Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China

Development and validation of a predictive risk tool for VTE in women with breast cancer under chemotherapy: a cohort study in China

OBJECTIVE

The incidence of venous thromboembolism (VTE) is significantly elevated in breast cancer patients, with a three-to-fourfold increase, and further escalates to sixfold in those undergoing chemotherapy. This study aims to identify the risk factors for VTE and develop a Nomogram risk prediction model distinct from the traditional Khorana score.

METHODS

Univariate Cox regression analysis assessed the impact of each variable on the occurrence of VTE, while stepwise multivariate Cox regression analysis identified independent predictors. Based on these results, we constructed a Nomogram model. The model's performance was validated using the C-index, receiver-operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Comparisons were made with the Khorana score to evaluate the practical application value.

RESULTS

Out of the 903 patients, 108 (11.96%) developed VTE. Cox regression analysis identified that Stage, undergoing surgery, age, white blood cells (WBC), D-dimer, and carcinoembryonic antigen (CEA) were significant risk factors for VTE (P < 0.05). The Nomogram model's C-index was 0.77 (95% CI 0.72-0.83) in the training set and 0.76 (95% CI 0.69-0.84) in the validation set. The model demonstrated excellent predictive accuracy and generalizability on the receiver-operating characteristic (ROC) curves and calibration curves. Compared to the traditional Khorana score, the Nomogram model showed superior predictive accuracy and greater clinical benefit.

CONCLUSIONS

This study established a VTE risk prediction model for breast cancer patients undergoing chemotherapy. The model is characterized by its intuitive and straightforward application, making it highly suitable for rapid VTE risk assessment in clinical practice.

Risk factors and clinical prediction models for low-level viremia in people living with HIV receiving antiretroviral therapy: an 11-year retrospective study

Objective

This study explores the risk factors for low-level viremia (LLV) occurrence after ART and develops a risk prediction model.

Method

Clinical data and laboratory indicators of people living with HIV (PLWH) at Hangzhou Xixi Hospital from 5 April 2011 to 29 December 2022 were collected. LASSO Cox regression and multivariate Cox regression analysis were performed to identify laboratory indicators and establish a nomogram for predicting LLV occurrence. The nomogram's discrimination and calibration were assessed via ROC curve and calibration plots. The concordance index (C-index) and decision curve analysis (DCA) were used to evaluate its effectiveness.

Result

Predictive factors, namely, age, ART delay time, white blood cell (WBC) count, baseline CD4+ T-cell count (baseline CD4), baseline viral load (baseline VL), and total bilirubin (TBIL), were incorporated into the nomogram to develop a risk prediction model. The optimal model (which includes 6 variables) had an AUC for LLV after 1-year, 3-year, and 5-year of listing of 0.68 (95% CI, 0.61-0.69), 0.69 (95% CI, 0.65-0.70), and 0.70 (95% CI, 0.66-0.71), respectively. The calibration curve showed high consistency between predicted and actual observations. The C-index and DCA indicated superior prediction performance of the nomogram. There was a significant difference in CD4 levels between LLV and non-LLV groups during the follow-up time. The dynamic SCR, ALT, TG and BG levels and occurrence of complications differed significantly between the high- and low-risk groups.

Conclusion

A simple-to-use nomogram containing 6 routinely detected variables was developed for predicting LLV occurrence in PLWH after ART.

Should we use nomograms for risk predictions in diffuse large B cell lymphoma patients? A systematic review

Models based on risk stratification are increasingly reported for Diffuse large B cell lymphoma (DLBCL). Due to a rising interest in nomograms for cancer patients, we aimed to review and critically appraise prognostic models based on nomograms in DLBCL patients. A literature search in PubMed/Embase identified 59 articles that proposed prognostic models for DLBCL by combining parameters of interest (e.g., clinical, laboratory, immunohistochemical, and genetic) between January 2000 and 2024. Of them, 40 studies proposed different gene expression signatures and incorporated them into nomogram-based prognostic models. Although most studies assessed discrimination and calibration when developing the model, many lacked external validation. Current nomogram-based models for DLBCL are mainly developed from publicly available databases, lack external validation, and have no applicability in clinical practice. However, they may be helpful in individual patient counseling, although careful considerations should be made regarding model development due to possible limitations when choosing nomograms for prognostication.