BACKGROUND: Accumulating evidence shows that free fatty acids (FFA) are associated with gestational diabetes mellitus (GDM). However, most of the studies focus on a few specific types of FFA, such as α-linolenic acid (C18:3n3) and Arachidonic acid (C20:4n6) or a total level of FFA.
OBJECTIVE: This study aimed to test the association between a variety of FFAs during the first trimester and the risk of GDM.
METHODS: The participants came from the Zhoushan Pregnant Women Cohort (ZWPC). A 1:2 nested case-control study was conducted: fifty mothers with GDM were matched with 100 mothers without GDM by age, pre-pregnancy body mass index (BMI), month of oral glucose tolerance test (OGTT) and parity. Thirty-seven FFAs (including 17 saturated fatty acids (SFA), 8 monounsaturated fatty acids (MUFA), 10 polyunsaturated fatty acids (PUFA) and 2 trans fatty acids (TFA)) in maternal plasma during the first trimester were tested by Gas Chromatography-Mass Spectrometry (GC-MS). Conditional logistic regression models were performed to assess the associations of FFA with the risk of GDM.
RESULTS: Nine FFAs were respectively associated with an increased risk of GDM (P < 0.05), and four FFAs were respectively associated with a decreased risk of GDM (P < 0.05). SFA risk score was associated with a greater risk of GDM (OR = 1.34, 95% CI: 1.12-1.60), as well as UFA risk score (OR = 1.26, 95% CI: 1.11-1.44), MUFA risk score (OR = 1.70, 95%CI: 1.27-2.26), PUFA risk score (OR = 1.32, 95%CI: 1.09-1.59) and TFA risk score (OR = 2.51, 95%CI: 1.23-5.13). Moreover, joint effects between different types of FFA risk scores on GDM were detected. For instance, compared with those with low risk scores of SFA and UFA, women with high risk scores of SFA and UFA had the highest risk of GDM (OR = 8.53, 95%CI: 2.41-30.24), while the Odds ratio in those with a low risk score of SFA and high risk score of UFA and those with a high risk score of SFA and low risk score of UFA was 6.37 (95%CI:1.33- 30.53) and 4.25 (95%CI: 0.97-18.70), respectively.
CONCLUSIONS: Maternal FFAs during the first trimester were positively associated with the risk of GDM. Additionally, there were joint effects between FFAs on GDM risk.
CONCLUSIONS: Elevated FFA levels in the first trimester increased the risk of GDM.

BACKGROUND: Risk scores facilitate the assessment of mortality risk in patients with community-acquired pneumonia (CAP). Despite their utilities, there is a scarcity of evidence comparing the various RS simultaneously. This study aims to evaluate and compare multiple risk scores reported in the literature for predicting 30-day mortality in adult patients with CAP.
METHODS: A retrospective cohort study on patients diagnosed with CAP was conducted across two hospitals in Colombia. The areas under receiver operating characteristic curves (ROC-curves) were calculated for the outcome of survival or death at 30 days using the scores obtained for each of the analyzed questionnaires.
RESULTS: A total of 7454 potentially eligible patients were included, with 4350 in the final analysis, of whom 15.2% (662/4350) died within 30 days. The average age was 65.4 years (SD: 21.31), and 59.5% (2563/4350) were male. Chronic kidney disease was 3.7% (9.2% vs. 5.5%; p < 0.001) (OR: 1.85) higher in subjects who died compared to those who survived. Among the patients who died, 33.2% (220/662) presented septic shock compared to 7.3% (271/3688) of the patients who survived (p < 0.001). The best performances at 30 days were shown by the following scores: PSI, SMART-COP and CURB 65 scores with the areas under ROC-curves of 0.83 (95% CI: 0.8-0.85), 0.75 (95% CI: 0.66-0.83), and 0.73 (95% CI: 0.71-0.76), respectively. The RS with the lowest performance was SIRS with the area under ROC-curve of 0.53 (95% CI: 0.51-0.56).
CONCLUSIONS: The PSI, SMART-COP and CURB 65, demonstrated the best diagnostic performances for predicting 30-day mortality in patients diagnosed with CAP. The burden of comorbidities and complications associated with CAP was higher in patients who died.

OBJECTIVE: In patients with atrial fibrillation (AF), recurrent AF and sinus rhythm during follow-up are determined by interactions between cardiovascular disease processes and rhythm-control therapy. Predictors of attaining sinus rhythm at follow-up are not well known.
METHODS: To quantify the interaction between cardiovascular disease processes and rhythm outcomes, 14 biomarkers reflecting AF-related cardiovascular disease processes in 1586 patients in the EAST-AFNET 4 biomolecule study (71 years old, 46% women) were quantified at baseline. Mixed logistic regression models including clinical features were constructed for each biomarker. Biomarkers were interrogated for interaction with early rhythm control. Outcome was sinus rhythm at 12 months. Results were validated at 24 months and in external datasets.
RESULTS: Higher baseline concentrations of three biomarkers were independently associated with a lower chance of sinus rhythm at 12 months: angiopoietin 2 (ANGPT2) (odds ratio [OR] 0.76 [95% confidence interval 0.65-0.89], p=0.001), bone morphogenetic protein 10 (BMP10) (OR 0.83 [0.71-0.97], p=0.017) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) (OR 0.73 [0.60-0.88], p=0.001). Analysis of rhythm at 24 months confirmed the results. Early rhythm control interacted with the predictive potential of NT-proBNP (pinteraction=0.033). The predictive effect of NT-proBNP was reduced in patients randomized to early rhythm control (usual care: OR 0.64 [0.51-0.80], p<0.001; early rhythm control: OR 0.90 [0.69-1.18], p=0.453). External validation confirmed that low concentrations of ANGPT2, BMP10 and NT-proBNP predict sinus rhythm during follow-up.
CONCLUSIONS: Low concentrations of ANGPT2, BMP10 and NT-proBNP identify patients with AF who are likely to attain sinus rhythm during follow-up. The predictive ability of NT-proBNP is attenuated in patients receiving rhythm control.

BACKGROUND: The CARDOT scores have been developed for prediction of respiratory complications after thoracic surgery. This study aimed to externally validate the CARDOT score and assess the predictive value of preoperative neutrophil-to-lymphocyte ratio (NLR) for postoperative respiratory complication.
METHODS: A retrospective cohort study of consecutive thoracic surgical patients at a single tertiary hospital in northern Thailand was conducted. The development and validation datasets were collected between 2006 and 2012 and from 2015 to 2021, respectively. Six prespecified predictive factors were identified, and formed a predictive score, the CARDOT score (chronic obstructive pulmonary disease, American Society of Anesthesiologists physical status, right-sided operation, duration of surgery, preoperative oxygen saturation on room air, thoracotomy), was calculated. The performance of the CARDOT score was evaluated in terms of discrimination by using the area under the receiver operating characteristic (AuROC) curve and calibration.
RESULTS: There were 1086 and 1645 patients included in the development and validation datasets. The incidence of respiratory complications was 15.7% (171 of 1086) and 22.5% (370 of 1645) in the development and validation datasets, respectively. The CARDOT score had good discriminative ability for both the development and validation datasets (AuROC 0.789 (95% CI 0.753-0.827) and 0.758 (95% CI 0.730-0.787), respectively). The CARDOT score showed good calibration in both datasets. A high NLR (≥ 4.5) significantly increased the risk of respiratory complications after thoracic surgery (P < 0.001). The AuROC curve of the validation cohort increased to 0.775 (95% CI 0.750-0.800) when the score was combined with a high NLR. The AuROC of the CARDOT score with the NLR showed significantly greater discrimination power than that of the CARDOT score alone (P = 0.008).
CONCLUSIONS: The CARDOT score showed a good discriminative performance in the external validation dataset. An addition of a high NLR significantly increases the predictive performance of CARDOT score. The utility of this score is valuable in settings with limited access to preoperative pulmonary function testing.

UNASSIGNED: A neonatal mortality prediction score can assist clinicians in making timely clinical decisions to save neonates\' lives by facilitating earlier admissions where needed. It can also help reduce unnecessary admissions.
UNASSIGNED: The study aimed to develop and validate a prognosis risk score for neonatal mortality within 28 days in public hospitals in the Amhara region, Ethiopia.
UNASSIGNED: The model was developed using a validated neonatal near miss assessment scale and a prospective cohort of 365 near-miss neonates in six hospitals between July 2021 and January 2022. The model\'s accuracy was assessed using the area under the receiver operating characteristics curve, calibration belt, and the optimism statistic. Internal validation was performed using a 500-repeat bootstrapping technique. Decision curve analysis was used to evaluate the model\'s clinical utility.
UNASSIGNED: In total, 63 of the 365 neonates died, giving a neonatal mortality rate of 17.3% (95% CI: 13.7-21.5). Six potential predictors were identified and included in the model: anemia during pregnancy, pregnancy-induced hypertension, gestational age less than 37 weeks, birth asphyxia, 5 min Apgar score less than 7, and birth weight less than 2500 g. The model\'s AUC was 84.5% (95% CI: 78.8-90.2). The model\'s predictive ability while accounting for overfitting via internal validity was 82%. The decision curve analysis showed higher clinical utility performance.
UNASSIGNED: The neonatal mortality predictive score could aid in early detection, clinical decision-making, and, most importantly, timely interventions for high-risk neonates, ultimately saving lives in Ethiopia.
Main findings: This prognosis risk score for neonatal mortality tested in Ethiopia had high performance accuracy and the decision curve analysis showed increased clinical utility performance.Added knowledge: The tool developed here can aid healthcare providers in identifying high-risk neonates and making timely clinical decisions to save lives.Global health impact for policy and action: The findings have the potential to be applied in local contexts to identify high-risk neonates and make treatment decisions that could improve child survival rates.

Background and Objective: Early discharge following robot-assisted kidney transplantation (RAKT) is a cost-effective strategy for reducing healthcare expenses while maintaining favorable short- and long-term prognoses. This study aims to identify predictors of postoperative delayed discharge in RAKT patients and develop a predictive model to enhance clinical outcomes. Materials and Methods: This retrospective study included 146 patients aged 18 years and older who underwent RAKT at a single tertiary medical center from August 2020 to January 2024. Data were collected on demographics, comorbidities, social and medical histories, preoperative labs, surgical information, intraoperative data, and postoperative outcomes. The primary outcome was delayed postoperative discharge (length of hospital stay > 7 days). Risk factors for delayed discharge were identified through univariate and multivariate regression analyses, leading to the development of a risk scoring system, the effectiveness of which was evaluated through receiver operating characteristic curve analysis. Results: 110 patients (74.8%) were discharged within 7 days post-transplant, while 36 (24.7%) remained hospitalized for 8 days or longer. Univariate and multivariate logistic regression analyses identified ABO incompatibility, BUN levels, anesthesia time, and vasodilator use as risk factors for delayed discharge. The RAKT score, incorporating these factors, demonstrated a predictive performance with a C-statistic of 0.789. Conclusions: This study identified risk factors for delayed discharge after RAKT and developed a promising risk scoring system for real-world clinical application, potentially improving postoperative outcome stratification in RAKT recipients.

Various scoring systems are available for COVID-19 risk stratification. This study aimed to validate their performance in predicting severe COVID-19 course in a large, heterogeneous Swiss cohort. Scores like the National Early Warning Score (NEWS), CURB-65, 4C mortality score (4C), Spanish Society of Infectious Diseases and Clinical Microbiology score (COVID-SEIMC), and COVID Intubation Risk Score (COVID-IRS) were assessed in patients hospitalized for COVID-19 in 2020 and 2021. Predictive accuracy for severe course (defined as all-cause in-hospital death or invasive mechanical ventilation (IMV)) was evaluated using receiver operating characteristic curves and the area under the curve (AUC). The new \'COVID-COMBI\' score, combining parameters from the top two scores, was also validated. This study included 1,051 patients (mean age 65 years, 60% male), with 162 (15%) experiencing severe course. Among the established scores, 4C had the best accuracy for predicting severe course (AUC 0.76), followed by COVID-IRS (AUC 0.72). COVID-COMBI showed significantly higher accuracy than all established scores (AUC 0.79, p = 0.001). For predicting in-hospital death, 4C performed best (AUC 0.83), and, for IMV, COVID-IRS performed best (AUC 0.78). The 4C and COVID-IRS scores were robust predictors of severe COVID-19 course, while the new COVID-COMBI showed significantly improved accuracy but requires further validation.

Gastric cancer (GC) is a leading cause of cancer-related mortality and is characterized by significant heterogeneity, highlighting the need for further studies aimed at personalized treatment strategies. Tumor angiogenesis is critical for tumor development and metastasis, yet its role in molecular subtyping and prognosis prediction remains underexplored. This study aims to identify angiogenesis-related subtypes and develop a prognostic model for GC patients. Using data from The Cancer Genome Atlas (TCGA), we performed consensus cluster analysis on differentially expressed angiogenesis-related genes (ARGs), identifying two patient subtypes with distinct survival outcomes. Differentially expressed genes between the subtypes were analyzed via Cox and LASSO regression, leading to the establishment of a subtype-based prognostic model using a machine learning algorithm. Patients were classified into high- and low-risk groups based on the risk score. Validation was performed using independent datasets (ICGC and GSE15459). We utilized a deconvolution algorithm to investigate the tumor immune microenvironment in different risk groups and conducted analyses on genetic profiling, sensitivity and combination of anti-tumor drug. Our study identified ten prognostic signature genes, enabling the calculation of a risk score to predict prognosis and overall survival. This provides critical data for stratified diagnosis and treatment upon patient admission, monitoring disease progression throughout the entire course, evaluating immunotherapy efficacy, and selecting personalized medications for GC patients.

OBJECTIVE: Emergent vascular imaging identifies a subset of patients requiring immediate specialized care (i.e. carotid stenosis > 50%, dissection or free-floating thrombus). However, most TIA patients do not have these findings, so it is inefficient to image all TIA patients in crowded emergency departments (ED). Our objectives were to derive and internally validate a clinical prediction score for clinically significant carotid artery disease in TIA patients.
METHODS: This was a planned secondary analysis of a prospective cohort study from 14 Canadian EDs. Among 11555 consecutive adult ED patients with TIA/minor stroke symptoms over 12 years, 9882 had vascular imaging and were included in the analysis. Our main outcome was clinically significant carotid artery disease, defined as extracranial internal carotid stenosis ≥ 50%, dissection, or thrombus in the internal carotid artery, with contralateral symptoms.
RESULTS: Of 9882 patients, 888 (9.0%) had clinically significant carotid artery disease. Logistic regression was used to derive a 13-variable reduced model. We simplified the model into a score (Symcard [Symptomatic carotid artery disease] Score), with suggested cut-points for high, medium, and low-risk stratification. A substantial portion (38%) of patients were classified as low-risk, 33.8% as medium risk, and 28.2% as high risk. At the low-risk cut-point, sensitivity was 92.9%, specificity 41.1%, and diagnostic yield 1.7%.
CONCLUSIONS: This simple score can predict carotid artery disease in TIA patients using readily available information. It identifies low-risk patients who can defer vascular imaging to an outpatient or specialty clinic setting. Medium-risk patients may undergo imaging immediately or with slight delay, depending on local resources. High-risk patients should undergo urgent vascular imaging.
RéSUMé: OBJECTIFS: L’imagerie vasculaire émergente permet d’identifier un sous-ensemble de patients nécessitant des soins spécialisés immédiats (c.-à-d. sténose carotidienne >50 %, dissection ou thrombus flottant). Cependant, la plupart des patients atteints de RTI ne présentent pas ces résultats, il est donc inefficace d’effectuer une imagerie de tous les patients atteints de RTI dans les services d’urgence (ER) surpeuplés. Nos objectifs étaient de calculer et de valider en interne un score de prédiction clinique pour la maladie carotide cliniquement significative chez les patients atteints d’une AIT MéTHODES: Il s’agissait d’une analyse secondaire planifiée d’une étude de cohorte prospective menée auprès de 14 DE canadiens. Parmi les 11555 patients adultes consécutifs atteints d’un EI présentant des symptômes d’AIT/AVC mineur au cours des 12 dernières années, 9882 ont reçu une imagerie vasculaire et ont été inclus dans l’analyse. Notre principal critère de jugement était la maladie carotide cliniquement significative, définie comme une sténose extracrânienne de la carotide interne à 50 %, une dissection ou un thrombus dans l’artère carotide interne, avec des symptômes contralatéraux. RéSULTATS: Sur 9882 patients, 888 (9,0 %) présentaient une maladie de l’artère carotide cliniquement significative. La régression logistique a été utilisée pour obtenir un modèle réduit à 13 variables. Nous avons simplifié le modèle en un score (Symcard [Symptomatic carotid artery disease] Score), avec des points de coupure suggérés pour la stratification à risque élevé, moyen et faible. Une proportion importante (38,0 %) des patients ont été classés à faible risque, 33,8 % à risque moyen et 28,2 % à risque élevé. Au seuil de faible risque, la sensibilité était de 92,9 %, la spécificité de 41,1 % et le rendement diagnostique de 1,7 %. CONCLUSIONS: Ce score simple permet de prédire la maladie de l’artère carotide chez les patients atteints d’AIT en utilisant des informations facilement disponibles. Il identifie les patients à faible risque qui peuvent reporter l’imagerie vasculaire à un établissement de consultation externe ou de spécialité. Les patients à risque moyen peuvent subir une imagerie immédiatement ou avec un léger délai, selon les ressources locales. Les patients à haut risque doivent subir une imagerie vasculaire urgente.

BACKGROUND: Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome.
RESULTS: We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017).
CONCLUSIONS: We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.