ABSTRACT Preoperative cognitive impairment is common among older adults preparing for surgery. Despite growing evidence that preoperative cognitive/neuronal integrity is a risk factor for perioperative insults and post- operative adverse outcomes, health care systems do not systematically pre-operatively screen for cognition. Clinical researchers have yet to identify a pragmatic approach to pre-operative cognitive screening. Our team members have developed the digital Clock Drawing Test (dCDT), a tool that captures subtle behavioral variables during a rapid (5-minute) clock drawing assessment. The data and benefit afforded by this tool have yet to be considered across perioperative contexts. We will apply the dCDT within a large number of pre- surgical patients (n=5,000 per year) coupled with novel machine learning algorithms to address three specific aims. Aim 1: examine range and distribution of preoperative neurocognitive impairment with older adult preoperative patients relative to non-surgical older adult demographically matched peers (available n=2,400 via NIH/Boston University Framingham Heart Study) using novel previously unobserved dCDT graphomotor and decision making variables; Aim 2: examine the predictive validity of presurgical dCDT variables on postoperative, clinician reported/hospital recorded events; Aim 3: examine pre to postoperative 6-week, 3- month, and 1-year change in dCDT and NIH PROMIS metrics for thoracic (n=70), orthopedic (n=70), major abdominal-pelvic patients (n=70), and non-surgery peers (n=70). For the observational studies (Aim 1 and 2), individuals > 65 years presenting to the UFHealth presurgical clinic will complete the dCDT as well as a three- word memory test and frailty assessment as part of the standard clinical evaluation. Surgical and anesthetic details will be acquired via the electronic medical record. Clinically-relevant outcomes will include complications, length of stay, cost of care, functional capacity, and mortality. Outcomes will be supplemented by a separate longitudinally-studied subgroup (Aim 3) completing NIH PROMIS metrics at 6 weeks, 3 months, and 1year after surgery. Analyses will focus on stratifying distributions and clusters of dCDT characteristics across numerous sociodemographic, surgical, and anesthetic factors. The predictive value of the dCDT will be modeled relative to clinical outcomes. Changes in dCDT and baseline NIH PROMIS domains will be compared pre- and post-operatively and examined for interactions with longitudinal perioperative events. Subaims: We will apply `deep learning' approaches to drawings to identify novel features of pre-surgical patients relative to a large sample of demographically equated dCDT data points available through the Framingham Heart Study. Symbolic aggregate approximation (SAX)-based machine learning approaches will characterize interactions between preoperative dCDT features and intraoperative anesthetic sensitivities.