A novel differential diagnosis algorithm for chronic lymphocytic leukemia using immunophenotyping with flow cytometry.

INTRODUCTION: The availability of a clinical decision algorithm for diagnosis of chronic lymphocytic leukemia (CLL) may greatly contribute to the diagnosis of CLL, particularly in cases with ambiguous immunophenotypes. Herein we propose a novel differential diagnosis algorithm for the CLL diagnosis using immunophenotyping with flow cytometry. METHODS: The hierarchical logistic regression model (Backward LR) was used to build a predictive algorithm for the diagnosis of CLL, differentiated from other lymphoproliferative disorders (LPDs). RESULTS: A total of 302 patients, of whom 220 (72.8%) had CLL and 82 (27.2%), B-cell lymphoproliferative disorders other than CLL, were included in the study. The Backward LR model comprised the variables CD5, CD43, CD81, ROR1, CD23, CD79b, FMC7, sIg and CD200 in the model development process. The weak expression of CD81 and increased intensity of expression in markers CD5, CD23 and CD200 increased the probability of CLL diagnosis, (p < 0.05). The odd ratio for CD5, C23, CD200 and CD81 was 1.088 (1.050 - 1.126), 1.044 (1.012 - 1.077), 1.039 (1.007 - 1.072) and 0.946 (0.921 - 0.970) [95% C.I.], respectively. Our model provided a novel diagnostic algorithm with 95.27% of sensitivity and 91.46% of specificity. The model prediction for 97.3% (214) of 220 patients diagnosed with CLL, was CLL and for 91.5% (75) of 82 patients diagnosed with an LPD other than CLL, was others. The cases were correctly classified as CLL and others with a 95.7% correctness rate. CONCLUSIONS: Our model highlighting 4 markers (CD81, CD5, CD23 and CD200) provided high sensitivity and specificity in the CLL diagnosis and in distinguishing of CLL among other LPDs.

A novel differential diagnosis algorithm for chronic lymphocytic leukemia using immunophenotyping with flow cytometry

Abstract Introduction The availability of a clinical decision algorithm for diagnosis of chronic lymphocytic leukemia (CLL) may greatly contribute to the diagnosis of CLL, particularly in cases with ambiguous immunophenotypes. Herein we propose a novel differential diagnosis algorithm for the CLL diagnosis using immunophenotyping with flow cytometry. Methods The hierarchical logistic regression model (Backward LR) was used to build a predictive algorithm for the diagnosis of CLL, differentiated from other lymphoproliferative disorders (LPDs). Results A total of 302 patients, of whom 220 (72.8%) had CLL and 82 (27.2%), B-cell lymphoproliferative disorders other than CLL, were included in the study. The Backward LR model comprised the variables CD5, CD43, CD81, ROR1, CD23, CD79b, FMC7, sIg and CD200 in the model development process. The weak expression of CD81 and increased intensity of expression in markers CD5, CD23 and CD200 increased the probability of CLL diagnosis, (p < 0.05). The odd ratio for CD5, C23, CD200 and CD81 was 1.088 (1.050 - 1.126), 1.044 (1.012 - 1.077), 1.039 (1.007 - 1.072) and 0.946 (0.921 - 0.970) [95% C.I.], respectively. Our model provided a novel diagnostic algorithm with 95.27% of sensitivity and 91.46% of specificity. The model prediction for 97.3% (214) of 220 patients diagnosed with CLL, was CLL and for 91.5% (75) of 82 patients diagnosed with an LPD other than CLL, was others. The cases were correctly classified as CLL and others with a 95.7% correctness rate. Conclusions Our model highlighting 4 markers (CD81, CD5, CD23 and CD200) provided high sensitivity and specificity in the CLL diagnosis and in distinguishing of CLL among other LPDs.

Association between hospital characteristics and 30-day mortality of patients hospitalized for acute myocardial infarction in Sichuan, China.

OBJECTIVE: Because acute myocardial infarction (AMI) is a major cause of death, China faces the challenge of improving its quality of care. This study provides context-specific evidence of association between 30-day mortality and hospital characteristics in China to extend the understanding of hospitalized AMI patients. METHODS: We conducted a retrospective cohort study of 67,619 hospitalized AMI patients at 372 tertiary and secondary hospitals in Sichuan, China, between January 1, 2018 and December 31, 2020. Using a hierarchical logistic regression model to control risk factors, we explored relationships among 30-day mortality, hospital level, AMI volume, and percutaneous coronary intervention (PCI) timeliness. Locally weighted scatterplot smoothing was used to observe the trends of 30-day mortality with increased AMI volume and PCI timeliness. RESULTS: After risk factor adjustment, the 30-day mortality model demonstrated that a lower hospital level and smaller AMI volume were associated with higher 30-day mortality (medium-volume: OR = 1.511, 95% CI (1.195, 1.910); small-volume: OR = 1.636, 95% CI (1.277, 2.096); other tertiary: OR = 1.190, 95% CI (1.037, 1.365); secondary: OR = 1.524, 95% CI (1.289, 1.800)). Similarly, 30-day mortality was higher for patients at hospitals with a low PCI timeliness (low timeliness: OR = 1.318, 95% CI (1.079, 1.610)). Scatterplot smoothing showed hospital 30-day mortality first reduced quickly and gradually stabilized with increased AMI volume and PCI timeliness. CONCLUSION: Patients admitted to tertiary grade A hospitals, large-volume hospitals, and high- or medium-timeliness hospitals were more likely to survive at 30 days. Policymakers should focus on improving the outcomes at hospitals without these characteristics.

Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention.

BACKGROUND: Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. OBJECTIVES: This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. METHODS: Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. RESULTS: In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%). CONCLUSIONS: The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts.