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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  
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 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  
Status 
Not Applicable 
 
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

 
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