背景:这项研究旨在应用反向传播神经网络(BPNN)开发一种预测危重病患者多药耐药生物体(MDRO)感染的模型。
方法:本研究收集了2021年8月至2022年1月青岛大学附属医院重症监护病房(ICU)收治的患者信息。将所有入选的患者随机分为训练集(80%)和测试集(20%)。使用最小绝对收缩率和选择算子和逐步回归分析来确定MDRO感染的独立危险因素。基于这些因素构建了BPNN模型。然后,我们从2022年5月至2022年7月在同一中心对患者进行了外部验证.通过校准曲线评估模型性能,曲线下面积(AUC),灵敏度,特异性,和准确性。
结果:在主要队列中,纳入688例患者,其中MDRO感染患者109例(15.84%)。MDRO感染的危险因素,由主要队列确定,包括住院时间,ICU住院时间,长期卧床休息,在ICU前使用抗生素,急性生理和慢性健康评估II,ICU前的侵入性手术,抗生素的数量,慢性肺病,和低蛋白血症。验证集中有238名患者,其中MDRO感染患者31例(13.03%)。该BPNN模型产生良好的校准。训练集的AUC,测试集和验证集为0.889(95%CI0.852-0.925),0.919(95%CI0.856-0.983),和0.811(95%CI0.731-0.891),分别。
结论:本研究证实了MDRO感染的9个独立危险因素。BPNN模型表现良好,可能用于预测ICU患者的MDRO感染。
BACKGROUND: This study aimed to apply the backpropagation neural network (BPNN) to develop a model for predicting multidrug-resistant organism (MDRO) infection in critically ill patients.
METHODS: This study collected patient information admitted to the intensive care unit (ICU) of the Affiliated Hospital of Qingdao University from August 2021 to January 2022. All patients enrolled were divided randomly into a training set (80%) and a test set (20%). The least absolute shrinkage and selection operator and stepwise regression analysis were used to determine the independent risk factors for MDRO infection. A BPNN model was constructed based on these factors. Then, we externally validated this model in patients from May 2022 to July 2022 over the same center. The model performance was evaluated by the calibration curve, the area under the curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: In the primary cohort, 688 patients were enrolled, including 109 (15.84%) MDRO infection patients. Risk factors for MDRO infection, as determined by the primary cohort, included length of hospitalization, length of ICU stay, long-term bed rest, antibiotics use before ICU, acute physiology and chronic health evaluation II, invasive operation before ICU, quantity of antibiotics, chronic lung disease, and hypoproteinemia. There were 238 patients in the validation set, including 31 (13.03%) MDRO infection patients. This BPNN model yielded good calibration. The AUC of the training set, the test set and the validation set were 0.889 (95% CI 0.852-0.925), 0.919 (95% CI 0.856-0.983), and 0.811 (95% CI 0.731-0.891), respectively.
CONCLUSIONS: This study confirmed nine independent risk factors for MDRO infection. The BPNN model performed well and was potentially used to predict MDRO infection in ICU patients.