Using Artificial Intelligence (AI)-Assisted Pulse Diagnosis Analysis on Precision Critical Medicine.

Chang Gung Medical Foundation

Status: Completed

Conditions: Shock; ICU Patients

Treatments: Device: ANSwatch

Study type: Observational

Funder types: Other

Identifiers

NCT04675424

NMRPG3K6131

Details and patient eligibility

About

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.

Enrollment: 45 patients

Sex: All

Ages: 20 to 80 years old

Volunteers: No Healthy Volunteers

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.