PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach
NCT ID: NCT05789953
Last Updated: 2025-04-10
Study Results
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Basic Information
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RECRUITING
512 participants
OBSERVATIONAL
2023-04-25
2025-12-31
Brief Summary
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Detailed Description
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Sonography is becoming increasingly important as a non-invasive examination method that can be performed at the bedside. Various sonographic scores and models have already been developed to predict pulmonary complications. Image processing methods and machine learning, in particular deep learning are also increasingly being used in ultrasound diagnostics. A combination of routine clinical data and imaging data to develop a machine learning algorithm has not yet been tested. However, augmented algorithms using pre- and intraoperative clinical information in addition to ultrasound imaging promise better predictive accuracy than the respective individual methods. In addition, prospective clinical evaluation of machine learning algorithm-based prediction models is lacking to date, although they show good values for "area under the receiver operating characteristic" (AUROC), accuracy and precision in the respective test and validation datasets, which are considered common measures of the predictive quality of such models.
Measures for the prevention of POPC are known, but are probably not consistently applied in clinical routine due to the increased demand, especially for human resources. Therefore, the aim of the study is to identify patients at risk of POPC on the basis of a machine learning algorithm.
All patients are undergoing the same study protocol to develop 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 POPC according to the criteria elaborated by the StEP- collaboration.
According to the case number calculation, 512 adult patients undergoing elective, surgical procedures under general anaesthesia are going to be included. Perioperative routine data will be assessed and stored in a hospital-internal database, as well as data from postoperative clinical examination. Image data from lung sonography will be archived in the PACS for further processing. Based on the collected data, a machine learning algorithm based on neural networks will be trained to predict POPC. The model is created with the anonymized data using the statistics-oriented programming language R and the framework TensorFlow, a deep learning software library based on the programming language Python. The prediction quality of the created prediction model is assessed using the area under the receiver operator characteristics (AUROC) as well as the area under the precision recall curve (AUPRC) and compared with the values of the ARISCAT score, a common score to estimate the risk of POPC.
Precise risk assessment by means of an augmented machine-learning algorithm that uses clinical routine as well as imaging data has great potential to improve patient outcomes and could also help to reduce health care costs.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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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.
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* elective, surgical procedure
* general anaesthesia
Exclusion Criteria
* outpatient surgery
* postoperative admission to intensive care unit
18 Years
ALL
No
Sponsors
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Britta Trautwein
OTHER
Responsible Party
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Britta Trautwein
Resident doctor
Locations
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University Hospital Ulm
Ulm, , Germany
Countries
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Central Contacts
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Facility Contacts
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References
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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.
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.
Miskovic A, Lumb AB. Postoperative pulmonary complications. Br J Anaesth. 2017 Mar 1;118(3):317-334. doi: 10.1093/bja/aex002.
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.
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.
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.
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.
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.
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.
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.
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.
Other Identifiers
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UHUlm
Identifier Type: -
Identifier Source: org_study_id
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