Rapid identification of slow healing wounds
Rapid identification of slow healing wounds
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DOI：
10.1111/wrr.12384
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发表时间：
2016-01-01
影响因子：
2.9
通讯作者：
Shah, Nigam H.
中科院分区：
文献类型：
--
作者：
Jung, Kenneth;Covington, Scott;Shah, Nigam H.
在美国，慢性不愈合伤口的患病率为2%，每年估计花费500亿美元。对伤口愈合缓慢的风险进行准确的分层可能有助于指导治疗和转诊决策。我们应用现代机器学习方法和特征工程开发了一种伤口延迟愈合的预测模型，该模型使用门诊伤口护理中心常规护理期间收集的信息。患者和伤口数据收集于Healogics Inc.运营的68家门诊伤口护理中心。在2009年至2013年间，该数据集包括59，953名患者的基本人口统计信息，以及180，696处伤口的定量和分类信息。通过将患者随机分配到训练集和测试集，将伤口分为训练集和测试集。如果在伤口护理中心就诊后伤口愈合时间超过15周，则认为伤口愈合时间延迟。该数据集中11%的伤口符合这一标准。根据护理第一周的训练数据开发了预后模型，以预测延迟愈合的伤口。使用训练集的保留子集进行模型选择，并在测试集上评价最终模型以评价区分能力和校准。该模型实现了延迟愈合结局的曲线下面积为0.842（95%置信区间0.834-0.847），Brier可靠性评分为0.00018。早期准确预测延迟愈合的伤口可以通过允许临床医生增加对风险最大的患者进行干预的积极性来改善患者护理。
Chronic nonhealing wounds have a prevalence of 2% in the United States, and cost an estimated $50 billion annually. Accurate stratification of wounds for risk of slow healing may help guide treatment and referral decisions. We have applied modern machine learning methods and feature engineering to develop a predictive model for delayed wound healing that uses information collected during routine care in outpatient wound care centers. Patient and wound data was collected at 68 outpatient wound care centers operated by Healogics Inc. in 26 states between 2009 and 2013. The dataset included basic demographic information on 59,953 patients, as well as both quantitative and categorical information on 180,696 wounds. Wounds were split into training and test sets by randomly assigning patients to training and test sets. Wounds were considered delayed with respect to healing time if they took more than 15 weeks to heal after presentation at a wound care center. Eleven percent of wounds in this dataset met this criterion. Prognostic models were developed on training data available in the first week of care to predict delayed healing wounds. A held out subset of the training set was used for model selection, and the final model was evaluated on the test set to evaluate discriminative power and calibration. The model achieved an area under the curve of 0.842 (95% confidence interval 0.834-0.847) for the delayed healing outcome and a Brier reliability score of 0.00018. Early, accurate prediction of delayed healing wounds can improve patient care by allowing clinicians to increase the aggressiveness of intervention in patients most at risk.