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.

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.

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.

Macrophages played an important role in the progression and treatment of head and neck squamous cell carcinoma (HNSCC). We employed weighted gene co-expression network analysis (WGCNA) to identify macrophage-related genes (MRGs) and classify patients with HNSCC into two distinct subtypes. A macrophage-related risk signature (MRS) model, comprising nine genes: IGF2BP2, PPP1R14C, SLC7A5, KRT9, RAC2, NTN4, CTLA4, APOC1, and CYP27A1, was formulated by integrating 101 machine learning algorithm combinations. We observed lower overall survival (OS) in the high-risk group and the high-risk group showed elevated expression levels in most of the immune checkpoint and human leukocyte antigen (HLA) genes, suggesting a strong immune evasion capacity. Correspondingly, TIDE score positively correlated with risk score, implying that high-risk tumors may resist immunotherapy more effectively. At the single-cell level, we noted macrophages in the tumor microenvironment (TME) predominantly stalled in the G2/M phase, potentially hindering epithelial-mesenchymal transition and playing a crucial role in the inhibition of tumor progression. Finally, the proliferation and migration abilities of HNSCC cells significantly decreased after the expression of IGF2BP2 and SLC7A5 reduced. It also decreased migration ability of macrophages and facilitated their polarization towards the M1 direction. Our study constructed a novel MRS for HNSCC, which could serve as an indicator for predicting the prognosis, immune infiltration and immunotherapy for HNSCC patients.

UNASSIGNED: Breast cancer (BC) is a highly common form of cancer that occurs in many parts of the world. However, early -stage BC is curable. Many patients with BC have poor prognostic outcomes owing to ineffective diagnostic and therapeutic tools. The ubiquitination system and associated proteins were found influencing the outcome of individuals with cancer. Therefore, developing a biomarker associated with ubiquitination genes to forecast BC patient outcomes is a feasible strategy.
UNASSIGNED: The primary goal of this work was to develop a novel risk score signature capable of accurately estimate the future outcome of patients with BC by targeting ubiquitinated genes.
UNASSIGNED: Univariate Cox regression analysis was conducted utilizing the E1, E2, and E3 ubiquitination-related genes in the GSE20685 dataset. Genes with p < 0.01 were screened again using the Non-negative Matrix Factorization (NMF) algorithm, and the resulting hub genes were composed of a risk score signature. Patients were categorized into two risk groups, and the predictive effect was tested using Kaplan-Meier (KM) and Receiver Operating Characteristic (ROC) curves. This risk score signature was later validated using multiple external datasets, namely TCGA-BRAC, GSE1456, GSE16446, GSE20711, GSE58812 and GSE96058. Immuno-microenvironmental, single-cell, and microbial analyses were also performed.
UNASSIGNED: The selected gene signature comprising six ubiquitination-related genes (ATG5, FBXL20, DTX4, BIRC3, TRIM45, and WDR78) showed good prognostic power in patients with BC. It was validated using multiple externally validated datasets, with KM curves showing significant differences in survival (p < 0.05). The KM curves also demonstrated superior predictive ability compared to traditional clinical indicators. Single-cell analysis revealed that Vd2 gd T cells were less abundantin the low-risk group, whereas patients in the high-risk group lacked myeloid dendritic cells. Tumor microbiological analysis revealed a notable variation in microorganism diversity between the high- and low-risk groups.
UNASSIGNED: This study established an risk score signature consisting of six ubiquitination genes, that can accurately forecast the outcome of patients with BC using multiple datasets. It can provide personalized and targeted assistance to provide the evaluation and therapy of individuals having BC.

UNASSIGNED: Colon cancer is associated with multiple levels of molecular heterogeneity. RNA processing converts primary transcriptional RNA to mature RNA, which drives tumourigenesis and its maintenance. The characterisation of RNA processing genes in colon cancer urgently needs to be elucidated.
UNASSIGNED: In this study, we obtained 1033 relevant samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to explore the heterogeneity of RNA processing phenotypes in colon cancer. Firstly, Unsupervised hierarchical cluster analysis detected 4 subtypes with specific clinical outcomes and biological features via analysis of 485 RNA processing genes. Next, we adopted the least absolute shrinkage and selection operator (LASSO) as well as Cox regression model with penalty to characterise RNA processing-related prognostic features.
UNASSIGNED: An RNA processing-related prognostic risk model based on 10 genes including FXR1, MFAP1, RBM17, SAGE1, SNRPA1, SRRM4, ADAD1, DDX52, ERI1, and EXOSC7 was identified finally. A composite prognostic nomogram was constructed by combining this feature with the remaining clinical variables including TNM, age, sex, and stage. Genetic variation, pathway activation, and immune heterogeneity with risk signatures were also analysed via bioinformatics methods. The outcomes indicated that the high-risk subgroup was associated with higher genomic instability, increased proliferative and cycle characteristics, decreased tumour killer CD8+ T cells and poorer clinical prognosis than the low-risk group.
UNASSIGNED: This prognostic classifier based on RNA-edited genes facilitates stratification of colon cancer into specific subgroups according to TNM and clinical outcomes, genetic variation, pathway activation, and immune heterogeneity. It can be used for diagnosis, classification and targeted treatment strategies comparable to current standards in precision medicine. It provides a rationale for elucidation of the role of RNA editing genes and their clinical significance in colon cancer as prognostic markers.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of the digestive system. RNA methylation plays an important role in tumorigenesis and metastasis, which could alter gene expression and even function at multiple levels, such as RNA splicing, stability, translocation, and translation. In this study, we aimed to conduct a comprehensive analysis of RNA methylation-related genes (RMGs) in HCC and their relationship with survival and clinical features.
METHODS: A retrospective analysis was performed using publicly available HCC-related datasets. The differentially expressed genes (DEGs) between HCC and controls were identified from TCGA-LlHC and intersected with RMGs to obtain differentially expressed RNA methylation-related genes (DERMGs). Regression analysis was used to screen for prognostic genes and construct risk models. Simultaneously, clinical, immune infiltration and therapeutic efficacy analyses were performed. Finally, multivariate cox regression was used to identify independent risk factors, and quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression levels of the core genes of the model.
RESULTS: A 21-gene risk model for HCC was established with excellent performance based on ROC curves and survival analysis. Risk scores correlated with tumor grade, pathologic T, and TNM stage. Immune infiltration analysis showed correlations with immune scores, 11 immune cells, and 30 immune checkpoints. Low-risk patients showed a higher susceptibility to immunotherapy. The risk score and TNM stage were independent prognostic factors. qRT-PCR confirmed higher expression of PRDM9, ALPP, and GAD1 in HCC.
CONCLUSIONS: This study identified RNA methylation-related signature genes in HCC and constructed a risk model that predicts patient outcomes and reflects the immune microenvironment. Prognostic genes are involved in complex regulatory mechanisms, which may be useful for cancer diagnosis, prognosis, and therapy.