Akiki Ronald K, Anand Rajsavi S, Borrelli Mimi, Sarkar Indra Neil, Liu Paul Y, Chen Elizabeth S
Alpert Medical School, Brown University, Providence, RI, USA.
Center for Biomedical Informatics, Brown University, Providence, RI, USA.
J Emerg Crit Care Med. 2021 Apr;5:13. doi: 10.21037/jeccm-20-154. Epub 2021 Apr 25.
Open wounds have a significant impact on the health of patients causing pain, loss of function, and death. Labeled as a comorbid condition, open wounds represent a "silent epidemic" that affect a large portion of the US population. Due to their burden of care, open wound patients face an increased risk of ICU stay and mortality. There is a dearth of studies that investigate mortality among wound patients in the ICU. We sought to develop a model that predicts the risk of mortality among wound patients in the ICU.
Random forest and binomial logistic regression models were developed to predict the risk of mortality among open wound patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database. MIMIC-III includes de-identified data for patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. Six variables were used to develop the model (wound location, gender, age, admission type, minimum platelet count and hyperphosphatemia). The Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index were used to assess model strength.
A total of 3,937 patients were included with a mean age of 76.57. Of those, 3,372 (85%) survived and 565 (15%) died during their ICU stay. The random forest model achieved an area under the curve (AUC) of 0.924. The CCI and Elixhauser models resulted in AUC of 0.528 and 0.565, respectively.
Machine learning models may allow clinicians to provide better care and management to open wound patients in the ICU.
开放性伤口对患者健康有重大影响，会导致疼痛、功能丧失甚至死亡。开放性伤口被视为一种合并症，是影响很大一部分美国人口的“隐性流行病”。由于护理负担，开放性伤口患者入住重症监护病房（ICU）和死亡的风险增加。目前缺乏对ICU中伤口患者死亡率的研究。我们试图开发一种模型来预测ICU中伤口患者的死亡风险。
开发了随机森林模型和二项逻辑回归模型，以预测重症监护医学信息集市三期（MIMIC-III）数据库中开放性伤口患者的死亡风险。MIMIC-III包含2001年至2012年间在贝斯以色列女执事医疗中心重症监护病房住院患者的去识别数据。使用六个变量来开发模型（伤口位置、性别、年龄、入院类型、最低血小板计数和高磷血症）。使用查尔森合并症指数（CCI）和埃利克斯豪泽合并症指数来评估模型强度。
共纳入3937例患者，平均年龄为76.57岁。其中，3372例（85%）在ICU住院期间存活，565例（15%）死亡。随机森林模型的曲线下面积（AUC）为0.924。CCI模型和埃利克斯豪泽模型的AUC分别为0.528和0.565。
机器学习模型可能使临床医生能够为ICU中的开放性伤口患者提供更好的护理和管理。
