Clinical trial NCT04675424: Using Artificial Intelligence (AI)-Assisted Pulse Diagnosis Analysis on Precision Critical Medicine.
Official title: Using Artificial Intelligence (AI)-Assisted Pulse Diagnosis Analysis on Precision Critical Medicine.
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: ICU Patients
Shock
Keywords: traditional Chinese medicine
pulse diagnosis
artificial intelligence
precision critical medicine
Lead sponsor: Chang Gung Memorial Hospital
Sponsor class: OTHER
First posted: 2020-12-19
Enrollment: 45
Sex: ALL
Minimum age (years): 20
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: The ICU patient under acute circulatory failure

Brief summary: Precision/personalized medicine becomes an important part of modern medical system in the recent years. In the past, the treatments for patients have been decided by doctors according patients' symptoms and/or regular biochemical profiles. However, it is not uncommon that patients' condition varies tremendously even they have same diagnosis, and under such condition, treatment efficacy may be limited due to the heterogeneity among patients. Therefore, lack of therapeutic efficacy may be not really ineffective, and the main reason may be inadequate patient classification. For this reason, the "omics"-based personal/precision medicine emerges recently and becomes more and more important. However, in contrast to feasible and common "personalized" medicine, the approach of precision medicine to the molecular medicine level is still difficult, especially among patients in intensive critical units (ICUs). In contrast to cancer, which has remarkable advances in the past decades, the precision/personal medicine is more difficult in critical and emergent medicine. One reason is the amount of omics data is quite huge and thus dealing with omics data is time consuming. Therefore, it is not effective in daily clinical practice in ICUs care. For this condition, the investigators propose that the combination of clinical data, including pulse diagnosis by traditional Chinese medicine (TCM) doctor or ANSwatch wrist sphygmomanometer, fluid responsiveness by "Masimo" Radical-7 Pulse CO-Oximeter, and the specific database from monitors in ICUs may be a feasible way to predict outcome among ICU patients. There are two main goals for this study: (1) After establishing clinical traditional Chinese medicine (TCM) pulse diagnosis and ICU clinical parameters databases, acquiring and features of pulse diagnosis by applying AI and (2) analyzing the correlations between the features of pulse diagnosis and important clinical parameters.


Eligibility criteria: Inclusion Criteria:

* 1.The patient in ICU is willing to participate in the research project or patient's family member is willing patient to participate in the research project.
* 2.Acute circulatory failure

Exclusion Criteria:

* 1. The patient do not want to participate in the research project or patient's family member do not want the patient to participate in the research project.
* 2. Pregnant woman.
* 3. Goals of treatment is palliative care.
* 4. Patient is heritable immunodeficiency or acquired immunodeficiency.
* 5. Known existing cardiac arrhythmia, valvular heart disease, right ventricular dysfunction, intra-cardiac shunt, air leaking from chest drains, abdominal compartment syndrome.

Primary outcomes:
measure: 28-days mortality
timeFrame: 28 days
measure: Pulse diagnosis
timeFrame: Day1, Day4, Day8, Day11, Day15, Day18, Day22, Day25
Secondary outcomes:
measure: Hemodynamic status
timeFrame: Day1, Day4, Day8, Day11, Day15, Day18, Day22, Day25
measure: Fluid-responsiveness
timeFrame: Day1, Day4, Day8, Day11, Day15, Day18, Day22, Day25
measure: Heart rate variability
timeFrame: Day1, Day4, Day8, Day11, Day15, Day18, Day22, Day25,
Other outcomes:
Locations:
city: Taoyuan
country: Taiwan
facility: Chang Gung Memorial Hospital, Linkou branch
Study officials:
References: