Mariska E. Te Pas

Objectives: An increasing number of older adults, including those with mild cognitive impairment, are undergoing cardiac surgery. Despite strong recommendations for preoperative cognitive screening and peri-operative monitoring, routine implementation faces challenges, such as limited time in busy outpatient clinics and lack of patient motivation. To address this issue, gamification and self-administration of cognitive screening using BAMCOG were explored in patients undergoing transcatheter aortic valve replacement (TAVR). Methods: A multi-methods prospective repeated-measures within-subject cohort study was conducted between January 2021 and December 2022 to assess usability and concurrent validity. The initial part after game development focused on qualitatively examining the usability of BAMCOG in eight patients, using the System Usability Scale (SUS). The second part, with 40 patients, evaluated concurrent validity by comparing BAMCOG with the widely used Montreal Cognitive Assessment (MoCA). Results: The average SUS score was 79.7, indicating good usability. In the preoperative period, the correlation between BAMCOG and MoCA scores was r = 0.33 (p < 0.05), which increased to r = 0.59 (p < 0.001) on the first postoperative day. Conclusions: In conclusion, peri-operative self-monitoring of cognition around a TAVR procedure is feasible, but the concurrent validity of the BAMCOG and the MoCA scores is moderate to low. This warrants further research on gamified cognitive screeners to optimize their use in perioperative cognitive monitoring.

Postoperative delirium (POD) and postoperative encephalopathy (POE) are common complications in older adults undergoing aortic valve replacement (AVR), yet the predictive accuracy of cognitive screening tools remains uncertain. In this prospective cohort study, 50 patients aged 65 years and older scheduled for AVR between January and October 2022 underwent preoperative assessment with the Brain Aging Monitor Cognitive Assessment (BAMCOG) and Montreal Cognitive Assessment (MoCA). Postoperatively, POD was evaluated with the Delirium Observation Screening (DOS) scale and POE with electroencephalography (EEG). BAMCOG and MoCA showed poor accuracy in predicting POE, with AUROC values of 0.67 and 0.59 respectively, but BAMCOG demonstrated good accuracy for POD prediction (AUROC 0.85) compared with MoCA (AUROC 0.53). Higher BAMCOG scores were significantly associated with reduced POD incidence, with each 10% increase in score lowering the risk by 16%. These findings suggest that BAMCOG may be a valuable preoperative screening tool for POD, though larger studies are needed to confirm its clinical utility and establish optimal cutoff values.

Cognitive impairment predisposes patients to the development of delirium and postoperative cognitive dysfunction. In particular, in older patients, the adverse sequelae of cognitive decline in the perioperative period may contribute to adverse outcomes after surgical procedures. Subtle signs of cognitive impairment are often not previously diagnosed. Therefore, the aim of this review is to describe the available cognitive screeners suitable for preoperative screening and their psychometric properties for identifying mild cognitive impairment, as preoperative workup may improve perioperative care for patients at risk for postoperative cognitive dysfunction. Electronic systematic and snowball searches of PubMed, PsycInfo, ClinicalKey, and ScienceDirect were conducted for the period 2015–2020. Major inclusion criteria for articles included those that discussed a screener that included the cognitive domain ‘memory’, that had a duration time of less than 15 min, and that reported sensitivity and specificity to detect mild cognitive impairment. Studies about informant-based screeners were excluded. We provided an overview of the characteristics of the cognitive screener, such as interrater and test-retest reliability correlations, sensitivity and specificity for mild cognitive impairment and cognitive impairment, and duration of the screener and cutoff points. Of the 4775 identified titles, 3222 were excluded from further analysis because they were published prior to 2015. One thousand four hundred and forty-eight titles did not fulfill the inclusion criteria. All abstracts of 52 studies on 45 screeners were examined of which 10 met the inclusion criteria. For these 10 screeners, a further snowball search was performed to obtain related studies, resulting in 20 articles. Screeners included in this review were the Mini-Cog, MoCA, O3DY, AD8, SAGE, SLUMS, TICS(-M), QMCI, MMSE2, and Mini-ACE. The sensitivity and specificity range to detect MCI in an older population is the highest for the MoCA, with a sensitivity range of 81–93% and a specificity range of 74–89%. The MoCA, with the highest combination of sensitivity and specificity, is a feasible and valid routine screening of pre-surgical cognitive function. This warrants further implementation and validation studies in surgical pathways with a large proportion of older patients.

Background: Respondent engagement of questionnaires in health care is fundamental to ensure adequate response rates for the evaluation of services and quality of care. Conventional survey designs are often perceived as dull and unengaging, resulting in negative respondent behavior. It is necessary to make completing a questionnaire attractive and motivating. Objective: The aim of this study is to compare the user experience of a chatbot questionnaire, which mimics intelligent conversation, with a regular computer questionnaire. Methods: The research took place at the preoperative outpatient clinic. Patients completed both the standard computer questionnaire and the new chatbot questionnaire. Afterward, patients gave their feedback on both questionnaires by the User Experience Questionnaire, which consists of 26 terms to score. Results: The mean age of the 40 included patients (25 [63%] women) was 49 (SD 18-79) years; 46.73% (486/1040) of all terms were scored positive for the chatbot. Patients preferred the computer for 7.98% (83/1040) of the terms and for 47.88% (498/1040) of the terms there were no differences. Completion (mean time) of the computer questionnaire took 9.00 minutes by men (SD 2.72) and 7.72 minutes by women (SD 2.60; P=.148). For the chatbot, completion by men took 8.33 minutes (SD 2.99) and by women 7.36 minutes (SD 2.61; P=.287). Conclusions: Patients preferred the chatbot questionnaire over the computer questionnaire. Time to completion of both questionnaires did not differ, though the chatbot questionnaire on a tablet felt more rapid compared to the computer questionnaire. This is an important finding because it could lead to higher response rates and to qualitatively better responses in future questionnaires.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Respondent engagement of questionnaires in health care is fundamental to ensure adequate response rates for the evaluation of services and quality of care. Conventional survey designs are often perceived as dull and unengaging, resulting in negative respondent behavior. It is necessary to make completing a questionnaire attractive and motivating. Objective: The aim of this study is to compare the user experience of a chatbot questionnaire, which mimics intelligent conversation, with a regular computer questionnaire. Methods: The research took place at the preoperative outpatient clinic. Patients completed both the standard computer questionnaire and the new chatbot questionnaire. Afterward, patients gave their feedback on both questionnaires by the User Experience Questionnaire, which consists of 26 terms to score. Results: The mean age of the 40 included patients (25 [63%] women) was 49 (SD 18-79) years; 46.73% (486/1040) of all terms were scored positive for the chatbot. Patients preferred the computer for 7.98% (83/1040) of the terms and for 47.88% (498/1040) of the terms there were no differences. Completion (mean time) of the computer questionnaire took 9.00 minutes by men (SD 2.72) and 7.72 minutes by women (SD 2.60; P=.148). For the chatbot, completion by men took 8.33 minutes (SD 2.99) and by women 7.36 minutes (SD 2.61; P=.287). Conclusions: Patients preferred the chatbot questionnaire over the computer questionnaire. Time to completion of both questionnaires did not differ, though the chatbot questionnaire on a tablet felt more rapid compared to the computer questionnaire. This is an important finding because it could lead to higher response rates and to qualitatively better responses in future questionnaires.