Details

Autor(en) / Beteiligte

• Fritsch, Sebastian Johannes
• Riedel, Morris
• Marx, Gernot
• Bickenbach, Johannes
• Schuppert, Andreas

Titel

Development of a machine learning model for prediction of the duration of unassisted spontaneous breathing in patients during prolonged weaning from mechanical ventilation

Ist Teil von

• Journal of critical care, 2024-08, Vol.82, p.154795-154795, Article 154795

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2024

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Treatment of patients undergoing prolonged weaning from mechanical ventilation includes repeated spontaneous breathing trials (SBTs) without respiratory support, whose duration must be balanced critically to prevent over- and underload of respiratory musculature. This study aimed to develop a machine learning model to predict the duration of unassisted spontaneous breathing. Structured clinical data of patients from a specialized weaning unit were used to develop (1) a classifier model to qualitatively predict an increase of duration, (2) a regressor model to quantitatively predict the precise duration of SBTs on the next day, and (3) the duration difference between the current and following day. 61 features, known to influence weaning, were included into a Histogram-based gradient boosting model. The models were trained and evaluated using separated data sets. 18.948 patient-days from 1018 individual patients were included. The classifier model yielded an ROC-AUC of 0.713. The regressor models displayed a mean absolute error of 2:50 h for prediction of absolute durations and 2:47 h for day-to-day difference. The developed machine learning model showed informed results when predicting the spontaneous breathing capacity of a patient in prolonged weaning, however lacking prognostic quality required for direct translation to clinical use. •Setting aims for spontaneous breathing in prolonged weaning is a challenging task.•Both over- and underloading are unfavourable and prolong weaning unnecessarily.•Structured clinical data could help to predict a patient's respiratory capability.•A machine learning model was developed to predict duration of spontaneous breathing.•The developed models showed only mediocre accuracy and partly relevant deviations.