Admission Laboratory Values Accurately Predict In-hospital Mortality: a Retrospective Cohort Study
Admission Laboratory Values Accurately Predict In-hospital Mortality: a Retrospective Cohort Study
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DOI：
10.1007/s11606-019-05282-2
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发表时间：
2020-03-01
影响因子：
5.7
通讯作者：
Harris, A. D.
中科院分区：
文献类型：
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作者：
Blanco, N.;Leekha, S.;Harris, A. D.
背景：患者的病情越严重，患者的医院预后就越差。然而，医院范围内的疾病严重程度评分，简单，广泛可用，而不是特定的诊断仍然是需要的。实验室检测可能被用作估计疾病严重程度的替代方法。目的评价医院实验室检查作为疾病严重程度指标预测住院患者住院死亡率的能力，并探讨其作为疾病严重程度风险调整替代方法的潜力。设计和患者：对2015年11月至2017年11月马里兰大学医学中心收治的38,367名成人非创伤患者进行回顾性队列研究。实验室检查（血红蛋白、血小板计数、白细胞计数、尿素氮、肌酐、葡萄糖、钠、钾和总碳酸氢盐（HCO3））在入院后24小时内进行。使用我们的部分队列（n = 21,003）构建了预测住院死亡率的多变量logistic回归模型。采用c统计量和Hosmer-Lemeshow （HL）检验评价模型的性能。此外，构建了一条校准带，确定了校准曲线周围的置信区间，以识别错标范围。患者年龄和所有实验室检查预测死亡率具有良好的判别性（c = 0.79）。入院时HCO3水平或白细胞计数异常的患者在住院期间死亡的可能性是结果正常患者的两倍。模型校正和拟合良好（HL = 13.9, p = 0.18）。结论入院实验室检查能够较准确地预测住院死亡率，为疾病严重程度风险调整提供了一种客观、广泛可及的方法。
Background The greater the severity of illness of a patient, the more likely the patient will have a poor hospital outcome. However, hospital-wide severity of illness scores that are simple, widely available, and not diagnosis-specific are still needed. Laboratory tests could potentially be used as an alternative to estimate severity of illness. Objective To evaluate the ability of hospital laboratory tests, as measures of severity of illness, to predict in-hospital mortality among hospitalized patients, and therefore, their potential as an alternative method to severity of illness risk adjustment. Designs and Patients A retrospective cohort study among 38,367 adult non-trauma patients admitted to the University of Maryland Medical Center between November 2015 and November 2017 was performed. Laboratory tests (hemoglobin, platelet count, white blood cell count, urea nitrogen, creatinine, glucose, sodium, potassium, and total bicarbonate (HCO3)) were included when ordered within 24 h from the time of hospital admission. A multivariable logistic regression model to predict in-hospital mortality was constructed using a section of our cohort (n = 21,003). Main Measures Model performance was evaluated using the c-statistic and the Hosmer-Lemeshow (HL) test. In addition, a calibration belt was constructed to determine a confidence interval around the calibration curve with the purpose of identifying ranges of miscalibration. Key Results Patient age and all laboratory tests predicted mortality with good discrimination (c = 0.79). Patients with abnormal HCO3 levels or leukocyte counts at admission were twice as likely to die during their hospital stay as patients with normal results. A good model calibration and fit were observed (HL = 13.9, p = 0.18). Conclusions Admission laboratory tests are able to predict in-hospital mortality with good accuracy, providing an objective and widely accessible approach to severity of illness risk adjustment.