A Nomogram for Predicting Risk of Thromboembolism in Gastric Cancer Patients Receiving Chemotherapy

Prediction of mortality risk in critically ill patients with systemic lupus erythematosus: a machine learning approach using the MIMIC-IV database

OBJECTIVE

Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. Our study aims to develop and validate predictive models for the mortality risk.

METHODS

This observational study identified patients with SLE requiring hospital admission from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We downloaded data from Fujian Provincial Hospital as an external validation set. Variable selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Then, we constructed two predictive models: a traditional nomogram based on logistic regression and a machine learning model employing a stacking ensemble approach. The predictive ability of the models was evaluated by the areas under the receiver operating characteristic curve (AUC) and the calibration curve.

RESULTS

A total of 395 patients and 100 patients were enrolled respectively from MIMIC-IV database and the validation cohort. The LASSO regression identified 18 significant variables. Both models demonstrated good discrimination, with AUCs above 0.8. The machine learning model outperformed the nomogram in terms of precision and specificity, highlighting its potential superiority in risk prediction. The SHapley additive explanations analysis further elucidated the contribution of each variable to the model's predictions, emphasising the importance of factors such as urine output, age, weight and alanine aminotransferase.

CONCLUSIONS

The machine learning model provides a superior tool for predicting mortality risk in patients with SLE, offering a basis for clinical decision-making and potential improvements in patient outcomes.

Venous thromboembolism in patients with acute myeloid leukemia: development of a predictive model

BACKGROUND

Patients with acute myeloid leukemia (AML) are at increased risk of venous thromboembolic events (VTE). However, thromboprophylaxis is largely underused.

OBJECTIVES

This study aimed to determine possible VTE development risk factors and to develop a novel predictive model.

METHODS

We conducted a retrospective cohort study of adult patients with newly diagnosed AML. We used univariate and multivariable logistic regression to estimate binary outcomes and identify potential predictors. Based on our final model, a dynamic nomogram was constructed with the goal of facilitating VTE probability calculation.

RESULTS

Out of 626 eligible patients with AML, 72 (11.5%) developed VTE during 6 months of follow-up. Six parameters were independent predictors: male sex (odds ratio [OR] 1.82, 95% confidence interval [CI]: 1.077-2.065), prior history of thrombotic events (OR 2.27, 95% CI: 1.4-4.96), international normalized ratio (OR 0.21, 95% CI: 0.05-0.95), Eastern Cooperative Oncology Group performance status (OR 0.71, 95% CI: 0.53-0.94), and intensive therapy (OR 2.05, 95% CI: 1.07-3.91). The C statistics for the model was 0.68. The model was adequately calibrated and internally validated. The decision-curve analysis suggested the use of thromboprophylaxis in patients with VTE risks between 8 and 20%.

CONCLUSION

We developed a novel and convenient tool that may assist clinicians in identifying patients whose VTE risk is high enough to warrant thromboprophylaxis.

A Nomogram Model to Predict Deep Vein Thrombosis Risk After Surgery in Patients with Hip Fractures

Aims

This study aimed to establish a nomogram model for predicting the probability of postoperative deep vein thrombosis (DVT) risk in patients with hip fractures.

Methods

504 patients were randomly assigned to the training set and validation set, and then divided into a DVT group and a non-DVT group. The study analysed the risk factors for DVT using univariate and multivariate analyses. Based on these parameters, a nomogram model was constructed and validated. The predicting performance of nomogram was evaluated by discrimination, calibration, and clinical usefulness.

Results

The predictors contained in the nomogram model included age, surgical approach, 1-day postoperative D-dimer value and admission ultrasound diagnosis of the lower limb vein. Furthermore, the area under the ROC curve (AUC) for the specific DVT risk-stratification nomogram model (0.815; 95% CI 0.746-0.884) was significantly higher than the current model (Caprini) (0.659; 95% CI 0.572-0.746, P < 0.05). According to the calibration plots, the prediction and actual observation were in good agreement. In the range of threshold probabilities of 0.2-0.8, the predictive performance of the model on DVT risk could be maximized.

Conclusions

The current predictive model could serve as a reliable tool to quantify the possibility of postoperative DVT in hip fractures patients.

Risk assessment of venous thromboembolism in head and neck cancer patients and its establishment of a prediction model

IMPORTANCE

Venous thromboembolism (VTE) is closely relevant to head and neck cancer (HNC) prognosis, but little data exist on the risk prediction of VTE in patients with HNC.

OBJECTIVE

To study the risk factors regarding VTE in HNC patients and construct a nomogram model for its prediction.

DESIGN, SETTING, AND PARTICIPANTS

A cross-sectional retrospective study was implemented to comparatively analyze 220 HNC patients from January 2018 to December 2021. The Lasso algorithm was used to optimize the selection of variables. A nomogram model for predicting HNC-associated VTE was established using multivariate logistic regression analysis. Internal validation of the model was performed by bootstrap resampling (1000 times). Calibration plot and decision curve analysis (DCA) were applied to evaluate the calibration capability of the prediction model.

MAIN OUTCOME AND MEASURE

The demographics, medical history, blood biochemical indicators, and modalities of treatment were included for analysis.

RESULTS

The incidence of HNC-associated VTE was 2.8% (55/1967) in authors' affiliation. Five variables of risk factors, including surgery, radiochemotherapy, D-dimer, aspartate transaminase, and globulin, were screened and selected as predictors by Lasso algorithm. A prediction model that incorporated these independent predictors was developed and presented as the nomogram. The model showed good discrimination with a C-index of 0.972 (95% CI: 0.934-0.997), and had an area under the receiver operating characteristic curve value of 0.981 (p < 0.001, 95% CI: 0.964-0.998). The calibration curve displayed good agreement of the predicted probability with the actual observed probability for HNC-associated VTE. The DCA plot showed that the application of this nomogram was associated with net benefit gains in clinical practice.

CONCLUSIONS AND RELEVANCE

The high-performance nomogram model developed in this study may help early diagnose the risk of VTE in HNC patients and to guide individualized decision-making on thromboprophylaxis.

Hypoxia-associated genes predicting future risk of myocardial infarction: a GEO database-based study

Background

Patients with unstable angina (UA) are prone to myocardial infarction (MI) after an attack, yet the altered molecular expression profile therein remains unclear. The current work aims to identify the characteristic hypoxia-related genes associated with UA/MI and to develop a predictive model of hypoxia-related genes for the progression of UA to MI.

Methods and results

Gene expression profiles were obtained from the GEO database. Then, differential expression analysis and the WGCNA method were performed to select characteristic genes related to hypoxia. Subsequently, all 10 hypoxia-related genes were screened using the Lasso regression model and a classification model was established. The area under the ROC curve of 1 shows its excellent classification performance and is confirmed on the validation set. In parallel, we construct a nomogram based on these genes, showing the risk of MI in patients with UA. Patients with UA and MI had their immunological status determined using CIBERSORT. These 10 genes were primarily linked to B cells and some inflammatory cells, according to correlation analysis.

Conclusion

Overall, GWAS identified that the CSTF2F UA/MI risk gene promotes atherosclerosis, which provides the basis for the design of innovative cardiovascular drugs by targeting CSTF2F.

Derivation, validation and assessment of a novel nomogram-based risk assessment model for venous thromboembolism in hospitalized patients with lung cancer: A retrospective case control study

Purpose

This study aimed to develop and validate a specific risk-stratification nomogram model for the prediction of venous thromboembolism(VTE) in hospitalized patients with lung cancer using readily obtainable demographic, clinical and therapeutic characteristics, thus guiding the individualized decision-making on thromboprophylaxis on the basis of VTE risk levels.

Methods

We performed a retrospective case–control study among newly diagnosed lung cancer patients hospitalized between January 2016 and December 2021. Included in the cohort were 234 patients who developed PTE and 936 non-VTE patients. The patients were randomly divided into the derivation group (70%, 165 VTE patients and 654 non-VTE patients) and the validation group (30%, 69 VTE patients and 282 non-VTE patients). Cut off values were established using a Youden´s Index. Univariate and multivariate regression analyses were used to determine independent risk factors associated with VTE. Variance Inflation Factor(VIF) was used for collinearity diagnosis of the covariates in the model. The model was validated by the consistency index (C-index), receiver operating characteristic curves(ROC) and the calibration plot with the Hosmer-Lemeshow goodness-of-fit test. The clinical utility of the model was assessed through decision curve analysis(DCA). Further, the comparison of nomogram model with current models(Khorana, Caprini, Padua and COMPASS-CAT) was performed by comparing ROC curves using the DeLong’s test.

Results

The predictive nomogram modle comprised eleven variables: overweight(24-28) defined by body mass index (BMI): [odds ratio (OR): 1.90, 95% confidence interval (CI): 1.19-3.07], adenocarcinoma(OR:3.00, 95% CI: 1.88-4.87), stageIII-IV(OR:2.75, 95%CI: 1.58-4.96), Central venous catheters(CVCs) (OR:4.64, 95%CI: 2.86-7.62), D-dimer levels≥2.06mg/L(OR:5.58, 95%CI:3.54-8.94), PT levels≥11.45sec(OR:2.15, 95% CI:1.32-3.54), Fbg levels≥3.33 g/L(OR:1.76, 95%CI:1.12-2.78), TG levels≥1.37mmol/L (OR:1.88, 95%CI:1.19-2.99), ROS1 rearrangement(OR:2.87, 95%CI:1.74-4.75), chemotherapy history(OR:1.66, 95%CI:1.01-2.70) and radiotherapy history(OR:1.96, 95%CI:1.17-3.29). Collinearity analysis with demonstrated no collinearity among the variables. The resulting model showed good predictive performance in the derivation group (AUC 0.865, 95% CI: 0.832-0.897) and in the validation group(AUC 0.904,95%CI:0.869-0.939). The calibration curve and DCA showed that the risk-stratification nomogram had good consistency and clinical utility. Futher, the area under the ROC curve for the specific VTE risk-stratification nomogram model (0.904; 95% CI:0.869-0.939) was significantly higher than those of the KRS, Caprini, Padua and COMPASS-CAT models(Z=12.087, 11.851, 9.442, 5.340, all P<0.001, respectively).

Conclusion

A high-performance nomogram model incorporated available clinical parameters, genetic and therapeutic factors was established, which can accurately predict the risk of VTE in hospitalized patients with lung cancer and to guide individualized decision-making on thromboprophylaxis. Notably, the novel nomogram model was significantly more effective than the existing well-accepted models in routine clinical practice in stratifying the risk of VTE in those patients. Future community-based prospective studies and studies from multiple clinical centers are required for external validation.

A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

Purpose

To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification.

Methods

A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves.

Results

Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit.

Conclusions

A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00483-1.

Prognostic nomograms for gastric carcinoma after surgery to assist decision-making for postoperative treatment with chemotherapy cycles <9 or chemotherapy cycles ≥9

Objective

We sought to develop novel nomograms to accurately predict overall survival (OS) of chemotherapy cycles &lt;9 and chemotherapy cycles ≥9 and construct risk stratification to differentiate low-risk and high-risk of two cohorts.

Methods

Patients who underwent curative-intent resection for gastric cancer between January 2002 and May 2020 at a single China institution were identified. Variables associated with OS were recorded and analyzed according to multivariable Cox models. Nomograms predicting 3- and 5-year OS were built according to variables resulting from multivariable Cox models. Discrimination ability was calculated using the Harrell's Concordance Index. The constructed nomogram was subjected to 1,000 resamples bootstrap for internal validation. Calibration curves for the new nomograms were used to test the consistency between the predicted and actual 3- and 5-year OS. Decision curve analysis (DCA) was performed to assess the clinical net benefit. The Concordance index (C-index) and time-dependent receiver operating characteristic (t-ROC) curves were used to evaluate and compare the discriminative abilities of the new nomograms. Finally, prognostic risk stratification of gastric cancer was conducted with X-tile software and nomograms converted into a risk-stratified prognosis model.

Results

For the nomogram predict OS of chemotherapy cycles &lt;9, C-index was 0.711 (95% CI, 0.663–0.760) in internal validation and 0.722 (95% CI, 0.662–0.783) in external validation, which was better than AJCC 8th edition TNM staging (internal validation: 0.627, 95% CI, 0.585–0.670) and (external validation: 0.595,95% CI, 0.543–0.648). The C-index of the nomogram for chemotherapy cycles ≥9 in internal validation was 0.755 (95% CI, 0.728–0.782) and 0.785 (95% CI, 0.747–0.823) in external validation, which was superior to the AJCC 8th edition TNM staging (internal validation: 0.712 95% CI, 0.688–0.737) and (external validation 0.734, 95% CI, 0.699–0.770).The calibration curves, t-ROC curves and DCA of the two nomogram models show that the recognition performance of the two nomogram models was outstanding. The statistical differences in the prognosis among the two risk stratification groups further showed that our model had an excellent risk stratification performance.

Conclusion

This is first reported risk stratification for chemotherapy cycles of gastric carcinoma. Our proposed nomograms can effectively evaluate postoperative prognosis of patients with different chemotherapy cycles of gastric carcinoma. Chemotherapy cycles ≥9 is therefore recommended for high-risk patients with chemotherapy cycles &lt;9, but not for low-risk patients. Meanwhile, combination with multiple therapies are essential to high-risk patients with chemotherapy cycles ≥9 and unnecessary for low-risk patients.