Construction of a prediction model for Non-tuberculous mycobacterial lung disease based on clinical characteristics and analysis of its application value

doi:10.21203/rs.3.rs-3927845/v1

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Construction of a prediction model for Non-tuberculous mycobacterial lung disease based on clinical characteristics and analysis of its application value

https://doi.org/10.21203/rs.3.rs-3927845/v1

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Objective

The objective of this study was to explore the differential clinical features between Nontuberculous mycobacterial pulmonary disease (NTM-PD) and pulmonary tuberculosis (PTB), and to develop a predictive model for the differential diagNosis of these two conditions. The study aimed to provide clinical guidance for the diagNosis and treatment of NTM-PD.

Methods

The study included 145 patients with NTM-PD and 206 patients with PTB, whose clinical characteristics,imaging findings,and inflammatory markers were compared.A binary logistic regression model was used to analyze the influencing factors and evaluate the predictive performance and calibration accuracy of the model.

Results

A comparative analysis of clinical, imaging, and inflammatory markers between NTM-PD and PTB groups revealed significant differences in demographics (age, gender, occupation, BMI), symptoms (dyspnea, loss of appetite, fever), risk factors (smoking, alcohol consumption history, diabetes), and comorbidities (bronchiectasis, emphysema, COPD, cystic-columnar, honeycomb, lung cavitation, MONo%; P < 0.05). Multivariate binary logistic regression identified gender and diabetes as protective, while bronchiectasis, COPD, and lung cavitation as risk factors. The model's predictive performance was strong with an AUC of 0.874 (95% CI 0.837 ~ 0.910; P < 0.001) and a Youden index of 0.611, yielding sensitivity of 83.4% and specificity of 77.7%. Model calibration was assessed by the Hosmer-Lemeshow test, showing no significant difference between predicted and observed values (χ²=7.895, P = 0.444 > 0.05).

Conclusion

In female patients without diabetes or underlying conditions such as bronchiectasis or COPD, when high-resolution computed tomography (HRCT) of the chest reveals predominantly cavitated lesions, it is imperative to give high priority to the differential diagnosis for possible NTM-PD, given its clinical resemblance to PTB. A meticulous distinction between these diagnoses is essential during the diagnostic process to prevent misdiagnosis.

Nontuberculous Mycobacterial Pulmonary Disease

Clinical Characteristics

Predictive Models

Non-tuberculous mycobacteria (NTM) encompass a diverse array of mycobacterial species that exclude those belonging to the Mycobacterium tuberculosis complex and Mycobacterium leprae, with over 190 unique NTM strains recognized thus far, which are further classified into at least 14 distinct subspecies[1, 2]. These organisms pervasively inhabit natural environments such as water bodies, soil, and dust, possessing the potential to infect humans and certain animals. NTM can infiltrate various tissues within the human body, including but not limited to the lungs, lymph nodes, skin, bones, and joints, with the capability to cause both localized and disseminated infections, predominantly affecting the pulmonary system [3]. Worldwide, there is an escalating trend in both the prevalence and incidence rates of NTM-Pulmonary Disease (NTM-PD) [4–7].Currently, the diagnosis of NTM-PD relies heavily on positive culture results complemented by pathological evidence or acid-fast bacilli smear findings. However, the diagnostic pathway presents numerous challenges in clinical practice. For instance, physicians may underestimate the likelihood of NTM-PD, sputum samples are susceptible to contamination during collection and processing, and the prolonged incubation period required for NTM cultures often complicates matters. These issues frequently culminate in misdiagnosis, where NTM-PD is mistaken for pulmonary tuberculosis or other respiratory diseases, thereby delaying accurate diagnosis and timely intervention. Notably, a significant number of cases have been incorrectly attributed to drug-resistant tuberculosis, leading to inappropriate treatments that can exacerbate conditions and, in some instances, result in fatalities[8].Thus, delving into the clinical manifestations, radiological features, and laboratory inflammation indices associated with NTM-PD holds substantial potential to enhance clinicians' ability to accurately identify and manage NTM-PD, thereby optimizing therapeutic strategies. This study retrospectively examines relevant factors and laboratory inflammatory markers among hospitalized patients diagnosed with NTM-PD compared to PTB, aiming to elucidate further the risk factors specifically linked to NTM-PD.

Object and methods

Study Subjects

Between January 2021 and December 2023, this study enrolled a total of 145 patients diagnosed with NTM-PD and 206 patients with PTB who were hospitalized at Fuyang Second People's Hospital. The research was conducted in strict accordance with the ethical principles laid out in the Helsinki Declaration and its subsequent amendments. It has been meticulously reviewed and received formal approval from the Institutional Ethics Committee of Fuyang Second People's Hospital(approval number: 20211231014).

Inclusion and exclusion criteria

Inclusion Criteria:(1)Patients who met the diagnostic criteria for NTM-PD as outlined in the "Guidelines for Diagnosis and Treatment of Nontuberculous Mycobacterial Diseases (2020 Edition)" [9];(2)Patients diagnosed with PTB according to the "WS288—2017 Diagnostic Criteria for Pulmonary Tuberculosis"[10];(3)Cases with a pathogenetically confirmed diagnosis, supported by complete medical record information;(4)Confirmed diagnoses of NTM-PD and PTB through culture and identification tests during hospitalization.Exclusion Criteria:(1)Patients presenting with co-infections of both NTM-PD and PTB;(2)Cases where clinical records were incomplete or insufficient for comprehensive analysis.

Patients data collection

Clinical data collected for hospitalized patients encompassed the following categories:(1)Demographic information (gender, age, body mass index-BMI, smoking history, alcohol consumption history);(2)Chronic disease background and concurrent comorbidities;(3)Clinical manifestations encompassing a range of symptoms and signs;(4)Radiological features including patchy infiltrates, nodular lesions, cystic or columnar changes, linear opacities, ground-glass opacities, honeycombing patterns, and flocculent abnormalities.Moreover, in addition to these clinical characteristics, laboratory inflammation markers were meticulously retrieved from the patients' medical records.

Case grouping

Patients were stratified into two distinct cohorts: the NTM-Pulmonary Disease (NTM-PD) group and the Pulmonary Tuberculosis (PTB) group.

Clinical laboratory data

The Hitachi 7600 Automated Biochemistry Analyzer was utilized for the quantitative assessment of C-reactive protein (CRP) and Serum Amyloid A (SAA) concentrations in blood samples, while the SYSMEX XE2100 Fully Automated Hematology Analyzer was employed to enumerate and quantify a variety of hematological parameters, which included: White Blood Cell Count (WBC), Neutrophil Percentage (NEUT%), Lymphocyte Percentage (LYMPH%), Monocyte Percentage (MONo%), Absolute Neutrophil Count (NEUT#), Absolute Lymphocyte Count (LYMPH#), Absolute Monocyte Count (MONo#), and Platelet Count (PLT).Utilizing these measurements, several ratios were computed, namely the SAA-to-CRP ratio (SAA/CRP), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Lymphocyte-to-Monocyte Ratio (LMR). All procedures were meticulously executed in strict adherence to the manufacturer's instructions detailed within the respective assay kit manuals.

Statistical analysis

Statistical analyses were performed using IBM SPSS Statistics version 26.0 software. Continuous variables underwent Normality testing utilizing the Kolmogorov-Smirnov (K-S) test. For those demonstrating a Normal distribution, comparisons between two independent samples were executed using an independent-samples t-test and are reported as mean ± standard deviation (mean ± SD). Conversely, non-Normally distributed continuous variables were subjected to the Mann-Whitney U test and are presented as [median(M) (interquartile range: P25, P75)].Categorical data are expressed in terms of frequency (n) and percentage (%) and were analyzed utilizing chi-square (χ²) tests. When the theoretical frequency (T) was less than 5 or over 20% of Ts were below 1, Fisher's exact test was employed instead.Multivariate binary logistic regression models were constructed to appraise multiple influencing factors. The calibration of these models was rigorously evaluated by means of the Hosmer-Lemeshow goodness-of-fit test. Statistical significance was established at a threshold of P < 0.05.

Comparison of Clinical Characteristics Between Two Groups of Patients

Age, sex, occupation, BMI, dyspnea, loss of appetite, fever, smoking history, alcohol consumption history, diabetes, bronchiectasis, emphysema, and COPD differences were found to be statistically significant (P < 0.05) between the two groups of patients. However, coughing, sputum production, hemoptysis, and hypertension did Not show statistical significance (P > 0.05) (Table 1).

Table 1

Comparison of Clinical Characteristics Between Two Groups of Patients
Characteristic	NTM-PD	PTB	Statistical value	P
Age[M(P25, P75)]	68(59,75)	59.5(44.75,71)	-4.425	< 0.001
Gender[n(%)]			4.478	0.034
Male	94(64.83)	155(75.24)
Female	51(35.17)	51(24.76)
Occupation[n(%)]			-^a	0.010
Peasants	128(88.28)	154(74.76)
Clerk/Office Worker	7(4.83)	18(8.74)
Retiree	5(3.45)	7(3.40)
Student	0(0.00)	7(3.40)
Freelancer	5(3.45)	20(9.71)
BMI(mean ± SD)	18.71 ± 2.70	20.41 ± 3.50	-4.913	< 0.001
Coughing[n(%)]	135(93.10)	181(87.86)	2.602	0.107
Sputum production[n(%)]	129(88.97)	169(82.04)	0.862	0.353
Hemoptysis[n(%)]	28(19.31)	31(15.05)	1.105	0.293
Dyspnea[n(%)]	92(63.45)	74(35.92)	25.867	< 0.001
Loss of appetite[n(%)]	77(53.10)	60(29.13)	20.559	< 0.001
Fever[n(%)]	75(51.72)	77(37.38)	7.133	0.008
Smoking history[n(%)]	52(35.86)	96(46.60)	4.025	0.045
Alcohol consumption history[n(%)]	35(24.14)	76(36.89)	6.403	0.011
Diabetes[n(%)]	12(8.28)	57(27.67)	20.267	< 0.001
Hypertension[n(%)]	17(11.72)	33(16.02)	1.285	0.257
Comorbidities[n(%)]
Bronchiectasis[n(%)]	42(28.97)	11(5.34)	37.053	< 0.001
Emphysema[n(%)]	18(12.41)	11(5.34)	5.618	0.018
COPD[n(%)]	43(29.66)	13(6.31)	34.486	< 0.001
Note: a: Fisher's exact test was employed.

Comparison of Imaging Features and Inflammatory Biomarkers Between the Two Groups of Patients

Cystic-columnar, honeycomb, and lung cavitation, as well as MONo%, showed statistically significant differences (P < 0.05) between the two groups of patients. The remaining imaging characteristics and inflammatory markers did Not exhibit statistical significance (P > 0.05) (Table 2).

Table 2

Comparison of Imaging Features and Inflammatory Biomarkers Between the Two Groups of Patients
Imaging Features and Inflammatory Markers	NTM-PD	PTB	Statistical value	P
Patchy[n(%)]	126(86.90)	189(91.75)	2.176	0.140
Nodular[n(%)]	68(46.90)	112(54.37)	1.902	0.168
Cystic-columnar[n(%)]	17(11.72)	6(2.91)	10.791	0.001
Linear[n(%)]	65(44.83)	72(34.95)	3.488	0.062
Ground-glass[n(%)]	5(3.45)	3(1.46)	-^a	0.283
Honeycomb[n(%)]	9(6.21)	2(0.97)	-^a	0.009
Fibrotic[n(%)]	2(1.38)	0(0.00)	-^a	0.170
Lung cavitation[n(%)]	107(73.79)	97(47.09)	24.935	< 0.001
CRP[M(P25, P75)]	35.50(8.75,78.90)	23.55(5.35,57.75)	-1.800	0.072
SAA[M(P25, P75)]	64.50(23.35,149.10)	50.90(14.38,109.98)	-1.590	0.112
WBC[M(P25, P75)]	6.90(5.56,9.28)	6.77(5.12,8.60)	-1.386	0.166
NEUT%(mean ± SD)	67.70 ± 11.77	65.72 ± 12.23	1.519	0.130
LYMPH%[M(P25, P75)]	19.00(12.05,27.30)	20.30(14.08,27.53)	-0.743	0.457
MONo%[M(P25, P75)]	8.90(7.20,11.80)	10.05(8.10,12.23)	-2.701	0.007
NEUT#[M(P25, P75)]	4.68(3.33,6.77)	4.36(3.08,6.20)	-1.566	0.117
LYMPH#[M(P25, P75)]	1.32(0.92,1.79)	1.30(0.94,1.74)	-0.225	0.822
MONo#[M(P25, P75)]	0.64(0.48,0.84)	0.71(0.51,0.88)	-1.339	0.180
PLT(mean ± SD)	276.99 ± 106.10	282.58 ± 110.16	-0.475	0.635
SAA/CRP[M(P25, P75)]	1.89(0.94,4.32)	2.26(0.99,4.40)	-0.766	0.444
NLR[M(P25, P75)]	3.70(2.10,6.23)	3.30(2.07,5.42)	-0.935	0.350
PLR[M(P25, P75)]	203.28(136.60,295.80)	198.26(139.50,315.82)	-0.609	0.543
LMR[M(P25, P75)]	2.17(1.36,2.91)	1.96(1.23,2.79)	-1.293	0.196
Note: a: Fisher's exact test was employed.

Multivariate Binary Logistic Regression Analysis

For the purpose of multivariate binary logistic regression analysis, variables such as gender, dyspnea, Loss of appetite, fever, smoking history, alcohol consumption history, diabetes, bronchiectasis, emphysema, COPD, cystic-columnar, honeycomb, and lung cavitation were dichotomously categorized (Table 3).In the multivariate binary logistic regression analysis, NTM-PD was set as the dependent variable, while age, gender, occupation, BMI, dyspnea, Loss of appetite, fever, smoking history, alcohol consumption history, diabetes, bronchiectasis, emphysema, COPD, cystic-columnar, honeycomb, lung cavitation, and MONo% were considered as covariates. The results showed that gender, diabetes, bronchiectasis, COPD, and lung cavitation had statistically significant associations (P < 0.05) with the presence of NTM-PD. The remaining factors did Not demonstrate statistical significance (P > 0.05) (Table 4).

Table 3

Variable Coding Table
Independent variable	Assign	Independent variable	Assign
Gender	female = 0;male = 1	Bronchiectasis	No = 0;Yes = 1
Gyspnea	No = 0;Yes = 1	Emphysema	No = 0;Yes = 1
Loss of appetite	No = 0;Yes = 1	COPD	No = 0;Yes = 1
Fever	No = 0;Yes = 1	Cystic-columnar	No = 0;Yes = 1
Smoking history	No = 0;Yes = 1	Honeycomb	No = 0;Yes = 1
Alcohol consumption history	No = 0;Yes = 1	Lung cavitation	No = 0;Yes = 1
Diabetes	No = 0;Yes = 1

Table 4

Multivariate Binary Logistic Regression Analysis
Variable	β	SE	Wald χ²	P	OR	95%CI
Variable	β	SE	Wald χ²	P	OR	lower limit	upper limit
Age	0.019	0.010	3.661	0.056	1.019	1.000	1.040
Gender	-0.833	0.408	4.172	0.041	0.435	0.196	0.967
Occupation	-0.178	0.159	1.241	0.265	0.837	0.613	1.144
BMI	-0.073	0.047	2.435	0.119	0.929	0.847	1.019
Dyspnea	0.491	0.298	2.712	0.100	1.633	0.911	2.928
Loss of appetite	0.153	0.310	0.243	0.622	1.165	0.635	2.137
Fever	0.275	0.290	0.897	0.344	1.316	0.745	2.325
Smoking history	0.166	0.445	0.139	0.710	1.180	0.493	2.825
Alcohol consumption history	-0.592	0.438	1.824	0.177	0.553	0.235	1.306
Diabetes	-1.698	0.440	14.88	< 0.001	0.183	0.077	0.434
Bronchiectasis	1.556	0.467	11.095	0.001	4.740	1.897	11.843
Emphysema	0.769	0.533	2.084	0.149	2.157	0.760	6.128
COPD	1.211	0.432	7.873	0.005	3.358	1.441	7.825
Cystic-columnar	0.638	0.602	1.120	0.290	1.892	0.581	6.163
Honeycomb	1.478	0.932	2.516	0.113	4.385	0.706	27.235
Lung cavitation	1.767	0.317	31.039	< 0.001	5.853	3.143	10.897
MONO%	-0.069	0.039	3.104	0.078	0.933	0.865	1.008

The predictive validity and calibration of the logistic regression model were assessed.

The multi-factor binary logistic regression analysis model was subjected to ROC curve analysis, which revealed an AUC of 0.874 (P < 0.001, 95%CI:0.837–0.910). At a Youden Index of 0.611, the sensitivity was 83.4%, and specificity was 77.7% (Fig. 1). The calibration of the model was evaluated using the Hosmer-Lemeshow (HL) goodness-of-fit test, resulting in a χ² statistic of 7.895, with P = 0.444 > 0.05, indicating adequate model calibration (Fig. 2).

China, ranking third globally in tuberculosis incidence[11],is a nation grappling with diagnostic challenges due to the striking clinical and radiological resemblance between NTM-PD and PTB. Symptoms such as coughing, expectoration, fever, night sweats, and hemoptysis are common to both conditions, while chest computed tomography (CT) imaging can reveal similar features like patchy infiltrates, nodular lesions, or calcifications. This high degree of overlap complicates accurate diagnosis.Moreover, disparities in medical resources and technical capacities across grassroots healthcare facilities in China exacerbate this issue. Many primary care institutions struggle with the implementation of NTM culture or polymerase chain reaction (PCR) testing, which are crucial for confirming NTM-PD. Consequently, a subset of patients with genuine NTM-PD often receive misdiagnoses as having pulmonary tuberculosis within clinical settings. This diagnostic dilemma highlights the pressing need to enhance diagnostic tools and standardize procedures across various tiers of healthcare institutions in China.

The current study demonstrates that significant statistical differences were observed in age, gender, occupation, body mass index (BMI), dyspnea, loss of appetite, fever, smoking history, alcohol consumption history, diabetes, bronchiectasis, emphysema, and COPD when comparing the two patient cohorts. The rationale for these disparities can be elucidated as follows:NTM is an opportunistic pathogen with the potential to become pathogenic under favorable conditions. In elderly individuals, physiological aging and comorbidities diminish their capacity to combat diseases, significantly elevating their susceptibility to NTM lung disease, a notion supported by Yang XY[12] and other researchers;In contrast to PTB patients, there was a higher proportion of females in the NTM-PD group. The potentially weaker constitution of women might contribute to a lower resistance against infections, serving as a potential risk factor for NTM lung disease development. This observation warrants further investigation, aligning with studies conducted by Zheng LY[13] and Chen XH [14];Occupational groups such as farmers, frequently exposed to soil and water through their work environment, may be at heightened risk of NTM infection due to increased environmental contact.Abnormal BMI, whether low or high, can impair immune system function. A high BMI could be associated with COPD and diabetes, which may influence the onset of either NTM or TB infections.Clinical symptoms like dyspnea, loss of appetite, and fever may reflect varying stages of disease progression and pathophysiological mechanisms. In NTM-PD patients, unique symptom characteristics might arise due to the complexity of the condition and coexisting morbidities, although some studies[15] suggest non-typical differences between NTM lung disease and tuberculosis symptoms, the absence of distinct clinical presentations complicates definitive differentiation between the two diseases.Smoking and alcohol consumption both damage the respiratory mucosal barrier and weaken the immune system, thereby increasing the risk of NTM and TB infections. They may also affect disease severity and treatment outcomes. Among NTM-PD patients in this study, 43 cases (29.66%) had COPD, 42 cases (28.97%) had bronchiectasis, 18 cases (12.41%) had emphysema, 17 cases (11.72%) had hypertension, and 12 cases (8.28%) had diabetes. Many of these patients had a history of long-term inhaled corticosteroid treatment and maintenance therapy using oral small doses of corticosteroids, coupled with suboptimal blood glucose control. These conditions led to increased energy expenditure, gradual physical decline, and a significant decrease in immunity, rendering them more vulnerable to NTM-PD. These findings provide a basis for inherent susceptibility to NTM-PD, consistent with reports from several domestic and international scholars [16–20].

The present study conducted a comparative radiological analysis of the two patient cohorts, revealing significant disparities in imaging characteristics pertaining to cystic-columnar, honeycomb, and lung cavity formations. It is posited that cystic-columnar and honeycomb changes may be more prevalent in NTM-PD patients, potentially associated with chronic inflammatory reactions and localized tissue destruction resulting from NTM infection, thereby giving rise to relatively unique patterns of structural reorganization. In contrast, tuberculosis typically manifests distinct imaging attributes such as tree-in-bud sign and nodular or flocculent infiltrates[21]. The mechanisms underlying cavity formation and appearance vary according to etiology; for example, cavities arising from NTM lung disease often exhibit multiple, irregular, or multi-walled characteristics, whereas those due to tuberculosis are more frequently accompanied by satellite lesions or possess well-defined margins.Despite the non-significant differences observed in several inflammation markers between the two groups in this investigation, and an isolated alteration in MONO% not demonstrating clear clinical relevance, both CRP and SAA levels were significantly elevated above normal thresholds in both populations, indicative of a heightened inflammatory state. However, these biomarkers failed to differentiate directly between the two diseases, which concurs with findings reported by Getahun H[22], Zhang Yang[23], and aligns with the results presented by He HQ [24]. This highlights the variability within the research literature regarding inflammatory markers and underscores the necessity for further exploration with larger sample sizes to substantiate these observations.

This study successfully developed a logistic regression model for NTM-PD. Employing multivariate regression analysis, the influence of various independent variables—age, gender, occupation, body mass index (BMI), dyspnea, loss of appetite, fever, smoking history, alcohol consumption history, diabetes, bronchiectasis, emphysema, COPD, cystic-columnar changes, honeycomb patterns, lung cavitation, and MONO%—on the dependent variable (NTM-PD) was thoroughly investigated. The derived model revealed that being female and having diabetes were protective factors against NTM-PD, whereas bronchiectasis, COPD, and lung cavitation emerged as significant risk factors.The receiver operating characteristic (ROC) curve analysis demonstrated an area under the curve (AUC) value of 0.874 for discriminating NTM-PD, indicating that this model possesses a high predictive accuracy. Moreover, the absence of substantial divergence between the predicted and observed prevalence rates suggests that the model exhibits good calibration properties, thereby enabling more objective and precise differentiation between NTM-PD and PTB. This underscores the potential clinical utility of the model in enhancing the diagnostic process for NTM-PD.

In summary, the logistic regression model constructed with five pivotal factors—female gender, absence of diabetes, and the presence of bronchiectasis, COPD, and lung cavitation—offers valuable clinical insights for differentiating NTM-PD from PTB. This model contributes to a more accurate diagnosis and highlights the necessity of considering these parameters when discerning between the two diseases in a clinical context.However, this study is not without limitations. The sample size employed was relatively modest, and there exists potential for enhancing the precision of the research methodologies used. Moreover, the findings might be influenced by other pertinent variables that were not fully considered, which could exhibit temporal fluctuations and uncertainty as time and environmental conditions evolve. To bolster the credibility of the conclusions, it is imperative to expand the sample size, employ more meticulous and comprehensive data collection strategies, and conduct additional longitudinal studies to address the current inadequacies. These measures would serve to strengthen the validity and generalizability of the results.

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Second People's Hospital of Fuyang City (approval number: 20211231014). Due to the retrospective nature of the study, the need for informed consent was waived and waiver was approved by the Ethics Committee of the Second People's Hospital of Fuyang City.

Consent for publication

Not applicable.

Availability of data and materials

Data are provided in the supplementary information document.

Competing interests

The authors declare no competing interests.

Funding

This work was supported by Scientific Research Project of Fuyang Municipal Health Commission (FY2021-052).

Author contributions

Conception and design of the research: LHQ,HMF.Acquisition of data:CGL.Analysis and interpretation of the data: LHQ,YH,Statistical analysis: LHQ, ZW,Obtaining financing: HMF,CXY,Writing of the manuscript: LHQ,HJ, Critical revision of the manuscript for intellectual content: ZW, LHQ,All authors read and approved the final draft.

Authors' information

¹The Second People's Hospital of Fuyang City,Fuyang Infection Disease Clinical College of Anhui Medical university.

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Construction of a prediction model for Non-tuberculous mycobacterial lung disease based on clinical characteristics and analysis of its application value

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Introduction

Object and methods

Study Subjects

Inclusion and exclusion criteria

Methods

Patients data collection

Case grouping

Clinical laboratory data

Statistical analysis

Results

Comparison of Clinical Characteristics Between Two Groups of Patients

Comparison of Imaging Features and Inflammatory Biomarkers Between the Two Groups of Patients

Multivariate Binary Logistic Regression Analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1