Machine Learning Prediction of Parameters of Early Warning Scores in General Wards

NCT06574906 | Active Not Recruiting DMC

• 1 other identifier: AIM-PEW-WAR
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

Brief Summary

In the event of illness or injury, patients are medically evaluated and initially treated in acute medical outpatient clinics, emergency rooms and surgeries. If medically indicated, care and treatment can also be provided in hospital. Depending on the severity of the illness and the main medical problem, this care is provided on hospital wards, which are primarily looked after by specific specialist disciplines and assigned to them in the form of clinical departments, for example. As part of the inpatient stay, treatment and care is usually provided through ward rounds by the medical staff. However, ward rounds are spot checks of individual measured values at predefined times. Qualified nursing staff carry out the agreed treatment plans and check the patient's general condition several times a day. In contrast to intensive medical monitoring, however, there is no continuous monitoring and therefore an aggravation of a patient's condition is not always immediately apparent. Furthermore, in addition to known complications of existing conditions, new or unexpected complications can also occur. Although non-intensive care monitoring is based on discontinuous monitoring, incidents and complications can sometimes be life-threatening, especially if there is no immediate response to a deterioration in the patient's condition. Even if there are early warning systems such as scores, their ability to react is limited, partly due to the frequency with which they are collected. In addition to patient-specific limitations of inpatient monitoring, such as patient cooperation in the sense of self-monitoring, medical limitations, such as the frequency of the survey, there are also economic limitations, such as the availability of staff who can be deployed for more frequent monitoring. Although there are telemedical approaches to monitoring, setting these up is often limited both economically and by the additional training required, for example. Even if threshold values are (or can be) defined for the measured data (vital signs, laboratory parameters, clinical impression and others), if these are exceeded or not reached, a consequence, e.g. a therapy step, 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 affected patients in a hospital ward are potentially in mortal danger. One way of averting the dangers described above could be to use a reduced combination of monitoring methods compared to intensive care monitoring. At the same time, the use of artificial intelligence enables the automated evaluation of the collected data and can thus lead to the prediction of changes in parameters, which enables early alerting, i.e. before the occurrence of pathophysiological decompensation.

Conditions

Patient Safety

Outcome Measures

Primary Outcomes (4)

• Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) for Prediction of Parameters of Early Warning Scores

  Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) for Prediction of Parameters of Early Warning Scores

  2024-10-01 to 2026-10-31

• Area Under the Curve of the Precision-Recall Curve (AUC-PRC) for Prediction of Parameters of Early Warning Scores

  Area Under the Curve of the Precision-Recall Curve (AUC-PRC) for Prediction of Parameters of Early Warning Scores

  2024-10-01 to 2026-10-31

• F-Beta Score with Beta = 1 (F1-Score) for Prediction of Parameters of Early Warning Scores

  F-Beta Score with Beta = 1 (F1-Score) for Prediction of Parameters of Early Warning Scores

  2024-10-01 to 2026-10-31

• Confusion Matrix for Prediction of Parameters of Early Warning Scores

  Confusion Matrix for Prediction of Parameters of Early Warning Scores

  2024-10-01 to 2026-10-31

Secondary Outcomes (6)

• SHapley's Additive exPlanations (SHAP) Values for Prediction Models

  2024-10-01 to 2026-10-31

• Prediction of Routine Laboratory Values

  2024-10-01 to 2026-10-31

• Prediction of Parameters Measured by Photophlethysmogram (PPG)

  2024-10-01 to 2026-10-31

• Prediction of Medical Emergency Team or Emergency Critical Care Treatment

  2024-10-01 to 2026-10-31

• Prediction of Unplanned Intensive Care Unit (ICU) Admission

  2024-10-01 to 2026-10-31

Interventions

Parameters of Early Warning Scores OTHER

Parameters of Early Warning Scores

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients treated in general wards.

You may qualify if:

• Treated in general ward between 2024-10-01 and 2026-10-31 at the study center.

You may not qualify if:

• None.

Sponsors & Collaborators

• Kepler University Hospital: lead
• RISC Software GmbH: collaborator
• innovethic eU: collaborator
• FiveSquare GmbH: collaborator

Study Sites (1)

Johannes Kepler University, Kepler University Hospital

Linz, Upper Austria, 4020, Austria

Location

Study Officials

• Thomas Tschoellitsch, MD

  Johannes Kepler University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 14, 2024
• First Posted: August 28, 2024
• Study Start: August 15, 2025
• Primary Completion (Estimated): October 31, 2026
• Study Completion (Estimated): October 31, 2026
• Last Updated: September 3, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

AU (1)