GraphGPSM: a global scoring model for protein structure using graph neural networks.

The scoring models used for protein structure modeling and ranking are mainly divided into unified field and protein-specific scoring functions. Although protein structure prediction has made tremendous progress since CASP14, the modeling accuracy still cannot meet the requirements to a certain extent. Especially, accurate modeling of multi-domain and orphan proteins remains a challenge. Therefore, an accurate and efficient protein scoring model should be developed urgently to guide the protein structure folding or ranking through deep learning. In this work, we propose a protein structure global scoring model based on equivariant graph neural network (EGNN), named GraphGPSM, to guide protein structure modeling and ranking. We construct an EGNN architecture, and a message passing mechanism is designed to update and transmit information between nodes and edges of the graph. Finally, the global score of the protein model is output through a multilayer perceptron. Residue-level ultrafast shape recognition is used to describe the relationship between residues and the overall structure topology, and distance and direction encoded by Gaussian radial basis functions are designed to represent the overall topology of the protein backbone. These two features are combined with Rosetta energy terms, backbone dihedral angles and inter-residue distance and orientations to represent the protein model and embedded into the nodes and edges of the graph neural network. The experimental results on the CASP13, CASP14 and CAMEO test sets show that the scores of our developed GraphGPSM have a strong correlation with the TM-score of the models, which are significantly better than those of the unified field score function REF2015 and the state-of-the-art local lDDT-based scoring models ModFOLD8, ProQ3D and DeepAccNet, etc. The modeling experimental results on 484 test proteins demonstrate that GraphGPSM can greatly improve the modeling accuracy. GraphGPSM is further used to model 35 orphan proteins and 57 multi-domain proteins. The results show that the average TM-score of the models predicted by GraphGPSM is 13.2 and 7.1% higher than that of the models predicted by AlphaFold2. GraphGPSM also participates in CASP15 and achieves competitive performance in global accuracy estimation.

A novel scoring model for predicting efficacy and guiding individualised treatment in immune thrombocytopaenia.

Despite diverse therapeutic options for immune thrombocytopaenia (ITP), drug efficacy and selection challenges persist. This study systematically identified potential indicators in ITP patients and followed up on subsequent treatment. We initially analysed 61 variables and identified 12, 14, and 10 candidates for discriminating responders from non-responders in glucocorticoid (N = 215), thrombopoietin receptor agonists (TPO-RAs) (N = 224), and rituximab (N = 67) treatments, respectively. Patients were randomly assigned to training or testing datasets and employing five machine learning (ML) models, with eXtreme Gradient Boosting (XGBoost) area under the curve (AUC = 0.89), Decision Tree (DT) (AUC = 0.80) and Artificial Neural Network (ANN) (AUC = 0.79) selected. Cross-validated with logistic regression and ML finalised five variables (baseline platelet, IP-10, TNF-α, Treg, B cell) for glucocorticoid, eight variables (baseline platelet, TGF-ß1, MCP-1, IL-21, Th1, Treg, MK number, TPO) for TPO-RAs, and three variables (IL-12, Breg, MAIPA-) for rituximab to establish the predictive model. Spearman correlation and receiver operating characteristic curve analysis in validation datasets demonstrated strong correlations between response fractions and scores in all treatments. Scoring thresholds SGlu ≥ 3 (AUC = 0.911, 95% CI, 0.865-0.956), STPO-RAs ≥ 5 (AUC = 0.964, 95% CI 0.934-0.994), and SRitu = 3 (AUC = 0.964, 95% CI 0.915-1.000) indicated ineffectiveness in glucocorticoid, TPO-RAs, and rituximab therapy, respectively. Regression analysis and ML established a tentative and preliminary predictive scoring model for advancing individualised treatment.

Integrative analysis regarding the correlation between collagen-related genes and prostate cancer.

PURPOSE: Prostate cancer (PCa) is a common malignancy in men, with an escalating mortality rate attributed to Recurrence and metastasis. Recent studies have illuminated collagen's critical regulatory role within the tumor microenvironment, significantly influencing tumor progression. Accordingly, this investigation is dedicated to examining the relationship between genes linked to collagen and the prognosis of PCa, with the objective of uncovering any possible associations between them. METHODS: Gene expression data for individuals with prostate cancer were obtained from the TCGA repository. Collagen-related genes were identified, leading to the development of a risk score model associated with biochemical recurrence-free survival (BRFS). A prognostic nomogram integrating the risk score with essential clinical factors was crafted and evaluated for efficacy. The influence of key collagen-related genes on cellular behavior was confirmed through various assays, including CCK8, invasion, migration, cell cloning, and wound healing. Immunohistochemical detection was used to evaluate PLOD3 expression in prostate cancer tissue samples. RESULTS: Our study identified four key collagen-associated genes (PLOD3, COL1A1, MMP11, FMOD) as significant. Survival analysis revealed that low-risk groups, based on the risk scoring model, had significantly improved prognoses. The risk score was strongly associated with prostate cancer prognosis. Researchers then created a nomogram, which demonstrated robust predictive efficacy and substantial clinical applicability.Remarkably, the suppression of PLOD3 expression notably impeded the proliferation, invasion, migration, and colony formation capabilities of PCa cells. CONCLUSION: The risk score, derived from four collagen-associated genes, could potentially act as a precise prognostic indicator for BRFS of patients. Simultaneously, our research has identified potential therapeutic targets related to collagen. Notably, PLOD3 was differentially expressed in cancer and para-cancer tissues in clinical specimens and it also was validated through in vitro studies and shown to suppress PCa tumorigenesis following its silencing.

Prediction of neoplastic gallbladder polyps in patients with different age level based on preoperative ultrasound: a multi-center retrospective real-world study.

BACKGROUND: The prevalence of neoplastic polyps in gallbladder polyps (GPs) increases sharply with age and is associated with gallbladder carcinoma (GBC). This study aims to predict neoplastic polyps and provide appropriate treatment strategies based on preoperative ultrasound features in patients with different age level. METHODS: According to the age classification of WHO, 1523 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China were divided into young adults group (n=622), middle-aged group (n=665) and elderly group (n=236). Linear scoring models were established based on independent risk variables screened by the Logistic regression model in different age groups. The area under ROC (AUC) to evaluate the predictive ability of linear scoring models, long- and short- diameter of GPs. RESULTS: Independent risk factors for neoplastic polyps included the number of polyps, polyp size (long diameter), and fundus in the young adults and elderly groups, while the number of polyps, polyp size (long diameter), and polyp size (short diameter) in the middle-aged groups. In different age groups, the AUCs of its linear scoring model were higher than the AUCs of the long- and short- diameter of GPs for differentiating neoplastic and non-neoplastic polyps (all P<0.05), and Hosmer-Lemeshow goodness of fit test showed that the prediction accuracy of the linear scoring models was higher than the long- and short- diameter of GPs (all P>0.05). CONCLUSION: The linear scoring models of the young adults, middle-aged and elderly groups can effectively distinguish neoplastic polyps from non-neoplastic polyps based on preoperative ultrasound features.

Enhancing prognostic prediction of invasive candidiasis among cancer patients with a serum C5a-based scoring model.

PURPOSE: Invasive candidiasis poses a life-threatening risk, and early prognosis assessment is vital for timely interventions to reduce mortality. Serum C5a levels have recently been linked to prognosis, but confirmation in cancer patients is pending. METHODS: We detected the concentrations of serum C5a in hospitalized cancer patients with invasive candidiasis from 2020 to 2023, and retrospectively analyzed the clinical data. RESULTS: 372 cases were included in this study, with a 90-day mortality rate of 21.8%. Candida albicans (48.7%) remained the predominant pathogen, followed by Candida glabrata (25.5%), Candida tropicalis (12.4%), and Candida parapsilosis (8.3%). Gastrointestinal cancer was the most diagnosed pathology type (37.6%). Serum C5a demonstrated a noteworthy correlation with 90-day mortality, and employing a cutoff value of 36.7 ng/ml revealed significantly higher 90-day mortality in low-C5a patients (41.2%) compared to high-C5a patients (6.3%) (p < 0.001). We also identified no source control, no surgery, metastasis, or chronic renal failure independently correlated with the 90-day mortality. Based on this, a prognostic model combining C5a and clinical parameters was constructed, which performed better than models built solely on C5a or clinical parameters. Furthermore, we weighted scores to each parameter in the model and presented diagnostic sensitivity and specificity corresponding to different score points calculated by the model. CONCLUSION: We constructed a prognostic scoring model including serum C5a and clinical parameters, which would contribute to precise prognosis assessment and benefit the outcome among cancer patients.

Construction and validation of a risk-scoring model to predict lymph node metastasis in T1b-T2 esophageal cancer.

BACKGROUND: Lymph node status is an important factor in determining preoperative treatment strategies for stage T1b-T2 esophageal cancer (EC). Thus, the aim of this study was to investigate the risk factors for lymph node metastasis (LNM) in T1b-T2 EC and to establish and validate a risk-scoring model to guide the selection of optimal treatment options. METHODS: Patients who underwent upfront surgery for pT1b-T2 EC between January 2016 and December 2022 were analyzed. On the basis of the independent risk factors determined by multivariate logistic regression analysis, a risk-scoring model for the prediction of LNM was constructed and then validated. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminant ability of the model. RESULTS: The incidence of LNM was 33.5% (214/638) in our cohort, 33.4% (169/506) in the primary cohort and 34.1% (45/132) in the validation cohort. Multivariate analysis confirmed that primary site, tumor grade, tumor size, depth, and lymphovascular invasion were independent risk factors for LNM (all P < 0.05), and patients were grouped based on these factors. A 7-point risk-scoring model based on these variables had good predictive accuracy in both the primary cohort (AUC, 0.749; 95% confidence interval 0.709-0.786) and the validation cohort (AUC, 0.738; 95% confidence interval 0.655-0.811). CONCLUSION: A novel risk-scoring model for lymph node metastasis was established to guide the optimal treatment of patients with T1b-T2 EC.

Early Identification of Patients with Potential Failure of Nonoperative Management for Gastroduodenal Peptic Ulcer Perforation.

INTRODUCTION: We aimed to identify objective factors associated with failure of nonoperative management (NOM) of gastroduodenal peptic ulcer perforation (GDUP) and establish a scoring model for early identification of patients in whom NOM of GDUP may fail. METHODS: A total of 71 patients with GDUP were divided into NOM (cases of NOM success) and operation groups (cases requiring emergency operation or conversion from NOM to operation). Using logistic regression analysis, a scoring model was established based on the independent factors. The patients were stratified into low-risk and high-risk groups according to the scores. RESULTS: Of the 71 patients, 18 and 53 were in the NOM and operation groups, respectively. Ascites in the pelvic cavity on computed tomography (CT) and sequential organ failure assessment (SOFA) score at admission were identified as independent factors for NOM failure. The scoring model was established based on the presence of ascites in the pelvic cavity on CT and SOFA score ≥2 at admission. The operation rates for GDUP were 28.6% and 86.0% in the low-risk (score, 0) and high-risk groups (scores, 2 and 4), respectively. CONCLUSION: Our scoring model may help determine NOM failure or success in patients with GDUP and make decisions regarding initial treatment.

[Establishment of a predictive scoring model and preventive measures for bladder neck contracture after laparoscopic enucleation of the prostate with urethra preservation].

OBJECTIVE: To establish a predictive scoring model for bladder neck contracture (BNC) after laparoscopic enucleation of the prostate with preservation of the urethra (Madigan surgery) and explore the preventive measures against this postoperative complication. METHODS: We included 362 cases of BPH treated by laparoscopic Madigan surgery from January 2019 to March 2022 (45 with and 317 without postoperative BNC) in the training group and another 120 cases treated the same way in the verification group, collected the clinical data on the patients and evaluated the results of surgery. Using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression, we analyzed the risk factors for postoperative BNC and constructed a predictive scoring model for evaluation of the factors. RESULTS: Compared with the baseline, the IPSS, quality of life (QOL) score and postvoid residual urine volume (PVR) were significantly decreased (P < 0.05) while the maximum urinary flow rate (Qmax) remarkably increased (P < 0.05) in the BPH patients at 3 months after surgery. Eight non-zero characteristic predictors were identified by LASSO regression analysis. Multivariate logistic regression analysis showed that short clinical experience of the surgeon, concurrent prostatitis, bladder rinse solution temperature <34℃, catheter blockage, urethral balloon injection volume >40 ml and postoperative constipation were independent risk factors for postoperative BNC (P < 0.05). The best cut-off value was 2.36 points in both the training and the verification groups. The results of evaluation exhibited a high discriminability of the predictive scoring model. CONCLUSION: Laparoscopic Madigan surgery is a safe and effective method for the treatment of BPH. Short clinical experience of the surgeon, concurrent prostatitis, bladder rinse solution temperature <34℃, catheter blockage, water injected into the urethral balloon >40 ml and postoperative constipation were independent risk factors for postoperative BNC. The predictive scoring model constructed in this study has a good discriminability and is simple and feasible, contributive to the prediction of postoperative BNC in BPH patients undergoing laparoscopic Madigan surgery.

Development of a scoring model based on objective factors to predict gangrenous/perforated appendicitis.

BACKGROUND: The mortality rate of gangrenous/perforated appendicitis is higher than that of uncomplicated appendicitis. However, non-operative management of such patients is ineffective. This necessitates their careful exam at presentation to identify gangrenous/perforated appendicitis and aid surgical decision-making. Therefore, this study aimed to develop a new scoring model based on objective findings to predict gangrenous/perforated appendicitis in adults. METHODS: We retrospectively analyzed 151 patients with acute appendicitis who underwent emergency surgery between January 2014 and June 2021. We performed univariate and multivariate analyses to identify independent objective predictors of gangrenous/perforated appendicitis, and a new scoring model was developed based on logistic regression coefficients for independent predictors. Receiver operating characteristic (ROC) curve analysis and the Hosmer-Lemeshow test were performed to assess the discrimination and calibration of the model. Finally, the scores were classified into three categories based on the probability of gangrenous/perforated appendicitis. RESULTS: Among the 151 patients, 85 and 66 patients were diagnosed with gangrenous/perforated appendicitis and uncomplicated appendicitis, respectively. Using the multivariate analysis, C-reactive protein level, maximal outer diameter of the appendix, and presence of appendiceal fecalith were identified as independent predictors for developing gangrenous/perforated appendicitis. Our novel scoring model was developed based on three independent predictors and ranged from 0 to 3. The area under the ROC curve was 0.792 (95% confidence interval, 0.721-0.863), and the Hosmer-Lemeshow test showed a good calibration of the novel scoring model (P = 0.716). Three risk categories were classified: low, moderate, and high risk with probabilities of 30.9%, 63.8%, and 94.4%, respectively. CONCLUSIONS: Our scoring model can objectively and reproducibly identify gangrenous/perforated appendicitis with good diagnostic accuracy and help in determining the degree of urgency and in making decisions about appendicitis management.

Fasting serum fructose is associated with metabolic dysfunction-associated fatty liver disease: A prospective study.

AIM: The association between sugar-sweetened beverages and metabolic disorders has been well studied. However, it has not been determined whether fasting serum fructose is associated with metabolic dysfunction-associated fatty liver disease (MAFLD). METHODS: Participants were enrolled from 2011 to 2012 in Shanghai. Fasting serum fructose concentration was measured with a validated liquid chromatography-tandem mass spectrometry method. RESULTS: A total of 954 participants without diabetes were included. They were followed for an average of 3.5 years. A total of 320 (33.5%) participants had MAFLD at baseline. With the increase in fasting serum fructose level by quartile, the MAFLD prevalence was increased by 27.0%, 25.0%, 37.4%, and 44.5%, respectively (p < 0.001). Each SD increase in fasting serum fructose level was associated with a 60% increased risk of MAFLD (odds ratio 1.60; 95% confidence interval [CI], 1.36-1.88; p < 0.001). Fasting serum fructose levels were more closely associated with four components of MAFLD (hepatic steatosis, prediabetes, insulin resistance, and low high-density lipoprotein). We built a diagnostic model named the fructose fat index (FFI). The area under the receiver operating characteristic curve of the FFI was 0.879 (95% CI, 0.850-0.908) in the derivation cohort and 0.827 (95% CI, 0.776-0.878) in the validation cohort. Subsequent prospective studies found that the incidence risk of MAFLD was 2.26 times higher in the high-fructose group than in the low-fructose group among female participants (95% CI, 1.46-3.49; p < 0.001). CONCLUSION: Fasting serum fructose concentration, which mostly reflects endogenous fructose, was associated with a higher risk of MAFLD. The FFI derived from fasting serum fructose could be used to predict MAFLD.

A new dyslipidemia-based scoring model to predict transplant-free survival in patients with hepatitis E-triggered acute-on-chronic liver failure.

BACKGROUND/AIMS: Hepatitis E virus (HEV)-triggered acute-on-chronic liver failure (ACLF) has unacceptably high short-term mortality. However, it is unclear whether the existing predictive scoring models are applicable to evaluate the prognosis of HEV-triggered ACLF. METHODS: We screened datasets of patients with HEV-triggered ACLF from a regional tertiary hospital for infectious diseases in Shanghai, China, between January 2011 and January 2021. Clinical and laboratory parameters were recorded and compared to determine a variety of short-term mortality risk factors, which were used to develop and validate a new prognostic scoring model. RESULTS: Out of 4952 HEV-infected patients, 817 patients with underlying chronic liver disease were enrolled in this study. Among these, 371 patients with HEV-triggered ACLF were identified and allocated to the training set (n = 254) and test set (n = 117). The analysis revealed that hepatic encephalopathy (HE), ascites, triacylglycerol and apolipoprotein A (apoA) were associated with 90-day mortality (P < 0.05). Based on these significant indicators, we designed and calculated a new prognostic score = 0.632 × (ascites: no, 1 point; mild to moderate, 2 points; severe, 3 points) + 0.865 × (HE: no, 1 point; grade 1-2, 2 points; grade 3-4, 3 points) - 0.413 × triacylglycerol (mmol/L) - 2.171 × apoA (g/L). Compared to four well-known prognostic models (MELD score, CTP score, CLIF-C OFs and CLIF-C ACLFs), the new scoring model is more accurate, with the highest auROCs of 0.878 and 0.896, respectively, to predict 28- and 90-day transplantation-free survival from HEV-triggered ACLF. When our model was compared to COSSH ACLF IIs, there was no significant difference. The test data also demonstrated good concordance. CONCLUSIONS: This study is one of the first to address the correlation between hepatitis E and serum lipids and provides a new simple and efficient prognostic scoring model for HEV-triggered ACLF.

Scoring model with serum albumin and CA19-9 for metastatic pancreatic cancer in second-line treatment: results from the NAPOLEON study.

BACKGROUND: Patients with metastatic pancreatic cancer refractory to first-line chemotherapy (CTx) have few treatment options. It is unclear what kind of patients could be brought about survival benefit by 2nd-line CTx after refractory to gemcitabine + nab-PTX (GnP) or FOLFIRINOX. METHODS: This analysis was conducted as part of a multicenter retrospective study of GnP or FOLFIRINOX in patients with metastatic pancreatic cancer. Excluding censored cases, 156 and 77 patients, respectively, received second-line chemotherapy (CTx) and best supportive care (BSC). Using prognostic factors for post-discontinuation survivals (PDSs) at the first-line determination in multivariate analysis, we developed a scoring system to demonstrate the benefit of second-line CTx. RESULTS: The second-line CTx group had a median PDS of 5.2 months, whereas the BSC group had a median PDS of 2.7 months (hazard ratio 0.42; 95% confidence interval [CI] 0.31-0.57; p < 0.01). According to the Cox regression model, serum albumin levels below 3.5 g/dL, and CA19-9 levels above 1000 U/mL were independent prognostic factors (p < 0.01). Serum albumin (≥ and < 3.5 g/dL allotted to scores 0 and 1) and CA19-9 (< and ≥ 1000 U/mL allotted to scores 0 and 1) at first-line determination were used to develop the scoring system. The PDSs of patients with scores of 0 and 1 were significantly better than those of the BSC group; however, there was no significant difference between the PDSs of patients with score 2 and the BSC group. CONCLUSION: The survival advantage of second-line CTx, was observed in patients with scores of 0 and 1 but not in those with score 2.

The expression of autophagy markers in IVIG-resistant Kawasaki disease and the establishment of prediction model.

BACKGROUND: The aim of this study was to find early predictors of Intravenous Immunoglobulin (IVIG)-Resistant Kawasaki Disease. METHODS: Patients diagnosed with Kawasaki disease were enrolled in this study. Univariate analysis and multiple logistic regression were used to analyze the clinical characteristics and laboratory findings of patients in both groups before IVIG treatment. Independent predictors of Intravenous Immunoglobulin-Resistant Kawasaki Disease were analyzed, and a prediction model for children with Intravenous Immunoglobulin-Resistant Kawasaki Disease was constructed. RESULTS: A total of 108 children (67 males and 41 females) with IVIG-sensitive Kawasaki disease and 31 children (20 males and 11 females) with IVIG-resistant Kawasaki disease participated in this study. Compared with the IVIG-sensitive group, the duration of hospitalization, ALT, AST, GLB, r-GT, IgG, PCT, and ESR was elevated in the IVIG-resistant KD group, and ATG16L1, LC3II, BECN1, RBC, HGB, ALB, A/G, and CK were significantly lower (P < 0.05). mRNA expression of ESR, BECN1, and LC3II were independent risk factors for IVIG-resistant Kawasaki disease. A logistic regression model and scoring system were established, and the cut-off values of independent risk factors were derived from ROC curves: ESR ≥ 79.5 mm/h, BECN1 ≤ 0.645, LC3II ≤ 0.481. A new scoring system was established according to the respective regression coefficients as follows: ESR ≥ 79.5 mm/h (1 point), BECN1 ≤ 0.645 (1 point). LC3II ≤ 0.481 (2 points), 0-1 as low risk for IVIG non-response, and ≥ 2 as high risk. Applied to this group of study subjects, the sensitivity was 87.10%, specificity 83.33%, Youden index 0.70, AUC 0.9. CONCLUSIONS: Autophagy markers ATG16L1, BECN1, and LC3II are down-regulated in the expression of IVIG -resistant KD. ESR, BECN1, and LC3II mRNAs are independent risk factors for IVIG-resistant KD and may be involved in the development of IVIG-resistant KD. This study established a new model that can be used to predict IVIG-resistant KD, and future validation in a larger population is needed.

The state of lead scoring models and their impact on sales performance.

Although lead scoring is an essential component of lead management, there is a lack of a comprehensive literature review and a classification framework dedicated to it. Lead scoring is an effective and efficient way of measuring the quality of leads. In addition, as a critical Information Technology tool, a proper lead scoring model acts as an alleviator to weaken the conflicts between sales and marketing functions. Yet, little is known regarding lead scoring models and their impact on sales performance. Lead scoring models are commonly categorized into two classes: traditional and predictive. While the former primarily relies on the experience and knowledge of salespeople and marketers, the latter utilizes data mining models and machine learning algorithms to support the scoring process. This study aims to review and analyze the existing literature on lead scoring models and their impact on sales performance. A systematic literature review was conducted to examine lead scoring models. A total of 44 studies have met the criteria and were included for analysis. Fourteen metrics were identified to measure the impact of lead scoring models on sales performance. With the increased use of data mining and machine learning techniques in the fourth industrial revolution, predictive lead scoring models are expected to replace traditional lead scoring models as they positively impact sales performance. Despite the relative cost of implementing and maintaining predictive lead scoring models, it is still beneficial to supersede traditional lead scoring models, given the higher effectiveness and efficiency of predictive lead scoring models. This study reveals that classification is the most popular data mining model, while decision tree and logistic regression are the most applied algorithms among all the predictive lead scoring models. This study contributes by systematizing and recommending which machine learning method (i.e., supervised and/or unsupervised) shall be used to build predictive lead scoring models based on the integrity of different types of data sources. Additionally, this study offers both theoretical and practical research directions in the lead scoring field.

Identification of ruptured intracranial aneurysms using the aneurysm-specific prediction score in patients with multiple aneurysms with subarachnoid hemorrhages- a Chinese population based external validation study.

BACKGROUND: For patients with aneurysmal subarachnoid hemorrhages (SAHs) and multiple intracranial aneurysms (MIAs), a simple and fast imaging method that can identify ruptured intracranial aneurysms (RIAs) may have great clinical value. We sought to use the aneurysm-specific prediction score to identify RIAs in patients with MIAs and evaluate the aneurysm-specific prediction score. METHODS: Between May 2018 and May 2021, 134 patients with 290 MIAs were retrospectively analyzed. All patients had an SAH due to IA rupture. CT angiography (CTA) was used to assess the maximum diameter, shape, and location of IAs to calculate the aneurysm-specific prediction score. Then, the aneurysm-specific prediction score was applied to RIAs in patients with MIAs. RESULTS: The IAs with the highest aneurysm-specific prediction scores had not ruptured in 17 (12.7%) of the 134 patients with 290 MIAs. The sensitivity, specificity, false omission rate, diagnostic error rate, and diagnostic accuracy of the aneurysm-specific prediction score were higher than those of the maximum diameter, shape, and location of IAs. CONCLUSIONS: The present study suggests that the aneurysm-specific prediction score has high diagnostic accuracy in identifying RIAs in patients with MIAs and SAH, but that it needs further evaluation.

A TTP-incorporated scoring model for predicting mortality of solid tumor patients with bloodstream infection caused by Escherichia coli.

BACKGROUND: Few mortality-scoring models are available for solid tumor patients who are predisposed to develop Escherichia coli-caused bloodstream infection (ECBSI). We aimed to develop a mortality-scoring model by using information from blood culture time to positivity (TTP) and other clinical variables. METHODS: A cohort of solid tumor patients who were admitted to hospital with ECBSI and received empirical antimicrobial therapy was enrolled. Survivors and non-survivors were compared to identify the risk factors of in-hospital mortality. Univariable and multivariable regression analyses were adopted to identify the mortality-associated predictors. Risk scores were assigned by weighting the regression coefficients with corresponding natural logarithm of the odds ratio for each predictor. RESULTS: Solid tumor patients with ECBSI were distributed in the development and validation groups, respectively. Six mortality-associated predictors were identified and included in the scoring model: acute respiratory distress (ARDS), TTP ≤ 8 h, inappropriate antibiotic therapy, blood transfusion, fever ≥ 39 °C, and metastasis. Prognostic scores were categorized into three groups that predicted mortality: low risk (< 10% mortality, 0-1 points), medium risk (10-20% mortality, 2 points), and high risk (> 20% mortality, ≥ 3 points). The TTP-incorporated scoring model showed excellent discrimination and calibration for both groups, with AUC being 0.833 vs 0.844, respectively, and no significant difference in the Hosmer-Lemeshow test (6.709, P = 0.48) and the chi-square test (6.993, P = 0.46). Youden index showed the best cutoff value of ≥ 3 with 76.11% sensitivity and 79.29% specificity. TTP-incorporated scoring model had higher AUC than no TTP-incorporated model (0.837 vs 0.817, P < 0.01). CONCLUSIONS: Our TTP-incorporated scoring model was associated with improving capability in predicting ECBSI-related mortality. It can be a practical tool for clinicians to identify and manage bacteremic solid tumor patients with high risk of mortality.

A scoring model for diagnosis of tuberculous pleural effusion.

BACKGROUND: Due to the low efficiency of a single clinical feature or laboratory variable in the diagnosis of tuberculous pleural effusion (TBPE), the diagnosis of TBPE is still challenging. This study aimed to build a scoring diagnostic model based on laboratory variables and clinical features to differentiate TBPE from non-tuberculous pleural effusion (non-TBPE). METHODS: A retrospective study of 125 patients (63 with TBPE; 62 with non-TBPE) was undertaken. Univariate analysis was used to select the laboratory and clinical variables relevant to the model composition. The statistically different variables were selected to undergo binary logistic regression. Variables B coefficients were used to define a numerical score to calculate a scoring model. A receiver operating characteristic (ROC) curve was used to calculate the best cut-off value and evaluate the performance of the model. Finally, we add a validation cohort to verify the model. RESULTS: Six variables were selected in the scoring model: Age ≤ 46 years old (4.96 points), Male (2.44 points), No cancer (3.19 points), Positive T-cell Spot (T-SPOT) results (4.69 points), Adenosine Deaminase (ADA) ≥ 24.5U/L (2.48 point), C-reactive Protein (CRP) ≥ 52.8 mg/L (1.84 points). With a cut-off value of a total score of 11.038 points, the scoring model's sensitivity, specificity, and accuracy were 93.7%, 96.8%, and 99.2%, respectively. And the validation cohort confirms the model with the sensitivity, specificity, and accuracy of 92.9%, 93.3%, and 93.1%, respectively. CONCLUSION: The scoring model can be used in differentiating TBPE from non-TBPE.

Elevated bilirubin, alkaline phosphatase at onset, and drug metabolism are associated with prolonged recovery from DILI.

BACKGROUND & AIMS: Although most drug-induced liver injury (DILI) cases resolve after the offending medication is discontinued, time to recovery varies among patients, with 6 -12% developing a chronic disease. Herein, we investigated clinical factors and drug properties as potential risk determinants that influence the time course for DILI recovery and developed a model to predict its trajectory. METHODS: We applied an accelerated failure time model to 294 cases collected by the International Drug-Induced Liver Network Consortium (iDILIC). Factors included in the multivariate recovery score model were selected through univariate analysis. The model was externally validated using 257 cases from the Spanish DILI Registry and 191 cases from the LiverTox database. RESULTS: Higher serum bilirubin and alkaline phosphatase (ALP) at DILI onset, a longer time to onset, and non-significant drug metabolism were associated with a longer recovery and were included in the recovery score model. We defined high- and low-risk groups based on the scores assigned by the model. The estimated probability of recovery by 6 months was 0.46 (95% CI 0.26-0.61) for the high-risk group and 0.93 (95% CI 0.58-0.99) for the low-risk group in the iDILIC. Model performance was validated in both validation sets. The high- and low-risk cases identified by the model showed a significantly different time course for recovery, with a majority of low-risk cases recovering sooner. CONCLUSION: The trajectory of biochemical recovery from DILI is predicted by the extent of drug metabolism, serum bilirubin and ALP at DILI onset. The model can be used to compute an estimated DILI recovery and, when a significant delay is predicted, clinicians may consider additional investigations such as histologic evaluation or extended follow-up. LAY SUMMARY: In this study, we investigated whether drug properties and clinical factors are associated with the time it takes to recover from drug-induced liver injury (DILI). We found that total bilirubin, alkaline phosphatase level at DILI onset, time to onset, and extent of drug metabolism were consistently associated with recovery time. Using these factors, we built a model to predict the trajectory of recovery from DILI and validated this model in 2 independent cohorts. Our findings offer important insights into the factors influencing the trajectory of recovery from DILI. Additional investigations and longer follow-ups can be planned in those for whom a delayed recovery is predicted.

Evaluation of a Kawasaki Disease Risk Model for Predicting Coronary Artery Aneurysms in a Japanese Population: An Analysis of Post RAISE.

OBJECTIVES: To test the performance of the Son risk score, which was created to predict coronary artery abnormalities from baseline variables in North American patients with Kawasaki disease. STUDY DESIGN: The dataset from Post RAISE, the largest prospective cohort study of Japanese patients with Kawasaki disease to date, was used for the present study. With high risk defined as ≥3 points, sensitivity, specificity, positive predictive value, and negative predictive value for coronary artery abnormality development were calculated. To evaluate the effect of each risk factor in the Son score, the OR and 95% CIs were calculated using logistic regression analysis with the presence of coronary artery abnormality at 1 month after disease onset. RESULTS: Post RAISE enrolled 2628 consecutive patients with Kawasaki disease, and 304 patients had a high-risk score, of whom 15.1% showed coronary artery abnormality. At the cutoff ≥3 points, the sensitivity was 37.7%, and the specificity was 87.2%. The maximum z score at baseline ≥2.0 (OR 3.5, 95% CI 2.3-5.2) and age <6 months at disease onset (OR 3.2, 95% CI 1.9-5.4), were significantly associated with coronary artery abnormality development. However, a high concentration of C-reactive protein was not associated with coronary artery abnormality. The area under the receiver operating characteristic curve for the Son score was 0.65 (95% CI 0.59-0.71). CONCLUSIONS: The Son score had insufficient sensitivity and good specificity in a Japanese cohort of patients with Kawasaki disease. Among the variables comprising the Son score, a large baseline z score and young age at disease onset were significant, independent predictors of coronary artery abnormality development.

Myelitis in inflammatory disorders associated with myelin oligodendrocyte glycoprotein antibody and aquaporin-4 antibody: A comparative study in Chinese Han patients.

BACKGROUND AND PURPOSE: Myelitis is an important clinical component of myelin oligodendrocyte glycoprotein antibody (MOG-ab)-associated disease (MOGAD) and aquaporin-4 antibody (AQP4-ab)-positive neuromyelitis optica spectrum disorder (NMOSD). The aim of this work was to evaluate the differentiating features of myelitis between the two diseases. METHODS: Myelitis-related clinical and radiologic data from 130 patients with MOGAD and 125 patients with AQP4-ab-positive NMOSD were retrospectively reviewed and compared. A scoring model was established to differentiate MOG-ab-associated myelitis from AQP4-ab-associated myelitis. RESULTS: Overall, 29.2% (38/130) of patients with MOGAD and 66.4% (83/125) of patients with AQP4-ab-positive NMOSD had ever experienced myelitis. Compared with those with NMOSD, patients with MOGAD exhibited a lower frequency of myelitis, either during the first episode (p < 0.0001) or throughout the disease duration (p < 0.0001). Compared with AQP4-ab-associated myelitis, MOG-ab-associated myelitis manifested a higher male-to-female ratio (p < 0.0001), younger age at disease onset (p = 0.0004), more prodromic influenza-like symptoms (p = 0.030), more prodromic fever (p = 0.0003), more bowel and bladder dysfunction (p = 0.011), less painful tonic spasms (p < 0.0001), and lower Expanded Disability Status Scale scores after treatment (p < 0.0001). On magnetic resonance imaging, lower spinal cord lesions (p = 0.023), short-segment lesions (p = 0.021), conus involvement (p = 0.0001), and H sign (p < 0.0001) were more common in MOG-ab-associated myelitis. A scoring model with a cutoff value of 4 differentiated MOG-ab-associated myelitis from AQP4-ab-associated myelitis with a sensitivity of 87.9% and a specificity of 90.1%. CONCLUSIONS: Myelitis was less commonly observed in MOGAD and exhibited distinct features compared to those of AQP4-ab-positive NMOSD.