PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach

NCT05789953 | Recruiting

• 1 other identifier: UHUlm
• Study Type: observational
• Target: 512
• Locations: 1 country
• Sites: 1

Brief Summary

Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective, tie up human and technical resources. The aim of the planned research project is therefore to enable reliable identification of high-risk patients on the basis of a tailored machine learning algorithm using perioperative clinical routine data and sonographic imaging data collected in the recovery room. The randomized clinical trial will include 512 patients undergoing elective surgery in general anaesthesia. The primary outcome will be the development of POPC. The goal of the study is to detect postoperative pulmonary complications before they become clinically manifest.

Conditions

Postoperative Pulmonary Complications

Keywords

machine learning; lung ultrasound

Outcome Measures

Primary Outcomes (1)

• Number of patients with postoperative pulmonary complications (POPC)

  POPC according to criteria by the StEP-collaboration. This includes a clinical examination and interview of the patients on postoperative day 1,3 and 7.

  postoperative day 7 or day of discharge

Study Arms (1)

Development of the machine learning model

Perioperative clinical routine data are going to be assessed as per standard. Postoperatively, a standardized lung sonography is going to be performed in the recovery room. Patients will then be visited on the ward on postoperative day 1, 3 and 7 for clinical examination to detect postoperative pulmonary complications according to the criteria elaborated by the StEP- collaboration.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

adult patients in a university hospital

You may qualify if:

• adult patients
• elective, surgical procedure
• general anaesthesia

You may not qualify if:

• patients younger than 18 years of age
• outpatient surgery
• postoperative admission to intensive care unit

Sponsors & Collaborators

• Britta Trautwein: lead

Study Sites (1)

University Hospital Ulm

Ulm, 89081, Germany

RECRUITING

Related Publications (11)

• Fernandez-Bustamante A, Frendl G, Sprung J, Kor DJ, Subramaniam B, Martinez Ruiz R, Lee JW, Henderson WG, Moss A, Mehdiratta N, Colwell MM, Bartels K, Kolodzie K, Giquel J, Vidal Melo MF. Postoperative Pulmonary Complications, Early Mortality, and Hospital Stay Following Noncardiothoracic Surgery: A Multicenter Study by the Perioperative Research Network Investigators. JAMA Surg. 2017 Feb 1;152(2):157-166. doi: 10.1001/jamasurg.2016.4065.

  PMID: 27829093 BACKGROUND

• Ferreyra GP, Baussano I, Squadrone V, Richiardi L, Marchiaro G, Del Sorbo L, Mascia L, Merletti F, Ranieri VM. Continuous positive airway pressure for treatment of respiratory complications after abdominal surgery: a systematic review and meta-analysis. Ann Surg. 2008 Apr;247(4):617-26. doi: 10.1097/SLA.0b013e3181675829.

  PMID: 18362624 BACKGROUND

• Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth. 2017 Mar 1;118(3):317-334. doi: 10.1093/bja/aex002.

  PMID: 28186222 BACKGROUND

• Abbott TEF, Fowler AJ, Pelosi P, Gama de Abreu M, Moller AM, Canet J, Creagh-Brown B, Mythen M, Gin T, Lalu MM, Futier E, Grocott MP, Schultz MJ, Pearse RM; StEP-COMPAC Group. A systematic review and consensus definitions for standardised end-points in perioperative medicine: pulmonary complications. Br J Anaesth. 2018 May;120(5):1066-1079. doi: 10.1016/j.bja.2018.02.007. Epub 2018 Mar 27.

  PMID: 29661384 BACKGROUND

• Ball L, Pelosi P. Predictive scores for postoperative pulmonary complications: time to move towards clinical practice. Minerva Anestesiol. 2016 Mar;82(3):265-7. Epub 2015 Sep 4. No abstract available.

  PMID: 26344668 BACKGROUND

• Nithiuthai J, Siriussawakul A, Junkai R, Horugsa N, Jarungjitaree S, Triyasunant N. Do ARISCAT scores help to predict the incidence of postoperative pulmonary complications in elderly patients after upper abdominal surgery? An observational study at a single university hospital. Perioper Med (Lond). 2021 Dec 8;10(1):43. doi: 10.1186/s13741-021-00214-3.

  PMID: 34876228 BACKGROUND

• Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications. JAMA Netw Open. 2021 Mar 1;4(3):e212240. doi: 10.1001/jamanetworkopen.2021.2240.

  PMID: 33783520 BACKGROUND

• Szabo M, Bozo A, Darvas K, Soos S, Ozse M, Ivanyi ZD. The role of ultrasonographic lung aeration score in the prediction of postoperative pulmonary complications: an observational study. BMC Anesthesiol. 2021 Jan 14;21(1):19. doi: 10.1186/s12871-021-01236-6.

  PMID: 33446103 BACKGROUND

• van Sloun RJG, Demi L. Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, In-Vivo Results. IEEE J Biomed Health Inform. 2020 Apr;24(4):957-964. doi: 10.1109/JBHI.2019.2936151. Epub 2019 Aug 19.

  PMID: 31425126 BACKGROUND

• Brusasco C, Santori G, Tavazzi G, Via G, Robba C, Gargani L, Mojoli F, Mongodi S, Bruzzo E, Tro R, Boccacci P, Isirdi A, Forfori F, Corradi F; UCARE (Ultrasound in Critical care and Anesthesia Research Group). Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema. J Clin Monit Comput. 2022 Feb;36(1):131-140. doi: 10.1007/s10877-020-00629-1. Epub 2020 Dec 12.

  PMID: 33313979 BACKGROUND

• Trautwein B, Beer M, Blobner M, Jungwirth B, Kagerbauer SM, Gotz M. Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. PLoS One. 2025 Aug 19;20(8):e0329076. doi: 10.1371/journal.pone.0329076. eCollection 2025.

  PMID: 40828817 DERIVED

Central Study Contacts

Britta Trautwein, MD

CONTACT

00731 500 60227; britta.trautwein@uniklinik-ulm.de

Simone Kagerbauer, PD

CONTACT

00731 500 60254; simone.kagerbauer@uni-ulm.de

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Resident doctor

Study Record Dates

• First Submitted: March 16, 2023
• First Posted: March 29, 2023
• Study Start: April 25, 2023
• Primary Completion: July 1, 2025
• Study Completion: December 1, 2025
• Last Updated: April 10, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

DE (1)