Development and validation of a machine learning algorithm–based risk prediction model of pressure injury in the intensive care unit.
The study aimed to establish a machine learning–based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (ICU) using large‐scale clinical data. A retrospective cohort study design was employed to develop and validate a top‐performing clinical feature panel a...
| Publicado en: | International Wound Journal Vol. 19; no. 7; pp. 1637 - 1650 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Nov2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159936181&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159936181 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17424801 1725 jtl: International Wound Journal issn: 17424801 maglogo: Y pubinfo: dt: Nov2022 vid: 19 iid: 7 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 159936181 154844071 159936181 159936181 10.1111/iwj.13764 159936181 ppf: 1637 ppct: 13 formats: tig: atl: Development and validation of a machine learning algorithm–based risk prediction model of pressure injury in the intensive care unit. aug: au: Xu, Jie Chen, Danxiang Deng, Xiaofang Pan, Xiaoyun Chen, Yu Zhuang, Xiaoming Sun, Caixia affil: Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China sug: subj: Pressure Ulcer Prognosis Critically Ill Patients Machine Learning Risk Assessment Prediction Models Electronic Health Records Instrument Validation Instrument Construction Early Diagnosis Human Nonexperimental Studies Retrospective Design Logistic Regression Braden Scale for Predicting Pressure Sore Risk Scales Funding Source ROC Curve Sensitivity and Specificity Validation Studies ab: The study aimed to establish a machine learning–based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (ICU) using large‐scale clinical data. A retrospective cohort study design was employed to develop and validate a top‐performing clinical feature panel accessibly in the electronic medical records (EMRs), which was in the mode of a quantifiable nomogram. Clinical factors regarding demographics, admission cause, clinical laboratory index, medical history and nursing scales were extracted as risk candidates. The performance improvement was based on the application of the machine learning technique, comprising logistic regression, decision tree and random forest algorithm with five‐fold cross‐validation (CV) technique. The comprehensive assessment of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC) was considered in the evaluation of predictive performance. The receiver operating characteristic curves revealed the top performance for the logistic regression model in respect to machine learning improvement, achieving the highest sensitivity and AUC among three types of classifiers. Compared against the 23‐point Braden scale routinely recorded online, an incorporated nomogram of logistic regression model and Braden scale achieved the best performance with an AUC of 0.87 ± 0.07 and 0.84 ± 0.05 in training and test cohort, respectively. Our findings suggest that the machine learning technique potentiated the limited predictive validity of routinely recorded clinical data on pressure injury development during ICU hospitalisation. Easily accessible electronic records held the potentials to substitute the traditional Braden score in the prediction of pressure injury in intensive care unit. Preoperative prediction of pressure injury facilitates the exemption from the severe consequences. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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