Annals of surgery · 2025
Recovery Patterns: Longitudinal Cluster Analysis of Physical Function Following Abdominal Surgery.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Toben Daan J, de Wind Astrid, van der Meij Eva, Huirne Judith A F, Hoogendoorn Mark, Anema Johannes R
Аннотация
BACKGROUND: A rise in the proportion of day surgery has seen a concomitant increase in the proportion of patients recovering at home. Blended eHealth is well situated to provide this group with medical support and supervision. However, a data-driven description of the heterogeneity is missing.
OBJECTIVE: To identify clinically meaningful patterns of functional recovery following abdominal surgery and describe how the emergent patient characteristics differ between them.
METHODS: This was a secondary data analysis of 2 data sets collected through 2 previously conducted RCTs. We used k-medoids clustering and growth mixture modeling on the longitudinal patient-reported outcome measurement information system physical function t-scores of 649 patients. Differences in patient characteristics between the resultant clusters were identified through statistical tests.
RESULTS: Three clusters-fast, intermediate, and uneven recovery-were identified regardless of the data set or statistical technique. A fourth cluster-relapse-was identified by both statistical techniques but only in the presence of heavy surgery. The fifth and sixth clusters-low gain and high gain-were identified for both light and heavy surgery, but only through k-medoids clustering.
CONCLUSIONS: Trajectories of physical function following abdominal surgery are heterogenous but distinct clinically meaningful patterns can be extracted. This classification may facilitate shared decision-making during preoperative care, and future research may utilize them as targets for prediction.
OBJECTIVE: To identify clinically meaningful patterns of functional recovery following abdominal surgery and describe how the emergent patient characteristics differ between them.
METHODS: This was a secondary data analysis of 2 data sets collected through 2 previously conducted RCTs. We used k-medoids clustering and growth mixture modeling on the longitudinal patient-reported outcome measurement information system physical function t-scores of 649 patients. Differences in patient characteristics between the resultant clusters were identified through statistical tests.
RESULTS: Three clusters-fast, intermediate, and uneven recovery-were identified regardless of the data set or statistical technique. A fourth cluster-relapse-was identified by both statistical techniques but only in the presence of heavy surgery. The fifth and sixth clusters-low gain and high gain-were identified for both light and heavy surgery, but only through k-medoids clustering.
CONCLUSIONS: Trajectories of physical function following abdominal surgery are heterogenous but distinct clinically meaningful patterns can be extracted. This classification may facilitate shared decision-making during preoperative care, and future research may utilize them as targets for prediction.