Stratifying risk of failure to achieve textbook outcomes among patients undergoing hepatectomy for hepatocellular carcinoma: A multicenter score validation study.

BACKGROUND AND AIMS: The concept of textbook outcomes (TOs) has gained increased attention as a critical metric to assess the quality and success of outcomes following complex surgery. A simple yet effective scoring system was developed and validated to predict risk of not achieving textbook outcomes (non-TOs) following hepatectomy for hepatocellular carcinoma (HCC). METHODS: Using a multicenter prospectively collected database, risk factors associated with non-TO among patients who underwent hepatectomy for HCC were identified. A predictive scoring system based on factors identified from multivariate regression analysis was used to risk stratify patients relative to non-TO. The score was developed using 70 % of the overall cohort and validated in the remaining 30 %. RESULTS: Among 3681 patients, 1458 (39.6 %) failied to experience a TO. Based on the derivation cohort, obesity, American Society of Anaesthesiologists score(ASA score), Child-Pugh grade, tumor size, and extent of hepatectomy were identified as independent predictors of non-TO. The scoring system ranged from 0 to 10 points. Patients were categorized into low (0-3 points), intermediate (4-6 points), and high risk (7-10 points) of non-TO. In the validation cohort, the predicted risk of developing non-TOs was 39.0 %, which closely matched the observed risk of 39.9 %. There were no differences among the predicted and observed risks within the different risk categories. CONCLUSIONS: A novel scoring system was able to predict risk of non-TO accurately following hepatectomy for HCC. The score may enable early identification of individuals at risk of adverse outcomes and inform surgical decision-making, and quality improvement initiatives.

Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients.

OBJECTIVE: This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images. METHODS: A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan-Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA). RESULTS: BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction. CONCLUSION: This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.

Evolution of Life Cycles of Nematodes Parasitizing Woody Plants As a Result of Ecological and Phylogenetic Co-Adaptations with Hosts and Vectors.

Fundamental aspects in the evolution of nematodes parasitizing woody plants are reviewed. (1) Nematode faunal lists of natural refugia are useful to predict the risks of opportunistic pathogens becoming true pathogens in the forest and park communities. (2) Nematode composition in natural refugia gives a chance to identify nematode antagonists of insect vectors of dangerous fungal and nematode infections, which can be potentially used as the biological agents for woody plants' protection. (3) Dauers in the ancestors of wood-inhabiting nematodes played a role as a survival stage in the detritus decomposition succession, and they later acquired the functions of dispersal and adaptations for transmission using insect vectors. (4) When inspecting wilted trees, it is necessary to use dauers for diagnostics, as sexually mature nematodes may be absent in tree tissues. (5) Plant parasitic nematodes originated from members of the detritus food web and retained a detritivorous phase in the life cycle as a part of the propagative generation. (6) Vectors in the life cycles of plant parasitic nematodes are inherited from the ancestral detritivorous nematode associations, rather than inserted in the dixenic life cycle of the 'nematode-fungus-plant' association. (7) Despite the significant difference in the duration of the nematode-tree and nematode-vector phases of the life cycle, the actual parasitic nematode specificity is dual: firstly to the vector and secondly to the natural host plant (as demonstrated in phytotests excluding a vector).

Plasma metabolomics identifies key metabolites and improves prediction of diabetic retinopathy: development and validation across multi-national cohorts.

PURPOSE: To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and evaluate their utility in predicting DR development and progression. DESIGN: Multicenter, multi-ethnic cohort study. PARTICIPANTS: This study included 17,675 participants with baseline pre-diabetes/diabetes, in accordance with the 2021 American Diabetes Association guideline, and free of baseline DR from the UK Biobank (UKB); and an additional 638 diabetic participants from the Guangzhou Diabetic Eye Study (GDES) for external validation. METHODS: Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort. Model assessments included the C-statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical utility in both cohorts. MAIN OUTCOME MEASURES: DR development, progression, and retinal microvascular damage. RESULTS: Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C-statistic: 0.802, 95% CI, 0.760-0.843 vs. 0.751, 95% CI, 0.706-0.796; P = 5.56×10-4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C-statistic: 0.807, 95% CI, 0.711-0.903 vs. 0.617, 95% CI, 0.494, 0.740; P = 1.68×10-4) and progression (C-statistic: 0.797, 95% CI, 0.712-0.882 vs. 0.665, 95% CI, 0.545-0.784; P = 0.003) in the external cohort. Improvements in NRIs, IDIs, and clinical utility were also evident in both cohorts (all P <0.05). In addition, lactate and citrate were associated to microvascular damage across macular and optic disc regions (all P <0.05). CONCLUSIONS: Metabolomic profiling has proven effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology.

Risk of Transfusion in Isolated Coronary Artery Bypass Graft: Models developed from the STS Database.

BACKGROUND: Perioperative blood transfusion is associated with adverse outcomes and higher costs following coronary artery bypass graft surgery (CABG). We developed risk assessments for patients' probability of perioperative transfusion and the expected transfusion volume, to improve clinical management and resource use. METHODS: Among 1,266,545 consecutive (2008-2016) isolated-CABG operations in STS's Adult Cardiac Surgery Database, 657,821 (51.9%) received perioperative blood transfusions (red blood cell [RBC], fresh frozen plasma [FFP], cryoprecipitate, and/or platelets). We developed "full" models to predict perioperative transfusion of any blood product, and of RBC, FFP, or platelets. Using least absolute shrinkage and selection operator model selection, we built a rapid risk score based on 5 variables (age, body surface area, sex, preoperative hematocrit and use of intra-aortic balloon pump). RESULTS: Full model C-statistics were 0.785, 0.815, 0.707, and 0.699 for any blood product, RBC, FFP, and platelets. Rapid risk assessments' C-statistics were 0.752, 0.785, 0.670, and 0.661 for any blood product, RBC, FFP, and platelets. The observed versus expected risk plots showed strong calibration for full models and risk assessment tools; absolute differences between observed and expected risks of transfusion were <10.8% in each percentile of expected risk. Risk-assessments' predicted probabilities of transfusion were strongly and non-linearly associated (p<.0001) with total units transfused. CONCLUSIONS: These robust and well-calibrated risk assessment tools for perioperative transfusion in CABG can inform surgeons regarding patients' risks and number of RBC, FFP, and platelets units they can expect to need. This can aid in optimizing outcomes and increasing efficient use of blood products.

Prediction of diabetic retinopathy among type 2 diabetic patients in University of Gondar Comprehensive Specialized Hospital, 2006-2021: A prognostic model.

BACKGROUND: There has been a paucity of evidence for the development of a prediction model for diabetic retinopathy (DR) in Ethiopia. Predicting the risk of developing DR based on the patient's demographic, clinical, and behavioral data is helpful in resource-limited areas where regular screening for DR is not available and to guide practitioners estimate the future risk of their patients. METHODS: A retrospective follow-up study was conducted at the University of Gondar (UoG) Comprehensive Specialized Hospital from January 2006 to May 2021 among 856 patients with type 2 diabetes (T2DM). Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The data were validated by 10-fold cross-validation. Four ML techniques (naïve Bayes, K-nearest neighbor, decision tree, and logistic regression) were employed. The performance of each algorithm was measured, and logistic regression was a well-performing algorithm. After multivariable logistic regression and model reduction, a nomogram was developed to predict the individual risk of DR. RESULTS: Logistic regression was the best algorithm for predicting DR with an area under the curve of 92%, sensitivity of 87%, specificity of 83%, precision of 84%, F1-score of 85%, and accuracy of 85%. The logistic regression model selected seven predictors: total cholesterol, duration of diabetes, glycemic control, adherence to anti-diabetic medications, other microvascular complications of diabetes, sex, and hypertension. A nomogram was developed and deployed as a web-based application. A decision curve analysis showed that the model was useful in clinical practice and was better than treating all or none of the patients. CONCLUSIONS: The model has excellent performance and a better net benefit to be utilized in clinical practice to show the future probability of having DR. Identifying those with a higher risk of DR helps in the early identification and intervention of DR.

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.

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 roadmap for modelling radiation-induced cardiac disease.

Cardiac risk mitigation is a major priority in improving outcomes for cancer survivors as advances in cancer screening and treatments continue to decrease cancer mortality. More than half of adult cancer patients will be treated with radiotherapy (RT); therefore it is crucial to develop a framework for how to assess and predict radiation-induced cardiac disease (RICD). Historically, RICD was modelled solely using whole heart metrics such as mean heart dose. However, data over the past decade has identified cardiac substructures which outperform whole heart metrics in predicting for significant cardiac events. Additionally, non-RT factors such as pre-existing cardiovascular risk factors and toxicity from other therapies contribute to risk of future cardiac events. In this review, we aim to discuss the current evidence and knowledge gaps in predicting RICD and provide a roadmap for the development of comprehensive models based on three interrelated components, (1) baseline CV risk assessment, (2) cardiac substructure radiation dosimetry linked with cardiac-specific outcomes and (3) novel biomarker development.

Investigating the features of risky driving behaviors on expressway diverge area based on conflict and modeling analysis.

Driving behaviors are important cause of expressway crash. In this study, method based on modified time-to-collision (MTTC) to identify risky driving behaviors on an expressway diverge area is proposed, thus investigating the impact of velocity and acceleration features of risky driving behavior. Firstly, MTTC is applied to judge whether the behavior is risky. Then, the relationships between velocity, acceleration and different driving behavior on the expressway diverge area were fit by binary logistic regression models (BLR) with L2 regularization and random forests (RF) models, and the models were interpreted by feature importance plots and partial dependency plots. The results show that the AUC metric of 4 RF models for 4 types of driving behaviors, namely, left lane change, right lane change, acceleration and deceleration, are 0.932, 0.845, 0.846 and 0.860 separately. The interpretation of models demonstrates that velocity and absolute value of acceleration greatly affect the risk of the driving behaviors. Different driving behaviors with a certain acceleration have a range of safety speed range. The range will get narrower with the growth of maximum absolute value of acceleration rate, and will be nearly non-exist when the acceleration is over 5 m/s2. In conclusion, this study provided a methodology to measure the risk of driving behaviors and establish a model to recognize of risky driving behaviors. The results can lay the foundation for making countermeasures to prevent risky driving behaviors by managing the vehicle speed.

Risk of Death Among Nursing Home Residents: A Cross-National Perspective.

OBJECTIVES: Describe the rate of death over 4 consecutive quarters and determine optimal categorization of residents into risk-of-death categories, expanding the Changes in Health, Endstage Disease & Symptoms and Signs (CHESS) scale. DESIGN: Using secondary analysis design with Minimum Data Set (MDS) data, the CHESS scale provided the base upon which the DeathRisk-NH scale was developed. SETTING AND PARTICIPANTS: Baseline and 4 quarterly follow-up analyses of Canadian (n = 109,145) and US (n = 1,075,611) nursing home resident data were completed. METHODS: Logistic regression analyses identified predictors of death, additive to CHESS, to form the DeathRisk-NH scale. The independent variable set used MDS items, focusing on clinical complexity indicators, diagnostic conditions, and measures of severe clinical distress. RESULTS: Country cohorts had similar percentages of residents with mean activities of daily living hierarchy scores, dependence in mobility, continence, memory, and overall, CHESS scores. The percentage of individuals who died increased from 10.5% (3 months) to 30.7% (12 months). The average annual death rate for this cohort was 5.5 times higher than the national annual death rate of approximately 5.6%. CONCLUSIONS AND IMPLICATIONS: The DeathRisk-NH is an effective prediction model to identify residents at risk of death within the first 12 months after admission to the nursing home. The tool may be helpful in patient care planning, resource allocation, and excess death monitoring.

Development and evaluation of a chronic kidney disease risk prediction model using random forest.

This research aims to advance the detection of Chronic Kidney Disease (CKD) through a novel gene-based predictive model, leveraging recent breakthroughs in gene sequencing. We sourced and merged gene expression profiles of CKD-affected renal tissues from the Gene Expression Omnibus (GEO) database, classifying them into two sets for training and validation in a 7:3 ratio. The training set included 141 CKD and 33 non-CKD specimens, while the validation set had 60 and 14, respectively. The disease risk prediction model was constructed using the training dataset, while the validation dataset confirmed the model's identification capabilities. The development of our predictive model began with evaluating differentially expressed genes (DEGs) between the two groups. We isolated six genes using Lasso and random forest (RF) methods-DUSP1, GADD45B, IFI44L, IFI30, ATF3, and LYZ-which are critical in differentiating CKD from non-CKD tissues. We refined our random forest (RF) model through 10-fold cross-validation, repeated five times, to optimize the mtry parameter. The performance of our model was robust, with an average AUC of 0.979 across the folds, translating to a 91.18% accuracy. Validation tests further confirmed its efficacy, with a 94.59% accuracy and an AUC of 0.990. External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model's reliability. In summary, the study identified critical genetic biomarkers and successfully developed a novel disease risk prediction model for CKD. This model can serve as a valuable tool for CKD disease risk assessment and contribute significantly to CKD identification.

Health Literacy, Individual and Community Engagement, and Cardiovascular Risks and Disparities: JACC: CardioOncology State-of-the-Art Review.

Cardiovascular and cancer outcomes intersect within the realm of cardio-oncology survivorship care, marked by disparities across ethnic, racial, social, and geographical landscapes. Although the clinical community is increasingly aware of this complex issue, effective solutions are trailing. To attain substantial public health impact, examinations of cancer types and cardiovascular risk mitigation require complementary approaches that elicit the patient's perspective, scale it to a population level, and focus on actionable population health interventions. Adopting such a multidisciplinary approach will deepen our understanding of patient awareness, motivation, health literacy, and community resources for addressing the unique challenges of cardio-oncology. Geospatial analysis aids in identifying key communities in need within both granular and broader contexts. In this review, we delineate a pathway that navigates barriers from individual to community levels. Data gleaned from these perspectives are critical in informing interventions that empower individuals within diverse communities and improve cardio-oncology survivorship.

Self-Report Tool for Identification of Individuals With Coronary Atherosclerosis: The Swedish CardioPulmonary BioImage Study.

BACKGROUND: Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether nonimaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis. METHODS AND RESULTS: We used data from the population-based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25 182) and coronary artery calcification score (n=28 701), aged 50 to 64 years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self-report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self-report (clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and benchmarked against factors in the pooled cohort equation. The self-report tool (n=14 variables) and the clinical tool (n=23 variables) showed high-to-excellent discriminative ability to identify a segment involvement score ≥4 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 0.76, P<0.001). The tools showed a larger net benefit in clinical decision-making at relevant threshold probabilities. The self-report tool identified 65% of all individuals with a segment involvement score ≥4 in the top 30% of the highest-risk individuals. Tools developed for coronary artery calcification score ≥100 performed similarly. CONCLUSIONS: We have developed a self-report tool that effectively identifies individuals with moderate to severe coronary atherosclerosis. The self-report tool may serve as prescreening tool toward a cost-effective computed tomography-based screening program for high-risk individuals.

Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis.

Objective: This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models. Methods: We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software. Results: A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance. Conclusion: Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings. Registration: This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.

Prediction models for the risk of ventilator-associated pneumonia in patients on mechanical ventilation: A systematic review and meta-analysis.

BACKGROUND: Identifying patients at risk for ventilator-associated pneumonia (VAP) through prediction models can facilitate medical decision-making. Our objective was to systematically evaluate the current models for VAP in patients with mechanical ventilation (MV). METHODS: Nine databases systematically retrieved from establishment to March 6, 2024. Two independent reviewers performed study selection, data extraction, and quality assessment, respectively. The Prediction Model Risk of Bias Assessment Tool was used to evaluate the risk of model bias and applicability. Stata 17.0 was used for conducting a meta-analysis of discrimination of model validation. RESULTS: The total of 34 studies were included, with reported 52 prediction models. More than 50% of the models were developed using logistic regression, and the AUCs of the included models ranged from 0.509 to 0.982. Predictors that appeared more frequently in the models were MV duration, length of ICU stay, age. Each study was essentially considered having an overall high risk of bias. A meta-analysis of 17 studies containing 33 models with validated and complete data was performed with a pooled AUC of 0.80 (95% CI: 0.78-0.83). CONCLUSION: Despite the relatively excellent performance of the models, there is a high risk of bias of the model development process. Enhancing the methodological quality and revealing the details of study process, especially the external validation, practical application and optimization of the models need urgent attention.

Association of serum resolvin D1 with the risk of major adverse cardiovascular events in hemodialysis patients.

PURPOSE: Resolvin D1 (RvD1) inhibits inflammation, reduces oxidative stress, and forecasts the risk of cardiovascular events, but relevant evidence in hemodialysis patients is lacking. This study intended to investigate the predictive value of RvD1 for major adverse cardiovascular events (MACE) risk in hemodialysis patients. METHODS: Totally, 252 patients who underwent hemodialysis were included. Serum RvD1 was measured by enzyme-linked immunosorbent assay. Patients were followed up with a median of 12.1 months. MACE was recorded during the follow-up period. RESULTS: RvD1 was inversely correlated with diabetes history (P = 0.002), cardiac troponin T (TnT) (P = 0.029), and high sensitivity C-reactive protein (hsCRP) (P < 0.001) in hemodialysis patients. 25 hemodialysis patients experienced MACE. RvD1 was reduced in hemodialysis patients with MACE versus those without MACE (P = 0.004). RvD1 exhibited a certain value in forecasting MACE risk, with an area under curve (AUC) of 0.675 [95% confidence interval CI: 0.565-0.786]. Increased RvD1 cut by median (P = 0.043) and cut by quartile (P = 0.042) were related to decreased accumulating MACE in hemodialysis patients. Moreover, RvD1 independently predicted declined MACE risk [odds ratio (OR) = 0.644, P = 0.045], but age (OR = 1.048, P = 0.039) and TnT (OR = 1.006, P = 0.005) independently predicted ascended MACE risk in hemodialysis patients. The combination of these independent factors displayed a good value for estimating MACE risk in hemodialysis patients with an AUC of 0.744 (95% CI: 0.640-0.849). CONCLUSION: Serum RvD1 is inversely correlated with diabetes history, TnT, and hsCRP in hemodialysis patients. More importantly, it could serve as a potential marker to predict MACE risk in these patients.

Integration of Radiomics and Immune-Related Genes Signatures for Predicting Axillary Lymph Node Metastasis in Breast Cancer.

BACKGROUND: To develop a radiogenomics nomogram for predicting axillary lymph node (ALN) metastasis in breast cancer and reveal underlying associations between radiomics features and biological pathways. MATERIALS AND METHODS: This study included 1062 breast cancer patients, 90 patients with both DCE-MRI and gene expression data. The optimal immune-related genes and radiomics features associated with ALN metastasis were firstly calculated, and corresponding feature signatures were constructed to further validate their performances in predicting ALN metastasis. The radiogenomics nomogram for predicting the risk of ALN metastasis was established by integrating radiomics signature, immune-related genes (IRG) signature, and critical clinicopathological factors. Gene modules associated with key radiomics features were identified by weighted gene co-expression network analysis (WGCNA) and submitted to functional enrichment analysis. Gene set variation analysis (GSVA) and correlation analysis were performed to investigate the associations between radiomics features and biological pathways. RESULTS: The radiogenomics nomogram showed promising predictive power for predicting ALN metastasis, with AUCs of 0.973 and 0.928 in the training and testing groups, respectively. WGCNA and functional enrichment analysis revealed that gene modules associated with key radiomics features were mainly enriched in breast cancer metastasis-related pathways, such as focal adhesion, ECM-receptor interaction, and cell adhesion molecules. GSVA also identified pathway activities associated with radiomics features such as glycogen synthesis, integration of energy metabolism. CONCLUSION: The radiogenomics nomogram can serve as an effective tool to predict the risk of ALN metastasis. This study provides further evidence that radiomics phenotypes may be driven by biological pathways related to breast cancer metastasis.

Temporal and external validation of the algorithm predicting first trimester outcome of a viable pregnancy.

BACKGROUND: Symptoms like vaginal bleeding or abdominal pain in early pregnancy can create anxiety about potential miscarriage. Previous studies have demonstrated ultrasonographic variables at the first trimester transvaginal scan (TVS) which can assist in predicting outcomes by 12 weeks gestation. AIM: To validate the miscarriage risk prediction model (MRP) in women who present with a viable intrauterine pregnancy (IUP) at the primary ultrasound. MATERIALS AND METHODS: A multi-centre diagnostic study of 1490 patients was performed between 2011 and 2019 for retrospective external and 2017-2019 for prospective temporal validation. The reference standard was a viable pregnancy at 12 + 6 weeks. The MRP model is a multinomial logistic regression model based on maternal age, embryonic heart rate, logarithm (gestational sac volume/crown-rump length (CRL)) ratio, CRL and presence or absence of clots. RESULTS: Temporal validation data from 290 viable IUPs were collected: 225 were viable at the end of the first trimester, 31 had miscarried and 34 were lost to follow-up. External validation data from 1203 viable IUPs were collected at two other ultrasound units: 1062 were viable, 69 had miscarried and 72 were lost to follow-up. Temporal validation with a cut-off of 0.1 demonstrated: area under the curve (AUC) of 0.8 (0.7-0.9), sensitivity 66.7%, specificity 83.9%, positive predictive value (PPV) 35.7%, negative predictive value (NPV) 94.9%, positive likelihood ration (LR+) 4.1 and negative LR (LR-) 0.4. External validation demonstrated: AUC 0.7 (0.7-0.8), sensitivity 44.9%, specificity 90.4%, PPV 23.3%, NPV 96.2%, LR+ 4.6 and LR- 0.6 (0.4-0.7). CONCLUSION: The MRP model is not able to be used in real time for counselling, and management should be individualised.

Performance evaluation of predictive models for detecting MMR gene mutations associated with Lynch syndrome in cancer patients in a Chinese cohort in Taiwan.

Identifying Lynch syndrome significantly impacts cancer risk management, treatment, and prognosis. Validation of mutation risk predictive models for mismatch repair (MMR) genes is crucial for guiding genetic counseling and testing, particularly in the understudied Asian population. We evaluated the performance of four MMR mutation risk predictive models in a Chinese cohort of 604 patients with colorectal cancer (CRC), endometrial cancer (EC), or ovarian cancer (OC) in Taiwan. All patients underwent germline genetic testing and 36 (6.0%) carried a mutation in the MMR genes (MLH1, MSH2, MSH6, and PMS2). All models demonstrated good performance, with area under the receiver operating characteristic curves comparable to Western cohorts: PREMM5 0.80 (95% confidence interval [CI], 0.73-0.88), MMRPro 0.88 (95% CI, 0.82-0.94), MMRPredict 0.82 (95% CI, 0.74-0.90), and Myriad 0.76 (95% CI, 0.67-0.84). Notably, MMRPro exhibited exceptional performance across all subgroups regardless of family history (FH+ 0.88, FH- 0.83), cancer type (CRC 0.84, EC 0.85, OC 1.00), or sex (male 0.83, female 0.90). PREMM5 and MMRPredict had good accuracy in the FH+ subgroup (0.85 and 0.82, respectively) and in CRC patients (0.76 and 0.82, respectively). Using the ratio of observed and predicted mutation rates, MMRPro and PREMM5 had good overall fit, while MMRPredict and Myriad overestimated mutation rates. Risk threshold settings in different models led to different positive predictive values. We suggest a lower threshold (5%) for recommending genetic testing when using MMRPro, and a higher threshold (20%) when using PREMM5 and MMRPredict. Our findings have important implications for personalized mutation risk assessment and counseling on genetic testing.