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首页> 外文期刊>BMC Medical Informatics and Decision Making >Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model
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Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

机译：心脏手术后重症监护病房出院的计算机化预测：高斯过程模型的开发和验证

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Background The intensive care unit (ICU) length of stay (LOS) of patients undergoing cardiac surgery may vary considerably, and is often difficult to predict within the first hours after admission. The early clinical evolution of a cardiac surgery patient might be predictive for his LOS. The purpose of the present study was to develop a predictive model for ICU discharge after non-emergency cardiac surgery, by analyzing the first 4 hours of data in the computerized medical record of these patients with Gaussian processes (GP), a machine learning technique. Methods Non-interventional study. Predictive modeling, separate development (n = 461) and validation (n = 499) cohort. GP models were developed to predict the probability of ICU discharge the day after surgery (classification task), and to predict the day of ICU discharge as a discrete variable (regression task). GP predictions were compared with predictions by EuroSCORE, nurses and physicians. The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration. The regression task was evaluated by comparing median actual and predicted discharge, loss penalty function (LPF) ((actual-predicted)/actual) and calculating root mean squared relative errors (RMSRE). Results Median (P25-P75) ICU length of stay was 3 (2-5) days. For classification, the GP model showed an aROC of 0.758 which was significantly higher than the predictions by nurses, but not better than EuroSCORE and physicians. The GP had the best calibration, with a Brier Score of 0.179 and Hosmer-Lemeshow p-value of 0.382. For regression, GP had the highest proportion of patients with a correctly predicted day of discharge (40%), which was significantly better than the EuroSCORE (p Conclusions A GP model that uses PDMS data of the first 4 hours after admission in the ICU of scheduled adult cardiac surgery patients was able to predict discharge from the ICU as a classification as well as a regression task. The GP model demonstrated a significantly better discriminative power than the EuroSCORE and the ICU nurses, and at least as good as predictions done by ICU physicians. The GP model was the only well calibrated model.
机译：背景技术接受心脏手术的患者的重症监护病房（ICU）住院时间（LOS）可能相差很大，并且通常难以在入院后的最初几个小时内预测。心脏外科手术患者的早期临床发展可能是其LOS的预测指标。本研究的目的是通过分析高斯过程（GP）（一种机器学习技术）的患者的计算机病历中的前4个小时数据，来开发非急诊心脏手术后ICU出院的预测模型。方法非干预性研究。预测建模，独立开发（n = 461）和验证（n = 499）队列。开发GP模型来预测术后ICU出院的可能性（分类任务），并预测ICU出院的天数作为离散变量（回归任务）。将GP预测与EuroSCORE，护士和医师的预测进行了比较。使用aROC对分类任务进行评估以进行区分，并使用Brier分数，Brier分数定标和Hosmer-Lemeshow检验进行校准。通过比较实际中位数和预期排放中位数，损失损失函数（LPF）（（实际预测）/实际）并计算均方根相对误差（RMSRE）来评估回归任务。结果中位（P25-P75）ICU住院时间为3（2-5）天。对于分类，GP模型显示的aROC为0.758，显着高于护士的预测，但并不优于EuroSCORE和医生。 GP的校准效果最佳，其Brier得分为0.179，Hosmer-Lemeshow p值为0.382。对于回归分析，GP出院日正确预测的患者比例最高（40％），这明显优于EuroSCORE（p结论在GP的ICU中使用入院后最初4小时使用PDMS数据的GP模型。有计划的成年心脏手术患者能够预测出ICU的分类和回归任务GP模型显示出比EuroSCORE和ICU护士更好的判别力，至少与ICU的预测一样好医生GP模型是唯一经过良好校准的模型。

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