Ignite Creation Date:
2024-10-25 @ 7:55 PM
Last Modification Date:
2024-10-26 @ 3:39 PM
Study NCT ID:
NCT06574906
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-08-14
Brief Title:
Machine Learning Prediction of Parameters of Early Warning Scores in General Wards
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Machine Learning Prediction of Parameters of Early Warning Scores in General Wards
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 patients general condition several times a day In contrast to intensive medical monitoring however there is no continuous monitoring and therefore an aggravation of a patients 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 patients 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 eg 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 ie before the occurrence of pathophysiological decompensation
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 patients general condition several times a day In contrast to intensive medical monitoring however there is no continuous monitoring and therefore an aggravation of a patients 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 patients 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 eg 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 ie before the occurrence of pathophysiological decompensation
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None