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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.

External Validation of a Machine-Learning derived risk prediction model for Post Operative Pulmonary Complications

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2024/07/070709
Enrollment
400
Registered
2024-07-16
Start date
Unknown
Completion date
Unknown
Last updated
2024-08-19

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: O- Medical and Surgical

Interventions

Intervention1: NIL: NIL

Sponsors

Dr. Aumkar Kishore Shah
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Undergoing major (duration more than 2hours) abdominal surgery Elective or Emergency

Exclusion criteria

Exclusion criteria: Pregnancy Post-partum up to 6 weeks Moribund patients not expected to survive more than 48 hours

Design outcomes

Primary

MeasureTime frame
To externally validate a machine learning model in an independent population for predicting POPC as per Melbourne group scaleTimepoint: 2 years—

Secondary

MeasureTime frame
To evaluate the calibration of the model in the external validation datasetTimepoint: 2 years;To explore the performance of the model across different subgroups age gender comorbidity status and type of surgery Timepoint: 2 years;To validate the model to predict postoperative respiratory failure up to day 7 Timepoint: 2 years—

Countries

India

Contacts

Public ContactDr Souvik Maitra

Department of Anaesthesiology, Pain Medicine and Critical Care, AIIMS, New Delhi

souvikmaitra@live.com8146727891

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026