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头孢哌酮/舒巴坦致血小板减少列线图预测模型的建立与验证
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| 篇名： | 头孢哌酮/舒巴坦致血小板减少列线图预测模型的建立与验证 |
| TITLE： | Establishment and validation of nomogram prediction model of cefoperazone/sulbactam-induced thrombocytopenia |
| 摘要： | 目的 探讨头孢哌酮/舒巴坦致成人住院患者血小板减少的预测因子，建立列线图预测模型并进行验证。方法回顾性收集西安市中心医院2021年6月30日至2023年6月30日使用头孢哌酮/舒巴坦治疗的成人住院患者资料，按7∶3随机分为训练集和内部验证集。采用单因素/多因素Logistic回归分析筛选头孢哌酮/舒巴坦致血小板减少的独立预测因子，通过R4.0.3软件“RMS”包绘制列线图；采用受试者工作特征曲线及C-index值评估模型的预测效能；采用Hosmer-Lemeshow拟合优度检验评价模型的校正度。以相同标准，收集西安市第一医院同期使用头孢哌酮/舒巴坦治疗的成人住院患者的临床资料，对列线图预测模型进行外部验证。结果共纳入西安市中心医院患者1045例，其中头孢哌酮/舒巴坦致血小板减少患者67例，发生率为6.41%。排除假阳性患者后，最终纳入患者473例，其中训练集331例、内部验证集142例。多因素Logistic回归分析结果显示，患者年龄[OR＝1.043，95%CI（1.017，1.070）]、估算的肾小球滤过率（eGFR）[OR＝0.988，95%CI（0.977，0.998）]、基线血小板[OR＝0.989，95%CI（0.982，0.996）]、营养风险[OR＝3.863，95%CI（1.884，7.921）]和累计限定日剂量数（DDDs）[OR＝1.082，95%CI（1.020，1.147）]是头孢哌酮/舒巴坦致血小板减少的独立预测因子（P＜0.05）。训练集和内部验证集的C-index值分别为0.824[95%CI（0.759，0.890）]和0.828[95%CI（0.749，0.933）]，Hosmer-Lemeshow检验的χ2值分别为0.441、1.804（P值分别为0.802、0.406）。外部验证集中，C-index值为0.808[95%CI（0.672，0.945）]，Hosmer-Lemeshow检验的χ2值为0.899（P值为0.638）。结论患者年龄、基线血小板、eGFR、营养风险和累计DDDs是头孢哌酮/舒巴坦致血小板减少的独立预测因子；所建列线图预测模型具有良好的预测效能和外推性，有助于临床快速、准确地识别头孢哌酮/舒巴坦致血小板减少的潜在风险。 |
| ABSTRACT： | OBJECTIVE To explore the predictive factors of cefoperazone/sulbactam-induced thrombocytopenia in adult inpatients， and to establish and validate the nomogram prediction model. METHODS Data of adult inpatients treated with cefoperazone/sulbactam in Xi’an Central Hospital from Jun. 30th， 2021 to Jun. 30th， 2023 were retrospectively collected. The training set and internal validation set were randomly constructed in a 7∶3 ratio. Singler factor and multifactor Logistic regression analysis were used to screen the independent predictors of cefoperazone/sulbactam-induced thrombocytopenia. The nomogram was drawn by using “RMS” of R 4.0.3 software， and the predictive performance of the model was evaluated by the receiver operating characteristic curve and C-index curve. Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration degree of the model. Using the same standard， the clinical data of hospitalized patients receiving cefoperazone/sulbactam in Xi’an First Hospital in the same period were collected for external validation of the nomogram prediction model. RESULTS A total of 1 045 patients in Xi’an Central Hospital were included in this study， among which 67 patients suffered from cefoperazone/sulbactam-induced thrombocytopenia， with an incidence of 6.41%. After the false positive patients were excluded， 473 patients were included finally， including 331 in the training set and 142 in theinternal validation set. Multifactor Logistic regression analysis showed that age [OR＝1.043， 95%CI （1.017， 1.070）]， estimated glomerular filtration rate （eGFR） [OR＝0.988，95%CI（0.977， 0.998）]， baseline platelet （PLT） [OR＝0.989， 95%CI（0.982， 0.996）]， nutritional risk [OR＝3.863， 95%CI（1.884， 7.921）] and cumulative defined daily doses （DDDs） [OR＝1.082， 95%CI（1.020， 1.147）] were independent predictors for cefoperazone/sulbactam-induced thrombocytopenia （P＜0.05）. The C-index values of the training set and the internal validation set were 0.824 [95%CI （0.759， 0.890）] and 0.828 [95%CI （0.749， 0.933）]， respectively. The results of the Hosmer-Lemeshow test showed that χ 2 values were 0.441 （P＝0.802） and 1.804 （P＝0.406）. In the external validation set， the C-index value was 0.808 [95%CI （0.672， 0.945）]， the χ 2 value of the Hosmer-Lemeshow test was 0.899 （P＝0.638）. CONCLUSIONS The independent predictors of cefoperazone/sulbactam-induced thrombocytopenia include age， baseline PLT， eGFR， nutritional risk and cumulative DDDs. The model has good predictive efficacy and extrapolation ability， which can help clinic identify the potential risk of cefoperazone/sulbactam-induced thrombocytopenia quickly and accurately. |
| 期刊： | 2024年第35卷第08期 |
| 作者： | 白荷荷；彭莉蓉；王园姬；聂晓静；王金萍；马莉；王冠 |
| AUTHORS： | BAI Hehe，PENG Lirong，WANG Yuanji，NIE Xiaojing，WANG Jinping，MA Li，WANG Guan |
| 关键字： | 头孢哌酮/舒巴坦；血小板减少；药物不良反应；列线图预测模型；预测因子 |
| KEYWORDS： | cefoperazone/sulbactam； thrombocytopenia； adverse drug reaction； nomogram prediction model； independent |
| 阅读数： | 7 次 |
| 本月下载数： | 1 次 |
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