Prediction model for haematoma after tissue expander placement: A retrospective cohort study of 7080 cases over 20 years

Qiang He,Chen Dong, Yajuan Song,Yinke Tang, Feifei Chu,Wei Liu, Yu Zhang,Jing Du, Zhou Yu,Xianjie Ma

JOURNAL OF PLASTIC RECONSTRUCTIVE AND AESTHETIC SURGERY(2024)

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摘要
Haematoma is an early complication of tissue expander placement and can lead to in- fection, capsule contracture and various complications, hindering successful reconstruction. However, no scientific models can accurately predict the risk of haematoma following tissue ex- pansion. Therefore, this study aimed to develop and validate a prediction model for haematoma following tissue expander placement. The medical records of patients who underwent expander placement between 2001 and 2021 were obtained from the clinical database of the Department of Plastic Surgery at the Xijing Hospital. A total of 4579 consecutive patients with 7080 expanders and 179 expanded pocket haematomas were analysed. Multivariate logistic regression analysis identified adult age (P = 0.006), male sex (P < 0.001), scar reconstruction (P = 0.019), perioperative hy- pertension (P < 0.001), face and neck location (P = 0.002) and activated partial thromboplastin time above the normal range (P < 0.001) as risk factors for haematoma. Therefore, these were included in the prediction model, and a nomogram was constructed. The discrimination of the nomogram was robust (area under the curve: 0.78; 95% confidence interval: 0.72-0.83). Further, the prediction model had a strong fit (Hosmer-Lemeshow test, P = 0.066) and maintained similar discrimination after considering performance optimism (bootstrapped area under the curve: 0.79; 95% confidence interval: 0.73-0.84). This clinical prediction model was created using a generalisable dataset and can be utilised to obtain valid haematoma predictions after expander placement, assisting surgeons in implementing preventive measures or interventions to reduce the occurrence of haematoma.
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关键词
Tissue expansion,Expander,Haematoma,Prediction model,Risk factors,Nomogram
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