A potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms

Abstract

Background: Aplastic anemia (AA) and myelodysplastic neoplasms (MDS) have similar peripheral blood manifestations and are clinically characterized by reduced hematological triad. It is challenging to distinguish and diagnose these two diseases. Hence, utilizing machine learning methods, we employed and validated an algorithm that used cell population data (CPD) parameters to diagnose AA and MDS.

Methods: In this study, CPD parameters were obtained from the Beckman Coulter DxH800 analyzer for 160 individuals diagnosed with AA or MDS through a comprehensive retrospective analysis. The individuals were unselectively assigned to a training cohort (77%) and a testing cohort (23%). Additionally, an external validation cohort consisting of eighty-six elderly patients with AA and MDS from two additional centers was established. The discriminative parameters were carefully analyzed through univariate analysis, and the most predictive variables were selected using least absolute shrinkage and selection operator (LASSO) regression. Six machine learning algorithms were utilized to compare the performance of forecasting AA and MDS patients. The area under the curves (AUCs), calibration curves, decision curves analysis (DCA), and shapley additive explanations (SHAP) plots were employed to interpret and assess the model's predictive accuracy, clinical utility, and stability.

Results: After the comparative evaluation of various models, the logistic regression model emerged as the most suitable machine learning model for predicting the probability of AA and MDS, which utilized five principal variables (age, MNVLY, SDVLY, MNLALSEGC, and MNCEGC) to accurately estimate the risk of these diseases. The best model delivered an AUC of 0.791 in the testing cohort and had a high specificity (0.850) and positive predictive value (0.818). Furthermore, the calibration curve indicated excellent agreement between actual and predicted probabilities. The DCA curve further supported the clinical utility of our model and offered significant clinical advantages in guiding treatment decisions. Moreover, the model's performance was consistent in an external validation group, with an AUC of 0.719.

Conclusions: We developed a novel model that effectively distinguished elderly patients with AA and MDS, which had the potential to provide physicians assistance in early diagnosis and the proper treatment for the elderly.

Keywords: Aplastic anemia; Cell population data; Machine learning; Myelodysplastic neoplasms; Parameters.

Fig. 1

The flow chart demonstrated the participants encompassed within in the study

Fig. 2

Screening the optimal predictors via LASSO regression. A Regression coefficient path plot in LASSO regression. Diverse colored lines indicate that different variables will gradually become zero, and the later they become zero, the more important the indicator. B The cross-validation curve of LASSO regression. The minimum standard is on the left line and the 1-SE standard is on the right line. In the current study, we selected 9 non-zero predictors according to the 1-SE standard. SE, the standard error

Fig. 3

The ROC curves of AA and MDS were independently predicted by 9 predictors between the two groups

Fig. 4

A The weight importance of nine filtered indicators. B Heat map of correlation of top five indicators. The correlation degree is from low (blue) to high (red)

Fig. 5

The performance of six machine learning models. A ROC curve of the training cohort; B ROC curve of the testing cohort; C Calibration curve; (D) Decision curve analysis

Fig. 6

Model explainability via the SHAP algorithm. A The horizontal SHAP value represents the influence on the prediction result, and the vertical coordinate is each indicator, the contribution degree is from low (blue) to high (red). B The importance ranking of independent variables. C The SHAP force plot of patients with myelodysplastic neoplasms. D The SHAP force plot of patients with aplastic anemia

Fig. 7

ROC for the external validation cohort