A nomogram for predicting survival of head and neck mucosal melanoma

The nomogram for predicting nasal bleeding after endoscopic transsphenoidal resection of pituitary adenomas: a retrospective study

Objective

This study aimed to develop and validate a dynamic nomogram to assess the risk of nasal bleeding after endoscopic transnasal transsphenoidal pituitary tumor resection.

Methods

A retrospective analysis was conducted on patients who underwent endoscopic transnasal transsphenoidal pituitary tumor resection from June 2019 to June 2021. Univariate and multivariate logistic regression analyses were used to screen for independent risk factors for nasal bleeding from the training set. A multivariate logistic regression model was established, a nomogram was plotted, and it was validated in an internal validation set. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

The nomogram indicators included anticoagulant use, sphenoid sinus artery injury, nasal irrigation, platelet count (PLT), and constipation. The predictive model had an area under the ROC curve of 0.932 (95% CI: 0.873-0.990) and 0.969 (95% CI: 0.940-0.997) for the training and validation sets, respectively, indicating good discrimination. The calibration curve showed good consistency between the actual and predicted incidence of nasal bleeding (p > 0.05). DCA indicated that the nomogram had good clinical net benefit in predicting postoperative nasal bleeding in patients.

Conclusion

In summary, this study explored the incidence and influencing factors of nasal bleeding after endoscopic transnasal transsphenoidal pituitary tumor resection and established a predictive model to assist clinical decision-making.

Development and validation of a nomogram for predicting overall survival in patients with sinonasal mucosal melanoma

BACKGROUND

Sinonasal mucosal melanoma (SNMM) is a relatively rare malignant tumour with a poor prognosis. This study was designed to identify prognostic factors and establish a nomogram model to predict the overall survival (OS) of patients with SNMM.

METHODS

A total of 459 patients with SNMM were selected from the Surveillance, Epidemiology, and End Results (SEER) database as the training cohort. Univariate and multivariate Cox regression analyses were used to screen for independent factors associated with patient prognosis and develop the nomogram model. In addition, external validation was performed to evaluate the effectiveness of the nomogram with a cohort of 34 patients with SNMM from Peking Union Medical College Hospital.

RESULTS

The median OS in the cohort from the SEER database was 28 months. The 1-year, 3-year and 5-year OS rates were 69.8%, 40.4%, and 30.0%, respectively. Multivariate Cox regression analysis indicated that age, T stage, N stage, surgery and radiotherapy were independent variables associated with OS. The areas under the receiver operating characteristic curves (AUCs) of the nomograms for predicting 1-, 3- and 5-year OS were 0.78, 0.71 and 0.71, respectively, in the training cohort. In the validation cohort, the area under the curve (AUC) of the nomogram for predicting 1-, 3- and 5-year OS were 0.90, 0.75 and 0.78, respectively. Patients were classified into low- and high-risk groups based on the total score of the nomogram. Patients in the low-risk group had a significantly better survival prognosis than patients in the high-risk group in both the training cohort (P < 0.0001) and the validation cohort (P = 0.0016).

CONCLUSION

We established and validated a novel nomogram model to predict the OS of SNMM patients stratified by age, T stage, N stage, surgery and radiotherapy. This predictive tool is of potential importance in the realms of patient counselling and clinical decision-making.

Prognostic nomogram for external ear melanoma patients in the elderly: a SEER-based study

AIM

The aim of this study was to construct and validate a nomogram to predict the 1-, 3- and 5-year overall survival (OS) in external ear melanoma (EEM) patients in the elderly based on the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

The information of patients diagnosed with EEM in the elderly between 2010 and 2014 was downloaded from the SEER database. Univariable and multivariable Cox analyses were carried out to identify the independent characteristics, and the independent factors were further included to construct a nomogram. The discriminative ability and calibration of the nomogram to predict OS were tested using C-index value, and calibration plots. Based on the risk score of the nomogram, the patients were divided into high- and low-risk subgroup. Finally, the survival differences of different subgroups were explored by Kaplan-Meier curves. All statistical analyses were performed by R 4.2.0.

RESULTS

A total of 710 elderly EMM patients were included and randomly divided into training cohort and validation cohort. Univariable Cox regression were used to identify age, race, sex, American Joint Committee on Cancer (AJCC), T, surgery, radiation, chemotherapy, and tumor size as independent risk factors. Then, multivariable Cox model to determine significant risk factors was used to establish the selected factors. A nomogram for predicting the 1-, 3- and 5-year OS was constructed using the independent variables including age, AJCC, T, surgery and chemotherapy. The C-index values were 0.78 (95% CI 0.75-0.81) in training set and 0.72 (95% CI 0.66-0.78) in validation set. The calibration curves were closer to ideal curves indicated the accurate predictive ability of this nomogram. The elderly patients with EEM in the low-risk group showed a longer OS than patients in the high-risk group in both training and validation cohorts.

CONCLUSIONS

Our study established and validated a novel model to predict 1-, 3- and 5-year OS for EEM. The individualized nomogram has a good prognostic ability and can be used as a new survival prediction tool for the elderly patients with EMM.

Risk factors and the nomogram model for intraoperatively acquired pressure injuries in children with brain tumours: A retrospective study

This study aimed to investigate the clinical features and incidence of Intraoperatively Acquired Pressure Injuries (IAPIs) of brain tumours in children, to screen the risk factors and to establish a nomogram model for making prevention strategies against the development of IAPIs. Clinical data of 628 children undergoing brain tumour surgery from August 2019 to August 2021 were extracted from the adverse events and the electronic medical systems. They were randomly divided into a training cohort(n = 471) and a validation cohort(n = 157). The univariate and multivariate analysis was performed to identify the risk factors in training cohort; R software was used to construct a nomogram model; the area under the receiver operator characteristic curve (AUC) and calibration plots were used to judge the predictive performance of the nomogram model; decision curve analysis (DCA) was used to assess the clinical usefulness of the nomogram model. Age, haemorrhage, use of vasopressor, temperature, operation time and operation position were considered as significant risk factors, and enrolled to construct a nomogram model. The results of AUC showed satisfactory discrimination of the nomogram; the calibration plots indicated favourable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts; DCA showed better net benefit and threshold probability of the nomogram model. The nomogram model illustrates significant predictive ability, which can provide scientific and individual guidance for preventing development of IAPIs.

Prognostic model for predicting overall and cancer-specific survival among patients with superficial spreading melanoma: A SEER based study

Skin malignant melanoma is one of the most aggressive skin tumors. Superficial spreading melanoma (SSM) is the most common histological type, which can originate from different body skin sites, and some patients can still accumulate regional lymph nodes and even have distant metastasis in some cases. This study used the relevant data from the monitoring, epidemiology and results database of the National Cancer Institute database to study the overall survival (OS) and cancer-specific survival (CSS) of SSM patients and established an SSM nomogram to evaluate the prognosis of patients. A total of 13,922 patients were collected from the monitoring, epidemiology and results database of the National Cancer Institute and randomly divided into a training cohort (8353 cases) and a validation cohort (5569 cases). Univariate and multivariate Cox regression analysis were used to determine prognostic factors, and these factors were used to construct OS and CSS nomograms for patients with SSM. Finally, the discrimination and consistency of the nomogram model were evaluated by the consistency index (C-index), area under the curve (AUC) and calibration curve. Multivariate Cox regression analysis suggested that age, sex, tumor site, the American joint committee on cancer T stage and the first primary melanoma were independent predictors of OS and CSS in patients with SSM and that the American joint committee on cancer N stage was also an independent predictor of CSS in patients with SSM. Based on the above prognostic factors, this study constructed a predictive model. The C-index of the model OS and CSS for this training cohort was 0.805 [95% CI: 0.793-0.817] and 0.896 [95% CI: 0.878-0.913], respectively. The AUC values for 1-, 3-, and 5-year OS were 0.822, 0.820, and 0.821, respectively, and the AUC values for CSS were 0.914, 0.922, and 0.893, respectively. The data indicated that both nomograms showed better predictive accuracy. The calibration curves of the training cohort and the validation cohort were in good agreement. The nomogram has superior predictive performance in predicting 1-, 3-, and 5-year OS and CSS prognosis in patients with SSM and can provide a reference for individualized treatment and clinical counseling of SSM.

Establishment and validation of a nomogram for predicting immune-related prognostic features in trunk melanoma-specific death

Background

Trunk melanoma is one of the most common and deadly types of melanomas. Multiple factors are associated with the prognosis of patients with trunk melanoma. Currently, direct, and reliable clinical tools for early assessment of individual specific risk of death are limited, and most of them are prediction models for all-cause death. Their accuracy in predicting competitiveness events, which make up a relatively large portion, may be substantially compromised. Hence, we conducted this study to investigate the risk factors of trunk melanoma-specific death to establish a comprehensive prediction model suitable for clinical application.

Methods

Patients with trunk melanoma analyzed in this study were from the SEER program [2010-2015]. The random sampling method was used to split the included cases into the training and validation cohorts at a ratio of 7:3. Univariate and multivariate competing risk models were used to screen the independent influencing factors of specific death, and then a nomogram covering these independent predictors was constructed. The concordance index (C-index) and a calibration curve were used to evaluate the calibration degree and accuracy of the nomogram.

Results

We identified 21,198 patients with trunk melanoma from the SEER database, and 3,814 of them died (17.99%). Among the death cases, deaths from other causes accounted for 66.50%The prognostic nomogram included 8 variables and 16 independent influencing factors. The overall C-index in the training set was 0.89, and the receiver operating characteristic (ROC) curve for predicting 1-, 3-, and 5-year survival was 0.928 [95% confidence interval (CI): 0.911-0.945], 0.907 (95% CI: 0.895-0.918), and 0.891 (95% CI: 0.879-0.902), respectively. The C-index of the model in the validation set was 0.89, and the area under the ROC curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific death (CSD) was 0.927 (95% CI: 0.899-0.955), 0.916 (95% CI: 0.901-0.930), and 0.905 (95% CI: 0.899-0.921). Both the training set and the validation set showed the ideal calibration degree.

Conclusions

This model can be used as a potential tool for prognostic risk management of trunk melanoma in the presence of many competing events.

A Simple Nomogram for Predicting Osteoarthritis Severity in Patients with Knee Osteoarthritis

Objective

To explore the influencing factors of knee osteoarthritis (KOA) severity and establish a KOA nomogram model.

Methods

Inpatient data collected in the Department of Joint Surgery, Chengde Medical University Affiliated Hospital from January 2020 to January 2022 were used as the training cohort. Patients with knee osteoarthritis who were admitted to the Third Hospital of Hebei Medical University from February 2022 to May 2022 were taken as the external validation group of the model. In the training group, the least absolute shrinkage and selection operator (LASSO) method was used to screen the factors of KOA severity to determine the best prediction index. Then, after combining the significant factors from the LASSO and multivariate logistic regressions, a prediction model was established. All potential prediction factors were included in the KOA severity prediction model, and the corresponding nomogram was drawn. The consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), GiViTi calibration band, net classification improvement (NRI) index, and integrated discrimination improvement (IDI) index evaluation of a model predicted KOA severity. Decision curve analysis (DCA) and clinical influence curves were used to study the model's potential clinical value. The validation group also used the above evaluation indexes to measure the diagnostic efficiency of the model. Spearman correlation was used to investigate the relationship between nomogram-related markers and osteoarthritis severity.

Results

The total sample included 572 patients with knee osteoarthritis, including 400 patients in the training cohort and 172 patients in the validation cohort. The nomogram's predictive factors were age, pulse, absolute value of lymphocytes, mean corpuscular haemoglobin concentration (MCHC), and blood urea nitrogen (BUN). The C-index and AUC of the model were 0.802. The GiViTi calibration band (P = 0.065), NRI (0.091), and IDI (0.033) showed that the modified model can distinguish between severe KOA and nonsevere KOA. DCA showed that the KOA severity nomogram has clinical application value with threshold probabilities between 0.01 and 0.78. The external verification results also show the stability and diagnosis of the model. Age, pulse, MCHC, and BUN are correlated with osteoarthritis severity.

Conclusions

A nomogram model for predicting KOA severity was established for the first time that can visually identify patients with severe KOA and is novel for indirectly evaluating KOA severity by nonimaging means.