stanford pipra validation study in an elderly orthopedic population

Conditions: Delirium; Postoperative Delirium; Cognitive Impairment

Summary: the purpose of this study is to determine the accuracy of an ai-based tool in the prediction of postoperative delirium (pod), in a population at moderately high risk of developing this syndrome (i.e., elderly orthopedic subjects). the population to be studied has already been enrolled in a parallel study regarding the likelihood of developing delirium, its relationship to the type of anesthetic, and the relationship between anesthetic type, development of delirium and risk for post-operative cognitive impairment (including risk for dementia) (protocol id#55169 \[david drover-principal investigator; josé maldonado-co-investigator\]).

study investigators would like to determine the real-life accuracy of a new tool developed for the prediction of delirium: pre-interventional preventive risk assessment (pipra) tool. the importance of assessing the risk for post-operative delirium (pod) includes: providing clinicians and patients with accurate predictive information regarding the patient's risk for developing delirium as part of the risk/benefit calculation for surgical procedures, and thus potential risk of subsequent cognitive impairment; as well as the ability to introduce timely prophylactic techniques that may prevent its onset. the pipra tools consists of nine items commonly found in any presurgical patient's electronic medical record (emr). the tool has been designed to run in the background of the emr and automatically calculate the patient's risk for developing delirium upon admission for surgical intervention. for our study, we will be applying the pipra tool to the emr of patients already enrolled in a parallel study as detailed above. the pipra tool predicts the risk of developing pod delirium based on its algorithm that takes into consideration the following nine clinical variables: age, height/weight or body mass index, the american society of anesthesiologist physical status classification system (asa), past history of delirium, past history of cognitive impairment (including dementia), number of medications, preoperative c-reactive protein levels, surgical risk (as determined by the european society of anesthesiology), and type of surgery. the subsequent result predicts the risk (in percentage) of a patient developing pod following surgery. the pipra tool is fully integrated into emr systems, operating in the background, extracting relevant information, and automatically generating a delirium prediction score. in addition, this software possesses the flexibility to recalibrate the delirium risk based on the availability of the nine clinical variables.

Eligibility criteria:
inclusion criteria: * surgical patients * greater than or equal to 65 years old * asa i, ii or iii * greater than 90 min of surgery * non-cardiac surgery exclusion criteria: * dementia * alzheimer's * parkinson's * current benzodiazepine use * alcoholism * previous surgery within the last 12 months or expected in the 12 months to come (except the expected surgery for which they are being enrolled). * color-blindness

Keywords: artificial intelligence delirium risk factors delirium postoperative delirium cognitive impairment