Predicting tachycardia as a surrogate for instability in the intensive care unit.
Tachycardia is a strong though non-specific marker of cardiovascular stress that proceeds hemodynamic instability. We designed a predictive model of tachycardia using multi-granular intensive care unit (ICU) data by creating a risk score and dynamic trajectory. A subset of clinical and numerical sig...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 33; no. 6; pp. 973 - 986 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2019
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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=139439718&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139439718 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Dec2019 vid: 33 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139439718 139439718 NLM30767136 10.1007/s10877-019-00277-0 NLM30767136 139439718 ppf: 973 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting tachycardia as a surrogate for instability in the intensive care unit. aug: au: Yoon, Joo Heung Mu, Lidan Chen, Lujie Dubrawski, Artur Hravnak, Marilyn Pinsky, Michael R. Clermont, Gilles affil: Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA sug: subj: Critical Care Methods Cardiovascular Diseases Diagnosis Intraoperative Monitoring Methods Lung Diseases Diagnosis Tachycardia Diagnosis Aged Logistic Regression Relative Risk Middle Age Resource Databases Hospitals, Special Hospital Mortality ROC Curve Young Adult Data Collection Adult Heart Rate Pharmacokinetics Regression Algorithms Reproducibility of Results Intensive Care Units Scales Aged: 65+ years Middle Aged: 45-64 years Adult: 19-44 years ab: Tachycardia is a strong though non-specific marker of cardiovascular stress that proceeds hemodynamic instability. We designed a predictive model of tachycardia using multi-granular intensive care unit (ICU) data by creating a risk score and dynamic trajectory. A subset of clinical and numerical signals were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database. A tachycardia episode was defined as heart rate ≥ 130/min lasting for ≥ 5 min, with ≥ 10% density. Regularized logistic regression (LR) and random forest (RF) classifiers were trained to create a risk score for upcoming tachycardia. Three different risk score models were compared for tachycardia and control (non-tachycardia) groups. Risk trajectory was generated from time windows moving away at 1 min increments from the tachycardia episode. Trajectories were computed over 3 hours leading up to the episode for three different models. From 2809 subjects, 787 tachycardia episodes and 707 control periods were identified. Patients with tachycardia had increased vasopressor support, longer ICU stay, and increased ICU mortality than controls. In model evaluation, RF was slightly superior to LR, which accuracy ranged from 0.847 to 0.782, with area under the curve from 0.921 to 0.842. Risk trajectory analysis showed average risks for tachycardia group evolved to 0.78 prior to the tachycardia episodes, while control group risks remained < 0.3. Among the three models, the internal control model demonstrated evolving trajectory approximately 75 min before tachycardia episode. Clinically relevant tachycardia episodes can be predicted from vital sign time series using machine learning algorithms. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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