Development and validation of a risk prediction model for PICC-related venous thrombosis in patients with cancer: A prospective cohort study

doi:10.21203/rs.3.rs-5009878/v1

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Development and validation of a risk prediction model for PICC-related venous thrombosis in patients with cancer: A prospective cohort study

https://doi.org/10.21203/rs.3.rs-5009878/v1

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Objective: To develop and validate a risk prediction model for predicting the risk of Peripherally Inserted Central Catheter-Related venous thrombosis (PICC-RVT) in cancer patients with PICCs.

Method: A prospective cohort study of 281 cancer patients with PICCs was conducted from April 2023 to January 2024. Data on patient-, laboratory- and catheter-related risk factors were collected on the day of catheterization. Patients were investigated for PICC-RVT by Doppler sonography in the presence of PICC-RVT signs and symptoms. Univariate and multivariate regression analyses were used to identify independently associated risk factors of PICC-RVT and develop a risk prediction model.

Results: 275 patients were finally included for data analysis, and 18 (6.5%) developed PICC-RVT. Four risk factors were identified as key predictors of PICC-RVT, including “diabetes requiring insulin (OR:8.016; 95%CI:1.157-55.536), major surgery (within 1 month and operation time >45 minutes) (OR:0.023; 95%CI:1.296-30.77), reduced limb activities of the PICC arm (OR:6.687; 95%CI:2.024-22.09)” and “catheter material (OR:3.319; 95%CI:0.940-11.723)”. The nomogram model was developed and internally validated with an area under the receiver operating characteristics curve (AUC) of 0.796 (95%CI:0.707-0.885). The Hosmer–Lemeshow goodness-of-ft was 1.685 (p=0.194).

Conclusion: The nomogram prediction model had good predictive performance. This model could help identify patients at the highest risk for PICC-RVT to guide effective prophylaxis. Further external validation studies of this nomogram model on a large sample are required.

Health sciences/Risk factors

Health sciences/Health care/Disease prevention

Peripherally inserted central catheter

Cancer

Thrombosis

Risk prediction model

Peripherally inserted central venous catheters (PICCs) are commonly used in cancer patients to administer chemotherapy and supportive treatment¹. PICCs are easy to insert, safe, and cost-effective², and carry a lower risk of mechanical injury (e.g., pneumothorax) than centrally inserted venous catheters (CVCs)³. However, PICCs are associated with a higher risk of PICC-related venous thrombosis (PICC-RVT), one of the most common and harmful catheter-related complications. The incidence of symptomatic PICC-RVT varies from 6.7–10.6% in cancer patients^4,5. PICC-RVT could cause patient discomfort, unplanned catheter removal, prolonged hospital stays, and increased financial burden⁶. In severe cases, PICC-RVT could lead to lethal pulmonary embolism and post-thrombotic syndrome^7,8. Therefore, identifying patients at greater risk of PICC-RVT earlier is clinically significant, which helps guide subsequent prophylaxis.

Researchers have constructed some risk prediction models for PICC-RVT, such as Seeley⁹ and Michigan¹⁰, Peng et al.¹¹, and Hu et al.¹². Nevertheless, some prediction models were built based on retrospective studies with selection bias^9–11_, and some studies failed to consider catheter-related risk factors as candidate predictors^9,12. The current guideline¹³ does not recommend a specific predictive PICC-RVT model and states that the Caprini model may be an alternative but with the risk of over-prophylaxis. To summarize, the predictive model for PICC-RVT needs to be further explored. Thus, we conduct a prospective cohort study to construct a new prediction model for PICC-RVT in patients with cancer. The result of our study would add evidence and guide clinicians in the risk assessment of PICC-RVT.

Study setting and population

Between April 2023 and January 2024, a prospective cohort study of patients with cancer receiving PICC insertions was conducted at a cancer center in Guangzhou, China. The study passed the review of the hospital Ethics Committee. All patients signed the informed consent before the study. Inclusion criteria are as follows: (1) received PICC insertion (4F/5F, Bard Access System); (2) diagnosed with cancer; (3) age ≥18 years old; (4) received subsequent catheter maintenance in our hospital until catheter removal. Exclusion criteria include patients known or suspected to be allergic to the components contained in PICC catheters and PICC inserted in the lower limbs. This study was performed in accordance with the Declaration of Helsinki.

PICC placement and maintenance

All PICC catheterization had been inserted by an experienced (≥10 years) and qualified professional nurse (“PICC nurse”) under ultrasound guidance. PICC operation procedures were performed under the guidelines¹³. The catheter tip position was located with an intra-cavitary electrocardiogram (IC-ECG) and confirmed via X-ray. The PICCs used in this study were single-lumen (4-French) or double-lumens (5-French). Catheter maintenance was implemented by specialist nurses under the standard protocol and guidelines in our intravenous catheter clinic.

Data collection

Data extracted were (1) patient-related: gender, age, body mass index (BMI), smoking, cancer type, comorbidities (e.g. hypertension, diabetes, hyperlipidemia, coronary heart disease, severe lung disease, superior vena cava syndrome), history of thrombosis/anticoagulants/radiotherapy/chemotherapy/trauma/major surgery/CVC placement, the activity of the catheterized limb (evaluated with Muscle Strength Testing¹⁴); (2) laboratory test results before PICC insertion: D-dimer level, white blood cell count (WBC), and Platelet (PLT); (3) PICC insertion data: catheter type, number of catheter lumens, catheter dwell time, vein/arm of insertion, catheter-to-vein ratio, catheter tip position, puncture times, number of catheter malposition times. The researcher and the PICC nurse independently extracted all data from electronic health records and via face-to-face communication with patients on the day of catheterization. Following catheter insertion, all patients were followed up within 48 hours and weekly until PICC-RVT or catheter removal occurred. Follow-up consisted of catheter maintenance and evaluation of signs and symptoms of PICC-RVT (e.g., pain, swelling). The PICC-RVT outcome was diagnosed by ultrasonography in the presence of signs and symptoms. On ultrasonic examination, PICC-related thrombosis appeared as an anechoic or hypoechoic image, partially or fully occluding the vessel lumen. Specialist nurses recorded data on the PICC-RVT occurrence during catheter maintenance. The PICC nurse, specialist nurses for catheter maintenance, and the radiologist did not participate in the study design. The researchers checked missing data weekly to ensure data quality.

Statistical analysis

Data analysis was performed with SPSS 29.0 for model development and R package (version 4.4.0) for model internal validation. Categorical data were quantified with numerical values and percentages, while continuous data were presented as mean±standard deviation. Statistical differences among categorical data were assessed using the Chi-square or Fisher’s exact tests. Those with a P value < 0.2 in the univariate logistic regression analysis were entered into the multivariate analysis. Using stepwise regression analysis, the independent risk factors of PICC-RVT were determined. A nomogram prediction model was constructed based on the multivariable logistic regression analysis. The area under the receiver-operating characteristic curve (AUC) was reported for discrimination evaluation. The model calibration was evaluated using the Hosmer–Lemeshow test. For internal validation, a bootstrap with 1000 bootstrap resamples was used to quantify the overfitting of the developed model and optimism in its predictive performance. A two-sided P value of less than 0.05 was considered statistically significant.

Study design and patients

From April 2023 to January 2024, 281 patients received PICC insertions at the cancer center, accounting for 30799 catheter days. The sample size for the final analysis was 275 after the exclusion of 3 cases for transferring to other hospitals and 3 cases discontinuing the scheme for catheter maintenance scheme. 203(73.8%) patients were men, and 72 (26.2%) were women, with a mean age of 53.21 ± 12.67 years (range 18–87 years). Head and neck cancer (58.9%) are the most common cancer types, followed by digestive system cancer (12.0%) and reproductive system cancer (10.9%). Most patients (98.5%) were inserted with single-lumen catheters. 63.3% of the catheters inserted were polyurethane, and 36.7% were silicone. The most common punctured vein was the basilic one (76.3%), and the other 23.7% was the brachial one. The mean catheter dwelling time was 114.6 ± 45.6 days (range 3-274 days). The demographic-, laboratory-, and catheter-related data of both groups are shown in Table 1 and Table 2.

Characteristics of PICCRVT in cancer patients

18 patients (6.5%) developed symptomatic PICC-RVT. The mean duration from PICC insertion to the occurrence of PICC-RVT was 83.4 ± 34.6 days (range 28–143 days). The most common sites of PICC-RVT were basilic veins (n = 14), followed by axillary veins (n = 11), and subclavian veins (n = 5).

Univariate and multivariate analyses for PICCRVT

Univariate analysis showed that diabetes requiring insulin (P = 0.036), trauma (within 1 month) (P = 0.174), major surgery (within 1 month and operation time > 45 minutes) (P = 0.017), reduced limb activities of the PICC arm (P < 0.001), catheter material (P = 0.196), number of catheter malposition during insertion (≥3 times) (P = 0.102) were correlated with PICC-RVT (P < 0.2) (Table 1 and Table 2).

Table 1

Demographic characteristics (N = 275)
Variables	ALL (N = 275)	Non-PICC-RVT group (N = 257)	PICC-RVT group (N = 18)	P-value
Gender
Male	203 (73.8%)	191 (74.3%)	12 (66.7%)	0.477
Female	72 (26.2%)	66 (25.7%)	6 (33.3%)
Age				0.43
≤ 60	205 (74.5)	193 (75.1%)	12 (66.7%)
> 60	70 (25.5)	64 (24.9%)	6 (33.3%)
BMI				0.881
<25	192 (69.8%)	180 (70.0%)	12 (66.7%)
≥25, < 30	79 (28.7%)	73 (28.4%)	6 (33.3%)
≥30	4 (1.5%)	4 (1.6%)	0 (0%)
Smoking	83 (30.2%)	77 (30.0%)	6 (33.3%)	0.763
Cancer type:				0.995
Head and neck cancer	162(58.9%)	152 (59.1%)	10 (55.6%)
Digestive system cancer	33 (12.0%)	29 (11.3%)	4 (22.2%)
Reproductive system cancer	30 (10.9%)	29 (11.3%)	1 (5.6%)
Urological tumor	19 (6.91%)	18 (7.00%)	1 (5.6%)
Hematological malignancy	8 (2.9%)	8 (3.1%)	0 (0%)
Lung cancer	7 (2.55%)	7 (2.72%)	0 (0%)
Breast cancer	5 (1.82%)	3 (1.17%)	2 (11.1%)
Others	11 (4.00%)	11 (4.28%)	0 (0%)
Hypertension	54 (19.6%)	50 (19.5%)	4 (22.2%)	0.775
Diabetes	26 (9.5%)	22 (8.6%)	4 (22.2%)	0.067
Hyperlipidemia	18 (6.5%)	17 (6.6%)	1 (5.6%)	0.861
Coronary heart disease	3 (1.1%)	3 (1.2%)	0 (0%)	0.999
Severe lung disease	12 (4.4%)	11 (4.3%)	1 (5.6%)	0.798
Superior vena cava syndrome	1 (0.4%)	1 (0.4%)	0 (0.0%)	1
History of thrombosis	9 (3.3%)	8 (3.1%)	1 (5.6%)	0.579
History of anticoagulants use	11 (4.0%)	10 (3.9%)	1 (5.6%)	0.729
History of radiotherapy	25 (9.1%)	24 (9.3%)	1 (5.6%)	0.594
History of chemotherapy	41 (14.9%)	38 (14.8%)	3 (16.7%)	0.829
History of trauma	4 (1.5%)	3 (1.2%)	1 (5.6%)	0.174
History of major surgery in the past month lasting > 45 minutes	12 (4.4%)	9 (3.5%)	3 (16.7%)	0.017
History of CVC placement	36 (13.1%)	33 (12.8%)	3 (16.7%)	0.643
Severe infection	3 (1.1%)	3 (1.2%)	0 (0%)	0.999
Continuous bed rest > 72 h	13 (4.7%)	11 (4.3%)	2 (11.1%)	0.205
Reduced limb activities of the PICC arm	25 (9.1%)	19 (7.4%)	6 (33.3%)	< 0.001

Table 2

Laboratory test and catheterization characteristics (N = 275)
Variables	ALL (N = 275)	Non-PICC-RVT group(N = 257)	PICC-RVT group (N = 18)	P-value
D-dimer levels (mg/L)				0.275
≤ 0.5	171 (62.2%)	162 (63.0%)	9 (50%)
>0.5	104 (37.8%)	95 (37.0%)	9 (50%)
White blood cell count (10^9/L)				0.999
≤12	269 (97.8%)	251 (97.7%)	18 (100%)
>12	6 (2.2%)	6 (2.3%)	0 (0%)
Platelets (10^9/L)				0.267
≤300	238 (86.5%)	224 (87.2%)	14 (77.8%)
>300	37 (13.5%)	33 (12.8%)	4 (22.2%)
Catheter type				0.196
Silicone	101 (36.7%)	97 (37.7%)	4 (22.2%)
Polyurethane	174 (63.3%)	160 (62.3%)	14 (77.8%)
Number of catheter lumens:				0.926
Single	261 (94.9%)	244 (94.9%)	17 (94.4%)
Double	14 (5.1%)	13 (5.1%)	1 (5.6%)
Catheter dwell time (days)				0.382
< 90	70(25.5%)	67(26.1%)	3(16.7%)
≥ 90	205(74.5%)	190(73.9%)	15(83.3%)
Arm selected for insertion				0.87
Right	163 (59.3%)	152 (59.1%)	11 (61.1%)
Left	112 (40.7%)	105 (40.9%)	7 (38.9%)
Vein selected for insertion				0.47
Brachial vein	65 (23.7%)	62 (24.2%)	3 (16.7%)
Basilic vein	209 (76.3%)	194 (75.8%)	15 (83.3%)
Catheter-to-vein ratio (CVT)				0.712
≤0.45	251 (91.3%)	235 (91.4%)	16 (88.9%)
>0.45	24 (8.7%)	22 (8.6%)	2 (11.1%)
Catheter tip position at the lower third of the superior vena cava	273 (99.3%)	255 (99.2%)	18 (100%)	0.999
Number of puncture times				1
<3	274 (99.6%)	256 (99.6%)	18 (100%)
≥3	1 (0.4%)	1 (0.4%)	0 (0%)
Number of catheter malposition times during insertion				0.102
<3	265 (96.4%)	249 (96.9%)	16 (88.9%)
≥3	10 (3.6%)	8 (3.1%)	2 (11.1%)

Multivariate analysis revealed that diabetes requiring insulin (P = 0.035, OR:8.016, 95%CI:1.157–55.536), major surgery (within 1 month and operation time > 45 minutes) (P = 0.023, OR:0.023,95%CI:1.296–30.77), reduced limb activities of the PICC arm (P < 0.001, OR:6.687, 95%CI:2.024–22.09) were found to be independently associated with PICC-RVT (Table 3). Although catheter material was not statistically significant in the multivariate analysis with a P value of 0.062 (OR:3.319, 95%CI:0.940-11.723), it was considered clinically important. Thus, it was included in the final model.

Table 3

PICC-RVT Risk factors in a stepwise entry process multivariate analysis
Characteristic	B	SE	Wald	P value	OR	95% CI for OR
Diabetes requiring insulin	2.081	0.988	4.442	0.035	8.016	(1.157, 55.536)
Reduced activities of PICC arm	1.9	0.61	9.713	0.002	6.687	(2.024, 22.09)
Major surgery (> 45 mins)	1.843	0.808	5.203	0.023	6.315	(1.296, 30.77)
Catheter material (Polyurethane)	1.2	0.6	3.472	0.062	3.319	(0.940,11.723)

Nomogram for PICC-RVT and validation

Four risk factors including diabetes requiring insulin, major surgery, reduced limb activities of the PICC arm, and catheter material, were retained in the final model (Fig. 1). The AUC of the model was 0.796 (95%CI: 0.695–0.897), indicating good discriminatory ability (Fig. 2). The results of the Hosmer–Lemeshow goodness-of-fit test (1.685, P = 0.194) indicated that the model had a satisfactory prediction effect, and no further calibration is required.

This study observed an incidence of 6.5% for symptomatic PICC-RVT in cancer patients, comparable to results from relevant studies of 3.6%¹⁵ and 6.7%⁵. Nevertheless, the incidence of PICC-RVT was assumed to be higher as this study did not include asymptomatic cases. A meta-analysis¹⁶ found that asymptomatic cases (46.7%, 77/165) nearly accounted for half of the total PICC-RVT events. Thus, the thrombotic complications of PICC insertions remain a significant source of morbidity and mortality in patients with cancer.

The study identified four risk factors independently correlated with PICC-RVT: diabetes requiring insulin, reduced limb activities of the PICC arm, major surgery, and catheter material. A systematic review¹⁷concluded that diabetes was a significant predictor of PICC-RVT. According to guidelines¹⁸, diabetes requiring insulin treatment indicates more severe hyperglycemia (≥ 16.7 mmol/L), leading to vascular injury and predisposing to a pro-thrombotic state¹⁹. Our study differentiates the impact of diabetes and diabetes requiring insulin treatment on the incidence of PICC-RVT. Thus, for diabetic patients receiving insulin therapy, caution should be exercised regarding the risk of thrombotic complications, and regular assessment and careful monitoring are required²⁰.

Another patient-related risk factor identified was reduced limb activities of the PICC arm. This finding was in accord with many former studies^9,21–24. The reason might be that reduced activity causes blood stasis and thrombosis due to prolonged bedridden and immobility²⁵. However, the definition of reduced activity amount varied across studies such as “continuous bed rest > 72 h”²², “daily activity amount at the insertion site compared to pre-tubing activity²⁴”, “activity with catheter side arm²¹”, use of the Eastern Cooperative Oncology Group (ECOG)²⁶/ Karnofsky Performance Status (KPS)²⁷ score, “impaired activities of daily living”²³, “recently bedridden⁹”. The reduced limb activities of the PICC arm in our study were evaluated with muscle strength testing (1–5). Patients with a score ≤ of 4 for the catheter side were considered to have reduced limb activities. In future studies, this prompts a uniform definition of reduced activity amount, including the extent (duration, intensity) and parts (catheter arm vs. whole body). Patients with reduced activity on the PICC arm should be educated about the importance of strengthening arm exercises. The 2024 Infusion Therapy Standards of Practice¹³ encouraged upper extremity exercise to reduce venous stasis. It further indicated that handgrip exercise with an elastic ball 3 or 6 times every day for 3 weeks could decrease the incidence of PICC-RVT in patients with cancer¹³. For patients unable to engage in active exercise, passive exercises are encouraged and future studies focusing on exercise regimens are required in this population.

Major surgery is a well-established risk factor for the development of thrombosis. Caprini²⁸ first introduced the predictor of “major surgery > 45 minutes” (2 points) into their model for the risk of venous thromboembolism (VTE) in surgical patients. Our study builds on the work of Caprini et al. and further advances by focusing specifically on patients with PICCs. Previous literature has powerfully demonstrated independent correlations between surgery and PICC-RVT, such as “breast surgery” in Peng et al.¹¹, and surgery lasting > 1 h²⁹. The mechanisms behind surgery for thrombosis formation were the collective impact of decreased blood flow and stasis, hypercoagulable state, and endothelial injury³⁰. Thus, those receiving PICCs with a history of major surgery lasting > 45 minutes should be assessed earlier and closely monitored for the risk of PICC-RVT.

A novel finding of our study was the catheter material, which has not yet been included in any models. Evidence³¹ has revealed that polyurethane catheters are more susceptible to PICC-RVT than silicone catheters. Nevertheless, thrombosis-induced polyurethane material allows a high-flow injection due to high stiffness³². Current guidelines do not recommend a specific catheter material. A choice of catheter material should be made based on the patient’s clinical requirement before catheter insertion. For those who require examinations of high-pressure injection (e.g., enhanced CT), the choice of polyurethane catheters should also consider the risk of PICC-RVT. For those who do not, silicone catheters are preferred.

The AUC of our RAM is 0.796, indicating good discriminatory ability. The Hosmer–Lemeshow goodness-of-ft test showed good calibration with a result of 1.685 (P = 0.194). The model incorporated four risk factors i.e., diabetes requiring insulin, major surgery (within 1 month and lasting > 45 minutes), reduced limb activities of the PICC arm, and catheter material, which are easy to access for risk assessment before PICC insertion. Our results showed that the model has acceptable predictive performance and could be a potentially helpful tool for predicting the risk of PICC-RVT. External validation of this model on a large sample size is needed.

Strengths and limitations

The study has some important strengths. First, our study is a prospective design. Second, a comprehensive list of patient-, laboratory-and catheter-related risk factors were considered as candidate predictors during model development. Nevertheless, our sample size was small from a single center, which could have diminished the ability to detect potentially significant risk factors. Besides, as it was mainly derived from head and neck cancers, this model should be used with caution when applied to other cancer types.

We developed and internally validated a nomogram prediction model, including diabetes requiring insulin, major surgery, reduced limb activities of the PICC arm, and catheter material. This model has a good predictive ability, which could help evaluate the risk of PICC-RVT in patients with cancer.

Data availability

The data generated and analyzed in the study are available from the corresponding author upon reasonable request.

Acknowledgments

We thank the specialist nurses at the catheter clinic for PICC placement, PICC maintenance, and record of RAM scores and the PICC-RVT occurrence information.

Author contributions

Y.F. and J.L. designed the study; Z.H. and M.L. collected the data; R.H. performed the data analyses; Z.H., M.L., and Z.W. helped perform the analysis with constructive discussions. All the authors had read and approved the manuscript.

Funding

This research was funded by the Chinese Medical Association Journal Research project (CMAPH-NRG-2022017). The funders did not participate in the study design, the collection, analysis, interpretation of data, or the writing.

Competing interests

The authors declare no competing interests.

Ethics declarations

The study was approved by the Ethics Committee of Sun Yat-sen University (number： B2023-078-01, 29th March 2023).

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No competing interests reported.

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Development and validation of a risk prediction model for PICC-related venous thrombosis in patients with cancer: A prospective cohort study

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Abstract

Figures

Introduction

Methods

Study setting and population

PICC placement and maintenance

Data collection

Statistical analysis

Results

Study design and patients

Characteristics of PICCRVT in cancer patients

Univariate and multivariate analyses for PICCRVT

Nomogram for PICC-RVT and validation

Discussion

Strengths and limitations

Conclusions

Declarations

References

Additional Declarations

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