Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead).

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.

Development of a complex intervention to prevent delirium in older hospitalized patients by optimizing discharge and transfer processes and involving caregivers: A multi-method study.

BACKGROUND: Delirium is a common yet challenging condition in older hospitalized patients, associated with various adverse outcomes. Environmental factors, such as room changes, may contribute to the development or severity of delirium. Most previous research has focused on preventing and reducing this condition by addressing risk factors and facilitating reorientation during hospital stay. OBJECTIVE: We aimed to systematically develop a complex intervention to prevent delirium in older hospitalized patients by optimizing discharge and transfer processes and involving caregivers during and after these procedures. The intervention combines stakeholder and expert opinions, evidence, and theory. This article provides guidance and inspiration to research groups in developing complex interventions according to the recommendations in the Medical Research Council framework for complex interventions. DESIGN AND METHODS: A stepwise multi-method study was conducted. The preparation phase included analysis of the context and current practice via focus groups. Based on these results, an expert workshop was organized, followed by a Delphi survey. Finally, the intervention was modeled and a program theory was developed, including a logic model. RESULTS: A complex intervention was developed in an iterative process, involving healthcare professionals, delirium experts, researchers, as well as caregiver and patient representatives. The key intervention component is an 8-point-program, which provides caregivers with recommendations for preventing delirium during the transition phase and in the post-discharge period. Information materials (flyers, handbook, videos, posters, defined "Dos and Don'ts", discharge checklist), training for healthcare professionals, and status analyses are used as implementation strategies. In addition, roles were established for gatekeepers to act as leaders, and champions to serve as knowledge multipliers and trainers for the multi-professional team in the hospitals. CONCLUSIONS: This study serves as an example of how to develop a complex intervention. In an additional step, the intervention and implementation strategies will be investigated for feasibility and acceptability in a pilot study with an accompanying process evaluation. TWEETABLE ABSTRACT: Delirium prevention can benefit from optimizing discharge and transfer processes and involving caregivers of older patients in these procedures. STUDY REGISTRATION: DRKS00017828, German Register of Clinical Studies, date of registration 17.09.2019.

Artificial Intelligence-Based Clinical Decision Support Systems in Geriatrics: An Ethical Analysis.

OBJECTIVES: To provide an ethical analysis of the implications of the usage of artificial intelligence-supported clinical decision support systems (AI-CDSS) in geriatrics. DESIGN: Ethical analysis based on the normative arguments regarding the use of AI-CDSS in geriatrics using a principle-based ethical framework. SETTING AND PARTICIPANTS: Normative arguments identified in 29 articles on AI-CDSS in geriatrics. METHODS: Our analysis is based on a literature search that was done to determine ethical arguments that are currently discussed regarding AI-CDSS. The relevant articles were subjected to a detailed qualitative analysis regarding the ethical considerations Supplementary Datamentioned therein. We then discussed the identified arguments within the frame of the 4 principles of medical ethics according to Beauchamp and Childress and with respect to the needs of frail older adults. RESULTS: We found a total of 5089 articles; 29 articles met the inclusion criteria and were subsequently subjected to a detailed qualitative analysis. We could not identify any systematic analysis of the ethical implications of AI-CDSS in geriatrics. The ethical considerations are very unsystematic and scattered, and the existing literature has a predominantly technical focus emphasizing the technology's utility. In an extensive ethical analysis, we systematically discuss the ethical implications of the usage of AI-CDSS in geriatrics. CONCLUSIONS AND IMPLICATIONS: AI-CDSS in geriatrics can be a great asset, especially when dealing with patients with cognitive disorders; however, from an ethical perspective, we see the need for further research. By using AI-CDSS, older patients' values and beliefs might be overlooked, and the quality of the doctor-patient relationship might be altered, endangering compliance to the 4 ethical principles of Beauchamp and Childress.

Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician.

INTRODUCTION: Geriatric co-management is known to improve treatment of older adults in various clinical settings, however, widespread application of the concept is limited due to restricted resources. Digitalization may offer options to overcome these shortages by providing structured, relevant information and decision support tools for medical professionals. We present the SURGE-Ahead project (Supporting SURgery with GEriatric co-management and Artificial Intelligence) addressing this challenge. METHODS: A digital application with a dashboard-style user interface will be developed, displaying 1) evidence-based recommendations for geriatric co-management and 2) artificial intelligence-enhanced suggestions for continuity of care (COC) decisions. The development and implementation of the SURGE-Ahead application (SAA) will follow the Medical research council framework for complex medical interventions. In the development phase a minimum geriatric data set (MGDS) will be defined that combines parametrized information from the hospital information system with a concise assessment battery and sensor data. Two literature reviews will be conducted to create an evidence base for co-management and COC suggestions that will be used to display guideline-compliant recommendations. Principles of machine learning will be used for further data processing and COC proposals for the postoperative course. In an observational and AI-development study, data will be collected in three surgical departments of a University Hospital (trauma surgery, general and visceral surgery, urology) for AI-training, feasibility testing of the MGDS and identification of co-management needs. Usability will be tested in a workshop with potential users. During a subsequent project phase, the SAA will be tested and evaluated in clinical routine, allowing its further improvement through an iterative process. DISCUSSION: The outline offers insights into a novel and comprehensive project that combines geriatric co-management with digital support tools to improve inpatient surgical care and continuity of care of older adults. TRIAL REGISTRATION: German clinical trials registry (Deutsches Register für klinische Studien, DRKS00030684), registered on 21st November 2022.

Artificial neural networks for non-linear age correction of diffusion metrics in the brain.

Human aging is characterized by progressive loss of physiological functions. To assess changes in the brain that occur with increasing age, the concept of brain aging has gained momentum in neuroimaging with recent advancements in statistical regression and machine learning (ML). A common technique to assess the brain age of a person is, first, fitting a regression model to neuroimaging data from a group of healthy subjects, and then, using the resulting model for age prediction. Although multiparametric MRI-based models generally perform best, models solely based on diffusion tensor imaging have achieved similar results, with the benefits of faster data acquisition and better replicability across scanners and field strengths. In the present study, we developed an artificial neural network (ANN) for brain age prediction based upon tract-based fractional anisotropy (FA). Consequently, we investigated if this age-prediction model could also be used for non-linear age correction of white matter diffusion metrics in healthy adults. The brain age prediction accuracy of the ANN (R 2 = 0.47) was similar to established multimodal models. The comparison of the ANN-based age-corrected FA with the tract-wise linear age-corrected FA resulted in an R 2 value of 0.90 [0.82; 0.93] and a mean difference of 0.00 [-0.04; 0.05] for all tract systems combined. In conclusion, this study demonstrated the applicability of complex ANN models to non-linear age correction of tract-based diffusion metrics as a proof of concept.

Comparison of delirium detection tools in acute care : A rapid review.

BACKGROUND: Delirium is a frequent psychopathological syndrome in geriatric patients. It is sometimes the only symptom of acute illness and bears a high risk for complications. Therefore, feasible assessments are needed for delirium detection. OBJECTIVE AND METHODS: Rapid review of available delirium assessments based on a current Medline search and cross-reference check with a special focus on those implemented in acute care hospital settings. RESULTS: A total of 75 delirium detection tools were identified. Many focused on inattention as well as acute onset and/or fluctuating course of cognitive changes as key features for delirium. A range of assessments are based on the confusion assessment method (CAM) that has been adapted for various clinical settings. The need for a collateral history, time resources and staff training are major challenges in delirium assessment. Latest tests address these through a two-step approach, such as the ultrabrief (UB) CAM or by optional assessment of temporal aspects of cognitive changes (4 As test, 4AT). Most delirium screening assessments are validated for patient interviews, some are suitable for monitoring delirium symptoms over time or diagnosing delirium based on collateral history only. CONCLUSION: Besides the CAM the 4AT has become well-established in acute care because of its good psychometric properties and practicability. There are several other instruments extending and improving the possibilities of delirium detection in different clinical settings.

A complex intervention to promote prevention of delirium in older adults by targeting caregiver's participation during and after hospital discharge - study protocol of the TRAnsport and DElirium in older people (TRADE) project.

BACKGROUND: Among potentially modifiable risk factors for delirium, transfers between wards, hospitals and other facilities have been mentioned with low evidence. TRADE (TRAnsport and DElirium in older people) was set up to investigate i) the impact of transfer and/or discharge on the onset of delirium in older adults and ii) feasibility and acceptance of a developed complex intervention targeting caregiver's participation during and after hospital discharge or transfer on cognition and the onset of delirium in older adults. METHODS: The study is designed according to the guidelines of the UK Medical Research Council (MRC) for development and evaluation of complex interventions and comprises two steps: development and feasibility/piloting. The development phase includes i) a multicenter observational prospective cohort study to assess delirium incidence and cognitive decline associated with transfer and discharge, ii) a systematic review of the literature, iii) stakeholder focus group interviews and iv) an expert workshop followed by a Delphi survey. Based on this information, a complex intervention to better and systematically involve family caregivers in discharge and transport was developed. The intervention will be tested in a pilot study using a stepped wedge design with a detailed process and health economic evaluation. The study is conducted at four acute care hospitals in southwest Germany. Primary endpoints are the delirium incidence and cognitive function. Secondary endpoints include prevalence of caregiver companionship, functional decline, cost and cost effectiveness, quality of discharge management and quality of admission management in admitting hospitals or nursing homes. Data will be collected prior to discharge as well as after 3, 7 and 90 days. DISCUSSION: TRADE will help to evaluate transfer and discharge as a possible risk factor for delirium. In addition, TRADE evaluates the impact and modifiability of caregiver's participation during patient's transfer or discharge on delirium incidence and cognitive decline providing the foundation for a confirmatory implementation study. TRIAL REGISTRATION: DRKS (Deutsches Register für klinische Studien) DRKS00017828 . Registered on 17th September 2019. Retrospectively registered.