Clinical trial NCT06567574: Stanford PIPRA Validation Study in an Elderly Orthopedic Population
Official title: Stanford Study of the Validation of the Pre-Interventional Preventive Risk Assessment (PIPRA) Tool In The At-Risk Orthopedic Surgery Population
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Delirium
Postoperative Delirium
Cognitive Impairment
Keywords: Artificial Intelligence
Delirium Risk Factors
Delirium
Postoperative Delirium
Cognitive Impairment
Lead sponsor: Stanford University
Sponsor class: OTHER
First posted: 2024-08-22
Enrollment: 250
Sex: ALL
Minimum age (years): 65
Standard ages: OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Stanford Hospital geriatric patients having surgery

Brief 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 \[David Drover-Principal investigator; José Maldonado-Co-investigator\]).

Detailed description: 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

Primary outcomes:
measure: Number of subjects with postoperative delirium accurately predicted by the PIPRA Tool
timeFrame: Baseline measurement of variables and detection of delirium presenting up to 72 hours post-operatively
measure: Development of post-operative Delirium
timeFrame: Up to 72 hours post-operatively
measure: Determination of Delirium Phenotype
timeFrame: Up to 72 hours post-operatively.
measure: Impact of delirium on post-operative cognitive impairment
timeFrame: Up to 10-year post-operatively.
measure: Sensitivity and specificity of the PIPRA tool for detecting postoperative delirium.
timeFrame: Baseline measurement of variables and detection of delirium presenting up to 72 hours post-operatively
measure: Receiver operating characteristic (ROC) curve analysis of PIPRA tool
timeFrame: Baseline measurement of variables and detection of delirium presenting up to 72 hours post-operatively
Secondary outcomes:
measure: Immediate Postoperative Mortality
timeFrame: From date of post-operative admission up to time of postoperative discharge, an average of 72 hours.
measure: Length of Hospital Length of Stay
timeFrame: From date of post-operative admission up to time of postoperative discharge (assessed up to 1 year after admission).
measure: Discharge Site
timeFrame: Type of facility the patient was discharge to from the hospital (assessed up to 6 months from admission)
measure: Requirement of Intensive Care Unit Admission
timeFrame: Time to discharge from the hospital (assessed up to 6 months from admission)
measure: Need of pharmacological protocol
timeFrame: From post-operative hospital admission up to time of postoperative discharge (assessed up to 6 months from admission)
Other outcomes:
Locations:
city: Palo Alto
country: United States
facility: Stanford Medical Center
state: California
Study officials:
affiliation: Stanford University
name: José R Maldonado, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Buchan TA, Sadeghirad B, Schmutz N, Goettel N, Foroutan F, Couban R, Mbuagbaw L, Dodsworth BT. Preoperative prognostic factors associated with postoperative delirium in older people undergoing surgery: protocol for a systematic review and individual patient data meta-analysis. Syst Rev. 2020 Nov 14;9(1):261. doi: 10.1186/s13643-020-01518-z.
citation: Dodsworth BT, Reeve K, Falco L, Hueting T, Sadeghirad B, Mbuagbaw L, Goettel N, Schmutz Gelsomino N. Development and validation of an international preoperative risk assessment model for postoperative delirium. Age Ageing. 2023 Jun 1;52(6):afad086. doi: 10.1093/ageing/afad086.
citation: Sadeghirad B, Dodsworth BT, Schmutz Gelsomino N, Goettel N, Spence J, Buchan TA, Crandon HN, Baneshi MR, Pol RA, Brattinga B, Park UJ, Terashima M, Banning LBD, Van Leeuwen BL, Neerland BE, Chuan A, Martinez FT, Van Vugt JLA, Rampersaud YR, Hatakeyama S, Di Stasio E, Milisen K, Van Grootven B, van der Laan L, Thomson Mangnall L, Goodlin SJ, Lungeanu D, Denhaerynck K, Dhakharia V, Sampson EL, Zywiel MG, Falco L, Nguyen AV, Moss SJ, Krewulak KD, Jaworska N, Plotnikoff K, Kotteduwa-Jayawarden S, Sandarage R, Busse JW, Mbuagbaw L. Perioperative Factors Associated With Postoperative Delirium in Patients Undergoing Noncardiac Surgery: An Individual Patient Data Meta-Analysis. JAMA Netw Open. 2023 Oct 2;6(10):e2337239. doi: 10.1001/jamanetworkopen.2023.37239.
citation: Maldonado JR. Acute Brain Failure: Pathophysiology, Diagnosis, Management, and Sequelae of Delirium. Crit Care Clin. 2017 Jul;33(3):461-519. doi: 10.1016/j.ccc.2017.03.013.
citation: Robinson TN, Eiseman B. Postoperative delirium in the elderly: diagnosis and management. Clin Interv Aging. 2008;3(2):351-5. doi: 10.2147/cia.s2759.