Postoperative delirium is frequent in older patients. Its association with higher mortality,^1,2 cognitive decline,³ loss of autonomy, and increased hospitalization and costs^4,5 warrant preventive initiatives in frail or high-risk patients.^4-6 The 16% of US individuals 65 years and older accounted for more than 40% of surgical interventions in 2019.⁷ This demographic/surgical health care utilization disproportion will worsen as the number of people 65 years and older is expected to double by 2050. In medical patients, one-third of delirium cases are considered preventable^8-10 with multimodal nonpharmacological interventions.^10-12 Once delirium occurs, no treatment changes its course or outcome,¹³ highlighting the importance of postoperative delirium prevention.

Postoperative delirium is a common disorder in older adults that is associated with higher morbidity and mortality, prolonged cognitive impairment, development of dementia, higher institutionalization rates, and rising healthcare costs. The probability of delirium after surgery increases with patients’ age, with pre-existing cognitive impairment, and with comorbidities, and its diagnosis and treatment is dependent on the knowledge of diagnostic criteria, risk factors, and treatment options of the medical staff. In this study, we will investigate whether a cross-sectoral and multimodal intervention for preventing delirium can reduce the prevalence of delirium and postoperative cognitive decline (POCD) in patients older than 70 years undergoing elective surgery. Additionally, we will analyze whether the intervention is cost-effective. The study will be conducted at five medical centers (with two or three surgical departments each) in the southwest of Germany. The study employs a stepped-wedge design with cluster randomization of the medical centers. Measurements are performed at six consecutive points: preadmission, preoperative, and postoperative with daily delirium screening up to day 7 and POCD evaluations at 2, 6, and 12 months after surgery. Recruitment goals are to enroll 1500 patients older than 70 years undergoing elective operative procedures (cardiac, thoracic, vascular, proximal big joints and spine, genitourinary, gastrointestinal, and general elective surgery procedures). Results of the trial should form the basis of future standards for preventing delirium and POCD in surgical wards. Key aims are the improvement of patient safety and quality of life, as well as the reduction of the long-term risk of conversion to dementia. Furthermore, from an economic perspective, we expect benefits and decreased costs for hospitals, patients, and healthcare insurances. German Clinical Trials Register, DRKS00013311 . Registered on 10 November 2017.

Introduction: Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14–56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop a machine learning (ML) model for POD prediction in older patients, in close cooperation with the PAWEL (patient safety, cost-effectiveness and quality of life in elective surgery) project. Methods: The model was trained on the PAWEL study’s dataset of 878 patients (no intervention, age ≥ 70, 209 with POD). Presence of POD was determined by the Confusion Assessment Method and a chart review. We selected 15 features based on domain knowledge, ethical considerations and a recursive feature elimination. A logistic regression and a linear support vector machine (SVM) were trained, and evaluated using receiver operator characteristics (ROC). Results: The selected features were American Society of Anesthesiologists score, multimorbidity, cut-to-suture time, estimated glomerular filtration rate, polypharmacy, use of cardio-pulmonary bypass, the Montreal cognitive assessment subscores ‘memory’, ‘orientation’ and ‘verbal fluency’, pre-existing dementia, clinical frailty scale, age, recent falls, post-operative isolation and pre-operative benzodiazepines. The linear SVM performed best, with an ROC area under the curve of 0.82 [95% CI 0.78–0.85] in the training set, 0.81 [95% CI 0.71–0.88] in the test set and 0.76 [95% CI 0.71–0.79] in a cross-centre validation. Conclusion: We present a clinically useful and explainable ML model for POD prediction. The model will be deployed in the Supporting SURgery with GEriatric Co-Management and AI project.