Risk factors analysis of lung cancer combined with venous thromboembolism and the construction of a clinic prediction model

doi:10.21203/rs.3.rs-4829684/v1

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Research Article

Risk factors analysis of lung cancer combined with venous thromboembolism and the construction of a clinic prediction model

https://doi.org/10.21203/rs.3.rs-4829684/v1

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Objective

The purpose of this study is to explore the disparities among lung cancer patients who also experience venous thromboembolism (VTE) and to establish a predictive model utilizing patients' clinical data and standard laboratory indicators for accurate assessment of VTE risk.

Method

A retrospective analysis was conducted on the clinical records of lung cancer patients at the First Affiliated Hospital of Ningbo University from January 2018 to December 2023. The patients were randomly divided into a training set and a validation set in a 7:3 ratio. 27 clinical parameters were chosen. Independent risk factors were selected by lasso regression and multivariate logistic regression. A nomogram was constructed for all variables showing significance at p < 0.05 in the multiple variable logistic analysis, and it was internally validated.

Result

A total of 300 lung cancer cases were examined, with 64 cases of VTE and 236 cases without VTE. Following screening by Lasso regression and multiple logistic regression, 6 variables were determined to be significant for the final model, including metastasis, surgery, chemotherapy, targeted therapy, hemoglobin (HB), and platelet count (PLT). The modeling cohort (AUC 0.804) and the validation cohort (AUC 0.799) indicated strong discrimination. Calibration curve and decision curve analysis (DCA) demonstrated favorable consistency and clinical usefulness of the model.

Conclusion

The developed nomogram in this study has the capacity to predict the likelihood of VTE events in lung cancer patients to a certain degree, thus assisting healthcare providers in formulating appropriate prevention and treatment strategies.

pulmonary malignancy

venous thromboembolism

risk factors

prediction model

Lung cancer stands out as one of the most prevalent malignant tumors in clinical settings, claiming the top spot as the primary cause of cancer-related fatalities^{[1, 2]}. Recent advances in the field have brought forth a plethora of comprehensive treatment options for lung cancer, resulting in an increase in the survival rates of patients^[3]. However, this progress has also led to a surge in the incidence of complications. Venous thromboembolism (VTE), a common complication associated with malignant tumors, particularly lung cancer^[4], encompasses deep vein thrombosis (DVT) and pulmonary embolism (PE). The VTE risk was amplified 5–6 times in active cancer patients compared to non-cancer patients^[5], with PE standing as the second leading cause of death following lung cancer itself^[6]. The grim reality is that cancer patients with VTE face a bleaker outlook than their counterparts without such complications, further complicating the treatment and prognosis of lung cancer patients and escalating both medical costs and the financial burden on patients^[7].

In light of the distinctive needs of various patient cohorts, multiple VTE risk assessment models have been formulated and integrated into clinical practice to enable early detection, identification, and intervention for at-risk populations^[8–10]. Noteworthy examples include the acclaimed Khorana score tailored for patients undergoing chemotherapy, the Caprini model designed for assessing VTE risk in surgical patients, and the Padua risk model utilized for predicting VTE risk in medical inpatients. Acknowledging the inadequacy of existing tools for lung cancer patients, a dedicated team led by Qin et al.^[11] is honing in on crafting a specialized cancer-related VTE risk assessment tool customized for the Chinese populace. To address this pressing need, the development of a VTE prediction model targeted specifically at lung cancer patients is crucial for informing strategies aimed at VTE prevention and treatment in this vulnerable population.

A nomogram is a visually intuitive tool that translates complex statistical models into easily comprehensible graphical representations^[12]. Extensive research underscores the efficacy of nomograms in accurately gauging VTE risk across various diseases^[13–18]. Focusing on Chinese lung cancer patients, this study strives to design a nomogram model for this population and evaluate its predictive performance.

Patient

From January 2018 to December 2023, participants in this study were sourced from the First Affiliated Hospital of Ningbo University, Zhejiang, China. The inclusion criteria for patients were: (a) with pathologically or histologically confirmed lung cancer; (b) a minimum hospitalization period of 48 hours; (c) age ≥ 18 years. The exclusive criteria in this study were as follows: (a) VTE occurred before lung cancer diagnosis or combined with other causes(e.g. trauma within 1 month); (b) patients with a combination of other primary tumors or hematological disorders; (c) incomplete medical records. It is noteworthy that in the analysis, patients with multiple hospitalizations were counted only once.

Nomogram Development and Internal Validation

Patients were screened based on the criteria and randomly divided into a training group and a validation group in a 7:3 ratio. A total of 27 predictive variables were collected, encompassing patient demographics, tumor characteristics, treatment details, and laboratory findings. The mean (SD) of variables with normally distributed data is shown; the median (interquartile range) of variables with non-normal distributions is shown. Categorical data are shown as percentages and frequencies.

The research utilized the Lasso regression method on the training set to identify relevant factors with non-zero regression coefficients, followed by multiple logistic regression analysis to establish the final predictive model. Model efficacy was tested by model discrimination, calibration, and clinical applicability. The receiver operating characteristic (ROC) curve was drawn to assess the ability of the model to correctly distinguish between the occurrence of VTE based on the area under the curve (AUC). The Hosmer-Lemeshow test was performed and calibration curves generated using 1000 times Bootstrapping were used to assess whether the true probability fits the predicted probability. Clinical decision curve (DCA) and and clinical impact curves (CIC) were drawn to assess the clinical applicability against the net benefit of the model. The R programming language (version 4.4.0) was used for statistical analyses, with a significance level of 0.05.

Patient Characteristics

A total of 300 patients were ultimately included in the study, with 64 patients diagnosed with VTE, comprising 21.33% of the overall study population. The baseline characteristics table (Table 1) highlights important findings related to outcomes, gender, age, BMI, comorbidities, treatment modalities, and laboratory parameters among the study participants. Noteworthy observations include that there were no significant differences in the distribution of VTE incidence, gender and treatment modalities across the two groups. The prevalence of hypertension varied between the groups (83.8% in the Training group, 71.1% in the Validation group, p = 0.012), while other comorbidities such as diabetes, smoking, and metastasis did not show significant variations. There is a difference in HB levels between the two groups in the laboratory parameters (p = 0.029).

Table 1

Characteristics of patients' demographics and clinical conditions.
Characteristic	Overall, N = 300	group		p-value
Characteristic	Overall, N = 300	Training, N = 210	Validation, N = 90	p-value
Outcome				0.389
Non-VTE	236 (78.7%)	168 (80.0%)	68 (75.6%)
VTE	64 (21.3%)	42 (20.0%)	22 (24.4%)
Gender				0.469
Male	184 (61.3%)	126 (60.0%)	58 (64.4%)
Female	116 (38.7%)	84 (40.0%)	32 (35.6%)
Age(year)	60 (53, 70)	60 (53, 70)	60 (50, 70)	0.539
BMI(kg/m²)	22.9 (20.4, 25.6)	23.0 (20.5, 25.6)	22.7 (20.2, 25.3)	0.562
CHD				0.301
NO	283 (94.3%)	200 (95.2%)	83 (92.2%)
YES	17 (5.7%)	10 (4.8%)	7 (7.8%)
Hypertension				0.012
NO	240 (80.0%)	176 (83.8%)	64 (71.1%)
YES	60 (20.0%)	34 (16.2%)	26 (28.9%)
Diabetes				0.793
NO	261 (87.0%)	182 (86.7%)	79 (87.8%)
YES	39 (13.0%)	28 (13.3%)	11 (12.2%)
Smoking				0.625
NO	206 (68.7%)	146 (69.5%)	60 (66.7%)
YES	94 (31.3%)	64 (30.5%)	30 (33.3%)
Subtype				0.743
Adenocarcinoma	139 (46.3%)	96 (45.7%)	43 (47.8%)
Others	161 (53.7%)	114 (54.3%)	47 (52.2%)
Stage				0.776
I-II	80 (26.7%)	55 (26.2%)	25 (27.8%)
III-IV	220 (73.3%)	155 (73.8%)	65 (72.2%)
Metastasis				0.720
NO	274 (91.3%)	191 (91.0%)	83 (92.2%)
YES	26 (8.7%)	19 (9.0%)	7 (7.8%)
Pleural effussion				0.319
NO	256 (85.3%)	182 (86.7%)	74 (82.2%)
YES	44 (14.7%)	28 (13.3%)	16 (17.8%)
Pulmonary infection				0.398
NO	207 (69.0%)	148 (70.5%)	59 (65.6%)
YES	93 (31.0%)	62 (29.5%)	31 (34.4%)
Surgery				0.631
NO	163 (54.3%)	116 (55.2%)	47 (52.2%)
YES	137 (45.7%)	94 (44.8%)	43 (47.8%)
Chemotherapy				0.656
NO	191 (63.7%)	132 (62.9%)	59 (65.6%)
YES	109 (36.3%)	78 (37.1%)	31 (34.4%)
Radiotherapy				0.439
NO	278 (92.7%)	193 (91.9%)	85 (94.4%)
YES	22 (7.3%)	17 (8.1%)	5 (5.6%)
Immunotherapy				0.520
NO	226 (75.3%)	156 (74.3%)	70 (77.8%)
YES	74 (24.7%)	54 (25.7%)	20 (22.2%)
Targeted therapy				0.703
NO	253 (84.3%)	176 (83.8%)	77 (85.6%)
YES	47 (15.7%)	34 (16.2%)	13 (14.4%)
CVC				> 0.999
NO	286 (95.3%)	200 (95.2%)	86 (95.6%)
YES	14 (4.7%)	10 (4.8%)	4 (4.4%)
CRP(mg/L)	0 (0, 10)	0 (0, 10)	0 (0, 8)	0.777
*WBC (10⁹/L)**	5.90 (5.40, 8.21)	5.90 (5.27, 8.20)	5.87 (5.55, 8.43)	0.658
HB(g/L)	135 (127, 147)	133 (126, 146)	137 (129, 151)	0.029
*RBC (10¹²/L)**	4.41 (4.20, 4.76)	4.40 (4.20, 4.70)	4.51 (4.25, 4.89)	0.090
*PLT (10⁹/L)**	216 (181, 254)	216 (179, 254)	218 (181, 262)	0.794
TG (mmol/L)	1.05 (0.78, 1.60)	1.04 (0.78, 1.55)	1.07 (0.77, 1.70)	0.631
HDL-C (mmol/L)	1.20 (1.03, 1.38)	1.20 (0.99, 1.38)	1.20 (1.04, 1.37)	0.794
LDL-C (mmol/L)	2.90 (2.33, 3.28)	2.89 (2.29, 3.27)	2.92 (2.50, 3.30)	0.361
D-Dimer(ng/mL)	116 (79, 211)	119 (79, 228)	107 (78, 190)	0.641
Data was shown as median (IQR) or number (%). Abbreviations: VTE venous thromboembolism; CHD coronary heart disease; CVC central venous catheter; CRP C-reactive protein; WBC white blood cell; HB Hemoglobin; RBC red blood cell; PLT blood platelet; TG triglyceride; HDL-C high density lipoprotein cholesterol; LDL-C low density lipoprotein cholesterol.

Table 1 Characteristics of patients' demographics and clinical conditions.

Significant predictive factors in the training set

A Lasso regression analysis was conducted based on selected predictive factors. A vertical line was drawn at the minimum value of λ, resulting in the selection of 11 non-zero coefficient predictive variables (Fig. 1). These variables included subtype, stage, metastasis, surgery, chemotherapy, targeted therapy, age, hypertension, diabetes, hemoglobin (HB) and platelet count (PLT). Based on further multivariate logistic analysis results, distant metastasis (OR 4.82), surgery (OR 3.48), chemotherapy (OR 8.46), targeted therapy (OR 3.72), HB (OR 1.03), and PLT (OR 1.01) were selected as predictive variables included in the ultimate model (Table 2).

Figure 1 LASSO regression cross-validation for forecasting VTE.

Table 2

Key determinants of VTE risk.
Predictor	β	SE	p-value	OR
Intercept	-10.22852	2.77811	< 0.001	0.000
Age	-0.02801	0.01738	0.107	0.972
Hypertension
YES – NO	0.82891	0.56107	0.140	2.291
Diabetes
YES – NO	-1.30114	0.69998	0.063	0.272
Subtype
Adenocarcinoma – Others	0.80223	0.44873	0.074	2.231
Stage
III-IV – I-II	1.13297	0.58209	0.052	3.105
Metastasis
YES – NO	1.57269	0.67148	0.019	4.820
Surgery
YES – NO	1.24719	0.46163	0.007	3.481
Chemotherapy
YES – NO	2.13492	0.48216	< 0.001	8.456
Targeted therapy
YES – NO	1.31455	0.54598	0.016	3.723
HB	0.03500	0.01526	0.022	1.036
PLT	0.01007	0.00366	0.006	1.010

Development and calibration of the nomogram model

After identifying the key predictors through multivariate logistic regression analysis, a nomogram was produced (Fig. 2). Each variable was assigned a specific score, and the total score was calculated by summing the scores of all factors. This cumulative score was then utilized to estimate the probability of VTE occurrence.

Figure 2 The nomogram for predicting VTE risk of lung cancer patients.

The AUC values for the development and validation groups were 0.804 (95% CI: 0.737–0.870) and 0.799 (95% CI: 0.699–0.899) respectively, indicating the model's strong predictive performance (Fig. 3). Internal validation was conducted on the development cohort using 1000 bootstrap repetitions, resulting in a corrected C-index of 0.772, showcasing the model's reasonable predictive accuracy. Furthermore, the calibration curves for the training (Fig. 4A) and validation (Fig. 4B) cohorts showed good agreement between the predictions and the observations.

The DCA results revealed that the model's DCA curve did not intersect with extreme curves when the threshold probability ranged from 0.1 to 0.7, indicating its sound clinical efficacy (Fig. 5A). Clinical impact curves (CIC) aided in evaluating the level of clinical benefit. There are two types of curves in CIC: a red curve that shows how many individuals are classified as being high risk at each level of probability, and a blue curve that shows how likely individuals are to experience VTE at each level. The two curves closely converged at a predicted probability of 0.4, and overlapped at a predicted probability of 0.6, underscoring the model's predictive precision (Fig. 5B).

Figure 3 ROC curve for the training dataset (A) and validation dataset (B).

Figure 4 Calibration plot of the model in the training cohort(A) and validation (B) cohort.

Figure 5A: Decision curve analysis (DCA) of the model. B: Clinical impact curve (CIC) of the model.

The incidence rate of VTE in patients with lung cancer is to be 3.92% per year, with rates varying between 2% and 15%^[19]. Risk assessment, prevention, and treatment of VTE play a critical role in effectively managing VTE in lung cancer patients. Although risk assessment tools like Caprini, Padua, and Khorana scores have been commonly used in clinical settings for risk assessment, their effectiveness in assessing VTE risk in lung cancer patients remains controversial^[10]. Furthermore, previous models often faced limitations due to the dichotomization of predictor variables, which could result in decreased predictive accuracy when continuous variables were categorized into multiple risk groups. In contrast, nomograms serve as statistical models that can generate individual probabilities of clinical events by incorporating various prognostic and determinant variables, making them valuable for personalized treatment^[20]. In this investigation, we developed a nomogram predictive model for assessing VTE risk, incorporating variables such as distant metastasis, surgery, chemotherapy, targeted therapy, HB and PLT.

Our research revealed a notable link between distant metastasis and an elevated risk of VTE in lung cancer patients (OR 4.82). This finding is consistent with previous conclusions drawn from relevant clinical trial articles on the association between distant metastasis and VTE events in patients with different types of cancer, both domestically and internationally^[21–26]. A study utilizing the Khorana and COMPASS-CAT RAM tools to examine VTE prevalence in patients with non-small cell lung cancer (NSCLC) observed a significantly higher incidence of VTE in metastatic (M1) patients compared to those with early-stage disease (M0)^[27]. The mechanism of this relationship is thought to be related to the hypercoagulability promoted by tumor factors and the prothrombotic state induced by endothelial dysfunction^[6].

VTE is one of the serious complications following surgery in the field of surgery. Numerous studies have confirmed a close relationship between surgical treatment methods and the development of VTE^[28–31]. Recent data consistently indicate that patients undergoing bilobectomy or pneumonectomy, along with prolonged surgical duration, face an increased risk of VTE^{[29, 32]}. Moreover, Thomas et al.^[33] found that patients undergoing thoracotomy have a higher incidence of VTE compared to those undergoing video-assisted thoracoscopic surgery (0. 8% vs. 0. 6%, P<0. 01). It is also noteworthy that although the early postoperative hospitalization phase is considered a critical period for the development of VTE, this risk persists even after discharge^[34–36].

Chemotherapy remains the cornerstone of lung cancer treatment. This study suggests that there is a close correlation between chemotherapy and VTE (OR 8.46). Previous research has also established chemotherapy as an independent risk factor for VTE events with patients in lung cancer^[37–40]. Among lung cancer patients undergoing chemotherapy, the incidence rate of VTE after 14 months from the initiation of chemotherapy is 1.35%^[41]. VTE risk with cytotoxic drugs (including drugs with the same mechanism of action) is not consistent^[42]. Especially platinum chemotherapy, may increase the incidence of VTE^{[43, 44]}. Further subgroup analysis indicated that the increased risk of VTE was associated with elevated cisplatin administration^[45]. The mechanism by which chemotherapy-induced thrombosis may be related to direct endothelial damage and enhanced procoagulant activity^[39], while the exact mechanism by which carboplatin and cisplatin lead to thrombosis has not been determined^[46].

In our risk prediction model, targeted therapy has been shown to be a strong predictive factor for the development of VTE (OR 3.72). Epidermal growth factor receptor (EGFR) mutation is the most common and well-studied genetic mutation. A meta-analysis of 17 Phase II/III randomized controlled trials on cetuximab (EGFR targeting monoclonal antibody) shows a 1.5-fold increased risk of VTE in tumor patients receiving cetuximab-containing regimens compared to those receiving the same regimen without cetuximab^[47]. This conclusion is also supported by another study^[48]. It follows that anti-EGFR drugs, especially cetuximab, are associated with an increased risk of VTE.

Additionally, this study includes a range of non-invasive and easily accessible indicators for analysis. There are significant independent risks associated with both HB levels and PLT counts for VTE among lung cancer patients, with odds ratios of 1.03 and 1.01, respectively.

Several limitations of this research must be recognized. Firstly, all cases registered in this study were from a single center, lacking external data from different centers for validation, inevitably introducing bias and weakening statistical power. Secondly, the study's retrospective design inherently brings significant limitations. Thirdly, due to the limited number of cases, data may be flawed, and the nomogram's clinical effectiveness in practical application may not be entirely accurate. Therefore, to address these crucial issues, there is a pressing need for a well-designed prospective, multi-center study involving a substantial sample size in the future.

VTE is a common complication in patients with lung cancer. This study combines demographic and clinical characteristics to develop a novel nomogram model based on six predictive factors to assess the risk of VTE in lung cancer patients. Internal validation demonstrated its strong predictive performance, providing guidance for clinical thrombus assessment and prevention.

CONFLICT OF INTEREST STATEMENT

The authors report no conflict of interest.

ETHICS STATEMENT

The research adhered to the ethical principles outlined in the Declaration of Helsinki for medical research involving human subjects and received approval from the Ethics Committee of the First Affiliated Hospital of Ningbo University (No.2024138R-01). Informed consent was not required for this retrospective study.

FUNDING INFORMATION

This work was supported by Zhejiang Provincial Medical and Health Science and Technology Plan (Grant No. 2024KY1537) and the fourth Ningbo health youth technical backbone personnel training program.

Author Contribution

Study design: Shiyu Chen and Zhuanbo Luo; Data collection: Shiyu Chen and Zhuanbo Luo; Data analysis: Shiyu Chen and Zhuanbo Luo; Writing: Shiyu Chen.

DATA AVAILABILITY STATEMENT

The datasets utilized in this study can be requested from the corresponding author.

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Risk factors analysis of lung cancer combined with venous thromboembolism and the construction of a clinic prediction model

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Nomogram Development and Internal Validation

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Significant predictive factors in the training set

Development and calibration of the nomogram model

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