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预测创伤和急诊普通外科患者的住院时间。

Predicting length of stay for trauma and emergency general surgery patients.

机构信息

Northwestern University Feinberg School of Medicine, United States.

Cleveland Clinic, Department of General Surgery, United States.

出版信息

Am J Surg. 2020 Sep;220(3):757-764. doi: 10.1016/j.amjsurg.2020.01.055. Epub 2020 Feb 7.

Abstract

BACKGROUND

Predicting length of stay (LOS) is difficult for trauma and emergency general surgery (TEGS) patients. Our aim was to determine the accuracy of LOS predictions by TEGS team members and the NSQIP Risk Calculator and the patient factors associated with inaccurate predictions.

METHODS

LOS for 200 TEGS patients were predicted. Full-model univariate and multivariable linear regressions were used to determine associations between patient characteristics and inaccurate predictions.

RESULTS

There were 1,518 predictions of LOS. LOS predictions were rarely correct (TEGS team: 30.7% all patients, 35.6% surgical; NSQIP: 33.0% surgical). No individual group nor NSQIP was significantly better at predicting LOS. Inaccurate predictions were associated with female patients, longer LOS, trauma, frailty, higher comorbidity and injury severity scores, and lesser disposition.

CONCLUSION

Both the TEGS team and NSQIP are poor at predicting LOS for TEGS patients. Further work helping to guide LOS predictions for TEGS patients is warranted.

摘要

背景

创伤和急诊普通外科 (TEGS) 患者的住院时间 (LOS) 预测较为困难。我们的目的是确定 TEGS 团队成员和 NSQIP 风险计算器对 LOS 预测的准确性,以及与预测不准确相关的患者因素。

方法

对 200 例 TEGS 患者的 LOS 进行了预测。使用全模型单变量和多变量线性回归来确定患者特征与不准确预测之间的关联。

结果

共预测了 1518 次 LOS。 LOS 预测很少准确(TEGS 团队:所有患者为 30.7%,手术患者为 35.6%;NSQIP:手术患者为 33.0%)。没有任何一个组或 NSQIP 在 LOS 预测方面表现明显更好。不准确的预测与女性患者、较长的 LOS、创伤、虚弱、更高的合并症和损伤严重程度评分以及较少的处置有关。

结论

TEGS 团队和 NSQIP 均不擅长预测 TEGS 患者的 LOS。需要进一步研究以帮助指导 TEGS 患者的 LOS 预测。

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