Clinical trial NCT06574906: Machine Learning Prediction of Parameters of Early Warning Scores in General Wards
Official title: Machine Learning Prediction of Parameters of Early Warning Scores in General Wards
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Patient Safety
Lead sponsor: Kepler University Hospital
Sponsor class: OTHER
Collaborators: name: RISC Software GmbH
name: innovethic eU
name: FiveSquare GmbH
First posted: 2024-08-28
Enrollment: 3000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients treated in general wards.

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.


Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* None.

Primary outcomes:
measure: Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) for Prediction of Parameters of Early Warning Scores
timeFrame: 2024-10-01 to 2026-10-31
measure: Area Under the Curve of the Precision-Recall Curve (AUC-PRC) for Prediction of Parameters of Early Warning Scores
timeFrame: 2024-10-01 to 2026-10-31
measure: F-Beta Score with Beta = 1 (F1-Score) for Prediction of Parameters of Early Warning Scores
timeFrame: 2024-10-01 to 2026-10-31
measure: Confusion Matrix for Prediction of Parameters of Early Warning Scores
timeFrame: 2024-10-01 to 2026-10-31
Secondary outcomes:
measure: SHapley's Additive exPlanations (SHAP) Values for Prediction Models
timeFrame: 2024-10-01 to 2026-10-31
measure: Prediction of Routine Laboratory Values
timeFrame: 2024-10-01 to 2026-10-31
measure: Prediction of Parameters Measured by Photophlethysmogram (PPG)
timeFrame: 2024-10-01 to 2026-10-31
measure: Prediction of Medical Emergency Team or Emergency Critical Care Treatment
timeFrame: 2024-10-01 to 2026-10-31
measure: Prediction of Unplanned Intensive Care Unit (ICU) Admission
timeFrame: 2024-10-01 to 2026-10-31
measure: Prediction of Electrocardiogram (ECG) Waveform
timeFrame: 2024-10-01 to 2026-10-31
Other outcomes:
Locations:
Study officials:
affiliation: Johannes Kepler University
name: Thomas Tschoellitsch, MD
role: PRINCIPAL_INVESTIGATOR
References: