Risk scores for prediction of paroxysmal atrial fibrillation after acute ischemic stroke or transient ischemic attack: A systematic review and meta-analysis

Introduction

Detection of paroxysmal atrial fibrillation (PAF) is crucial for secondary prevention in patients with recent strokes of unknown etiology. This systematic review and meta-analysis assess the predictive power of available risk scores for detecting new PAF after acute ischemic stroke (AIS).

Methods

PubMed, Embase, Scopus, and Web of Science databases were searched until September 2023 to identify relevant studies. A bivariate random effects meta-analysis model pooled data on sensitivity, specificity, and area under the curve (AUC) for each score. The QUADAS-2 tool was used for the quality assessment.

Results

Eventually, 21 studies with 18 original risk scores were identified. Age, left atrial enlargement, and NIHSS score were the most common predictive factors, respectively. Seven risk scores were meta-analyzed, with iPAB showing the highest pooled sensitivity and AUC (sensitivity: 89.4%, specificity: 74.2%, AUC: 0.83), and HAVOC having the highest pooled specificity (sensitivity: 46.3%, specificity: 82.0%, AUC: 0.82). Altogether, seven risk scores displayed good discriminatory power (AUC ≥0.80) with four of them (HAVOC, iPAB, Fujii, and MVP scores) being externally validated.

Conclusion

Available risk scores demonstrate moderate to good predictive accuracy and can help identify patients who would benefit from extended cardiac monitoring after AIS. External validation is essential before widespread clinical adoption.

Single-cell analysis extracted CAFs-related genes to established online app to predict clinical outcome and radiotherapy prognosis of prostate cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) play a significant role in regulating the clinical outcome and radiotherapy prognosis of prostate cancer (PCa). The aim of this study is to identify CAFs-related genes (CAFsRGs) using single-cell analysis and evaluate their potential for predicting the prognosis and radiotherapy prognosis in PCa.

METHODS

We acquire transcriptome and single-cell RNA sequencing (scRNA-seq) results of PCa and normal adjacent tissues from The GEO and TCGA databases. The "MCPcounter" and "EPIC" R packages were used to assess the infiltration level of CAFs and examine their correlation with PCa prognosis. ScRNA-seq and differential gene expression analyses were used to extract CAFsRGs. We also applied COX and LASSO analysis to further construct a risk score (CAFsRS) to assess biochemical recurrence-free survival (BRFS) and radiotherapy prognosis of PCa. The predictive efficacy of CAFsRS was evaluated by ROC curves and subgroup analysis. Finally, we integrated the CAFsRS gene signature with relevant clinical features to develop a nomogram, enhancing the predictive accuracy.

RESULTS

The abundance of CAFs is associated with a poor prognosis of PCa patients. ScRNA-seq and differential gene expression analysis revealed 323 CAFsRGs. After COX and LASSO analysis, we obtained seven CAFsRGs with prognostic significance (PTGS2, FKBP10, ENG, CDH11, COL5A1, COL5A2, and SRD5A2). Additionally, we established a risk score model based on the training set (n = 257). The ROC curve was used to confirm the performance of CAFsRS (The AUC values for 1, 3 and 5-year survival were determined to be 0.732, 0.773, and 0.775, respectively.). The testing set (n = 129), GSE70770 set (n = 199) and GSE116918 set (n = 248) revealed that the model exhibited exceptional predictive performance. This was also confirmed by clinical subgroup analysis. The violin plot demonstrated a statistically significant disparity in the CAFs infiltrations between the high-risk and low-risk groups of CAFsRS. Further analysis confirmed that both CAFsRS and T stage were independent prognostic factors for PCa. The nomogram was then established and its excellent predictive performance was demonstrated through calibration and ROC curves. Finally, we developed an online prognostic prediction app ( https://sysu-symh-cafsnomogram.streamlit.app/ ) to facilitate the practical application of the nomogram.

CONCLUSIONS

The prognostic prediction risk score model we constructed could accurately predict BRFS and radiotherapy prognosis PCa, which can provide new ideas for clinicians to develop personalized PCa treatment and follow-up programs.

Magnetic resonance imaging-based deep learning imaging biomarker for predicting functional outcomes after acute ischemic stroke

PURPOSE

Clinical risk scores are essential for predicting outcomes in stroke patients. The advancements in deep learning (DL) techniques provide opportunities to develop prediction applications using magnetic resonance (MR) images. We aimed to develop an MR-based DL imaging biomarker for predicting outcomes in acute ischemic stroke (AIS) and evaluate its additional benefit to current risk scores.

METHOD

This study included 3338 AIS patients. We trained a DL model using deep neural network architectures on MR images and radiomics to predict poor functional outcomes at three months post-stroke. The DL model generated a DL score, which served as the DL imaging biomarker. We compared the predictive performance of this biomarker to five risk scores on a holdout test set. Additionally, we assessed whether incorporating the imaging biomarker into the risk scores improved the predictive performance.

RESULTS

The DL imaging biomarker achieved an area under the receiver operating characteristic curve (AUC) of 0.788. The AUCs of the five studied risk scores were 0.789, 0.793, 0.804, 0.810, and 0.826, respectively. The imaging biomarker's predictive performance was comparable to four of the risk scores but inferior to one (p = 0.038). Adding the imaging biomarker to the risk scores improved the AUCs (p-values) to 0.831 (0.003), 0.825 (0.001), 0.834 (0.003), 0.836 (0.003), and 0.839 (0.177), respectively. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements (all p < 0.001).

CONCLUSIONS

Using DL techniques to create an MR-based imaging biomarker is feasible and enhances the predictive ability of current risk scores.

Prediction of severe infections in chronic lymphocytic leukemia: a simple risk score to stratify patients at diagnosis

Chronic Lymphocytic Leukemia (CLL) is well-known for increasing susceptibility to infections. Factors such as immune dysregulation, IGHV status, hypogammaglobulinemia, and patient comorbidity and treatment, contribute to higher infection rates and mortality. However, the impact of hypogammaglobulinemia on infection rates is controversial. We aimed to identify clinical and biological parameters linked to the risk of severe infectious events. Additionally, we set up a straightforward risk infection score to stratify CLL patients at diagnosis, thereby enabling the development of suitable infection prevention strategies. We retrospectively evaluated 210 unselected CLL patients diagnosed between 1988 and 2018. This evaluation encompassed demographics, Binet stage, immunoglobulin (Ig) levels, treatment history, comorbidities, and IGHV mutational status at diagnosis. The frequency and severity of infectious events were recorded. Analysis revealed that age, IGHV mutational status, Binet stage, and hypogammaglobulinemia were statistically associated with the Time to First Infection (TTFI) in univariate and multivariate analyses. Using hazard ratios from the multivariate analysis, we finally devised a risk scoring system that integrated age, IGHV mutational status, immunoglobulin levels, and Binet stage to stratify patients at diagnosis based on their specific infection risk. In our cohort, disease progression and infections were the leading cause of death. These findings pointed out the clinical need for a screening process strategic for defining infectious risk at the time of CLL diagnosis, with a significant enhancement in the clinical management of these patients.

Predicting Recurrence After Radical Surgery for High-Risk Renal Cell Carcinoma: Development and Internal Validation of the "TOWARDS" Score

BACKGROUND

Considering the reported greater benefits of immunotherapy and its unignorable adverse events in adjuvant therapy for high-risk renal cell carcinoma (hrRCC), accurate prediction may optimize drug use.

METHODS

The primary objective of this study was to generate a score-based prognostic model of recurrence-free survival in hrRCC. The study retrospectively evaluated 456 patients at two institutions who underwent radical surgery for nonmetastatic pT3-4 and/or N1-2 or pT2 and G4 disease. Clinical variables deemed universally available were selected through backward stepwise analysis and fitted by a multivariable Cox proportional hazards regression model. A point-based score was derived from regression coefficients. Discrimination, calibration, and decision curve analyses were conducted to evaluate predictive performance. Internal validation with bootstrapping was performed to correct for optimism.

RESULTS

The mean follow-up period was 55.3 months, and the median follow-up period was 28.0 months. During the follow-up period, the recurrence rate was 48.2% (n = 220) during a median of 75.7 months. Stepwise variable selection retained age, Eastern Cooperative Oncology Group (ECOG) performance status, presence or absence of symptoms, size of the primary tumor, pathologic T stage, pathologic N stage, tumor grade, and histology. Subsequently, the TOWARDS score (range 0-53) was developed from these variables. Internal validation showed an optimism-corrected C-index of 0.723 and a calibration slope of 0.834. The decision curve analysis showed the superiority of this score over the University of California, Los Angeles (UCLA) Integrated Staging System and GRade, Age, Nodes, and Tumor score.

CONCLUSIONS

The authors' novel TOWARDS scoring model had good accuracy for predicting disease recurrence in patients with hrRCC, and the clinical practicability was superior to that of the existing models.

Validation of a Stenotrophomonas maltophilia bloodstream infection prediction score in the hematologic malignancy population

Stenotrophomonas maltophilia (SM) bloodstream infections (BSIs) contribute to significant mortality in hematologic malignancy (HM) and hematopoietic stem cell transplantation (HSCT) patients. A risk score to predict SM BSI could reduce time to appropriate antimicrobial therapy (TTAT) and improve patient outcomes. A single center cohort study of hospitalized adults with HM/HSCT was conducted. Patients had ≥ 1 blood culture with a Gram-negative (GN) organism. A StenoSCORE was calculated for each patient. The StenoSCORE2 was developed using risk factors for SM BSI identified via logistic regression. Receiver operating characteristic (ROC) curves were plotted. Sensitivity and specificity for the StenoSCORE and StenoSCORE2 were calculated. Thirty-six SM patients and 534 non-SM patients were assessed. A StenoSCORE ≥ 33 points was 80% sensitive, 68% specific, and accurately classified 69% of GN BSIs. StenoSCORE2 variables included acute leukemia, prolonged neutropenia, mucositis, ICU admission, recent meropenem and/or cefepime exposure. The StenoSCORE2 performed better than the StenoSCORE (ROC AUC 0.84 vs. 0.77). A StenoSCORE2 ≥ 4 points was 86% sensitive, 76% specific, and accurately classified 77% of GN BSIs. TTAT was significantly longer for patients with SM BSI compared with non-SM BSI (45.16 h vs. 0.57 h; p < 0.0001). In-hospital and 28-day mortality were significantly higher for patients with SM BSI compared to non-SM BSI (58.3% vs. 18.5% and 66.7% vs. 26.4%; p-value < 0.0001). The StenoSCORE and StenoSCORE2 performed well in predicting SM BSIs in patients with HM/HSCT and GN BSI. Clinical studies evaluating whether StenoSCORE and/or StenoSCORE2 implementation improves TTAT and clinical outcomes are warranted.

Prognostic Analysis of Lactic Acid Metabolism Genes in Oral Squamous Cell Carcinoma

OBJECTIVES

Oral squamous cell carcinoma (OSCC) is the most common malignant tumour in the oral and maxillofacial region. Lactic acid accumulation in the tumour microenvironment (TME) has gained attention for its dual role as an energy source for cancer cells and an activator of signalling pathways crucial to tumour progression. This study aims to reveal the impact of lactate-related genes (LRGs) on the prognosis, TME, and immune characteristics of OSCC, with the ultimate goal of developing a novel prognostic model.

METHODS

Unsupervised clustering analysis of LRGs in OSCC patients from The Cancer Genome Atlas database was conducted to evaluate and compare TME, immune features, and clinical characteristics across various lactate subtypes. A refined prognostic model was developed through the application of Cox and Least absolute shrinkage and selection operator (LASSO) regression techniques. External validation sets were then utilised to improve model accuracy, along with a detailed correlation analysis of drug sensitivity.

RESULTS

The Cancer Genome Atlas-OSCC patients were categorised into 4 distinct lactate subtypes based on LRGs. Notably, patients in subtype 1 and subtype 2 exhibited the least and most favourable prognoses, respectively. Subtype 1 patients showed elevated expression levels of immune checkpoint genes. Further analysis identified 1086 genes with significant expression differences between cancer and noncancer tissues, as well as between subtype 1 and subtype 2 patients. Selected genes for the prognostic model included ZNF662, CGNL1, VWCE, and ZFP42. The high-risk group defined by this model had a significantly poorer prognosis (P < .0001) and functioned as an independent prognostic factor (P < .001), accurately predicting 1-, 3-, and 5-year survival rates. Additionally, individuals in the high-risk category exhibited heightened sensitivity to chemotherapy drugs such as AZ6102 and Venetoclax.

CONCLUSIONS

The predictive model based on the genes ZNF662, CGNL1, VWCE, and ZFP42 can serve as a reliable biomarker, providing accurate prognostic predictions for OSCC patients and potential opportunities for pharmaceutical interventions.

Early Detection and Prediction of Anthracycline-Induced Cardiotoxicity - A Prospective Cohort Study

BACKGROUND

In the present study, we aimed to investigate whether early cardiac biomarker alterations and echocardiographic parameters, including left atrial (LA) strain, can predict anthracycline-induced cardiotoxicity (AIC) and thus develop a predictive risk score.Methods and Results: The AIC registry is a prospective, observational cohort study designed to gather serial echocardiographic and biomarker data before and after anthracycline chemotherapy. Cardiotoxicity was defined as a reduction in left ventricular ejection fraction (LVEF) ≥10 percentage points from baseline and <55%. In total, 383 patients (93% women; median age, 57 [46-66] years) completed the 2-year follow-up; 42 (11.0%) patients developed cardiotoxicity (median time to onset, 292 [175-440] days). Increases in cardiac troponin T (TnT) and B-type natriuretic peptide (BNP) and relative reductions in the left ventricular global longitudinal strain (LV GLS) and LA reservoir strain [LASr] at 3 months after anthracycline administration were independently associated with subsequent cardiotoxicity. A risk score containing 2 clinical variables (smoking and prior cardiovascular disease), 2 cardiac biomarkers at 3 months (TnT ≥0.019 ng/mL and BNP ≥31.1 pg/mL), 2 echocardiographic variables at 3 months (relative declines in LV GLS [≥6.5%], and LASr [≥7.5%]) was generated.

CONCLUSIONS

Early decline in LASr was independently associated with subsequent cardiotoxicity. The AIC risk score may provide useful prognostication in patients receiving anthracyclines.

Comparison of the performance of cardiovascular risk prediction tools in rural India: the Rishi Valley Prospective Cohort Study

AIMS

We compared the performance of cardiovascular risk prediction tools in rural India.

METHODS AND RESULTS

We applied the World Health Organization Risk Score (WHO-RS) tools, Australian Risk Score (ARS), and Global risk (Globorisk) prediction tools to participants aged 40-74 years, without prior cardiovascular disease, in the Rishi Valley Prospective Cohort Study, Andhra Pradesh, India. Cardiovascular events during the 5-year follow-up period were identified by verbal autopsy (fatal events) or self-report (non-fatal events). The predictive performance of each tool was assessed by discrimination and calibration. Sensitivity and specificity of each tool for identifying high-risk individuals were assessed using a risk score cut-off of 10% alone or this 10% cut-off plus clinical risk criteria of diabetes in those aged >60 years, high blood pressure, or high cholesterol. Among 2333 participants (10 731 person-years of follow-up), 102 participants developed a cardiovascular event. The 5-year observed risk was 4.4% (95% confidence interval: 3.6-5.3). The WHO-RS tools underestimated cardiovascular risk but the ARS overestimated risk, particularly in men. Both the laboratory-based (C-statistic: 0.68 and χ2: 26.5, P = 0.003) and non-laboratory-based (C-statistic: 0.69 and χ2: 20.29, P = 0.003) Globorisk tools showed relatively good discrimination and agreement. Addition of clinical criteria to a 10% risk score cut-off improved the diagnostic accuracy of all tools.

CONCLUSION

Cardiovascular risk prediction tools performed disparately in a setting of disadvantage in rural India, with the Globorisk performing best. Addition of clinical criteria to a 10% risk score cut-off aids assessment of risk of a cardiovascular event in rural India.

LAY SUMMARY

In a cohort of people without prior cardiovascular disease, tools used to predict the risk of cardiovascular events varied widely in their ability to accurately predict who would develop a cardiovascular event.The Globorisk, and to a lesser extent the ARS, tools could be appropriate for this setting in rural India.Adding clinical criteria, such as sustained high blood pressure, to a cut-off of 10% risk of a cardiovascular event within 5 years could improve identification of individuals who should be monitored closely and provided with appropriate preventive medications.

Impact of a patient risk scoring tool pilot on prioritization of pharmacy-conducted medication histories

BACKGROUND

Approximately 50-70% of patients have at least one medication discrepancy in their initial medication history. These discrepancies can lead to errors on admission and discharge orders and have the potential to cause patient harm and incur added costs associated with increased length of stay and readmission rates. Several studies have demonstrated improved medication history accuracy with pharmacy-conducted services, but variations in practice exist due to challenges with workflow and resources.

OBJECTIVE

This study aims to assess the impact of implementing a patient risk scoring tool for the prioritization of medication history review by pharmacy staff.

METHODS

This quasi-experimental, single-center study was conducted at a 948-bed academic medical center as a pilot study with the medication history team which consists of pharmacists and technicians in the emergency department (ED). The endpoints assessed included pharmacy completion rate of patients in the high-risk category, overall pharmacy conducted medication history rate, and the proportion of medication discrepancies identified after reconciliation.

RESULTS

The number of medication histories completed by pharmacy (n=849) decreased by 5.7% in the post-intervention period (P=0.002). Between the pre- and post-intervention period, there were less low risk patients being captured by pharmacy (89.7% to 59.9%, respectively). There was also an increase in the number of medium-risk (Δ=25.4%) and high-risk patients (Δ=4.4%) being captured by pharmacy staff (P<0.017, α=0.017).

CONCLUSION

Use of a risk scoring tool allowed pharmacy staff to prioritize workflow and capture more high-risk patients.

An exosome mRNA-related gene risk model to evaluate the tumor microenvironment and predict prognosis in hepatocellular carcinoma

BACKGROUND

The interplay between exosomes and the tumor microenvironment (TME) remains unclear. We investigated the influence of exosomes on the TME in hepatocellular carcinoma (HCC), focusing on their mRNA expression profile.

METHODS

mRNA expression profiles of exosomes were obtained from exoRBase. RNA sequencing data from HCC patients' tumors were acquired from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). An exosome mRNA-related risk score model of prognostic value was established. The patients in the two databases were divided into high- and low-risk groups based on the median risk score value, and used to validate one another. Functional enrichment analysis was performed based on a differential gene prognosis model (DGPM). CIBERSORT was used to assess the abundance of immune cells in the TME. The correlation between the expression levels of immune checkpoint-related genes and DGPM was analyzed alongside the prediction value to drug sensitivity.

RESULTS

A prognostic exosome mRNA-related 4-gene signature (DYNC1H1, PRKDC, CCDC88A, and ADAMTS5) was constructed and validated. A prognostic nomogram had prognostic ability for HCC. The genes for this model are involved in extracellular matrix, extracellular matrix (ECM)-receptor interaction, and the PI3K-Akt signaling pathway. Expression of genes here had a positive correlation with immune cell infiltration in the TME.

CONCLUSIONS

Our study results demonstrate that an exosome mRNA-related risk model can be established in HCC, highlighting the functional significance of the molecules in prognosis and risk stratification.

Identification of cellular senescence-associated genes as new biomarkers for predicting the prognosis and immunotherapy response of non-small cell lung cancer and construction of a prognostic model

Background

Globally, lung carcinoma remains the leading cause of death, with its associated morbidity and mortality rates remaining elevated. Despite the slow advancement of treatment, the outlook remains bleak. Cellular senescence represents a halt in the cell cycle, encompassing a range of physiological and pathological activities, along with diverse phenotypic alterations, including variations in secretory phenotype, macromolecular harm, and metabolic disturbances. Research has revealed its vital function in the formation and growth of tumors. This study aimed to examine cellular senescence-related mRNAs linked to the outlook of non-small cell lung cancer (NSCLC) and to formulate a predictive risk framework for NSCLC.

Methods

We acquired the NSCLC expression data from The Cancer Genome Atlas (TCGA) to examine mRNAs linked to cellular senescence. Both single-variable and multiple-variable cox proportion risk assessments were utilized to determine the traits of cellular senescence-related mRNAs linked to NSCLC prognosis. Subsequently, the prognostic model for cellular senescence-related mRNAs was integrated with clinical-pathological characteristics to create a prognostic nomogram. Furthermore, the study delved into the risk-oriented predictive model, examining immune infiltration and responses to immunotherapy among both high and low-risk categories.

Results

Utilizing both univariate and multivariate Cox proportion risk assessments, a risk model comprising 12 mRNAs associated with cellular aging was ultimately developed: IGFBP1, TLR3, WT1, ID1, PTTG1, ERRFI1, HEPACAM, MAP2K3, RAD21, NANOG, PRKCD, SOX5. Univariate analysis and multivariate analysis illustrated that the risk score served as a standalone indicator for prognosis, and the hazard ratio (HR) of the risk score were 1.182 (1.139-1.226) (p < 0.001) and 1.162 (1.119 - 1.206) (p < 0.001), respectively. Individual prognoses were forecasted using nomogram, c-index, and principal component analysis (PCA). Furthermore, the risk-oriented model revealed notable statistical variances in immune infiltration and response to immunotherapy among the high and low risk categories.

Conclusions

This study shows that mRNAs related to cell senescence associated with prognosis are reliable predictors of NSCLC immunotherapy reaction and prognosis.

Predictive value of a novel digital risk calculator to determine early patient outcomes after major surgery: a proof-of-concept pilot study

BACKGROUND

A structured risk assessment of patients with validated and evidence-based tools can help to identify modifiable factors before major surgeries. The Protego Maxima trial investigated the value of a new digitized risk assessment tool that combines tools which can be easily used and implemented in the clinical workflow by doctors and qualified medical staff. The hypothesis was that the structured assessment and risk-grouping is predictive of short-term surgical quality reflected by complications and overall survival.

METHODS

The Protego Maxima Trial was a prospective cohort analysis of patients undergoing major surgery (visceral, thoracic, urology, vascular and gynecologic surgeries) as key inclusion criterion and the absence of an acute or acute on chronically decompensated pulmo-cardiovascular decompensation. Patients were risk-scored with the software (The Prehab App) that includes a battery of evidence-based risk assessment tools that allow a structured risk assessment. The data were grouped to predefined high and low risk groups and aggregate and individual scores. The primary outcome was to validate the predictive value of the RAI score and the TUG for overall survival in the high and low risk groups. Secondary outcomes were surgical outcomes at 90-days after surgery (overall survival, Clavien-Dindo (CD) 1-5 (all complications), and CD 3-5 (major complications)). The study was carried out in accordance with the DIN ISO 14,155, and the medical device regulation (MDR) at Frankfurt University Hospital between March 2022 and January 2023.

RESULTS

In total 267 patients were included in the intention to treat analysis. The mean age was 62.1 ± 12.4 years. Patients with a RAI score > 25 and/or a timed up and go (TUG) > 8 s had a higher risk for mortality at 90 days after surgery. The low-risk group predicted beneficial outcome and the high-risk group predicted adverse outcome in the ROC analysis (Area Under the Curve Receiver Operator Characteristics: AUROC > 0.800; p = 0.01). Risk groups (high vs. low) showed significant differences for 90-day survival (99.4% vs. 95.5%; p = 0.04) and major complications (16.4% vs. 32.4%; p < 0.001).

CONCLUSION

The proof-of-concept trial showed that a risk assessment with 'The Prehab App' may be viable to estimate the preoperative risk for mortality and major complications before major surgeries. The overall performance in this initial set of data indicated a certain reliability of the scoring and risk grouping, especially of the RAI score and the TUG. A larger data set will be required to proof the generalizability of the risk scoring to every subgroup and may be fostered by artificial intelligence approaches.

TRIAL REGISTRATION

Ethics number: 2021-483-MDR/MPDG-zuständig monocentric; The Federal Institute for Pharmaceuticals and Medical Devices/BfArM, reference number: 94.1.04-5660-13655; Eudamed: CIV-21-07-0307311; German Clinical Trial Registry: DRKS 00026985.

Predictors of heart failure readmission and all-cause mortality in patients with acute heart failure

BACKGROUND

Predischarge risk stratification of patients with acute heart failure (AHF) could facilitate tailored treatment and follow-up, however, simple scores to predict short-term risk for HF readmission or death are lacking.

METHODS

We sought to develop a congestion-focused risk score using data from a prospective, two-center observational study in adults hospitalized for AHF. Laboratory data were collected on admission. Patients underwent physical examination, 4-zone, and in a subset 8-zone, lung ultrasound (LUS), and echocardiography at baseline. A second LUS was performed before discharge in a subset of patients. The primary endpoint was the composite of HF hospitalization or all-cause death.

RESULTS

Among 350 patients (median age 75 years, 43% women), 88 participants (25%) were hospitalized or died within 90 days after discharge. A stepwise Cox regression model selected four significant independent predictors of the composite outcome, and each was assigned points proportional to its regression coefficient: NT-proBNP ≥2000 pg/mL (admission) (3 points), systolic blood pressure < 120 mmHg (baseline) (2 points), left atrial volume index ≥60 mL/m2 (baseline) (1 point) and ≥ 9 B-lines on predischarge 4-zone LUS (3 points). This risk score provided adequate risk discrimination for the composite outcome (HR 1.48 per 1 point increase, 95% confidence interval: 1.32-1.67, p < 0.001, C-statistic: 0.70). In a subset of patients with 8-zone LUS data (n = 176), results were similar (C-statistic: 0.72).

CONCLUSIONS

A four-variable risk score integrating clinical, laboratory and ultrasound data may provide a simple approach for risk discrimination for 90-day adverse outcomes in patients with AHF if validated in future investigations.

Stroke risk prediction models: A systematic review and meta-analysis

BACKGROUND

Prediction algorithms/models are viable methods for identifying individuals at high risk of stroke across diverse populations for timely intervention. However, evidence summarizing the performance of these models is limited. This study examined the performance and weaknesses of existing stroke risk-score-prediction models (SRSMs) and whether performance varied by population and region.

METHODS

PubMed, EMBASE, and Web of Science were searched for articles on SRSMs from the earliest records until February 2022. The Prediction Model Risk of Bias Assessment Tool was used to assess the quality of eligible articles. The performance of the SRSMs was assessed by meta-analyzing C-statistics (0 and 1) estimates from identified studies to determine the overall pooled C-statistics by fitting a linear restricted maximum likelihood in a random effect model.

RESULTS

Overall, 17 articles (cohort study = 15, nested case-control study = 2) comprising 739,134 stroke cases from 6,396,594 participants from diverse populations/regions (Asia; n = 8, United States; n = 3, and Europe and the United Kingdom; n = 6) were eligible for inclusion. The overall pooled c-statistics of SRSMs was 0.78 (95%CI: 0.75, 0.80; I2 = 99.9%), with most SRSMs developed using cohort studies; 0.78 (95%CI: 0.75, 0.80; I2 = 99.9%). The subgroup analyses by geographical region: Asia [0.81 (95%CI: 0.79, 0.83; I2 = 99.8%)], Europe and the United Kingdom [0.76 (95%CI: 0.69, 0.83; I2 = 99.9%)] and the United States only [0.75 (95%CI: 0.72, 0.78; I2 = 73.5%)] revealed relatively indifferent performances of SRSMs.

CONCLUSION

SRSM performance varied widely, and the pooled c-statistics of SRSMs suggested a fair predictive performance, with very few SRSMs validated in independent population group(s) from diverse world regions.

Prognostic impact of MitraScore in elderly Asian patients with heart failure: sub-analysis of FRAGILE-HF

AIMS

MitraScore is a novel, simple, and manually calculatable risk score developed as a prognostic model for patients undergoing transcatheter edge-to-edge repair (TEER) for mitral regurgitation. As its components are considered prognostic in heart failure (HF), we aimed to investigate the usefulness of the MitraScore in HF patients.

METHODS AND RESULTS

We calculated MitraScore for 1100 elderly patients (>65 years old) hospitalized for HF in the prospective multicentre FRAGILE-HF study and compared its prognostic ability with other simple risk scores. The primary endpoint was all-cause deaths, and the secondary endpoints were the composite of all-cause deaths and HF rehospitalization and cardiovascular deaths. Overall, the mean age of 1100 patients was 80 ± 8 years, and 58% were men. The mean MitraScore was 3.2 ± 1.4, with a median of 3 (interquartile range: 2-4). A total of 326 (29.6%), 571 (51.9%), and 203 (18.5%) patients were classified into low-, moderate-, and high-risk groups based on the MitraScore, respectively. During a follow-up of 2 years, 226 all-cause deaths, 478 composite endpoints, and 183 cardiovascular deaths were observed. MitraScore successfully stratified patients for all endpoints in the Kaplan-Meier analysis (P < 0.001 for all). In multivariate analyses, MitraScore was significantly associated with all endpoints after covariate adjustments [adjusted hazard ratio (HR) (95% confidence interval): 1.22 (1.10-1.36), P < 0.001 for all-cause deaths; adjusted HR 1.17 (1.09-1.26), P < 0.001 for combined endpoints; and adjusted HR 1.24 (1.10-1.39), P < 0.001 for cardiovascular deaths]. The Hosmer-Lemeshow plot showed good calibration for all endpoints. The net reclassification improvement (NRI) analyses revealed that the MitraScore performed significantly better than other manually calculatable risk scores of HF: the GWTG-HF risk score, the BIOSTAT compact model, the AHEAD score, the AHEAD-U score, and the HANBAH score for all-cause and cardiovascular deaths, with respective continuous NRIs of 0.20, 0.22, 0.39, 0.39, and 0.29 for all-cause mortality (all P-values < 0.01) and 0.20, 0.22, 0.42, 0.40, and 0.29 for cardiovascular mortality (all P-values < 0.02).

CONCLUSIONS

MitraScore developed for patients undergoing TEER also showed strong discriminative power in HF patients. MitraScore was superior to other manually calculable simple risk scores and might be a good choice for risk assessment in clinical practice for patients receiving TEER and those with HF.

Circulating ceramide levels and ratios in Emirati youth under 18 years: associations with cardiometabolic risk factors

BACKGROUND

Circulating ceramide (Cer) drives various pathological processes associated with cardiovascular diseases, liver illness, and diabetes mellitus. Although recognized as predictors of cardiometabolic diseases (CMD) in research and clinical settings, their potential for predicting CMD risk in individuals under 18 remains unexplored.

OBJECTIVES

This study was designed to utilize Liquid Chromatography-Mass Spectrometry (LC-MS/MS) methodology to determine the biological reference ranges for Cer in plasma samples of Emirati children and develop a risk assessment score (CERT-1) based on Cer concentrations.

METHODS

Using LC-MS/MS, we developed a method to measure five Cer species in plasma samples of 582 Emirati participants aged 5-17. We used the circulating concentrations of these Cer to determine their reference intervals in this population. We employed traditional statistical analyses to develop a risk score (CERT-1) and assess the association between Cer levels and conventional biomarkers of CMD.

RESULTS

We validated a high-throughput methodology using LC-MS/MS to quantify five Cer species in human plasma. Reference values for this population (n = 582) were quantified: CerC16:0 (0.12-0.29 µmol/L), CerC18:0 (0.019-0.067 µmol/L), CerC22:0 (0.102-0.525 µmol/L), CerC24:0 (0.65-1.54 µmol/L) and CerC24:1 (0.212-0.945 µmol/L). We devised a risk assessment score (CERT-1) based on plasma Cer content in the study participants, showing that 72.5% have low to moderate risk and 9.3% are at a higher risk of developing CMD. Our analyses also revealed a significant correlation (P < 0.05) between this score and the conventional risk factors linked to CMD, indicating its potential clinical implication.

CONCLUSION

This study presents a clinical-scaled LC-MS/MS methodology for assessing clinically relevant Cer, setting reference ranges, and developing a risk score (CERT-1) for young Emirati individuals. Our findings can enhance primary risk prediction and inform the management and follow-up of CMD from an early age.

Biomarkers of Physical and Mental Health for Prediction of Parkinson's Disease: A Population-Based Study from 15 European Countries

OBJECTIVES

Early diagnosis of Parkinson's disease (PD) is critical for optimal treatment. However, the predictive potential of physical and mental health in PD is poorly characterized.

METHODS

We evaluated the potential of multiple demographic, physical, and mental factors in predicting the future onset of PD in older adults aged 50 years or older from 15 European countries. Individual study participants were followed over four waves of the Survey of Health, Ageing, and Retirement in Europe (SHARE) from 2013-2020.

RESULTS

Of 57,980 study participants, 442 developed PD during the study period. We identified male sex and advancing age from the sixth decade of life onward as significant predictors of future PD. Among physical factors, a low handgrip strength (HGS; men <27 kg, women <16 kg), being bothered by frailty, and recent falls were significantly associated with future PD. Among mental factors, a higher depression (Euro-D depression score >6) emerged as an independent predictor of future PD. Finally, the presence of hypertension or Alzheimer's disease (AD) increases the risk of future PD.

CONCLUSIONS

Altogether, male sex, advancing age, low HGS, frailty, depression, hypertension, and AD were identified as critical risk factors for future PD. Our results may be useful in the early identification and treatment of populations at risk for PD.

Predicting exacerbations in COPD in the Danish general population

BACKGROUND

Risk of exacerbations in individuals with mild chronic obstructive pulmonary disease (COPD) in the general population is less well described than in more advanced disease. We hypothesized that in addition to history of previous exacerbation also other clinical characteristics predict future moderate exacerbations.

METHODS

In 96,462 individuals in the Copenhagen General Population Study, we identified 3175 with clinical COPD defined as forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) < 0.70 and FEV1 <80% predicted in symptomatic individuals without asthma. We estimated the importance of age, sex, FEV1, modified Medical Research Council (mMRC) dyspnea scale, chronic bronchitis, exacerbation history, comorbidities, cohabitation, body mass index, smoking, and blood eosinophils for the 1-year and 3-year future risk of moderate COPD exacerbations and developed a prediction tool for future exacerbations in COPD in the general population based on easily available clinical information.

RESULTS

We observed 265 exacerbations in 2543 maintenance treatment naïve individuals with COPD and 197 exacerbations in 632 individuals with COPD on maintenance treatment. In the maintenance treatment naïve group, exacerbation history (hazard ratio (HR): 8.53), low FEV1 (HR: 4.82 for <30% predicted versus 50-79% predicted), and higher age (HR: 1.46 for ≥75 years versus <65 years) were significant predictors of future exacerbations. In the group on maintenance treatment, male sex and mMRC ≥2 also predicted higher risk with borderline significance.

CONCLUSIONS

In addition to exacerbation history also higher age and lower FEV1 predict future exacerbation risk in COPD in the general population.

Diagnostic value of liver stiffness measurement combined with risk scores for esophagogastric variceal bleeding in patients with hepatitis B cirrhosis

PURPOSE

To assess the predictive value of liver stiffness measurement (LSM) and three bleeding risk scoring systems for esophagogastric varices bleeding (EGVB) in patients with hepatitis B cirrhosis during hospitalization.

METHODS

In this study, 210 patients who had hepatitis B cirrhosis were selected as the subjects. They were categorized into two groups based on whether EGVB occurred during hospitalization: a bleeding group (70 cases) and a non-bleeding group (140 cases). Logistic regression was used to analyze the factors related to the occurrence of EGVB, and the diagnostic performance was evaluated using a receiver operating characteristic (ROC) curve.

RESULTS

Significant differences were observed between the two groups in systolic blood pressure, platelet count, albumin, urea nitrogen, LSM, pre-endoscopic Rockall score (PRS), Glasgow-Blatchford score (GBS), and AIMS65 score (P < 0.05). The correlation analysis showed that LSM had significant positive relationship with PRS, GBS and AIMS65 score. Logistic regression analysis revealed that LSM and GBS score were independent risk factors for EGVB occurrence during hospitalization. ROC curve analysis showed that the combined prediction model of LSM and GBS score had the best prediction performance for EGVB occurrence, with an ROC curve area of 0.811, which was significantly better than the three risk scoring systems (P < 0.05), but similar to the predicted value of LSM (P = 0.335).

CONCLUSIONS

The combination of LSM and GBS score can significantly improve the predictive efficacy of EGVB occurrence in patients with hepatitis B cirrhosis during hospitalization, which has important clinical significance for patients' prognosis.

Exploring prognostic microbiota markers in patients with endometrial carcinoma: Intratumoral insights

Endometrial cancer, a leading gynecological malignancy, is profoundly influenced by the uterine microbiota, a key factor in disease prognosis and treatment. Our study underscores the distinct microbial compositions in endometrial cancer compared to adjacent non-cancerous tissues, revealing a dominant presence of p_Actinobacteria in cancerous tissues as opposed to p_Firmicutes in surrounding areas. Through comprehensive analysis, we identified 485 unique microorganisms in cancer tissues, 26 of which correlate with patient prognosis. Employing univariate Cox regression and LASSO regression analyses, we devised a microbial risk scoring model, effectively stratifying patients into high and low-risk categories, thereby providing predictive insights into their overall survival. We further developed a nomogram that incorporates the microbial risk score along with age, grade, and clinical stage, significantly enhancing the accuracy of our clinical prediction model for endometrial cancer. Moreover, our study delves into the differential immune landscapes of high-risk and low-risk patients. The low-risk group displayed a higher prevalence of activated B cells and increased T cell co-stimulation, indicative of a robust immune response. Conversely, high-risk patients showed elevated tumor immune dysfunction and exclusion scores, suggesting less favorable outcomes in immunotherapy. Notably, the efficacy of IPS-CTLA4 and PD1/PD-L1/PD-L2 blockers was substantially higher in the low-risk group, pointing to a more responsive immunotherapeutic approach. In summary, our research elucidates the unique microbial patterns in endometrial cancer and adjacent tissues, and establishes both a microbial risk score model and a clinical prediction nomogram. These findings highlight the potential of uterine microbiota as a biomarker for customizing treatment strategies, enabling precise interventions for high-risk patients while preventing overtreatment in low-risk cases. This study emphasizes the microbiota's role in tailoring immunotherapy, offering a novel perspective in the treatment and prognosis of endometrial cancer. Significantly, our study's expansive sample analysis from the TCGA-UCEC cohort, employing linear discriminant analysis effect size methodology, not only validates but also enhances our understanding of the microbiota's role in endometrial cancer, paving the way for novel diagnostic and therapeutic approaches in its management.

[Usefulness of the MPB-INFURG-SEMES model to predict bacteremia in the patient with solid tumor in the Emergency Department]

OBJECTIVE

To analyse a new risk score to predict bacteremia (MPB-INFURG-SEMES) in the patients with solid tumor attender for infection in the emergency departments (ED).

METHODS

Prospective, multicenter observational cohort study of blood cultures (BC) obtained from adult patients with solid neoplasia treated in 63 EDs for infection from November 1, 2019, to March 31, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The prognostic performance for true bacteremia was calculated with the chosen cut-off for getting the sensitivity, specificity, positive predictive value and negative predictive value.

RESULTS

A total of 857 blood samples wered cultured. True cases of bacteremia were confirmed in 196 (22.9%). The remaining 661 cultures (77.1%) wered negative. And, 42 (4.9%) were judged to be contaminated. The model's area under the receiver operating characteristic curve was 0.923 (95% CI,0.896-0.950). The prognostic performance with a model's cut-off value of ≥ 5 points achieved 95.74% (95% CI, 94,92-96.56) sensitivity, 76.06% (95% CI, 75.24-76.88) specificity, 53.42%(95% CI, 52.60-54.24) positive predictive value and 98.48% (95% CI, 97.66- 99.30) negative predictive value.

CONCLUSIONS

The MPB-INFURG-SEMES score is useful for predicting bacteremia in the adults patients with solid tumor seen in the ED.

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer

BACKGROUND

Treatment options for patients with gastric cancer (GC) continue to improve, but the overall prognosis is poor. The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present, and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups.

AIM

To determined the effects of differentially expressed immune-related genes (DEIRGs) on the development, prognosis, tumor microenvironment (TME), and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations.

METHODS

Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were searched from ImmPort. DEIRGs were extracted from the intersection of the differentially-expressed genes (DEGs) and IRGs lists. The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes (KEGGs) and Gene Ontology (GO) databases. To identify genes associated with prognosis, a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression. The tumor risk score was divided into high- and low-risk groups. The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model. The infiltration of immune cells was obtained using 'CIBERSORT,' and the infiltration of immune subgroups in high- and low-risk groups was analyzed. The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed.

RESULTS

We collected 412 GC and 36 adjacent tissue samples, and identified 3627 DEGs and 1311 IRGs. A total of 482 DEIRGs were obtained. GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes, receptor ligand activity, and signaling receptor activators. KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors, neuroactive ligand receptor interactions, and viral protein interactions. We ultimately obtained an immune-related signature based on 10 genes, including 9 risk genes (LCN1, LEAP2, TMSB15A mRNA, DEFB126, PI15, IGHD3-16, IGLV3-22, CGB5, and GLP2R) and 1 protective gene (LGR6). Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, and risk curves confirmed that the risk model had good predictive ability. Multivariate COX analysis showed that age, stage, and risk score were independent prognostic factors for patients with GC. Meanwhile, patients in the low-risk group had higher tumor mutation burden and immunophenotype, which can be used to predict the immune checkpoint inhibitor response. Both cytotoxic T lymphocyte antigen4+ and programmed death 1+ patients with lower risk scores were more sensitive to immunotherapy.

CONCLUSION

In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME. By providing risk factor analysis and prognostic information, our risk model can provide new directions for immunotherapy in GC patients.

Identification of therapeutic targets in osteoarthritis by combining heterogeneous transcriptional datasets, drug-induced expression profiles, and known drug-target interactions

BACKGROUND

Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course.

METHODS

We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data.

RESULTS

We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient's risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922).

CONCLUSIONS

The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient's risk scores can be used in clinical settings.

Advances in familial hypercholesterolemia

Familial hypercholesterolemia (FH), a semi-dominant genetic disease affecting more than 25 million people worldwide, is associated with severe hypercholesterolemia and premature atherosclerotic cardiovascular disease. Over the last decade, advances in data analysis, screening, diagnosis and cardiovascular risk stratification has significantly improved our ability to deliver precision medicine for these patients. Furthermore, recent updates on guideline recommendations and new therapeutic approaches have also proven to be highly beneficial. It is anticipated that both ongoing and upcoming clinical trials will offer further insights for the care and treatment of FH patients.

Postoperative Cerebrospinal Fluid-Related Complications After Posterior Fossa and Posterolateral Skull Base Surgeries: Development of a Predictive Model and Clinical Risk Score

BACKGROUND

Postoperative pseudomeningocele (PMC) and cerebrospinal fluid (CSF) leak are common complications following posterior fossa and posterolateral skull base surgeries. We sought to 1) determine the rate of CSF-related complications and 2) develop a perioperative model and risk score to identify the highest risk patients for these events.

METHODS

We performed a retrospective cohort of 450 patients undergoing posterior fossa and posterolateral skull base procedures from 2016 to 2020. Logistic regressions were performed for predictor selection for 3 prespecified models: 1) a priori variables, 2) predictors selected by large effect sizes, and 3) predictors with P ≤ 0.100 on univariable analysis. A final model was created by elimination of nonsignificant predictors, and the integer-based postoperative CSF-related complications (POCC) clinical risk score was derived. Internal validation was done using 10-fold cross-validation and bootstrapping with uniform shrinkage.

RESULTS

A total of 115 patients (25.6%) developed PMC and/or CSF leakage. Age >55 years (odds ratio [OR], 0.560; 95% confidence interval [CI], 0.328-0.954), body mass index >30 kg/m2 (OR, 1.88; 95% CI, 1.14-3.10), and postoperative CSF diversion (OR, 2.85; 95% CI, 1.64-5.00) were associated with CSF leak and PMC. Model 2 was the most predictive (cross-validated area under the receiver operating characteristic curve, 0.690). The final risk score was devised using age, body mass index class, dural repair technique, use of bone substitute, and duration of postoperative CSF diversion. The POCC score performed well (cross-validated area under the receiver operating characteristic curve, 0.761) and was highly specific (96.1%).

CONCLUSIONS

We created the first generalizable and predictive risk score to identify patients at risk of CSF-related complications. The POCC score could improve surveillance, inform doctor-patient discussions regarding the risks of surgery, and assist in perioperative management.

Modified Harborview Risk Score accurately predicts mortality for patients with ruptured abdominal aortic aneurysm

OBJECTIVE

The modified Harborview Risk Score (HRS) is a simple measure initially derived from a single institutional dataset used to predict ruptured abdominal aortic aneurysm (rAAA) repair survival preoperatively using basic labs and vital signs collected upon presentation. However, validation of this widely applicable scoring system has not been performed. This study aims to validate this scoring system using a large multi-institutional database.

METHODS

All patients who underwent repair of an rAAA from 2011 to 2018 in the National Surgical Quality Improvement Program (NSQIP) and at a single academic medical center were included. The modified HRS was calculated by assigning 1 point for each of the following: age >76 years, creatinine >2 mg/dL, international normalized ratio >1.8, and any systolic blood pressure less than 70 mmHg. Assessment of the prediction model was then completed. Using a primary outcome measure of 30-day mortality, the receiver operating characteristic area under the curve was calculated. The discrimination between datasets was compared using a Delong test. Mortality rates for each score were compared between datasets using the Pearson χ2 test. Comparative analysis for patients with a score of 4 was limited due to a small sample size.

RESULTS

A total of 1536 patients were identified using NSQIP, and 163 patients were assessed in the institutional dataset. There were 518 patients with a score of 0 (455 NSQIP, 63 institutional), 676 patients with a score of 1 (617 NSQIP, 59 institutional), 391 patients with a score of 2 (364 NSQIP, 27 institutional), 106 with a score of 3 (93 NSQIP, 13 institutional), and 8 patients with a score of 4 (7 NSQIP, 1 institutional). No difference was found in the receiver operating characteristic area under the curves between datasets (P = .78). Thirty-day mortality was 10% NSQIP vs 22% institutional for a score of 0; 28% NSQIP vs 36% institutional for a score of 1; 41% NSQIP vs 44% institutional for a score of 2; 45% NSQIP vs 69% institutional for a score of 3; and 57% NSQIP vs 100% institutional for a score of 4. Score 0 was the only score with a significant mortality rate difference between datasets (P = .01).

CONCLUSIONS

The modified HRS is confirmed to be broadly applicable as a clinical decision-making tool for patients presenting with rAAAs. Therefore, this easily applicable model should be applied for all patients presenting with rAAAs to assist with provider and patient decision-making prior to proceeding with repair.

Identification of prognostic risk score of disulfidptosis-related genes and molecular subtypes in glioma

Background

Programmed cell death is closely related to glioma. As a novel kind of cell death, the mechanism of disulfidptosis in glioma remains unclear. Therefore, it is of great importance to study the role of disulfidptosis-related genes (DRGs) in glioma.

Methods

We first investigated the genetic and transcriptional alterations of 15 DRGs. Two consensus cluster analyses were used to evaluate the association between DRGs and glioma subtypes. In addition, we constructed prognostic DRG risk scores to predict overall survival (OS) in glioma patients. Furthermore, we developed a nomogram to enhance the clinical utility of the DRG risk score. Finally, the expression levels of DRGs were verified by immunohistochemistry (IHC) staining.

Results

Most DRGs (14/15) were dysregulated in gliomas. The 15 DRGs were rarely mutated in gliomas, and only 50 of 987 samples (5.07 %) showed gene mutations. However, most of them had copy number variation (CNV) deletions or amplifications. Two distinct molecular subtypes were identified by cluster analysis, and DRG alterations were found to be related to the clinical characteristics, prognosis, and tumor immune microenvironment (TIME). The DRG risk score model based on 12 genes was developed and showed good performance in predicting OS. The nomogram confirmed that the risk score had a particularly strong influence on the prognosis of glioma. Furthermore, we discovered that low DRG scores, low tumor mutation burden, and immunosuppression were features of patients with better prognoses.

Conclusion

The DRG risk model can be used for the evaluation of clinical characteristics, prognosis prediction, and TIME estimation of glioma patients. These DRGs may be potential therapeutic targets in glioma.

Modified Harborview Risk Score improves ease in predicting mortality after ruptured abdominal aortic aneurysm repair

OBJECTIVE

The Harborview Risk Score (HRS) is a simple, accurate 4-point preoperative risk scoring system used to predict 30-day mortality following ruptured abdominal aortic aneurysm (rAAA) repair. The HRS assigns 1 point for each of the following: age >76 years, pH <7.2, creatinine >2 mg/dL, and any episode of severe hypotension (systolic blood pressure <70 mmHg). One potential limitation of this risk scoring system is that arterial blood gas (ABG) analysis is required to determine arterial pH. Because ABG analysis is not routinely performed prior to patient transfer or rAAA repair, we sought to determine if the HRS could be modified by replacing pH with the international normalized ratio (INR), a factor that has been previously shown to have a strong and independent association with 30-day death after rAAA repair.

METHODS

A retrospective review of all rAAA repairs done at a single academic medical center between January 2002 and December 2018 was performed. Our traditional HRS was compared with a modified score, in which pH <7.2 was replaced with INR >1.8. Patients were included if they underwent rAAA repair (open or endovascular), and if they had preoperative laboratory values available to calculate both the traditional and modified HRS.

RESULTS

During the 17-year study period, 360 of 391 repairs met inclusion criteria. Observed 30-day mortality using the modified scoring system was 17% (18/106) for a score of 0 points, 43% (53/122) for 1 point, 54% (52/96) for 2 points, 84% (27/32) for 3 points, and 100% (4/4) for 4 points. Receiver operating characteristic analysis revealed similar ability of the two scoring systems to predict 30-day death: there was no significant difference in the area under the curve (AUC) comparing the traditional (AUC = 0.74) and modified (AUC = 0.72) HRS (P = .3).

CONCLUSIONS

Although previously validated among a modern cohort of patients with rAAA, our traditional 4-point risk score is limited in real-world use by the need for an ABG. Substituting INR for pH improves the usefulness of our risk scoring system without compromising accuracy in predicting 30-day mortality after rAAA repair.

Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia

Preterm birth is one of the most common obstetric complications in low- and middle-income countries, where access to advanced diagnostic tests and imaging is limited. Therefore, we developed and validated a simplified risk prediction tool to predict preterm birth based on easily applicable and routinely collected characteristics of pregnant women in the primary care setting. We used a logistic regression model to develop a model based on the data collected from 481 pregnant women. Model accuracy was evaluated through discrimination (measured by the area under the Receiver Operating Characteristic curve; AUC) and calibration (via calibration graphs and the Hosmer-Lemeshow goodness of fit test). Internal validation was performed using a bootstrapping technique. A simplified risk score was developed, and the cut-off point was determined using the "Youden index" to classify pregnant women into high or low risk for preterm birth. The incidence of preterm birth was 19.5% (95% CI:16.2, 23.3) of pregnancies. The final prediction model incorporated mid-upper arm circumference, gravidity, history of abortion, antenatal care, comorbidity, intimate partner violence, and anemia as predictors of preeclampsia. The AUC of the model was 0.687 (95% CI: 0.62, 0.75). The calibration plot demonstrated a good calibration with a p-value of 0.713 for the Hosmer-Lemeshow goodness of fit test. The model can identify pregnant women at high risk of preterm birth. It is applicable in daily clinical practice and could contribute to the improvement of the health of women and newborns in primary care settings with limited resources. Healthcare providers in rural areas could use this prediction model to improve clinical decision-making and reduce obstetrics complications.

Long-term all-cause death prediction by coronary, aortic, and valvular calcification in patients with acute ST-segment elevation myocardial infarction

BACKGROUND

To determine the prognostic value of cumulative calcification score of coronary artery calcification (CAC), thoracic aortic calcification (TAC) and aortic valve calcification (AVC) in acute ST segment elevation myocardial infarction (STEMI) patients.

METHODS

This was a retrospective, single-center cohort study. A total of 332 STEMI patients who received primary percutaneous coronary intervention (PPCI) were enrolled in this study between January 2010 to October 2018. We assessed the calcification in the left anterior descending branch (LAD), left circumflex branch (LCX), right coronary artery (RCA), thoracic aorta, and aortic valve. Calcification of each part was counted as 1 point, and the cumulative calcification score was calculated as the sum of all points. The primary endpoint was all-cause mortality. Multivariate Cox proportional hazards models were used to determine association of cumulative calcification score with end points. The performance of the score was evaluated by receiver operating characteristic (ROC) curve analysis and absolute net reclassification improvement (NRI), compared with the Global Registry of Acute Coronary Events (GRACE) risk score.

RESULTS

The overall population's calcification score was 2.0 ± 1.6. During a mean follow-up time of 69.8 ± 29.3 months, the all-cause mortality rate was 12.1%. Kaplan-Meier curve showed that the score was significantly associated with mortality (log-rank p < 0.001). The multivariable Cox proportional hazard analyses showed that a calcification score of 4-5 was independently associated with all-cause death in STEMI patients [hazard ratio (HR) = 2.32, 95% confidence interval (CI): 1.01-5.31, p = 0.046]. The area under the ROC curve (AUC) of the calcification score was 0.67 (95% CI: 0.61-0.72), and the AUC of the GRACE score was 0.80 (95% CI: 0.75-0.84). There was no statistical difference in the predictive value between both scores for 3-year mortality in STEMI patients after PPCI (p = 0.06). Based on the NRI analysis, the calcification score showed better risk classification compared with the GRACE score (absolute NRI = 6.63%, P = 0.027).

CONCLUSION

The cumulative calcification score is independently associated with the long-term prognosis of STEMI patients after PPCI.

Pregnancy and cardiac maternal outcomes in women with inherited cardiomyopathy: interest of the CARPREG II risk score

AIMS

Inherited cardiomyopathies are relatively rare but carry a high risk of cardiac maternal morbidity and mortality during pregnancy and postpartum. However, data for risk stratification are scarce. The new CARPREG II score improves prediction of prognosis in pregnancies associated with heart disease, though its role in inherited cardiomyopathies is unclear. We aim to describe characteristics and cardiac maternal outcomes in patients with inherited cardiomyopathy during pregnancy, and to evaluate the interest of the CARPREG II risk score in this population.

METHODS AND RESULTS

In this retrospective single-centre study, 90 consecutive pregnancies in 74 patients were included (mean age 32 ± 5 years), including 28 cases of dilated cardiomyopathy (DCM), 46 of hypertrophic cardiomyopathy, 11 of arrhythmogenic right ventricular cardiomyopathy and 5 of left ventricular noncompaction, excluding peripartum cardiomyopathy. The discriminatory power of several risk scores was assessed by the area under the receiver-operating characteristic curve (AUC). Median CARPREG II score was 2 [0;3] and was higher in the DCM subgroup. A severe cardiac maternal complication was observed in 18 (20%) pregnancies, mainly driven by arrhythmia and heart failure (each event in 10 pregnancies), with 3 cardiovascular deaths. Forty-three pregnancies (48%) presented foetal/neonatal complications (18 premature delivery, 3 foetal/neonatal death). CARPREG II was significantly associated with cardiac maternal complications (P < 0.05 for all) and showed a higher AUC (0.782) than CARPREG (0.755), mWHO (0.697) and ZAHARA (0.604).

CONCLUSIONS

Pregnancy in women with inherited cardiomyopathy carries a high risk of maternal cardiovascular complications. CARPREG II is the most efficient predictor of cardiovascular complications in this population.

Predicting long-term time to cardiovascular incidents using myocardial perfusion imaging and deep convolutional neural networks

Myocardial perfusion imaging (MPI) is a clinical tool which can assess the heart's perfusion status, thereby revealing impairments in patients' cardiac function. Within the MPI modality, the acquired three-dimensional signals are typically represented as a sequence of two-dimensional grayscale tomographic images. Here, we proposed an end-to-end survival training approach for processing gray-scale MPI tomograms to generate a risk score which reflects subsequent time to cardiovascular incidents, including cardiovascular death, non-fatal myocardial infarction, and non-fatal ischemic stroke (collectively known as Major Adverse Cardiovascular Events; MACE) as well as Congestive Heart Failure (CHF). We recruited a total of 1928 patients who had undergone MPI followed by coronary interventions. Among them, 80% (n = 1540) were randomly reserved for the training and 5- fold cross-validation stage, while 20% (n = 388) were set aside for the testing stage. The end-to-end survival training can converge well in generating effective AI models via the fivefold cross-validation approach with 1540 patients. When a candidate model is evaluated using independent images, the model can stratify patients into below-median-risk (n = 194) and above-median-risk (n = 194) groups, the corresponding survival curves of the two groups have significant difference (P < 0.0001). We further stratify the above-median-risk group to the quartile 3 and 4 group (n = 97 each), and the three patient strata, referred to as the high, intermediate and low risk groups respectively, manifest statistically significant difference. Notably, the 5-year cardiovascular incident rate is less than 5% in the low-risk group (accounting for 50% of all patients), while the rate is nearly 40% in the high-risk group (accounting for 25% of all patients). Evaluation of patient subgroups revealed stronger effect size in patients with three blocked arteries (Hazard ratio [HR]: 18.377, 95% CI 3.719-90.801, p < 0.001), followed by those with two blocked vessels at HR 7.484 (95% CI 1.858-30.150; p = 0.005). Regarding stent placement, patients with a single stent displayed a HR of 4.410 (95% CI 1.399-13.904; p = 0.011). Patients with two stents show a HR of 10.699 (95% CI 2.262-50.601; p = 0.003), escalating notably to a HR of 57.446 (95% CI 1.922-1717.207; p = 0.019) for patients with three or more stents, indicating a substantial relationship between the disease severity and the predictive capability of the AI for subsequent cardiovascular inciidents. The success of the MPI AI model in stratifying patients into subgroups with distinct time-to-cardiovascular incidents demonstrated the feasibility of proposed end-to-end survival training approach.

A Novel KIF4A-Related Model for Predicting Immunotherapy Response and Prognosis in Kidney Renal Clear Cell Carcinoma

BACKGROUND

The efficacy of chemotherapy in treating Kidney Renal Clear Cell Carcinoma (KIRC) is limited, whereas immunotherapy has shown some promising clinical outcomes. In this context, KIF4A is considered a potential therapeutic target for various cancers. Therefore, identifying the mechanism of KIF4A that can predict the prognosis and immunotherapy response of KIRC would be of significant importance.

METHODS

Based on the TCGA Pan-Cancer dataset, the prognostic significance of the KIF4A expression across 33 cancer types was analyzed by univariate Cox algorithm. Furthermore, overlapping differentially expressed genes (DEGs1) between the KIF4A high- and lowexpression groups and DEGs2 between the KIRC and normal groups were also analyzed. Machine learning and Cox regression algorithms were performed to obtain biomarkers and construct a prognostic model. Finally, the role of KIF4A in KIRC was analyzed using quantitative real-time PCR, transwell assay, and EdU experiment.

RESULTS

Our analysis revealed that KIF4A was significant for the prognosis of 13 cancer types. The highest correlation with KIF4A was found for KICH among the tumour mutation burden (TMB) indicators. Subsequently, a prognostic model developed with UBE2C, OTX1, PPP2R2C, and RFLNA was obtained and verified with the Renal Cell Cancer-EU/FR dataset. There was a positive correlation between risk score and immunotherapy. Furthermore, the experiment results indicated that KIF4A expression was considerably increased in the KIRC group. Besides, the proliferation, migration, and invasion abilities of KIRC tumor cells were significantly weakened after KIF4A was knocked out.

CONCLUSION

We identified four KIF4A-related biomarkers that hold potential for prognostic assessment in KIRC. Specifically, early implementation of immunotherapy targeting these biomarkers may yield improved outcomes for patients with KIRC.

The metabolism-related lncRNA signature predicts the prognosis of breast cancer patients

Long non-coding RNAs (lncRNAs) involved in metabolism are recognized as significant factors in breast cancer (BC) progression. We constructed a novel prognostic signature for BC using metabolism-related lncRNAs and investigated their underlying mechanisms. The training and validation cohorts were established from BC patients acquired from two public sources: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The prognostic signature of metabolism-related lncRNAs was constructed using the least absolute shrinkage and selection operator (LASSO) cox regression analysis. We developed and validated a new prognostic risk model for BC using the signature of metabolism-related lncRNAs (SIRLNT, SIAH2-AS1, MIR205HG, USP30-AS1, MIR200CHG, TFAP2A-AS1, AP005131.2, AL031316.1, C6orf99). The risk score obtained from this signature was proven to be an independent prognostic factor for BC patients, resulting in a poor overall survival (OS) for individuals in the high-risk group. The area under the curve (AUC) for OS at three and five years were 0.67 and 0.65 in the TCGA cohort, and 0.697 and 0.68 in the GEO validation cohort, respectively. The prognostic signature demonstrated a robust association with the immunological state of BC patients. Conventional chemotherapeutics, such as docetaxel and paclitaxel, showed greater efficacy in BC patients classified as high-risk. A nomogram with a c-index of 0.764 was developed to forecast the survival time of BC patients, considering their risk score and age. The silencing of C6orf99 markedly decreased the proliferation, migration, and invasion capacities in MCF-7 cells. Our study identified a signature of metabolism-related lncRNAs that predicts outcomes in BC patients and could assist in tailoring personalized prevention and treatment plans.

A simple clinical risk score (ABCDMP) for predicting mortality in patients with AECOPD and cardiovascular diseases

BACKGROUND

The morbidity and mortality among hospital inpatients with AECOPD and CVDs remains unacceptably high. Currently, no risk score for predicting mortality has been specifically developed in patients with AECOPD and CVDs. We therefore aimed to derive and validate a simple clinical risk score to assess individuals' risk of poor prognosis.

STUDY DESIGN AND METHODS

We evaluated inpatients with AECOPD and CVDs in a prospective, noninterventional, multicenter cohort study. We used multivariable logistic regression analysis to identify the independent prognostic risk factors and created a risk score model according to patients' data from a derivation cohort. Discrimination was evaluated by the area under the receiver-operating characteristic curve (AUC), and calibration was assessed by the Hosmer-Lemeshow goodness-of-fit test. The model was validated and compared with the BAP-65, CURB-65, DECAF and NIVO models in a validation cohort.

RESULTS

We derived a combined risk score, the ABCDMP score, that included the following variables: age > 75 years, BUN > 7 mmol/L, consolidation, diastolic blood pressure ≤ 60 mmHg, mental status altered, and pulse > 109 beats/min. Discrimination (AUC 0.847, 95% CI, 0.805-0.890) and calibration (Hosmer‒Lemeshow statistic, P = 0.142) were good in the derivation cohort and similar in the validation cohort (AUC 0.811, 95% CI, 0.755-0.868). The ABCDMP score had significantly better predictivity for in-hospital mortality than the BAP-65, CURB-65, DECAF, and NIVO scores (all P < 0.001). Additionally, the new score also had moderate predictive performance for 3-year mortality and can be used to stratify patients into different management groups.

CONCLUSIONS

The ABCDMP risk score could help predict mortality in AECOPD and CVDs patients and guide further clinical research on risk-based treatment.

CLINICAL TRIAL REGISTRATION

Chinese Clinical Trail Registry NO.:ChiCTR2100044625; URL: http://www.chictr.org.cn/showproj.aspx?proj=121626 .

S2 I2 N0-3 score predicts short- and long-term mortality and morbidity in HFrEF: a post-hoc analysis of the GUIDE-IT trial

AIMS

This study investigated the S2 I2 N0-3 score, a simple tool comprising stroke history, insulin-treated diabetes, and N-terminal pro-brain natriuretic peptide, for forecasting mortality and morbidity in heart failure (HF) with reduced ejection fraction (HFrEF).

METHODS AND RESULTS

Analysing 890 GUIDE-IT HFrEF trial participants, we stratified them by baseline S2 I2 N0-3 risk score into three risk groups. We examined the score's association with five adverse outcomes over short (90 days) and extended periods (median follow-up of 15 months) using Cox and competing risk models. Our analysis revealed significant positive associations between the S2 I2 N0-3 strata and adverse outcomes. When analysed as a continuous variable, each point increment of the S2 I2 N0-3 score was associated with a higher risk of short- and long-term cardiovascular death [short term: hazard ratio (HR) 1.43, 95% confidence interval (CI) 1.03-1.98; long term: HR 1.18, 95% CI 1.02-1.38], all-cause death (HR 1.52, 95% CI 1.12-2.07; HR 1.18, 95% CI 1.03-1.36), HF hospitalization (HR 1.39, 95% CI 1.20-1.62; HR 1.18, 95% CI 1.06-1.31), any hospitalization (HR 1.19, 95% CI 1.06-1.34; HR 1.09, 95% CI 1.00-1.19), and the composite outcome of cardiovascular death and HF hospitalization (HR 1.39, 95% CI 1.21-1.60; HR 1.17, 95% CI 1.06-1.30). The S2 I2 N0-3 demonstrated reliable prognostic value, with C-indices ranging from 0.619 to 0.753 across outcomes and time points. When compared with the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score using Z-statistics, net reclassification index, and integrated discrimination improvement, the S2 I2 N0-3 showed comparable predictive power for all outcomes during both short- and long-term follow-ups.

CONCLUSIONS

The S2 I2 N0-3 risk score had modest predictive values for both short- and long-term clinical outcomes in HFrEF patients, offering equivalent performance to the established MAGGIC score.

Identification of a potential signature to predict the risk of postmenopausal osteoporosis

BACKGROUND

Postmenopausal osteoporosis (PMOP) is related to the elevated risk of fracture in postmenopausal women. Thus, to effectively predict the occurrence of PMOP, we explored a novel gene signature for the prediction of PMOP risk.

METHODS

The WGCNA analysis was conducted to identify the PMOP-related gene modules based on the data from GEO database (GSE56116 and GSE100609). The "limma" R package was applied for screening differentially expressed genes (DEGs) based on the data from GSE100609 dataset. Next, LASSO Cox algorithm were applied to identify valuable PMOP-related risk genes and construct a risk score model. GSEA was then conducted to analyze potential signaling pathways between high-risk (HR) score and low-risk (LR) score groups.

RESULTS

A novel risk model with five PMOP-related risk genes (SCUBE3, TNNC1, SPON1, SEPT12 and ULBP1) was developed for predicting PMOP risk status. RT-qPCR and western blot assays validated that compared to postmenopausal non-osteoporosis (non-PMOP) patients, SCUBE3, ULBP1, SEPT12 levels were obviously elevated, and TNNC1 and SPON1 levels were reduced in blood samples from PMOP patients. Additionally, PMOP-related pathways such as MAPK signaling pathway, PI3K-Akt signaling pathway and HIF-1 signaling pathway were significantly activated in the HR-score group compared to the LR-score group. The circRNA-gene-miRNA and gene-transcription factor networks showed that 533 miRNAs, 13 circRNAs and 40 TFs might be involved in regulating the expression level of these five PMOP-related genes.

CONCLUSION

Collectively, we developed a PMOP-related gene signature based on SCUBE3, TNNC1, SPON1, SEPT12 and ULBP1 genes, and higher risk score indicated higher risk suffering from PMOP.

Identification and Validation of a Novel Anoikis-Related Gene Signature for Predicting Survival in Patients With Serous Ovarian Cancer

Background

Ovarian cancer is an extremely deadly gynecological malignancy, with a 5-year survival rate below 30%. Among the different histological subtypes, serous ovarian cancer (SOC) is the most common. Anoikis significantly contributes to the progression of ovarian cancer. Therefore, identifying an anoikis-related signature that can serve as potential prognostic predictors for SOC is of great significance.

Methods

We intersected 308 anoikis-related genes (ARGs) and identified those significantly associated with SOC prognosis using univariate Cox regression. A LASSO Cox regression model was constructed and evaluated using Kaplan-Meier and receiver operating characteristic (ROC) analyses in TCGA (The Cancer Genome Atlas) and GSE26193 cohorts. We conducted quantitative real-time polymerase chain reaction (qPCR) to assess mRNA levels and applied bioinformatics to investigate the correlation between risk groups and gene expression, mutations, pathways, tumor immune microenvironment (TIME), and drug sensitivity in SOC.

Results

Among 308 ARGs, 28 were significantly associated with SOC prognosis. A 13-gene prognostic model was established through LASSO Cox regression in TCGA cohort. High-risk group had poorer prognosis than low-risk group (median overall survival (mOS): 34.2 vs. 57.1 months, hazard ratio (HR): 2.590, 95% confidence interval (CI): 0.159 - 6.00, P < 0.001). The area under the curve (AUC) values of 0.63, 0.65, and 0.74 reflected the predictive performance for 3-, 5-, and 8-year overall survival (OS) in GSE26193 validation cohort. Functional enrichment, pathway analysis, and TIME analysis identified distinct characteristics between risk groups. Drug sensitivity analysis revealed potential drug advantages for each group. Furthermore, qPCR validation once again confirmed the effectiveness of the risk model in SOC patients.

Conclusions

We developed and validated a robust ARG model, which could be used to predict OS in SOC patients. By systematically analyzing the correlation between the risk score of the ARGs signature model and various patterns, including the TIME and drug sensitivity, our findings suggest that this prognostic model contributes to the advancement of personalized and precise therapeutic strategies. Nevertheless, further validation studies and investigations into the underlying mechanisms are warranted.

Integrated bioinformatics analysis identifies a Ferroptosis-related gene signature as prognosis model and potential therapeutic target of bladder cancer

Background

Bladder cancer (BLCA) is one of the most prevalent cancers worldwide. Ferroptosis is a newly discovered form of non-apoptotic cell death that plays an important role in tumors. However, the prognostic value of ferroptosis-related genes (FRGs) in BLCA has not yet been well studied.

Method and materials

In this study, we performed consensus clustering based on FRGS and categorized BLCA patients into 2 clusters (C1 and C2). Immune cell infiltration score and immune score for each sample were computed using the CIBERSORT and ESTIMATE methods. Functional annotation of differentially expressed genes were performed by Gene Ontology (GO) and KEGG pathway enrichment analysis. Protein expression validation were confirmed in Human Protein Atlas. Gene expression validation were performed by qPCR in human bladder cancer cell lines lysis samples.

Result

C2 had a significant survival advantage and higher immune infiltration levels than C1. Additionally, C2 showed substantially higher expression levels of immune checkpoint markers than C1. According to the Cox and LASSO regression analyses, a novel ferroptosis-related prognostic signature was developed to predict the prognosis of BLCA effectively. High-risk and low-risk groups were divided according to risk scores. Kaplan-Meier survival analyses showed that the high-risk group had a shorter overall survival than the low-risk group throughout the cohort. Furthermore, a nomogram combining risk score and clinical features was developed. Finally, SLC39A7 was identified as a potential target in bladder cancer.

Discussion

In conclusion, we identified two ferroptosis-clusters with different prognoses using consensus clustering in BLCA. We also developed a ferroptosis-related prognostic signature and nomogram, which could indicate the outcome.

A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma

BACKGROUND

Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions.

METHODS

RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses.

RESULTS

ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients.

CONCLUSIONS

The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

Establishing and validation of the VBV score for assessing Lung ground-glass nodules based on high-resolution computed tomography

BACKGROUND

The widespread utilization of chest High-resolution Computed Tomography (HRCT) has prompted detection of pulmonary ground-glass nodules (GGNs) in otherwise asymptomatic individuals. We aimed to establish a simple clinical risk score model for assessing GGNs based on HRCT.

METHODS

We retrospectively analyzed 574 GGNs in 574 patients undergoing HOOK-WIRE puncture and pulmonary nodule surgery from January 2014 to November 2018. Clinical characteristics and imaging features of the GGNs were assessed. We analyzed the differences between malignant and benign nodules using binary logistic regression analysis and constructed a simple risk score model, the VBV Score, for predicting the malignancy status of GGNs. Then, we validated this model via other 1200 GGNs in 1041 patients collected from three independent clinical centers in 2022.

RESULTS

For the exploratory phase of this study, out of the 574 GGNs, 481 were malignant and 93 were benign. Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. Then, we derived a VBV Score = vacuole sign + air bronchogram + intra-nodular vessel sign, to predict the malignancy of GGNs, with a sensitivity, specificity, and accuracy of 95.6%, 80.6%, and 93.2%, respectively. We also validated it on other 1200 GGNs, with a sensitivity, specificity, and accuracy of 96.0%, 82.6%, and 95.0%, respectively.

CONCLUSIONS

Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. VBV Score showed good sensitivity, specificity, and accuracy for differentiating benign and malignant pulmonary GGNs.

Development and validation of epigenetic modification-related signals for the diagnosis and prognosis of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the world's most common malignancies. Epigenetics is the study of heritable changes in characteristics beyond the DNA sequence. Epigenetic information is essential for maintaining specific expression patterns of genes and the normal development of individuals, and disorders of epigenetic modifications may alter the expression of oncogenes and tumor suppressor genes and affect the development of cancer. This study elucidates the relationship between epigenetics and the prognosis of CRC patients by developing a predictive model to explore the potential value of epigenetics in the treatment of CRC.

METHODS

Gene expression data of CRC patients' tumor tissue and controls were downloaded from GEO database. Combined with the 720 epigenetic-related genes (ERGs) downloaded from EpiFactors database, prognosis-related epigenetic genes were selected by univariate cox and LASSO analyses. The Kaplan-Meier and ROC curve were used to analyze the accuracy of the model. Data of 238 CRC samples with survival data downloaded from the GSE17538 were used for validation. Finally, the risk model is combined with the clinical characteristics of CRC patients to perform univariate and multivariate cox regression analysis to obtain independent risk factors and draw nomogram. Then we evaluated the accuracy of its prediction by calibration curves.

RESULTS

A total of 2906 differentially expressed genes (DEGs) were identified between CRC and control samples. After overlapping DEGs with 720 ERGs, 56 epigenetic-related DEGs (DEERGs) were identified. Combining univariate and LASSO regression analysis, the 8 epigenetic-related genes-based risk score model of CRC was established. The ROC curves and survival difference of high and low risk groups revealed the good performance of the risk score model based on prognostic biomarkers in both training and validation sets. A nomogram with good performance to predict the survival of CRC patients were established based on age, NM stage and risk score. The calibration curves showed that the prognostic model had good predictive performance.

CONCLUSION

In this study, an epigenetically relevant 8-gene signature was constructed that can effectively predict the prognosis of CRC patients and provide potential directions for targeted therapies for CRC.

Prevalence and predictors of stone passage after double J stenting for symptomatic ureteral stones: a cross-sectional, real-life study

PURPOSE

To evaluate the rate of and predictors of stone passage (SP) after urgent retrograde stenting for symptomatic ureteral stones.

METHODS

We retrospectively analysed data from 249 consecutive patients presenting to the emergency department for symptomatic ureteral stones and treated with retrograde stenting. Demographic, clinical and laboratory characteristics were collected. Stones parameters were collected before stenting and SP was evaluated at 1 month with computerized tomography. Descriptive statistics and logistic regression models tested the association between predictors and SP.

RESULTS

Overall, median (IQR) age and stone diameter were 56 (45-68) years and 7.1 (4.4-9.8) mm, respectively. Stones were located in the proximal, mid and distal ureter in 102 (41.0%), 48 (19.3%) and 99 (39.8%) cases. SP was observed in 65 (26.2%) individuals. Stone diameter (3.2 vs. 7.7 mm, p < 0.001) and stone density (416 vs. 741, p < 0.001) were lower and a higher rate of distal stones (76.9% vs. 26.7%, p < 0.001) was found in the SP group compared to that with persistent stones. Multivariable logistic regression analysis showed that distal ureteral stone location (OR 7.9, p < 0.01) and lower HU (OR 0.9, p < 0.01) were associated with SP, after accounting for stone volume. Patients with a distal stone of 500 HU had a 75% probability of SP.

CONCLUSION

Stone passage occurred in 26% of patients with indwelling stent due to symptomatic ureteral stones. Lower stone density and distal stone location were independent predictors of stone passage. Patients with these criteria should be managed with follow-up imaging and stent removal instead of ureteroscopy.

FDrisk: development of a validated risk assessment tool for Fabry disease utilizing electronic health record data

Purpose

Fabry disease (FD) is a rare, X-linked, lysosomal storage disease characterized by great variability in clinical presentation and progressive multisystemic organ damage. Lack of awareness of FD and frequent misdiagnoses cause long diagnostic delays. To address the urgent need for earlier diagnosis, we created an online, risk-assessment scoring tool, the FDrisk, for predicting an individual's risk for FD and prompting diagnostic testing and clinical evaluation.

Methods

Utilizing electronic health records, data were collected retrospectively from randomly selected, deidentified patients with FD treated at the Emory Lysosomal Storage Disease Center. Deidentified, negative controls were randomly selected from the Fabry Disease Diagnostic Testing and Education project database, a program within the American Association of Kidney Patients Center for Patient Education and Research. Diagnosis of FD was documented by evidence of a pathogenic variant in GLA and/or an abnormal level of leukocyte α-Gal A. Thirty characteristic clinical features of FD were initially identified and subsequently curated into 16 clinical covariates used as predictors for the risk of FD. An overall prediction model and two sex-specific prediction models were built. Two-hundred and sixty samples (130 cases, 130 controls) were used to train the risk prediction models. One-hundred and ninety-seven independent samples (30 cases, 167 controls) were used for testing model performance. Prediction accuracy was evaluated using a threshold of 0.5 to determine a predicted case vs. control.

Results

The overall risk prediction model demonstrated 80% sensitivity, 83.8% specificity, and positive predictive value of 47.1%. The male model demonstrated 75% sensitivity, 95.8% specificity, and positive predictive value of 75%. The female model demonstrated 83.3% sensitivity, 81.3% specificity, and positive predictive value of 45.5%. Patients with risk scores at or above 50% are categorized as "at risk" for FD and should be sent for diagnostic testing.

Conclusion

We have developed a statistical risk prediction model, the FDrisk, a validated, clinician-friendly, online, risk-assessment scoring tool for predicting an individual's risk for FD and prompting diagnostic testing and clinical evaluation. As an easily accessible, user-friendly scoring tool, we believe implementing the FDrisk will significantly decrease the time to diagnosis and allow earlier initiation of FD-specific therapy.

Sex-specific genetic risks for adverse outcomes after coronary revascularization procedures

Men and women have differing risks of adverse events after revascularization procedures and these differences could be partially driven by genetics. We studied the sex-specific differences in associations of polygenic risk scores (PRSs) with atrial fibrillation (AF), ischaemic stroke (STR), intracranial haemorrhage (ICH), myocardial infarction (MI) and gastrointestinal haemorrhage (GIH) in coronary revascularization patients. The study cohort comprised 5561 and 17 578 revascularized women and men. All participants underwent genotyping and register-based follow-up from 1961 to 2021. We calculated PRSs for all individuals and used Cox models with interaction term to examine the sex-specific associations between the PRSs and adverse outcomes after revascularization. The AF-PRS was more strongly associated with AF in men [hazard ratio (HR) per 1 standard deviation increase, 1.16; 95% confidence interval (CI), 1.12-1.19; P = 7.6 × 10-22) than in women (P for interaction 0.006). Conversely, ICH-PRS was more strongly associated with ICH after revascularization in women (HR, 1.32; 95% CI, 1.08-1.62; P = 0.008) than in men (P for interaction 0.008). We observed no sex-specific differences for the associations of PRSs with STR, MI or GIH. The genetic risk of AF after revascularization is greater in men than in women, and vice versa for ICH. Sex-specific PRSs could be used to identify individuals in high genetic risk for these complications.

Value of HAVOC and Brown ESUS-AF scores for atrial fibrillation on implantable cardiac monitors after embolic stroke of undetermined source

OBJECTIVES

Up to 20 % of ischemic strokes are associated with overt atrial fibrillation (AF). Furthermore, silent AF was detected by an implantable cardiac monitor (ICM) in 1 in 3 cryptogenic strokes in the CRYSTAL AF study. An ESC position paper has suggested a HAVOC score ≥ 4 or a Brown ESUS-AF score ≥ 2 as criteria for ICM implantation after cryptogenic stroke, but neither of these criteria has been developed or validated in ICM populations. We assessed the performance of HAVOC and Brown ESUS-AF scores in a cohort of ICM patients implanted after embolic stroke of undetermined source (ESUS).

METHODS

All patients implanted with an ICM for ESUS between February 2016 and February 2022 at two French University Hospitals were retrospectively included. Demographic data, cardiovascular risk factors, and clinical and biological data were collected after a review of electronic medical records. HAVOC and Brown ESUS-AF scores were calculated for all patients.

FINDINGS

Among the 384 patients included, 106 (27 %) developed AF during a mean follow-up of 33 months. The scores performances for predicting AF during follow-up were: HAVOC= AUC: 68.5 %, C-Index: 0.662, and Brown ESUS-AF=AUC: 72.9 %, C-index 0.712. Compared with the CHA2DS2-VASc score, only the Brown ESUS-AF score showed significant improvement in NRI/IDI. Furthermore, classifying patients according to the suggested HAVOC and Brown ESUS-AF thresholds, only 24 % and 31 % of the cohort, respectively, would have received an ICM, and 58 (55 %) and 47 (44 %) of the AF patients, respectively, would not have been implanted with an ICM.

CONCLUSION

HAVOC and Brown ESUS-AF scores showed close and moderate performance in predicting AF on ICM after cryptogenic stroke, with a significant lack of sensitivity. Specific risk scores should be developed and validated in large ICM cohorts.

PrOgnosis in Pulmonary Embolism (PoPE): 30-Day mortality risk score based on five admission parameters

INTRODUCTION AND OBJECTIVE

Several scoring systems have been developed for risk stratification in patients with acute pulmonary embolism (PE). The Pulmonary Embolism Severity Index (PESI) and its simplified version (sPESI) are among the most used, however the high number of variables hinder its application. Our aim was to derive an easy-to-perform score based on simple parameters obtained at admission to predict 30-day mortality in acute PE patients.

METHODS

Retrospective study in 1115 patients with acute PE from two institutions (derivation cohort n=835, validation cohort n=280). The primary endpoint was all-cause mortality at 30 days. Statistically and clinically relevant variables were selected for multivariable Cox regression analysis. We derived and validated a multivariable risk score model and compared to other established scores.

RESULTS

The primary endpoint occurred in 207 patients (18.6%). Our model included five variables weighted as follows: modified shock index ≥1.1 (hazard ratio [HR] 2.57, 1.68-3.92, p<0.001), active cancer (HR 2.27, 1.45-3.56, p<0.001), altered mental state (HR 3.82, 2.50-5.83, p<0.001), serum lactate concentration ≥2.50 mmol/L (HR 5.01, 3.25-7.72, p<0.001), and age ≥80 years (HR 1.95, 1.26-3.03, p=0.003). The prognostic ability was superior to other scores (area under curve [AUC] 0.83 [0.79-0.87] vs 0.72 [0.67-0.79] in PESI and 0.70 [0.62-0.75] in sPESI, p<0.001) and its performance in the validation cohort was deemed good (73 events in 280 patients, 26.1%, AUC=0.76, 0.71-0.82, p<0.0001) and superior to other scores (p<0.05).

CONCLUSIONS

The PoPE score (https://tinyurl.com/ybsnka8s) is an easy tool with superior performance to predict early mortality in patients admitted for PE with non-high-risk PE.

Atrial fibrillation in embolic stroke of undetermined source: role of advanced imaging of left atrial function

AIMS

Atrial fibrillation (AF) is detected in over 30% of patients following an embolic stroke of undetermined source (ESUS) when monitored with an implantable loop recorder (ILR). Identifying AF in ESUS survivors has significant therapeutic implications, and AF risk is essential to guide screening with long-term monitoring. The present study aimed to establish the role of left atrial (LA) function in subsequent AF identification and develop a risk model for AF in ESUS.

METHODS AND RESULTS

We conducted a single-centre retrospective case-control study including all patients with ESUS referred to our institution for ILR implantation from December 2009 to September 2019. We recorded clinical variables at baseline and analysed transthoracic echocardiograms in sinus rhythm. Univariate and multivariable analyses were performed to inform variables associated with AF. Lasso regression analysis was used to develop a risk prediction model for AF. The risk model was internally validated using bootstrapping. Three hundred and twenty-three patients with ESUS underwent ILR implantation. In the ESUS population, 293 had a stroke, whereas 30 had suffered a transient ischaemic attack as adjudicated by a senior stroke physician. Atrial fibrillation of any duration was detected in 47.1%. The mean follow-up was 710 days. Following lasso regression with backwards elimination, we combined increasing lateral PA (the time interval from the beginning of the P wave on the surface electrocardiogram to the beginning of the A' wave on pulsed wave tissue Doppler of the lateral mitral annulus) [odds ratio (OR) 1.011], increasing Age (OR 1.035), higher Diastolic blood pressure (OR 1.027), and abnormal LA reservoir Strain (OR 0.973) into a new PADS score. The probability of identifying AF can be estimated using the formula. Model discrimination was good [area under the curve (AUC) 0.72]. The PADS score was internally validated using bootstrapping with 1000 samples of 150 patients showing consistent results with an AUC of 0.73.

CONCLUSION

The novel PADS score can identify the risk of AF on prolonged monitoring with ILR following ESUS and should be considered a dedicated risk stratification tool for decision-making regarding the screening strategy for AF in stroke.

Prevalence of lifestyle cardiovascular risk factors and estimated framingham 10-year risk scores of adults with psychotic disorders compared to controls at a referral hospital in Eldoret, Kenya

INTRODUCTION

Lifestyle factors such as smoking, alcohol use, suboptimal diet, and inadequate physical activity have been associated with increased risk of cardiovascular diseases. There are limited data on these risk factors among patients with psychosis in low- and middle-income countries.

OBJECTIVES

This study aimed to establish the prevalence of lifestyle cardiovascular risk factors, and the 10-year cardiovascular risk scores and associated factors in patients with psychosis compared to controls at Moi Teaching and Referral Hospital in Eldoret, Kenya.

METHODS

A sample of 297 patients with schizophrenia, schizoaffective disorder, or bipolar mood disorder; and 300 controls matched for age and sex were included in this analysis. A study specific researcher-administered questionnaire was used to collect data on demographics, antipsychotic medication use, smoking, alcohol intake, diet, and physical activity. Weight, height, abdominal circumference, and blood pressure were also collected to calculate the Framingham 10-year Cardiovascular Risk Score (FRS), while blood was drawn for measurement of glucose level and lipid profile. Pearson's chi-squared tests and t-tests were employed to assess differences in cardiovascular risk profiles between patients and controls, and a linear regression model was used to determine predictors of 10-year cardiovascular risk in patients.

RESULTS

Compared to controls, patients with psychosis were more likely to have smoked in their lifetimes (9.9% vs. 3.3%, p = 0.006) or to be current smokers (13.8% vs. 7%, p = 0.001). Over 97% of patients with psychosis consumed fewer than five servings of fruits and vegetables per week; 78% engaged in fewer than three days of vigorous exercise per week; and 48% sat for more than three hours daily. The estimated 10-year risk of CVD was relatively low in this study: the FRS in patients was 3.16, compared to 2.93 in controls. The estimated 10-year cardiovascular risk in patients was significantly associated with female sex (p = 0.007), older patients (p < 0.001), current tobacco smoking (p < 0.001), and metabolic syndrome (p < 0.001).

CONCLUSION

In the setting of Eldoret, there is suboptimal physical exercise and intake of healthy diet among patients with psychosis and controls. While the estimated risk score among patients is relatively low in our study, these data may be useful for informing future studies geared towards informing interventions to promote healthy lifestyles in this population.