Establishment of a Nomogram for Predicting Early Death in Viral Myocarditis.
Ontology highlight
ABSTRACT: Objective
This research aimed to establish a nomogram for predicting early death in viral myocarditis (VMC) patients.Method
A total of 362 consecutive VMC patients in Fujian Medical University Affiliated First Quanzhou Hospital between January 1, 2009, and December 31, 2019, were included. A least absolute shrinkage and selection operator (LASSO) regression model was used to detect the risk factors that most consistently and correctly predicted early death in VMC. The performance of the nomogram was assessed by calibration, discrimination, and clinical utility.Result
9 factors were screened by LASSO regression analysis for predicting the early death of VMC. Combined with the actual clinical situation, the heart failure (HF) (OR: 2.13, 95% CI: 2.76-5.95), electrocardiogram (ECG) (OR: 6.11, 95% CI: 1.05-8.66), pneumonia (OR: 3.62, 95% CI: 1.43-9.85), brain natriuretic peptide (BNP) (OR: 4.66, 95% CI: 3.07-24.06), and lactate dehydrogenase (LDH) (OR: 1.90, 95% CI: 0.19-9.39) were finally used to construct the nomogram. The nomogram's C-index was 0.908 in the training cohort and 0.924 in the validation cohort. And the area under the receiver operating characteristic curve of the nomogram was 0.91 in the training cohort and 0.924 in the validating cohort. Decision curve analysis (DCA) also showed that the nomogram was clinically useful.Conclusion
This nomogram achieved an good prediction of the risk of early death in VMC patients.
SUBMITTER: Sun X
PROVIDER: S-EPMC8133858 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:AbstractWe aimed to identify potential clinical predictors associated with the risk of fulminant myocarditis, and further to establish and assess a nomogram model based on significant attributes for clinical practicability.This is a retrospective, cross-sectional study, involving 28 patients with fulminant myocarditis and 35 age-, and sex-matched patients with non-fulminant myocarditis. Effect-size estimates are expressed as odds ratio (OR) and 95% confidence interval (CI).Fifteen factors were primarily identified to be associated with the significant risk of fulminant myocarditis after adjusting for confounders. Due to strong correlation, 6 factors were retained, including mean arterial pressure (OR, 95% CI, P: .82, .72-.94, .005), creatinine (2.15, 1.13-4.10, 0.020), blood urea nitrogen (1.45, 1.04-2.02, 0.028), aspartate aminotransferase (2.62, 1.16-5.91, 0.021), troponin I (1.43, 1.07-1.90, 0.015), and ventricular wall motion abnormality (25.81, 2.52-264.69, 0.006). The contribution of the 6 significant factors to predicting fulminant myocarditis risk was significant from multi-angle analyses, and regressing these factors in a nomogram model exhibited good predictive accuracy, as reflected by both C-index (>90%, P < .001).We have identified 6 clinical factors in significant association with fulminant myocarditis, and their prediction capability was more obvious in a nomogram model. Further investigations with larger sample sizes and longer follow-up intervals are warranted.
| S-EPMC8084052 | biostudies-literature
Project description:BackgroundEndoscopic submucosal dissection (ESD) has been accepted as the standard treatment for the appropriate indication of early gastric cancer (EGC). Determining the risk of lymph node metastasis (LNM) is critical for the following treatment selection after ESD. This study aimed to develop a predictive model to quantify the probability of LNM in EGC to help minimize the invasive procedures.MethodsA total of 952 patients with EGC who underwent radical gastrectomy were retrospectively reviewed. LASSO regression was used to help screen the potential risk factors. Multivariate logistic regression was used to establish a predictive nomogram, which was subjected to discrimination and calibration evaluation, bootstrapping internal validation, and decision curve analysis.ResultsResults of multivariate analyses revealed that gender, fecal occult blood test, CEA, CA19-9, histologic differentiation grade, lymphovascular invasion, depth of infiltration, and Ki67 labeling index were independent prognostic factors for LNM. The nomogram had good discriminatory performance, with a concordance index of 0.816 (95% CI 0.781-0.853). The validation dataset yielded a corrected concordance index of 0.805 (95% CI 0.770-0.842). High agreements between ideal curves and calibration curves were observed.ConclusionsThe nomogram is clinically useful for predicting LNM after ESD in EGC, which is beneficial to identifying patients who are at low risk for LNM and would benefit from avoiding an unnecessary gastrectomy.
| S-EPMC9651963 | biostudies-literature
Project description:PurposeOne of the most frequent side effects of radical prostatectomy (RP) is urinary incontinence. The primary cause of urine incontinence is usually thought to be impaired urethral sphincter function; nevertheless, the pathophysiology and recovery process of urine incontinence remains unclear. This study aimed to identify potential risk variables, build a risk prediction tool that considers preoperative urodynamic findings, and direct doctors to take necessary action to reduce the likelihood of developing early urinary incontinence.MethodsWe retrospectively screened patients who underwent radical prostatectomy between January 1, 2020 and December 31, 2023 at the First People 's Hospital of Nantong, China. According to nomogram results, patients who developed incontinence within three months were classified as having early incontinence. The training group's general characteristics were first screened using univariate logistic analysis, and the LASSO method was applied for the best prediction. Multivariate logistic regression analysis was carried out to determine independent risk factors for early postoperative urine incontinence in the training group and to create nomograms that predict the likelihood of developing early urinary incontinence. The model was internally validated by computing the performance of the validation cohort. The nomogram discrimination, correction, and clinical usefulness were assessed using the c-index, receiver operating characteristic curve, correction plot, and clinical decision curve.ResultsThe study involved 142 patients in all. Multivariate logistic regression analysis following RP found seven independent risk variables for early urinary incontinence. A nomogram was constructed based on these independent risk factors. The training and validation groups' c-indices showed that the model had high accuracy and stability. The calibration curve demonstrates that the corrective effect of the training and verification groups is perfect, and the area under the receiver operating characteristic curve indicates great identification capacity. Using a nomogram, the clinical net benefit was maximised within a probability threshold of 0.01-1, according to decision curve analysis (DCA).ConclusionThe nomogram model created in this study can offer a clear, personalised analysis of the risk of early urine incontinence following RP. It is highly discriminatory and accurate, and it can help create efficient preventative measures and identify high-risk populations.
| S-EPMC11373233 | biostudies-literature
Project description:Prostate cancer (PCa), a severe health burden for males, accounts for the second frequent cancer and fifth tumor specific death cancer around the world. Several studies on tumor-infiltrating immune cells (TIICs) have shown inconsistent and controversial results to PCa. We downloaded a gene expression matrix and clinical information from TCGA, and CIBERSORT was used to identify the proportion of TIICs. Wilcoxon's Sign Rank Test evaluated different gene expression levels in PCa and normal tissues. Kaplan-Meier curves were used to evaluate the associations of TIICs and recurrence-free survival (RFS). Finally, based on the preset P-value of .05, the distribution of TIICs in 73 PCa tissues and 11 normal tissues was illustrated. Activated CD4+ T cells and M0 macrophages account for a high proportion in PCa tissues, while neutrophils and monocytes were found at a high density in normal tissues. Further results showed that the density of plasma cells, Treg cells and resting mast cells were associated with advanced PCa. Additionally, M2 macrophages affected the RFS of PCa patients, and AR was also involved. In the current study, we first evaluated the immune infiltration among PCa and revealed that M2 macrophages could predict the prognosis of PCa patients. Meanwhile, based on the LASSO regression analysis, we established a novel nomogram to assess the recurrence risk of PCa based on immune cell proportions and clinical features.
| S-EPMC6718526 | biostudies-literature
Project description:The features related to the prognosis of patients with mucinous breast cancer (MBC) remain controversial. We aimed to explore the prognostic factors of MBC and develop a nomogram for predicting survival outcomes.The Surveillance, Epidemiology, and End Results (SEER) database was searched to identify 139611 women with resectable breast cancer from 1990 to 2007. Survival curves were generated using Kaplan-Meier methods. The 5-year and 10-year cancer-specific survival (CSS) rates were calculated using the Life-Table method. Based on Cox models, a nomogram was constructed to predict the probabilities of CSS for an individual patient. The competing risk regression model was used to analyse the specific survival of patients with MBC.There were 136569 (97.82%) infiltrative ductal cancer (IDC) patients and 3042 (2.18%) MBC patients. Patients with MBC had less lymph node involvement, a higher frequency of well-differentiated lesions, and more estrogen receptor (ER)-positive tumors. Patients with MBC had significantly higher 5 and10-year CSS rates (98.23 and 96.03%, respectively) than patients with IDC (91.44 and 85.48%, respectively). Univariate and multivariate analyses showed that MBC was an independent factor for better prognosis. As for patients with MBC, the event of death caused by another disease exceeded the event of death caused by breast cancer. A competing risk regression model further showed that lymph node involvement, poorly differentiated grade and advanced T-classification were independent factors of poor prognosis in patients with MBC. The Nomogram can accurately predict CSS with a high C-index (0.816). Risk scores developed from the nomogram can more accurately predict the prognosis of patients with MBC (C-index = 0.789) than the traditional TNM system (C-index = 0.704, P< 0.001).Patients with MBC have a better prognosis than patients with IDC. Nomograms could help clinicians make more informed decisions in clinical practice. The competing risk regression model, as a more rational model, is recommended for use in the survival analysis of patients with MBC in the future.
| S-EPMC5070827 | biostudies-literature
Project description:The prognosis of multiple myeloma (MM) patients was poor in white-American patients as compared to black-American patients. This study aimed to predict the death of MM patients in whites based on the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. A total of 28,912 white MM patients were included in this study. Data were randomly divided into a training set and a test set (7 : 3). The random forest and 5-fold cross-validation were used for developing a prediction model. The performance of the model was determined by calculating the area under the curve (AUC) with 95% confidence interval (CI). MM patients in the death group had older age, higher proportion of tumor distant metastasis, bone marrow as the disease site, receiving radiotherapy, and lower proportion of receiving chemotherapy than that in the survival group (all P < 0.001). The AUC of the random forest model in the training set and testing set was 0.741 (95% CI, 0.740-0.741) and 0.703 (95% CI, 0.703-0.704), respectively. In addition, the AUC of the age-based model was 0.688 (95% CI, 0.688-0.689) in the testing set. The results of the DeLong test indicated that the random forest model had better predictive effect than the age-based model (Z = 7.023, P < 0.001). Further validation was performed based on age and marital status. The results presented that the random forest model was robust in different age and marital status. The random forest model had a good performance to predict the death risk of MM patients in whites.
| S-EPMC9398791 | biostudies-literature
Project description:BackgroundThe aim of this study was to determine the risk factors and develop a nomogram for blood transfusions after posterior lumbar spinal fusion (PSL).MethodsWe conducted a retrospective, single-center study based on 885 patients receiving PSL, and data was obtained from May 2015 to September 2019. Univariable and multivariable logistics regression analysis were conducted to identify risk factors for blood transfusion, and a nomogram was constructed to individually evaluate the risk of blood transfusion. Discrimination, calibration, and clinical usefulness were validated by the receiver operating characteristics (ROC), C-index, calibration plot, and decision curve analysis, respectively. Bootstrapping validation was performed to assess the performance of the model.ResultsOf 885 patients, 885 were enrolled in the final study population, and 289 received blood transfusion. Statistical analyses showed that low preoperative hemoglobin (Hb), longer time to surgery, operative time, levels of fusion > 1, longer surgery duration, and higher total intraoperative blood loss (IBL) were the risk factors for transfusion. The C-index was 0.898 (95% CI 0.847-0.949) in this dataset and 0.895 in bootstrapping validation, respectively. Calibration curve showed satisfied discrimination and calibration of the nomogram. Decision curve analysis (DCA) shown that the nomogram was clinical utility.ConclusionsIn summary, we investigated the relationship between the blood transfusion requirement and predictors: levels of fusion, operative time, time to surgery, total intraoperative EBL, and preoperative Hb level. Our nomogram with a robust performance in the assessment of risk of transfusion can contribute to clinicians in making clinical decision. However, external validation is still needed in the further.
| S-EPMC7798229 | biostudies-literature
Project description:Background:Medulloblastoma is the common pediatric malignant tumor with poor prognosis in cerebellum. However, MB is always with clinical heterogeneity. To provide patients with more clinically beneficial treatment strategies, there is a pressing need to develop a new prognostic prediction model as a supplement to the prediction outcomes of clinical judgment. Materials and Methods:Four datasets of mRNA expression and clinical data were downloaded from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and functionally enriched among GSE50161, GSE74195, GSE86574. Then we used STRING and Cytoscape to constructed and analyze protein-protein interaction network (PPI) and hub genes. Univariate cox regression analysis was performed to identify overall survival-related hub genes in an unique dataset from GSE85217 as train cohort. Lasso Cox regression model was used to construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, univariate and multivariate Cox regression analysis were used to assess the prognostic capacity of the twelve-gene signature. A unique dataset from GSE85217 was downloaded to further validate the results. Finally, we established the nomogram by using the gene signature and validated it with ROC curve. Gene set enrichment analysis (GSEA) was carried out to further investigate its potential molecular mechanism. Besides, the twelve genes expression at the mRNA and protein levels was validated using external database such as Oncomine, cBioportal and HPA, respectively. Results:A twelve-gene signature comprising FOXM1, NEK2, CCT2, ACTL6A, EIF4A3, CCND2, ABL1, SYNCRIP, ITGB1, NRXN2, ENAH, and UMPS was established to predict overall survival of medulloblastoma. The ROC curve showed good performance in survival prediction in both the train cohort and the validation cohort. The twelve-gene signature could stratify patients into a high risk and low risk group which had significantly different survival. Univariate and multivariate Cox regression revealed that the twelve-gene signature was an independent prognostic factor in medulloblastoma. Nomogram, which included twelve-gene signatures, was established and showed some clinical benefit. Conclusion:Our study identified a twelve-gene signature and established a prognostic nomogram that reliably predicts overall survival in medulloblastoma. The above results will help us to better analyze the pathogenesis and treatment of medulloblastoma in the future.
| S-EPMC7495025 | biostudies-literature
Project description:Background and objectivesThe clinicopathological risk factors to predict recurrence of papillary thyroid cancer (PTC) patients remain controversial.MethodsPTC patients treated with thyroidectomy between January 1997 and December 2011 at the First Affiliated Hospital of Zhejiang University (Zhejiang cohort) were included. Multivariate Cox regression analysis was conducted to identify independent recurrence predictors. Then, the nomogram model for predicting probability of recurrence was built.ResultsAccording to Zhejiang cohort (N = 1,697), we found that the 10-year event-free survival (EFS) rates of PTC patients with early-stage (TNM stages I, II, and III) were not well discriminated (91.6%, 89.0%, and 90.7%; P=0.768). The multivariate Cox model identified age, bilaterality, tumor size, and nodal status as independent risk factors for tumor recurrence in PTC patients with TNM stages I-III. We then developed a nomogram with the C-index 0.70 (95% CI, 0.64 to 0.76), which was significantly higher (P < 0.0001) than the AJCC staging system (0.52). In the validation group, the C-index remained at a similar level.ConclusionsIn this study, we build up a new recurrence predicting system and establish a nomogram for early-stage PTC patients. This prognostic model may better predict individualized outcomes and conduct personalized treatments.
| S-EPMC6754965 | biostudies-literature
Project description:PurposeOvarian cancer is a common gynecological malignant tumor. Poor prognosis is strongly associated with early death, but there is no effective tool to predict this. This study aimed to construct a nomogram for predicting cancer-specific early death in patients with ovarian cancer.MethodsWe used data from the Surveillance, Epidemiology, and End Results database of patients with ovarian cancer registered from 1988 to 2016. Important independent prognostic factors were determined by univariate and multivariate logistic regression and LASSO Cox regression. Several risk factors were considered in constructing the nomogram. Nomogram discrimination and calibration were evaluated using C-index, internal validation, and receiver operating characteristic (ROC) curves.ResultsA total of 4769 patients were included. Patients were assigned to the training set (n = 3340; 70%) and validation set (n = 1429; 30%). Based on the training set, eight variables were shown to be significant factors for early death and were incorporated in the nomogram: American Joint Committee on Cancer (AJCC) stage, residual lesion size, chemotherapy, serum CA125 level, tumor size, number of lymph nodes examined, surgery of primary site, and age. The concordance indices and ROC curves showed that the nomogram had better predictive ability than the AJCC staging system and good clinical practicability. Internal validation based on validation set showed good consistency between predicted and observed values for early death.ConclusionCompared with predictions made based on AJCC stage or residual lesion size, the nomogram could provide more robust predictions for early death in patients with ovarian cancer.
| S-EPMC9166879 | biostudies-literature