Risk Stratification Using Multivariable Fractional Polynomials in Diffuse Large B-Cell Lymphoma

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

A nomogram model for predicting 5-year risk of prediabetes in Chinese adults

Early identification is crucial to effectively intervene in individuals at high risk of developing pre-diabetes. This study aimed to create a personalized nomogram to determine the 5-year risk of pre-diabetes among Chinese adults. This retrospective cohort study included 184,188 participants without prediabetes at baseline. Training cohorts (92,177) and validation cohorts (92,011) were randomly assigned (92,011). We compared five prediction models on the training cohorts: full cox proportional hazards model, stepwise cox proportional hazards model, multivariable fractional polynomials (MFP), machine learning, and least absolute shrinkage and selection operator (LASSO) models. At the same time, we validated the above five models on the validation set. And we chose the LASSO model as the final risk prediction model for prediabetes. We presented the model with a nomogram. The model's performance was evaluated in terms of its discriminative ability, clinical utility, and calibration using the area under the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration analysis on the training cohorts. Simultaneously, we also evaluated the above nomogram on the validation set. The 5-year incidence of prediabetes was 10.70% and 10.69% in the training and validation cohort, respectively. We developed a simple nomogram that predicted the risk of prediabetes by using the parameters of age, body mass index (BMI), fasting plasma glucose (FBG), triglycerides (TG), systolic blood pressure (SBP), and serum creatinine (Scr). The nomogram's area under the receiver operating characteristic curve (AUC) was 0.7341 (95% CI 0.7290-0.7392) for the training cohort and 0.7336 (95% CI 0.7285-0.7387) for the validation cohort, indicating good discriminative ability. The calibration curve showed a perfect fit between the predicted prediabetes risk and the observed prediabetes risk. An analysis of the decision curve presented the clinical application of the nomogram, with alternative threshold probability spectrums being presented as well. A personalized prediabetes prediction nomogram was developed and validated among Chinese adults, identifying high-risk individuals. Doctors and others can easily and efficiently use our prediabetes prediction model when assessing prediabetes risk.

Nomogram model and risk score to predict 5-year risk of progression from prediabetes to diabetes in Chinese adults: Development and validation of a novel model

AIM

To develop a personalized nomogram and risk score to predict the 5-year risk of diabetes among Chinese adults with prediabetes.

METHODS

There were 26 018 participants with prediabetes at baseline in this retrospective cohort study. We randomly stratified participants into two cohorts for training (n = 12 947) and validation (n = 13 071). The least absolute shrinkage and selection operator (LASSO) model was applied to select the most significant variables among candidate variables. And we further established a stepwise Cox proportional hazards model to screen out the risk factors based on the predictors chosen by the LASSO model. We presented the model with a nomogram. The model's discrimination, clinical use and calibration were assessed using the area under the receiver operating characteristic (ROC) curve, decision curve and calibration analysis. The associated risk factors were also categorized according to clinical cut-points or tertials to create the diabetes risk score model. Based on the total score, we divided it into four risk categories: low, middle, high and extremely high. We also evaluated our diabetes risk score model's performance.

RESULTS

We developed a simple nomogram and risk score that predicts the risk of prediabetes by using the variables age, triglyceride, fasting blood glucose, body mass index, alanine aminotransferase, high-density lipoprotein cholesterol and family history of diabetes. The area under the ROC curve of the nomogram was 0.8146 (95% CI 0.8035-0.8258) and 0.8147 (95% CI 0.8035-0.8259) for the training and validation cohort, respectively. The calibration curve showed a perfect fit between predicted and observed diabetes risks at 5 years. Decision curve analysis presented the clinical use of the nomogram, and there was a wide range of alternative threshold probability spectrums. A total risk score of 0 to 2.5, 3 to 4.5, 5 to 7.5 and 8 to 13.5 is associated with low, middle, high and extremely high diabetes risk status, respectively.

CONCLUSIONS

We developed and validated a personalized prediction nomogram and risk score for 5-year diabetes risk among Chinese adults with prediabetes, identifying individuals at a high risk of developing diabetes. Doctors and other healthcare professionals can easily and quickly use our diabetes score model to assess the diabetes risk status in patients with prediabetes. In addition, the nomogram model and risk score we developed need to be validated in a prospective cohort study.

Clinical Characteristics, Outcomes, and Risk Factors for Patients with Diffuse Large B-Cell Lymphoma and Development of Nomogram to Identify High-Risk Patients

Objectives. To analyse the clinical features, outcomes, and risk factors of patients with diffuse large B-cell lymphoma (DLBCL) in China, with the aim to establish a new prognostic model based on risk factors. Methods. Clinical features and outcomes of 564 patients newly diagnosed with DLBCL from Jan 2009 to May 2017 were analyzed retrospectively. Variables were screened by LASSO regression and nomogram was constructed. Results. The 5-year overall survival (OS) of the cohort was 75%. The 5-year OS of patients differentiated by International Prognostic Index (IPI) score was 90% (score 0–2), 73% (score 3), and 51% (score 4-5), respectively. Age > 60, Eastern Cooperative Oncology Group (ECOG) > 1, Ann Arbor stage III-IV, bone marrow involvement, low level of albumin (ALB), and lymphatic/monocyte ratio (LMR) were independent predictors of OS. The predictive model was developed based on factors including age, bone marrow involvement, LMR, ALB, and ECOG scores. The predictive ability of the model (AUC, 0.77) was better than that of IPI (AUC, 0.74) and NCCN-IPI (AUC, 0.69). The 5-year OS of patients in the low-, intermediate-, and high-risk groups identified by the new predictive model was 89%, 70%, and 33%, respectively. Conclusions. The new prediction model had better predictive performance and could better identify high-risk patients.

Development and Validation of a Prediction Model for Chronic Post-Surgical Pain After Thoracic Surgery in Elderly Patients: A Retrospective Cohort Study

Purpose

Chronic post-surgical pain (CPSP) is one of the adverse outcomes after surgery, especially in thoracotomy. However, the prevalence of CPSP in elderly adults (≥65 years), is still limited. Therefore, the present study was undertaken to establish and validate the prediction model of CPSP in those patients after thoracic surgery, including thoracotomy and video-assisted thoracoscopic surgery.

Patients and Methods

This retrospective, observational single-center cohort study was conducted in Nanfang Hospital, Southern Medical University, which randomly and consecutively collected 577 elderly patients who underwent thoracic surgery between January 1, 2017, and December 31, 2020. According to the Akaike information criterion, the prediction model was built based on all the data and was validated by calibration with 500 bootstrap samples.

Results

The mean age of participants was 69.09±3.80 years old, and 63.1% were male. The prevalence of CPSP was 26.9%. Age more than 75 years, BMI, blood loss, longer length of hospital stays, and higher pre-operative neutrophil count were associated with CPSP. Except for these factors, we incorporated history of drinking to build up the prediction model. The areas under the curve (AUCs) of the prediction models were 0.66 (95% CI, 0.61–0.71) and 0.64 (95% CI, 0.59–0.69) in the observational and validation cohorts, respectively. And the calibration curve of the predictive model showed a good fit between the predicted risk of CPSP and observed outcomes in elderly patients.

Conclusion

The present developed model may help clinicians to find high-risk elderly patients with CPSP after thoracic surgery and take corresponding measures in advance to reduce the incidence of CPSP and improve their life quality.

Significance of Single-cell Level Dual Expression of BCL2 and MYC Determined With Multiplex Immunohistochemistry in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a fatal heterogenous neoplasm. Recent clinical trials have failed partly due to nebulous criteria for defining high-risk patients. Patients with double-expresser lymphoma (DEL) have a poor prognosis and are resistant to conventional treatment. However, many diagnostic and clinical controversies still surround DEL partly due to the arbitrariness of criteria for the diagnosis of DEL. In this study, we suggest a refined method for diagnosing DEL by evaluating the concurrent expression of BCL2 and MYC at the single-cell level (dual-protein-expressing lymphoma [DUEL]). For the proof of concept, a multiplex immunofluorescence assay for CD20, BCL2, and MYC was performed and quantitatively analyzed using spectral image analysis in patients. The analysis results and clinical applicability were verified by using dual-color immunohistochemistry performed on 353 independent multicenter patients who had been uniformly treated with standard therapy. DUEL showed significantly worse overall survival (OS) and event-free survival (EFS) (P=0.00011 and 0.00035, respectively). DUEL status remained an independent adverse prognostic variable with respect to the International Prognostic Index risk and the cell of origin. Moreover, the advantage of determining DUEL status by dual-color immunohistochemistry was shown by more robust classification and more homogeneous high-risk subgroup patient identification in both training (n=271) (OS: P<0.0001; EFS: P<0.0001) and validation sets (n=82) (OS: P=0.0087; EFS: P<0.0001). This concept of DUEL is more consistent with carcinogenesis and has greater practical utility, hence it may provide a better basis for both basic and clinical research for the development of new therapeutics.

A prediction nomogram for the 3-year risk of incident diabetes among Chinese adults

Identifying individuals at high risk for incident diabetes could help achieve targeted delivery of interventional programs. We aimed to develop a personalized diabetes prediction nomogram for the 3-year risk of diabetes among Chinese adults. This retrospective cohort study was among 32,312 participants without diabetes at baseline. All participants were randomly stratified into training cohort (n = 16,219) and validation cohort (n = 16,093). The least absolute shrinkage and selection operator model was used to construct a nomogram and draw a formula for diabetes probability. 500 bootstraps performed the receiver operating characteristic (ROC) curve and decision curve analysis resamples to assess the nomogram's determination and clinical use, respectively. 155 and 141 participants developed diabetes in the training and validation cohort, respectively. The area under curve (AUC) of the nomogram was 0.9125 (95% CI, 0.8887-0.9364) and 0.9030 (95% CI, 0.8747-0.9313) for the training and validation cohort, respectively. We used 12,545 Japanese participants for external validation, its AUC was 0.8488 (95% CI, 0.8126-0.8850). The internal and external validation showed our nomogram had excellent prediction performance. In conclusion, we developed and validated a personalized prediction nomogram for 3-year risk of incident diabetes among Chinese adults, identifying individuals at high risk of developing diabetes.

Quantitative analysis of tumor-specific BCL2 expression in DLBCL: refinement of prognostic relevance of BCL2

BCL2 overexpression has been reported to be associated with poor prognosis in patients with diffuse large B-cell lymphoma (DLBCL). However, currently there is no consensus on the evaluation of BCL2 expression and only the proportion of BCL2 positive cells are evaluated for the determination of BCL2 positivity. This study aimed to define BCL2 positivity by quantitative analysis integrating both the intensity and proportion of BCL2 expression. BCL2 expression of 332 patients (221 patients for the training set and 111 patients for the validation set) with newly diagnosed DLBCL who received R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) were analyzed using the tumor-specific automated quantitative analysis (AQUA) scoring method based on multiplex immunofluorescence. In the training set, high BCL2 AQUA score (N = 86, 38.9%) was significantly associated with poor prognosis (p = 0.01, HR 2.00; 95% CI [1.15–3.49]) independent of international prognostic index, cell of origin, and MYC expression. The poor prognostic impact of the high BCL2 AQUA score was validated in the validation set. AQUA scoring of BCL2 expression incorporating both the intensity and proportion of BCL2 positive cells was independently associated with survival outcomes of patients with primary DLBCL treated with R-CHOP.

Consecutive Hypoalbuminemia Predicts Inferior Outcome in Patients With Diffuse Large B-Cell Lymphoma

The prognostic value of albumin changes between diagnosis and end-of-treatment (EoT) in diffuse large B-cell lymphoma (DLBCL) remains unknown. We retrospectively analyzed 574 de novo DLBCL patients treated with R-CHOP from our and two other centers. All patients were divided into a training cohort (n = 278) and validation cohort (n = 296) depending on the source of the patients. Overall survival (OS) and progression-free survival (PFS) were analyzed by the method of Kaplan-Meier and Cox proportional hazard regression model. In the training cohort, 163 (58.6%) patients had low serum albumin at diagnosis, and 80 of them were present with consecutive hypoalbuminemia at EoT. Patients with consecutive hypoalbuminemia showed inferior OS and PFS (p = 0.010 and p = 0.079, respectively). Similar survival differences were also observed in the independent validation cohort (p = 0.006 and p = 0.030, respectively). Multivariable analysis revealed that consecutive hypoalbuminemia was an independent prognostic factor OS [relative risk (RR), 2.249; 95% confidence interval (CI), 1.441-3.509, p < 0.001] and PFS (RR, 2.001; 95% CI, 1.443-2.773, p < 0.001) in all DLBCL patients independent of IPI. In conclusion, consecutive hypoalbuminemia is a simple and effective adverse prognostic factor in patients with DLBCL, which reminds us to pay more attention to patients with low serum albumin at EoT during follow-up.