1.解放军总医院医疗保障中心药剂科,北京 100853
2.蚌埠医科大学药学院,安徽 蚌埠 233030
3.沈阳药科大学生命科学与生物制药学院,沈阳 110016
Received:30 September 2025,
Revised:2026-02-05,
Accepted:05 February 2026,
Published:28 February 2026
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蔡乐,赵雨晴,崔佳珠,等.巴曲酶相关严重低纤维蛋白原血症的危险因素分析及风险预测模型构建[J].中国药房,2026,37(04):462-467.
CAI Le,ZHAO Yuqing,CUI Jiazhu,et al.Analysis of risk factors and construction of risk prediction model for batroxobin-related severe hypofibrinogenemia[J].ZHONGGUO YAOFANG,2026,37(04):462-467.
蔡乐,赵雨晴,崔佳珠,等.巴曲酶相关严重低纤维蛋白原血症的危险因素分析及风险预测模型构建[J].中国药房,2026,37(04):462-467. DOI: 10.6039/j.issn.1001-0408.2026.04.09.
CAI Le,ZHAO Yuqing,CUI Jiazhu,et al.Analysis of risk factors and construction of risk prediction model for batroxobin-related severe hypofibrinogenemia[J].ZHONGGUO YAOFANG,2026,37(04):462-467. DOI: 10.6039/j.issn.1001-0408.2026.04.09.
目的
2
探讨巴曲酶相关严重低纤维蛋白原血症(HFIB)的临床特征及危险因素,并构建其风险预测模型。
方法
2
回顾性收集2020年1月1日至2024年12月31日某三甲医院第一医学中心使用巴曲酶的住院患者资料,并根据发生HFIB的严重程度分为非严重HFIB组和严重HFIB组。采用单因素和多因素Logistic回归分析筛选巴曲酶相关严重HFIB的独立影响因素。采用R 4.5软件“rms”程序包绘制列线图;采用受试者工作特征曲线检验模型的预测性能;采用Bootstrap自助抽样法评估模型的校准度;采用Hosmer-Lemeshow检验评价模型的拟合优度。
结果
2
共纳入1 472例患者,其中1 445例发生HFIB,发生率为98.17%;895例为严重HFIB,发生率为60.80%。多因素Logistic回归分析结果显示,年龄增长、较高的首剂量/10 kg体重、使用维持剂量、合并使用糖皮质激素是巴曲酶相关严重HFIB的独立危险因素,较高的纤维蛋白原(FIB)基线值为其独立保护因素(
P
<0.05)。模型的受试者工作特征曲线下面积为0.760[95%置信区间为(0.735,0.785)
]
;校准曲线的平均绝对误差为0.006;Hosmer-Lemeshow检验的
P
值为0.609。
结论
2
巴曲酶可快速、显著降低FIB,易引起严重HFIB;年龄增长、使用较高首剂量/10 kg体重、使用维持剂量以及合并使用糖皮质激素患者的巴曲酶相关严重HFIB的发生风险较高,而FIB基线值较高患者的巴曲酶相关严重HFIB的发生风险较低;基于上述因素建立的风险预测模型可用于预测巴曲酶相关严重HFIB的发生风险。
OBJECTIVE
2
To investigate the clinical characteristics and risk factors for batroxobin-related severe hypofibrinogenemia (HFIB) and construct a risk prediction model.
METHODS
2
A retrospective analysis was conducted on inpatients treated with batroxobin in the First Medical Center of a tertiary hospital from January 1, 2020, to December 31, 2024. Patients were categorized into non-severe HFIB group and severe HFIB group based on the severity of HFIB. Univariate and multivariate Logistic regression analyses were performed to identify the independent influencing factors for batroxobin-related severe HFIB. A nomogram was developed using the “rms” package in R 4.5 software. The predictive performance of the model was evaluated using the receiver operating characteristic curve. Calibration was assessed via the Bootstrap resampling method, and goodness-of-fit was evaluated with the Hosmer-Lemeshow test.
RESULTS
2
A total of 1 472 patients were included in this study. Of these, 1 445 developed HFIB, yi
elding an incidence of 98.17%. Furthermore, 895 were classified as severe HFIB, accounting for 60.80% of the cohort. Multivariate Logistic regression analysis showed that increased age, high initial dose per 10 kg body weight, use of maintenance dose, and concomitant glucocorticoid use were independent risk factors for batroxobin-related severe HFIB, while high baseline fibrinogen (FIB) level was identified as a protective factor. The model demonstrated an area under the curve of 0.760 (95% CI: 0.735-0.785). The mean absolute error of the calibration curve was 0.006. The
P
value of the Hosmer-Lemeshow test was 0.609.
CONCLUSIONS
2
Batroxobin can rapidly and significantly reduce FIB levels and carries a risk of inducing severe HFIB. Patients with advanced age, high initial dose per 10 kg body weight, use of maintenance dose and concomitant glucocorticoid use had a higher risk of batroxobin-related severe HFIB, while high baseline FIB level had a lower risk of batroxobin-related severe HFIB. The risk prediction model developed based on these factors can be used to predict the likelihood of batroxobin-related severe HFIB.
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