The value of neuroinflammation-related factors in predicting the occurrence of postoperative delirium in elderly patients with spinal fractures and the construction of predictive models
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摘要:
目的 为探究神经炎症相关因子与老年脊柱骨折患者谵妄发生的关系,纳入老年脊柱骨折患者测量其基线神经炎症相关因子水平,并据此构建风险预测模型。 方法 纳入2022年1月—2024年6月浙江中医药大学附属第二医院426例老年脊柱骨折手术患者,根据7∶3的比例将患者分为训练集(298例)与验证集(128例)。根据患者术后谵妄发生情况分组(发生组和未发生组),探讨老年脊柱骨折术后谵妄发生的影响因素以构建预测模型,并分析其预测效能。 结果 多因素logistic回归分析显示,年龄、手术时间、白介素-6(IL-6)、IL-1β、C-反应蛋白(CRP)、中枢神经特异蛋白(S100β)均为老年脊柱骨折手术患者术后谵妄发生的独立影响因素(P < 0.05);ROC曲线分析显示,基于各项影响因素构建的模型预测老年脊柱骨折手术患者术后谵妄发生的曲线下面积为0.881,灵敏度为85.71%、特异度为79.75%,约登指数为0.655。验证集验证显示,该预测模型的曲线下面积为0.858(95% CI: 0.782~0.916),灵敏度为80.65%,特异度为76.47%,约登指数为0.571;决策曲线分析显示训练集和验证集的阈值概率分别为1%和2%,提示该预测模型具有较高的净收益。 结论 年龄、手术时间、IL-6、IL-1β、CRP、S100β均为老年脊柱骨折患者谵妄发生的影响因素,据此构建的预测模型具有较高的预测效能。 Abstract:Objective To explore the relationship between neuroinflammation neuroinflammation-related factors and delirium in elderly patients suffering from spinal fractures, this study constituted an investigation into the measurement of baseline neuroinflammation-related factors in elderly patients with spinal fractures. The construction of a risk prediction model was then based on the findings. Methods From January 2022 to June 2024, a total of 426 elderly patients afflicted with spinal fractures were enrolled in the Second Affiliated Hospital of Zhejiang Chinese Medicine University. According to the ratio of 7∶3, the patients were divided into a training set (n=298) and a verification set (n=128). According to the incidence of postoperative delirium, the patients were categorized into two groups (the occurrence group and the non-occurrence group) to explore the influencing factors of delirium. Meanwhile, a prediction model was also constructed. Results Multivariate logistic regression analysis showed that age, operation time, interleukin-6 (IL-6), IL-1β, C-reactive protein (CRP), and central nervous system-specific protein (S100β) were all independent influencing factors for the occurrence of postoperative delirium in elderly patients undergoing spinal fracture surgery (P < 0.05). ROC curve analysis indicated that the area under the curve (AUC) of the model constructed based on the above influencing factors for predicting postoperative delirium in elderly patients undergoing spinal fracture surgery was 0.881, with a sensitivity of 85.71%, a specificity of 79.75%, and a Youden index of 0.655. Validation by the validation set showed that the AUC of this prediction model was 0.858 (95% CI: 0.782-0.916), with a sensitivity of 80.65%, a specificity of 76.47%, and a Youden index of 0.571. Decision curve analysis revealed that the threshold probabilities of the training set and the validation set were 1% and 2% respectively, suggesting that this prediction model had a relatively high net benefit. Conclusion Age, operation time, IL-6, IL-1β, CRP and S100β as the influencing factors of delirium in elderly patients with spinal fracture, and the prediction model based on these factors has high prediction efficiency. -
表 1 训练集和验证集老年脊柱骨折手术患者一般资料比较
Table 1. Comparison of general data of patients in training set and verification set
项目 训练集(n=298) 验证集(n=128) 统计量 P值 年龄(x ±s,岁) 68.12±6.50 69.23±7.88 1.402a 0.162 性别[例(%)] 1.901b 0.168 男性 182(61.07) 69(53.91) 女性 116(38.93) 59(46.09) BMI(x ±s) 25.23±2.42 24.96±2.28 1.100a 0.272 受教育年限[例(%)] 2.101b 0.147 ≤12 201(67.45) 77(60.16) >12年 97(32.55) 51(39.84) 基础疾病[例(%)] 高血压 95(31.88) 31(24.22) 2.5234b 0.112 糖尿病 89(29.87) 33(25.78) 0.731b 0.393 高脂血症 61(20.47) 29(22.66) 0.257b 0.612 冠心病 43(14.43) 19(14.84) 0.012b 0.911 慢性阻塞性肺疾病 24(8.05) 6(4.69) 1.550b 0.213 美国麻醉医师协会分级[例(%)] 3.206b 0.201 Ⅰ级 157(52.68) 59(46.09) Ⅱ级 113(37.92) 50(39.06) Ⅲ级 28(9.40) 19(14.84) 手术部位[例(%)] 1.419b 0.234 颈椎 135(45.30) 50(39.06) 腰椎 163(54.70) 78(60.94) 手术时间(x ±s,min) 177.83±23.64 173.76±26.52 1.499a 0.135 术中低血压[例(%)] 9(3.02) 3(2.34) 0.150b 0.699 IL-6(x ±s,pg/mL) 23.00±5.72 23.71±4.81 1.317a 0.189 IL-1β(x ±s,pg/mL) 3.65±1.07 3.58±1.14 0.592a 0.555 CRP(x ±s,pg/mL) 5.17±1.89 5.04±1.81 0.671a 0.503 S100β(x ±s,pg/mL) 59.17±15.14 62.14±16.08 1.778a 0.077 注:a为t值,b为χ2值。 表 2 训练集发生组与未发生组老年脊柱骨折手术患者一般资料比较
Table 2. Comparison of general data of elderly patients undergoing spinal fracture surgery between the occurrence group and the non-occurrence group in the training set
项目 发生组(n=56) 未发生组(n=242) 统计量 P值 年龄(x ±s,岁) 71.82±5.34 66.33±5.50 6.894a < 0.001 性别[例(%)] 0.299b 0.584 男性 36(64.29) 146(60.33) 女性 20(35.71) 96(39.67) BMI(x ±s) 24.97±2.11 25.55±2.44 1.642a 0.102 受教育年限[例(%)] 0.497b 0.481 ≤12年 40(71.43) 161(66.53) >12年 16(28.57) 81(33.47) 基础疾病[例(%)] 高血压 17(30.36) 78(32.23) 0.074b 0.786 糖尿病 20(35.71) 66(28.51) 1.126b 0.289 高脂血症 13(223.21) 48(19.83) 0.319b 0.572 冠心病 10(17.86) 33(13.64) 0.656b 0.418 慢性阻塞性肺疾病 6(10.71) 18(7.44) 0.659b 0.417 美国麻醉医师协会分级[例(%)] 8.641b 0.013 Ⅰ级 25(44.64) 132(54.55) Ⅱ级 20(35.71) 93(38.43) Ⅲ级 11(19.64) 17(7.02) 手术部位[例(%)] 1.170b 0.275 颈椎 29(51.79) 106(43.80) 腰椎 27(48.21) 136(56.20) 手术时间(x ±s,min) 179.37±19.71 168.22±16.71 3.920a < 0.001 术中低血压[例(%)] 5(8.93) 4(1.65) 8.219b 0.004 IL-6(x ±s,pg/mL) 24.62±1.81 23.18±1.17 5.685a < 0.001 IL-1β(x ±s,pg/mL) 3.89±1.11 3.56±0.98 2.048a 0.044 CRP(x ±s,pg/mL) 5.75±1.72 4.95±1.51 3.206a 0.002 S100β(x ±s,pg/mL) 65.45±12.35 58.91±13.26 3.521a 0.001 注:a为t值,b为χ2值。 表 3 老年脊柱骨折手术患者术后谵妄风险因素的logistic回归分析
Table 3. Logistic regression analysis of postoperative delirium risk factors in elderly patients undergoing spinal fracture surgery
变量 B SE Waldχ2 P值 OR值 95% CI 年龄 0.171 0.037 21.501 < 0.001 1.186 1.104~1.275 美国麻醉医师协会分级 0.349 0.278 1.577 0.209 1.418 0.822~2.444 手术时间 0.023 0.011 4.083 0.043 1.023 1.001~1.046 术中低血压 1.623 0.883 3.378 0.066 5.067 0.898~28.592 IL-6 0.600 0.144 17.428 < 0.001 1.822 1.375~2.415 IL-1β 0.483 0.198 5.937 0.015 1.621 1.099~2.389 CRP 0.268 0.119 5.017 0.025 1.307 1.034~1.652 S100β 0.057 0.016 13.466 < 0.001 1.059 1.027~1.091 注:赋值方法如下,术后是否发生谵妄(因变量),是=1,否=0;年龄、手术时间、IL-6、IL-1β、CRP、S100β均以实际值赋值;ASA分级,Ⅰ级=0,Ⅱ级=1,Ⅲ级=2;术中低血压,未发生=0,发生=1。 表 4 结肠息肉患者术后复发风险因素的预测效能
Table 4. Predictive efficacy of risk factors for postoperative recurrence in patients with colonic polyps
模型 曲线下面积(95% CI) 约登指数 敏感性(%) 特异性(%) 训练集预测模型 0.881(0.838~0.915) 0.655 85.71 79.75 验证集预测模型 0.858(0.782~0.916) 0.571 80.65 76.47 -
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