{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T05:25:02Z","timestamp":1787894702632,"version":"build-2784847793"},"reference-count":40,"publisher":"IOP Publishing","issue":"11","license":[{"start":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T00:00:00Z","timestamp":1700784000000},"content-version":"vor","delay-in-days":23,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T00:00:00Z","timestamp":1700784000000},"content-version":"tdm","delay-in-days":23,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Less invasive surfactant administration (LISA) has been introduced to preterm infants with respiratory distress syndrome on continuous positive airway pressure (CPAP) support in order to avoid intubation and mechanical ventilation. However, after this LISA procedure, a significant part of infants fails CPAP treatment (CPAP-F) and requires intubation in the first 72 h of life, which is associated with worse complication free survival chances. The aim of this study was to predict CPAP-F after LISA, based on machine learning (ML) analysis of high resolution vital parameter monitoring data surrounding the LISA procedure.\n                    <jats:italic>Approach<\/jats:italic>\n                    . Patients with a gestational age (GA) &lt;32 weeks receiving LISA were included. Vital parameter data was obtained from a data warehouse. Physiological features (HR, RR, peripheral oxygen saturation (SpO\n                    <jats:sub>2<\/jats:sub>\n                    ) and body temperature) were calculated in eight 0.5 h windows throughout a period 1.5 h before to 2.5 h after LISA. First, physiological data was analyzed to investigate differences between the CPAP-F and CPAP-Success (CPAP-S) groups. Next, the performance of two types of ML models (logistic regression: LR, support vector machine: SVM) for the prediction of CPAP-F were evaluated.\n                    <jats:italic>Main results<\/jats:italic>\n                    . Of 51 included patients, 18 (35%) had CPAP-F. Univariate analysis showed lower SpO2, temperature and heart rate variability (HRV) before and after the LISA procedure. The best performing ML model showed an area under the curve of 0.90 and 0.93 for LR and SVM respectively in the 0.5 h window directly after LISA, with GA, HRV, respiration rate and SpO\n                    <jats:sub>2<\/jats:sub>\n                    as most important features. Excluding GA decreased performance in both models.\n                    <jats:italic>Significance<\/jats:italic>\n                    . In this pilot study we were able to predict CPAP-F with a ML model of patient monitor signals, with best performance in the first 0.5 h after LISA. Using ML to predict CPAP-F based on vital signals gains insight in (possibly modifiable) factors that are associated with LISA failure and can help to guide personalized clinical decisions in early respiratory management.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ad0ab6","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T17:27:50Z","timestamp":1699464470000},"page":"115005","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Predicting CPAP failure after less invasive surfactant administration (LISA) in preterm infants by machine learning model on vital parameter data: a pilot study"],"prefix":"10.1088","volume":"44","author":[{"given":"R M J S","family":"Kloonen","sequence":"first","affiliation":[{"name":"Eindhoven University of Technology"},{"name":"Maxima Medical Centre Veldhoven, Department of Clinical Physics, Po Box 7777, 5600 MB,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9107-0420","authenticated-orcid":true,"given":"G","family":"Varisco","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E","family":"de Kort","sequence":"additional","affiliation":[{"name":"Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P","family":"Andriessen","sequence":"additional","affiliation":[{"name":"Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0844-7208","authenticated-orcid":true,"given":"H J","family":"Niemarkt","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology"},{"name":"Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C","family":"van Pul","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology"},{"name":"Maxima Medical Centre Veldhoven, Department of Clinical Physics, Po Box 7777, 5600 MB,"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2023,11,24]]},"reference":[{"key":"pmeaad0ab6bib1","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/technologies9030052","type":"journal-article","article-title":"Effect of data scaling methods on machine learning algorithms and model performance","volume":"9","author":"Ahsan","year":"2021","journal-title":"Technologies (Basel)"},{"key":"pmeaad0ab6bib2","doi-asserted-by":"publisher","first-page":"F17","DOI":"10.1136\/archdischild-2015-310299","type":"journal-article","article-title":"Less invasive surfactant administration versus intubation for surfactant delivery in preterm infants with respiratory distress syndrome: a systematic review and meta-analysis","volume":"102","author":"Aldana-Aguirre","year":"2017","journal-title":"ADC Fetal Neonatal Ed."},{"key":"pmeaad0ab6bib3","doi-asserted-by":"publisher","first-page":"1751","DOI":"10.1002\/ppul.25798","type":"journal-article","article-title":"Incidence, predictors of success and outcome of LISA in very preterm infants","volume":"57","author":"Balazs","year":"2022","journal-title":"Pediatric Pulmonol."},{"key":"pmeaad0ab6bib4","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.physa.2005.08.080","type":"journal-article","article-title":"Phase-rectified signal averaging detects quasi-periodicities in non-stationary data","volume":"364","author":"Bauer","year":"2006","journal-title":"Physica"},{"key":"pmeaad0ab6bib5","doi-asserted-by":"publisher","DOI":"10.1097\/CCE.0000000000000302","type":"journal-article","article-title":"Prediction of late-onset sepsis in preterm infants using monitoring signals and machine learning","volume":"3","author":"Cabrera-Quiros","year":"2021","journal-title":"Crit. 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