Predictive Models for Screening of Postoperative Cognitive Dysfunction in Older Surgical Patients
Issued Date
2026-03-01
Resource Type
eISSN
22288082
Scopus ID
2-s2.0-105032160162
Journal Title
Siriraj Medical Journal
Volume
78
Issue
3
Start Page
196
End Page
206
Rights Holder(s)
SCOPUS
Bibliographic Citation
Siriraj Medical Journal Vol.78 No.3 (2026) , 196-206
Suggested Citation
Siriussawakul A., Suraarunsumrit P., Srinonprasert V., Somnuke P., Limratana P., Sura-amonrattana U., Morkphrom E., Pratumvinit B., Tornsatitkul S., Jiraphorncharas C. Predictive Models for Screening of Postoperative Cognitive Dysfunction in Older Surgical Patients. Siriraj Medical Journal Vol.78 No.3 (2026) , 196-206. 206. doi:10.33192/smj.v78i3.279441 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115678
Title
Predictive Models for Screening of Postoperative Cognitive Dysfunction in Older Surgical Patients
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Corresponding Author(s)
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Abstract
Objective: Postoperative cognitive dysfunction (POCD) substantially impacts the long-term quality of life of patients and caregivers. Early detection of POCD is essential. We devised quick vigilance screening models for application preoperatively (model one) and during the postoperative period (model two) to predict the development of early POCD (one week after surgery). Materials and Methods: We conducted a cohort study on patients aged ≥ 60 years undergoing cardiac or noncardiac surgeries. POCD was defined as a postoperative Montreal Cognitive Assessment decrease of ≥ two points from the baseline preoperative score. We stipulated that predictive factors should be simple and obtainable by health professionals or trained caregivers. Multivariate analysis results informed our selection of clinically significant variables for constructing the POCD predictive models. Results: Of the 465 patients in the final analysis, the early POCD incidence was 24.9%. The equation used for predictive model one was (1 x education level lower than high school) + (2 x ischemic heart disease) + (2 x warfarin) + (1.5 x frailty score of 3–5). The equation for model two was (-1 x IADL score) + (6 x isoflurane anesthesia) + (7 x any type of intraoperative blood transfusion). Both models displayed well-calibrated curves. The optimal cut-off values of model one and model two to discriminate between a high and low probability of POCD were 2 and 0, respectively. Conclusions: The preoperative and immediate postoperative POCD predictive models perform reliably. These models may effectively guide early POCD detection and risk modification in older surgical patients.