title: Heart rate time series characteristics for early detection of infections in critically ill patients.
abstract: It is difficult to make a distinction between inflammation and infection. Therefore, new strategies are required to allow accurate detection of infection. Here, we hypothesize that we can distinguish infected from non-infected ICU patients based on dynamic features of serum cytokine concentrations and heart rate time series. Serum cytokine profiles and heart rate time series of 39 patients were available for this study. The serum concentration of ten cytokines were measured using blood sampled every 10 min between 2100 and 0600 hours. Heart rate was recorded every minute. Ten metrics were used to extract features from these time series to obtain an accurate classification of infected patients. The predictive power of the metrics derived from the heart rate time series was investigated using decision tree analysis. Finally, logistic regression methods were used to examine whether classification performance improved with inclusion of features derived from the cytokine time series. The AUC of a decision tree based on two heart rate features was 0.88. The model had good calibration with 0.09 Hosmer-Lemeshow p value. There was no significant additional value of adding static cytokine levels or cytokine time series information to the generated decision tree model. The results suggest that heart rate is a better marker for infection than information captured by cytokine time series when the exact stage of infection is not known. The predictive value of (expensive) biomarkers should always be weighed against the routinely monitored data, and such biomarkers have to demonstrate added value.
authors.affiliation: Division Animal and Human Health Engineering, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001, Louvain, Belgium.
authors.forename: T
authors.initials: T
authors.lastname: Tambuyzer
authors.affiliation: Laboratory of Intensive Care Medicine, Division Cellular and Molecular Medicine, Clinical Department, KU Leuven, Herestraat 49, 3000, Louvain, Belgium.
authors.forename: F
authors.initials: F
authors.lastname: Guiza
authors.affiliation: Laboratory of Intensive Care Medicine, Division Cellular and Molecular Medicine, Clinical Department, KU Leuven, Herestraat 49, 3000, Louvain, Belgium.
authors.forename: E
authors.initials: E
authors.lastname: Boonen
authors.affiliation: Laboratory of Intensive Care Medicine, Division Cellular and Molecular Medicine, Clinical Department, KU Leuven, Herestraat 49, 3000, Louvain, Belgium.|Medical Intensive Care Unit, Department of Internal Medicine, KU Leuven, Herestraat 49, 3000, Louvain, Belgium.
authors.forename: P
authors.initials: P
authors.lastname: Meersseman
authors.affiliation: Laboratory of Intensive Care Medicine, Division Cellular and Molecular Medicine, Clinical Department, KU Leuven, Herestraat 49, 3000, Louvain, Belgium.
authors.forename: H
authors.initials: H
authors.lastname: Vervenne
authors.affiliation: Immunoendocrine Research Unit, Medical Department M, Aarhus University Hospital, Norrebrogade 44, 8000, Aarhus C, Denmark.
authors.forename: T K
authors.initials: TK
authors.lastname: Hansen
authors.affiliation: Immunoendocrine Research Unit, Medical Department M, Aarhus University Hospital, Norrebrogade 44, 8000, Aarhus C, Denmark.
authors.forename: M
authors.initials: M
authors.lastname: Bjerre
authors.affiliation: Laboratory of Intensive Care Medicine, Division Cellular and Molecular Medicine, Clinical Department, KU Leuven, Herestraat 49, 3000, Louvain, Belgium.
authors.forename: G
authors.initials: G
authors.lastname: Van den Berghe
authors.affiliation: Division Animal and Human Health Engineering, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001, Louvain, Belgium.
authors.forename: D
authors.initials: D
authors.lastname: Berckmans
authors.affiliation: Division Animal and Human Health Engineering, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001, Louvain, Belgium. jean-marie.aerts@biw.kuleuven.be.
authors.forename: J M
authors.initials: JM
authors.lastname: Aerts
authors.affiliation: Laboratory of Intensive Care Medicine, Division Cellular and Molecular Medicine, Clinical Department, KU Leuven, Herestraat 49, 3000, Louvain, Belgium.
authors.forename: G
authors.initials: G
authors.lastname: Meyfroidt
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood; D002138:Calibration; D003422:Critical Care; D016638:Critical Illness; D003428:Cross Infection / Q000175:diagnosis*; D016207:Cytokines / Q000097:blood; D003663:Decision Trees; D006339:Heart Rate; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012121:Respiration, Artificial; D012306:Risk; D013997:Time Factors; D055815:Young Adult
chemical_list: D015415:Biomarkers; D016207:Cytokines
keywords: Cytokines; Heart rate; ICU; Infection monitoring