Predictive value of the first blood routine parameters within 24 hours of admission for critical illness in children
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摘要:
目的 探讨入院24 h内首次血常规参数与儿童危重症的相关性,并建立ROC曲线和列线图模型以评价其对儿童危重症的预测价值。 方法 以2015年4月—2019年12月蚌埠医学院第一附属医院儿童重症监护室入院24 h内行血常规检查并完成小儿危重病例评分的患儿为研究对象。将患儿随机分为训练队列和验证队列,训练队列患儿变量与危重症的相关性采用logistic回归分析。采用受试者工作特征曲线分析变量对2个队列危重症的预测效能,采用R语言构建训练队列列线图预测模型评估危重症的发生概率。 结果 共纳入496例患儿,男283例,女213例,中位年龄2.0(0.57,5.88)岁。训练队列347例,验证队列149例。白细胞计数(WBC)、红细胞分布宽度CV(RDW-CV)及网织红细胞百分比(RET%)与训练队列患儿危重症显著相关(均P < 0.05),WBC+RDW-CV+RET%联合指标预测训练队列及验证队列危重症的曲线下面积分别为0.644和0.711,在最佳截断值为0.357和0.290时,联合指标预测2个队列危重症的灵敏度分别为46.4%和79.6%,特异度分别为80.0%和60.0%。以训练队列WBC、RDW-CV及RET%构建列线图模型,一致性指数、校准曲线、决策曲线和临床影响曲线分析表明列线图可预测儿童危重症。 结论 入院24 h内首次WBC+RDW-CV+RET%对儿童危重症具有较好的预测效能,以WBC、RDW-CV及RET%构建的列线图可预测儿童危重症的发生概率。 Abstract:Objective To explore the correlation between the first blood routine parameters within 24 hours of admission and critical illness in children, and establish ROC curve and a nomogram model to evaluate their predictive value for critical illness in children. Methods Patients who underwent blood routine test and completed pediatric critical illness score within 24 hours of admission in the Pediatric Intensive Care Unit of the First Affiliated Hospital of Bengbu Medical College from April 2015 to December 2019 were enrolled. The patients were randomly divided into a training cohort and a validation cohort. The correlation between the variables of the training cohort and critical illness was analyzed by logistic regression analysis. The predictive performances of variables for critical illness in the two cohorts were analyzed by receiver operating characteristic curve. The nomogram prediction model of the training cohort was constructed using R language to assess the occurrence probability of critical illness. Results Among 496 patients, there were 283 males and 213 females, with a median age of 2.0 (0.57, 5.88) years. There were 347 patients in the training cohort, and 149 patients in the validation cohort. White cell count (WBC), red cell distribution width-coefficient of variation (RDW-CV), and reticulocyte percentage (RET%) were significantly associated with critical illness in children in the training cohort (all P < 0.05). The receiver operating characteristic curve analysis showed that the areas under the curve of WBC+RDW-CV+RET% combined index for predicting critical illness in the training cohort and the validation cohort were 0.644 and 0.711, respectively. When the optimal cut-off values of 0.357 for the training cohort and 0.290 for the validation cohort were used, sensitivities of the combined index for predicting critical illness were 46.4% and 79.6%, and specificities were 80.0% and 60.0%, respectively. The nomogram prediction model was constructed using WBC, RDW-CV, and RET% of the patients in the training cohort. The concordance index, calibration curve, decision curve, and clinical impact curve analyses indicated that this nomogram could be used to predict critical illness in children. Conclusion The first WBC+RDW-CV+RET% combined index within 24 hours of admission has a good predictive performance for critical illness in children. The nomogram constructed by WBC, RDW-CV, and RET% can be used to predict the occurrence probability of critical illness in children. -
Key words:
- Critical illness /
- Blood cell count /
- ROC Curves /
- Nomogram /
- Children
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危重症的早期评估对于患儿的生存和预后至关重要,建立病情严重性和预后评估工具是儿童重症医学发展的必需。国内以小儿危重病例评分(pediatric critical illness score,PCIS)和简化PCIS应用最为普遍[1-3]。然而简化PCIS仍需对除动脉血氧分压和pH之外的8项指标进行评分,简便性仍显不够[3]。血常规是一种便捷、便宜和普遍开展的血细胞成分检测方法,为一些临床预测模型所采用,如第三代儿童死亡风险评分纳入WBC和PLT[4]。此外,血常规参数也常用于预测儿童和成人疾病严重性和死亡结局[1-3, 5-7]。本研究以PCIS和简化PCIS为儿童危重症的判断依据,探讨入院24 h内首次血常规参数与儿童危重症的相关性,并建立ROC曲线和危重症列线图预测模型,进一步探讨血常规参数对儿童危重症的预测价值,达到早期诊断的目的。
1. 资料与方法
1.1 临床资料
本研究为回顾性研究,以2015年4月—2019年12月入住蚌埠医学院第一附属医院儿童重症监护室的患儿为研究对象。纳入标准:(1)入院24 h内完成血常规检查;(2)入院24 h内完成PCIS评分;(3)年龄29 d~15岁。排除标准:(1)血常规参数缺失;(2)血液系统疾病。本研究获得蚌埠医学院第一附属医院医学伦理委员会批准(BYYFY-2015KY04),均获得家长知情同意。
1.2 队列定义
采用R语言sample函数按7∶ 3比例将患儿随机分为训练队列和验证队列[8]。训练队列用于筛选危重症危险因素并构建危重症列线图预测模型,验证队列用于验证训练队列获得的结果。
1.3 危重症定义
根据入院24 h内生理参数和实验室指标的最差值进行PCIS,总分100分,>80分为非危重症组,≤80分为危重症组[2],如有缺项则以简化PCIS为依据,总分80分,>64分为非危重症组,≤64分为危重症组[3]。
1.4 资料收集
通过电子病历系统收集资料:(1)人口学资料:年龄,性别。(2)入院24 h内第1次血常规:WBC,中性粒细胞百分比(neutrophil percentage,N%),红细胞计数(red cell count,RBC),血红蛋白(hemoglobin,HB),红细胞压积(hematocrit,HCT),平均红细胞体积(mean erythrocyte volume,MCV),平均血红蛋白(mean hemoglobin,MCH),平均血红蛋白浓度(mean hemoglobin concentration,MCHC),红细胞分布宽度-CV(red cell distribution width-coefficient of variation,RDW-CV),红细胞分布宽度-SD(red cell distribution width-standard deviation,RDW-SD),PLT,平均血小板体积(mean platelet volume,MPV),血小板分布宽度(platelet distribution width,PDW),血小板压积(plateletcrit,PCT),大型血小板比例(large platelet ratio,P-LCR),网织红细胞百分比(reticulocyte percentage,RET%),未成熟网织红细胞比值(immature reticulocyte ratio,IRF)。(3)入院24 h内PCIS评分所需的生理参数和实验室指标:心率、收缩压、呼吸频率、未吸氧下动脉血氧分压、pH值、血钠、血钾、肌酐、HB、胃肠系统有无应激性溃疡和/或肠麻痹[1]。血常规检测采用日本Sysmex-XN900全自动血液分析仪,血钠、血钾和血肌酐检测采用美国BECKMAN COULTER AU5800全自动生化分析系统,动脉血气分析采用美国雅培i-STAT300血气分析仪。
1.5 统计学方法
数据录入Excel软件。采用SPSS 25.0、R语言4.1.2及MedCalc 19.3.1统计学软件进行数据处理。非正态分布的计量资料以M(P25, P75)表示,组间比较采用Mann-Whitney U检验。计数资料以例(%)表示,组间比较采用χ2检验。训练队列患儿危重症危险因素采用单因素和多因素logistic回归分析,单因素分析有统计学意义的变量进入多因素logistic回归模型。以多因素分析有统计学意义的变量为自变量,通过logistic回归方程产生新的预测变量,构建ROC曲线,计算曲线下面积(AUC),并确定新的预测变量对训练队列和验证队列危重症预测的最佳截断值及灵敏度、特异度、阳性预测值、阴性预测值。采用R语言构建列线图预测模型,确定个体危重症的发生概率,应用一致性指数和校准曲线验证列线图预测模型的准确性,决策曲线和临床影响曲线评价列线图模型对2个队列危重症预测的临床应用价值。P < 0.05为差异有统计学意义。
2. 结果
2.1 一般资料
共纳入522例患儿,其中血常规参数缺失0例,血液系统疾病20例,指标不完整无法完成PCIS评分6例。最终纳入496例,男283例(57.1%),女213例(42.9%),年龄0.08~15.00岁,中位年龄2.00(0.57,5.88)岁。传染性疾病12例(2.4%),感染性疾病19例(3.8%),呼吸系统疾病155例(31.3%),免疫系统疾病3例(0.6%),内分泌系统疾病11例(2.2%),神经系统疾病167例(33.7%),肾脏疾病4例(0.8%),消化系统疾病54例(10.9%),心血管系统疾病12例(2.4%),中毒50例(10.1%),其他9例(1.8%)。训练队列347例,其中危重症112例,非危重症235例;验证队列149例,其中危重症49例,非危重症100例。训练队列和验证队列患儿基线资料差异无统计学意义(均P>0.05),见表 1。
表 1 训练队列和验证队列基线特征比较Table 1. Comparison of baseline characteristics between training queue and validation queue项目 训练队列(n=347) 验证队列(n=149) 统计量 P值 危重症[例(%)] 112(32.3) 49(32.9) 0.018a 0.894 年龄[(岁)] 5.70(3.20,11.40) 1.83(0.62,6.00) -0.671b 0.502 性别[例(%)] 男 201(57.9) 82(55.0) 0.356a 0.551 女 146(42.1) 67(45.0) WBC(×109/L) 12.06(8.35,17.63) 12.04(8.36,19.15) -0.719b 0.472 N(%) 67.20(48.90,81.80) 69.40(52.90,80.15) -0.088b 0.930 RBC(×1012/L) 4.26(3.78,4.59) 4.20(3.77,4.53) -0.946b 0.344 HB(g/L) 115(103,125) 115(101.5,124.0) -0.436b 0.663 HCT 0.35(0.31,0.37) 0.34(0.31,0.37) -0.982b 0.326 PLT(×109/L) 310(234,310) 305(233,391) -0.225b 0.822 MCV(fL) 81.6(78.0,85.7) 81.6(77.65,85.25) -0.309b 0.758 MCH(pg) 27.4(26.1,28.8) 27.6(26.05,28.7) -0.381b 0.703 MCHC(g/L) 334(325,345) 337(327,345) -1.030b 0.303 RDW-CV(%) 13.6(12.9,14.6) 13.6(13.0,14.7) -0.042b 0.967 RDW-SD(fL) 40.3(37.9,43.3) 40.5(38.25,43.55) -0.338b 0.735 MPV(fL) 7.2(6.7,8.8) 7.5(6.8,9.4) -1.992b 0.065 PCT(ml/L) 0.30(0.23,0.39) 0.30(0.23,0.39) -0.116b 0.908 PDW(%) 11.1(10.1,12.2) 11.0(10.1,12.3) -0.187b 0.852 P-LCR(%) 23.7(19.2,28.8) 23.2(19.15,29.25) -0.170b 0.865 RET% 0.88(0.65,1.27) 0.89(0.64,1.38) -0.185b 0.854 IRF(%) 5.7(3.2,11.4) 5.3(2.7,11.3) -0.671b 0.502 注:a为χ2值,b为Z值。 2.2 危重症危险因素分析
Logistic回归分析中自变量赋值:性别,男=1, 女=2;其余自变量为连续变量, 以实际值赋值。因变量赋值:非危重症=0,危重症=1。单因素logistic回归分析显示WBC(OR=1.033)、RBC(OR=0.695)、HB(OR=0.986)、RDW-CV(OR=1.254)、RDW-SD(OR=1.063)、RET%(OR=1.444)及IRF(OR=1.042)与训练队列危重症显著相关(均P < 0.05),见表 2。多因素logistic回归分析显示WBC(OR=1.032)、RDW-CV(OR=1.179)及RET%(OR=1.334)与训练队列危重症显著相关(表 3)。
表 2 训练队列危重症危险因素单因素logistic回归分析Table 2. Univariate logistic regression analysis of critical illness risk factors in training cohort变量 β SE Waldχ2 P值 OR值 95% CI 年龄(岁) -0.191 0.156 1.504 0.220 0.826 0.777~0.905 性别 -0.061 0.233 0.068 0.794 0.941 0.596~1.486 WBC(×109/L) 0.032 0.014 5.308 0.021 1.033 1.005~1.061 N(%) -0.009 0.005 2.922 0.087 0.991 0.980~1.001 RBC(×1012/L) -0.364 0.173 4.438 0.035 0.695 0.496~0.975 HB(g/L) -0.014 0.006 4.880 0.027 0.986 0.973~0.998 HCT -3.997 2.287 3.055 0.080 0.018 0.000~1.624 PLT(×109/L) 0.001 0.001 0.792 0.374 1.001 0.999~1.002 MCV(fL) 0.018 0.017 1.079 0.299 1.018 0.984~1.054 MCH(pg) -0.010 0.042 0.060 0.806 0.990 0.911~1.075 MCHC(g/L) -0.013 0.007 3.506 0.061 0.987 0.974~1.001 RDW-CV(%) 0.226 0.076 8.878 0.003 1.254 1.080~1.455 RDW-SD(fL) 0.061 0.020 9.132 0.003 1.063 1.022~1.106 MPV(fL) 0.071 0.079 0.812 0.368 1.074 0.919~1.255 PCT(ml/L) 0.988 0.854 1.336 0.248 2.685 0.503~14.332 PDW(%) 0.011 0.055 0.041 0.839 1.011 0.907~1.127 P-LCR(%) 0.005 0.014 0.141 0.707 1.005 0.978~1.034 RET% 0.367 0.125 8.579 0.003 1.444 1.129~1.846 IRF 0.041 0.015 7.394 0.007 1.042 1.011~1.073 表 3 训练队列危重症危险因素多因素logistic回归分析Table 3. Multivariate logistic regression analysis of critical illness risk factors in training cohort变量 β SE Waldχ2 P值 OR值 95% CI WBC(×109/L) 0.031 0.014 4.809 0.028 1.032 1.003~1.061 RDW-CV(%) 0.164 0.080 4.181 0.041 1.179 1.007~1.380 RET% 0.288 0.132 4.782 0.029 1.334 1.030~1.728 2.3 危重症预测ROC曲线特征
以WBC、RDW-CV及RET%为自变量进行二元logistic回归分析产生一个新的预测指标(WBC+RDW-CV+RET%),根据回归系数得出Logit(P)=-3.837+0.031×WBC+0.164×RDW-CV+0.288×RET%,联合指标对儿童危重症的预测概率P=eLogit(P)/[1+eLogit(P)]。以危重症发生概率为自变量,危重症实际发生情况为因变量构建ROC曲线(图 1),联合指标预测训练队列和验证队列危重症的AUC分别为0.644和0.711,在最佳截断值为0.357和0.290时,联合指标预测训练队列和验证队列危重症的灵敏度分别为46.4%(95% CI:37.0~56.1)和79.6%(95% CI:65.7~89.8),特异度分别为80.0%(95% CI:74.3~84.9)和60.0%(95% CI:49.7~69.7),阳性预测值分别为52.5%(95% CI=44.5~60.5)和49.4%(95% CI:42.5~56.3),阴性预测值分别为75.8%(95% CI:72.3~79.0)和85.7%(95% CI:77.1~91.4)。
2.4 危重症列线图预测模型
为确定个体危重症的发生概率,我们采用R语言以训练队列患儿的WBC、RDW-CV和RET%构建危重症列线图预测模型(图 2)。列线图模型对训练队列和验证队列危重症预测的一致性指数分别为0.644(95% CI: 0.580~0.708)和0.689(95% CI: 0.598~0.781)。对训练队列患儿采用Bootstrap 1 000次内部自抽样获得列线图模型的校准曲线[9](图 3A),将验证队列患儿带入训练队列列线图预测模型获得验证队列危重症的校准曲线(图 3B)。决策曲线分析显示训练队列和验证队列的模型曲线均在无治疗线和治疗线之上(图 3C和3D)。临床影响曲线分析显示训练队列和验证队列危重症预测概率与实际发生概率均在风险阈值0.4后差距减小,吻合良好(图 3E和3F)。
图 3 列线图预测训练队列和验证队列危重症发生的校准曲线、决策曲线及临床影响曲线分析注:A为训练队列校准曲线,B为验证队列校准曲线。长虚线为45°对角线,代表最佳预测结果,短虚线为危重症发生曲线,实线为危重症预测曲线。C为训练队列决策曲线,D为验证队列决策曲线。横坐标为风险阈概率,纵坐标为净获益,无治疗线(蓝色实线)表示所有患儿被认为是非危重症不治疗的净获益,治疗线(绿色实线)表示所有患儿被认为是危重症接受治疗的净获益,红色实线分别为2个队列的模型曲线。E为训练队列临床影响曲线,F为验证队列临床影响曲线。横坐标为风险阈概率,纵坐标为危重症高危人数,红色曲线表示在各个风险阈概率下,被模型判定为危重症高风险的预测曲线,蓝色虚线为各个风险阈概率下危重症的发生曲线。Figure 3. Analysis of calibration curve, decision curve and clinical influence curve of critical illness occurrence in training cohort and validation cohort3. 讨论
本研究发现WBC、RDW-CV及RET%是儿童危重症的独立危险因素,三者联合指标预测训练队列和验证队列危重症的AUC分别为0.644和0.711,均在0.6以上,表明联合指标对儿童危重症具有较好的预测效能。尽管本研究联合指标对危重症预测的阳性预测值较低,但是阴性预测值高,训练队列为75.8%,验证队列为85.7%,表明联合指标小于最佳截断值时,对非危重症的预测效能高,可进一步以训练队列患儿WBC、RDW-CV及RET%构建列线图模型预测个体患儿危重症的发生概率。列线图是一种图形工具,无需借助计算机或计算器即可快速得出与复杂的计算相近似的结果,可以通过图形方式表示每个预测变量对结局的影响,进而获得所有预测变量对某一患者的预测结果[10]。一致性指数用于衡量预测危重症和实际危重症的一致性,范围0.5~1.0,0.5表示无预测能力,1.0表示完美预测[11],通常认为一致性指数大于0.7预测能力好[12]。本研究显示列线图模型对训练队列和验证队列危重症预测的一致性指数较为接近,分别为0.644和0.689,均接近0.7,表明所建立的列线图模型对儿童危重症具有较好的预测效能。校准曲线用于比较列线图危重症预测结果和危重症发生的一致性,由图 3A和图 3B可见,训练队列和验证队列校准曲线吻合程度好,均接近45°对角线,表明列线图模型对儿童危重症具有较好的预测效能[8]。决策曲线和临床影响曲线反映列线图模型的临床应用价值。本研究决策曲线分析显示训练队列和验证队列的模型曲线均位于无治疗线和治疗线上方区域,提示列线图的临床价值较好[13-14]。临床影响曲线分析发现训练队列和验证队列危重症预测概率与实际概率在风险阈值大于0.4后吻合良好[15],表明本研究构建的列线图对儿童危重症的预测有较好的临床价值。
外周血WBC是临床常用的感染指标。SHAH S等[16]研究发现WBC异常与儿童全身炎症反应综合征进展至脓毒性休克显著相关。CORONADO M A等[17]报道高WBC计数的新型冠状病毒急性呼吸衰竭患儿的死亡率显著升高。红细胞分布宽度是反映红细胞体积差异的指标[18]。KIM D H等[19]报道红细胞分布宽度≥14.50%是儿童重症监护室患儿死亡率的独立危险因素。LEE J等[20]研究发现红细胞分布宽度≥15.00%与儿童严重社区获得性肺炎的风险增加显著相关。杨剑秋等[21]研究发现,红细胞分布宽度≥14.92%可用于成人脓毒症病情严重程度及不良预后评估。网织红细胞成熟的前3.0~3.5 d发生在骨髓中,最后24 h发生在外周血中,当失血、溶血或缺氧导致红细胞生成素刺激红细胞生成时,网织红细胞会过早释放到外周血中,在骨髓中的成熟时间缩短至不到1 d,而在外周血中的成熟时间相应延长,这导致网织红细胞在外周血中有更长的寿命,增加了网织红细胞在循环红细胞中的比例[22]。这可能解释本研究RET%升高是儿童危重症的独立危险因素。
本研究为单中心研究,后续有必要开展多中心前瞻性研究进一步探讨入院24 h内首次血常规参数对儿童危重症的预测价值。
综上所述,入院24 h内首次WBC、RDW-CV及RET%是儿童危重症发生的独立危险因素,三者联合指标对儿童危重症具有较好的预测效能,以WBC、RDW-CV及RET%构建的列线图模型可预测危重症的发生概率,具有较好的准确性、简便性和临床应用价值。
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图 3 列线图预测训练队列和验证队列危重症发生的校准曲线、决策曲线及临床影响曲线分析
注:A为训练队列校准曲线,B为验证队列校准曲线。长虚线为45°对角线,代表最佳预测结果,短虚线为危重症发生曲线,实线为危重症预测曲线。C为训练队列决策曲线,D为验证队列决策曲线。横坐标为风险阈概率,纵坐标为净获益,无治疗线(蓝色实线)表示所有患儿被认为是非危重症不治疗的净获益,治疗线(绿色实线)表示所有患儿被认为是危重症接受治疗的净获益,红色实线分别为2个队列的模型曲线。E为训练队列临床影响曲线,F为验证队列临床影响曲线。横坐标为风险阈概率,纵坐标为危重症高危人数,红色曲线表示在各个风险阈概率下,被模型判定为危重症高风险的预测曲线,蓝色虚线为各个风险阈概率下危重症的发生曲线。
Figure 3. Analysis of calibration curve, decision curve and clinical influence curve of critical illness occurrence in training cohort and validation cohort
表 1 训练队列和验证队列基线特征比较
Table 1. Comparison of baseline characteristics between training queue and validation queue
项目 训练队列(n=347) 验证队列(n=149) 统计量 P值 危重症[例(%)] 112(32.3) 49(32.9) 0.018a 0.894 年龄[(岁)] 5.70(3.20,11.40) 1.83(0.62,6.00) -0.671b 0.502 性别[例(%)] 男 201(57.9) 82(55.0) 0.356a 0.551 女 146(42.1) 67(45.0) WBC(×109/L) 12.06(8.35,17.63) 12.04(8.36,19.15) -0.719b 0.472 N(%) 67.20(48.90,81.80) 69.40(52.90,80.15) -0.088b 0.930 RBC(×1012/L) 4.26(3.78,4.59) 4.20(3.77,4.53) -0.946b 0.344 HB(g/L) 115(103,125) 115(101.5,124.0) -0.436b 0.663 HCT 0.35(0.31,0.37) 0.34(0.31,0.37) -0.982b 0.326 PLT(×109/L) 310(234,310) 305(233,391) -0.225b 0.822 MCV(fL) 81.6(78.0,85.7) 81.6(77.65,85.25) -0.309b 0.758 MCH(pg) 27.4(26.1,28.8) 27.6(26.05,28.7) -0.381b 0.703 MCHC(g/L) 334(325,345) 337(327,345) -1.030b 0.303 RDW-CV(%) 13.6(12.9,14.6) 13.6(13.0,14.7) -0.042b 0.967 RDW-SD(fL) 40.3(37.9,43.3) 40.5(38.25,43.55) -0.338b 0.735 MPV(fL) 7.2(6.7,8.8) 7.5(6.8,9.4) -1.992b 0.065 PCT(ml/L) 0.30(0.23,0.39) 0.30(0.23,0.39) -0.116b 0.908 PDW(%) 11.1(10.1,12.2) 11.0(10.1,12.3) -0.187b 0.852 P-LCR(%) 23.7(19.2,28.8) 23.2(19.15,29.25) -0.170b 0.865 RET% 0.88(0.65,1.27) 0.89(0.64,1.38) -0.185b 0.854 IRF(%) 5.7(3.2,11.4) 5.3(2.7,11.3) -0.671b 0.502 注:a为χ2值,b为Z值。 表 2 训练队列危重症危险因素单因素logistic回归分析
Table 2. Univariate logistic regression analysis of critical illness risk factors in training cohort
变量 β SE Waldχ2 P值 OR值 95% CI 年龄(岁) -0.191 0.156 1.504 0.220 0.826 0.777~0.905 性别 -0.061 0.233 0.068 0.794 0.941 0.596~1.486 WBC(×109/L) 0.032 0.014 5.308 0.021 1.033 1.005~1.061 N(%) -0.009 0.005 2.922 0.087 0.991 0.980~1.001 RBC(×1012/L) -0.364 0.173 4.438 0.035 0.695 0.496~0.975 HB(g/L) -0.014 0.006 4.880 0.027 0.986 0.973~0.998 HCT -3.997 2.287 3.055 0.080 0.018 0.000~1.624 PLT(×109/L) 0.001 0.001 0.792 0.374 1.001 0.999~1.002 MCV(fL) 0.018 0.017 1.079 0.299 1.018 0.984~1.054 MCH(pg) -0.010 0.042 0.060 0.806 0.990 0.911~1.075 MCHC(g/L) -0.013 0.007 3.506 0.061 0.987 0.974~1.001 RDW-CV(%) 0.226 0.076 8.878 0.003 1.254 1.080~1.455 RDW-SD(fL) 0.061 0.020 9.132 0.003 1.063 1.022~1.106 MPV(fL) 0.071 0.079 0.812 0.368 1.074 0.919~1.255 PCT(ml/L) 0.988 0.854 1.336 0.248 2.685 0.503~14.332 PDW(%) 0.011 0.055 0.041 0.839 1.011 0.907~1.127 P-LCR(%) 0.005 0.014 0.141 0.707 1.005 0.978~1.034 RET% 0.367 0.125 8.579 0.003 1.444 1.129~1.846 IRF 0.041 0.015 7.394 0.007 1.042 1.011~1.073 表 3 训练队列危重症危险因素多因素logistic回归分析
Table 3. Multivariate logistic regression analysis of critical illness risk factors in training cohort
变量 β SE Waldχ2 P值 OR值 95% CI WBC(×109/L) 0.031 0.014 4.809 0.028 1.032 1.003~1.061 RDW-CV(%) 0.164 0.080 4.181 0.041 1.179 1.007~1.380 RET% 0.288 0.132 4.782 0.029 1.334 1.030~1.728 -
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