Digital Early Warning System for Acute Lung Injury in Liver Surgery

Condition(s) studied

Study summary

This study aims to develop an explainable machine learning model that takes into account the characteristics of cardiopulmonary interactions. This model will enable early prediction of acute lung injury (ALI) in patients undergoing major liver surgery. The research will create a digital early-warning system for ALI, thereby supporting clinical diagnosis and treatment decisions. This, in turn, should help reduce the incidence and mortality rates associated with ALI.

Eligibility

Primary outcome measure(s)

• Occurrence of ALI within 7 Days after Surgery — Perioperative period (Perioperative): Refers to the entire process from the determination of surgical treatment to postoperative rehabilitation (e.g., from 1 day before surgery to 7 days after surgery).
  Berlin Definition: 1. Onset: Acute exacerbation of known injury or new/worsening respiratory symptoms within 1 week. 2. Chest Imaging (X-ray or CT): Bilateral pulmonary shadows not fully explained by exudation, atelectasis, or nodules. 3. Pulmonary Edema Etiology: Respiratory failure not fully attributed to heart failure or fluid overload; if no related risk factors, objective tests (e.g., Doppler echocardiography) are needed to exclude hydrostatic pulmonary edema. 4. Oxygenation Levels: Mild - With CPAP/PEEP \>5 cmH2O, 200 mmHg \< PaO2/FiO2 \< 300 mmHg; Moderate - With CPAP/PEEP \>5 cmH2O, 100 mmHg \< PaO2/FiO2 \< 200 mmHg; Severe - With CPAP/PEEP \>5 cmH2O, PaO2/FiO2 \< 100 mmHg.

Trial sites (4)

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