
==== Front
J Cancer Res Clin Oncol
J Cancer Res Clin Oncol
Journal of Cancer Research and Clinical Oncology
0171-5216
1432-1335
Springer Berlin Heidelberg Berlin/Heidelberg

39237750
5914
10.1007/s00432-024-05914-z
Research
Construction of a clinical prediction model for the diagnosis of immune thrombocytopenia based on clinical laboratory parameters
Zhong Kangying
Pei Yuqing
Yang Ziyan
Zheng Qin zhengqinhx@scu.edu.cn

https://ror.org/007mrxy13 grid.412901.f 0000 0004 1770 1022 Department of Laboratory Medicine, West China Hospital of Sichuan University, Sichuan, China
6 9 2024
6 9 2024
2024
150 9 41225 4 2024
31 7 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Purpose

Primary immune thrombocytopenia (ITP) is an autoimmune bleeding disorder characterized by isolated thrombocytopenia that is often misdiagnosed due to the lack of a gold standard for diagnosis and currently relies on exclusionary approaches. This project combines several laboratory parameters to construct a clinical prediction model for adult ITP patients.

Methods

A total of 428 patients with thrombocytopenia who visited the West China Hospital of Sichuan University between January 2021 and March 2023 were enrolled. Based on the diagnostic criteria, we divided those patients into an ITP group and a non-ITP group. A total of 34 laboratory parameters were analyzed via univariate analysis and correlation analysis, and the least absolute shrinkage and selection operator regression analysis was used to establish the model. The training and validation sets were divided at a ratio of 7:3, and we used a fivefold cross-validation method to construct the model.

Results

The model included the following variables: red blood cell, mean corpuscular hemoglobin concentration, red blood cell distribution width-standard deviation, platelet variability index score, immature platelet fraction, lymphocyte absolute value. The prediction model exhibited good performance, with a sensitivity of 0.89 and a specificity of 0.83 in the training set and a sensitivity of 0.90 and a specificity of 0.87 in the validation set.

Conclusion

The clinical prediction model can assess the probability of ITP in thrombocytopenic patients and has good predictive accuracy for the diagnosis of ITP.

Keywords

Primary immune thrombocytopenia
Clinical prediction model
Platelet count variability
Immature platelet fraction
the Science and Technology Projects of Sichuan Province of China2023NSFSC1485 2023YFS0187 issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
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pmcIntroduction

Primary immune thrombocytopenia (ITP) is an autoimmune disease characterized by a platelet count less than 100×109/L. The incidence rate of ITP among adults is approximately 10 cases per 100,000 individuals annually. ITP causes bleeding in two-thirds of patients, particularly in the skin and mucous membranes (Segal and Powe 2006). Moreover, approximately 80% of adults with ITP will develop a chronic disease course with a significant decline in quality of life (Provan et al. 2019). At present, high-dose dexamethasone therapy still represents the first choice for the treatment of ITP (Neunert et al. 2019). The presentations of ITP vary in terms of bleeding risk, with significant individual variation ranging from thrombocytopenia without other clinical signs to cutaneous mucosal bleeding, severe visceral bleeding, and fatal intracranial hemorrhage (Cooper et al. 2021). Given the unclear association between platelet count and bleeding risk in patients with ITP, as well as the challenges in predicting such risk, early diagnosis assumes paramount importance.

The clinical diagnosis of ITP requires a combination of patient history, physical examination, complete blood count, peripheral blood smear, and the exclusion of other causes of isolated thrombocytopenia (Sandal et al. 2021). The diagnosis of ITP presents challenges, primarily due to the lack of a universally recognized “gold standard” methodology. Additionally, distinguishing ITP from various underlying causes of thrombocytopenia across different patient populations is a complex process. Although bone marrow examinations are effective for diagnosing ITP, they are not commonly used in patients primarily diagnosed with thrombocytopenia due to concerns about invasiveness. Currently, with intensive research on the pathogenic mechanisms of ITP, other parameters, such as the immature platelet fraction (IPF), are being found to be an alternative to bone marrow examination. An elevated IPF implies increased thrombopoiesis, so IPF can distinguish ITP from thrombocytopenia due to bone marrow failure. However, whether IPF can be used as an independent parameter is still controversial because there are several types of thrombocytopenia associated with elevated IPF; current IPF-related studies are based on small samples, and there is no clear cutoff value for IPF (Jeon et al. 2020).

Based on existing studies and clinical observations, we hypothesize that concurrent assessment of hematology-related parameters and bone marrow examinations in patients suspected of ITP holds substantial clinical significance for the differential diagnosis of ITP. However, clinical diagnostic models for ITP that include the above laboratory test parameters are lacking. Therefore, this project proposes establishing a clinical diagnostic model for ITP in adults to help clinicians accurately differentiate patients with ITP from those with other thrombocytopenic disorders, which could shorten the clinical diagnostic period and improve the accuracy of disease diagnosis to provide patients with a rapid and accurate treatment plan.

Materials and methods

Patients and study design

We retrospectively analyzed 625 patients with thrombocytopenia (platelet count less than 100 × 109/L) at West China Hospital of Sichuan University from January 2021 to March 2023. We divided the patients into two groups: those diagnosed with ITP (ITP group) and those diagnosed with thrombocytopenia due to a cause other than ITP (non-ITP group). According to the American Society of Hematology 2019 ITP guidelines (Neunert et al. 2019) and/or the 2020 Chinese Adult ITP Diagnosis and Treatment Guidelines (Thrombosis and Hemostasis Group, 2020), the following criteria were used as the diagnostic criteria for ITP: (1) a platelet count less than 100 × 109/L and a microscopic examination of peripheral blood smears showing no obvious abnormalities in blood cell morphology; and (2) experience of clinicians combined with a number of laboratory tests to exclude other causes of thrombocytopenia (including autoimmune diseases, lymphoproliferative disorders, myelodysplastic neoplasms, aplastic anemia, acute leukemia, solid tumors, chronic liver diseases, infectious diseases, other conditions resulting in secondary thrombocytopenia.)

Data collection

This study compiled a list of potential predictive variables related to the diagnosis of ITP based on existing studies. The baseline clinical information included age, sex, and clinical diagnosis. The laboratory parameters included complete blood count, the lowest platelet count, and the platelet variability index (PVI) (which requires three or more platelet count measurements per patient). The PVI can describe the degree of platelet fluctuation over time and the severity of platelet reduction in ITP patients (Li et al. 2021). Considering the pathogenesis and clinical characteristics of ITP, this study initially planned to include additional laboratory parameters and medical history data, such as transfusion history, acute-phase reactant proteins, quantification of immunoglobulins, absolute counts of immune cells, previous treatments for ITP, and ITP bleeding scores. However, due to the high ratio of missing values for the aforementioned parameters and the difficulty in quantifying history-related indicators, these variables were not included in the model. The PVI is a derived index that can reflect both the severity of thrombocytopenia and the variability in the platelet. In the present study, we used each of these two components, the severity of thrombocytopenia and the variability of platelet counts, to independently evaluate their contributions (Audia et al. 2017).

In the process of model construction, the evaluation of predictive variables was independent of the assessment of diagnostic results, aiming to minimize information bias to the greatest extent.

Statistical analysis

All the data were analyzed using R (version 4.2.1). The significance level for all the statistical tests was set at 0.05, and all the tests were two-tailed. Continuous variables are presented as the mean and standard deviation or median and interquartile range, while categorical variables are presented as frequency and proportions. The clinical baseline characteristics of the participants were compared using the t test for normally distributed continuous variables, the Mann‒Whitney U test for skewed continuous variables and the χ2 test for categorical variables.

Model construction and validation

In this study, the Pearson correlation coefficient was used to analyze the correlations among the 27 variables that demonstrated intergroup differences. The variance inflation factor (VIF) was used to evaluate multicollinearity among the predictive variables, with the cutoff value set at 5. A VIF exceeding 5 indicated relatively high multicollinearity. Variables showing significant differences (p < 0.05) between groups were subjected to the least absolute shrinkage and selection operator (LASSO) regression analysis for evaluation and model construction. During the model construction process, we divided the original dataset into a training set and a validation set at a 7:3 ratio. The data in the validation set were completely independent of the data in the training set and were not included in the model training process. It was only used for the final model evaluation. We used the training and validation datasets from the constructed LASSO regression model for training and validation, respectively. LASSO regression with fivefold cross-validation and lambda. min as the criterion was employed to select the optimal combination of influencing factors. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity and Youden index (Schisterman et al. 2005) were used to assess the discrimination of the training group and validation group.

Results

Patient characteristics

A total of 625 patients with thrombocytopenia were enrolled at West China Hospital of Sichuan University from January 2021 to March 2023; 133 patients were excluded due to insufficient clinical information, and 64 patients were excluded due to less than three previous platelet counts. The remaining 428 patients were divided into an ITP group (n = 69) and a non-ITP group (n = 359). The non-ITP group consisted of 359 patients with autoimmune diseases (n = 65); lymphoproliferative disorders (n = 51); myelodysplastic neoplasms (n = 22); aplastic anemia (n = 14); acute leukemia (n = 47); solid tumors (n = 88); chronic liver diseases (n = 42); infectious diseases (n = 21); other conditions resulting in secondary thrombocytopenia (n = 9, including chronic kidney disease, megaloblastic anemia, trauma, thrombotic thrombocytopenia). The inclusion of patients and their groupings is illustrated in Fig. 1.

Fig. 1 Flow chart of patient enrollment and grouping in this study. Abbreviations: ITP: primary immune thrombocytopenia

The ITP group had a median age of 51 [34,58] years, with 49/69 of patients being female. The non-ITP group had a median age of 54 [43,64] years, with 189/359 of patients being female. Baseline characteristics of the patients are presented in Table 1.

Table 1 Baseline characteristics of patients included in the study

Characteristics	ITP(n = 69)	non-ITP(n = 359)	p-value	
Age (years)	51(34,58)	54 (43,64)	0.0482*	
Female (n, %)	49(71.01%)	189(52.65%)	0.007**	
Immature granulocyte (%)	0.40 (0.30,0.70)	0.60 (0.30,1.40)	0.1558	
Immature granulocyte (×109/L)	0.03 (0.02,0.07)	0.03 (0.01,0.07)	0.1096	
High fluorescent lymphocyte (×109/L)	0.10 (0,0.20)	0 (0,0.30)	0.6983	
Micro red blood cell (%)	1.20 (0.80,2.00)	1.10 (0.70,2.08)	0.4893	
Nucleated red blood cell (%)	0 (0,0)	0 (0,0.30)	0.0020**	
Nucleated red blood cell (×109/L)	0 (0,0)	0 (0,0.01)	0.0011**	
Red blood cell (×1012/L)	4.38 (3.81,4.89)	2.97 (2.39,3.87)	< 0.0001**	
Hemoglobin (g/L)	127 (112,139)	96.0 (73.0,124)	<0.0001**	
Hematocrit (L/L)	0.40 (0.35,0.43)	0.30 (0.23,0.37)	<0.0001**	
Mean corpuscular volume (fL)	91.2 (87.7,95.8)	96.2 (91.1,102)	<0.0001**	
Mean corpuscular hemoglobin (g/L)	318 (310,328)	324 (315,333)	0.0038**	
Mean corpuscular hemoglobin concentration (pg)	29.4 ± 2.88	31.5 ± 3.07	<0.0001**	
Red blood cell volume distribution width- standard deviation (fL)	45.1 (42.5,50.4)	51.8 (46.3,61.3)	<0.0001**	
Red blood cell volume distribution width-coefficient of variation (%)	13.6 (13.0,15.0)	14.7 (13.5,17.2)	<0.0001**	
Lowest platelet count (×109/L)	19.0 (6.00,28.0)	36.0 (16.5,52.0)	<0.0001**	
Platelet variability index score	4.00 (3.00,5.00)	3.00 (1.00,3.00)	<0.0001**	
Immature platelet fraction (%)	17.6 (9.00,27.5)	8.90 (4.45,14.7)	<0.0001**	
Platelet large cell ratio (%)	48.4 ± 10.2	40.8 ± 11.2	<0.0001**	
Plateletocrit (L/L)	0.05 (0.03,0.08)	0.06 (0.04,0.08)	0.6034	
Mean platelet volume (fL)	13.2 (12.1,14.1)	11.9 (10.9,13.1)	<0.0001**	
Platelet distribution width (%)	18.3 (3.8,21.6)	15.2 (12.3,18.6)	0.0008**	
White blood cell (×109/L)	7.24 (5.53,9.15)	4.12 (2.49,6.72)	< 0.0001**	
Neutrophilic segmented granulocyte (%)	63.8 (52.7,71.8)	63.4 (49.3,75.6)	0.8358	
Lymphocyte (%)	26.1 (20.0,38.1)	22.3 (12.2,35.8)	0.0337*	
Monocyte (%)	6.50 (5.10,8.00)	7.70 (5.45,10.0)	0.0071**	
Eosinophilic granulocyte (%)	0.90 (0.30,1.70)	1.00 (0.20,2.20)	0.769	
Basophilic granulocyte (%)	0.50 (0.30,0.60)	0.30 (0.10,0.60)	0.0185*	
Neutrophilic segmented granulocyte (×109/L)	4.62 (2.88,6.42)	2.44 (1.23,4.56)	<0.0001**	
Monocyte (×109/L)	0.45 (0.34,0.67)	0.33 (0.18,0.49)	<0.0001**	
Lymphocyte (×109/L)	1.84 (1.44,2.55)	0.83 (0.47,1.35)	<0.0001**	
Eosinophilic granulocyte (×109/L)	0.07 (0.03,0.11)	0.03 (0.01,0.08)	0.0072**	
Basophilic granulocyte (×109/L)	0.03 (0.02,0.05)	0.01 (0.01,0.03)	<0.0001**	
*p < 0.05; **p < 0.01; Data were shown as mean ± standard deviation for normal distributed continuous variables, median (25th percentile and 75th percentile) for skew continuous variables, or number (%) for categorical variables

Variable selection for the prediction model

When comparing the differences between groups, most of the variables included in this study were significantly different between ITP patients and non-ITP patients (Table 1). In this study, the Pearson correlation coefficient was used to analyze the correlations among the 27 variables that demonstrated intergroup differences. To ensure the accuracy and stability of the model, it is necessary to conduct multicollinearity analysis on these 19 variables before constructing the model. The VIF was used to assess the degree of multicollinearity between independent variables. This analysis aimed to determine whether there was a problem with distorted model estimates or difficulties in accurate estimation due to the correlation among the independent variables. The VIFs of the variables that were ultimately included in the model construction are shown in Table 2.

Table 2 Variance inflation factor (VIF) calculated values for the 19 variables

Variables	VIF	Variables	VIF	
Sex	1.1	Immature platelet fraction	1.63	
Age	1.11	Platelet distribution width	1.39	
Nucleated red blood cell%	1.22	Lymphocyte%	1.7	
Nucleated red blood cell#	1.15	Monocyte%	1.16	
Red blood cell	1.32	Basophilic granulocyte%	1.07	
Mean corpuscular volume	1.83	Neutrophilic segmented granulocyte#	1.55	
Mean corpuscular hemoglobin concentration	1.34	Monocyte#	1.29	
Red blood cell volume distribution width- standard deviation	2.13	Lymphocyte#	1.86	
Lowest platelet count	1.32	Eosinophilic granulocyte#	1.14	
Platelet variability index score	1.08			
%: percentage; #: absolute value

Development of predictive models

LASSO regression was employed to further analyze the 19 variables listed in Table 2. The model identified the following 6 variables as the best matching factors: red blood cell (RBC), mean corpuscular hemoglobin (MCHC), red blood cell distribution width- standard deviation (RDW-SD), PVI score, IPF and lymphocyte absolute value, as shown in Fig. 2. Among these factors, the MCHC and RDW-SD were determined to be independent protective factors for ITP patients. Conversely, RBC, PVI score, IPF and lymphocyte absolute value were identified as independent risk factors. The specific variables and their corresponding coefficients are detailed in Table 3.

Fig. 2 Best match factor screening by LASSO regression. A. LASSO regression path diagram; B. Plot of the best matching factors screened by the fivefold cross-validation method, and the best matching factors were selected using lambda. min as the criterion

Table 3 The corresponding coefficients and log changes of the 6 variables in the model

Predictor	𝜷 (Coefficient)	
(Intercept)	-4.2391	
Red blood cell	0.4345	
Mean corpuscular hemoglobin concentration	-0.0065	
Red blood cell volume distribution width- standard deviation	-0.001	
Platelet variability index score	0.6801	
Immature platelet fraction	0.0245	
Lymphocyte absolute value	0.6999	

Validation of the accuracy and discrimination of the diagnostic model

The accuracy and discrimination of the diagnostic model were validated using ROC. In the training cohort, the model’s AUC reached 0.9149 (95% CI: 0.862–0.967, p < 0.05), at a cutoff value of 0.2404, the corresponding maximum Youden index was 0.7192, with a sensitivity of 0.8932 and specificity of 0.8261. In the validation set, the model’s AUC for the ROC curve reached 0.9040 (95% CI: 0.814–0.994, p < 0.05), at a cutoff value of 0.2295, the corresponding maximum Youden index was 0.7736, with a sensitivity of 0.904 and specificity of 0.8696. The clinical diagnostic model suggested excellent discrimination ability in diagnosing ITP (Fig. 3).

Fig. 3 ROC curves of the LASSO regression model in the training and validation sets

Discussion

Due to the intricate pathogenesis of ITP and the absence of definitive diagnostic indicators and gold standards, this study integrated a variety of common laboratory parameters that are routinely measurable in primary hospital to construct a clinical diagnostic model for ITP. In this study, we initially considered relevant indicators from the general baseline data, such as age and sex, because according to the large-scale sample, the prevalence of ITP was greater in women and elderly people aged older than 60 years. The complete blood count can be used to determine whether anemia is caused by insufficient or impaired utilization of hematopoietic materials (vitamin B12, folate and/or iron deficiency), as indicated by laboratory parameters such as the mean red blood cell volume and red blood cell distribution width. Notably, we calculated an index to evaluate the variability in platelet counts based on three consecutive platelet count measurements; this index has value in assessing platelet counts variability in patients and holds diagnostic significance for ITP (Li et al. 2021). The unstable platelet counts in ITP patients may be related to premature destruction of platelet or impaired platelet production caused by immune regulation disorders caused by autoantibodies, cytotoxicity, complement or other immune mechanisms (Audia et al. 2017; Zufferey et al. 2017, Toltl et al. 2011; Shrestha et al. 2020). Rapid fluctuations in platelet counts can be observed in patients infected, vaccinated, or receiving other immune stimuli (Rinaldi et al. 2014). Conversely, patients with platelet reduction caused by non-immune factors usually exhibit more stable platelet counts, with stable rates of peripheral platelet clearance and platelet production. The PVI can be calculated based solely on routine platelet count values, making it highly clinically applicable compared to other laboratory parameters. Moreover, the PVI is a dynamic metric that shows improved accuracy as more platelet count values accumulate over time. Therefore, incorporating PVI score along with other relevant laboratory parameters is clinically important when developing clinical prediction models (Li et al. 2021).

We selected 6 variables from the aforementioned 34 laboratory parameters, namely RBC, MCHC, RDW-SD, PVI score, IPF, lymphocyte absolute value. In other thrombocytopenic conditions, a decrease in platelet count is typically not isolated. For example, patients with myelodysplastic neoplasms, aplastic anemia, and liver diseases often exhibit concurrent anemia making the RBC and MCHC valuable discriminative indicators. The IPF can reflect the functionality of bone marrow megakaryocytes (Arshad et al. 2021). In patients with ITP, bone marrow proliferation is usually normal, while IPF levels are elevated. In some cases of ITP, the platelet can be severely reduced, but relying solely on the lowest platelet count is inadequate for diagnosis. The pathogenesis of ITP is complex and diverse and involves humoral immune disorders, cellular immune disorders, abnormal cytokine secretion, platelet apoptosis, and genetic and environmental factors (Miltiadous et al. 2020). Since the first description of regulatory T cell abnormalities in ITP was provided (Liu et al. 2007), a large body of literature has described new aspects of T cell, B cell, and dendritic cell biology in this disorder, and animal models are continuing to yield relevant results. However, to our surprise, lymphocyte absolute value was helpful in the diagnosis of ITP. We suspect that this difference partly reflects the number of T cells in the peripheral blood, but since the pathogenesis of ITP involves the interaction of multiple immune cells, our hypothesis needs to be verified in a larger cohort.

This study also has certain limitations. Firstly, glycoprotein-platelet antibody and detailed immunoglobulin profiles are potentially informative indicators for ITP, but because of the high proportion of missing values, we excluded these variables from our current model. Secondly, relying solely on a diagnostic model containing these 6 variables in clinical practice may overlook important clinical information compared to the laboratory diagnostic indicators of ITP mentioned in the existing scientific literature. Thirdly, while the study included a substantial number of patients, the relatively small size of the ITP group may affect the performance of the diagnostic model. In the next stage, additional specialized diagnostic tests will be incorporated, along with relevant medical history information such as transfusion history, megakaryocyte platelet generation capacity, and previous treatment history. Our model will be further optimized with larger and more balanced sample sizes.

In conclusion, we used the aforementioned 6 variables to construct a diagnostic model that had a strong ability to distinguish between patients with ITP and those with other thrombocytopenic conditions, with a sensitivity and specificity in the validation set of 0.90 and 0.87, respectively, suggesting that applying this model to the initial diagnosis of patients with thrombocytopenia can provide strong indications for suspected ITP, shorten the clinical diagnostic process, and facilitate prompt initiation of ITP-specific therapies.

Author contributions

All authors contributed to the study conception and design. Ziyang Yang and Kangying Zhong collected and analyzed the data, Kangying Zhong and Yuqing Pei wrote the main manuscript text and the manuscript was reviewed and edited by Qin Zheng. All authors read and approved the final manuscript.

Funding

This work was supported by the Science and Technology Projects of Sichuan Province of China (Grant Nos. 2023NSFSC1485 and 2023YFS0187) and the Health Care Committee Project of Sichuan Province (Grant No. 2024–114).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee.

Competing interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Kangying Zhong, Yuqing Pei and Ziyan Yang contributed equally to this work.
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