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Study
Design: Cross-sectional Analytical – Both Retrospective and Prospective
Study
Setting: Departments of Emergency Medicine, Trauma,
Medicine, medicine, Pulmonology, Anaesthesiology and Critical Care, AIIMS
Rishikesh
Study Population: All patients (minimum
500 patients) admitted to the above departments at AIIMS
Rishikesh (irrespective of COVID Status) during the study period satisfying
the following criteria.
Inclusion
Criteria:
1.
Aged 18 years and above.
2.
Able and willing to give informed written consent (Signature/Thumb
impression)
Exclusion Criteria:
1.
Unable or unwilling to give informed consent
Requirement for
informed consent will be waived off for retrospective patient data with IEC
approval.
Methods:
Phase 1: In Phase 1 of the study,
retrospective data of 10-100 patients will be analysed for Model
evaluation on GEHC site, iteration and development of initial proof of
concept.
Phase 2: In Phase 2, prospective data of 100 patients
will be used for Testing of
initial proof of concept in the clinical setting on prospectively collected
datasets.
Phase 3: In Phase 3, Testing of the iterated model in the
clinical setting on prospectively collected datasets will be done in 500
patients for Performance evaluation of model as a decision support tool in
the clinical setting
Phase 4: Clinical
deployment across multiple hospitals (through separate MOUs)
Data
Collection
1. Patient demographics, history, monitor
parameters, ventilator parameters, imaging results and lab tests for COVID
related illness that are readily available in a consistent format at AIIMS
Rishikesh inpatient
files/records/systems/e-Hospital will be collected
2. The associated metadata (ie DICOM tag extraction,
if possible EPR data) will be collected and aggregated
3. Data will be anonymized to remove any patient
protected health information (PHI)
a. Site to keep a PHI-rich copy of the data on-hand
b. A PHI free copy of the data
4. Transfer the data via secure media to GE for
algorithm development and validation
Retrospective/Prospective
Data Collection - De-identified datasets for minimum of 100 patients
a.
Demographic datasets including but not
limited to Age, Gender, Ethnicity, Weight, Height,
ICD-9 code diagnosis, ICD-9 code procedures, ICU Admit Time, ICU Discharge
Time, Past Medical History, Hospital Admit Time, Hospital Discharge Time,
Mortality (Yes or no, if yes then the time of death)
b.
Lab tests including but not limited to ABG
tests, Urea, Creatinine, Electrolytes, Monitor
parameters including RR, HR, SpO2,
ECG, Blood Pressure Parameters (Invasive & Non-Invasive BP parameters),
Temperature
c. Ventilator parameters including FiO2, FeO2, PEEP,
EtCO2, MV, TV, PIP, PP, Intubation Time, Extubation Time, Mode of
Ventilation, Ventilator Settings Parameters
d. Radiology imaging (CT,
longitudinal X-ray)
Prospective
testing and performance evaluation
1. Deploy a version of the developed algorithm at AIIMS
Rishikesh to run on prospectively
recruited patient datasets
2. Record performance of the algorithm against the expected outcome and determine if further development is required
3. Determine the relevance of the algorithm
Clinical
deployment across multiple hospitals (through MOUs)
1. Determine quickest and most efficient deployment
platform accessible for all AIIMS Rishikesh partners
Outcomes:
1. Data Collection and initial proof of concept model build
a. De-identified datasets to transferred to GEHC
data scientists
b. Proof of concept model results shared with
broader collaboration
2. Prospective evaluation
a. The model developed is evaluated within 2 weeks of
readiness in a controlled environment in AIIMS Rishikesh as a pilot
3. Deploy algorithm on mass
a. The deployment model identified and implemented at collaboration sites
b.
Review the option
to deploy models nationally
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