| CTRI Number |
CTRI/2024/07/070709 [Registered on: 16/07/2024] Trial Registered Prospectively |
| Last Modified On: |
13/08/2024 |
| Post Graduate Thesis |
Yes |
| Type of Trial |
Observational |
|
Type of Study
|
Cohort Study |
| Study Design |
Single Arm Study |
|
Public Title of Study
|
A study to check the accuracy of a machine learning derived model that predicts the risk of a patient developing pulmonary complications ( lung ) after a surgery (Post Operative ) based on the certain characteristics of the patient. |
|
Scientific Title of Study
|
External Validation of a Machine-Learning derived risk prediction model for Post Operative Pulmonary Complications |
| Trial Acronym |
|
|
Secondary IDs if Any
|
| Secondary ID |
Identifier |
| NIL |
NIL |
|
|
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
|
| Name |
Dr Aumkar Kishore Shah |
| Designation |
Junior Resident |
| Affiliation |
AIIMS, New Delhi |
| Address |
Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi
South West
DELHI
110029
India |
| Phone |
8551858954 |
| Fax |
|
| Email |
shahaumkar2000@gmail.com |
|
Details of Contact Person
Scientific Query
|
| Name |
Dr Souvik Maitra |
| Designation |
Additional Professor |
| Affiliation |
Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi |
| Address |
Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi
South West
DELHI
110029
India |
| Phone |
8146727891 |
| Fax |
|
| Email |
souvikmaitra@live.com |
|
Details of Contact Person
Public Query
|
| Name |
Dr Souvik Maitra |
| Designation |
Additional Professor |
| Affiliation |
Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi |
| Address |
Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi
South West
DELHI
110029
India |
| Phone |
8146727891 |
| Fax |
|
| Email |
souvikmaitra@live.com |
|
|
Source of Monetary or Material Support
|
| All India Institute of Medical Sciences, AIIMS, New Delhi, India 110029 |
|
|
Primary Sponsor
|
| Name |
Dr. Aumkar Kishore Shah |
| Address |
Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi |
| Type of Sponsor |
Government medical college |
|
|
Details of Secondary Sponsor
|
|
|
Countries of Recruitment
|
India |
|
Sites of Study
|
| No of Sites = 1 |
| Name of Principal
Investigator |
Name of Site |
Site Address |
Phone/Fax/Email |
| Dr Aumkar Kishore Shah |
All India Institute of Medical Sciences, AIIMS |
Ansari Nagar, New Delhi 110029
South West
DELHI |
8551858954
shahaumkar2000@gmail.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| Ethics Committee, All India Institute of Medical Sciences, New Delhi |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Patients |
(1) ICD-10 Condition: O||Medical and Surgical, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Intervention |
NIL |
NIL |
|
|
Inclusion Criteria
|
| Age From |
18.00 Year(s) |
| Age To |
99.00 Year(s) |
| Gender |
Both |
| Details |
Undergoing major (duration more than 2hours) abdominal surgery
Elective or Emergency
|
|
| ExclusionCriteria |
| Details |
Pregnancy
Post-partum up to 6 weeks
Moribund patients not expected to survive more than 48 hours
|
|
|
Method of Generating Random Sequence
|
Not Applicable |
|
Method of Concealment
|
Not Applicable |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
| To externally validate a machine learning model in an independent population for predicting POPC as per Melbourne group scale |
2 years |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
To validate the model to predict postoperative respiratory failure up to day 7
|
2 years |
| To evaluate the calibration of the model in the external validation dataset |
2 years |
To explore the performance of the model across different subgroups age gender comorbidity status and type of surgery
|
2 years |
|
|
Target Sample Size
|
Total Sample Size="400"
Sample Size from India="400"
Final Enrollment numbers achieved (Total)= "Applicable only for Completed/Terminated trials"
Final Enrollment numbers achieved (India)="Applicable only for Completed/Terminated trials" |
|
Phase of Trial
|
N/A |
|
Date of First Enrollment (India)
|
29/07/2024 |
| Date of Study Completion (India) |
Applicable only for Completed/Terminated trials |
| Date of First Enrollment (Global) |
29/07/2024 |
| Date of Study Completion (Global) |
Applicable only for Completed/Terminated trials |
|
Estimated Duration of Trial
|
Years="2"
Months="0"
Days="0" |
Recruitment Status of Trial (Global)
Modification(s)
|
Not Applicable |
| Recruitment Status of Trial (India) |
Open to Recruitment |
|
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
|
Postoperative
pulmonary complications (POPC) are a significant cause of morbidity and
mortality following surgery, contributing to prolonged hospital stays and
increased healthcare costs.
Machine learning
(ML) algorithms have shown promise in predicting POPC risk based on
preoperative variables.
However,
external validation of these models is essential to evaluate their
generalizability and clinical utility.
A recent review
found that only a few of the developed scores have been externally validated
viz. ARISCAT Score
Recently, such a
model was developed in the department.
The machine
learning model performed reasonably well in the internal validation cohort, but
the investigators were unable to validate in a large external cohort at that
time.
This study will
aim to externally validate the developed model, thereby increasing the
generalizability of the model and pave the way for further multicentric
validations and development of a tool for the routine clinical use adapted to
the Indian population. |