stay. This study aimed to assess factors that affect the length of hospital stay (LHS) and its relation to the outcome of Fournier gangrene patients. METHODS: A retrospective study was performed at King Abdulaziz University Hospital (KAUH), Saudi Arabia, on patients diagnosed with Fournier gangrene between 2017 and 2023. Data about length of hospital stay (LHS), age, BMI, clinical and surgical data and outcome was obtained. RESULTS: The mean age of the studied patients was 59.23 ± 11.19 years, the mean body mass index (BMI) was 26.69 ± 7.99 kg/m2, and the mean duration of symptoms was 10.27 ± 9.16 days. The most common presenting symptoms were swelling or induration (64%), 88% had comorbidities with diabetes mellitus (DM) (84%), and 76% had uncontrolled DM. of patients, 24% had a poly-microbial infection, with E. coli being the most common (52%). The mean length of hospital stay (LHS) was 54.56 ± 54.57 days, and 24% of patients had an LHS of more than 50 days. Longer LHS (> 50 days) was associated with patients who did not receive a compatible initial antibiotic, whereas shorter LHS was associated with patients who received Impenem or a combination of vancomycin and meropenem as alternative antibiotics following incompatibility. Reconstruction patients had significantly longer LHS and a higher mean temperature. However, none of the studied variables were found to be predictors of long LHS in the multivariate regression analysis. CONCLUSION: Knowledge of the values that predict LHS allows for patient-centered treatment and may be useful in predicting more radical treatments or the need for additional treatment in high-risk patients. Future multicenter prospective studies with larger sample sizes are needed to assess the needed variables and predictors of long LHS.

Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical feature.

BACKGROUND: Radiomics provided opportunities to quantify the tumor phenotype non-invasively. This study extracted contrast-enhanced computed tomography (CECT) radiomic signatures and evaluated clinical features of bone metastasis in non-small-cell lung cancer (NSCLC). With the combination of the revealed radiomics and clinical features, the predictive modeling on bone metastasis in NSCLC was established. METHODS: A total of 318 patients with NSCLC at the Tianjin Medical University Cancer Institute & Hospital was enrolled between January 2009 and December 2019, which included a feature-learning cohort (n = 223) and a validation cohort (n = 95). We trained a radiomics model in 318 CECT images from feature-learning cohort to extract the radiomics features of bone metastasis in NSCLC. The Kruskal-Wallis and the least absolute shrinkage and selection operator regression (LASSO) were used to select bone metastasis-related features and construct the CT radiomics score (Rad-score). Multivariate logistic regression was performed with the combination of the Rad-score and clinical data. A predictive nomogram was subsequently developed. RESULTS: Radiomics models using CECT scans were significant on bone metastasis prediction in NSCLC. Model performance was enhanced with each information into the model. The radiomics nomogram achieved an AUC of 0.745 (95% confidence interval [CI]: 0.68,0.80) on predicting bone metastasis in the training set and an AUC of 0.808(95% confidence interval [CI]: 0.71,0.88) in the validation set. CONCLUSION: The revealed invisible image features were of significance on guiding bone metastasis prediction in NSCLC. Based on the combination of the image features and clinical characteristics, the predictive nomogram was established. Such nomogram can be used for the auxiliary screening of bone metastasis in NSCLC.

Global incidence and mortality trends of gastric cancer and predicted mortality of gastric cancer by 2035.

OBJECTIVE: To study the historical global incidence and mortality trends of gastric cancer and predicted mortality of gastric cancer by 2035. METHODS: Incidence data were retrieved from the Cancer Incidence in Five Continents (CI5) volumes I-XI, and mortality data were obtained from the latest update of the World Health Organization (WHO) mortality database. We used join-point regression analysis to examine historical incidence and mortality trends and used the package NORDPRED in R to predict the number of deaths and mortality rates by 2035 by country and sex. RESULTS: More than 1,089,000 new cases of gastric cancer and 769,000 related deaths were reported in 2020. The average annual percent change (AAPC) in the incidence of gastric cancer from 2003 to 2012 among the male population, South Korea, Japan, Malta, Canada, Cyprus, and Switzerland showed an increasing trend (P > 0.05); among the female population, Canada [AAPC, 1.2; (95%Cl, 0.5-2), P < 0.05] showed an increasing trend; and South Korea, Ecuador, Thailand, and Cyprus showed an increasing trend (P > 0.05). AAPC in the mortality of gastric cancer from 2006 to 2015 among the male population, Thailand [3.5 (95%cl, 1.6-5.4), P < 0.05] showed an increasing trend; Malta Island, New Zealand, Turkey, Switzerland, and Cyprus had an increasing trend (P > 0.05); among the male population aged 20-44, Thailand [AAPC, 3.4; (95%cl, 1.3-5.4), P < 0.05] showed an increasing trend; Norway, New Zealand, The Netherlands, Slovakia, France, Colombia, Lithuania, and the USA showed an increasing trend (P > 0.05). It is predicted that the mortality rate in Slovenia and France's female population will show an increasing trend by 2035. It is predicted that the absolute number of deaths in the Israeli male population and in Chile, France, and Canada female population will increase by 2035. CONCLUSION: In the past decade, the incidence and mortality of gastric cancer have shown a decreasing trend; however, there are still some countries showing an increasing trend, especially among populations younger than 45 years. Although mortality in most countries is predicted to decline by 2035, the absolute number of deaths due to gastric cancer may further increase due to population growth.

Prenatal anaemia and risk of postpartum haemorrhage: a cohort analysis of data from the Predict-PPH study.

BACKGROUND: Most previous clinical studies investigating the connection between prenatal anaemia and postpartum haemorrhage (PPH) have reported conflicting results. OBJECTIVES: We examined the association between maternal prenatal anaemia and the risk of PPH in a large cohort of healthy pregnant women in five health institutions in Lagos, Southwest Nigeria. METHODS: This was a prospective cohort analysis of data from the Predict-PPH study that was conducted between January and June 2023. The study enrolled n = 1222 healthy pregnant women giving birth in five hospitals in Lagos, Nigeria. The study outcome, WHO-defined PPH, is postpartum blood loss of at least 500 milliliters. We used a multivariable logistic regression model with a backward stepwise conditional approach to examine the association between prenatal anaemia of increasing severity and PPH while adjusting for confounding factors. RESULTS: Of the 1222 women recruited to the Predict-PPH study between January and June 2023, 1189 (97·3%) had complete outcome data. Up to 570 (46.6%) of the enrolled women had prenatal anaemia while 442 (37.2%) of those with complete follow-up data had WHO-defined PPH. After controlling for potential confounding factors, maternal prenatal anaemia was independently associated with PPH (adjusted odds ratio = 1.37, 95% confidence interval: 1.05-1.79). However, on the elimination of interaction effects of coexisting uterine fibroids and mode of delivery on this association, a sensitivity analysis yielded a lack of significant association between prenatal anaemia and PPH (adjusted odds ratio = 1.27, 95% confidence interval: 0.99-1.64). We also recorded no statistically significant difference in the median postpartum blood loss in women across the different categories of anaemia (P = 0.131). CONCLUSION: Our study revealed that prenatal anaemia was not significantly associated with PPH. These findings challenge the previously held belief of a suspected link between maternal anaemia and PPH. This unique evidence contrary to most previous studies suggests that other factors beyond prenatal anaemia may contribute more significantly to the occurrence of PPH. This highlights the importance of comprehensive assessment and consideration of various maternal health factors in predicting and preventing this life-threatening obstetric complication.

Usefulness of lactate to albumin ratio for predicting in-hospital mortality in atrial fibrillation patients admitted to the intensive care unit: a retrospective analysis from MIMIC-IV database.

BACKGROUND: High lactate to albumin ratio (LAR) has been reported to be associated to with poor prognosis in patients admitted to the intensive care unit (ICU). However, its role in predicting in-hospital mortality in AF patients admitted to ICU has not been explored. METHODS: The Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was used to retrieve information on patients who had been diagnosed with AF. X-tile software was utilized to determine the optimal cut-off LAR. Area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA) were conducted to assess the prediction performance of LAR for in-hospital mortality. RESULTS: Finally, 8,287 AF patients were included and 1,543 death (18.6%) occurred. The optimal cut-off value of LAR is 0.5. Patients in lower LAR (< 0.5) group showed a better in-hospital survival compared to patients in higher LAR (≥ 0.5) group (HR: 2.67, 95%CI:2.39-2.97, P < 0.001). A nomogram for in-hospital mortality in patients with AF was constructed based on multivariate Cox analysis including age, CCI, ß blockers usage, APSIII, hemoglobin and LAR. This nomogram exhibited excellent discrimination and calibration abilities in predicting in-hospital mortality for critically ill AF patients. CONCLUSION: LAR, as a readily available biomarker, can predict in-hospital mortality in AF patients admitted to the ICU. The nomogram that combined LAR with other relevant variables performed exceptionally well in terms of predicting in-hospital mortality.

Could emergency admission plasma D-dimer level predict first pass effect of stent retriever thrombectomy in acute ischemic stroke?

BACKGROUND: Evidence on plasma biomarkers to identify first pass effect (FPE) in patients with acute ischemic stroke (AIS) with large vessel occlusion (LVO) treated with thrombectomy is limited. PURPOSE: To evaluate whether plasma D-dimer could predict FPE. MATERIAL AND METHODS: Consecutive patients with LVO who underwent first-line stent retriever thrombectomy at our center between January 2018 and August 2021 were enrolled. Patients were classified into the FPE (modified Thrombolysis in Cerebral Infarction [mTICI] ≥2c) group or non-FPE (mTICI 0-2b) group based on angiographic outcomes. Logistic regression analysis was performed to determine the predictors of FPE. The overall ability of D-dimer levels in predicting FPE was evaluated using receiver operating characteristic (ROC) curves. RESULTS: In total, 313 patients were included; 88 (28.1%) patients achieved FPE. Compared to those with non-FPE, patients with FPE had more diabetes mellitus history, lower D-dimer levels, higher clot burden score, a higher proportion of M1 middle cerebral artery, and a higher proportion of main stem occlusion pattern (P <0.05). After adjusting for potential variables, D-dimer levels (OR=0.81, 95% CI=0.52-0.96), clot burden score (OR=1.76, 95% CI=1.38-2.87), and main stem occlusion pattern (OR=1.85, 95% CI=1.19-2.62) remained independently associated with FPE. Based on the ROC analysis, the D-dimer as a predictor for predicting FPE presented with a specificity of 79%, a negative predictive value of 87%, and an area under the curve of 0.761. CONCLUSION: Low emergency admission plasma D-dimer level is an independent predictor of FPE in patients with AIS treated with stent retriever thrombectomy.

A mathematical model to predict "low-lying" posterior communicating artery aneurysms in neurosurgical practice.

"Low-lying" posterior communicating artery (PCoA) aneurysms require great attention in surgical clipping due to their distinct anatomical characteristics. In this study, we propose an easy method to immediately recognize "low-lying" PCoA aneurysms in neurosurgical practice. A total of 89 cases with "low-lying" PCoA aneurysms were retrospectively analyzed. All patients underwent preoperative digital subtraction angiography (DSA) examinations and microsurgical clipping. Cases were classified into the "low-lying" and regular groups based on intraoperative findings. The distance- and angle-relevant parameters that reflected the relative location of the aneurysms and tortuosity of the internal carotid artery were measured using 3D-DSA images. The data were sequentially integrated into a mathematical analysis to obtain the prediction model. Finally, we proposed a novel mathematical formula to preoperatively predict the existence of "low-lying" PCoA aneurysms with great accuracy. Neurosurgeons might benefit from this model, which enables them to directly identify "low-lying" PCoA aneurysms and make appropriate surgical decisions accordingly.

Predictive Value of Angle of Progression in Term Nulliparous Women: A Comprehensive Study on Spontaneous Vaginal Delivery Correlation and Clinical Implications.

OBJECTIVES: This study aims to explore the correlation between the angle of progression (AOP) and spontaneous vaginal delivery (SVD) for term nulliparous women before the onset of labor. Additionally, it evaluates the diagnostic efficacy of AOP in predicting SVD in term nulliparous women. METHODS: In this retrospective observational study, data from nulliparous women without contraindications for vaginal delivery, with a singleton pregnancy ≥37 weeks, and before the onset of labor were included. Transperineal ultrasound was performed to collect AOP. The date and mode of delivery were tracked, to assess the correlation between AOP and SVD in term nulliparous women. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic efficacy of AOP in predicting SVD for term nulliparous women. RESULTS: The SVD-failure (SVD-f) group exhibited a significantly lower AOP compared with the SVD group (88.43° vs 95.72°, P < .001). Logistic regression analysis revealed that AOP was associated with SVD in term nulliparous women (OR = 1.051). ROC curve analysis demonstrated that the area under the ROC curve with AOP 84° as the threshold was 0.663, with a sensitivity of 85.25% and specificity of 43.18%. Considering a sensitivity and specificity of 90%, the dual cut-off values for term nulliparous women for SVD were 81° and 104°, respectively. CONCLUSIONS: A positive correlation was identified between AOP and SVD for nulliparous women after 37 weeks and before the onset of labor. Notably, term nulliparous women with AOP exceeding 104° exhibited a higher probability of SVD.

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. OBJECTIVE: The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. METHODS: The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles. RESULTS: The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). CONCLUSIONS: The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.