Clinical trial NCT02893462: Setting up a Warehouse of Physiological Data and Biomedical Signals in Adult Intensive Care
Official title: Setting up a Warehouse of Physiological Data and Biomedical Signals in Adult Intensive Care
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Critical Illness
Keywords: data warehousing
data aggregation
data mining
Lead sponsor: University Hospital, Brest
Sponsor class: OTHER
First posted: 2016-09-08
Enrollment: 1500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Population of patients admitted in the intensive care unit for monitoring of vital failure

Brief summary: The aim of this study is the establishment of a warehouse physiological data and biomedical signal in intensive care adult patients in acute situations from particular records from the Philips Intellivue MP70 monitor.

Detailed description: Cardiopulmonary failures are major public health concerns, due to the aging population. Each of these situations is burdened with a poor prognosis in the medium term and a source of prolonged hospitalizations, generating significant health costs. Early detection and prediction of organ failure could reduce health costs and risks for the patient, offering a reaction early and appropriate medical technology. The proposed approach aims to optimize the knowledge of a complex physiological domain and multi-system, while promoting the automatic transfer of knowledge. The approach proposed data-mining and development of algorithms for detecting and / or predicting a strong potential for disruption because it proposes to apply innovative automated analysis procedures to a fragile patient population, and then a transfer to the medical device industry.

From communicating tools of recording of the signals, the investigator envisage in a global way:

1. the constitution of a warehouse of physiological data of grown-up patients in acute situation (intensive care unit);
2. the development by data mining of a system of detection of organs failures or adverse events basing itself on the application of innovative algorithms, allowing the decision-making operational, from the fusion of arisen ill-assorted events;
3. the use of intelligent tools of auto-learning and elaboration of complex multimodal models for purposes of prediction of events;

Eligibility criteria: Inclusion Criteria:

* Any adult patient admitted in Brest University Hospital's intensive care unit for monitoring of vital failure

Exclusion Criteria:

* Refusal to participate

Primary outcomes:
measure: Number of participants with physiological signal abnormality
timeFrame: from two to twenty-four hours
Secondary outcomes:
Other outcomes:
Locations:
city: Brest
country: France
facility: CHRU de Brest
Study officials:
affiliation: CHRU de Brest
name: Erwan L'Her, Professor
role: PRINCIPAL_INVESTIGATOR
References:
citation: Bodenes L, N'Guyen QT, Le Mao R, Ferriere N, Pateau V, Lellouche F, L'Her E. Early heart rate variability evaluation enables to predict ICU patients' outcome. Sci Rep. 2022 Feb 15;12(1):2498. doi: 10.1038/s41598-022-06301-9.
citation: L'Her E, Nazir S, Pateau V, Visvikis D. Accuracy of noncontact surface imaging for tidal volume and respiratory rate measurements in the ICU. J Clin Monit Comput. 2022 Jun;36(3):775-783. doi: 10.1007/s10877-021-00708-x. Epub 2021 Apr 22.
citation: L'Her E, N'Guyen QT, Pateau V, Bodenes L, Lellouche F. Photoplethysmographic determination of the respiratory rate in acutely ill patients: validation of a new algorithm and implementation into a biomedical device. Ann Intensive Care. 2019 Jan 21;9(1):11. doi: 10.1186/s13613-019-0485-z.