Machine Learning Prediction of Parameters of Early Warning Scores in Intensive Care Units (AIM-PEW-ICU)

A large number of different organ functions are recorded in real time for patients being monitored in an intensive care unit. On the one hand, the measured values collected are used for continuous monitoring of vital parameters, e.g. blood pressure, heart rate and respiratory rate, but are also evaluated several times a day in conjunction with other data as part of ward rounds. In both cases, continuous monitoring from a limited number of parameters, but also in the distinct evaluation with a more extensive set of analyzable parameters, there are limitations in the evaluability even with all the care and expertise available: In continuous analysis, interpretation is limited by the restricted number of continuously recorded parameters described above. Although a large number of such measurements are possible, and at least theoretically a larger number of parameters could be measured, patient-specific limits such as patient cooperation, medical limits such as the significance of the measured values in specific situations, but also economic limits are often decisive in this context. Although accurate conclusions can be drawn from the continuous and therefore complete representation of aspects of human physiology, the limitation of the available parameters reduces the interpretability of the synthesis of different statuses. In the broader, more comprehensive assessments during visits at specific points in time, on the other hand, there are limitations due to, among other things, point recordings of individual measured values and the predefined visit times. Even if limit values are (or can be) defined for the measured data, and a consequence, e.g. a therapy step, is initiated if these values are exceeded or not reached, this alert can only be initiated retrospectively if these values are exceeded and a consequence can only be initiated retrospectively. In this situation, a pathophysiological change is already so far advanced that in many cases a compensation mechanism no longer functions adequately and turns into a decompensation situation. In this situation, the patients affected in an intensive care unit are in many cases in mortal danger. Both situations, continuous recording of a limited number of parameters and the evaluation of extensive data in the form of a snapshot could be optimized despite the limitations mentioned. Without changing the collection of data (time, scope, etc.), the possibilities for optimizing their interpretation and the consequences that can be derived from the interpretation remain. The interpretation of the data is primarily determined by the interpreters as the method of interpretation. Current approaches attempt to use machine learning (ML) methods to predict individual situations that recognize adverse events in the given data and at the same time allow alarms to be triggered pre-emptively, i.e. before a life-threatening situation occurs. Furthermore, there are already studies on the change of early warning scores in time series, which are, however, limited in their informative value for longer prediction periods.

Study Overview

• Status: Active, not recruiting
• Conditions: Patient Safety
• Intervention / Treatment: Other: Parameters of Early Warning Scores

• Study Type: Observational
• Enrollment (Estimated): 8000

Contacts and Locations

Study Locations

• Austria
  • Upper Austria
    • Linz, Upper Austria, Austria, 4020
      • Kepler University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Patients treated in intensive care.

Description

Inclusion Criteria:

• Treated in intensive care between 2010-01-01 and 2023-12-31 at the study center.

Exclusion Criteria:

• None.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
AUC-ROC for Prediction of Parameters of Early Warning Scores	AUC-ROC for Prediction of Parameters of Early Warning Scores	2010-01-01 to 2023-12-31
AUC-PRC for Prediction of Parameters of Early Warning Scores	AUC-PRC for Prediction of Parameters of Early Warning Scores	2010-01-01 to 2023-12-31
F1-Score for Prediction of Parameters of Early Warning Scores	F1-Score for Prediction of Parameters of Early Warning Scores	2010-01-01 to 2023-12-31
Confusion Matrix for Prediction of Parameters of Early Warning Scores	Confusion Matrix for Prediction of Parameters of Early Warning Scores	2010-01-01 to 2023-12-31

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
SHAP Values for Prediction Models	SHAP Values for Prediction Models	2010-01-01 to 2023-12-31
Confusion Matrix for Prediction of In Hospital-Mortality	Confusion Matrix for Prediction of In Hospital-Mortality	2010-01-01 to 2023-12-31

Collaborators and Investigators

• Sponsor: Kepler University Hospital
• Collaborators: RISC Software GmbH; innovethic eU; FiveSquare GmbH
• Investigators: Study Chair: Jens Meier, MD, Johannes Kepler University

Study record dates

Study Major Dates

• Study Start (Actual): May 1, 2024
• Primary Completion (Actual): September 15, 2024
• Study Completion (Estimated): September 15, 2025

Study Registration Dates

• First Submitted: February 7, 2024
• First Submitted That Met QC Criteria: February 7, 2024
• First Posted (Actual): February 14, 2024

Study Record Updates

• Last Update Posted (Actual): October 15, 2024
• Last Update Submitted That Met QC Criteria: October 12, 2024
• Last Verified: October 1, 2024

More Information

Terms related to this study

• Other Study ID Numbers: AIM-PEW-ICU

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No