Survival Status Of Patients Research Articles

MicroRNAs targeting CDKN2A gene as a potential prognostic marker in head and neck squamous cell carcinoma.

Epigenetic factors are known to markedly influence the functions of a gene by modification of transcripts, via methylation or acetylation and degradation of mRNA transcripts. The CDKN2A encodes cyclin-dependent kinase inhibitor 2A, a tumour suppressor protein. Genetic and epigenetic alterations in this gene have been demonstrated in several cancer types. The non-coding RNAs with a special emphasis on microRNAs have long been explored for their potential role in the epigenetic modification of gene expression. The present study aims to identify the microRNAs targeting CDKN2A gene transcripts and demonstrate their prognostic significance in head and neck squamous cell carcinoma (HNSCC). Computational approaches were employed to identify the microRNAs targeting CDKN2A. The gene and protein expression profile of CDKN2A was analyzed using UALCAN. A significant upregulation of CDKN2A was observed in the primary tumour tissues (p=<10-12). Interestingly, the protein expression, although found to be statistically significant (p=0.0129) did not correlate well with the gene expression profile. The microRNAs targeting CDKN2A were further analyzed to identify the possible reason for the decrease in protein expression. Among the 44 microRNAs targeting CDKN2A gene transcripts, hsa-miR-3681-3p, hsa-miR-542-5p, hsa-miR-4519 were found to be upregulated and hsa-miR-134-5p was found to be downregulated with a significant association with survival status of HNSCC patients. The hsa-miR-542-5p was found to correlate well with the survival and hence can be considered as the key microRNA associated with HNSCC. However, further validation of this microRNA is warranted to confirm its role in the process of carcinogenesis.

The Diagnostic and Prognostic Value of the Combination of Tumor M2-Pyruvate Kinase, Carcinoembryonic Antigen, and Cytokeratin 19 Fragment in Non-Small Cell Lung Cancer.

Objective: Finding biomarkers related to non-small cell lung cancer (NSCLC) is helpful for the diagnosis and precise treatment of lung cancer. The relationship between serum tumor M2-pyruvate kinase (TuM2-PK), carcinoembryonic antigen (CEA), and cytokeratin 19 fragment (CYFRA21-1) and NSCLC was analyzed. Methods: The serum levels of TuM2-PK, CEA, and CYFRA21-1 in 184 patients with the NSCLC group, 60 patients with the benign lung disease (BLD) group, and 90 healthy controls (HC) group were detected. The levels of TuM2-PK were measured by using an enzyme-linked immunosorbent assay. The detection methods of CEA and CYFRA21-1 were electrochemiluminescence. The receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic value of TuM2-PK, CEA, and CYFRA21-1 on NSCLC. The Kaplan-Meier survival curve was drawn to evaluate the survival status in NSCLC patients with different serum levels of TuM2-PK, CEA, and CYFRA21-1. Results: Serum levels of TuM2-PK, CEA, and CYFRA21-1 in the NSCLC group were significantly higher than those in the BLD group and the HC group (P < .01). Serum levels of TuM2-PK, CEA, and CYFRA21-1 in NSCLC patients were associated with the tumor lymph node metastasis stage (P < .05), lymph node metastasis (P < .05), and distant metastasis (P < .05). The ROC curve showed that the area under the curve of serum levels of TuM2-PK, CEA, and CYFRA21-1 was 0.814, 0.638, and 0.719, respectively, and that the combination of the above 3 was 0.918. The Kaplan-Meier survival curve showed that the 1-, 3- and 5-year survival rate in NSCLC patients with positive TuM2-PK, CEA, and CYFRA21-1 was significantly lower than that in NSCLC patients with negative TuM2-PK, CEA, and CYFRA21-1, respectively (P < .05). Conclusions: Serum TuM2-PK, CEA, and CYFRA21-1 levels have high clinical values in the diagnosis of NSCLC, and can effectively judge the prognosis of patients.

CRYL1 is a Potential Prognostic Biomarker of Clear Cell Renal Cell Carcinoma Correlated with Immune Infiltration and Cuproptosis.

Clear cell renal cell carcinoma (ccRCC) is a widespread urogenital neoplasm. However, the therapeutic efficacy of these methods is unsatisfactory. In-depth screening of biomarkers could aid early diagnosis and therapy and predict patient prognosis. The GEO datasets were selected with specific criteria. Differentially expressed gene (DEG), weighted gene coexpression network analysis (WGCNA), protein-protein interaction, LASSO, random forest, and Cox regression analyses were applied to identify the independent prognostic biomarkers. Survival analysis, correlation with clinical features, gene set enrichment analysis (GSEA), GO enrichment, immune infiltration analysis, and correlation with cuproptosis-related genes were carried out to determine the prognostic value and possible molecular mechanisms of the TSVR. Wound healing assays, transwell assays, cell colony formation experiments, flow cytometry, and immunohistochemistry (IHC) analysis were used to validate the functional attributes of CRYL1. Four GEO datasets were included to screen for hub genes. DEG combined with WGCNA showed a key module with 300 genes having the strongest correlation with "survival state" (R2 = -0.24 and P = 7e-8); six genes were identified by LASSO, random forest, and Cytoscape. Finally, CRYL1 (hazard ratio (HR) = 2.01, P < 0.001) was selected as an independent prognostic biomarker. The higher CRYL1 expression group had better DFS and overall survival (OS). GSEA demonstrated that the CRYL1-related DEGs were enriched mainly in the metabolism of sugar, fat, and amino acids. CRYL1 is positively correlated with FDX1 and the LIAS pathway, which are important molecule involved in cuproptosis. CRYL1 affects the infiltration abundance of four immune cells and can predict a positive OS. Wound healing, transwell, cell colony formation, and flow cytometry assays demonstrated that CRYL1 silencing enhances migration and proliferation and leads to a decreased apoptotic ratio. IHC analysis suggested that CRYL1 was highly expressed in adjacent tissues. CRYL1 is a robust predictive marker for clinicopathological characteristics and survival status in ccRCC patients.

Clinical significance of TLR7/IL-23/IL-17 signaling pathway in patients with acute respiratory distress syndrome

ObjectivesTo analyze the clinical significance of Toll-Like Receptor 7/Interleukin-23/Interleukin-17 (TLR7/IL-23/IL-17) signaling pathway in patients with Acute Respiratory Distress Syndrome (ARDS). MethodThe clinical data of 85 patients with ARDS were retrospectively analyzed and set as the ARDS group, and the clinical data of 85 healthy participants during the same period were set as the healthy control group. Univariate and multivariate logistic regression were used to analyze risk the factors affecting the prognosis of ARDS patients. ResultsTheTLR7 mRNA expression and IL-23 and IL-17 levels in peripheral blood mononuclear cells were higher in the ARDS group than in the control group (p < 0.05). TLR7 mRNA expression, IL-23, IL-17, Surfactant Protein-D (SP-D), and Clara Cell protein-16 (CC-16) levels were the highest in the severe group, followed by the moderate group, and the lowest in the mild group, while Oxygenation Index (OI) was the lowest in the severe group, followed by the moderate group, and the highest in the mild group (p < 0.05). Multivariate logistic regression analysis found that the disease grade (severe), TLR7 mRNA expression, IL-23 level, and IL-17 level were the risk factors affecting the 28-d survival status of ARDS patients (OR > 1, p < 0.05). ConclusionsIn ARDS patients, the TLR7/IL-23/IL-17 signaling pathway is activated. The expression of this pathway is closely related to the severity of the disease and the levels of lung injury markers, and it is a risk factor that may have a direct impact on the prognosis of ARDS patients.

A comprehensive analysis of essential meiotic endonuclease 1 to prognosis and immune infiltration in kidney renal clear cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common type of cancer, and its molecular pathogenesis is unclear. In this study, we investigated the prognostic value of essential meiotic endonuclease 1 (EME1) in kidney renal clear cell carcinoma (KIRC). We downloaded the RNA-Seq expression of 526 KIRC tissues and 72 normal tissues from the TCGA database and the corresponding clinical data. The gene expression profiles associated with four clear cell renal cell carcinomas were downloaded from the GEO database for analysis. The expression of EME1 in clear renal cell carcinoma and its correlation with the clinical baseline data were analyzed. Kaplan-Meier survival curve analysis was performed to assess the relationship between EME1 and patient survival. Enrichment analysis was performed to elucidate the possible functions of EME1. We also analyzed the relationship between the EME1 expression and immune infiltration through TIMER2.0 and TISIDB online databases as well as the relationship between EME1 and common immune checkpoints. EME1 was identified as a risk factor for overall survival in clear cell renal cell carcinoma with a hazard ratio of 3.201 (95% confidence interval: 2.430-4.215; p < 0.001). EME1 was highly expressed in KIRC compared to that in normal tissues (p < 0.001) and in the worse TNM stages and late stages (stage 3/4) (p < 0.001). High EME1 expression was strongly associated with the advanced T stage (p = 0.003), advanced N stage (p = 0.002), and advanced M stage (p = 0.006). Research data on KIRC were simultaneously collected and analyzed from the GEO database, including GSE40435, GSE53000, GSE68417, and GSE53757. EME1 predicted the survival status in KIRC patients (AUC = 0.62). We further established a nomogram including the correlation between the high and low EME1 expression, and EME1 was found to contribute to the prediction of the probability of patient survival with a c-index = 0.796. Kaplan-Meier analysis revealed a lower likelihood of survival with a high EME1 expression (p < 0.001). In addition, further bioinformatics analysis suggested that EME1 may be associated with the extent of immune infiltration in KIRC. An increased expression of EME1 in KIRC is thus associated with advanced clinicopathological features, possibly acting as a potential biomarker of poor prognosis in KIRC.

Development and Validation of a Dynamic Nomogram for Predicting 3-Month Mortality in Acute Ischemic Stroke Patients with Atrial Fibrillation.

Acute ischemic stroke (AIS) in patients with atrial fibrillation (AF) carries a substantial risk of mortality, emphasizing the need for effective risk assessment and timely interventions. This study aimed to develop and validate a practical dynamic nomogram for predicting 3-month mortality in AIS patients with AF. AIS patients with AF were enrolled and randomly divided into training and validation cohorts. The nomogram was developed based on independent risk factors identified by multivariate logistic regression analysis. The prediction performance of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, decision curve analysis (DCA), and Kaplan-Meier survival analysis. A total of 412 patients with AIS and AF entered final analysis, 288 patients in the training cohort and 124 patients in the validation cohort. The nomogram was developed using age, baseline National Institutes of Health Stroke Scale score, early introduction of novel oral anticoagulants, and pneumonia as independent risk factors. The nomogram exhibited good discrimination both in the training cohort (AUC, 0.851; 95% CI, 0.802-0.899) and the validation cohort (AUC, 0.811; 95% CI, 0.706-0.916). The calibration plots, DCA and Kaplan-Meier survival analysis demonstrated that the nomogram was well calibrated and clinically useful, effectively distinguishing the 3-month survival status of patients with AIS and AF, respectively. The dynamic nomogram can be obtained at the website: https://yanxiaodi.shinyapps.io/3-monthmortality/. The dynamic nomogram represents the first predictive model for 3-month mortality and may contribute to managing the mortality risk of patients with AIS and AF.

A practical online prediction platform to predict the survival status of laryngeal squamous cell carcinoma after 5 years

ObjectiveCurrently, there are few practical tools for predicting the prognosis of laryngeal squamous cell carcinoma (LSCC). This study aims to establish a model and a convenient online prediction platform to predict whether LSCC patients will survive 5 years after diagnosis, providing a reference for further evaluation of patient prognosis. MethodsThis is a retrospective study based on data collected from two centers. Center 1 included 117 LSCC patients with survival prognosis data, and center 2 included 33 patients, totaling 150 patients. All data were divided into independent training sets (60 %) and testing sets (40 %). Eight machine learning (ML) algorithms were used to establish models with 11 clinical parameters as input features. The accuracy, sensitivity, specificity, and receiver operating characteristic curve (ROC) of the testing set were used to evaluate the models, and the best model was selected. The model was then developed into a website-based 5-year survival status prediction platform for LSCC. In addition, we also used the SHapley Additive exPlanations (SHAP) tool to conduct interpretability analysis on the parameters of the model. ResultsThe LSCC 5-year survival status prediction model using the support vector machine (SVM) algorithm achieved the best results, with accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of 85.0 %, 87.5 %, 75.0 %, and 81.2 % respectively. The online platform for predicting the 5-year survival status of LSCC based on this model was successfully established. The SHAP analysis shows that the clinical stage is the most important feature of the model. ConclusionThis study successfully established a ML model and a practical online prediction platform to predict the survival status of laryngeal cancer patients after 5 years, which may help clinicians to better evaluate the prognosis of LSCC. Level of evidence3.

Identification and experimental validation of Stearoyl-CoA desaturase is a new drug therapeutic target for osteosarcoma

Osteosarcoma (OS) is the most common malignant bone tumor. Fatty acid reprogramming plays an essential role in OS progression. However, new fatty acid related therapeutic targets of OS have not been completely elucidated. Therefore, we firstly identified 113 differentially expressed fatty acid metabolism genes using bioinformatic analysis, 19 of which were found to be associated with OS prognosis. Then, 7 hub genes were screened out and yielded a strong prediction accuracy (AUC value = 0.88, at 3 years) for predicting the survival status of OS patients. Furthermore, we confirmed that SCD was highly expressed in OS cells and patients. And Knock-down of SCD impaired proliferation and migration of OS cells. Moreover, SCD was transcriptionally activated by c-Myc to promote proliferation and migration of OS cells. Finally, SCD inhibitor could significantly induce OS ferroptosis in vitro and in vivo. In conclusion, we identified that SCD was a reliable risk factor for OS patients. And SCD was activated by c-Myc. The inhibitor of SCD could significantly impaired OS growth and induce OS ferroptosis, which indicated that SCD was a potential drug target for OS treatment.

Risk Factors for Negative Emotions in Patients Undergoing Radical Resection of Hilar Cholangiocarcinoma and Their Influence on Prognosis.

Hilar cholangiocarcinoma (HCCA) is a common malignant tumor of the biliary system. Factors such as limited physical function, intractability, and high mortality caused by the tumor lead to negative emotions, such as anxiety and depression in patients. In this study, we investigated the risk factors for negative emotions in patients undergoing radical resection of HCCA during the perioperative period and its effect on prognosis to provide strategies for alleviating the negative emotional disorders of patients and improving prognosis. From September 2016 to August 2021, we retrospectively examined 205 patients with HCCA who underwent radical resection in our hospital. The incidence of negative emotions and the clinical parameters of patients were compared using Chi-square tests and t-tests. The independent risk factors for unfavorable feelings in patients during the perioperative period were determined using binary logistic regression. The key variables influencing the postoperative survival status of HCCA patients were identified using the log-rank univariate analysis and Cox proportional risk regression analysis. The results of the binary logistic regression analysis showed that perioperative negative emotions were independently influenced by family monthly income (OR = 0.069), medical insurance (OR = 0.089), family care (OR = 0.013), sleep quality (OR = 0.071), TNF-α (OR = 5.851), and bile leakage (OR = 29.412) (P < 0.05). The age of the patient (OR = 2.003), preoperative CA19-9 (OR = 2.038), lymph node metastases (OR = 2.327), and negative mood (OR = 3.054) were independent risk variables that affected the survival status of patients, as determined by the results of Cox regression analysis (P < 0.05). In this study, we found that anxiety and depression in patients undergoing radical operation of HCCA are related to family monthly income, medical insurance, sleep quality, family care, TNF-α, and bile leakage; also, negative emotions have adverse effects on prognosis.

Association of Cardiac Valve Calcification and 1-year Mortality after Lower-extremity Amputation in Diabetic Patients: A Retrospective Study.

Cardiac valve calcification predisposes patients to a higher risk of adverse cardiovascular events. This study aimed to investigate the association between cardiac valve calcification and 1-year mortality in diabetic patients after lower-extremity amputation. This was a retrospective study conducted on the clinical data of diabetic patients who underwent lower-extremity amputation admitted to the Sun Yat-sen Memorial Hospital, Guangzhou, Guangdong, China for diabetic foot ulcers needed lower extremity amputation surgery between July 2017 and March 2021. Detailed preoperative medical assessments were performed and recorded. Cardiac valve calcification was assessed using echocardiography at baseline. Oneyear follow-up assessments were conducted and included clinical visits, hospital record assessments, and telephone reviews to obtain the survival status of patients. Ninety-three diabetic patients participated in the study. The 1-year follow-up mortality rate after amputation was 24.7%. Compared to the survival group, the prevalence of cardiac valve calcification and the Revised Cardiac Risk Index (RCRI) were higher in the mortality group. In the Cox regression analysis, cardiac valvular calcification (HR=3.427, 95% CI=1.125- 10.443, p =0.030) was found to be an independent predictor of all-cause mortality after amputation. In addition, the patients with both aortic valve calcification and mitral annular calcification had a higher all-cause mortality rate (50%). Receiver operator characteristic curve analysis showed a stronger predictive ability when using a combination of calcified valve number and RCRI (AUC=0.786 95%, CI=0.676-0.896, p =0.000). In diabetic patients after lower-extremity amputation, cardiac valve calcification was associated with all-cause mortality during 1-year follow-up. Combination of calcified valve number and RCRI score showed a stronger predictive value for mortality.

Glucose metabolism-based signature predicts prognosis and immunotherapy strategies for colon adenocarcinoma.

The global prevalence and metastasis rates of colon adenocarcinoma (COAD) are high, and therapeutic success is limited. Although previous research has primarily explored changes in gene phenotypes, the incidence rate of COAD remains unchanged. Metabolic reprogramming is a crucial aspect of cancer research and therapy. The present study aims to develop cluster and polygenic risk prediction models for COAD based on glucose metabolism pathways to assess the survival status of patients and potentially identify novel immunotherapy strategies and related therapeutic targets. COAD-specific data (including clinicopathological information and gene expression profiles) were sourced from The Cancer Genome Atlas (TCGA) and two Gene Expression Omnibus (GEO) datasets (GSE33113 and GSE39582). Gene sets related to glucose metabolism were obtained from the MSigDB database. The Gene Set Variation Analysis (GSVA) method was utilized to calculate pathway scores for glucose metabolism. The hclust function in R, part of the Pheatmap package, was used to establish a clustering system. The mutation characteristics of identified clusters were assessed via MOVICS software, and differentially expressed genes (DEGs) were filtered using limma software. Signature analysis was performed using the least absolute shrinkage and selection operator (LASSO) method. Survival curves, survival receiver operating characteristic (ROC) curves and multivariate Cox regression were analyzed to assess the efficacy and accuracy of the signature for prognostic prediction. The pRRophetic program was employed to predict drug sensitivity, with data sourced from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Four COAD subgroups (i.e., C1, C2, C3 and C4) were identified based on glucose metabolism, with the C4 group having higher survival rates. These four clusters were bifurcated into a new Clust2 system (C1 + C2 + C3 and C4). In total, 2175 DEGs were obtained (C1 + C2 + C3 vs. C4), from which 139 prognosis-related genes were identified. ROC curves predicting 1-, 3- and 5-year survival based on a signature containing nine genes showed an area under the curve greater than 0.7. Meanwhile, the study also found this feature to be an important predictor of prognosis in COAD and accordingly assessed the risk score, with higher risk scores being associated with a worse prognosis. The high-risk and low-risk groups responded differently to immunotherapy and chemotherapeutic agents, and there were differences in functional enrichment pathways. This unique signature based on glucose metabolism may potentially provide a basis for predicting patient prognosis, biological characteristics and more effective immunotherapy strategies for COAD.

Prognostic value of PD‑L1 expression and CD68 macrophages in tumor nest of patients with primary gastric cancer.

The programmed death receptor 1/programmed death receptor ligand 1 axis (PD-1/PD-L1) is involved in tumor immune escape and is a potential prognostic biomarker and anti-tumor immunotherapy target in patients with gastric cancer (GC). However, the results of studies obtained in recent years have been inconsistent. The present study aimed to determine the possible predictive significance of PD-L1 in conjunction with three proteins linked with PD-L1 regulation in patients with primary GC. In the present study, the PD-L1, human epidermal growth factor receptor 2 (HER2), cluster of differentiation (CD)133 and microphage-associated CD68 expression levels were identified by multiplexed immunohistochemistry and assessed by automated pathological analysis system in 93 GC tumors and neighboring normal tissues arrayed on the same tissue microarray. All four proteins were statistically analyzed in relation to the clinicopathological characteristics. The expression levels of HER2, CD133 and CD68 were considerably higher in cancer tissues compared with neighboring normal tissues (P<0.05), however, the reverse trend was detected for PD-L1 expression (P=0.0577), particularly in tumor nest (TN; P<0.05). There was no significant correlation between the HER2 and CD133 expression levels and clinicopathological factors. However, significant relationships were found between PD-L1 expression and the TNM stage, pathological differentiation and survival status of patients (P<0.05). Moreover, survival time was prolonged in individuals with elevated PD-L1 expression in TN and GC tissues, but no significant correlation was identified (P=0.0881). The CD68 expression level in tumor stroma, but not in TN, was significantly correlated with poor pathological differentiation in patients with GC (P<0.05). However, PD-L1+CD68+ macrophages were strongly related to lower tumor size (diameter <5 cm), early TNM stage (stage I+II), good pathological differentiation and overall survival in TN (P<0.05). In conclusion, PD-L1+CD68+ macrophage infiltration in TN might be a potential indicator of prognosis in patients with primary GC and merits further investigation.

Impact of the number of citizens officially certified for government-organized civilian cardiopulmonary resuscitation courses on the prognosis for patients with out-of-hospital cardiac arrest in Japan

Abstract Background To improve the prognosis of patients with out-of-hospital cardiac arrest (OHCA), the rate of high-quality cardiopulmonary resuscitation (CPR) and immediate use of automated external defibrillators (AEDs) by citizens must be further increased. Japanese government launched certified CPR courses for citizens throughout Japan in 1994 and the number of citizens certified on the course is increasing. However, the impact of the number of citizens officially certified for the courses on the rate of CPR implementation by citizens and the outcomes in patients with OHCA is unclear. Methods From a prospective, nationwide, population-based registry of patients with OHCA in Japan, we identified 350,444 patients from 2005 through 2020 with bystander-witnessed OHCA with 72,069 ventricular-fibrillation (VF) and the other non-VF cardiac arrests. The association between the number of citizens certified for the CPR courses and the rate of CPR implementation by citizens throughout Japan was analyzed by Poisson distribution and Cochran–Armitage test for trend. The primary outcome was survival at 1 month. The secondary outcomes were favorable neurologic outcome at 1 month. The associations between the CPR attempted by citizens [categorized into three (chest-compression-only CPR, chest compression with rescue breathing, and use of AEDs with chest compression and rescue breathing)] and outcomes were determined by multivariable mixed logistic regression analyses. Results Cumulative number of citizens certified for the CPR courses increased from 717,808 in 2005 to 34,938,322 in 2020, which reached to 27.8% of total Japanese population. Annual course receivers were also linearly increased during the period (Incidence Rate Ratios 1.03, p&amp;lt;0.001). Similarly, the rate of patients receiving CPR by citizens increased linearly from 40.6% in 2005 to 56.8% in 2020 (P for trend&amp;lt;0.001). In the multivariable mixed logistic regression analyses, higher CPR activity by citizens were significantly associated with better 1-year survival in both VF and non-VF patients even after adjusted for covariates [VF = chest compression: odds ratio (OR) 1.17; 95% confidence interval (CI) 0.98–1.39; P =0.078; chest compression with rescue breathing: OR 1.22; 95% CI 1.02–1.49; P =0.030; use of AEDs with chest compression and rescue breathing: OR 1.54; 95% CI 1.54–1.92; P &amp;lt;0.001 vs no CPR, non-VF= chest compression: OR 1.19; 95% CI 0.76–1.87; P =0.45; chest compression with rescue breathing: OR 2.26; 95% CI 1.34–3.82; P =0.020 vs non-CPR]. Similar results were also found in neurological outcomes in 1 month. Conclusions The rate of CPR implementation by citizens throughout Japan has continuously and linearly increased in these 15 years in proportion to the increase in the number of citizens certified for the government-organized CPR courses. Higher CPR activity by citizens were significantly associated with better 1-year survival and favorable functional status in both VF and non-VF patients.

Impact of Thrombolysis Time Metrics When Participating in National Stroke Center Construction Project.

Intravenous thrombolysis has emerged as an effective approach to improve the long-term survival and functional status of patients with ischemic stroke. The aim of this study was to assess the impact of a national stroke project on the door-to-needle-time (DNT). The patients were divided into pre-construction and construction periods. Construction Measures were performed during the construction period. The DNT and onset-to-needle time (ONT) were compared in two period groups. After participating in the National Stroke Center Project and effective measurements, the thrombolysis treatment metrics were improved significantly. The DNT (IQR) was shortened from 65.0 (54.5,85.0) minutes in the Pre-Construction period to 40.0 (33.0,53.0) minutes in the Construction period (p < 0.001). Similarly, the ONT was reduced from 157.0 (IQR) (115.0,184.0) minutes to 116.0 (87.8,170.0) minutes (p = 0.035). The DNT time and ONT time can be shortened by National Stroke Center Construction projects. More suitable hospitals should be encouraged to participate as the National Stroke Center.

Assessment of the management and outcome of neutropenia in paediatric haemato-oncology inpatients at Kenyatta National Hospital

Background: Neutropenia is a common complication among the haemato-oncology pediatric patients. Children who develop such complications are prone to bacterial infections which further predispose them to death. This study aimed at improving the practice of management of neutropenia in children with the view of reducing pediatric haemato-oncology mortality. Methods: We retrospectively reviewed medical records of paediatric haemato-oncology inpatients aged 13 years and below, who developed neutropenia during treatment at Kenyatta National Hospital. A data abstraction form was used to collect data from 113 medical records of patients admitted between 1st January 2020 and 31st December 2020. Data were analyzed using R to obtain descriptive and inferential statistics using a 95% confidence interval. Results: Boys constituted most of the cases assessed. The median (IQR) age at admission was 6 (4, 10) years. Leukaemias were more common than lymphomas. Acute lymphocytic leukaemia (59%) and Hodgkin’s lymphoma (4%) were the most and least frequently diagnosed haemato-oncology cancers. Most patients were diagnosed during the induction stage, 47 (42%) and had febrile neutropenia 59 (52%). Vaccination and bone marrow suppression histories were poorly documented. Thirty-three (29%) patients were reported to have succumbed during their treatment. G-CSF prescription was statistically significantly associated (p=0.045) with the survival status (death or alive) of patients, particularly at the induction stage (p=0.007). Conclusions: Neutropenia management in KNH is done as per protocol, with room to improve on history taking. Although 29% of the children succumbed within the course of treatment, the majority (71%) of the patients were discharged.

A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a type of cancer and has some of the highest rates of both incidence and mortality globally. Developing accurate models for survival prediction provides a basis clinical judgment and decision making, improving the survival status of ESCC patients. Although many predictive models have been developed, there is still lack of highly accurate survival prediction models for ESCC patients. This study proposes a novel survival prediction model for ESCC patients based on principal component analysis (PCA) and least-squares support vector machine (LSSVM) optimized by an improved dragonfly algorithm with hybrid strategy (HSIDA). The original 17 blood indicators are condensed into five new variables by PCA, reducing data dimensionality and redundancy. An improved dragonfly algorithm based on hybrid strategy is proposed, which addresses the limitations of dragonfly algorithm, such as slow convergence, low search accuracy and insufficient vitality of late search. The proposed HSIDA is used to optimize the regularization parameter and kernel parameter of LSSVM, improving the prediction accuracy of the model. The proposed model is validated on the dataset of 400 patients with ESCC in the clinical database of First Affiliated Hospital of Zhengzhou University and the State Key Laboratory of Esophageal Cancer Prevention and Control of Henan Province. The experiment results demonstrate that the proposed HSIDA-LSSVM has the best prediction performance than LSSVM, HSIDA-BP, IPSO-LSSVM, COA-LSSVM and IBA-LSSVM. The proposed model achieves the accuracy of 96.25%, sensitivity of 95.12%, specificity of 97.44%, precision of 97.50%, and F1 score of 96.30%.

Risk factors influencing chemotherapy compliance and survival of elderly patients with non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) has high degree of malignance and proneness to recurrence and metastasis. The aim of this study was to analyse the risk factors influencing the chemotherapy compliance and survival status of elderly NSCLC patients. The clinical data of 110 patients admitted from January 2014 to March 2018 were retrospectively analysed. They were assigned to non-chemotherapy (n=25), partial chemotherapy (n=30) and complete chemotherapy (n=55) groups according to chemotherapy compliance, and followed up until March 2021. Their clinicopathological characteristics were investigated by univariate analysis and then multivariate Cox regression analysis. The survival rates were compared by Kaplan-Meier survival curve and log-rank test. Among the 110 NSCLC patients, 25 did not receive chemotherapy, 30 underwent partial chemotherapy and 55 received complete chemotherapy. Educational level, pathological tumor-node-metastasis (TNM) stage, pathological type, surgical approach, place of residence, payment mode and chemotherapy stage were independent risk factors influencing the chemotherapy compliance (P<0.05). Particular attention should be paid to improving the chemotherapy compliance of patients with low educational level, late TNM stage, medical history of squamous cell carcinoma, history of thoracotomy, living in rural areas and no medical insurance, and those in the recurrence period or consolidation period of chemotherapy.

Association between ferritin to albumin ratio and 28-day mortality in patients with sepsis: a retrospective cohort study

ObjectivesThe ratio of ferritin to albumin (FAR) has been proposed as a novel prognostic indicator for COVID-19. However, the role of FAR in predicting the all-cause mortality rate in patients with sepsis has not been evaluated. Therefore, the aim of this study is to elucidate the correlation between FAR and the 28-day all-cause mortality rate in patients with sepsis.MethodsThis study used data from the Medical Information Mart for Intensive Care IV database (v2.0) for a retrospective cohort analysis. The study focused on adult patients with sepsis who were admitted to the intensive care unit. The primary objective was to assess the predictive capability of FAR in determining the 28-day all-cause mortality rate among patients with sepsis.ResultsThe study involved 1553 sepsis patients in total. Based on the survival status of sepsis patients within 28 days, they were divided into two groups: a survival group consisting of 973 patients, and a death group consisting of 580 patients. The results revealed a 28-day mortality rate of 37.35% among sepsis patients. The multivariable Cox regression analysis revealed that FAR was an independent predictor of the 28-day all-cause mortality rate in patients with sepsis (hazard ratio [HR]: 1.17–1.19; 95% confidence interval 1.11–1.26; P < 0.001). The FAR demonstrated a higher area under the curve (AUC) of 61.01% (95% confidence interval 58.07–63.96%), compared to serum ferritin (60.48%), serum albumin (55.56%), and SOFA score (56.97%). Receiver operating characteristic curve (ROC) analysis determined the optimal cutoff value for FAR as 364.2215. Kaplan–Meier analysis revealed a significant difference in the 28-day all-cause mortality rate between patients with FAR ≥ 364.2215 and those with FAR < 364.2215 (P < 0.001). Furthermore, subgroup analysis showed no significant interaction between FAR and each subgroup.ConclusionsThis study revealed a significant correlation between FAR and the 28-day mortality rate in patients with sepsis. Higher FAR values were strongly associated with increased mortality rates within 28 days.

Association of graph-based spatial features with overall survival status of glioblastoma patients

Glioblastoma is the most common malignant brain tumor with less than 15 months median survival. To aid prognosis, there is a need for decision tools that leverage diagnostic modalities such as MRI to inform survival. In this study, we examine higher-order spatial proximity characteristics from habitats and propose two graph-based methods (minimum spanning tree and graph run-length matrix) to characterize spatial heterogeneity over tumor MRI-derived intensity habitats and assess their relationships with overall survival as well as the immune signature status of patients with glioblastoma. A data set of 74 patients was studied based on the availability of post-contrast T1-weighted and T2-weighted fluid attenuated inversion recovery (FLAIR) image data in The Cancer Image Archive (TCIA). We assessed the predictive value of MST- and GRLM-derived features from 2D images for prediction of 12-month survival status and immune signature status of patients with glioblastoma via a receiver operating characteristic curve analysis. For 12-month survival prediction using MST-based method, sensitivity and specificity were 0.82 and 0.79 respectively. For GRLM-based method, sensitivity and specificity were 0.73 and 0.77 respectively. For immune status, sensitivity and specificity were 0.91 and 0.69, respectively, for the GRLM-based method with an immune effector. Our results show that the proposed MST- and GRLM-derived features are predictive of 12-month survival status as well as the immune signature status of patients with glioblastoma. To our knowledge, this is the first application of MST- and GRLM-based proximity analyses for the study of radiologically-defined tumor habitats in glioblastoma.

One-year mortality prediction for patients with sepsis: a nomogram integrating lactic dehydrogenase and clinical characteristics

BackgroundTo explore the association between myocardial enzymes and one-year mortality, and establish a nomogram integrating myocardial enzymes and clinical characteristics to predict one-year mortality among sepsis patients.MethodsData of 1,983 sepsis patients were extracted from Medical Information Mart for Intensive Care III database in this retrospective cohort study. All participants were randomly split into the training set for the development of model and testing set for the internal validation at the ratio of 7:3. Univariate logistic regression was used to screen variables with statistical differences which were made for stepwise regression, obtaining the predictors associated with one-year mortality of sepsis patients. Adopted multivariate logistic regression to assess the relationship between myocardial enzymes and one-year mortality of sepsis patients. A nomogram was established in predicting the one-year survival status of sepsis patients, and the performance of developed model were compared with LDH alone, sequential organ failure assessment (SOFA), simplified acute physiology score II (SAPS II) by receiver operator characteristic, calibration, and decision curves analysis.ResultsThe result found that LDH was associated with one-year mortality of sepsis patients [odds ratio = 1.28, 95% confidence interval (CI): 1.18–1.52]. Independent predictors, including age, gender, ethnicity, potassium, calcium, albumin, hemoglobin, alkaline phosphatase, vasopressor, Elixhauser score, respiratory failure, and LDH were identified and used to establish the nomogram (LDH-model) for predicting one-year mortality for sepsis patients. The predicted performance [area under curve (AUC) = 0.773, 95%CI: 0.748–0.798] of this developed nomogram in the training and testing sets (AUC = 0.750, 95%CI: 0.711–0.789), which was superior to that of LDH alone, SOFA score, SAPS II score. Additionally, calibration curve indicated that LDH-model may have a good agreement between the predictive and actual outcomes, while decision curve analysis demonstrated clinical utility of the LDH-model.ConclusionLDH level was related to the risk of one-year mortality in sepsis patients. A prediction model based on LDH and clinical features was developed to predict one-year mortality risk of sepsis patients, surpassing the predictive ability of LDH alone as well as conventional SAPS II and SOFA scoring systems.