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NCT Number: NCT04675424

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

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.

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Key information

Conditions

ICU Patients Pathologic Processes Pathological Conditions, Signs and Symptoms Shock

Age range

20 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Chang Gung Memorial Hospital, Linkou branch

Taoyuan, 33378, Taiwan

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

ANSWatch

Device

ANSwatch wrist sphygmomanometer and the specific database from monitors in ICUs may be a feasible way to predict outcome among ICU patients. Two measurements will be obtained:

  • Pulse diagnosis: Pulse diagnosis done on radial artery is recorded by traditional Chinese medicine physician and ANSwatch wrist sphygmomanometer at the same time.
  • Heart rate variability: Heart Rate Variability represents significant information on autonomic nervous system (ANS)'s regulating function and balance status.

Primary outcomes

  1. 28-days mortality

    Time frame: 28 days

    The investigators track the enrolled ICU patients for 28 days and record the date and cause of participants' death.

  2. Pulse diagnosis

    Time frame: Day1, Day4, Day8, Day11, Day15, Day18, Day22, Day25

    Pulse diagnosis in radial artery is recorded by traditional Chinese medicine physician and ANSwatch wrist sphygmomanometer at the same time. With the help of artificial intelligence, the investigators will find the correlation/comparison of pulse diagnosis by physician or machine and hope to define which pulse waveform characteristics could predict the prognosis/outcome in ICU patients.

Secondary outcomes

  1. Hemodynamic status

    Time frame: Day1, Day4, Day8, Day11, Day15, Day18, Day22, Day25

    Pulse-induced contour cardiac output (PiCCO) is a cardiac output monitor that combines pulse contour analysis and trans-pulmonary thermos-dilution technique. PiCCO enables assessment of the patient's hemodynamic status to guide fluid or vasoactive drug therapy, especially if complex mixed forms of shock (e.g. septic and cardio-genic). Relatively invasive PiCCO measurement requires the insertion of a central venous pressure (CVP) catheter and a thermo-dilution arterial line. The non-invasive pulse waveform from radial artery in ANSwatch could be one kind of hemodynamic monitor for peripheral circulation. By artificial intelligence, the investigators could define some correlation or comparison in the pulse waveform between PiCCO and ANSwatch.

  2. Fluid-responsiveness

    Time frame: Day1, Day4, Day8, Day11, Day15, Day18, Day22, Day25

    Respiratory variations in the pulse oximeter plethysmographic waveform amplitude have been shown to be able predict fluid responsiveness in mechanically ventilated patients. The investigators use a device [ Masimo corp., Irvine, California, USA] which could automatically and continuously monitor the respiratory variations in the pulse oximeter waveform amplitude (Pleth Variability Index, PVI, unit in %) to predict fluid responsiveness in ICU patients under mechanical ventilation. This time, the investigators want to find the correlation in the pulse waveform from PVI, PiCCO, or ANSwatch by artificial intelligence analysis.

  3. Heart rate variability

    Time frame: Day1, Day4, Day8, Day11, Day15, Day18, Day22, Day25,

    Heart Rate Variability (unit in microseconds) represents significant information on autonomic nervous system (ANS)'s regulating function and balance status. The change (variation) of heart rate during short term (5 minutes) is analyzed with the method of time domain and frequency domain to provide the degree of balance and activity of autonomic nervous system. Low Frequency (unit in %) is a band of power spectrum range between 0.04 and 0.15 Hz. Generally, it is a strong indicator of sympathetic activity. High Frequency (unit in %) is a band of power spectrum range between 0.15 and 0.4 Hz and it reflects parasympathetic (vagal) activity. LF/HF Ratio indicates overall balance between sympathetic and parasympathetic systems. There could be some relationship between these parameters and ICU patients' outcome.

Sponsors and collaborators

Lead sponsor

Chang Gung Memorial Hospital

Other

Registry information

Important dates

Study start
2020
Primary completion
2022
Study completion
2022
First posted
Dec 19, 2020
Registry last updated
Sep 21, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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