Ki-67 as a prognostic marker in mantle cell lymphoma—consensus guidelines of the pathology panel of the European MCL Network

Mantle cell lymphoma (MCL) has a heterogeneous clinical course and is mainly an aggressive B cell non-Hodgkin lymphoma; however, there are some indolent cases The Ki-67 index, defined by the percentage of Ki-67-positive lymphoma cells on histopathological slides, has been shown to be a very powerful prognostic biomarker. The pathology panel of the European MCL Network evaluated methods to assess the Ki-67 index including stringent counting, digital image analysis, and estimation by eyeballing. Counting of 2 × 500 lymphoma cells is the gold standard to assess the Ki-67 index since this value has been shown to predict survival in prospective randomized trials of the European MCL Network. Estimation by eyeballing and digital image analysis showed a poor concordance with the gold standard (concordance correlation coefficients [CCC] between 0.29 and 0.61 for eyeballing and CCC of 0.24 and 0.37 for two methods of digital image analysis, respectively). Counting a reduced number of lymphoma cells (2 × 100 cells) showed high interobserver agreement (CCC = 0.74). Pitfalls of the Ki-67 index are discussed and guidelines and recommendations for assessing the Ki-67 index in MCL are given

Effects of Longitudinal Changes in Charlson Comorbidity on Prognostic Survival Model Performance Among Newly Diagnosed Patients with Hypertension

Objectives: To assess methods of defining comorbidities by comparing risk adjusted mortality predictive model fit and performance among newly diagnosed hypertensive population. Methods: We included nearly all patients 18 year and older with an incident diagnosis of hypertension from one Canadian Province. We compared prognostic model performance for Cox regression models using Charlson comorbidities as time-invariant covariates (TIC) at baseline and time-varying covariates (TVC). Cox regression was used to calculate hazard ratios. Model fit and performance was based on the comparison of the AIC and Likelihood Ratio. Results: All Cox regression time-varying covariate models (TVCMs) outperformed time-invariant covariate (TIVMs) baseline models, based on a comparison of AIC and Likelihood Ratio, regardless of the method used to adjust for individual risk using the Charlson Comorbidities. TVCMs included all 17 Charlson comorbidities as individual independent variables showed the best fit and performance compared with similar baseline models, AIC (1,670,491 to 1,720,126) and Likelihood Ratio (112,941.72 to 63,239.78) respectively. Conclusion: Accounting for changes in patient comorbidity status over time more accurately capture a patient’s health risk and improves predictive model fit and performance over longer follow-up periods than traditional baseline method

Effects of longitudinal changes in Charlson comorbidity on prognostic survival model performance among newly diagnosed patients with hypertension

Abstract Background To assess the use of updated comorbidity information over time on ability to predict mortality among adults with newly diagnosed hypertension. Methods We studied adults 18 years and older with an incident diagnosis of hypertension from Alberta, Canada. We compared the prognostic performance of Cox regression models using Charlson comorbidities as time-invariant covariates at baseline (TIC) versus models including Charlson comorbidities as time-varying covariates (TVC) using Akaike Information Criterion (AIC) for testing goodness of fit. Results The strength of the association between important prognostic clinical variables and mortality varied by modeling technique; for example, myocardial infarction was less strongly associated with mortality in the TIC model (Hazard Ratio 1.07; 95% Confidence Interval (CI): 1.05 to 1.1) than in the TVC model (HR 1.20; 95% CI: 1.18 to 1.22). All TVC models slightly outperformed TIC models, regardless of the method used to adjust for comorbid conditions (individual Charlson Comorbidities, count of comorbidities or indices). The TVC model including all 17 Charlson comorbidities as individual independent variables showed the best fit and performance. Conclusion Accounting for changes in patient comorbidity status over time more accurately captures a patient’s health risk and slightly improves predictive model fit and performance than traditional methods using TIC assessment

Correction: The 5th edition of The World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms (vol 36, pg 1720, 2022)

10.1038/s41375-023-01962-5LEUKEMIA3791944-195