Prognostic Predictor In Patients Research Articles

The N6-methyladenosine methylation landscape stratifies breast cancer into two subtypes with distinct immunological characteristics.

N6-methyladenosine (m6A) methylation modification affects the tumorigenesis and metastasis of breast cancer (BC). This study investigated the association between m6A regulator-mediated methylation modification patterns and characterization of the tumour microenvironment in BC, as well as their prognostic importance. Public gene expression data and clinical annotations were collected from The Cancer Genome Atlas (TCGA) database, the Gene Expression Omnibus website and the METABRIC program. We analysed the genetic expression, gene-gene interactions, gene mutations and copy number variations using R software. The data were screened for risk genes using the Cox risk regression model, and we developed an algorithm for risk score and its predictive value. Compared to adjacent normal tissue, we identified 16 differentially expressed m6A regulators in BC, including six writers and 10 readers. Under unsupervised clustering, two distinguished modification patterns were identified, cluster C1 and C2. Compared to m6A cluster C2, cluster C1 was found to be more involved in immune-related pathways, with a relatively higher immune score and stromal score (P < 0.05). Patients were divided into two groups based on their risk scores for survival analysis. The patients in the high-risk score group had significantly worse overall survival than patients in the low-risk score group, (P < 0.0001). The TCGA database validation revealed the same prognostic tendency. In summary, our study showed distinct m6A regulator modification patterns contribute to the immunological heterogeneity and diversity of BC. The development of m6A gene signatures and the m6A score aid in the prognostic prediction of patients with BC.

FT4-to-FT3 ratio is a novel prognostic marker in subacute combined spinal cord degeneration patients.

The FT4-to-FT3 ratio (FFR) variations in patients with subacute combined spinal cord degeneration (SCSD) as a potentially useful prognostic indicator are still unknown. This study aimed to investigate the changes of FFR as a potentially valuable prognostic predictor in patients with SCSD. This study included 144 consecutive SCSD patients who received standard diagnostic and therapeutic procedures between January 2015 and December 2021 and were admitted to the Department of Neurology at the First Affiliated Hospital of Bengbu Medical University. At the time of admission, we gathered data on all patients' demographics, daily routines, previous chronic conditions, medication histories, and other clinical details. For the purpose of measuring FFR, blood samples were specifically taken within 48 h of admission. The degree of neurological impairment of patients was assessed using the functional disability scale at the time of admission. At 6 months following discharge, the Modified Rankin Scale (mRS) was used to evaluate the clinical prognosis. To evaluate the relationship between the FFR and the risks of a poor outcome (mRS > 2), univariate and multivariate logistic regression analysis was utilized. The significance of the FT4/FT3 ratio in predicting the clinical outcomes in SCSD patients 6 months after discharge was assessed using the area under curve-receiver operating characteristic (AUC-ROC). About 90 patients (62.5%) of the 144 patients had poor outcomes, while 54 (37.5%) had favorable outcomes. Higher FFR at admission was independently linked to higher odds of a poor outcome, according to a logistic analysis. With an optimized cutoff value of >2.843, the FFR exhibited the maximum accuracy for predicting a poor outcome, according to the AUC‒ROC curve (AUC 0.731, P < 0.001; sensitivity, 77.8%; specificity, 83.3%). FFR was identified as an independent predictor of poor outcomes by multivariate logistic regression (OR, 2.244; 95% CI, 1.74-2.90; P < 0.001). We discovered that in patients who had a bad result 6 months after discharge, the FFR had dramatically increased at the time of admission, providing a unique prognostic marker in patients with SCSD.

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study.

Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information into a single signature is necessary to stratify patients with clear cell renal cell carcinoma (ccRCC) for adjuvant therapy after surgery. A total of 414 patients with whole slide images, computed tomography images, and clinical data from three patient cohorts were retrospectively analyzed. The authors performed deep learning and machine learning algorithm to construct three single-modality prediction models for disease-free survival of ccRCC based on whole slide images, cell segmentation, and computed tomography images, respectively. A multimodel prediction signature (MMPS) for disease-free survival were further developed by combining three single-modality prediction models and tumor stage/grade system. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. Single-modality prediction models performed well in predicting the disease-free survival status of ccRCC. The MMPS achieved higher area under the curve value of 0.742, 0.917, and 0.900 in three independent patient cohorts, respectively. MMPS could distinguish patients with worse disease-free survival, with HR of 12.90 (95% CI: 2.443-68.120, P <0.0001), 11.10 (95% CI: 5.467-22.520, P <0.0001), and 8.27 (95% CI: 1.482-46.130, P <0.0001) in three different patient cohorts. In addition, MMPS outperformed single-modality prediction models and current clinical prognostic factors, which could also provide complements to current risk stratification for adjuvant therapy of ccRCC. Our novel multimodel prediction analysis for disease-free survival exhibited significant improvements in prognostic prediction for patients with ccRCC. After further validation in multiple centers and regions, the multimodal system could be a potential practical tool for clinicians in the treatment for ccRCC patients.

Does Serum Uric Acid to Creatinine Ratio Predict Mortality Risk in Patients With Heart Failure?

Previous studies have established a positive correlation between serum uric acid to creatinine (SUA/Cr) ratio and cardiovascular disease, but the relationship between SUA/Cr ratio and the prognosis of heart failure (HF) remains unknown. This study investigated the potential of SUA/Cr ratio as a prognostic predictor for patients with HF. This single-center prospective cohort study enrolled 2,122 patients with HF between March 2013 and June 2017. All patients were divided into 3 groups according to SUA/Cr ratio tertiles and were followed up with until December 31, 2022. The association between SUA/Cr ratio and the prognosis of HF was assessed using the Cox proportional hazards model. The mean (SD) age and mean (SD) SUA/Cr ratio of the study cohort (66% male) were 59.3 (14.7) years and 4.71 (2.09), respectively. During a median follow-up period of 15 months (range, 11-26 months), 390 end-point events were observed. Prognosis analysis revealed that a high SUA/Cr ratio was associated with an increased mortality risk of HF (hazard ratio, 1.62 [95% CI, 1.26-2.09]; P < .001) compared with the SUA/Cr ratio in the lowest tertile. After adjusting for covariates, the hazard ratio for mortality risk of HF was 1.71 (95% CI, 1.23-2.37; P = .001). Subgroup analysis showed that mortality risk increased in direct proportion with the SUA/Cr ratio in female patients, patients with a history of hypertension and β-blocker use, and patients with UA levels below 428 μmol/L and creatinine levels less than 97 mg/dL. Stratification by age; by history of diabetes, hyperlipidemia, and smoking; and by level of fasting plasma glucose, however, had no obvious effect on the association between SUA/Cr ratio and HF prognosis. Patients with higher SUA/Cr ratios had reduced left ventricular ejection fraction and increased left ventricular end-diastolic diameter. A high SUA/Cr ratio was an independent risk factor for the mortality risk of HF.

Incremental value of myocardial global longitudinal strain in predicting major adverse cardiac events among patients with hypertrophic cardiomyopathy.

Endocardial global longitudinal strain (endo-GLS) measured with echocardiography (echo) has been demonstrated to be associated with myocardial fibrosis (MF) and is a prognostic predictor in patients with hypertrophic cardiomyopathy (HCM). Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging showed that MF is primarily located in the myocardial layer of the extremely hypertrophic septal or ventricular wall. We hypothesized that GLS of the myocardial layer (myo-GLS) is more strongly correlated with the extent of LGE (%LGE) and is a more powerful prognostic factor than endo-GLS. A total of 177 inpatients (54.0 [IQR: 43.0, 64.0] years, female 37.3%) with HCM were retrospectively included from May 2019 to April 2021. Among them, 162 patients underwent echocardiographic examination and contrast-enhanced CMR within 7 days. Myo-GLS and %LGE were blindly assessed in a core laboratory. All the patients were followed after they were discharged. During a mean follow-up of 33.77 [IQR 30.05, 35.40] months, 14 participants (7.91%) experienced major adverse cardiac events (MACE). The MACE (+) group showed lower absolute endo-GLS and myo-GLS than the MACE (-) group. Myo-GLS was more associated with %LGE (r=-.68, P<.001) than endo-GLS (r=-.64, P<.001). Cox multivariable analysis indicated that absolute myo-GLS was independently associated with MACE (adjusted hazard ratio=.75, P<.05). Myo-GLS was better than endo-GLS at detecting MACE (+) patients (-8.64%, AUC.939vs. - 16.375%, AUC.898, P<.05). Myo-GLS is a stronger predictor of MACE than endo-GLS in patients with HCM and is highly correlated with %LGE.

Clinical Features and Prognostic Predictors in Patients with Renal Transplant Complicated by SARS-CoV-2 Infection, a Retrospective Single-Center Study.

This study examines the clinical outcomes and prognostic factors of COVID-19 in renal transplant recipients. Given their immunosuppressed status, these patients are at higher risk of severe complications from COVID-19. The study aims to provide healthcare professionals with critical insights for diagnosing and managing this vulnerable population. This retrospective cohort study included adult renal transplant recipients diagnosed with COVID-19. Data on demographics, medical history, laboratory results, and patient outcomes were analyzed to identify clinical characteristics and prognostic factors. This study included 115 renal transplant recipients with COVID-19, predominantly male, with a mortality rate of 10.4% (12 deaths). The overall vaccination rate was 20%. Univariate analysis showed significant differences between survivors and non-survivors in initial serum creatinine levels, and percentages of neutrophils, monocytes, and lymphocytes, along with CRP levels on day 3. Additionally, CRP levels, hemoglobin, and platelet counts on day 7 also differed significantly. Multivariate analysis identified CRP levels on days 3 and 7, day 7 hemoglobin and platelet counts, and concurrent bacterial infections as independent risk factors for mortality. Elevated CRP levels, renal impairment, and bacterial co-infections play a significant role in the outcomes of COVID-19 in kidney transplant recipients. This study highlights the importance of monitoring these factors for early identification and management of high-risk patients.

Prognosis value of galectin-3 in patients with dilated cardiomyopathy: a meta-analysis.

Accurate prediction and assessment of myocardial fibrosis (MF) and adverse cardiovascular events (MACEs) are crucial in patients with dilated cardiomyopathy (DCM). Several studies indicate that galectin-3 (gal-3) as a promising prognostic predictor in patients with DCM. A comprehensive search was conducted in PubMed, EMBASE, the Cochrane Library, and Web of Science for relevant studies up to August 2023. The hazard ratios (HRs) of gal-3 for MACEs in DCM patients, and for MACEs in LGE(+) versus LGE(-) groups, were evaluated. Statistical analysis was performed using STATA SE 14.0 software. Seven studies, encompassing 945 patients, met the eligibility criteria. In DCM patients, abnormally elevated gal-3 levels were indicative of an increased MACEs risk (HR = 1.10, 95% CI [1.00-1.21], I2 = 65.7%, p=0.008). Compared with the LGE(-) group, the level of gal-3 in LGE(+) group was higher (HR = 1.12, 95% CI [1.05-1.19], I2 = 31.4%, p=0.233), and the combination of gal-3 and LGE significantly improved the prediction of MACEs. Sensitivity analysis confirmed the robustness of all results. This study's findings suggest that elevated gal-3 levels significantly correlate with increased MACE risk in DCM, highlighting its potential as a biomarker. However, significant heterogeneity among studies necessitates further research to ascertain gal-3's predictive and diagnostic value in DCM prognosis, particularly in conjunction with LGE. CRD42023471199.

Eosinophil Recovery Time Is Associated with Clinical Outcomes in Patients with Type A Acute Aortic Dissection: a Retrospective Cohort Study.

Type A acute aortic dissection (TA-AAD) patients are prone to life-threatening complications and death. This study aimed to analyze the association between eosinophil (EOS) recovery and clinical outcomes in TA-AAD. A total of 274 patients with TA-AAD were eligible for inclusion, and 54 patients died within 1month. The patients with poor clinical outcomes showed significantly lower EOS count within 8days after surgery. The time-dependent ROC analysis showed that EOS recovery days predicted 1-month death with an AUC of 0.886 and a cutoff of 6days. EOS recovery within 6days was associated with a lower incidence of postoperative infection, a poorer prognosis, and a lower risk of 1-month and 6-month mortality than those requiring more recovery days. Collectively, postoperative early recovery of EOS predicted lower mortality and better prognosis and may be applied as an effective, rapid, and simple tool for the risk stratification and prognostic prediction of patients with TA-AAD.Clinical trial registration number: NCT05409677.

Prognostic mutation signature would serve as a potential prognostic predictor in patients with diffuse large B-cell lymphoma

The present study aimed to elucidate the prognostic mutation signature (PMS) associated with long-term survival in a diffuse large B-cell lymphoma (DLBCL) cohort. All data including derivation and validation cohorts were retrospectively retrieved from The Cancer Genome Atlas (TCGA) database and whole-exome sequencing (WES) data. The Lasso Cox regression analysis was used to construct the PMS based on WES data, and the PMS was determined using the area under the receiver operating curve (AUC). The predictive performance of eligible PMS was analyzed by time-dependent receiver operating curve (ROC) analyses. After the initial evaluation, a PMS composed of 94 PFS-related genes was constructed. Notably, this constructed PMS accurately predicted the 12-, 36-, and 60-month PFS, with AUC values of 0.982, 0.983, and 0.987, respectively. A higher level of PMS was closely linked to a significantly worse PFS, regardless of the molecular subtype. Further evaluation by forest plot revealed incorporation of international prognostic index or tumor mutational burden into PMS increased the prediction capability for PFS. The drug-gene interaction and pathway exploration revealed the PFS-related genes were associated with DNA damage, TP53, apoptosis, and immune cell functions. In conclusion, this study utilizing a high throughput genetic approach demonstrated that the PMS could serve as a prognostic predictor in DLBCL patients. Furthermore, the identification of the key signaling pathways for disease progression also provides information for further investigation to gain more insight into novel drug-resistant mechanisms.

Glioblastoma and Internal Carotid Artery Calcium Score: A Possible Novel Prognostic Partnership?

Purpose: Clinical evidence suggests an association between comorbidities and outcome in patients with glioblastoma (GBM). We hypothesised that the internal carotid artery (ICA) calcium score could represent a promising prognostic biomarker in a competing risk analysis in patients diagnosed with GBM. Methods: We validated the use of the ICA calcium score as a surrogate marker of the coronary calcium score in 32 patients with lung cancer. Subsequently, we assessed the impact of the ICA calcium score on overall survival in GBM patients treated with radio-chemotherapy. Results: We analysed 50 GBM patients. At the univariate analysis, methyl-guanine-methyltransferase gene (MGMT) promoter methylation (p = 0.048), gross total tumour resection (p = 0.017), and calcium score (p = 0.011) were significant prognostic predictors in patients with GBM. These three variables also maintained statistical significance in the multivariate analysis. Conclusions: the ICA calcium score could be a promising prognostic biomarker in GBM patients.

Outcome predictors of post-COVID conditions in the European Academy of Neurology COVID-19 registry.

Several neurological manifestations are part of the post-COVID condition. We aimed to: (1) evaluate the 6-month outcome in the cohort of patients with neurological manifestations during the COVID-19 acute phase and surviving the infection, and find outcome predictors; (2) define the prevalence and type of neurological symptoms persistent at six months after the infection. Data source was an international registry of patients with COVID-19 infection and neurological symptoms, signs or diagnoses established by the European Academy of Neurology. Functional status at six-month follow-up was measured with the modified Rankin scale (mRS), and defined as: "stable/improved" if the mRS at six months was equal as or lower than the baseline score; "worse" if it was higher than the baseline score. By October 30, 2022, 1,003 lab-confirmed COVID-19 patients were followed up for a median of 6.5months. Compared to their pre-morbid status, 522 patients (52%) were stable/improved, whereas 465 (46%) were worse (functional status missing for 16). Age, hospitalization, several pre-COVID-19 comorbidities, and COVID-19 general complications were predictors of a worse status. Amongst neurological manifestations, stroke carried the highest risk for worse outcome (OR 5.96), followed by hyperactive delirium (2.8), and peripheral neuropathies (2.37). On the other hand, hyposmia/hypogeusia (0.38), headache (0.40), myalgia (0.45), and COVID-19 vaccination (0.52) were predictors of a favourable prognosis. Persisting neurological symptoms or signs were reported by 316/1003 patients (31.5%), the commonest being fatigue (n = 133), and impaired memory or concentration (n = 103). Our study identified significant long-term prognostic predictors in patients with COVID-19 and neurological manifestations.

Comprehensive evaluation of serum hepatic proteins in predicting prognosis among cancer patients with cachexia: an observational cohort study

BackgroundHepatic proteins, including albumin, prealbumin, and transferrin have been confirmed to be prognostic predictors in various cancers. This study aimed to comprehensively assess the prognostic value of these three serum markers in patients with cancer cachexia.MethodsThis multicenter prospective cohort study included 1303 cancer cachexia patients, among whom 592 deaths occurred during a median follow-up of 20.23 months. The definition of cachexia was based on the 2011 international consensus. Concordance index (C-index) and receiver operating characteristic (ROC) curves were applied to compare the prognostic performance. The primary outcome was overall survival, which was calculated using the Kaplan–Meier method generated by log-rank test. A Cox proportional hazard regression model was used to identify independent predictors associated with survival. The secondary outcomes included 90-days mortality and quality of life (QoL).ResultsC-index and ROC curves showed that albumin had the most accurate predictive capacity for survival, followed by transferrin and prealbumin. Multivariate Cox analysis confirmed that low albumin (hazard ratio [HR] = 1.51, 95% confidence interval [95%CI] = 1.28–1.80, P < 0.001), prealbumin (HR = 1.42, 95%CI = 1.19–1.69, P < 0.001), and transferrin (HR = 1.50, 95%CI = 1.25–1.80, P < 0.001) were independent risk factors for long-term survival in cancer patients with cachexia. In subgroup analysis, the prognostic value of low albumin was significant in patients with upper gastrointestinal, hepatobiliary and pancreatic, and colorectal cancers; low prealbumin was significant in colorectal cancer; and low transferrin was significant in patients with upper gastrointestinal and colorectal cancer. All three hepatic proteins were valuable as prognostic predictors for patients with advanced (Stage III and IV) cancer with cachexia. The risks of 90-days mortality and impaired QoL were higher in cachexia patients with low albumin, prealbumin, and transferrin levels.ConclusionLow albumin, prealbumin, and transferrin levels were all independent prognostic factors affecting patients with cancer cachexia, especially in patients in the advanced stages. These results highlight the value of routinely checking serum hepatic proteins in clinical practice to predict the prognosis of patients with cancer cachexia.

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage.

Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis. Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions. This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.

Prognostic impact of main pulmonary artery to ascending aorta diameter ratio in patients with severe aortic stenosis underwent transcatheter aortic valve implantation.

Pulmonary hypertension (PH) and right ventricular dysfunctionare poor prognostic predictors in patients underwent transcatheter aortic valve implantation (TAVI) for severe aortic stenosis (AS). The prognostic impact of the main pulmonary artery/ascending aorta diameter ratio (MPA/AOr), measured simply by computed-tomographic angiography (CTA), was investigated in this patient group. A total of 374 retrospectively evaluated patients (mean age 78.1 ± 8.4 years, 192 [51.3%]females)who underwent TAVI for severe AS were included. MPA/AOr was measured on preprocedural CTA in all patients and the effect of this measurement on the presence of PH, in-hospital and 2-year-overall long-term mortality was investigated. The presence of PH was defined as a systolic pulmonary artery pressure (sPAP) >42 mmHg measured by echocardiography. According to multivariate-logistic-regression analysis, MPA/AOr(adjusted [Adj] odds ratio [OR]: 1.188, confidence interval [CI] 95% [1.002-1.410], p = 0.048), tricuspid annular plane systolic excursion (TAPSE)(adj OR:0.736, CI 95% [0.663-0.816], p < 0.001)and left atrial diameter (adj OR:1.051, CI 95% [1.007-1.098], p = 0.024)were identified as independent predictors of PH. In addition, a statistically significant correlation was found between MPA/AOr and TAPSE (r: -0.283, p < 0.001). Furthermore, MPA/AOr was found to be an independent predictor of both in-hospital (adj OR:1.434, CI 95% [1.093-1.881], p = 0.009)and 2-year long-term (adj OR:1.518, CI 95% [1.243-1.853], p < 0.001)mortality in multivariate analysis including TAPSE, STS score and sPAP. In the 2-year Kaplan-Meier survival probability analysis, an MPA/AOr >0.86 was found to have ahazard ratioof 3.697 (95% CI: 2.341-5.840), with a log-rank p < 0.001. MPA/AOr, which can be measured simply by CTA, may be useful as an indicator of the presence of PH and poor prognosis in patients planned for TAVI for severe AS.

Simultaneous assessment of stress hyperglycemia ratio and glycemic variability to predict mortality in patients with coronary artery disease: a retrospective cohort study from the MIMIC-IV database

BackgroundStress hyperglycemia and glycemic variability (GV) can reflect dramatic increases and acute fluctuations in blood glucose, which are associated with adverse cardiovascular events. This study aimed to explore whether the combined assessment of the stress hyperglycemia ratio (SHR) and GV provides additional information for prognostic prediction in patients with coronary artery disease (CAD) hospitalized in the intensive care unit (ICU).MethodsPatients diagnosed with CAD from the Medical Information Mart for Intensive Care-IV database (version 2.2) between 2008 and 2019 were retrospectively included in the analysis. The primary endpoint was 1-year mortality, and the secondary endpoint was in-hospital mortality. Levels of SHR and GV were stratified into tertiles, with the highest tertile classified as high and the lower two tertiles classified as low. The associations of SHR, GV, and their combination with mortality were determined by logistic and Cox regression analyses.ResultsA total of 2789 patients were included, with a mean age of 69.6 years, and 30.1% were female. Overall, 138 (4.9%) patients died in the hospital, and 404 (14.5%) patients died at 1 year. The combination of SHR and GV was superior to SHR (in-hospital mortality: 0.710 vs. 0.689, p = 0.012; 1-year mortality: 0.644 vs. 0.615, p = 0.007) and GV (in-hospital mortality: 0.710 vs. 0.632, p = 0.004; 1-year mortality: 0.644 vs. 0.603, p < 0.001) alone for predicting mortality in the receiver operating characteristic analysis. In addition, nondiabetic patients with high SHR levels and high GV were associated with the greatest risk of both in-hospital mortality (odds ratio [OR] = 10.831, 95% confidence interval [CI] 4.494–26.105) and 1-year mortality (hazard ratio [HR] = 5.830, 95% CI 3.175–10.702). However, in the diabetic population, the highest risk of in-hospital mortality (OR = 4.221, 95% CI 1.542–11.558) and 1-year mortality (HR = 2.013, 95% CI 1.224–3.311) was observed in patients with high SHR levels but low GV.ConclusionsThe simultaneous evaluation of SHR and GV provides more information for risk stratification and prognostic prediction than SHR and GV alone, contributing to developing individualized strategies for glucose management in patients with CAD admitted to the ICU.

Prognostic Value of the Geriatric Nutritional Risk Index in Previously Untreated Patients with Advanced Non-Small Cell Lung Cancer Treated with a Combination Therapy of Anti-PD-1/-PD-L1 Antibodies and Platinum-Based Chemotherapy: A Multicenter Retrospective Study

Introduction: Combination therapy of anti-programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) antibodies and platinum-based chemotherapy has been widely used as a first-line treatment for patients with unresectable advanced non-small cell lung cancer (NSCLC) in clinical settings; however, prognostic biomarkers associated with survival outcomes have not been sufficiently investigated. Methods: We enrolled 147 previously untreated patients with advanced NSCLC who were treated with a combination therapy of anti-PD-1/-PD-L1 antibodies and platinum-based chemotherapy at eight institutions in Nagano Prefecture between December 2018 and April 2023. We evaluated the prognostic value of the geriatric nutritional risk index (GNRI), a systemic inflammatory nutritional biomarker calculated from body weight and serum albumin level, for patients with NSCLC treated with a combination therapy of anti-PD-1/-PD-L1 antibodies and platinum-based chemotherapy. Results: The cutoff value of the GNRI was set at 92. The high GNRI and low GNRI groups included 88 and 59 patients, respectively. The median follow-up period was 15.9 months. The overall survival (OS) in the high GNRI group was significantly longer than that in the low GNRI group (27.9 vs. 15.6 months, p = 0.015). Multivariate analysis revealed that a high GNRI was an independently favorable prognostic predictor for OS (hazard ratio, 1.73; 95% confidence interval, 1.06–2.86; p = 0.031). Conclusion: The present study demonstrates that the GNRI is a useful prognostic predictor in patients with NSCLC treated with a combination therapy of anti-PD-1/-PD-L1 antibodies and platinum-based chemotherapy in clinical settings.

A zinc metabolism-related gene signature for predicting prognosis and characteristics of breast cancer.

Breast cancer is one of the most serious and prevalent malignancies. Zinc is commonly known to play a crucial role in the development and progression of breast cancer; however, the detailed mechanisms underlying this role are not well understood. This study aimed to develop a zinc metabolism-related gene (ZMRG) signature based on a multi-database study to predict patient prognosis and investigate the relationship between drug therapy response and immune enrichment. Data for breast cancer samples from The Cancer Genome Atlas and Gene Expression Omnibus databases were screened for zinc metabolism-related genes using the Molecular Signature Database. Cox and Least Absolute Shrinkage and Selection Operator regressions were performed to construct a ZMRG signature. To assess the predictive performance of the gene signature, Kaplan-Meier analysis and receiver operating characteristic curves were used. Additionally, we utilised single-sample gene set enrichment analysis, the Tumour Immune Estimation Resource, the Genomics of Drug Sensitivity in Cancer database, and the Cancer Therapeutics Response Portal to investigate the association between the tumour microenvironment and drug sensitivity. Quantitative PCR was used to assess the expression of each gene in the signature in breast cancer cell lines and patient samples. Five ZMRGs were identified (ATP7B, BGLAP, P2RX4, SLC39A11, and TH) and a risk profile was constructed for each. Two risk groups, high- and low-risk, were identified in this way, and the high-risk score subgroups were found to have worse prognosis. This risk profile was validated using the GSE42568 dataset. Tumour microenvironment and drug sensitivity analyses showed that the expression of these five ZMRGs was significantly associated with immune response. The high-risk group showed substantial immune cell infiltration and enrichment of immune pathways, and patients were more sensitive to drugs commonly used in breast cancer. The ZMRG signature represents a new prognostic predictor for patients with breast cancer, and may also provide new insights into individualised treatment of breast cancer.

Left atrial conduit strain derived from cardiac magnetic resonance is an independent predictor of left ventricular reverse remodeling in patients with nonischemic cardiomyopathy

BackgroundIn some patients with nonischemic cardiomyopathy (NICM), left ventricular (LV) function improves with medical assistance, resulting in left ventricular reverse remodeling (LVRR). However, predictors of LVRR are not fully understood. The left atrium (LA) has been reported as a prognostic predictor in patients with heart failure (HF). The present study aimed to evaluate clinical predictors of LVRR related to LA function on cardiac magnetic resonance (CMR).MethodsA total of 103 patients with reduced left ventricular ejection fraction (LVEF) were enrolled in this retrospective study between September 2015 and July 2021. CMR parameters, including strain data, were measured in all patients. Echocardiographic data obtained approximately 2 years after enrollment were analyzed to assess LVRR.ResultsLVRR occurred in 46 patients (44.7%) during follow-up. The value of LA conduit strain was higher in the LVRR group than in the non-LVRR group (6.6 [interquartile range (IQR): 5.6–9.3]% versus 5.0 [IQR: 3.0-6.2]%; p < 0.001). The multivariate logistic regression analysis showed that LA conduit strain was an independent predictor of LVRR (odds ratio [OR]: 1.216, 95% confidence interval [CI]: 1.050–1.408; p = 0.009). The area under the receiver operating characteristic (ROC) curve of the LA conduit strain was 0.746, and the cutoff value was 6.2%. The Kaplan‒Meier analysis revealed that the incidence of adverse cardiac events was significantly lower in patients with LA conduit strain > 6.2% compared to those with ⩽6.2%. (log-rank test, p = 0.019).ConclusionsLA conduit strain derived from CMR is an independent predictor of LVRR in patients with NICM.

Systemic Immune-Inflammation Index is a Prognostic Predictor for Patients With Acute Ischemic Stroke Treated With Intravenous Thrombolysis.

To investigate whether baseline systemic immune-inflammation index (SII) is associated with 3-month poor prognosis and early neurological outcomes in patients with acute ischemic stroke treated with intravenous thrombolysis. A total of 221 consecutive patients were enrolled in the retrospective study. The primary endpoints were poor functional outcomes or death at 3 months. Secondary endpoints were early neurological deterioration (END) or symptomatic intracerebral hemorrhage within 24 hours. Receiver operating characteristic curve analyses was performed to assess the overall discriminative ability of SII in predicting the 4 endpoints. We also performed the Spearman correlation test to evaluate the relationship between SII and stroke severity. Univariable and multivariable logistic regression analyses were performed to evaluate the associations between SII and endpoints. The cutoff values of SII were 504.99×10 9 /L for predicting a 3-month poor prognosis (sensitivity, 70.9% and specificity, 69.6%), 524.47×10 9 /L for predicting 3-month death (sensitivity, 78.9% and specificity, 59.9%) and 504.99×10 9 /L for predicting END (sensitivity, 70.7% and specificity, 62.6%), respectively. A positive association between SII and the National Institutes of Health Stroke Scale was observed ( rs = 0.306, P < 0.001). Multivariable analyses indicated that SII was independently associated with 3-month poor prognosis [odds ratio (OR) = 5.384; 95% CI: 2.844-10.193; P < 0.001], 3-month death (OR = 2.592, 95% CI: 1.046-6.421, P = 0.040) and END (OR = 3.202, 95% CI: 1.796-5.707, P < 0.001). Increased baseline SII was associated with END and 3-month poor outcomes, and may act as a potential prognostic predictor for acute ischemic stroke patients treated with intravenous thrombolysis.

Development and validation of a model integrating clinical and coronary lesion-based functional assessment for long-term risk prediction in PCI patients.

To establish a scoring system combining the ACEF score and the quantitative blood flow ratio (QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention (PCI). In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263 consecutive cases of CAD patients after PCI in PANDA III trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort. In both the Random Forest Model and the DeepSurv Model, age, renal function (creatinine), cardiac function (LVEF) and post-PCI coronary physiological index (QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age (years)/EF (%) + 1 (if creatinine ≥ 2.0 mg/dL) + 1 (if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination (C-statistic = 0.651; 95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration (Hosmer-Lemeshow χ2 = 7.070; P = 0.529) for predicting 2-year patient-oriented composite endpoint (POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan-Meier analysis (adjusted HR = 1.89; 95% CI: 1.18-3.04; log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group. An improved scoring system combining clinical and coronary lesion-based functional variables (ACEF-QFR) was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.