| CTRI Number |
CTRI/2024/10/076042 [Registered on: 29/10/2024] Trial Registered Prospectively |
| Last Modified On: |
24/09/2024 |
| Post Graduate Thesis |
No |
| Type of Trial |
Observational |
|
Type of Study
|
Ambispective observational study |
| Study Design |
Other |
|
Public Title of Study
|
Development of a model to predict death at 30 days in patients with complicated abdominal infections |
|
Scientific Title of Study
|
Development and validation of an Artificial Intelligence and machine learning model to predict 30 day mortality in patients with secondary peritonitis |
| Trial Acronym |
NIL |
|
Secondary IDs if Any
|
| Secondary ID |
Identifier |
| NIL |
NIL |
|
|
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
|
| Name |
Puneet Khanna |
| Designation |
Additional Professor |
| Affiliation |
AIIMS New Delhi |
| Address |
Room no 5011
5th floor Administrative block
All India Institute of Medical Sciences
New Delhi
South DELHI 110029 India |
| Phone |
9873106516 |
| Fax |
|
| Email |
k.punit@yahoo.com |
|
Details of Contact Person Scientific Query
|
| Name |
Puneet Khanna |
| Designation |
Additional Professor |
| Affiliation |
AIIMS New Delhi |
| Address |
Room no 5011
5th floor Administrative block
All India Institute of Medical Sciences
New Delhi
DELHI 110029 India |
| Phone |
9873106516 |
| Fax |
|
| Email |
k.punit@yahoo.com |
|
Details of Contact Person Public Query
|
| Name |
Bhavana K |
| Designation |
Senior Resident |
| Affiliation |
AIIMS New Delhi |
| Address |
Room no 5011
5th floor Administrative block
All India Institute of Medical Sciences
New Delhi
South DELHI 110029 India |
| Phone |
9600717490 |
| Fax |
|
| Email |
bhavanakayarat27@gmail.com |
|
|
Source of Monetary or Material Support
|
| All India Institute of Medical Sciences,
Aurobindo Marg
Ansari Nagar East
New Delhi 110029 |
|
|
Primary Sponsor
|
| Name |
All India Institute of Medical Sciences New Delhi |
| Address |
Sri Aurobindo Marg
Ansari Nagar East
New Delhi 110029 |
| Type of Sponsor |
Research institution and hospital |
|
|
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 Bhavana K |
All India Institute of Medical Sciences, New Delhi |
Room no 5011
5 th floor
Dept of Anaesthesia Pain Medicine & Critical Care Ansari Nagar
New Delhi 110029 South DELHI |
9600717490
bhavanakayarat27@gmail.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| Institute Ethics Committee,AIIMS-New Delhi |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Patients |
(1) ICD-10 Condition: K67||Disorders of peritoneum in infectious diseases classified elsewhere, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Intervention |
Not applicable |
Not applicable |
| Comparator Agent |
Not applicable |
Not applicable |
|
|
Inclusion Criteria
|
| Age From |
18.00 Year(s) |
| Age To |
75.00 Year(s) |
| Gender |
Both |
| Details |
Patients with secondary peritonitis including perforation of the hollow viscus, undergoing emergency laparotomy |
|
| ExclusionCriteria |
| Details |
1. Patients in whom no significant intra abdominal pathology was found during laparotomy
2. Patients aged > 75 years
3. Patients aged < 18 years |
|
|
Method of Generating Random Sequence
|
Not Applicable |
|
Method of Concealment
|
Not Applicable |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
| Development of the Artificial Intelligence and Machine learning model to predict 30 day mortality |
30 days |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
| Validation and testing of the Artificial Intelligence and Machine learning model to predict 30 day mortality |
30 days |
|
|
Target Sample Size
|
Total Sample Size="500" Sample Size from India="500"
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)
|
03/07/2025 |
| Date of Study Completion (India) |
Applicable only for Completed/Terminated trials |
| Date of First Enrollment (Global) |
Date Missing |
| Date of Study Completion (Global) |
Applicable only for Completed/Terminated trials |
|
Estimated Duration of Trial
|
Years="1" Months="0" Days="0" |
|
Recruitment Status of Trial (Global)
|
Not Yet Recruiting |
| Recruitment Status of Trial (India) |
Not Yet Recruiting |
|
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
|
Survival
rates are lower following emergency abdominal surgery, with 30-day
mortality rates ranging from 4% to 8%. Early identification of risk factors
and proactive risk mitigation strategies could potentially avert some of these
fatalities. Although numerous tools exist to assist clinicians in assessing the
risk of mortality or serious complications post-surgery (6), most are designed
for preoperative risk evaluation and rely solely on variables available before
surgery (7). However, the patient’s intraoperative course provides additional
valuable information, offering the potential for early detection of modifiable
deterioration. Numerous risk stratification models are currently available, however these models were primarily
derived from and for elective surgery patients, raising questions about their
accuracy and applicability to emergency surgery patients.Therefore, we aim to
construct a deep-learning algorithm, an AI model, that will provide dynamic
predictions of postoperative mortality based on pre- and intraoperative
electronic medical record data. We hypothesize that our model will accurately
predict 30-day mortality and morbidity in patients undergoing EL in the Indian
population. Unlike existing risk assessment models that include
only preoperative data or both pre- and intraoperative data to develop a model,
we intend to incorporate both preoperative, intraoperative, and postoperative
electronic medical record data of the patients. By implementing deep-learning
algorithms, we aim to analyze complex interactions among patient
characteristics, intraoperative events, and postoperative outcomes. It offers
the potential for personalized risk assessment and modified intervention
strategies, ultimately aiming to improve outcomes and reduce mortality rates in
this high-risk patient population. A total of 500 patients will be included, divided into
three groups at a ratio of 7:1:2. For model development, 350 patients will be
included, while 50 patients will be allocated for validation, and 100 patients
for testing the model |