Nomograms and risk classification systems predicting overall and cancer‐specific survival in primary malignant cardiac tumor

Guan, Tianwang; Li, Yanfang; Qiu, Zicong; Zhang, Yichi; Lin, Wenrui; Lai, Yanxian; Wang, Kenie; Shen, Yan; Du, Liping; Liu, Cheng

doi:10.1111/jocs.14299

Cited by 10 publications

(23 citation statements)

References 29 publications

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“…Based on the maximum χ 2 and minimum P value in the X-tile software (18), the optimal cut-off value for age in PMCTs patients was objectively identified as 44 and 76 in the current study, which is considered to be more credible and accurate. In previous studies on PMCTs patients, age at diagnosis showed a strong prognostic association with OS (4,19). In our study, older age was found to be associated with higher odds of early mortality, with patients over 76-year old having the highest odds of early mortality, followed by the 44-76 age group and 44, which was similar to the optical age cut-off value for OS for PMCT patients (4).…”

Section: Discussionsupporting

confidence: 85%

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Development and validation of a nomogram prediction model for early mortality in patients with primary malignant cardiac tumors

Wang¹,

Jing²,

ZhiYong³

2021

Ann Transl Med

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Section: Discussionsupporting

confidence: 85%

“…Lymphoma is the main pathological type of malignant cardiac tumor and is sensitivity to chemotherapy making this the primary treatment modality (29,30). Sarcoma is also believed to benefit from chemotherapy treatment (4,31,32). In our study, chemotherapy was found to be significantly associated with a decreased incidence of early mortality.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a nomogram prediction model for early mortality in patients with primary malignant cardiac tumors

Wang¹,

Jing²,

ZhiYong³

2021

Ann Transl Med

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“…The validation of the nomogram model was evaluated using the Harrell's concordance index (C-index), calibration curves, and decision curve analyses (DCAs). C-index values ≥0.7 indicate a comparatively accurate prediction in general (17,18). Calibration curves (bootstrap analyses with 1,000 resamples) were integrated to test the calibration of the nomogram model, and DCAs were further used to evaluate the usefulness of the model constructed by previous steps.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram-Based Risk Classification System Predicting the Overall Survival of Patients With Newly Diagnosed Stage IVB Cervix Uteri Carcinoma

Yu¹,

Zhong

Song

et al. 2021

Front. Med.

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Background: This study constructed and demonstrated a model to predict the overall survival (OS) of newly diagnosed distant metastatic cervical cancer (mCC) patients.Methods: The SEER (Surveillance, Epidemiology, and End Results) database was used to collect the eligible data, which from 2010 to 2016. Then these data were separated into training and validation cohorts (7:3) randomly. Cox regression analyses was used to identify parameters significantly correlated with OS. Harrell's Concordance index (C-index), calibration curves, and decision curve analysis (DCA) were further applied to verify the performance of this model.Results: A total of 2,091 eligible patients were enrolled and randomly split into training (n = 1,467) and validation (n = 624) cohorts. Multivariate analyses revealed that age, histology, T stage, tumor size, metastatic sites, local surgery, chemotherapy, and radiotherapy were independent prognostic parameters and were then used to build a nomogram for predicting 1 and 2-year OS. The C-index of training group and validation group was 0.714 and 0.707, respectively. The calibration curve demonstrated that the actual observation was in good agreement with the predicted results concluded by the nomogram model. Its clinical usefulness was further revealed by the DCAs. Based on the scores from the nomogram, a corresponding risk classification system was constructed. In the overall population, the median OS time was 23.0 months (95% confidence interval [CI], 20.5–25.5), 12.0 months (95% CI, 11.1–12.9), and 5.0 months (95% CI, 4.4–5.6), in the low-risk group, intermediate-risk group, and high-risk group, respectively.Conclusion: A novel nomogram and a risk classification system were established in this study, which purposed to predict the OS time with mCC patients. These tools could be applied to prognostic analysis and should be validated in future studies.

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“…After the inclusion and exclusion criteria, there were 152 non-LAA stroke patients were admitted to the study. In previous studies, it is common and suitable to include 2/3-4/5 patients as the development group (Gao et al 2020;Du 2020;Wang 2020;Guan 2019). In our study, we used R (packge caret) to randomly select 2/3 of the patients as the development group, and the remaining patients as the validation group.…”

Section: Methodsmentioning

confidence: 99%

Cardiac parameters affect prognosis in patients with non-large atherosclerotic infarction

Zeng

Zhang

Cheng

et al. 2021

Mol Med

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Background Although large artery atherosclerosis (LAA) is the most common type of cerebral infarction, non-LAA is not uncommon. The purpose of this paper is to investigate the prognosis of patients with non-LAA and to establish a corresponding nomogram. Patients and methods Between June 2016 and June 2017, we had 1101 admissions for acute ischemic stroke (AIS). Of these, 848 were LAA and 253 were non-LAA. Patients were followed up every 3 months with a minimum of 1 year of follow-up. After excluding patients who were lost follow-up and patients who did not meet the inclusion criteria, a total of 152 non-LAA patients were included in this cohort study. After single-factor analysis and multifactor logistic regression analysis, the risk factors associated with prognosis were derived and different nomograms were developed based on these risk factors. After comparison, the best model is derived. Results Logistics regression found that the patient’s National Institutes of Health Stroke Scale (NIHSS) score, ejection fraction (EF), creatine kinase-MB (CK-MB), age, neutrophil-to-lymphocyte ratio (NLR), aspartate aminotransferase (AST), and serum albumin were independently related to the patient’s prognosis. We thus developed three models: model 1: single NIHSS score, AUC = 0.8534; model 2, NIHSS + cardiac parameters (CK-MB, EF), AUC = 0.9325; model 3, NIHSS + CK−MB + EF + age + AST + NLR + albumin, AUC = 0.9598. We compare the three models: model 1 vs model 2, z = − 2.85, p = 0.004; model 2 vs model 3, z = − 1.58, p = 0.122. Therefore, model 2 is considered to be the accurate and convenient model. Conclusions Predicting the prognosis of patients with non-LAA is important, and our nomogram, built on the NIHSS and cardiac parameters, can predict the prognosis accurately and provide a powerful reference for clinical decision making.

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Nomograms and risk classification systems predicting overall and cancer‐specific survival in primary malignant cardiac tumor

Cited by 10 publications

References 29 publications

Development and validation of a nomogram prediction model for early mortality in patients with primary malignant cardiac tumors

Development and validation of a nomogram prediction model for early mortality in patients with primary malignant cardiac tumors

A Nomogram-Based Risk Classification System Predicting the Overall Survival of Patients With Newly Diagnosed Stage IVB Cervix Uteri Carcinoma

Cardiac parameters affect prognosis in patients with non-large atherosclerotic infarction

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