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2024 ; 16
(7
): 2221-2231
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Establishment and validation of a predictive model for peripherally inserted
central catheter-related thrombosis in patients with liver cancer
#MMPMID39087116
Chen XF
; Wu HJ
; Li T
; Liu JB
; Zhou WJ
; Guo Q
World J Gastrointest Surg
2024[Jul]; 16
(7
): 2221-2231
PMID39087116
show ga
BACKGROUND: Peripherally inserted central catheters (PICCs) are commonly used in
hospitalized patients with liver cancer for the administration of chemotherapy,
nutrition, and other medications. However, PICC-related thrombosis is a serious
complication that can lead to morbidity and mortality in this patient population.
Several risk factors have been identified for the development of PICC-related
thrombosis, including cancer type, stage, comorbidities, and catheter
characteristics. Understanding these risk factors and developing a predictive
model can help healthcare providers identify high-risk patients and implement
preventive measures to reduce the incidence of thrombosis. AIM: To analyze the
influencing factors of PICC-related thrombosis in hospitalized patients with
liver cancer, construct a predictive model, and validate it. METHODS: Clinical
data of hospitalized patients with liver cancer admitted from January 2020 to
December 2023 were collected. Thirty-five cases of PICC-related thrombosis in
hospitalized patients with liver cancer were collected, and 220 patients who
underwent PICC placement during the same period but did not develop PICC-related
thrombosis were randomly selected as controls. A total of 255 samples were
collected and used as the training set, and 77 cases were collected as the
validation set in a 7:3 ratio. General patient information, case data,
catheterization data, coagulation indicators, and Autar Thrombosis Risk
Assessment Scale scores were analyzed. Univariate and multivariate unconditional
logistic regression analyses were performed on relevant factors, and the value of
combined indicators in predicting PICC-related thrombosis in hospitalized
patients with liver cancer was evaluated using receiver operating characteristic
(ROC) curve analysis. RESULTS: Univariate analysis showed statistically
significant differences (P < 0.05) in age, sex, Karnofsky performance status
score (KPS), bedridden time, activities of daily living impairment, parenteral
nutrition, catheter duration, distant metastasis, and bone marrow suppression
between the thrombosis group and the non-thrombosis group. Other aspects had no
statistically significant differences (P > 0.05). Multivariate regression
analysis showed that age ? 60 years, KPS score ? 50 points, parenteral nutrition,
stage III to IV, distant metastasis, bone marrow suppression, and activities of
daily living impairment were independent risk factors for PICC-related thrombosis
in hospitalized patients with liver cancer (P < 0.05). Catheter duration of 1-6
months and catheter duration > 6 months were protective factors for PICC-related
thrombosis (P < 0.05). The predictive model for PICC-related thrombosis was
obtained as follows: P predictive probability = [exp (Logit P)]/[1 + exp (Logit
P)], where Logit P = age × 1.907 + KPS score × 2.045 + parenteral nutrition ×
9.467 + catheter duration × 0.506 + tumor-node-metastasis (TNM) staging × 2.844 +
distant metastasis × 2.065 + bone marrow suppression × 2.082 + activities of
daily living impairment × 13.926. ROC curve analysis showed an area under the
curve (AUC) of 0.827 (95%CI: 0.724-0.929, P < 0.001), with a corresponding
optimal cut-off value of 0.612, sensitivity of 0.755, and specificity of 0.857.
Calibration curve analysis showed good consistency between the predicted
occurrence of PICC-related thrombosis and actual occurrence (P > 0.05). ROC
analysis showed AUCs of 0.888 and 0.729 for the training and validation sets,
respectively. CONCLUSION: Age, KPS score, parenteral nutrition, TNM staging,
distant metastasis, bone marrow suppression, and activities of daily living
impairment are independent risk factors for PICC-related thrombosis in
hospitalized patients with liver cancer, while catheter duration is a protective
factor for the disease. The predictive model has an AUC of 0.827, indicating high
predictive accuracy and clinical value.
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