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Summary: 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‐β1, 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. [ABSTRACT FROM AUTHOR]

Abstract Although intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) presents with persistent inflammatory stimulation of the blood vessels and an increased risk of coronary artery dilatation. However, the pathogenesis of this disease is unclear, with no established biomarkers to predict its occurrence. This study intends to explore the utility of S100A12/TLR2-related signaling molecules and clinical indicators in the predictive modeling of IVIG-resistant KD. The subjects were classified according to IVIG treatment response: 206 patients in an IVIG-sensitive KD group and 49 in an IVIG-resistant KD group. Real-time PCR was used to measure the expression of S100A12, TLR2, MYD88, and NF-κB in peripheral blood mononuclear cells of patients, while collecting demographic characteristics, clinical manifestations, and laboratory test results of KD children. Multi-factor binary logistic regression analysis identified procalcitonin (PCT) level (≥ 0.845 ng/mL), Na level (≤ 136.55 mmol/L), and the relative expression level of S100A12 (≥ 10.224) as independent risk factors for IVIG-resistant KD and developed a new scoring model with good predictive ability to predict the occurrence of IVIG-resistant KD.

Although intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) presents with persistent inflammatory stimulation of the blood vessels and an increased risk of coronary artery dilatation. However, the pathogenesis of this disease is unclear, with no established biomarkers to predict its occurrence. This study intends to explore the utility of S100A12/TLR2-related signaling molecules and clinical indicators in the predictive modeling of IVIG-resistant KD. The subjects were classified according to IVIG treatment response: 206 patients in an IVIG-sensitive KD group and 49 in an IVIG-resistant KD group. Real-time PCR was used to measure the expression of S100A12, TLR2, MYD88, and NF-κB in peripheral blood mononuclear cells of patients, while collecting demographic characteristics, clinical manifestations, and laboratory test results of KD children. Multi-factor binary logistic regression analysis identified procalcitonin (PCT) level (≥ 0.845 ng/mL), Na level (≤ 136.55 mmol/L), and the relative expression level of S100A12 (≥ 10.224) as independent risk factors for IVIG-resistant KD and developed a new scoring model with good predictive ability to predict the occurrence of IVIG-resistant KD. [ABSTRACT FROM AUTHOR]

IntroductionThis study aims to analyze the clinical features of Kawasaki disease (KD) shock syndrome (KDSS) and explore its early predictors.MethodsA retrospective case–control study was used to analyze KD cases from February 2016 to October 2023 in our hospital. A total of 28 children with KDSS and 307 children who did not develop KDSS were included according to matching factors. Baseline information, clinical manifestations, and laboratory indicators were compared between the two groups. Indicators of differences were analyzed based on univariate analysis; binary logistic regression analysis was used to identify the risk factors for KDSS, and then receiver operating characteristic analysis was performed to establish a predictive score model for KDSS.ResultsElevated neutrophil-to-lymphocyte ratio(NLR) and decreased fibrinogen (FIB) and Na were independent risk factors for KDSS; the scoring of the above risk factors according to the odds ratio value eventually led to the establishment of a new scoring system: NLR ≥ 7.99 (6 points), FIB ≤ 5.415 g/L (1 point), Na ≤ 133.05 mmol/L (3 points), and a total score of ≥3.5 points were high-risk factors for progression to KDSS; otherwise, they were considered to be low-risk factors.ConclusionChildren with KD with NLR ≥ 7.99, FIB ≤ 5.415 g/L, and Na ≤ 133.05 mmol/L, and those with two or more of the above risk factors, are more likely to progress to KDSS, which helps in early clinical diagnosis and treatment.

Abstract Background Surgery on primary tumor (SPT) has been a common treatment strategy for many types of cancer. Aims This study aimed to investigate whether SPT could be considered a treatment option for metastatic esophageal cancer and to identify the patient population that would benefit the most from SPT. Methods Data from 18 registration sites in the Surveillance, Epidemiology, and End Results Program database (SEER database) were analyzed to select patients with metastatic esophageal cancer. Multivariate Cox regression analysis was used to identify potential risk factors for pre‐treatment survival. Variables with a p‐value of less than 0.05 were used to construct a pre‐treatment nomogram. A pre‐surgery predictive model was then developed using the pre‐surgery factors to score patients, called the “pre‐surgery score”. The optimal cut‐off value for the “pre‐surgery score” was determined using X‐tile analysis, and patients were divided into high‐risk and low‐risk subsets. It was hypothesized that patients with a low “pre‐surgery score” risk would benefit the most from SPT. Results A total of 3793 patients were included in the analysis. SPT was found to be an independent risk factor for the survival of metastatic esophageal cancer patients. Subgroup analyses showed that patients with liver or lung metastases derived more benefit from SPT compared to those with bone or brain metastases. A pre‐treatment predictive model was constructed to estimate the survival rates at one, two, and three years, which showed good accuracy (C‐index: 0.705 for the training set and 0.701 for the validation set). Patients with a “pre‐surgery score” below 4.9 were considered to have a low mortality risk and benefitted from SPT (SPT vs. non‐surgery: median overall survival (OS): 24 months vs. 4 months, HR = 0.386, 95% CI: 0.303–0.491, p

Abstract 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.

Kang Sun,1,2 Wangping Li,1 Yu Li,3,4 Guangyu Li,5 Lei Pan,1 Faguang Jin1 1Department of Respiratory and Critical Care Medicine, Tang Du Hospital, Air Force Military Medical University, Xi’an, Shaanxi Province, 710038, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, The 989th Hospital of Joint Support Force of Chinese People’s Liberation Army, Luoyang, Henan Province, 471003, People’s Republic of China; 3Department of Infectious Diseases, Shaanxi Provincial People’s Hospital and The Affiliated Hospital of Xi’an Medical University, Xi’an, Shaanxi Province, 710068, People’s Republic of China; 4Shaanxi Center for Models of Clinical Medicine in International Cooperation of Science and Technology, Xi’an, Shaanxi Province, 710068, People’s Republic of China; 5Department of Pathology, University of Texas Medical Branch, Galveston, TX, 77555, USACorrespondence: Lei Pan; Wangping Li, Department of Respiratory and Critical Care Medicine, Tang Du Hospital, Air Force Military Medical University, Xi’an, Shaanxi Province, 710038, People’s Republic of China, Email panlei@fmmu.edu.cn; qxd25@163.comBackground: The prognosis of ABA-HAP patients is very poor. This study aimed to develop a scoring model to predict ABA-HAP in patients with GNB-HAP.Methods: A single center retrospective cohort study was performed among patients with HAP caused by GNB in our hospital during January 2019 to June 2019 (the derivation cohort, DC). The variables were assessed on the day when qualified respiratory specimens were obtained. A prediction score was formulated by using independent risk factors obtained from logistic regression analysis. It was prospectively validated with a subsequent cohort of GNB-HAP patients admitted to our hospital during July 2019 to Dec 2019 (the validation cohort, VC).Results: The final logistic regression model of DC included the following variables: transferred from other hospitals (3 points); blood purification (3 points); risk for aspiration (4 points); immunocompromised (3 points); pulmonary interstitial fibrosis (3 points); pleural effusion (1 points); heart failure (3 points); encephalitis (5 points); increased monocyte count (2 points); and increased neutrophils count (2 points). The AUROC of the scoring model was 0.845 (95% CI, 0.796 ∼ 0.895) in DC and 0.807 (95% CI, 0.759 ∼ 0.856) in VC. The scoring model clearly differentiated the low-risk patients (the score < 8 points), moderate-risk patients (8 ≤ the score < 12 points) and high-risk patients (the score ≥ 12 points), both in DC (P < 0.001) and in VC (P < 0.001).Conclusion: This simple scoring model could predict ABA-HAP with high predictive value and help clinicians to choose appropriate empirical antibiotic therapy.Keywords: Acinetobacter baumannii, hospital acquired pneumonia, Gram-negative bacilli, predictive scoring model, empirical antibiotic therapy

Objectives: To explore the predictive factors for non-response to intravenous immunoglobulin (IVIG) in children with Kawasaki disease (KD) and to establish an IVIG non-response prediction scoring model for the Sichuan region., Methods: A retrospective study was conducted by collecting clinical data from children with KD admitted to four tertiary hospitals in Sichuan Province between 2019 and 2023. Among them, 940 children responded to IVIG, while 74 children did not respond. Multivariate logistic regression analysis was used to identify the predictive factors for non-response to IVIG and to establish a predictive scoring model. The model's effectiveness was assessed using the receiver operating characteristic curve (ROC) and validated with an independent dataset., Results: Multivariate logistic regression analysis showed that the platelet-to-lymphocyte ratio (PLR), hemoglobin (Hb), serum creatinine, aspartate aminotransferase (AST), and platelet count (PLT) were closely related to non-response to IVIG in children with KD ( P <0.05). Based on these indicators, a predictive scoring model was established: PLR > 199, 0.4 points; Hb ≤ 116 g/L, 4 points; AST > 58 U/L, 0.2 points; serum creatinine > 38 µmol/L, 3.9 points; PLT count ≤ 275 × 10 9 /L, 0.3 points. Using this model, children with KD were scored, and a total score greater than 4.3 was considered high risk of non-response to IVIG. The sensitivity of the model in predicting non-response to IVIG was 77.0%, specificity was 65.7%, and the area under the ROC curve was 0.746 (95% CI : 0.688-0.805)., Conclusions: The predictive scoring model based on PLR, Hb, serum creatinine, AST, and PLT demonstrates good predictive performance for non-response to IVIG in children with KD in the Sichuan region and can serve as a reference for clinical decision-making.

Early identification of gastrointestinal (GI) bleeding in children with abdominal Henoch-Schönlein purpura (HSP) is essential for their subsequent treatment, and a risk prediction model for GI bleeding in abdominal HSP was constructed in this study to assist physicians in their decision-making. In a single-center retrospective study, the children collected were divided into two parts, a training set and a validation set, according to the time of admission. In the training set, univariate analysis was performed to compare demographic data and laboratory tests between the two groups of children with GI and non-GI bleeding, and the independent risk factors were derived using binary logistic equations to develop a scoring model for predicting GI bleeding in children by odds ratio (OR) values and receiver operating characteristic curves. The scoring model was then internally validated in validation set. The results showed that there were 11 indicators were statistically different between the two groups in the training set, including white blood cells, neutrophil-to-lymphocyte ratio, platelets, eosinophils (EO), high sensitivity C-reactive protein (hsCRP), activated partial thromboplastin time (APTT), sodium, potassium (K), albumin (ALB), Total bilirubin, and Immunoglobulin E (IgE) in the univariate analysis. Among them, the independent risk factors for GI bleeding included the six indicators of EO ≤ 0.045×10^9/L, hsCRP ≥ 14.5 mg/L, APTT ≤ 28.1 s, K ≥ 4.18 mmol/L, ALB ≤ 40.6 g/L, and IgE ≥ 136 ng/mL. According to the OR values, where EO ≤ 0.045 ×10^9/L, hsCRP ≥ 14.5 mg/L, APTT ≤ 28.1 s, ALB ≤ 40.6 g/L each scored 3 points, K ≥ 4.18 mmol/L, IgE ≥ 136 ng/mL each scored 2 points, and the total score was 0-16 points. The sensitivity and specificity of predicting GI bleeding were 88.7% and 64.2%, respectively, when the child scored ≥ 7 points. In the validation set, the sensitivity, specificity and accuracy of the model in predicting GI bleeding were 77.4%, 74.5% and 75.2%, respectively. In conclusion, the construction of a scoring model to predict the risk of GI bleeding from abdominal HSP would greatly assist pediatricians in predicting and identifying children at high risk for GI bleeding at an early stage. [ABSTRACT FROM AUTHOR]

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a histopathological marker and risk factor for HCC recurrence. We integrated diffusion-weighted imaging (DWI) and magnetic resonance (MR) image findings of tumors into a scoring system for predicting MVI. In total, 228 HCC patients with pathologically confirmed MVI who underwent surgical resection or liver transplant between November 2012 and March 2021 were enrolled retrospectively. Patients were divided into a right liver lobe group (n = 173, 75.9%) as the model dataset and a left liver lobe group (n = 55, 24.1%) as the model validation dataset. Multivariate logistic regression identified two-segment involved tumor (Score: 1; OR: 3.14; 95% CI: 1.22 to 8.06; p = 0.017); ADCmin ≤ 0.95 × 10−3 mm2/s (Score: 2; OR: 10.88; 95% CI: 4.61 to 25.68; p = 0.000); and largest single tumor diameter ≥ 3 cm (Score: 1; OR: 5.05; 95% CI: 2.25 to 11.30; p = 0.000), as predictive factors for the scoring model. Among all patients, sensitivity was 89.66%, specificity 58.04%, positive predictive value 68.87%, and negative predictive value 84.41%. For validation of left lobe group, sensitivity was 80.64%, specificity 70.83%, positive predictive value 78.12%, and negative predictive value 73.91%. The scoring model using ADCmin, largest tumor diameter, and two-segment involved tumor provides high sensitivity and negative predictive value in MVI prediction for use in routine functional MR. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Background Although the risk factors for positive follow-up blood cultures (FUBCs) in gram-negative bacteremia (GNB) have not been investigated extensively, FUBC has been routinely carried out in many acute care hospitals. We attempted to identify the risk factors and develop a predictive scoring model for positive FUBC in GNB cases. Methods All adults with GNB in a tertiary care hospital were retrospectively identified during a 2-year period, and GNB cases were assigned to eradicable and non-eradicable groups based on whether removal of the source of infection was possible. We performed multivariate logistic analyses to identify risk factors for positive FUBC and built predictive scoring models accordingly. Results Out of 1473 GNB cases, FUBCs were carried out in 1268 cases, and the results were positive in 122 cases. In case of eradicable source of infection, we assigned points according to the coefficients from the multivariate logistic regression analysis: Extended spectrum beta-lactamase-producing microorganism (+ 1 point), catheter-related bloodstream infection (+ 1), unfavorable treatment response (+ 1), quick sequential organ failure assessment score of 2 points or more (+ 1), administration of effective antibiotics (− 1), and adequate source control (− 2). In case of non-eradicable source of infection, the assigned points were end-stage renal disease on hemodialysis (+ 1), unfavorable treatment response (+ 1), and the administration of effective antibiotics (− 2). The areas under the curves were 0.861 (95% confidence interval [95CI] 0.806–0.916) and 0.792 (95CI, 0.724–0.861), respectively. When we applied a cut-off of 0, the specificities and negative predictive values (NPVs) in the eradicable and non-eradicable sources of infection groups were 95.6/92.6% and 95.5/95.0%, respectively. Conclusions FUBC is commonly carried out in GNB cases, but the rate of positive results is less than 10%. In our simple predictive scoring model, zero scores—which were easily achieved following the administration of effective antibiotics and/or adequate source control in both groups—had high NPVs. We expect that the model reported herein will reduce the necessity for FUBCs in GNB cases.

BackgroundEarly identification of intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) is important for making a suitable therapeutic strategy for children with KD.MethodsThis study included a training set and an external validation set. The training set included 635 children (588 IVIG-sensitive and 47 IVIG-resistant KD) hospitalized in Wuhan Children’s Hospital, Hubei, China. Univariate analyses and binary logistic regression equation was incorporated to find the associated variables of the IVIG-resistant KD. A scoring model for predicting IVIG-resistant KD was established according to odds ratio (OR) values and receiver operating characteristic curves. The external validation set consisted of 391 children (358 IVIG-sensitive and 33 IVIG-resistant KD) hospitalized in Peking University First Hospital, Beijing, China. The predictive ability of the model of IVIG-resistant KD were externally validated by the real clinically diagnosed KD cases.ResultsFifteen variables in the training set were statistically different between IVIG-sensitive and IVIG-resistant KD children, including rash, duration of fever, peripheral blood neutrophil-to-lymphocyte ratio (NLR), prognostic nutritional index (PNI), percentage of monocytes and percentage of eosinophils, and serum alanine aminotransferase, aspartate aminotransferase, total bilirubin (TB), direct bilirubin, glutamyl transpeptidase, prealbumin, sodium ion, potassium ion and high-sensitivity C-reactive protein. According to logistic equation analysis, the final three independent correlates to IVIG-resistant KD were serum TB ≥ 12.8 μmol/L, peripheral blood NLR ≥ 5.0 and peripheral blood PNI ≤ 52.4. According to the OR values, three variables were assigned the points of 2, 2 and 1, respectively. When the score was ≥ 3 points, the sensitivity to predict IVIG-resistant KD was 80.9% and the specificity was 77.6%. In the validation set, the sensitivity, specificity and accuracy of the predictive model of IVIG-resistant KD were 72.7%, 84.9%, and 83.9%, respectively.ConclusionA scoring model was constructed to predict IVIG-resistant KD, which would greatly assist pediatricians in the early prediction of IVIG-resistant KD.

Objective: Ectopic pregnancy (EP) is a serious condition. Delayed diagnosis could lead to life-threatening outcomes. The study aimed to develop a diagnostic predictive model for EP to approach suspected cases with prompt intervention before the rupture occurred.Methods: A retrospective cross-sectional study enrolled 347 pregnant women presenting first-trimester complications (abdominal pain or vaginal bleeding) with diagnosis suspected of pregnancy of unknown location, who were eligible and underwent chart review. The data including clinical risk factors, signs and symptoms, serum human chorionic gonadotropin (hCG), and ultrasound findings were analyzed. The statistical predictive score was developed by performing logistic regression analysis. The testing data of 30 patients were performed to test the validation of predictive scoring.Results: From a total of 22 factors, logistic regression method–derived scoring model was based on five potent factors (history of pelvic inflammatory disease, current use of emergency pills, cervical motion tenderness, serum hCG ≥1,000 mIU/ml, and ultrasound finding of adnexal mass) using a cutoff score ≥3. This predictive index score was able to determine ectopic pregnancy with an accuracy of 77.8% [95% confidence interval (CI) = 73.1–82.1], specificity of 91.0% (95% CI = 62.1–72.0), sensitivity of 67.0% (95% CI = 88.0–94.0), and area under the curve of 0.906 (95% CI = 0.875–0.937). In the validation group, no patient with negative result of this score had an EP.Conclusion: Statistical predictive score was derived with high accuracy and applicable performance for EP diagnosis. This score could be used to support clinical decision making in routine practice for management of EP.

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. [ABSTRACT FROM AUTHOR]

Background: The rupture risk of anterior communicating artery aneurysms (ACoAAs) has been known to be higher than that of aneurysms at other locations. Thus, the aim of this study is to investigate the clinical and morphological characteristics associated with risk factors for the rupture of ACoAAs.Methods: In total, 361 consecutive patients with 361 ACoAAs between August 2011 and December 2017 were retrospectively reviewed. Patients and ACoAAs were divided into ruptured and unruptured groups. In addition to clinical characteristics, ACoAA characteristics were evaluated by CT angiography (CTA). A multiple logistic regression analysis was used to identify the independent risk factors associated with ACoAA rupture. The assignment score of these variables depends on the β coefficient. A receiver operating characteristic (ROC) curve analysis was used to calculate the optimal thresholds.Results: The multiple logistic regression model revealed that A1 dominance [odds ratio (OR) 3.034], an irregular shape (OR 3.358), and an aspect ratio ≥1.19 (AR; OR 3.163) increased the risk of rupture, while cerebral atherosclerosis (OR 0.080), and mean diameters ≥2.48 mm (OR 0.474) were negatively correlated with ACoAA rupture. Incorporating these five factors, the ROC analysis revealed that the threshold value of the multifactors was one, the sensitivity was 88.3%, and the specificity was 66.0%.Conclusions: The scoring model is a simple method that is based on A1 dominance, irregular shape, aspect ratio, cerebral atherosclerosis, and mean diameters from CTA and is of great value in the prediction of the rupture risk of ACoAAs.

Background: The rupture risk of anterior communicating artery aneurysms (ACoAAs) has been known to be higher than that of aneurysms at other locations. Thus, the aim of this study is to investigate the clinical and morphological characteristics associated with risk factors for the rupture of ACoAAs. Methods: In total, 361 consecutive patients with 361 ACoAAs between August 2011 and December 2017 were retrospectively reviewed. Patients and ACoAAs were divided into ruptured and unruptured groups. In addition to clinical characteristics, ACoAA characteristics were evaluated by CT angiography (CTA). A multiple logistic regression analysis was used to identify the independent risk factors associated with ACoAA rupture. The assignment score of these variables depends on the β coefficient. A receiver operating characteristic (ROC) curve analysis was used to calculate the optimal thresholds. Results: The multiple logistic regression model revealed that A1 dominance [odds ratio (OR) 3.034], an irregular shape (OR 3.358), and an aspect ratio ≥1.19 (AR; OR 3.163) increased the risk of rupture, while cerebral atherosclerosis (OR 0.080), and mean diameters ≥2.48 mm (OR 0.474) were negatively correlated with ACoAA rupture. Incorporating these five factors, the ROC analysis revealed that the threshold value of the multifactors was one, the sensitivity was 88.3%, and the specificity was 66.0%. Conclusions: The scoring model is a simple method that is based on A1 dominance, irregular shape, aspect ratio, cerebral atherosclerosis, and mean diameters from CTA and is of great value in the prediction of the rupture risk of ACoAAs. [ABSTRACT FROM AUTHOR]

Purpose: Microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) cannot be accurately predicted preoperatively. This study aimed to establish a predictive scoring model of MVI in solitary HCC patients without macroscopic vascular invasion.Methods: A total of 309 consecutive HCC patients who underwent curative hepatectomy were divided into the derivation (n=206) and validation cohort (n=103). A predictive scoring model of MVI was established according to the valuable predictors in the derivation cohort based on multivariate logistic regression analysis. The performance of the predictive model was evaluated in the derivation and validation cohorts.Results: Preoperative imaging features on CECT, such as intratumoral arteries, non-nodular type of HCC and absence of radiological tumor capsule were independent predictors for MVI. The predictive scoring model was established according to the β coefficients of the 3 predictors. Area under receiver operating characteristic (AUROC) of the predictive scoring model was 0.872 (95% CI, 0.817-0.928) and 0.856 (95% CI, 0.771-0.940) in the derivation and validation cohorts. The positive and negative predictive values were 76.5% and 88.0% in the derivation cohort and 74.4% and 88.3% in the validation cohort. The performance of the model was similar between the patients with tumor size ≤5cm and >5cm in AUROC (P=0.910).Conclusions: The predictive scoring model based on intratumoral arteries, non-nodular type of HCC, and absence of the radiological tumor capsule on preoperative CECT is of great value in the prediction of MVI regardless of tumor size. [ABSTRACT FROM AUTHOR]

Background The potential severity of postoperative pancreatic fistula ( POPF) after laparoscopy-assisted gastrectomy ( LAG) necessitates efforts to identify predictive factors for POPF. The aim of the present study was to identify predictive factors for POPF and to establish a predictive scoring system for POPF after LAG. Patients and Methods Between June 2004 and March 2011, 277 gastric cancer patients who underwent curative resection with LAG were enrolled. POPF was defined according to the International Study Group for Pancreatic Fistula grading system. Risk factors for POPF were evaluated using logistic regression analysis, and a scoring system for POPF was established. Results In the derivation cohort, multivariate analysis revealed the risk factors for POPF as patient age ≤70 years (5 points), amylase level of postoperative day 1 drainage fluid >454 IU/ L(5 points), total number of retrieved lymph nodes >21 (5 points), body mass index >21.45 kg/m2 (4 points), and operating time >337 min (2 points). In the validation cohort, at the cut-off point for high risk (score ≥15), the model had a negative predictive value of 94.5%, a positive predictive value of 57.4%, a sensitivity of 88.6%, and a specificity of 75.0% ( C statistic = 0.857). Conclusion This study demonstrated that POPF after LAG is associated with specific preoperative and postoperative factors. With a simple predictive scoring system, patients at high risk for POPF can be accurately identified. This simple predictive scoring system will be useful for many clinicians to assess the risk of POPF after LAG and start treating at-risk patients earlier. [ABSTRACT FROM AUTHOR]

Background: Patients undergoing hemodialysis are prone to cardiac arrests. Methods: This study aimed to develop a risk score to predict in-hospital cardiac arrest (IHCA) in emergency department (ED) patients undergoing emergency hemodialysis. Patients were included if they received urgent hemodialysis within 24 h after ED arrival. The primary outcome was IHCA within three days. Predictors included three domains: comorbidity, triage information (vital signs), and initial biochemical results. The final model was generated from data collected between 2015 and 2018 and validated using data from 2019. Results: A total of 257 patients, including 52 with IHCA, were analyzed. Statistical analysis selected significant variables with higher sensitivity cutoff, and scores were assigned based on relative beta coefficient ratio: K > 5.5 mmol/L (score 1), pH < 7.35 (score 1), oxygen saturation < 85% (score 1), and mean arterial pressure < 80 mmHg (score 2). The final scoring system had an area under the curve of 0.78 (p < 0.001) in the primary group and 0.75 (p = 0.023) in the validation group. The high-risk group (defined as sum scores ≥ 3) had an IHCA risk of 47.2% and 41.7%, while the low-risk group (sum scores < 3) had 18.3% and 7%, in the primary and validation databases, respectively. Conclusions: This predictive score model for IHCA in emergent hemodialysis patients could help healthcare providers to take necessary precautions and allocate resources. [ABSTRACT FROM AUTHOR]

Background: Although the risk factors for positive follow-up blood cultures (FUBCs) in gram-negative bacteremia (GNB) have not been investigated extensively, FUBC has been routinely carried out in many acute care hospitals. We attempted to identify the risk factors and develop a predictive scoring model for positive FUBC in GNB cases.Methods: All adults with GNB in a tertiary care hospital were retrospectively identified during a 2-year period, and GNB cases were assigned to eradicable and non-eradicable groups based on whether removal of the source of infection was possible. We performed multivariate logistic analyses to identify risk factors for positive FUBC and built predictive scoring models accordingly.Results: Out of 1473 GNB cases, FUBCs were carried out in 1268 cases, and the results were positive in 122 cases. In case of eradicable source of infection, we assigned points according to the coefficients from the multivariate logistic regression analysis: Extended spectrum beta-lactamase-producing microorganism (+ 1 point), catheter-related bloodstream infection (+ 1), unfavorable treatment response (+ 1), quick sequential organ failure assessment score of 2 points or more (+ 1), administration of effective antibiotics (- 1), and adequate source control (- 2). In case of non-eradicable source of infection, the assigned points were end-stage renal disease on hemodialysis (+ 1), unfavorable treatment response (+ 1), and the administration of effective antibiotics (- 2). The areas under the curves were 0.861 (95% confidence interval [95CI] 0.806-0.916) and 0.792 (95CI, 0.724-0.861), respectively. When we applied a cut-off of 0, the specificities and negative predictive values (NPVs) in the eradicable and non-eradicable sources of infection groups were 95.6/92.6% and 95.5/95.0%, respectively.Conclusions: FUBC is commonly carried out in GNB cases, but the rate of positive results is less than 10%. In our simple predictive scoring model, zero scores-which were easily achieved following the administration of effective antibiotics and/or adequate source control in both groups-had high NPVs. We expect that the model reported herein will reduce the necessity for FUBCs in GNB cases. [ABSTRACT FROM AUTHOR]