Nomogram predicted survival of patients with adenocarcinoma of esophagogastric junction

Nomograms for prognosis prediction in esophageal adenocarcinoma: realities and challenges

Prognostic assessment is of great significance for individualized treatment and care of cancer patients. Although the TNM staging system is widely used as the primary prognostic classifier for solid tumors in clinical practice, the complexity of tumor occurrence and development requires more personalized probability prediction models than an ordered staging system. By integrating clinical, pathological, and molecular factors into digital models through LASSO and Cox regression, a nomogram could provide more accurate personalized survival estimates, helping clinicians and patients develop more appropriate treatment and care plans. Esophageal adenocarcinoma (EAC) is a common pathological subtype of esophageal cancer with poor prognosis. Here, we screened and comprehensively reviewed the studies on EAC nomograms for prognostic prediction, focusing on performance evaluation and potential prognostic factors affecting survival. By analyzing the strengths and limitations of the existing nomograms, this study aims to provide assistance in constructing high-quality prognostic models for EAC patients.

Construction of a novel nomogram for predicting overall survival in patients with Siewert type II AEG based on LODDS: a study based on the seer database and external validation

Background

In recent years, the incidence of adenocarcinoma of the esophagogastric junction (AEG) has been rapidly increasing globally. Despite advances in the diagnosis and treatment of AEG, the overall prognosis for AEG patients remains concerning. Therefore, analyzing prognostic factors for AEG patients of Siewert type II and constructing a prognostic model for AEG patients is important.

Methods

Data of primary Siewert type II AEG patients from the SEER database from 2004 to 2015 were obtained and randomly divided into training and internal validation cohort. Additionally, data of primary Siewert type II AEG patients from the China Medical University Dandong Central Hospital from 2012 to 2018 were collected for external validation. Each variable in the training set underwent univariate Cox analysis, and variables with statistical significance (p < 0.05) were added to the LASSO equation for feature selection. Multivariate Cox analysis was then conducted to determine the independent predictive factors. A nomogram for predicting overall survival (OS) was developed, and its performance was evaluated using ROC curves, calibration curves, and decision curves. NRI and IDI were calculated to assess the improvement of the new prediction model relative to TNM staging. Patients were stratified into high-risk and low-risk groups based on the risk scores from the nomogram.

Results

Age, Differentiation grade, T stage, M stage, and LODDS (Log Odds of Positive Lymph Nodes)were independent prognostic factors for OS. The AUC values of the ROC curves for the nomogram in the training set, internal validation set, and external validation set were all greater than 0.7 and higher than those of TNM staging alone. Calibration curves indicated consistency between the predicted and actual outcomes. Decision curve analysis showed moderate net benefit. The NRI and IDI values of the nomogram were greater than 0 in the training, internal validation, and external validation sets. Risk stratification based on the nomogram's risk score demonstrated significant differences in survival rates between the high-risk and low-risk groups.

Conclusion

We developed and validated a nomogram for predicting overall survival (OS) in patients with Siewert type II AEG, which assists clinicians in accurately predicting mortality risk and recommending personalized treatment strategies.

Development of a risk prediction model for subsequent infection after colonization with carbapenem-resistant Enterobacterales: a retrospective cohort study

BACKGROUND

Colonization of carbapenem-resistant Enterobacterale (CRE) is considered as one of vital preconditions for infection, with corresponding high morbidity and mortality. It is important to construct a reliable prediction model for those CRE carriers with high risk of infection.

METHODS

A retrospective cohort study was conducted in two Chinese tertiary hospitals for patients with CRE colonization from 2011 to 2021. Univariable analysis and the Fine-Gray sub-distribution hazard model were utilized to identify potential predictors for CRE-colonized infection, while death was the competing event. A nomogram was established to predict 30-day and 60-day risk of CRE-colonized infection.

RESULTS

879 eligible patients were enrolled in our study and divided into training (n = 761) and validation (n = 118) group, respectively. There were 196 (25.8%) patients suffered from subsequent CRE infection. The median duration of subsequent infection after identification of CRE colonization was 20 (interquartile range [IQR], 14-32) days. Multisite colonization, polymicrobial colonization, catheterization and receiving albumin after colonization, concomitant respiratory diseases, receiving carbapenems and antimicrobial combination therapy before CRE colonization within 90 days were included in final model. Model discrimination and calibration were acceptable for predicting the probability of 60-day CRE-colonized infection in both training (area under the curve [AUC], 74.7) and validation dataset (AUC, 81.1). Decision-curve analysis revealed a significantly better net benefit in current model. Our prediction model is freely available online at https://ken-zheng.shinyapps.io/PredictingModelofCREcolonizedInfection/ .

CONCLUSIONS

Our nomogram has a good predictive performance and could contribute to early identification of CRE carriers with a high-risk of subsequent infection, although external validation would be required.

Clinical characteristics and prognostic outcomes for adenocarcinoma of esophagogastric junction in early-onset patients: a population-based appraisal

PURPOSE

Malignancies of the upper gastrointestinal tract are rare in early-onset patients outside the hereditary genetic disorders. There are few reports describing adenocarcinoma of the esophagogastric junction (AEG) in extremely early-onset patients aged under 50 years old. The aim of this study was to describe the clinicopathological features and prognosis of adenocarcinoma of esophagogastric junction (AEG) in early-onset patients among three successive periods: 1975-1989 (period 1), 1990-2004 (period 2), and 2005-2017 (period 3).

METHODS

Between 1975 and 2017, data were extracted from the Surveillance, Epidemiology, and End Results database, and 18,278 patients with AEG were enrolled. Three age groups of patients were identified: < 50, 50-69, and ≥ 70 years of age. Clinicopathological characteristics and prognostic outcomes were reviewed and compared among three groups over three periods (1975-89, 1990-04, and 2005-2017). Multivariate Cox regression analysis was performed to adjust for covariate effects related to both overall survival (OS) and cancer-specific survival (CSS).

RESULTS

Among three age groups, early-onset patients were more likely to present with higher tumor grade, advanced nodal, and distant metastatic disease at diagnosis than other groups (p < 0.01 for both). In comparison to the older group, a higher proportion of patients in the early-onset group received chemotherapy and radiation treatment. After adjusting for covariates, early-onset patients had a better CSS and OS than elderly patients.

CONCLUSIONS

Early-onset AEG patients were more likely than other age groups to present with advanced disease upon diagnosis. However, the prognosis of early-onset patients was better than their older counterparts after adjustment for covariates. The dissimilarities in tolerance to treatment among early-onset, middle-aged, and elderly patients could be the reason for this difference.

The GLIM criteria as an effective tool for survival prediction in gastric cancer patients

BACKGROUND

The Global Leadership Initiative on Malnutrition released a new version of the malnutrition criteria (GLIM criteria). To investigate the influence of the GLIM criteria on the long-term efficacy of radical gastric cancer surgery and establish a nomogram to predict the long-term prognosis of patients with gastric cancer.

METHODS

A retrospective analysis of 1121 patients with gastric cancer in our department from 2010 to 2013 was performed. A nomogram was established to predict overall survival (OS) based on the GLIM criteria. Patients were divided into the low-risk group (LRG) and high-risk group (HRG) based on the established nomogram.

RESULTS

Multivariate Cox regression analyses showed that GLIM criteria was an independent risk factor for the 5-year OS (HR = 1.768, Cl:1.341-2.329, p < 0.001). The C index, AUC and Time-ROC of the nomogram were significantly better than that of GLIM criteria and traditional criteria. The 5-year OS of patients receiving adjuvant chemotherapy in the high-risk group was significantly higher than that of patients without chemotherapy (45.77% vs. 24.73%,p < 0.001).

CONCLUSIONS

The GLIM criteria independently influence the long-term outcome of patients after radical gastric cancer surgery. The established nomogram can predict the long-term survival of patients with gastric cancer, and postoperative adjuvant chemotherapy for HRG can significantly improve the 5-year OS of patients.

Development and validation of a risk prediction nomogram for disposition of acute clozapine intoxicated patients to intensive care unit

BACKGROUND

Clozapine is an atypical antipsychotic drug used for the treatment of refractory schizophrenia. It is reported as the most toxic in its class. Using serum clozapine level as a severity indicator is doubtful and unfeasible, particularly in low resourced countries.

METHODS

This is an extended two-phase retrospective study that utilized medical records of patients diagnosed with acute clozapine intoxication and admitted to Tanta University Poison Control Center, Egypt during the past 6 years. Two hundred and eight medical records were used to establish and validate a nomogram for predicting the need for intensive care unit (ICU) admission in acute clozapine intoxicated patients.

RESULTS

A reliable simple bedside nomogram was developed and proved its significant ability to predict the need for ICU admission, with an area under the curve (AUC) of 83.9% and 80.8% accuracy. It encompassed the age of admitted patients (AUC = 64.8%, p = .003), respiratory rate (AUC = 74.7%, p < .001), O2 saturation (AUC = 71.7%, p < .001), and random blood glucose level upon admission (AUC = 70.5%, p < .001). External validation of the proposed nomogram showed a high AUC (99.2%) with an overall accuracy of 96.2%.

CONCLUSION

There is a need to develop a reliable objective tool predicting the severity and need for ICU admission in acute clozapine intoxication. The proposed nomogram is a substantially valuable tool to estimate ICU admission probabilities among patients with acute clozapine intoxication and will help clinical toxicologists make rapid decisions for ICU admission, especially in countries with low resources.

Validation of the Memorial Sloan Kettering Gastric Cancer Post-Resection Survival Nomogram: Does It Stand the Test of Time?

BACKGROUND

The Memorial Sloan Kettering Cancer Center (MSK) nomogram combined both gastroesophageal junction (GEJ) and gastric cancer patients and was created in an era from patients who generally did not receive neoadjuvant chemotherapy. We sought to reevaluate the MSK nomogram in the era of multidisciplinary treatment for GEJ and gastric cancer.

STUDY DESIGN

Using data on patients who underwent R0 resection for GEJ or gastric cancer between 2002 and 2016, the C-index of prediction for disease-specific survival (DSS) was compared between the MSK nomogram and the American Joint Committee on Cancer (AJCC) 8th edition staging system after segregating patients by tumor location (GEJ or gastric cancer) and neoadjuvant treatment. A new nomogram was created for the group for which both systems poorly predicted prognosis.

RESULTS

During the study period, 886 patients (645 gastric and 241 GEJ cancer) underwent up-front surgery, and 999 patients (323 gastric and 676 GEJ) received neoadjuvant treatment. Compared with the AJCC staging system, the MSK nomogram demonstrated a comparable C-index in gastric cancer patients undergoing up-front surgery (0.786 vs 0.753) and a better C-index in gastric cancer patients receiving neoadjuvant treatment (0.796 vs 0.698). In GEJ cancer patients receiving neoadjuvant chemotherapy, neither the MSK nomogram nor the AJCC staging system performed well (C-indices 0.647 and 0.646). A new GEJ nomogram was created based on multivariable Cox regression analysis and was validated with a C-index of 0.718.

CONCLUSIONS

The MSK gastric cancer nomogram's predictive accuracy remains high. We developed a new GEJ nomogram that can effectively predict DSS in patients receiving neoadjuvant treatment.

Nomograms for Predicting Disease-Free Survival in Patients With Siewert Type II/III Adenocarcinoma of the Esophagogastric Junction Receiving Neoadjuvant Therapy and Radical Surgery

Objective

This study aimed to develop prognostic prediction models for patients with Siewert type II/III adenocarcinoma of the esophagogastric junction (AEG) who received neoadjuvant therapy (neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy) and radical surgery. A baseline nomogram and a post-operative nomogram were constructed before neoadjuvant therapy and after surgery. The predictive performance of the constructed nomograms was internally validated and compared to the TNM staging system.

Materials and Methods

A total of 245 patients diagnosed with Siewert type II/III AEG and treated with neoadjuvant therapy followed by radical surgery at The Fourth Hospital of Hebei Medical University between January 2011 and December 2017 were enrolled. The variables before neoadjuvant therapy were defined as baseline factors, while the variables of baseline factors along with the variables of treatment and postoperative pathology were defined as post-operative factors. To construct the corresponding nomograms, independent predictors of baseline and post-operative factors were identified. The C-index and a time-dependent receiver operating characteristic curve were used to evaluate the model’s discrimination ability. The calibration ability of the model was determined by comparing the probability of predicted free-recurrence to the actual free-recurrence. Decision curve analysis (DCA) was used to determine the clinical usefulness of the nomogram.

Results

Among the baseline factors, age, cT stage, cN stage, Borrmann type, and staging laparoscopy were independent prognostic predictors. In contrast, among the post-operative factors, age, cN stage, staging laparoscopy, ypT stage, clinical response, number of positive lymph nodes, number of negative lymph nodes, laurén classification, and lymphatic, or perineural invasion (VELPI) were independent prognostic predictors. The two nomograms were constructed using the independent predictors of prognosis. The C-indexes for the baseline and post-operative nomograms were 0.690 (95% CI, 0.644-0.736) and 0.817 (95% CI, 0.782-0.853), respectively. The AUCs of the baseline nomogram at 3 and 5 years were both greater than cTNM (73.1 vs 58.8, 76.1 vs 55.7). Similarly, the AUCs of the post-operative nomogram were both greater than ypTNM (85.2 vs 69.1, 88.2 vs 71.3) at 3 and 5 years. The calibration curves indicated that both models had a high degree of calibration ability. By comparing the DCA at 3 and 5 years, we determined that the two nomograms constructed had better clinical utility than the TNM staging system.

Conclusions

The constructed nomograms have a more accurate predictive ability than the eighth edition TNM staging system, which can be useful for treatment selection and follow-up monitoring of patients.

Systematic review and validation of clinical models predicting survival after oesophagectomy for adenocarcinoma

BACKGROUND

Oesophageal adenocarcinoma poses a significant global health burden, yet the staging used to predict survival has limited ability to stratify patients by outcome. This study aimed to identify published clinical models that predict survival in oesophageal adenocarcinoma and to evaluate them using an independent international multicentre dataset.

METHODS

A systematic literature search (title and abstract) using the Ovid Embase and MEDLINE databases (from 1947 to 11 July 2020) was performed. Inclusion criteria were studies that developed or validated a clinical prognostication model to predict either overall or disease-specific survival in patients with oesophageal adenocarcinoma undergoing surgical treatment with curative intent. Published models were validated using an independent dataset of 2450 patients who underwent oesophagectomy for oesophageal adenocarcinoma with curative intent.

RESULTS

Seventeen articles were eligible for inclusion in the study. Eleven models were suitable for testing in the independent validation dataset and nine of these were able to stratify patients successfully into groups with significantly different survival outcomes. Area under the receiver operating characteristic curves for individual survival prediction models ranged from 0.658 to 0.705, suggesting poor-to-fair accuracy.

CONCLUSION

This study highlights the need to concentrate on robust methodologies and improved, independent, validation, to increase the likelihood of clinical adoption of survival predictions models.

Transhiatal esophagectomy as a treatment for locally advanced adenocarcinoma of the gastroesophageal junction: postoperative and oncologic results of a single-center cohort THE for locally advanced GEJC

Background and purpose

To report the postoperative and oncological outcomes of transhiatal esophagectomy for locally advanced cancer of the gastroesophageal junction.

Methods

Medical records of 120 consecutive patients who underwent transhiatal esophagectomy for locally advanced cancer of the gastroesophageal junction with curative intent after neoadjuvant treatment between February 2006 and December 2018 at our center were reviewed.

Results

All patients received either chemotherapy (46.7%) or chemoradiation (53.3%). The 90-day mortality and overall morbidity rates were 0.8% and 56.7%, respectively. Respiratory complications were the most common (30.8%). Anastomotic leakage occurred in 19 patients (15.8%), who were treated by local wound care (n = 13) or surgical drainage (n = 6). Recurrent laryngeal nerve injury occurred in 12 patients (9.9%). The median length of hospital stay was 15.5 days. The rate of R0 resection was 95.8%, and the median number of nodes removed was 17.5. Over a median follow-up of 77 months, the rate of recurrence was 40.8%, and the overall survival rates at 1, 3, and 5 years were 91%, 75%, and 65%, respectively. The median survival time was not reached. In multivariate analysis, disease stage was the only independent significant prognostic factor.

Conclusions

Transhiatal esophagectomy is a safe and effective procedure with good long-term oncological outcomes for locally advanced tumors after neo-adjuvant treatment. It can be recommended for all patients with cancer of the gastroesophageal junction, regardless of the Siewert classification, tumor stage, and comorbidities.

The probability of Lymph node metastasis with a tumor size larger than and smaller than 4 cm is different in stages T1-T3 of Siewert type II adenocarcinoma of esophagogastric junction: A Population-Based Study

Background: In adenocarcinoma of esophagogastric junction (AEG), the relationship between tumor size (TS) and lymph node metastasis (LNM) is unclear. This study aimed to explore the relationship between TS and LNM, and to construct a prediction model for LNM.

Materials and Methods: Data from 4649 Siewert type II AEG patients were retrospectively acquired from the Surveillance, Epidemiology, and End Result (SEER) database. TS data was analyzed as a continuous variable, but also divided into 1-cm-interval categorical groups for further analysis. The logistic regression model and restricted cubic spline (RCS) model was used to explore the relationship between TS and LNM, after adjusting for covariates. Internal validations as well as external validation (Single-Center data) were used to check our LNM prediction model.

Results: TS and LNM showed a significant relationship in the logistic regression analysis, regardless of the TS data being entered as a continuous or a categorical variable, after adjusting for covariates. The logistic regression model and RCS consistently showed that larger TS resulted in larger Odds Ratio (OR) values. When tumors were larger than 4 cm, the OR value remained relatively constant. The receiver operator characteristic curve evaluated the nomogram by the area under the curve (AUC) (AUC=0.737, in internal validation; AUC=0.626, in external validation), and the calibration curve of the nomogram showed an improved prediction system.

Conclusions: In Siewert type II T1-T3 stage AEG patients, we reported that LNM increased with TS up to 4-cm, and our nomogram provided a simple tool to predict LNM.

Development and Validation of a Clinical Prognostic Nomogram for Esophageal Adenocarcinoma Patients

Background

Clinical staging is essential for clinical decisions but remains imprecise. We purposed to construct a novel survival prediction model for improving clinical staging system (cTNM) for patients with esophageal adenocarcioma (EAC).

Methods

A total of 4180 patients diagnosed with EAC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and included as the training cohort. Significant prognostic variables were identified for nomogram model development using multivariable Cox regression. The model was validated internally by bootstrap resampling, and then subjected to external validation with a separate cohort of 886 patients from 2 institutions in China. The prognostic performance was measured by concordance index (C-index), Akaike information criterion (AIC) and calibration plots. Different risk groups were stratified by the nomogram scores.

Results

A total of six variables were determined related with survival and entered into the nomogram construction. The calibration curves showed satisfied agreement between nomogram-predicted survival and actual observed survival for 1-, 3-, and 5-year overall survival. By calculating the AIC and C-index values, our nomogram presented superior discriminative and risk-stratifying ability than current TNM staging system. Significant distinctions in survival curves were observed between different risk subgroups stratified by nomogram scores.

Conclusion

The established and validated nomogram presented better risk-stratifying ability than current clinical staging system, and could provide a convenient and reliable tool for individual survival prediction and treatment strategy making.

A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center

Background

The incidence rate of adenocarcinoma of the oesophagogastric junction (AEG) has significantly increased over the past decades, with a steady increase in morbidity. The aim of this study was to explore a variety of clinical factors to judge the survival outcomes of AEG patients.

Methods

We first obtained the clinical data of AEG patients from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and least absolute shrinkage and selection operator (LASSO) regression models were used to build a risk score system. Patient survival was analysed using the Kaplan-Meier method and the log-rank test. The specificity and sensitivity of the risk score were determined by receiver operating characteristic (ROC) curves. Finally, the internal validation set from the SEER database and external validation sets from our center were used to validate the prognostic power of this model.

Results

We identified a risk score system consisting of six clinical features that can be a good predictor of AEG patient survival. Patients with high risk scores had a significantly worse prognosis than those with low risk scores (log-rank test, P-value < 0.0001). Furthermore, the areas under ROC for 3-year and 5-year survival were 0.74 and 0.75, respectively. We also found that the benefits of chemotherapy and radiotherapy were limited to stage III/IV AEG patients in the high-risk group. Using the validation sets, our novel risk score system was proven to have strong prognostic value for AEG patients.

Conclusions

Our results may provide new insights into the prognostic evaluation of AEG.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08558-1.

Development and validation of a survival nomogram for patients with Siewert type II/III adenocarcinoma of the esophagogastric junction based on real-world data

Background

The clinical staging systems for adenocarcinoma of the esophagogastric junction (AEG) are controversial. We aimed to propose a prognostic nomogram based on real-world data for predicting survival of Siewert type II/III AEG patients after surgery.

Methods

A total of 396 patients with Siewert type II/III AEG diagnosed and treated at the Center for Gastrointestinal Surgery, the First Affiliated Hospital, Sun Yat-sen University, from June 2009 to June 2017 were enrolled. The original data of 29 variables were exported from the electronic medical records system. The nomogram was established based on multivariate Cox regression coefficients, and its performance was measured using Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve analysis and calibration curve.

Results

A nomogram was constructed based on nine variables. The C-index for overall survival (OS) prediction was 0.76 (95% CI, 0.72 to 0.80) in the training cohort, in the validation-1 cohort was 0.79 (95% CI, 0.72 to 0.86), and 0.73 (95% CI, 0.67 to 0.80) in the validation-2 cohort. Time-dependent ROC curves and calibration curves in all three cohorts showed good prognostic predictive accuracy. We further proved the superiority of the nomogram in predictive accuracy for OS to pathological TNM (pTNM) staging system and other independent prognostic factors. Kaplan-Meier survival curves demonstrated the pTNM stage, grade of differentiation, positive lymph node, log odds of positive lymph node and organ invasion were prognostic factors with good discriminative ability.

Conclusion

The established nomogram demonstrated a more precise prognostic prediction for patients with Siewert type II/III AEG.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08249-x.

Nomograms to Predict Overall and Cancer-Specific Survival in Gastric Signet-Ring Cell Carcinoma

BACKGROUND

The objective of our study was to develop and validate nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with signet-ring cell carcinoma (SRCC) of the stomach.

METHODS

Data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. A total of 1781 patients were randomly allocated to a training set (n = 1335) and a validation set (n = 446). Univariate and multivariate analyses were used to determine the prognostic effect of variables. Nomograms were developed to estimate OS and CSS and assessed using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC), and decision curve analyses (DCA). DCA was utilized to compare the nomograms and the Tumor-Node-Metastasis (TNM) staging system.

RESULTS

Age, race, tumor size, T, N, M stage, and use of surgery and/or radiotherapy were included in the nomograms. C-indexes for OS and CSS were 0.74 and 0.75 in the training set, respectively. C-indexes for OS and CSS were 0.76 and 0.76 in the validation set. Calibration plots and receiver operating characteristic (ROC) curves showed good predictive accuracy. According to the decision curve analyses (DCA), the new model was more useful than the TNM staging system.

CONCLUSIONS

We developed nomograms to predict OS and CSS in patients with SRCC of the stomach. Nomograms may be a valuable clinical supplement of the conventional TNM staging system.

Prognostic Nomogram for Postoperative Patients With Gastroesophageal Junction Cancer of No Distant Metastasis

Background

Gastroesophageal junction (GEJ) was one of the most common malignant tumors. However, the value of clinicopathological features in predicting the prognosis of postoperative patients with GEJ cancer and without distant metastasis was still unclear.

Methods

The 3425 GEJ patients diagnosed and underwent surgical resection without distant metastasis in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015 were enrolled,and they were randomly divided into training and validation cohorts with 7:3 ratio. Univariate and multivariate Cox regression analysis were used to determine the predictive factors that constituted the nomogram. The predictive accuracy and discriminability of Nomogram were determined by the area under the curve (AUC), C index, and calibration curve, and the influence of various factors on prognosis was explored.

Results

2,400 patients were designed as training cohort and 1025 patients were designed as validation cohort. The percentages of the distribution of demographic and clinicopathological characteristics in the training and validation cohorts tended to be the same. In the training cohort, multivariate Cox regression analysis revealed that the age, tumor grade, T stage and N stage were independent prognostic risk factors for patients with GEJ cancer without distant metastasis. The C index of nomogram model was 0.667. The AUC of the receiver operating characteristic (ROC) analysis for 3- and 5-year overall survival (OS) were 0.704 and 0.71, respectively. The calibration curve of 3- and 5-year OS after operation showed that there was the best consistency between nomogram prediction and actual observation. In the validation cohort, the C index of nomogram model, the AUC of 3- and 5-year OS, and the calibration curve were similar to the training cohort.

Conclusions

Nomogram could evaluate the prognosis of patients with GEJ cancer who underwent surgical resection without distant metastasis.

Nomogram for predicting the overall survival of the patients with oesophageal signet ring cell carcinoma

Background

The purpose of this study was to explore the prognostic factors of oesophageal signet ring cell (SRC) carcinoma and to construct a nomogram for predicting the outcome of SRC carcinoma of oesophagus.

Methods

A total of 968 cases of oesophageal SRC carcinoma were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016. Cases were divided into training cohort and validation cohort. Univariate and multivariable Cox analyses was performed to select the predictors of overall survival (OS for the nomogram. The performance of nomogram was validated with Harrell’s concordance index (C-index), calibration curves and decision curve analysis (DCA).

Results

The 1- and 5-year OS in the training cohort were 0.446 and 0.146, respectively, and the 1- and 5-year OS in the validation cohort were 0.459 and 0.138. The independent prognostic factors for establishing the nomogram were marital status, invasion of the surrounding tissue, lymph node metastasis, distant metastasis, surgery and chemotherapy. The Harrell’s c-index value of the training cohort and validation cohort were 0.723 and 0.708. In the calibration curves, the predicted survival probability and the actual survival probability have a considerable consistency. DCA indicated the favourable potential clinical utility of the nomogram.

Conclusions

A nomogram to predict the OS of patients with oesophageal SRC carcinoma was established. The validation of the nomogram fully demonstrates its great performance.

Multilevel Associations Between Patient- and Hospital-Level Factors and In-Hospital Mortality Among Hospitalized Patients With Head and Neck Cancer

Importance

Risk factors for in-hospital mortality of patients with head and neck cancer (HNC) are multilevel. Studies have examined the effect of patient-level characteristics on in-hospital mortality; however, there is a paucity of data on multilevel correlates of in-hospital mortality.

Objective

To examine the multilevel associations of patient- and hospital-level factors with in-hospital mortality and develop a nomogram to predict the risk of in-hospital mortality among patients diagnosed with HNC.

Design, Setting, and Participants

This cross-sectional study used the 2008-2013 National Inpatient Sample database. Hospitalized patients 18 years and older diagnosed (both primary and secondary diagnosis) as having HNC using the International Classification of Diseases, Ninth Revision, Clinical Modification codes were included. Analysis began December 2018.

Main Outcomes and Measures

The primary outcome of interest was in-hospital mortality. A weighted multivariable hierarchical logistic regression model estimated patient- and hospital-level factors associated with in-hospital mortality. Moreover, a multivariable logistic regression analysis was used to build an in-hospital mortality prediction model, presented as a nomogram.

Results

A total of 85 440 patients (mean [SD] age, 62.2 [13.5] years; 61 281 men [71.1%]) were identified, and 4.2% (n = 3610) died in the hospital. Patient-level risk factors associated with higher odds of in-hospital mortality included age (adjusted odds ratio [aOR], 1.03 per 1-year increase; 95% CI, 1.02-1.03), male sex (aOR, 1.23; 95% CI, 1.12-1.35), higher number of comorbidities (aOR, 1.14; 95% CI, 1.11-1.17), having a metastatic cancer (aOR, 1.49; 95% CI, 1.36- 1.64), having a nonelective admission (aOR, 3.26; 95% CI, 2.83-3.75), and being admitted to the hospital on a weekend (aOR, 1.30; 95% CI, 1.16-1.45). Of the hospital-level factors, admission to a nonteaching hospital (aOR, 1.48; 95% CI, 1.24-1.77) was associated with higher odds of in-hospital mortality. The nomogram showed fair in-hospital mortality discrimination (area under the curve of 72%).

Conclusions and Relevance

This cross-sectional study found that both patient- and hospital-level factors were associated with in-hospital mortality, and the nomogram estimated with fair accuracy the probability of in-hospital death among patients with HNC. These multilevel factors are critical indicators of survivorship and should thus be considered when planning programs or interventions aimed to improve survival among this unique population.

Development and validation of a novel competing risk model for predicting survival of esophagogastric junction adenocarcinoma: a SEER population-based study and external validation

Background

Adenocarcinoma in Esophagogastric Junction (AEG) is a severe gastrointestinal malignancy with a unique clinicopathological feature. Hence, we aimed to develop a competing risk nomogram for predicting survival for AEG patients and compared it with new 8th traditional tumor-node-metastasis (TNM) staging system.

Methods

Based on data from the Surveillance, Epidemiology, and End Results (SEER) database of AEG patients between 2004 and 2010, we used univariate and multivariate analysis to filter clinical factors and then built a competing risk nomogram to predict AEG cause-specific survival. We then measured the clinical accuracy by comparing them to the 8th TNM stage with a Receiver Operating Characteristic (ROC) curve, Brier score, and Decision Curve Analysis (DCA). External validation was performed in 273 patients from China National Cancer Center.

Results

A total of 1755 patients were included in this study. The nomogram was based on five variables: Number of examined lymph nodes, grade, invasion, metastatic LNs, and age. The results of the nomogram was greater than traditional TNM staging with ROC curve (1-year AUC: 0.747 vs. 0.641, 3-year AUC: 0.761 vs. 0.679, 5-year AUC: 0.759 vs. 0.682, 7-year AUC: 0.749 vs. 0.673, P < 0.001), Brier score (3-year: 0.198 vs. 0.217, P = 0.012; 5-year: 0.198 vs. 0.216, P = 0.008; 7-year: 0.199 vs. 0.215, P = 0.014) and DCA. In external validation, the nomogram also showed better diagnostic value than traditional TNM staging and great prediction accuracy.

Conclusion

We developed and validated a novel nomogram and risk stratification system integrating clinicopathological characteristics for AEG patients. The model showed superior prediction ability for AEG patients than traditional TNM classification.

Development and validation of a new clinical staging system to predict survival for esophageal squamous cell carcinoma patients: Application of the nomogram

INTRODUCTION

Survival of patients with the same clinical stage varies widely and effective tools to evaluate the prognosis utilizing clinical staging information is lacking. This study aimed to develop a clinical nomogram for predicting survival of patients with Esophageal Squamous Cell Carcinoma (ESCC).

MATERIALS AND METHODS

On the basis of data extracted from the SEER database (training cohort, n = 3375), we identified and integrated significant prognostic factors for nomogram development and internal validation. The model was then subjected to external validation with a separate dataset obtained from Jinling Hospital of Nanjing Medical University (validation cohort, n = 1187). The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index), Akaike information criterion (AIC) and calibration curves. And risk group stratification was performed basing on the nomogram scores.

RESULTS

On multivariable analysis of the training cohort, seven independent prognostic factors were identified and included into the nomogram. Calibration curves presented good consistency between the nomogram prediction and actual observation for 1-, 3-, and 5-year OS. The AIC value of the nomogram was lower than that of the 8th edition American Joint Committee on Cancer TNM (AJCC) staging system, whereas the C-index of the nomogram was significantly higher than that of the AJCC staging system. The risk groups stratified by CART allowed significant distinction between survival curves within respective clinical TNM categories.

CONCLUSIONS

The risk stratification system presented better discriminative ability for survival prediction than current clinical staging system and might help clinicians in decision making.

Survival nomogram for patients with metastatic siewert type II adenocarcinoma of the esophagogastric junction: a population-based study

BACKGROUND

The aim of this study was to construct a nomogram to predict the survival of patients with metastatic Siewert Type II adenocarcinomas of the esophagogastric junction (AEG).

METHODS

Patients were identified using the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was performed to assess the prognostic factors. A nomogram comprising independent prognostic factors was established and evaluated using C-indexes, calibration curves, and decision curve analyses.

RESULTS

In total 1616 eligible patients were enrolled. Race, age, bone metastasis, liver metastasis, lung metastasis, other metastasis sites, and distant lymph nodes metastasis were independent prognostic factors and were integrated to construct the nomogram. The nomogram had a C-index of 0.590 (95% CI: 0.569-0.611) in the training cohort and 0.569 (95% CI: 0.532-0.606) in the validation cohort. The calibration plots for the probabilities of 6-month and 1-year overall survival demonstrated there was an optimum between nomogram prediction and actual observation.

CONCLUSION

We developed and validated a nomogram to predict individual prognosis for patients with metastatic Siewert Type II AEG, and the risk stratification system based on the nomogram could effectively stratify the patients into two risk subgroups, which can help clinicians accurately predict mortality risk and recommend personalized treatment modalities.

Nomogram for predicting the survival of gastric adenocarcinoma patients who receive surgery and chemotherapy

BACKGROUND

Surgery is the only way to cure gastric adenocarcinoma (GAC), and chemotherapy is the basic adjuvant management for GAC. A significant prognostic nomogram for predicting the respective disease-specific survival (DSS) rates of GAC patients who receive surgery and chemotherapy has not been established.

OBJECTIVE

We were planning to establish a survival nomogram model for GAC patients who receive surgery and chemotherapy.

METHODS

We identified 5764 GAC patients who had received surgery and chemotherapy from the record of Surveillance, Epidemiology, and End Results (SEER) database. About 70% (n = 4034) of the chosen GAC patients were randomly assigned to the training set, and the rest of the included ones (n = 1729) were assigned to the external validation set. A prognostic nomogram was constructed by the training set and the predictive accuracy of it was validated by the validation set.

RESULTS

Based on the outcome of a multivariate analysis of candidate factors, a nomogram was developed that encompassed age at diagnosis, number of regional lymph nodes examined after surgery, number of positive regional lymph nodes, sex, race, grade, derived AJCC stage, summary stage, and radiotherapy status. The C-index (Harrell's concordance index) of the nomogram model was some larger than that of the traditional seventh AJCC staging system (0.707 vs 0.661). Calibration plots of the constructed nomogram displayed that the probability of DSS commendably accord with the survival rate. Integrated discrimination improvement (IDI) revealed obvious increase and categorical net reclassification improvement (NRI) showed visible enhancement. IDI for 3-, 5- and 10- year DSS were 0.058, 0.059 and 0.058, respectively (P > 0.05), and NRI for 3-, 5- and 10- year DSS were 0.380 (95% CI = 0.316-0.470), 0.407 (95% CI = 0.350-0.505), and 0.413 (95% CI = 0.336-0.519), respectively. Decision curve analysis (DCA) proved that the constructed nomogram was preferable to the AJCC staging system.

CONCLUSION

The constructed nomogram supplies more credible DSS predictions for GAC patients who receive surgery and chemotherapy in the general population. According to validation, the new nomogram will be beneficial in facilitating individualized survival predictions and useful when performing clinical decision-making for GAC patients who receive surgery and chemotherapy.

Nomogram for predicting cancer specific survival in inflammatory breast carcinoma: a SEER population-based study

The clinicopathological features of inflammatory breast carcinoma (IBC), the effect of therapeutic options on survival outcome and the identification of prognostic factors were investigated in this study. Information on IBC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Cox proportional hazard regression was used to determine potential significant prognostic factors of IBC. A nomogram was then constructed to evaluate patient survival based on certain variables. Univariate and multivariate analyses revealed that race (p < 0.001), M stage (p < 0.001), surgery (p = 0.010), chemotherapy (CT) (p < 0.001), tumor size (p = 0.010), estrogen receptor (p < 0.001), progesterone receptor (p = 0.04), and human epidermal growth factor receptor 2 (p < 0.001) were all independent risk factors. The concordance index (C-index) of the nomogram was 0.735, which showed good predictive efficiency. Survival analysis indicated that IBC patients without CT had poorer survival than those with CT (p < 0.001). Stratified analyses showed that modified radical mastectomy (MRM) had significant survival advantages over non-MRM in patients with stage IV IBC (p = 0.031). Patients treated with or without CT stratified by stage III and stage IV showed better survival than those without stage III and IV (p < 0.001). Trimodality therapy resulted in better survival than surgery combined with CT or CT alone (p < 0.001). Competing risk analysis also showed the same results. The nomogram was effectively applied to predict the 1, 3 and 5-year survival of IBC. Our nomogram showed relatively good accuracy with a C-index of 0.735 and is a visualized individually predictive tool for prognosis. Treatment strategy greatly affected the survival of patients. Trimodality therapy was the preferable therapeutic strategy for IBC. Further prospective studies are needed to validate these findings.

Comparison between gastric and esophageal classification system among adenocarcinomas of esophagogastric junction according to AJCC 8th edition: a retrospective observational study from two high-volume institutions in China

BACKGROUND

The new 8th TNM system attributes AEG Siewert type II to esophageal classification system. However, the gastric and esophageal classification system which was more suitable for type II remains in disputation. This study aimed to illuminate the 8th TNM-EC or TNM-GC system which was more rational for type II, especially for patients underwent transhiatal approaches.

METHODS

We collected the database of patients with AEG who underwent radical surgical resection from two high-volume institutions in China: West China Hospital (N = 773) and Xi Jing Hospital of Fourth Military University (N = 637). The cases were randomly matched into 705 training cohort and 705 validation cohort. All the cases were reclassified by the 8th edition of TNM-EC and TNM-GC. The distribution of patients in each stage, the hazard ratio of each stage, and the separation of the survival were compared. Multivariate analysis was performed using the Cox proportional hazard model. Comparisons between the different staging systems for the prognostic prediction were performed with the rcorrp.cens package in Hmisc in R (version 3.4.4. http://www.R-project.org/ ). The validity of these two systems was evaluated by Akaike information criterion (AIC) and concordance index (C-index).

RESULTS

By univariate analysis, the HRs from stage IA/IB to stage IV/IVB were monotonously increased according to TNM-GC scheme in both cohorts (training 2.63, 3.91, 5.02, 8.64, 15.51 and 29.64; validation 1.54, 3.55, 4.91, 7.14, 11.67, 18.71 and 48.32) whereas only a fluctuating increased tendency was found when staged by TNM-EC. After the multivariate analysis, TNM-GC (P < 0.001), TNM-EC (P = 0.001) in training cohort and TNM-GC (P < 0.001) TNM-EC (P < 0.001) in the validation cohort were both independent prognostic factors. The C-index value for the TNM-GC scheme was larger than that of TNM-EC system in both training (0.721 vs. 0.690, P < 0.001) and validation (0.721 vs. 0.696, P < 0.001) cohorts. After stratification analysis for Siewert type II, the C-index for TNM-GC scheme was still larger than that of TNM-EC in both training (0.724 vs. 0.694, P = 0.005) and validation (0.723 vs. 0.699, P < 0.001) cohorts.

CONCLUSIONS

The 8th TNM-GC scheme is superior to TNM-EC in predicting the prognosis of AEG especially for type II among patients underwent transhiatal approaches.

Treatment strategies and predicting prognoses in elderly patients with breast cancer

Objective

The prevalence of breast cancer in elderly women (older than 80 years) is expected to rise more dramatically than its incidence. In this study, we evaluated the evidence for treatment guidelines for elderly breast cancer patients.

Patients and methods

All included patients were enrolled from 2010 to 2013 from the Surveillance, Epidemiology, and End Results (SEER) database. The Akaike information criterion (AIC) and Harrell’s C statistic were used to perform comparisons. In addition, a propensity score analysis was used to avoid bias caused by data selection criteria. Prognostic factors were selected as nomogram parameters to develop a model to predict survival.

Results

A total of 16998 patients included in the SEER database from 2010 to 2013 had breast cancer and fulfilled the study criteria. Of whom, 13007 patients underwent surgery. Overall survival and cancer-specific survival were significantly better in patients who underwent surgery and/or radiotherapy than in those who did not (P<0.001). In addition, a nomogram system with a C index of 0.83 and an AIC index of 11112.85 was better able to predict prognoses and estimate cancer-specific survival in elderly patients with breast cancer.

Conclusion

A localized surgical approach might provide better results in elderly breast cancer patients. However, radiotherapy improved cancer-specific survival and overall survival in these patients. In addition, a prognostic nomogram directly quantified patient risk by accounting for various prognostic factors without forming risk groups and was better able to estimate cancer-specific survival.

Survival prediction tools for esophageal and gastroesophageal junction cancer: A systematic review

BACKGROUND

Clinical, pathological, and molecular information combined with cancer stage in prognostication algorithms can offer more personalized estimates of survival, which might guide treatment choices. Our aim was to evaluate the quality of prognostication tools in esophageal cancer.

METHODS

We systematically searched MedLine and Embase from 2005 to 2017 for studies reporting development or validation of models predicting long-term survival in esophageal cancer. We evaluated tools using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies guidelines and the American Joint Committee on Cancer acceptance criteria for risk models.

RESULTS

We identified 16 prognostication tools for patients treated with curative intent and 1 for patients with metastatic disease. These tools frequently excluded adenocarcinoma, contained outdated data, and were developed with a limited sample size. Nine tools were developed in China for squamous cell cancer, and 11 used data on patients diagnosed before 2010. Most tools excluded key prognostic factors such as age and sex. Tumor stage and grade were the most commonly, but not universally, included factors. Twelve tools were designed to predict overall survival; 5 predicted cancer-specific survival. Bootstrap internal validation was performed for most tools; c-statistics ranged from 0.63 to 0.77 and graphically evaluated calibration was "good." Five tools were externally validated; c-statistics ranged from 0.70 to 0.77.

CONCLUSIONS

Existing tools cannot be confidently used for esophageal cancer prognostication in current clinical practice. Better-quality tools might help to more individually and accurately estimate disease course, select further treatments, and risk-stratify for future clinical trials.

Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis

Background

Clinical prediction models are increasingly used to predict outcomes such as survival in cancer patients. The aim of this study was threefold. First, to perform a systematic review to identify available clinical prediction models for patients with esophageal and/or gastric cancer. Second, to evaluate sources of bias in the included studies. Third, to investigate the predictive performance of the prediction models using meta-analysis.

Methods

MEDLINE, EMBASE, PsycINFO, CINAHL, and The Cochrane Library were searched for publications from the year 2000 onwards. Studies describing models predicting survival, adverse events and/or health-related quality of life (HRQoL) for esophageal or gastric cancer patients were included. Potential sources of bias were assessed and a meta-analysis, pooled per prediction model, was performed on the discriminative abilities (c-indices).

Results

A total of 61 studies were included (45 development and 16 validation studies), describing 47 prediction models. Most models predicted survival after a curative resection. Nearly 75% of the studies exhibited bias in at least 3 areas and model calibration was rarely reported. The meta-analysis showed that the averaged c-index of the models is fair (0.75) and ranges from 0.65 to 0.85.

Conclusion

Most available prediction models only focus on survival after a curative resection, which is only relevant to a limited patient population. Few models predicted adverse events after resection, and none focused on patient’s HRQoL, despite its relevance. Generally, the quality of reporting is poor and external model validation is limited. We conclude that there is a need for prediction models that better meet patients’ information needs, and provide information on both the benefits and harms of the various treatment options in terms of survival, adverse events and HRQoL.

Prognosis of patients with primary malignant main stem bronchial tumors: 7,418 cases based on the SEER database

Background

The aim of this study was to identify risk factors for patients with malignant main stem bronchial tumors (MBTs) and to develop a nomogram for predicting prognosis in those patients using data from the Surveillance, Epidemiology, and End Results (SEER) database.

Method

A process was used for case screening from the SEER database. The effect of prognostic factors on survival was evaluated using the Kaplan–Meier method and log-rank test, a competing risk model, and the Cox proportional hazards regression model. A nomogram was established for predicting 1-, 3-, and 5-year overall survival (OS) in patients with MBTs.

Results

A total of 7,418 cases were included in this study. Age, gender, pathologic grade, histologic type, tumor size, involvement of lymph nodes, tumor extension, chemotherapy, and surgery were identified as independent risk factors by univariate and multivariate analyses. A nomogram was established based on the results of the Cox model, which was validated by a C-index of 0.672 (95% CI, 0.664–0.680), and a group of calibration plots.

Conclusion

Age, gender, pathologic grade, histologic type, tumor size, involvement of lymph nodes, tumor extension, chemotherapy, and surgery were independent risk factors for OS of patients with MBTs. A nomogram was formulated to predict 1-, 3-, and 5-year OS in patients with MBTs based on individual clinical characteristics.

Therapeutic Sequencing in Metastatic Renal Cell Carcinoma

The influx of multiple novel therapeutic options in the mRCC field has brought a challenge for treatment sequencing in this disease. In the past few years, cabozantinib, nivolumab and the combination of lenvatinib and everolimus have been approved in the second-line setting. As there is no direct comparison between these agents and the studies have failed to show improved benefit among a biomarker-selected patient population, appropriate patient selection based on clinical factors for individualized therapy is critical. Herein we provide a comprehensive overview of current data from each agent through the discussion of disease biology, clinical trials, potential biomarkers and distilling future perspectives in the field.

A nomogram for predicting the risk of neck node metastasis in pathologically node-negative oral cavity carcinoma

OBJECTIVE

To generate a nomogram for predicting the risk of neck node metastasis in pathologically node-negative patients using a combination of variables comprising of protein expression, ultrastructural alterations and clinicopathological parameters.

MATERIALS AND METHODS

Surgically removed oral tumours (n = 103) were analysed for the expression of desmosomal and hemidesmosomal assembly proteins by immunohistochemistry and ultrastructural alterations by transmission electron microscopy (TEM). Protein expression, ultrastructural alterations and clinicopathological variables were used to construct nomogram from the training set in 75 patients. Clinical utility of the nomogram was validated in a discrete set of 28 patients.

RESULTS

Univariate and multivariate analyses were performed on the training set, and obtained significant variables comprising of integrin β4 expression (p = .027), number of hemidesmosomes (p = .027)/desmosomes (p = .046), tumour differentiation grade (p = .033) and tumour thickness (p = .024) were used for construction of the nomogram. The area under the curve was calculated for both training 0.821 (95% CI 0.725-0.918) and validation sets 0.880 (95% CI 0.743-1.000). The nomogram demonstrated a predictive accuracy of 73.3% and 78.6% with the sensitivity of 81.4% and 83.3% in the training and validation sets, respectively.

CONCLUSIONS

The nomogram constructed on postsurgical tumour samples will be a value addition to histopathology for the detection of neck node metastasis in pathologically node-negative patients.

Optimal treatment for Siewert type II and III adenocarcinoma of the esophagogastric junction: A retrospective cohort study with long-term follow-up

AIM

To determine the optimal treatment strategy for Siewert type II and III adenocarcinoma of the esophagogastric junction.

METHODS

We retrospectively reviewed the medical records of 83 patients with Siewert type II and III adenocarcinoma of the esophagogastric junction and calculated both an index of estimated benefit from lymph node dissection for each lymph node (LN) station and a lymph node ratio (LNR: ratio of number of positive lymph nodes to the total number of dissected lymph nodes). We used Cox proportional hazard models to clarify independent poor prognostic factors. The median duration of observation was 73 mo.

RESULTS

Indices of estimated benefit from LN dissection were as follows, in descending order: lymph nodes (LN) along the lesser curvature, 26.5; right paracardial LN, 22.8; left paracardial LN, 11.6; LN along the left gastric artery, 10.6. The 5-year overall survival (OS) rate was 58%. Cox regression analysis revealed that vigorous venous invasion (v2, v3) (HR = 5.99; 95%CI: 1.71-24.90) and LNR of > 0.16 (HR = 4.29, 95%CI: 1.79-10.89) were independent poor prognostic factors for OS.

CONCLUSION

LN along the lesser curvature, right and left paracardial LN, and LN along the left gastric artery should be dissected in patients with Siewert type II or III adenocarcinoma of the esophagogastric junction. Patients with vigorous venous invasion and LNR of > 0.16 should be treated with aggressive adjuvant chemotherapy to improve survival outcomes.

Development and validation of a risk-prediction nomogram for in-hospital mortality in adults poisoned with drugs and nonpharmaceutical agents: An observational study

Acute poisoning with drugs and nonpharmaceutical agents represents an important challenge in the emergency department (ED).The objective is to create and validate a risk-prediction nomogram for use in the ED to predict the risk of in-hospital mortality in adults from acute poisoning with drugs and nonpharmaceutical agents.This was a prospective cohort study involving adults with acute poisoning from drugs and nonpharmaceutical agents admitted to a tertiary referral center for toxicology between January and December 2015 (derivation cohort) and between January and June 2016 (validation cohort). We used a program to generate nomograms based on binary logistic regression predictive models. We included variables that had significant associations with death. Using regression coefficients, we calculated scores for each variable, and estimated the event probability. Model validation was performed using bootstrap to quantify our modeling strategy and using receiver operator characteristic (ROC) analysis. The nomogram was tested on a separate validation cohort using ROC analysis and goodness-of-fit tests.Data from 315 patients aged 18 to 91 years were analyzed (n = 180 in the derivation cohort; n = 135 in the validation cohort). In the final model, the following variables were significantly associated with mortality: age, laboratory test results (lactate, potassium, MB isoenzyme of creatine kinase), electrocardiogram parameters (QTc interval), and echocardiography findings (E wave velocity deceleration time). Sex was also included to use the same model for men and women. The resulting nomogram showed excellent survival/mortality discrimination (area under the curve [AUC] 0.976, 95% confidence interval [CI] 0.954-0.998, P < 0.0001 for the derivation cohort; AUC 0.957, 95% CI 0.892-1, P < 0.0001 for the validation cohort).This nomogram provides more precise, rapid, and simple risk-analysis information for individual patients acutely exposed to drugs and nonpharmaceutical agents, and accurately estimates the probability of in-hospital death, exclusively using the results of objective tests available in the ED.