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剖宫产术后出院后阿片类药物使用预测模型的建立与验证。

Development and Validation of a Model to Predict Postdischarge Opioid Use After Cesarean Birth.

机构信息

Departments of Obstetrics & Gynecology, Biostatistics, Anesthesiology, and Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee.

出版信息

Obstet Gynecol. 2022 May 1;139(5):888-897. doi: 10.1097/AOG.0000000000004759. Epub 2022 Apr 5.

Abstract

OBJECTIVE

To develop and validate a prediction model for postdischarge opioid use in patients undergoing cesarean birth.

METHODS

We conducted a prospective cohort study of patients undergoing cesarean birth. Patients were enrolled postoperatively, and they completed pain and opioid use questionnaires 14 days after cesarean birth. Clinical data were abstracted from the electronic health record (EHR). Participants were prescribed 30 tablets of hydrocodone 5 mg-acetaminophen 325 mg at discharge and were queried about postdischarge opioid use. The primary outcome was total morphine milligram equivalents used. We constructed three proportional odds predictive models of postdischarge opioid use: a full model with 34 predictors available before hospital discharge, an EHR model that excluded questionnaire data, and a reduced model. The reduced model used forward selection to sequentially add predictors until 90% of the full model performance was achieved. Predictors were ranked a priori based on data from the literature and prior research. Predictive accuracy was estimated using discrimination (concordance index).

RESULTS

Between 2019 and 2020, 459 participants were enrolled and 279 filled the standardized study prescription. Of the 398 with outcome measurements, participants used a median of eight tablets (interquartile range 1-18 tablets) after discharge, 23.5% used no opioids, and 23.0% used all opioids. Each of the models demonstrated high accuracy predicting postdischarge opioid use (concordance index range 0.74-0.76 for all models). We selected the reduced model as our final model given its similar model performance with the fewest number of predictors, all obtained from the EHR (inpatient opioid use, tobacco use, and depression or anxiety).

CONCLUSION

A model with three predictors readily found in the EHR-inpatient opioid use, tobacco use, and depression or anxiety-accurately estimated postdischarge opioid use. This represents an opportunity for individualizing opioid prescriptions after cesarean birth.

摘要

目的

开发并验证一种预测剖宫产术后患者出院后阿片类药物使用的模型。

方法

我们进行了一项前瞻性队列研究,纳入了行剖宫产术的患者。患者在术后入组,并在剖宫产术后 14 天完成疼痛和阿片类药物使用问卷。从电子健康记录(EHR)中提取临床数据。患者在出院时被开处 30 片氢可酮 5mg-对乙酰氨基酚 325mg,并询问出院后阿片类药物的使用情况。主要结局为使用的总吗啡毫克当量。我们构建了三种术后阿片类药物使用的比例优势预测模型:一个包含 34 个预测因子的全模型,这些预测因子在出院前可用;一个排除问卷数据的 EHR 模型;一个简化模型。简化模型使用逐步向前选择来添加预测因子,直到达到全模型性能的 90%。根据文献和先前研究的数据,预先对预测因子进行了排序。使用判别(一致性指数)来估计预测准确性。

结果

在 2019 年至 2020 年间,纳入了 459 名参与者,其中 279 名完成了标准化研究处方。在有结局测量的 398 名患者中,参与者出院后中位数使用了 8 片(四分位间距 1-18 片),23.5%的患者未使用阿片类药物,23.0%的患者使用了所有阿片类药物。所有模型预测出院后阿片类药物使用的准确性均较高(所有模型的一致性指数范围为 0.74-0.76)。鉴于简化模型具有与预测因子数量最少的模型(均来自 EHR,包括住院期间阿片类药物使用、吸烟和抑郁或焦虑)相似的模型性能,我们选择了简化模型作为最终模型。

结论

一个模型使用三个在 EHR 中容易找到的预测因子——住院期间阿片类药物使用、吸烟和抑郁或焦虑——准确地估计了出院后的阿片类药物使用。这为个体化剖宫产术后阿片类药物处方提供了机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e1a/9015028/85eede828b8c/ong-139-888-g003.jpg

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