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CTRI Number  CTRI/2024/10/075928 [Registered on: 25/10/2024] Trial Registered Prospectively
Last Modified On: 10/10/2024
Post Graduate Thesis  Yes 
Type of Trial  Observational 
Type of Study   Cohort Study 
Study Design  Other 
Public Title of Study   Using Computers to Help Doctors Know If Lung Patients Will Get Better in the Hospitals Special Care Unit 
Scientific Title of Study   Using Machine Learning to Build Predictive Models on Patients with ARDS in medical Intensive Care Unit 
Trial Acronym  NIL 
Secondary IDs if Any  
Secondary ID  Identifier 
NIL  NIL 
 
Details of Principal Investigator or overall Trial Coordinator (multi-center study)  
Name  Dr Nada Thasneem 
Designation  Post graduate student 
Affiliation  Prasanna School Of Public Health 
Address  Department of healthcare and hospital management Prasanna School Of Public Health Tiger circle , Madhav Nagar manipal , udupi karnataka

Udupi
KARNATAKA
576104
India 
Phone  9074378157  
Fax    
Email  nada.psphmpl2023@learner.manipal.edu  
 
Details of Contact Person
Scientific Query  
Name  Dr Usha Rani 
Designation  Head Of the Department and Associate Professor  
Affiliation  Prasanna School Of Public Health 
Address  Department of healthcare and hospital management Prasanna School Of Public Health Tiger circle , Madhav Nagar manipal , udupi karnataka

Udupi
KARNATAKA
576104
India 
Phone  8310214742  
Fax    
Email  usha.rani@manipal.edu  
 
Details of Contact Person
Public Query  
Name  Dr Nada Thasneem 
Designation  Post graduate student 
Affiliation  Prasanna School Of Public Health 
Address  Department of healthcare and hospital management Prasanna School Of Public Health Tiger circle , Madhav Nagar manipal , udupi karnataka

Udupi
KARNATAKA
576104
India 
Phone  9074378157  
Fax    
Email  nada.psphmpl2023@learner.manipal.edu  
 
Source of Monetary or Material Support  
Department of healthcare and hospital management Prasanna School Of Public Health Tiger circle , Madhav Nagar manipal , udupi pincode : 576104 karnataka, India  
 
Primary Sponsor  
Name  Department of healthcare and hospital management 
Address  Department of healthcare and hospital management Prasanna School Of Public Health Tiger circle , Madhav Nagar manipal , udupi pincode : 576104 karnataka, India  
Type of Sponsor  Private medical college 
 
Details of Secondary Sponsor  
Name  Address 
NIL  NIL 
 
Countries of Recruitment     India  
Sites of Study  
No of Sites = 1  
Name of Principal Investigator  Name of Site  Site Address  Phone/Fax/Email 
Dr Nada Thasneem  Kasturba Hospital , Manipal  Department of Critical Care Kasturba Hospital , Manipal
Udupi
KARNATAKA 		 9074378157

nada.psphmpl2023@learner.manipal.edu 
 
Details of Ethics Committee  
No of Ethics Committees= 1  
Name of Committee  Approval Status 
Kasturba medical college and Kasturba hospital Institutional Ethics Committee-Student Research  Approved 
 
Regulatory Clearance Status from DCGI  
Status 
Not Applicable 
 
Health Condition / Problems Studied  
Health Type  Condition 
Patients  (1) ICD-10 Condition: J80||Acute respiratory distress syndrome,  
 
Intervention / Comparator Agent  
Type  Name  Details 
Intervention  nil  nil 
Comparator Agent  nil  nil 
 
Inclusion Criteria  
Age From  5.00 Year(s)
Age To  90.00 Year(s)
Gender  Both 
Details  Records of all Adult patients admitted in ICU 1 , ICU 2 and ICU 3 more than 48 hours from August 2021 to August 2024.
Record of all patients with mild , moderate, and severe Acute Respiratory Distress Syndrome (ARDS) admitted in ICU 1 , ICU 2 and ICU 3 more than 48 hours from August 2021 to August 2024 .
 
 
ExclusionCriteria 
Details  All patients who are not admitted more than 48 hours.
Patient not diagnosed with a disease .
 
 
Method of Generating Random Sequence   Not Applicable 
Method of Concealment   Not Applicable 
Blinding/Masking   Not Applicable 
Primary Outcome  
Outcome  TimePoints 
Predictive model for ARDs patients in intensive Care Unit  Retrospective Cohort Study 
 
Secondary Outcome  
Outcome  TimePoints 
helps clinicians to predict unfavorable medical conditions and their possible outcomes, produces reports, maneuver recommendations, or alarms in a timely manner  Retrospective cohort Study 
 
Target Sample Size   Total Sample Size="562"
Sample Size from India="562" 
Final Enrollment numbers achieved (Total)= "0"
Final Enrollment numbers achieved (India)="562" 
Phase of Trial   N/A 
Date of First Enrollment (India)   30/10/2024 
Date of Study Completion (India) 15/05/2025 
Date of First Enrollment (Global)  Date Missing 
Date of Study Completion (Global) Date Missing 
Estimated Duration of Trial   Years="1"
Months="0"
Days="0" 
Recruitment Status of Trial (Global)   Not Applicable 
Recruitment Status of Trial (India)  Completed 
Publication Details   N/A 
Individual Participant Data (IPD) Sharing Statement

Will individual participant data (IPD) be shared publicly (including data dictionaries)?  

Response - NO
Brief Summary  

OBJECTIVES

 

l  To Explore and Analyse Variables Contributing to Average Length of Stay, Severity of ARDS and Possible Outcome related to ARDS.

l  To Facilitate AI Experts in Developing Prediction Algorithm for Average Length of Stay, Severity of ARDS and Possible Outcome related to ARDS.

l  To Pilot Test Effectiveness of Developed AI Algorithms in Predicting Average Length of Stay, Severity of ARDS and Possible Outcome related to ARDS.


 Justification for study (whether of national significance with rationale)

 

Depending on the severity of the sickness and the duration of stay, an administrator must identify and predict any particular, frequent, and catastrophic condition that may increase hospital stays and result in greater costs, either from the patient directly or through a third party.


We hope that this study will be able to predict both the average length of stay for patients with acute respiratory distress syndrome (ARDS) and possible clinical outcomes. Additionally, we will identify the critical factors that will put ARDS patients into three severity categories: mild, moderate, and severe.Finding this would assist the administrator and the doctor in improving patient communication and preparing them for any potential ARDS-related clinical outcomes.

 

 


METHODOLOGY


The study aims to investigate and evaluate variables in ICU 1, 2, and 3 with the help of clinicians. Data will be collected retrospectively from the Medical Records Department (MRD) of 462 patients with ARDS admitted in ICU 1, ICU 2, and ICU 3 from August 2021 to August 2024. The study will use descriptive analysis, Chi square test, Univariate analysis, and Multivariate analysis to determine factors contributing to the severity of ARDS and potential outcomes.

After AI prediction model is build , it will be assessed using a pilot study, which will be conducted retrospectively on 100 patients from the MRD of IEC No. 591/2019 and CTRI/2019/11/021857. The pilot testing will evaluate the sensitivity and specificity of the model. The study aims to facilitate the machine learning development process with AI and machine learning experts. The data collected will be analyzed using a flowchart and will be used to develop a predictive model for predicting ARDS severity and potential outcomes.

 Detailed description of procedure / processes :

 

Objective 1 Methodology  :

 With the assistance of clinicians, investigate and evaluate the Variables  noted in ICU 1, 2, and 3. Finalize the Data Collection Form by removing all unnecessary variables.

 

After IEC and CTRI  approval ,  Data  will be collected Retrospectively gathering the information of 462 patients from Medical Records Department.

 

All Patients hospitalized to the ICU for longer than 48 hours will have their data retrospectively collected from MRD for the previous three years(august2021-august2024), including patients with   ARDS only . This will be done to ensure that we don’t miss anything that could have an impact on the average duration of stay, the severity of ARDS, and the potential results.

 

Study settings :

 

Departments including  Critical Care Department in Kasturba medical college , Manipal and Data Collected from the Medical Records Department.

 

Data collection form :

 

Data will be collected using excel sheet where new variables will be added depending upon the clinicians .

 

Analysis of Data:

Analysis using descriptive analysis, nominal variables will be reported using mean ± standard deviation and median with IQR depending on the normality of the data.

Variables in Respiratory chart includes  ratio of inspired oxygen fraction (FiO2) to partial pressure of oxygen (PaO2) is less than 300 mmHg , Positive end-expiratory pressure (PEEP) >-5 AP, Respiratory rate, heart rate,  sp02, pH, Partial pressure of Carbon dioxide (PaC02) , Saturation of oxygen (Sa02), HC03, Lac, AG.APACHE, SOFA  ,  Neutrophil/Lymphocyte ratio and  CBC

Serology include Hb , Hct /PCV, platelet, WBC, DLC, PT, INR , Aptt, LDH , urea , creatinine, sodium, potassium,  calcium, TB, DB, proteins, Albumin, Globulin, AST, ALT, ALP, PCT. CRP, Bicarbonate.

To determine the factors that contribute to the severity of ARDS and potential outcomes, we will use the Chi square test, Univariate analysis, and Multivariate analysis.

While categorical data are given as percentages and figures, continuous variables are typically presented as mean ± standard deviation or median (interquartile range), depending on the situation.

 

Objective 2 Methodology :

 

Identifying the essential elements of ARDS and using clinicians help to discover significant variables for patient outcomes in order to accelerate the development of machine learning employing machine learning professionals.

 

Objective 3 Methodology:

 

To assess the effectiveness of the AI prediction model in predicting length of stay, severity of ARDS, and potential outcomes, a pilot study will be initiated. This study will retrospectively collect data from 100 patients from the MRD of IEC No. 591/2019 and CTRI/2019/11/021857, which were prospectively collected in 2019. The pilot testing aims to evaluate the sensitivity and specificity of the model.


 
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