Nomograms are superior to staging and risk grouping systems for identifying high-risk patients: preoperative application in prostate cancer

Developing a Novel Prognostic Model Based on Muscle-Invasive Bladder Cancer Types: A Multicenter Retrospective Cohort Study of Patients Who Received Radical Cystectomy and Chemotherapy

BACKGROUND

To develop a prognostic model to manage patients with muscle-invasive bladder cancer (MIBC) undergoing radical cystectomy (RC) and chemotherapy.

PATIENTS AND METHODS

Clinicopathologic characteristics and survival data were collated from a North American database to develop a model. Genomic and clinicopathologic data were also obtained from European and Asian databases to externally validate the model. Patients were classified as either "primary" or "progressive" MIBC according to non-muscle invasive stage history. Optimized cancer-specific survival (CSS) models, based on MIBC types, were constructed using Cox's proportional hazard regression. Differences of biological function and tumor immunity, between two risk-based groups stratified according to the prognostic model, were estimated.

RESULTS

There were 2631 participants in the American cohort, 291 in the European cohort and 142 in the Asian cohort. Under Cox's regression analysis, tumor stage, lymph node stage, age, ethnicity, and MIBC types were independent CSS predictors (all p < 0.05). The constructed nomogram, which integrated these variables, improved the predictive power. This model had good discrimination and calibration. Patients were categorized into high- or low-risk groups according to the total points calculated. Kaplan-Meier curves revealed that patients in the high-risk group had poorer survival (p < 0.001). This was confirmed with two external validation cohorts (both with p < 0.001). Higher stromal scores and increased M0 and M2 macrophage numbers were observed in samples from the high-risk group, whereas regulatory T cells had lower infiltration in these populations (all with p < 0.05).

CONCLUSIONS

This MIBC type-based nomogram provides accurate CSS predictions, which could help improve patient management and clinical decision-making.

A prognostic nomogram for patients with HR+ mucinous breast carcinoma based on the SEER database and a Chinese cohort study

Purpose

The study aimed to develop a nomogram model for individual prognosis prediction in patients with hormone receptors positive (HR+) mucinous breast carcinoma (MBC) and assess the value of neoadjuvant chemotherapy (NAC) in this context.

Methods

A total of 6,850 HR+ MBC patients from the SEER database were identified and randomly (in a 7:3 ratio) divided into training cohorts and internal validation cohorts. 77 patients were enrolled from the Chongqing University Cancer Hospital as the external validation cohort. Independent risk factors affecting overall survival (OS) were selected using univariate and multivariate Cox regression analysis, and nomogram models were constructed and validated. A propensity score matching (PSM) approach was used in the exploration of the value of NAC versus adjuvant chemocherapy (AC) for long-term prognosis in HR+ MBC patients.

Results

Multivariate Cox regression analysis showed 8 independent prognostic factors: age, race, marital status, tumor size, distant metastasis, surgery, radiotherapy, and chemotherapy. The constructed nomogram model based on these 8 factors exhibited good consistency and accuracy. In the training group, internal validation group and external validation group, the high-risk groups demonstrated worse OS (p<0.0001). Subgroup analysis revealed that NAC had no impact on OS (p = 0.18), or cancer specific survival (CSS) (p = 0.26) compared with AC after PSM.

Conclusions

The established nomogram model provides an accurate prognostic prediction for HR+ MBC patients. NAC does not confer long-term survival benefits compared to AC. These findings provide a novel approach for prognostic prediction and clinical practice.

PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage.

METHODS

We first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis.

RESULTS

The datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation.

CONCLUSION

The new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients.

Construction and validation of a nomogram model to predict the poor prognosis in patients with pulmonary cryptococcosis

Background

Patients with poor prognosis of pulmonary cryptococcosis (PC) are prone to other complications such as meningeal infection, recurrence or even death. Therefore, this study aims to analyze the influencing factors in the poor prognosis of patients with PC, so as to build a predictive nomograph model of poor prognosis of PC, and verify the predictive performance of the model.

Methods

This retrospective study included 410 patients (78.1%) with improved prognosis of PC and 115 patients (21.9%) with poor prognosis of PC. The 525 patients with PC were randomly divided into the training set and validation set according to the ratio of 7:3. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to screen the demographic information, including clinical characteristics, laboratory test indicators, comorbidity and treatment methods of patients, and other independent factors that affect the prognosis of PC. These factors were included in the multivariable logistic regression model to build a predictive nomograph. The receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to verify the accuracy and application value of the model.

Results

It was finally confirmed that psychological symptoms, cytotoxic drugs, white blood cell count, hematocrit, platelet count, CRP, PCT, albumin, and CD4/CD8 were independent predictors of poor prognosis of PC patients. The area under the curve (AUC) of the predictive model for poor prognosis in the training set and validation set were 0.851 (95% CI: 0.818-0.881) and 0.949, respectively. At the same time, calibration curve and DCA results confirmed the excellent performance of the nomogram in predicting poor prognosis of PC.

Conclusion

The nomograph model for predicting the poor prognosis of PC constructed in this study has good prediction ability, which is helpful for improving the prognosis of PC and further optimizing the clinical management strategy.

Frailty in the prediction of delirium in the intensive care unit: A secondary analysis of the Deli study

BACKGROUND

Delirium is an acute disorder of attention and cognition with an incidence of up to 70% in the adult intensive care setting. Due to the association with significantly increased morbidity and mortality, it is important to identify who is at the greatest risk of an acute episode of delirium while being cared for in the intensive care. The objective of this study was to determine the ability of the cumulative deficit frailty index and clinical frailty scale to predict an acute episode of delirium among adults admitted to the intensive care.

METHODS

This study is a secondary analysis of the Deli intervention study, a hybrid stepped-wedge cluster randomized controlled trial to assess the effectiveness of a nurse-led intervention to reduce the incidence and duration of delirium among adults admitted to the four adult intensive care units in the south-west of Sydney, Australia. Important predictors of delirium were identified using a bootstrap approach and the absolute risks, based on the cumulative deficit frailty index and the clinical frailty scale are presented.

RESULTS

During the 10-mth data collection period (May 2019 and February 2020) 2566 patients were included in the study. Both the cumulative deficit frailty index and the clinical frailty scale on admission, plus age, sex, and APACHE III (AP III) score were able to discriminate between patients who did and did not experience an acute episode of delirium while in the intensive care, with AUC of 0.701 and 0.703 (moderate discriminatory ability), respectively. The addition of a frailty index to a prediction model based on age, sex, and APACHE III score, resulted in net reclassified of risk. Nomograms to individualize the absolute risk of delirium using these predictors are also presented.

CONCLUSION

We have been able to show that both the cumulative deficits frailty index and clinical frailty scale predict an acute episode of delirium among adults admitted to intensive care.

Longitudinal varying coefficient single-index model with censored covariates

It is of interest to health policy research to estimate the population-averaged longitudinal medical cost trajectory from initial cancer diagnosis to death, and understand how the trajectory curve is affected by patient characteristics. This research question leads to a number of statistical challenges because the longitudinal cost data are often non-normally distributed with skewness, zero-inflation, and heteroscedasticity. The trajectory is nonlinear, and its length and shape depend on survival, which are subject to censoring. Modeling the association between multiple patient characteristics and nonlinear cost trajectory curves of varying lengths should take into consideration parsimony, flexibility, and interpretation. We propose a novel longitudinal varying coefficient single-index model. Multiple patient characteristics are summarized in a single-index, representing a patient's overall propensity for healthcare use. The effects of this index on various segments of the cost trajectory depend on both time and survival, which is flexibly modeled by a bivariate varying coefficient function. The model is estimated by generalized estimating equations with an extended marginal mean structure to accommodate censored survival time as a covariate. We established the pointwise confidence interval of the varying coefficient and a test for the covariate effect. The numerical performance was extensively studied in simulations. We applied the proposed methodology to medical cost data of prostate cancer patients from the Surveillance, Epidemiology, and End Results-Medicare-Linked Database.

Re-exploration of prognosis in type B thymomas: establishment of a predictive nomogram model

OBJECTIVE

To explore the risk factors for disease progression after initial treatment of type B thymomas using a predictive nomogram model.

METHODS

A single-center retrospective study of patients with type B thymoma was performed. The Cox proportional hazard model was used for univariate and multivariate analyses. Variables with statistical and clinical significance in the multivariate Cox regression were integrated into a nomogram to establish a predictive model for disease progression.

RESULTS

A total of 353 cases with type B thymoma were retrieved between January 2012 and December 2021. The median follow-up was 58 months (range: 1-128 months). The 10-year progression-free survival (PFS) was 91.8%. The final nomogram model included R0 resection status and Masaoka stage, with a concordance index of 0.880. Non-R0 resection and advanced Masaoka stage were negative prognostic factors for disease progression (p < 0.001). No benefits of postoperative radiotherapy (PORT) were observed in patients with advanced stage and non-R0 resection (p = 0.114 and 0.284, respectively).

CONCLUSION

The best treatment strategy for type B thymoma is the detection and achievement of R0 resection as early as possible. Long-term follow-up is necessary, especially for patients with advanced Masaoka stage and who have not achieved R0 resection. No prognostic benefits were observed for PORT.

Development and Validation of Nomograms to Predict Risk and Prognosis in Salivary Gland Carcinoma Patient with Distant Metastases

Background: Salivary gland carcinoma (SGC) patients with distant metastasis (DM) are rare, and understanding this disease is insufficient. Nomograms can predict the prognostic probability of patients, while few studies have examined diagnostic and prognostic factors in SGC patients with DM. The purpose of this study was to establish and validate the risk and prognostic nomograms of SGC patients with DM. Methods: Based on the SEER database, we analyzed the data of SGC patients between 2004 and 2015. Logistic regression analyses and Cox proportional hazards regression analyses were used to identify risk and prognostic factors for DM in SGC patients. Based on the Akaike information criterion (AIC) value and likelihood ratio test, the best-fitting model was selected to build risk and prognostic nomograms, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and Kaplan-Meier (K-M) survival curves. ROC curves were also used to compare the nomograms with the American Joint Committee on Cancer (AJCC) staging system. Results: 7418 SGC patients were included in the study, and 307 (4.14%) of them were diagnosed with DM. This study identified that there are variables (age ≥ 80, no-parotid gland primary site, histologic type of mucoepidermoid carcinoma and squamous cell carcinoma, T stage ≥ T2, N staged ≥ N1, histologic grade ≥ III, and tumor size ≥ 41 mm) associated with the occurrence of DM in SGC patients. Therefore, we constructed diagnostic and prognostic nomograms after incorporating these variables. ROC curves illustrated the better predictive efficacy of 2 nomograms over the AJCC staging system. DCA curves, calibration curves, and K-M survival curves showed that 2 nomograms can accurately predict the occurrence and prognosis of DM among SGC patients in training and validation sets. Conclusion: It was shown that the nomograms were highly discriminative in predicting the diagnosis and prognosis of SGC patients with DM, and could identify high-risk patients, thereby providing SGC patients with individualized treatment plans.

Prediction of incidence of neurological disorders in HIV-infected persons in Taiwan: a nested case-control study

BACKGROUND

Neurological disorders are still prevalent in HIV-infected people. We aimed to determine the prevalence of neurological disorders and identify their risk factors in HIV-infected persons in Taiwan.

METHODS

We identified 30,101 HIV-infected people between 2002 and 2016 from the National Health Insurance Research Database in Taiwan, and analyzed the incidence of neurological disorders. We applied a retrospective, nested case-control study design. The individuals with (case group) and without (control group) a neurological disorder were then matched by age, sex and time. Factors associated with neurological disorders were analyzed using a conditional logistic regression model, and a nomogram was generated to estimate the risk of developing a neurological disorder.

RESULTS

The incidence of neurological disorders was 13.67 per 1000 person-years. The incidence remained stable during the observation period despite the use of early treatment and more tolerable modern anti-retroviral therapy. The conditional logistic regression model identified nine clinical factors and comorbidities that were associated with neurological disorders, namely age, substance use, traumatic brain injury, psychiatric illness, HIV-associated opportunistic infections, frequency of emergency department visits, cART adherence, urbanization, and monthly income. These factors were used to establish the nomogram.

CONCLUSION

Neurological disorders are still prevalent in HIV-infected people in Taiwan. To efficiently identify those at risk, we established a nomogram with nine risk factors. This nomogram could prompt clinicians to initiate further evaluations and management of neurological disorders in this population.

Prediction nomogram development and validation for postoperative radiotherapy in the management of oral squamous cell carcinoma

BACKGROUND

Predictive nomograms are useful tools to guide clinicians in estimating disease course. Oral squamous cell carcinoma (OSCC) patients would benefit from an interactive prediction calculator that defines their levels of survival-risk specific to their tumors to guide the use of postoperative radiotherapy (PORT).

METHODS

Patients with OSCC surgically treated with curative intent at four Head and Neck Cancer Centres were recruited retrospectively for development and validation of nomograms. Predictor variables include PORT, age, T and N classification, surgical margins, perineural invasion, and lymphovascular invasion. Outcomes were disease-free, disease-specific, and overall survivals over 5 years.

RESULTS

1296 patients with OSCC were in training cohort for nomogram analysis. Algorithms were developed to show relative benefit of PORT in survivals for higher-risk patients. External validation on 1212 patients found the nomogram to be robust with favorable discrimination and calibration.

CONCLUSION

The proposed calculator can assist clinicians and patients in the decision-making process for PORT.

Convolutional Neural Network Quantification of Gleason Pattern 4 and Association with Biochemical Recurrence in Intermediate Grade Prostate Tumors

Differential classification of prostate cancer (CaP) grade group (GG) 2 and 3 tumors remains challenging, likely due to the subjective quantification of percentage of Gleason pattern 4 (%GP4). Artificial intelligence assessment of %GP4 may improve its accuracy and reproducibility and provide information for prognosis prediction. To investigate this potential, a convolutional neural network (CNN) model was trained to objectively identify and quantify Gleason pattern (GP) 3 and 4 areas, estimate %GP4, and assess whether CNN-assessed %GP4 is associated with biochemical recurrence (BCR) risk in intermediate risk GG 2 and 3 tumors. The study was conducted in a radical prostatectomy cohort (1999-2012) of African American men from the Henry Ford Health System (Detroit, Michigan). A CNN model that could discriminate four tissue types (stroma, benign glands, GP3 glands, and GP4 glands) was developed using histopathologic images containing GG 1 (n=45) and 4 (n=20) tumor foci. The CNN model was applied to GG 2 (n=153) and 3 (n=62) for %GP4 estimation, and Cox proportional hazard modeling was used to assess the association of %GP4 and BCR, accounting for other clinicopathologic features including GG. The CNN model achieved an overall accuracy of 86% in distinguishing the four tissue types. Further, CNN-assessed %GP4 was significantly higher in GG 3 compared with GG 2 tumors (p=7.2*10^-11). %GP4 was associated with an increased risk of BCR (adjusted HR=1.09 per 10% increase in %GP4, p=0.010) in GG 2 and 3 tumors. Within GG 2 tumors specifically, %GP4 was more strongly associated with BCR (adjusted HR=1.12, p=0.006). Our findings demonstrate the feasibility of CNN-assessed %GP4 estimation, which is associated with BCR risk. This objective approach could be added to the standard pathological assessment for patients with GG 2 and 3 tumors and act as a surrogate for specialist genitourinary pathologist evaluation when such consultation is not available.

Pilot study for generating and assessing nomograms and decision curves analysis to predict clinically significant prostate cancer using only spatially registered multi-parametric MRI

Background

Current prostate cancer evaluation can be inaccurate and burdensome. To help non-invasive prostate tumor assessment, recent algorithms applied to spatially registered multi-parametric (SRMP) MRI extracted novel clinically relevant metrics, namely the tumor's eccentricity (shape), signal-to-clutter ratio (SCR), and volume.

Purpose

Conduct a pilot study to predict the risk of developing clinically significant prostate cancer using nomograms and employing Decision Curves Analysis (DCA) from the SRMP MRI-based features to help clinicians non-invasively manage prostate cancer.

Methods

This study retrospectively analyzed 25 prostate cancer patients. MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced) were resized, translated, and stitched to form SRMP MRI. Target detection algorithm [adaptive cosine estimator (ACE)] applied to SRMP MRI determines tumor's eccentricity, noise reduced SCR (by regularizing or eliminating principal components (PC) from the covariance matrix), and volume. Pathology assessed wholemount prostatectomy for Gleason score (GS). Tumors with GS >=4+3 (<=3+4) were judged as "Clinically Significant" ("Insignificant"). Logistic regression combined eccentricity, SCR, volume to generate probability distribution. Nomograms, DCA used all patients plus training (13 patients) and test (12 patients) sets. Area Under the Curves for (AUC) for Receiver Operator Curves (ROC) and p-values evaluated the performance.

Results

Combining eccentricity (0.45 ACE threshold), SCR (3, 4 PCs), SCR (regularized, modified regularization) with tumor volume (0.65 ACE threshold) improved AUC (>0.70) for ROC curves and p-values (<0.05) for logistic fit. DCA showed greater net benefit from model fit than univariate analysis, treating "all," or "none." Training/test sets achieved comparable AUC but with higher p-values.

Conclusions

Performance of nomograms and DCA based on metrics derived from SRMP-MRI in this pilot study were comparable to those using prostate serum antigen, age, and PI-RADS.

A Prognostic Model Generated from an Apparent Diffusion Coefficient Ratio Reliably Predicts the Outcomes of Oral Tongue Squamous Cell Carcinoma

This study aimed to develop an apparent diffusion coefficient (ADC) ratio-based prognostic model to predict the recurrence and disease-free survival (DFS) of oral tongue squamous cell carcinoma (OTSCC). A total of 188 patients with cT1-2 oral tongue squamous cell carcinoma were enrolled retrospectively. Clinical and laboratory data were extracted from medical records. The ADC values were measured at the regions of interest of the tumor and non-tumor tissues of the MRI images, and the ADC ratio was used for comparison between the patient with recurrence (n = 83 case, 44%) and patients without recurrence (n = 105 cases, 56%). Cox proportional hazards models were generated to analyze the risk factors of cancer recurrence. A nomogram was developed based on significant risk factors to predict 1-, 5- and 10-year DFS. The receiver operator characteristic (ROC) curves of predictors in the multivariable Cox proportional hazards prognostic model were generated to predict the recurrence and DFS. The integrated areas under the ROC curve were calculated to evaluate discrimination of the models. The ADC ratio, tumor thickness and lymph node ratio were reliable predictors in the final prognostic model. The final model had a 71.1% sensitivity and an 81.0% specificity. ADC ratio was the strongest predictor of cancer recurrence in prognostic performance. Discrimination and calibration statistics were satisfactory with C-index above 0.7 for both model development and internal validation. The calibration curve showed that the 5- and 10-year DFS predicted by the nomogram agreed with actual observations.

Nomogram for prediction of peritoneal metastasis risk in colorectal cancer

OBJECTIVE

Peritoneal metastasis is difficult to diagnose using traditional imaging techniques. The main aim of the current study was to develop and validate a nomogram for effectively predicting the risk of peritoneal metastasis in colorectal cancer (PMCC).

METHODS

A retrospective case-control study was conducted using clinical data from 1284 patients with colorectal cancer who underwent surgery at the First Affiliated Hospital of Guangxi Medical University from January 2010 to December 2015. Least absolute shrinkage and selection operator (LASSO) regression was applied to optimize feature selection of the PMCC risk prediction model and multivariate logistic regression analysis conducted to determine independent risk factors. Using the combined features selected in the LASSO regression model, we constructed a nomogram model and evaluated its predictive value via receiver operating characteristic (ROC) curve analysis. The bootstrap method was employed for repeated sampling for internal verification and the discrimination ability of the prediction models evaluated based on the C-index. The consistency between the predicted and actual results was assessed with the aid of calibration curves.

RESULTS

Overall, 96 cases of PMCC were confirmed via postoperative pathological diagnosis. Logistic regression analysis showed that age, tumor location, perimeter ratio, tumor size, pathological type, tumor invasion depth, CEA level, and gross tumor type were independent risk factors for PMCC. A nomogram composed of these eight factors was subsequently constructed. The calibration curve revealed good consistency between the predicted and actual probability, with a C-index of 0.882. The area under the curve (AUC) of the nomogram prediction model was 0.882 and its 95% confidence interval (CI) was 0.845-0.919. Internal validation yielded a C-index of 0.868.

CONCLUSION

We have successfully constructed a highly sensitive nomogram that should facilitate early diagnosis of PMCC, providing a robust platform for further optimization of clinical management strategies.

A new prognostic nomogram for heterotopic ossification formation after elbow trauma : the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction (STEHOP) model

AIMS

Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries.

METHODS

This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction model (STEHOP). STEHOP was validated by concordance index (C-index) and calibration plot. Internal validation was conducted using bootstrapping validation.

RESULTS

Male sex, obesity, open wound, dislocations, late definitive surgical treatment, and lack of use of non-steroidal anti-inflammatory drugs were identified as adverse predictors and incorporated to construct the STEHOP model. It displayed good discrimination with a C-index of 0.80 (95% confidence interval 0.75 to 0.84). A high C-index value of 0.77 could still be reached in the internal validation. The calibration plot showed good agreement between nomogram prediction and observed outcomes.

CONCLUSION

The newly developed STEHOP model is a valid and convenient instrument to predict HO formation after surgery for elbow trauma. It could assist clinicians in counselling patients regarding treatment expectations and therapeutic choices. Cite this article: Bone Joint J 2022;104-B(8):963-971.

A predictive nomogram for intracerebral hematoma expansion based on non-contrast computed tomography and clinical features

PURPOSE

To develop and validate a new nomogram utilizing non-contrast computed tomography (NCCT) signs and clinical factors for predicting hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (ICH).

METHODS

HE was defined as > 6 mL or 33% increase in baseline hematoma volume. Multivariable logistic regression analysis was performed to identify the predictors of HE. The discriminatory performance of the proposed model was evaluated via receiver operation characteristic (ROC) analysis, and the predictive accuracy was assessed by a calibration curve. The nomogram was established by R programming language. The decision curve analysis and clinical impact curve were drawn according to the related risk factors.

RESULTS

A total of 506 patients with spontaneous ICH were recruited in the development cohort, and 103 patients were registered as the external validation cohort. Among the development cohort, 132 (26.09%) experienced HE. Glasgow coma scale (GCS) (P < 0.001), neutrophil to lymphocyte ratio (NLR) (P < 0.001), blend sign (P < 0.001), swirl sign (P < 0.001), and hypodensities (P = 0.003) were significant predictors of HE, by which were used to establish the nomogram. The model demonstrated good performance with high area under the curve both in the development (AUC = 0.908; 95% confidence interval, 0.880-0.936) and the external validation (AUC = 0.844; 95% confidence interval, 0.760-0.908) cohort. The calibration curve illustrated a high accuracy for HE prediction.

CONCLUSION

The nomogram derived from NCCT markers and clinical factors outperformed the NCCT signs-only model in predicting HE for patients with ICH, thus providing an effective and noninvasive tool for the risk stratification of HE.

A Nutrition and Inflammation-Related Nomogram to Predict Overall Survival in Surgically Resected Esophageal Squamous Cell Carcinoma (ESCC) Patients

Pretreatment inflammation-based biomarkers and the prognostic nutrition index (PNI) have been used to evaluate prognosis in cancer patients. However, few studies have focused on the prognostic value of post-treatment inflammation-based biomarkers and PNI in ESCC patients. We aimed to investigate the values of pre/post-treatment inflammatory parameters and PNI for establishing a nomogram to predict overall survival (OS) in ESCC patients. A retrospective review was performed on 268 ESCC patients with esophagectomy. The prognostic values of inflammatory and nutrition indexes were evaluated. Based on the results of multivariable Cox analysis, a nomogram was developed. The predictive accuracy and discriminative ability of the nomogram were determined using the concordance-index (C-index) and a calibration curve and subsequently compared to tumor-node-metastasis (TNM) staging by C-index, receiver operating characteristic (ROC) and decision curve analysis (DCA). PreSII, PostSII, PrePNI, N stage, and TNM classification were assembled into a nomogram. The C-index of the nomogram was 0.774, and the area under curve (AUC) of the nomogram was 0.862. DCA demonstrated that the established nomogram was a better predictive model compared to the TNM system. The developed nomogram with superior predictive ability provides more valuable prognostic information for patients and clinicians than TNM classification.

Developing a Clinical Prediction Score: Comparing Prediction Accuracy of Integer Scores to Statistical Regression Models

Researchers often convert prediction tools built on statistical regression models into integer scores and risk classification systems in the name of simplicity. However, this workflow discards useful information and reduces prediction accuracy. We, therefore, investigated the impact on prediction accuracy when researchers simplify a regression model into an integer score using a simulation study and an example clinical data set. Simulated independent training and test sets (n = 1000) were randomly generated such that a logistic regression model would perform at a specified target area under the receiver operating characteristic curve (AUC) of 0.7, 0.8, or 0.9. After fitting a logistic regression with continuous covariates to each data set, continuous variables were dichotomized using data-dependent cut points. A logistic regression was refit, and the coefficients were scaled and rounded to create an integer score. A risk classification system was built by stratifying integer scores into low-, intermediate-, and high-risk tertiles. Discrimination and calibration were assessed by calculating the AUC and index of prediction accuracy (IPA) for each model. The optimism in performance between the training set and test set was calculated for both AUC and IPA. The logistic regression model using the continuous form of covariates outperformed all other models. In the simulation study, converting the logistic regression model to an integer score and subsequent risk classification system incurred an average decrease of 0.057-0.094 in AUC, and an absolute 6.2%-17.5% in IPA. The largest decrease in both AUC and IPA occurred in the dichotomization step. The dichotomization and risk stratification steps also increased the optimism of the resulting models, such that they appeared to be able to predict better than they actually would on new data. In the clinical data set, converting the logistic regression with continuous covariates to an integer score incurred a decrease in externally validated AUC of 0.06 and a decrease in externally validated IPA of 13%. Converting a regression model to an integer score decreases model performance considerably. Therefore, we recommend developing a regression model that incorporates all available information to make the most accurate predictions possible, and using the unaltered regression model when making predictions for individual patients. In all cases, researchers should be mindful that they correctly validate the specific model that is intended for clinical use.

Novel Prediction Models for Patients With Oral Squamous Cell Carcinoma at Different Anatomical Sites

PURPOSE

The individualized prediction of oral cavity squamous cell carcinoma (OC-SCC) is essential and should be as comprehensive as possible. The aim of this study was to identify new risk factors and develop nomograms comparing all anatomic sites of the oral cavity.

MATERIALS AND METHODS

We performed a retrospective cohort study using the Surveillance, Epidemiology, and End Results (SEER) database. All patients with OC-SCC diagnosed from 2004 to 2015 were selected and divided into the training cohort and the validation cohort. Age, gender, race, marital status, primary site, tumor grade, American Joint Committee on Cancer (AJCC) stage, TNM stage, surgical treatment, radiotherapy and chemotherapy were identified as predictor variables. The overall survival (OS) and disease specific survival (DSS) were identified as outcome variables. Kaplan-Meier method with log-rank test, univariate and multivariate cox regression analysis were performed. Independent prognostic factors were used to develop 3- and 5-year nomograms. Hazard ratio (HR) and corresponding 95% confidence interval (CI) showed the influence of each factor on OS or DSS. Concordance indexes (C-indexes) and calibration curves verified the nomograms internally and externally.

RESULTS

A total of 12,346 patients were included. Marital status and chemotherapy were independent prognostic factors (P < .05). Tumors occurring on the cheek mucosa had the highest risk in OS (HR, 2.0, 95% CI, 1.7-2.3) and DSS (HR, 4.7, 95% CI, 3.6-6.0), while tumors occurring on the lip had the lowest risk in OS (HR, 1.0) and DSS (HR,1.0). The C-indexes for OS in the training and validation sets were 0.767 and 0.770, respectively, and for DSS were 0.800 and 0.799, respectively.

CONCLUSION

Marital status and chemotherapy independently affect OC-SCC patients' survival. The prognosis is least favorable for tumors occurring on the cheek mucosa and most favorable for tumors occurring on the lip.

Co-expression Analysis of Genes and Tumor-Infiltrating Immune Cells in Metastatic Uterine Carcinosarcoma

Uterine carcinosarcoma (UCS) is a malignant tumor with a high tendency to invasion and metastasis. However, the underlying invasion and metastasis mechanisms of UCS remain poorly understood. Genetic alteration and tumor-infiltrating immune cells play important roles in tumorigenesis, progression, and metastasis. To better understand the underlying mechanisms of UCS, we screened tumor-infiltrating immune cells by applying CIBERSORT algorithm and constructed nomograms to predict the prognosis of UCS patients based on metastasis-specific tumor-infiltrating immune cells and genes, and demonstrated their utility by the high AUC values. Combining gene co-expression and experimental validation results, we propose a potential mechanism of AK8, MPZ, and mast cells activated might play important parts in UCS metastasis.

Development and validation of nomograms predicting cancer-specific survival of vulvar cancer patients: based on the Surveillance, Epidemiology, and End Results Program

OBJECTIVE

To explore potential prognostic factors and develop nomograms to predict the cancer-specific survival of patients with vulvar squamous cell carcinoma (SCC) and patients with vulvar melanoma.

METHODS

Cases of vulvar SCC and melanoma were retrieved from the Surveillance, Epidemiology, and End Results (SEER) Program, and randomly segregated into training and test sets. Based on the training set, univariate and multivariate Cox proportional hazard regressions evaluate the association between key demographic/clinical characteristics and vulvar cancer survival. Potential prognostic factors were included to construct nomograms for the prediction of 3-year and 5-year survival probabilities.

RESULTS

Age, tumor size, stage, surgery, and chemotherapy were potential factors associated with vulvar cancer survival. The C-indices for the training and test sets were 0.82 and 0.81 for SCC, and 0.73 and 0.70 for melanoma. Calibration curves revealed correlated agreements between nomogram-based probability and actual survival status.

CONCLUSION

Nomograms were developed to predict cancer-specific survival of patients with vulvar cancer, accordingly identifying the subgroup at high risk of cancer-specific mortality.

Neoadjuvant Approaches Prior To Radical Prostatectomy

Patients with high-risk localized prostate cancer benefit from multimodality therapy of curative intent. Androgen-deprivation therapy (ADT) combined with radiation improves survival in this population. However, prior clinical trials of neoadjuvant ADT and surgery failed to consistently demonstrate a survival advantage. The development of novel, more potent hormonal agents presents an opportunity to revisit the potential for neoadjuvant therapy to improve long-term outcomes for patients with localized prostate cancer. We review recent advances in neoadjuvant approaches for prostate cancer and emerging clinical trials data supporting the use of neoadjuvant therapy prior to radical prostatectomy.

Prognostic Exploration of All-Cause Death in Gingival Squamous Cell Carcinoma: A Retrospective Analysis of 2076 Patients

Background

We aimed to establish a prognostic model for gingival squamous cell carcinoma (GSCC) that was superior to traditional AJCC staging and to perform a comprehensive comparison of the newly established nomogram with the AJCC staging system.

Methods

We extracted 2,076 patients with gingival squamous cell carcinoma who had been entered into the SEER (Surveillance, Epidemiology, and End Results) database between 2004 and 2015, and randomly divided 70% of them into the training cohort and the other 30% into the validation cohort. Cox regression analysis was performed in combination with clinical experience and age, race, sex, marital status, tumor location, histological subtype, tumor grade, AJCC stage, chemotherapy status, radiotherapy status, and surgery status as possible prognostic factors. We evaluated and compared the two cohorts using the consistency index (C-index), area under the receiver operating characteristic curves, calibration curves, discriminant improvement index, and decision-curve analysis.

Results

The Cox retrospective analysis showed that age, AJCC stage, tumor grade, histological subtype, radiotherapy status, and surgery status were significant factors to include in the new model of gingival squamous cell carcinoma. The other indicators were also better for the new model than for the AJCC staging system.

Conclusion

We have developed and validated a nomogram for performing reliable gingival squamous cell carcinoma prognoses. The prognostic value of the nomogram is higher than that of the AJCC staging system. We expect that the inclusion of more-comprehensive and authoritative data (i.e., not just limited to residents of the United States) would also allow the construction of reliable nomograms for other populations.

Development of a Nomogram for Carbapenem-Resistant Enterobacteriaceae Acquisition Risk Prediction Among Patients in the Intensive Care Unit of a Secondary Referral Hospital

PURPOSE

This study aimed to identify the risk factors of carbapenem-resistant Enterobacteriaceae (CRE) acquisition to build a nomogram for CRE acquisition risk prediction and evaluate its performance.

METHODS

This unmatched case-control study included 352 adult patients (55 patients and 297 controls) admitted to the intensive care unit (ICU) of a 453-bed secondary referral hospital between January 1, 2018, and September 31, 2019, in Busan, South Korea. The nomogram was built with the identified risk factors using multiple logistic regression analysis. Its performance was analyzed using calibration-in-the-large, the slope of the calibration plot, concordance statistic (c-statistic), and the sensitivity and specificity of the training set, subsets, and a new test set.

RESULTS

The risk factors of CRE acquisition among ICU patients at a secondary referral hospital were Acute Physiology and Chronic Health Evaluation II score at the time of admission, use of a central venous catheter and a nasogastric tube, as well as use of cephalosporin antibiotics. At 20.0% of the predicted CRE acquisition risk in the training set, the calibration-in-the-large was 0, slope of the calibration plot was 1, c-statistic was .93, sensitivity was 85.5%, and specificity was 84.8%. The performance was relatively good in the subsets and new test set.

CONCLUSION

The nomogram can be used to monitor the CRE acquisition risk for ICU patients who have a similar case mix to patients in the study hospitals. Future studies need to involve more rigorous methodology and larger samples.

Trends of incidence and prognosis of primary adenocarcinoma of the bladder

BACKGROUND

Primary adenocarcinoma of the bladder (ACB) is a rare malignant tumor of the bladder with limited understanding of its incidence and prognosis.

METHODS

Patients diagnosed with ACB between 2004 and 2015 were obtained from the SEER database. The incidence changes of ACB patients between 1975 and 2016 were detected by Joinpoint software. Nomograms were constructed based on the results of multivariate Cox regression analysis to predict overall survival (OS) and cancer-specific survival (CSS) in patients with ACB, and the constructed nomograms were validated.

RESULTS

The incidence of ACB was trending down from 1991 to 2016. A total of 1039 patients were included in the study and randomly assigned to the training cohort (727) and validation cohort (312). In the training cohort, multivariate Cox regression showed that age, marital status, primary site, histology type, grade, AJCC stage, T stage, SEER stage, surgery, radiotherapy, and chemotherapy were independent prognostic factors for OS, whereas these were age, marital status, primary site, histology type, grade, AJCC stage, T/N stage, SEER stage, surgery, and radiotherapy for CSS. Based on the above Cox regression results, we constructed prognostic nomograms for OS and CSS in ACB patients. The C-index of the nomogram OS was 0.773 and the C-index of CSS was 0.785, which was significantly better than the C-index of the TNM staging prediction model. The area under the curve (AUC) and net benefit of the prediction model were higher than those of the TNM staging system. In addition, the calibration curves were very close to the ideal curve, suggesting appreciable reliability of the nomograms.

CONCLUSION

The incidence of ACB patients showed a decreasing trend in the past 25 years. We constructed a clinically useful prognostic nomogram for calculating OS and CSS of ACB patients, which can provide a personalized risk assessment for ACB patient survival.

Establishment and validation of nomogram model for survival predicting in patients with spinal metastases secondary to lung cancer

OBJECTIVES

To evaluate the prognostic effect of pre-treatment factors in patients with spinal metastases secondary to lung cancer, and establish a novel predicting nomogram for predicting the survival probability.

METHODS

A total of 209 patients operated for spinal metastases from lung cancer were consecutively enrolled, and divided into the training and validation samples with a ratio of 7:3, for model establishing and validating, respectively. Basing on the training sample, univariate and multivariate COX proportional hazard models were used for identifying the prognostic effect of pre-treatment factors, following which significant prognostic factors would be listed as items in nomogram to calculate the survival probabilities at 3, 6, 12 and 18 months. Then, the C-indexes and the calibration curves would be figured out to evaluate the discrimination ability and accuracy of the model both for the training and validation samples.

RESULTS

In the multivariate COX analysis, the gender, smoking history, location of spinal metastasis, visceral metastasis, Karnofsky performance status (KPS), adjuvant therapy, lymphocyte percentage and globulin were found to be significantly associated with the overall survival, and a novel nomogram was generated basing on these independent predictors. The C-indexes for the training and validation samples were 0.761 and 0.732, respectively. Favorable consistencies between the predicted and actual survival rates were demonstrated both in the internal and external validations.

DISCUSSION

Pre-treatment characteristics, including gender, smoking history, location of spinal metastasis, visceral metastasis, KPS, adjuvant therapy, percentage of lymphocyte, and serum globulin level, were identified to be significantly associated with overall survival of patients living with spinal metastases derived from lung cancer, and a user-friendly nomogram was established using these independent predictors.

A Novel Prediction Tool for Overall Survival of Patients Living with Spinal Metastatic Disease

OBJECTIVE

To identify the significant prognostic factors for overall survival in patients with spinal metastases and to establish an online widget for predicting survival with an interactive visual approach.

METHODS

Patients operated for spinal metastases between 2010 and 2018 were retrospectively enrolled and were randomly divided into training and validation samples with a ratio of 7:3. Patients' characteristics were analyzed with univariate and multivariate Cox analyses to identify independent prognostic factors basing on the training sample. A shiny web tool was developed by transforming the fitted multivariable Cox model into a visual interface. Time-dependent area under the curve plot and calibration curve were generated to assess the discrimination ability and consistency of the novel model, both for the training and validation samples.

RESULTS

A total of 265 consecutive patients were finally included, with 185 in the training sample and 80 in the validation sample. The primary tumor types, lesion site of metastasis, visceral metastasis, Frankel grade, operation category, number of surgical segments, and the preoperative percentage of lymphocyte were demonstrated to be significantly associated with overall survival. A novel shiny model (https://yang1209xg.shinyapps.io/predictspinalmetastasis/) that could provide predicted survival curve and median survival time was established, with favorable discrimination ability and consistency between predicted and actual survival both in internal and external data, according to time-dependent area under the curve plots and calibration curves.

CONCLUSIONS

A user-friendly shiny app with favorable discrimination ability and consistency was released online for predicting the survival of patients with spinal metastases. A continuous survival curve and the predicted median survival time are available to guide the treatment planning.

Treatment of localized prostate cancer and use of nomograms among urologists in the West Africa sub-region

INTRODUCTION

there is a high incidence of prostate cancer among men of African descent. The disease tends to occur at an early age with a tendency to be aggressive. The objective was to determine the practice of urologists in the West African sub-region regarding treatment of localized prostate cancer, the use of nomograms and their perception of the usefulness of nomograms.

METHODS

this was a cross-sectional study that involved urologists practicing in the West African sub-region attending urology and surgery conferences of the "Société Internationale d´Urologie", West African college of surgeons and the Ghana association of urological surgeons. A structured questionnaire was used that sort to ascertain the treatment modalities used for localized prostate cancer and the use of nomograms in the sub-region. The study period spanned the years 2018 and 2019.

RESULTS

fifty-six urologists practicing in eleven West African countries responded. Fifty percent had been in practice for less than 5 years. Sixty eight percent (38/56) had been involved in the treatment of localized prostate cancer. Radical prostatectomy was widely available and the treatment modality most used 94.7% (36/38). Nomograms was used by 57.9% of them (22/38) with the Partin tables being the most commonly used nomogram (34.2%). No Locally developed nomogram for treatment of localized prostate cancer was identified.

CONCLUSION

radical prostatectomy is the commonest treatment modality used for the management of localized prostate cancer in the West Africa sub-region. Majority of the urologists used nomograms with the Partin tables being the most used.

Development and validation of nomograms to accurately predict risk of recurrence for patients with laryngeal squamous cell carcinoma: Cohort study

BACKGROUND

Recurrence is still major obstacle to long-term survival in laryngeal squamous cell carcinoma (LSCC). We aimed to establish and validate a nomogram to precisely predict recurrence probability in patients with LSCC.

METHODS

A total of 283 consecutive patients with LSCC received curative-intend surgery between 2011 and 2014 at were enrolled in this study. Subsequently, 283 LSCC patients were randomly assigned to a training cohort (N = 171) and a validation cohort (N = 112) in a 3:2 ratio. According to the results of multivariable Cox regression analysis in the training cohort, we developed a nomogram. The predictive accuracy and discriminative ability of the nomogram were evaluated by calibration curve and concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate clinical value of our nomogram.

RESULTS

Six independent factors rooted in multivariable analysis of the training cohort to predict recurrence were age, tumor site, smoking, alcohol, N stage and hemoglobin, which were all integrated into the nomogram. The calibration curve for the probability of recurrence presented that the nomogram-based predictions were in good correspondence with actual observations. The C-index of the nomogram was 0.81 (0.75-0.88), and the area under curve (AUC) of nomogram in predicting recurrence free survival (RFS) was 0.894, which were significantly better than traditional TNM stage. Decision curve analysis further affirmed that our nomogram had a larger net benefit than TNM stage. The results were confirmed in the validation cohort.

CONCLUSION

A risk prediction nomogram for patients with LSCC, incorporating readily assessable clinicopathologic variables, generates more accurate estimations of the recurrence probability when compared TNM stage alone, but still needs additional data before being used in clinical implications.

Nomograms to predict individual prognosis of patients with squamous cell carcinoma of the urinary bladder

BACKGROUND

On the basis of some significant clinical parameters, we had an intent to establish nomograms for estimating the prognosis of patients with squamous cell carcinoma of the urinary bladder (SCCB), including overall survival (OS) and cancer-specific survival (CSS).

METHODS

The data of 1210 patients diagnosed with SCCB between 2004 and 2014,were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The Cox proportional hazards regression model was applied to evaluate the association between variables and survival. Nomograms were constructed to predict the OS and CSS of an individual patient based on the Cox model. In the end, the performance of nomograms was internally validated by using calibration curves, concordance index (C-index), and k-fold cross-validation.

RESULTS

Several common indicators were taken into the two nomograms (OS and CSS), including age at diagnosis, marital status, sex, TNM stage, surgical approach, tumor size, and lymph node ratio while the OS nomogram additionally contained race, grade, and chemotherapy. They had an excellent predictive accuracy on 1- and 3- year OS and CSS with C-index of 0.733 (95% confidence interval [CI], 0.717-0.749) for OS and 0.724 (95% CI, 0.707-0.741) for CSS. All calibration curves showed great consistency between actual survival and predictive survival.

CONCLUSIONS

The nomograms with improved accuracy and applicability on predicting the survival outcome of patients with SCCB would provide a reliable tool to help clinicians to evaluate the risk of patients and make individual treatment strategies.

Visualising statistical models using dynamic nomograms

Translational Statistics proposes to promote the use of Statistics within research and improve the communication of statistical findings in an accurate and accessible manner to diverse audiences. When statistical models become more complex, it becomes harder to evaluate the role of explanatory variables on the response. For example, the interpretation and communication of the effect of predictors in regression models where interactions or smoothing splines are included can be challenging. Informative graphical representations of statistical models play a critical translational role; static nomograms are one such useful tool to visualise statistical models. In this paper, we propose the use of dynamic nomogram as a translational tool which can accommodate models of increased complexity. In theory, all models appearing in the literature could be accompanied by the corresponding dynamic nomogram to translate models in an informative manner. The R package presented will facilitate this communication for a variety of linear and non-linear models.

Nomogram Identifies Age as the Most Important Predictor of Overall Survival in Oral Cavity Squamous Cell Cancer After Primary Surgery

Our goal was to determine the most important predictors and construct a nomogram for overall survival (OS) in oral cavity squamous cell cancer (OCSCC) treated with primary surgery followed by observation, adjuvant radiation or chemoradiation. Multivariable analysis was performed using Cox Proportional Hazard model of 9258 OCSCC patients from Surveillance, Epidemiology and End Results Program (SEER) database treated with surgery from 2003 to 2009. Potential predictors of OS were age, gender, race, tobacco use, oral cancer sub-sites, pathologic tumor stage and grade, pathologic nodal stage, extra-capsular invasion, clinical levels IV and V involvement, and adjuvant treatment selection. Weighted propensity scores for treatment were used to balance observed baseline characteristics between three treatment groups in order to reduce bias. Following primary surgery, patients underwent observation (56%), radiation alone (31%) or chemoradiation (13%). All tested predictors were statistically significant and included in our final nomogram. Most important predictor of OS was age, followed by pathologic tumor stage. SEER based-survival nomogram for OCSCC patients differs from published models derived from patients treated in a single or few academic treatment centers. An unexpected finding of patient age being the best OS predictor suggests that this factor may be more critical for the outcome than previously anticipated.

Predictive factors for short-term biochemical recurrence-free survival after robot-assisted laparoscopic radical prostatectomy in high-risk prostate cancer patients

BACKGROUND

We aimed to assess the short-term oncological outcomes of robot-assisted laparoscopic radical prostatectomy to determine the predictive factors associated with biochemical recurrence in high-risk prostate cancer patients.

METHODS

A total of 331 patients with localized prostate cancer underwent robot-assisted laparoscopic radical prostatectomy. Of them, 113 patients were diagnosed with high-risk prostate cancer according to the D'Amico risk group classification. We evaluated the association between pre- or postoperative predictive factors and biochemical recurrence using Cox regression analysis.

RESULTS

The 2-year biochemical recurrence-free survival rate was 65.0% in the high-risk group. On univariate analyses, PSA level > 20 ng/mL, Gleason pattern 5 component on biopsy, pathological stage T3 or higher, perineural invasion, and positive surgical margin were predictive factors for biochemical recurrence. On multivariate analysis, PSA level > 20 ng/mL, Gleason pattern 5 component on biopsy, perineural invasion, and positive surgical margin were identified as independent predictive factors. The 2-year biochemical recurrence-free survival rate was 36.5% for patients with PSA level > 20 ng/mL and/or Gleason pattern 5 component on biopsy.

CONCLUSIONS

PSA level > 20 ng/mL and/or presence of the Gleason pattern 5 component on biopsy are predictive factors for early biochemical recurrence after robot-assisted laparoscopic radical prostatectomy in high-risk prostate cancer patients. We considered that these patients require a combined modality therapy to improve their prognosis.

A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction

BACKGROUND AND OBJECTIVE

Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors.

METHODS

A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event.

RESULTS

A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation.

CONCLUSION

The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

Three prognostic indexes as predictors of response to adjuvant chemoradiotherapy in patients with oral squamous cell carcinoma after radical surgery: A large-scale prospective study

BACKGROUND

To develop and validate practical prognostic indexes (PIs) for predicting the prognosis and response to postoperative adjuvant therapy in patients with oral squamous cell carcinoma (OSCC).

METHODS

A large cohort of 1071 OSCC patients were randomized to either training set (N = 708) or validation set (N = 363). Three types of PIs were developed according to the nomogram scores, β coefficients and excess hazard ratios, respectively. Restricted cubic spline was used to demonstrate the relationship between PIs and the risks of death.

RESULTS

First, a nomogram was developed incorporating age at diagnosis, smoking status, clinical stage, tumor differentiation, lymph node status, comorbidity, and neutrophil to lymphocyte ratio levels. Then, three PIs were established with high survival predictive ability, and were superior to AJCC staging system (all P < .05). The risks of death were escalated continuously with the increasing number of PIs. Interestingly, adjuvant chemoradiotherapy was positively associated with poor overall survival in patients with low PIs, but exerted a beneficial effect on patients with high PIs.

CONCLUSION

Combined nomogram with further established PIs not only predicts the survival probability of OSCC patients, but also continuously quantifies the risk of death. High PIs could predict a beneficial response to adjuvant chemoradiotherapy, whereas low PIs indicate an unfavorable response.

Performance of prostate cancer recurrence nomograms by obesity status: a retrospective analysis of a radical prostatectomy cohort

Background

Obesity has been associated with aggressive prostate cancer and poor outcomes. It is important to understand how prognostic tools for that guide prostate cancer treatment may be impacted by obesity. The goal of this study was to evaluate the predicting abilities of two prostate cancer (PCa) nomograms by obesity status.

Methods

We examined 1576 radical prostatectomy patients categorized into standard body mass index (BMI) groups. Patients were categorized into low, medium, and high risk groups for the Kattan and CaPSURE/CPDR scores, which are based on PSA value, Gleason score, tumor stage, and other patient data. Time to PCa recurrence was modeled as a function of obesity, risk group, and interactions.

Results

As expected for the Kattan score, estimated hazard ratios (95% CI) indicated higher risk of recurrence for medium (HR = 2.99, 95% CI = 2.29, 3.88) and high (HR = 8.84, 95% CI = 5.91, 13.2) risk groups compared to low risk group. The associations were not statistically different across BMI groups. Results were consistent for the CaPSURE/CPDR score. However, the difference in risk of recurrence in the high risk versus low risk groups was larger for normal weight patients than the same estimate in the obese patients.

Conclusions

We observed no statistically significant difference in the association between PCa recurrence and prediction scores across BMI groups. However, our study indicates that there may be a stronger association between high risk status and PCa recurrence among normal weight patients compared to obese patients. This suggests that high risk status based on PCa nomogram scores may be most predictive among normal weight patients. Additional research in this area is needed.

Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record

Background

Electronic, personalized clinical decision support tools to optimize glycated hemoglobin (HbA_1c) screening are lacking. Current screening guidelines are based on simple, categorical rules developed for populations of patients. Although personalized diabetes risk calculators have been created, none are designed to predict current glycemic status using structured data commonly available in electronic health records (EHRs).

Objective

The goal of this project was to create a mathematical equation for predicting the probability of current elevations in HbA_1c (≥5.7%) among patients with no history of hyperglycemia using readily available variables that will allow integration with EHR systems.

Methods

The reduced model was compared head-to-head with calculators created by Baan and Griffin. Ten-fold cross-validation was used to calculate the bias-adjusted prediction accuracy of the new model. Statistical analyses were performed in R version 3.2.5 (The R Foundation for Statistical Computing) using the rms (Regression Modeling Strategies) package.

Results

The final model to predict an elevated HbA_1c based on 22,635 patient records contained the following variables in order from most to least importance according to their impact on the discriminating accuracy of the model: age, body mass index, random glucose, race, serum non–high-density lipoprotein, serum total cholesterol, estimated glomerular filtration rate, and smoking status. The new model achieved a concordance statistic of 0.77 which was statistically significantly better than prior models. The model appeared to be well calibrated according to a plot of the predicted probabilities versus the prevalence of the outcome at different probabilities.

Conclusions

The calculator created for predicting the probability of having an elevated HbA_1c significantly outperformed the existing calculators. The personalized prediction model presented in this paper could improve the efficiency of HbA_1c screening initiatives.

A Comparative Analysis of the Usefulness of Survival Prediction Models for Patients with Glioblastoma in the Temozolomide Era: The Importance of Methylguanine Methyltransferase Promoter Methylation, Extent of Resection, and Subventricular Zone Location

OBJECTIVE

Several survival prediction models for patients with glioblastoma have been proposed, but none is widely used. This study aims to identify the predictors of overall survival (OS) and to conduct an independent comparative analysis of 5 prediction models.

METHODS

Multi-institutional data from 159 patients with newly diagnosed glioblastoma who received adjuvant temozolomide concomitant chemoradiotherapy (CCRT) were collected. OS was assessed by Cox proportional hazards regression and adjusted for known prognostic factors. An independent CCRT patient cohort was used to externally validate the 1) RTOG (Radiation Therapy Oncology Group) recursive partitioning analysis (RPA) model, 2) Yang RPA model, and 3) Wee RPA model, Chaichana model, and the RTOG nomogram model. The predictive accuracy for each model at 12-month survival was determined by concordance indices. Calibration plots were performed to ascertain model prediction precision.

RESULTS

The median OS for patients who received CCRT was 19.0 months compared with 12.7 months for those who did not (P < 0.001). Independent predictors were: 1) subventricular zone II tumors (hazard ratio [HR], 1.6; 95% confidence interval [CI], 1.0-2.5); 2) methylguanine methyltransferase promoter methylation (HR, 0.36; 95% CI, 0.2-0.6); and 3) extent of resection of >85% (HR, 0.59; 95% CI, 0.4-0.9). For 12-month OS prediction, the RTOG nomogram model was superior to the RPA models with a c-index of 0.70. Calibration plots for 12-month survival showed that none of the models was precise, but the RTOG nomogram performed relatively better.

CONCLUSIONS

The RTOG nomogram best predicted 12-month OS. Methylguanine methyltransferase promoter methylation status, subventricular zone tumor location, and volumetric extent of resection should be considered when constructing prediction models.

The START nomogram for individualized prediction of the probability of unfavorable outcome after intravenous thrombolysis for stroke

Background and purpose

The nomogram is an important component of modern medical decision-making, which calculates the probability of an event entirely based on individual characteristics. We aimed to develop and validate a nomogram for individualized prediction of the probability of unfavorable outcome in intravenous thrombolysis-treated stroke patients included in the large multicenter Safe Implementation of Thrombolysis in Stroke-International Stroke Thrombolysis Register.

Methods

All patients registered in the Safe Implementation of Thrombolysis in Stroke-International Stroke Thrombolysis Register by 179 Italian centers between May 2001 and March 2016 were originally included. The main outcome measure was three-month unfavorable outcome (modified Rankin Scale 3–6). Four non-categorical predictors of unfavorable outcome (baseline National Institutes of Health (NIH) Stroke Scale score: 0–25, age ≥18 years, pre-stroke modified Rankin Scale score: 0–2, and onset-to-treatment time: 0–270 min) were identified a-priori by three neurologists with expertise in the management of stroke. To generate the NIHSS STroke Scale score, Age, pre-stroke mRS score, onset-to-treatment Time (START), the pre-established predictors were entered into a logistic regression model. The discriminative performance of the model was assessed using the area under the receiver operating characteristic curve.

Results

A total of 15,862 patients with complete data for generating the START was randomly dichotomized into training (2/3, n = 10,574) and test (1/3, n = 5288) sets. The area under the receiver operating characteristic curve of START was 0.800 (95% confidence interval: 0.792–0.809) in the training set and 0.815 (95% confidence interval: 0.804–0.822) in the test set.

Conclusions

By using a limited number of non-categorical predictors, the START is the first nomogram developed and validated in a large Safe Implementation of Thrombolysis in Stroke-International Stroke Thrombolysis Register cohort, which reliably calculates the probability of unfavorable outcome in intravenous thrombolysis-treated stroke patients.

Validation of nomograms for overall survival, cancer-specific survival, and recurrence in carcinoma of the major salivary glands

BACKGROUND

The purpose of this study was to investigate the performance of the Memorial Sloan Kettering Cancer Center salivary carcinoma nomograms predicting overall survival, cancer-specific survival, and recurrence with an external validation dataset.

METHODS

The validation dataset comprised 123 patients treated between 2010 and 2015 at our institution. They were evaluated by assessing discrimination (concordance index [C-index]) and calibration (plotting predicted vs actual probabilities for quintiles).

RESULTS

The validation cohort (n = 123) showed some differences to the original cohort (n = 301). The validation cohort had less high-grade cancers (P = .006), less lymphovascular invasion (LVI; P < .001) and shorter follow-up of 19 months versus 45.6 months. Validation showed a C-index of 0.833 (95% confidence interval [CI] 0.758-0.908), 0.807 (95% CI 0.717-0.898), and 0.844 (95% CI 0.768-0.920) for overall survival, cancer-specific survival, and recurrence, respectively.

CONCLUSION

The 3 salivary gland nomograms performed well using a contemporary validation dataset, despite limitations related to sample size, follow-up, and differences in clinical and pathology characteristics between the original and validation cohorts.

The Within-Group Discrimination Ability of the Cancer of the Prostate Risk Assessment Score for Men with Intermediate-Risk Prostate Cancer

BACKGROUND

Significant clinical heterogeneity within contemporary risk group is well known, particularly for those with intermediate-risk prostate cancer (IRPCa). Our study aimed to analyze the ability of the Cancer of the Prostate Risk Assessment (CAPRA) score to discern between favorable and non-favorable risk in patients with IRPCa.

METHODS

We retrospectively reviewed the data of 203 IRPCa patients who underwent extraperitoneal robot-assisted radical prostatectomy (RARP) performed by a single surgeon. Pathologic favorable IRPCa was defined as a Gleason score ≤ 6 and organ-confined stage at surgical pathology. The CAPRA score was compared with two established criteria for the within-group discrimination ability.

RESULTS

Overall, 38 patients (18.7% of the IRPCa cohort) had favorable pathologic features after RARP. The CAPRA score significantly correlated with established criteria I and II and was inversely associated with favorable pathology (all P < 0.001). The area under the receiver operating characteristic curve for the discriminative ability between favorable and non-favorable pathology was 0.679 for the CAPRA score and 0.610 and 0.661 for established criteria I and II, respectively. During a median 37.8 (interquartile range, 24.6-60.2) months of follow-up, 66 patients (32.5%) experienced biochemical recurrence (BCR). Cox regression analysis revealed that the CAPRA score, as a continuous sum score model or 3-group risk model, was an independent predictor of BCR after RARP.

CONCLUSION

The within-group discrimination ability of preoperative CAPRA score might help in patient counseling and selecting optimal treatments for those with IRPCa.

STARTING-SICH Nomogram to Predict Symptomatic Intracerebral Hemorrhage After Intravenous Thrombolysis for Stroke

BACKGROUND AND PURPOSE

Symptomatic intracerebral hemorrhage (sICH) is a rare but the most feared complication of intravenous thrombolysis for ischemic stroke. We aimed to develop and validate a nomogram for individualized prediction of sICH in intravenous thrombolysis-treated stroke patients included in the multicenter SITS-ISTR (Safe Implementation of Thrombolysis in Stroke-International Stroke Thrombolysis Register).

METHODS

All patients registered in the SITS-ISTR by 179 Italian centers between May 2001 and March 2016 were originally included. The main outcome measure was sICH per the European Cooperative Acute Stroke Study II definition (any type of intracerebral hemorrhage with increase of ≥4 National Institutes of Health Stroke Scale score points from baseline or death <7 days). On the basis of multivariate logistic model, the nomogram was generated. We assessed the discriminative performance by using the area under the receiver-operating characteristic curve and calibration of risk prediction model by using the Hosmer-Lemeshow test.

RESULTS

A total of 15 949 patients with complete data for generating the nomogram was randomly dichotomized into training (3/4; n=12 030) and test (1/4; n=3919) sets. After multivariate logistic regression, 10 variables remained independent predictors of sICH to compose the STARTING-SICH (systolic blood pressure, age, onset-to-treatment time for thrombolysis, National Institutes of Health Stroke Scale score, glucose, aspirin alone, aspirin plus clopidogrel, anticoagulant with INR ≤1.7, current infarction sign, hyperdense artery sign) nomogram. The area under the receiver-operating characteristic curve of STARTING-SICH was 0.739. Calibration was good (P=0.327 for the Hosmer-Lemeshow test).

CONCLUSIONS

The STARTING-SICH is the first nomogram developed and validated in a large SITS-ISTR cohort for individualized prediction of sICH in intravenous thrombolysis-treated stroke patients.

Validation of American Joint Committee on Cancer eighth staging system among prostate cancer patients treated with radical prostatectomy

Background

The objective in this paper was to validate the prognostic performance of the American Joint Committee on Cancer (AJCC) 7th and 8th systems among prostate cancer patients treated with radical prostatectomy.

Methods

The surveillance, epidemiology and end results (SEER) database (2006-2014) was accessed through the SEER*Stat program and AJCC 7th and 8th editions were calculated utilizing T, N and M stages, histological grade group, as well as baseline prostatic-specific antigen (PSA). Cancer-specific and overall survival analyses according to 7th and 8th editions were conducted. Moreover, multivariate analysis was conducted through a Cox proportional hazard model.

Results

A total of 72,999 patients with prostate cancer were identified in the period from 2006 to 2014. Overall survival was assessed according to AJCC 7th and 8th staging systems. The test for trend for overall survival was significant (p < 0.0001) for both staging systems. Concordance index for AJCC 7th system was: 0.791 [standard error of the mean (SE): 0.017; 95% CI: 0.758-0.825]; while concordance index for AJCC 8th system was: 0.840 (SE: 0.015; 95% CI: 0.811-0.869). In a multivariate analysis among patients with M0 disease, lower grade group, N0 stage and pT2 stage were associated with better cancer-specific survival (p < 0.01); while PSA level did not predict cancer-specific survival.

Conclusion

There is a clear improvement in the discriminatory ability for AJCC 8th versus AJCC 7th staging system in the postprostatectomy setting. This may be related to better integration of biological factors into the staging system.

Development and Validation of Nomograms Predictive of Overall and Progression-Free Survival in Patients With Oropharyngeal Cancer

Purpose Treatment of oropharyngeal squamous cell carcinoma (OPSCC) is evolving toward risk-based modification of therapeutic intensity, which requires patient-specific estimates of overall survival (OS) and progression-free survival (PFS). Methods To develop and validate nomograms for OS and PFS, we used a derivation cohort of 493 patients with OPSCC with known p16 tumor status (surrogate of human papillomavirus) and cigarette smoking history (pack-years) randomly assigned to clinical trials using platinum-based chemoradiotherapy (NRG Oncology Radiation Therapy Oncology Group [RTOG] 0129 and 0522). Nomograms were created from Cox models and internally validated by use of bootstrap and cross-validation. Model discrimination was measured by calibration plots and the concordance index. Nomograms were externally validated in a cohort of 153 patients with OPSCC randomly assigned to a third trial, NRG Oncology RTOG 9003. Results Both models included age, Zubrod performance status, pack-years, education, p16 status, and T and N stage; the OS model also included anemia and age × pack-years interaction; and the PFS model also included marital status, weight loss, and p16 × Zubrod interaction. Predictions correlated well with observed 2-year and 5-year outcomes. The uncorrected concordance index was 0.76 (95% CI, 0.72 to 0.80) for OS and 0.70 (95% CI, 0.66 to 0.74) for PFS, and bias-corrected indices were similar. In the validation set, OS and PFS models were well calibrated, and OS and PFS were significantly different across tertiles of nomogram scores (log-rank P = .003;< .001). Conclusion The validated nomograms provided useful prediction of OS and PFS for patients with OPSCC treated with primary radiation-based therapy.

Prognostic Nomogram for Advanced Hepatocellular Carcinoma Treated with FOLFOX 4

Background:

The Oxaliplatin plus 5-Fluorouracil /Leucovorin (FOLFOX4) regimen have been approved by Chinese Food and Drug Administration (CFDA), and covered by health insurance for patients with advanced hepatocellular carcinoma (HCC) in China. However, the efficacy of FOLFOX4 for HCC patients is still under debate. In this study, we aimed to establish a nomogram to identify HCC patients who might benefit from FOLFOX4 chemotherapy base on individual profile.

Methods:

A total of 184 patients from the EACH study who were treated with FOLFOX4 were included in this analysis. Backward Cox proportional hazards regression combined with clinical experience was used to select variables for construction of the nomogram. The nomogram performance was assessed in terms of discrimination and calibration. The results were validated using bootstrap resampling.

Results:

Six variables were included in the prognostic models based on their clinical relevance: age, maximum tumor diameter, lymph node status, aspartate aminotransferase (AST), total bilirubin (TBIL) and alpha-fetoprotein (AFP). The calibration curve showed that the predicted survival probabilities closely matched the actual observations. The C-index of the model was 0.75 (95% CI:0.71-0.80). This value was significantly superior to the one for the following staging systems: BCLC (0.67, P=0.004), CUPI (0.66, P<0.001), AJCC seventh edition (0.63, P=0.002), GRETCH (0.63, P<0.001).

Conclusions:

The proposed nomogram showed accurate prognostic prediction for 6-month overall survival of patients treated with FOLFOX4 and could be useful for clinicians counseling patients and making treatment decisions.