Suppr超能文献

新生儿随访血清胆红素的预测模型:模型开发与验证

Predictive Models for Neonatal Follow-Up Serum Bilirubin: Model Development and Validation.

作者信息

Chou Joseph H

机构信息

Massachusetts General Hospital, Boston, MA, United States.

出版信息

JMIR Med Inform. 2020 Oct 29;8(10):e21222. doi: 10.2196/21222.

Abstract

BACKGROUND

Hyperbilirubinemia affects many newborn infants and, if not treated appropriately, can lead to irreversible brain injury.

OBJECTIVE

This study aims to develop predictive models of follow-up total serum bilirubin measurement and to compare their accuracy with that of clinician predictions.

METHODS

Subjects were patients born between June 2015 and June 2019 at 4 hospitals in Massachusetts. The prediction target was a follow-up total serum bilirubin measurement obtained <72 hours after a previous measurement. Birth before versus after February 2019 was used to generate a training set (27,428 target measurements) and a held-out test set (3320 measurements), respectively. Multiple supervised learning models were trained. To further assess model performance, predictions on the held-out test set were also compared with corresponding predictions from clinicians.

RESULTS

The best predictive accuracy on the held-out test set was obtained with the multilayer perceptron (ie, neural network, mean absolute error [MAE] 1.05 mg/dL) and Xgboost (MAE 1.04 mg/dL) models. A limited number of predictors were sufficient for constructing models with the best performance and avoiding overfitting: current bilirubin measurement, last rate of rise, proportion of time under phototherapy, time to next measurement, gestational age at birth, current age, and fractional weight change from birth. Clinicians made a total of 210 prospective predictions. The neural network model accuracy on this subset of predictions had an MAE of 1.06 mg/dL compared with clinician predictions with an MAE of 1.38 mg/dL (P<.0001). In babies born at 35 weeks of gestation or later, this approach was also applied to predict the binary outcome of subsequently exceeding consensus guidelines for phototherapy initiation and achieved an area under the receiver operator characteristic curve of 0.94 (95% CI 0.91 to 0.97).

CONCLUSIONS

This study developed predictive models for neonatal follow-up total serum bilirubin measurements that outperform clinicians. This may be the first report of models that predict specific bilirubin values, are not limited to near-term patients without risk factors, and take into account the effect of phototherapy.

摘要

背景

高胆红素血症影响许多新生儿,若治疗不当,可导致不可逆的脑损伤。

目的

本研究旨在建立随访总血清胆红素测量的预测模型,并将其准确性与临床医生的预测进行比较。

方法

研究对象为2015年6月至2019年6月在马萨诸塞州4家医院出生的患者。预测目标是在前一次测量后<72小时获得的随访总血清胆红素测量值。2019年2月之前和之后出生的分别用于生成训练集(27428个目标测量值)和保留测试集(3320个测量值)。训练了多个监督学习模型。为进一步评估模型性能,还将保留测试集上的预测与临床医生的相应预测进行了比较。

结果

多层感知器(即神经网络,平均绝对误差[MAE]1.05mg/dL)和Xgboost(MAE 1.04mg/dL)模型在保留测试集上获得了最佳预测准确性。构建具有最佳性能并避免过拟合的模型,有限数量的预测变量就足够了:当前胆红素测量值、上次上升速率、光疗时间比例、下次测量时间、出生时的胎龄、当前年龄以及出生后体重变化分数。临床医生共做出210次前瞻性预测。与临床医生预测的MAE为1.38mg/dL相比,该神经网络模型在这部分预测中的准确性MAE为1.06mg/dL(P<0.0001)。在妊娠35周或更晚出生的婴儿中,该方法还用于预测随后超过光疗启动共识指南的二元结果,受试者工作特征曲线下面积为0.94(95%CI 0.91至0.97)。

结论

本研究建立的新生儿随访总血清胆红素测量预测模型优于临床医生。这可能是首个预测特定胆红素值、不限于无危险因素的近期患者且考虑光疗影响的模型报告。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15e9/7661258/bd7edb4d8a69/medinform_v8i10e21222_fig1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验