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BACKGROUND: Non-invasive risk stratification for patients with endometrial carcinoma (EC) is important for developing personalised treatment plans. Our study aimed to explore the ability of quantitative MRI parameters to predict the risk stratification of EC patients based on molecular classification. METHODS: Fifty-three patients with histologically proven EC who underwent pelvic MRI and surgical treatment at our hospital between January 2020 and August 2022 were assessed. The tumour volume (TV) and uterine volume (UV) were estimated with the ellipsoid formula and used to calculate the tumour volume ratio (TVR). The mean apparent diffusion coefficient (ADC) of the tumour was measured on a workstation. Quantitative MRI parameters were compared among different risk groups via unpaired Student's t-tests or Mann-Whitney's U-tests. RESULTS: The TV and TVR were significantly different between the low- and high-risk groups (p < 0.001), and cut-off values of 5342 mm3 and 0.055 allowed the differentiation of the high-risk group from the low-risk group, with 77% and 85% sensitivity and 78% and 78% specificity, respectively. There was a significant difference in the ADC between the two groups (p = 0.026), and a cut-off value of 0.65 × 10-3 mm2/s allowed differentiation of the risk groups, with 93% sensitivity and 39% specificity. CONCLUSIONS: Quantitative MRI parameters such as the TV, TVR and ADC may be helpful in preoperatively assessing the risk stratification of patients with EC based on molecular classification.

For patients with endometrial carcinoma (EC), it is important to assess the risk stratification based on molecular classification for developing treatment plans, but risk stratification is obtained most accurately from postoperative samples. We used non-invasive and easily accessible quantitative parameters of magnetic resonance imaging for preoperatively evaluating the risk stratification in these patients. We found that the quantitative parameters may be helpful in preoperatively assessing the risk stratification of patients with EC on the basis of molecular classification.

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OBJECTIVE: This study aims to analyse multiparametric MRI (mpMRI) characteristics of patients diagnosed with ISUP grade group (GG) 1 prostate cancer (PC) on initial target plus systematic MRI/TRUS fusion-guided biopsy and investigate histopathological progression during follow-up. METHODS: A retrospective single-centre cohort analysis was conducted on consecutive patients with mpMRI visible lesions (PI-RADS ≥ 3) and detection of ISUP-1-PC at the time of initial biopsy. The study assessed clinical, mpMRI, and histopathological parameters. Subcohorts were analysed with (1) patients who had confirmed ISUP-1-PC and (2) patients who experienced histopathological upgrading to ISUP ≥ 2 PC during follow-up either at re-biopsy or radical prostatectomy (RP). RESULTS: A total of 156 patients (median age 65 years) between March 2014 and August 2021 were included. Histopathological upgrading to ISUP ≥ 2 was detected in 55% of patients during a median follow-up of 9.5 months (IQR 2.2-16.4). When comparing subgroups with an ISUP upgrade and sustained ISUP 1 PC, they differed significantly in contact length of the index lesion to the pseudocapsule, ADC value, PI-RADS category, and the MRI grading group (mGG) (p < 0.05). In the ISUP GG ≥ 2 subgroup, 91% of men had PI-RADS category 4 or 5 and 82% exhibited the highest mGG (mGG3). In multivariate analysis, mGG was the only independent parameter for predicting ISUP ≥ 2-PC in these patients. CONCLUSIONS: MRI reveals important information about PC aggressiveness and should be incorporated into clinical decision-making when ISUP-1-PC is diagnosed. In cases of specific MRI characteristics adverse to the histopathology, early re-biopsy might be considered. CLINICAL RELEVANCE STATEMENT: In cases with clear MRI characteristics for clinically significant prostate cancer (e.g., mGG 3 and/or PI-RADS 5, cT3, or clear focal PI-RADS 4 lesions on MRI) and ISUP GG 1 PC diagnosed on initial prostate biopsy, MRI findings should be incorporated into clinical decision-making and early re-biopsy (e.g., within 6 months) might be considered. KEY POINTS: MRI reveals important information about prostate cancer (PC) aggressiveness. MRI should be incorporated into clinical decision-making when ISUP GG 1 PC is diagnosed on initial prostate biopsy. In cases of specific MRI characteristics adverse to the histopathology, early re-biopsy might be considered.

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Objective: This study aimed to compare the clinical risks and outcomes of COVID-19 and influenza. Methods: The search for relevant articles was conducted using both a database search method and a manual search, which involved searching through the reference lists of articles related to the topic for additional studies. The Quality assessment was carried out using the Newcastle Ottawa tool, and the data analysis was conducted using the Review Manager Software (RevMan 5.4.1). Results: The meta-analysis results indicated that COVID-19 patients had similar lengths of hospital stays (SMD: -0.25; 95% CI: -0.60-0.11; p=0.17). However, COVID-19 patients had significantly higher mortality rates (RR: 0.28; 95% CI: 0.21-0.37; p<0.0001), in-hospital complications (RR: 0.57; 95% CI: 0.50-0.65; p<0.00001), intensive care unit (ICU) admissions (OR: 0.48; 95% CI: 0.37-0.61; p<0.00001), length of ICU stay (SMD: -0.45; 95% CI: -0.83-0.06; p=0.02), and mechanical ventilation use (OR: 0.36; 95% CI: 0.28-0.46; p<0.00001). Conclusion: The findings suggested that COVID-19 was more severe than influenza. Therefore, "flu-like" symptoms should not be dismissed without a clear diagnosis, especially during the winter when influenza is more prevalent.

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BACKGROUND/OBJECTIVES: The Berlin-Frankfurt-Münster (BFM)-S classification is a crucial prognostic indicator in children experiencing first-relapsed acute lymphoblastic leukemia (ALL). Early molecular response to therapy, evaluated by measurable/minimal residual disease (MRD), has a significant impact on the survival of patients with childhood ALL. Applying risk stratification based on the BFM-S classification and MRD response after induction, the first nationwide prospective multicenter study, ALL-R08, was conducted in children with first-relapsed ALL in Japan. METHODS: The ALL-R08 study comprised two parts: ALL-R08-I, an observational study aimed at obtaining an overall picture of outcomes in first-relapsed childhood ALL, and ALL-R08-II, a clinical trial for the non-T-ALL S2 risk group. In ALL-R08-II, patients with an MRD level of ≥10-3 at the end of induction therapy were assigned to undergo allogeneic hematopoietic stem cell transplantation (allo-HCT), whereas those with an MRD level less than 10-3 and isolated extramedullary relapse continued to receive chemotherapy. RESULTS: In total, 163 patients were enrolled in the ALL-R08 study, and 82 and 81 patients were enrolled in the ALL-R08-I and the ALL-R08-II, respectively. In ALL-R08-I, the probability of 3-year event-free survival (EFS) for patients with S1, S2, S3, S4, and post-HCT groups was 83% ± 15%, 37% ± 11%, 28% ± 8%, 14% ± 7%, and 0%, respectively. In the ALL-R08-II trial, 3-year EFS in patients with post-induction MRD less than 10-3 and ≥10-3 was 70% ± 9% (n = 27) and 68% ± 8% (n = 31) (p = .591), respectively. CONCLUSIONS: ALL-REZ BFM-type treatment is equally effective for children with first-relapsed ALL treated according to the Japanese frontline protocols and for children with first-relapsed ALL treated according to the BFM-type frontline protocols.

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OBJECTIVE: To investigate the correlation between neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and risk stratification indicators as well as thrombus burden in patients with moderate-to-high risk acute pulmonary embolism (APE), and to assess the changes in these parameters following interventional therapy. METHODS: This study retrospectively included patients with moderate-to-high risk APE who were admitted to the Department of Interventional Vascular Surgery at Putian First Hospital from May 2020 to May 2024. All patients received anticoagulation therapy, pulmonary artery catheter-directed thrombolysis, and/or mechanical thrombectomy. Patients were further divided into subgroup A if they did not present with any of the following conditions at admission: a) acute inflammatory diseases (including lung infections); b) malignant tumors; c) history of trauma or surgery within the past 2 months. Patients with any of the aforementioned conditions were classified as subgroup B. Additionally, 50 healthy individuals were randomly selected as the healthy control group. RESULTS: The NLR and PLR in subgroup A were significantly lower than those in subgroup B (P < .01). Compared with the healthy control group, the NLR in the APE group and subgroup A was significantly higher (P < .001). There were no significant differences in NLR and PLR between the troponin I-negative and troponin I-positive groups (P > .05), or between the N-terminal pro-B-type natriuretic peptide (NT-proBNP)-negative and NT-proBNP-positive groups (P > .05). There were no significant correlations between NLR and PLR with risk stratification indicators and pulmonary artery embolism index (P > .05). Compared with before treatment, NLR, troponin I, NT-proBNP, right ventricular diameter/left ventricular diameter ratio, and pulmonary artery embolism index were significantly reduced after treatment (P < .05), while there was no significant difference in PLR before and after treatment (P > .05). CONCLUSION: Elevated NLR in patients with APE, which decreases after effective treatment, may be used for assessing disease status and treatment efficacy. However, there is no correlation between NLR and risk stratification indicators or thrombus burden. PLR does not demonstrate significant value in assessing APE.

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BACKGROUND: Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. AIM: To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. METHODS: The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators. RESULTS: Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature. CONCLUSION: The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

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Family history (FH) of cancer and polygenic risk scores (PRS) are pivotal for cancer risk assessment, yet their combined impact remains unclear. Participants in the UK Biobank (UKB) were recruited between 2006 and 2010, with complete follow-up data updated until February 2020 for Scotland and January 2021 for England and Wales. Using UKB data (N = 442,399), we constructed PRS and incidence-weighted overall cancer PRS (CPRS). FH was assessed through self-reported standardized questions. Among 202,801 men (34.6% with FH) and 239,598 women (42.0% with FH), Cox regression was used to examine the associations between FH, PRS, and cancer risk. We found a significant dose-response relationship between FH of cancer and corresponding cancer risk (Ptrend < .05), with over 10 significant pairs of cross-cancer effects of FH. FH and PRS are positively correlated and independent. Joint effects of FH of cancer (multiple cancers) and PRS (CPRS) on corresponding cancer risk were observed: for instance, compared with participants with no FH of cancer and low PRS, men with FH of cancer and high PRS had the highest risk of colorectal cancer (hazard ratio [HR]: 3.69, 95% confidence interval [CI]: 3.01-4.52). Additive interactions were observed in prostate and overall cancer risk for men and breast cancer for women, with the most significant result being a relative excess risk of interaction (RERI) of 2.98, accounting for ~34% of the prostate cancer risk. In conclusion, FH and PRS collectively contribute to cancer risk, supporting their combined application in personalized risk assessment and early intervention strategies.

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BACKGROUND: Risk stratification in patients with Brugada syndrome (BrS) is challenging, especially in those at intermediate risk. The Predicting Arrhythmic evenT (PAT) score has recently been demonstrated to be excellent for predicting future arrhythmic events in patients without prior ventricular fibrillation (VF). However, validation studies are lacking. OBJECTIVE: This study aimed to assess the performance of a novel risk stratification model in predicting future VF events in patients with BrS in a Japanese multicenter cohort. METHODS: The PAT score was calculated for 413 patients with BrS (mean age, 50.9±13.6 years; 395 men) from 59 hospitals in Japan, including 314 patients without prior VF. The incidence of developing VF during the follow-up period was investigated. RESULTS: During the 106.8-month follow-up period, 54 patients (13.1%) experienced VF events. Of the 314 patients without prior VF at enrollment, 14 (4.5%) experienced VF events. The incidence of VF events during the follow-up period was significantly higher in patients with PAT scores ≥10 than in those with scores <10 (41/173 [23.7%] vs. 13/240 [5.4%], p<0.0001) in the total cohort. No difference was observed in the incidence of VF events between patients with PAT scores ≥10 and <10 among the 314 patients without prior VF (6/86 [7.0%] vs. 8/228 [3.5%], p=0.22). PAT scores ≥10 predicted future VF events with a sensitivity and specificity of 42.9% and 73.3%, respectively. CONCLUSION: This Japanese multicenter registry demonstrated that the novel risk stratification model could not accurately predict future VF events in patients with BrS, but without prior VF.

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OBJECTIVE: We explored the preliminary value of abnormal spindle-like microcephaly- associated (ASPM) protein in aiding precise risk sub-stratification, prediction of metabolic heterogeneity, and prognosis of neuroblastoma (NB). METHODS: This retrospective study enrolled newly diagnosed patients with NB who underwent positron emission tomography/computed tomography (PET/CT) before therapy, and tumor tissue was collected after surgery. Regression analysis was used to evaluate ASPM expression and risk stratification in patients with NB. The expression levels of ASPM, clinical information, and PET/CT text features were analyzed using univariate and multivariate survival analyses. Finally, a correlation analysis was used to explore the relationship between ASPM and tumor metabolic heterogeneity. RESULTS: There were 48 patients with NB in this study (35 boys and 13 girls); 22 patients progressed and 16 died. We found that the level of ASPM was highly associated with risk stratification (OR = 5.295, 95%IC: 1.348-41.722, p = 0.021). Patients with NB and high-risk stratification with high ASPM level had a lower 3-year progression-free survival (PFS) rate (14.28%) and 1-year PFS rate (57.14%) than those with low ASPM level (57.14% and 93.75%, respectively). Using univariate and multivariate survival analyses, this study revealed that ASPM and LDH were independent risk factors for both PFS and overall survival (OS), whales GLZLM_ZLNU was only a risk factor for PFS. CONCLUSION: ASPM holds promise as a novel biomarker for refining current risk stratification and predicting prognosis in neuroblastoma. Elevated levels of ASPM, LDH, and GLZLM_ZLNU may be associated with poorer survival outcomes in neuroblastoma patients.

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BACKGROUND: To establish a nomogram predicting postoperative recurrence-free survival (RFS) in patients with nonmetastatic renal cell carcinoma (RCC) of pathological T3a (pT3a) stage undergoing nephrectomy. MATERIALS AND METHODS: A retrospective review included 668 patients with pT3a RCC between 2008 and 2019, randomly divided into training and validation groups (7:3 ratio). Cox regression analysis established the RFS-predicting nomogram in the training group. Nomogram performance was assessed using Harrell's concordance index (C-index), time-dependent receiver operating characteristic curve, decision curve analysis, and Kaplan-Meier survival analysis. RESULTS: Of the 668 patients with pT3a RCC, 167 patients experienced local recurrence or distant metastasis. Using multivariable Cox regression analysis, tumor size, ISUP grade, necrosis, capsular invasion, pT3a invasion pattern were identified as the significant predictors for RFS to establish the nomogram. The C-index of the nomogram was 0.753 (95% CI, 0.710-0.796) and 0.762 (95% CI, 0.701-0.822) for the training and validating group, respectively. The areas under the 1-year, 3-year and 5-year RFS receiver operating characteristic curves were 0.814, 0.769 and 0.768, respectively. Decision curve analysis showed the optimal application of the model in clinical decision-making. Patients with low risk T3a RCC have better RFS than those with high risk T3a RCC. CONCLUSION: Tumor size, ISUP grade, necrosis, capsular invasion and T3a invasion patterns were independent risk factors for worse RFS in patients with nonmetastatic pT3a RCC. The current nomogram could effectively predict the RFS of patients with nonmetastatic pT3a RCC.

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BACKGROUND: Chagas cardiomyopathy (CCM) is increasingly prevalent in developed countries due to migration from endemic areas. Accurate risk stratification is crucial due to the variable clinical course of CCM. OBJECTIVE: To analyze the association between Rassi score progression and electrophysiology study (EPS) changes in CCM patients. METHODS: This prospective, observational cohort study involved CCM patients from two tertiary hospitals. Patients were classified as low, intermediate, or high risk based on the Rassi score. Data collected included demographics, clinical history, and diagnostic tests. EPS assessed AH, HH, and HV intervals, and inducibility of ventricular arrhythmias. Follow-ups were at 30 days and six-month intervals, with individualized discussions for cardiac implantable electric devices (CIED) based on EPS results. RESULTS: Of 67 screened CCM patients, 59 underwent EPS. The mean Rassi score was 8.7 ±â€¯4.5 points, with 33.8 % low, 38.9 % intermediate, and 27.1 % high risk. EPS abnormalities were found in 57.6 % of patients, mainly VT/VF (52.5 %). Most induced ventricular arrhythmias were monomorphic VT (80.7 %). A significant association was found between Rassi score risk classification and EPS changes (OR = 1.88 95 %CI: 1.15-3.06 p = 0.02). Higher Rassi scores correlated with VT presence on EPS (p = 0.0036). Syncope/pre-syncope had an OR 2.45 95 %CI:1.21-4.94; p = 0.012, independent of Rassi risk. Decreased ejection fraction was linked to EPS changes (p = 0.04). CONCLUSION: EPS changes among CCM was associated with progression of the Rassi score, indicating its utility as a stratification tool. Factors such as the presence of syncope/pre-syncope, decreased LVEF and wall motion abnormalities emerged as independent predictors within Rassi scores for changes in EPS.

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Background: Identification of prognostic variables for poor outcomes following open reduction internal fixation (ORIF) of displaced proximal humerus fractures have been limited to singular, linear factors and subjective clinical intuition. Machine learning (ML) has the capability to objectively segregate patients based on various outcome metrics and reports the connectivity of variables resulting in the optimal outcome. Therefore, the purpose of this study was to (1) use unsupervised ML to stratify patients to high-risk and low-risk clusters based on postoperative events, (2) compare the ML clusters to the American Society of Anesthesiologists (ASA) classification for assessment of risk, and (3) determine the variables that were associated with high-risk patients after proximal humerus ORIF. Methods: The American College of Surgeons-National Surgical Quality Improvement Program database was retrospectively queried for patients undergoing ORIF for proximal humerus fractures between 2005 and 2018. Four unsupervised ML clustering algorithms were evaluated to partition subjects into "high-risk" and "low-risk" subgroups based on combinations of observed outcomes. Demographic, clinical, and treatment variables were compared between these groups using descriptive statistics. A supervised ML algorithm was generated to identify patients who were likely to be "high risk" and were compared to ASA classification. A game-theory-based explanation algorithm was used to illustrate predictors of "high-risk" status. Results: Overall, 4670 patients were included, of which 202 were partitioned into the "high-risk" cluster, while the remaining (4468 patients) were partitioned into the "low-risk" cluster. Patients in the "high-risk" cluster demonstrated significantly increased rates of the following complications: 30-day mortality, 30-day readmission rates, 30-day reoperation rates, nonroutine discharge rates, length of stay, and rates of all surgical and medical complications assessed with the exception of urinary tract infection (P < .001). The best performing supervised machine learning algorithm for preoperatively identifying "high-risk" patients was the extreme-gradient boost (XGBoost), which achieved an area under the receiver operating characteristics curve of 76.8%, while ASA classification had an area under the receiver operating characteristics curve of 61.7%. Shapley values identified the following predictors of "high-risk" status: greater body mass index, increasing age, ASA class 3, increased operative time, male gender, diabetes, and smoking history. Conclusion: Unsupervised ML identified that "high-risk" patients have a higher risk of complications (8.9%) than "low-risk" groups (0.4%) with respect to 30-day complication rate. A supervised ML model selected greater body mass index, increasing age, ASA class 3, increased operative time, male gender, diabetes, and smoking history to effectively predict "high-risk" patients.

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Background: The History, Electrocardiogram, Age, Risk factors, and Troponin I (HEART) score is a simple method to risk stratify patients with chest pain according to the risk for incidence of major adverse cardiac events (MACEs). Materials and methods: A 202-patient prospective, single center study at Sri Siddhartha Medical College, Tumkur. Patients included were those who were presented to the emergency department (ED) due to non-traumatic chest pain, irrespective of age or any previous medical treatments, and were later referred to the cardiac care unit (CCU), cardiology department (CD). The end point of the study was the incidence of MACE. Results: There was a high occurrence of endpoint-myocardial infarction (MI) as MACE among patients with a high-risk HEART score (p < 0.001). About 52 patients (81.3%) who had MI had a high-risk score and 2 patients (3.1%) who had an endpoint of MI had a low-risk score. Sensitivity of HEART score to anticipate MACE was 91%, and the specificity was 80%. Conclusions: Our prospective study demonstrates the high sensitivity of the HEART score to effectively risk stratify patients and project the phenomenon of MACE. We support the use of the HEART score as a fast and accurate risk stratification tool in the ED. How to cite this article: Anwar I, Sony D. HEART Score: Prospective Evaluation of Its Accuracy and Applicability. Indian J Crit Care Med 2024;28(8):748-752.

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Introduction: Urinary cytology (UrCy) is highly sensitive to diagnosing high-grade urothelial carcinoma (HGUC) but cannot predict muscularis propria invasion. Further, the atypical urothelial cell category (AUC) may have variable outcomes. Image morphometry (IM) may be a valuable adjunct technique in this setting. Hence, we evaluated IM in the AUC and HGUC categories to improve the diagnostic performance. Materials and Methods: The following six nuclear parameters were evaluated by IM on 3150 cells: nucleo-cytoplasmic (N:C) ratio, nuclear area, diameter, perimeter, standard deviation of the nuclear area (SDNA; pleomorphism) and integrated density (ID; nuclear chromasia), using the ImageJ software, in three cohorts based on the histopathology outcome: 20 cases of AUC - benign non-neoplastic outcome (AUC-B); 22 cases of HGUC Muscle invasive (HGUC-MI) and 21 cases of HGUC non-muscle invasive (HGUC-MF). Results: A retrospective analysis of urine cytology. The patient's ages ranged from 36 to 85 years, with a mean age of 60.6. The male-to-female ratio was 5.4:1. A total of 20 cases of AUC-B and 43 cases of HGUC were selected for IM analysis. HGUC cases had higher nuclear parameters than AUC-B, and HGUC-MI had higher SDNA, ID, diameter, and area than HGUC-MF. SDNA and ID predict muscularis propria invasion in HGUC. Conclusions: Image morphometry successfully differentiates HGUC cases from benign non-neoplastic ones and might help to identify muscularis propria invasion in HGUC using a combination of nuclear parameters.

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Purpose: Stage IV ovarian cancer is a tumor with a poor prognosis and lacks prognostic models. This study constructed and validated a model to predict overall survival (OS) in patients with newly diagnosed stage IV ovarian cancer. Methods: The data of this study were extracted from SEER database. Cox regression analysis was used to construct the nomogram model and implemented it in an online web application. Concordance index (C-index), calibration curve, area under receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to verify the performance of the model. Results: A total of 6062 patients were collected in this study. The analysis showed that age, race, histological grade, histological differentiation, T stage, CA125, liver metastasis, primary site surgery, and chemotherapy were independent prognostic parameters, and were used to construct the nomogram model. The C-index of the training group and the verification group was 0.704 and 0.711, respectively. Based on the score of the nomogram responding risk classification system is constructed. The online interface of Alfalfa-IVOC-OS is free to use. In addition, the racial analysis found that Asian or Pacific Islander people had higher survival rates than white and black people. Conclusion: This study established a new survival prediction model and risk classification system designed to predict OS time in patients with stage IV ovarian cancer to help clinicians evaluate the prognosis of patients with stage IV ovarian cancer.

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BackGround: Considerable studies have demonstrated a significant association between red blood cell distribution width (RDW) and clinical adverse events in cardiovascular or respiratory diseases, infections, and pulmonary embolism. However, there are limited data on prognostic predictions for patients suffering from chronic obstructive pulmonary disease (COPD). Methods: This study conducted a retrospective cohort analysis using data gathered from patients who diagnosed with COPD in the respiratory department of The Central hospital of Enshi Tujia and Miao Autonomous Prefecture between 2018 and 2021. Specifically, the RDW was recorded on their first admission. Multivariate logistic regression analysis were employed to examine the correlation between RDW and deterioration of COPD within one-year period. Results: The cohort of 1799 patients in the study comprised 74.7% male and had an average age of 68.9 ± 9.9 years. The fully adjusted model revealed that, the RDW-middle group (≤13.7,>12.8; OR 1.5, 95% CI 1.0-2.3, p=0.055) and the RDW-high group (>13.7; OR 1.7, 95% CI 1.1-2.6, p=0.013) had a 50% and 70% increased risk of deterioration within 1 year, respectively, in comparison with the RDW-low group (≤12.8). Subgroup analysis indicated that this trend was more significant in patients with hypertension (p for interaction = 0.016), and the probability of deterioration within 1 year in the RDW-high group was 3.3 times higher compared to the RDW-low group (OR 3.3, 95% CI 1.4-7.9, p=0.008). Conclusion: A significant association was observed between the increase in RDW and the heightened risk of deterioration within a year in patients diagnosed with COPD. Most importantly, our findings suggested the importance of RDW in enhancing the risk stratification and prevention of deterioration of COPD.

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Colon adenocarcinoma (COAD) represents a significant health concern within the population. Advancing our understanding of COAD is imperative for early detection, enabling personalized treatment interventions, and facilitating the development of effective preventive measures. The coagulation system plays a role in tumor-related pathological processes; however, its specific involvement in COAD and potential contributors remain unclear. This study aimed to establish a novel risk stratification approach by analyzing coagulation related genes (CRGs) associated with COAD. Through a comprehensive bioinformatics analysis of data from public databases, we screened COAD associated CRGs and characterized the associated molecular subtypes. After a comprehensive analysis of the characteristics of each subtype, we applied differentially expressed genes in CRG subtypes to establish a new risk stratification method. Clinical subgroup analysis, immunoinfiltration analysis, therapeutic reactivity prediction and other analytical methods suggest the potential clinical value of the established risk stratification method. As one of the selected targets, the effect of MS4A4A on the proliferation and invasion of COAD was confirmed by in vitro experiments, which partially verified the reliability of bioinformatics results. Our findings delineate CRGs potentially implicated in COAD pathogenesis and offer fresh insights into the influence of the coagulation process on tumorigenesis and progression.

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BACKGROUND: B-type natriuretic peptide or N-terminal pro-B-type natriuretic peptide is the only blood biomarker in established risk calculators for pulmonary arterial hypertension (PAH). Profiling systemic-originated plasma immunoglobulin G (IgG) N-glycans, which reflect different components of the pathophysiology of PAH including immune dysregulation and inflammation, may improve PAH risk assessment. OBJECTIVES: This study sought to identify plasma IgG N-glycan biomarkers that predict survival in PAH to improve risk assessment. METHODS: This cohort study examined 622 PAH patients from 2 national centers (Beijing [discovery] cohort: n = 273; Shanghai [validation] cohort: n = 349). Plasma IgG N-glycomes were profiled by a robust mass spectrometry-based method. Prognostic IgG N-glycan traits were identified and validated in the 2 cohorts using Cox regression and Kaplan-Meier survival analyses. The added value of IgG N-glycan traits to previously established risk models was assessed using Harrell C-indexes and survival analysis. RESULTS: Plasma IgG fucosylation was found to predict survival independent of age and sex in the discovery cohort (HR: 0.377; 95% CI: 0.168-0.845; P = 0.018) with confirmation in the validation cohort (HR: 0.445; 95% CI: 0.264-0.751; P = 0.005). IgG fucosylation remained a robust predictor of mortality in combined cohorts after full adjustment and in subgroup analyses. Integrating IgG fucosylation into previously established risk models improved their predictive capacity, marked by an overall elevation in Harrell C-indexes. IgG fucosylation was useful in further stratifying the intermediate-risk patients classified by a previously established model. CONCLUSIONS: Plasma IgG fucosylation informs PAH prognosis independent of established factors, offering additional value for predicting PAH outcomes.

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