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Frank B, Bandyopadhyay S, Dion C, Formanski E, Matusz E, Penney D, Davis R, O'Connor MK, Au R, Amini S, Rashidi P, Tighe P, Libon DJ, Price CC. A Network Analysis of Digital Clock Drawing for Command and Copy Conditions. Assessment 2024:10731911241236336. [PMID: 38494894 DOI: 10.1177/10731911241236336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Graphomotor and time-based variables from the digital Clock Drawing Test (dCDT) characterize cognitive functions. However, no prior publications have quantified the strength of the associations between digital clock variables as they are produced. We hypothesized that analysis of the production of clock features and their interrelationships, as suggested, will differ between the command and copy test conditions. Older adults aged 65+ completed a digital clock drawing to command and copy conditions. Using a Bayesian hill-climbing algorithm and bootstrapping (10,000 samples), we derived directed acyclic graphs (DAGs) to examine network structure for command and copy dCDT variables. Although the command condition showed moderate associations between variables (μ | β z | = 0.34) relative to the copy condition (μ | β z | = 0.25), the copy condition network had more connections (18/18 versus 15/18 command). Network connectivity across command and copy was most influenced by five of the 18 variables. The direction of dependencies followed the order of instructions better in the command condition network. Digitally acquired clock variables relate to one another but differ in network structure when derived from command or copy conditions. Continued analyses of clock drawing production should improve understanding of quintessential normal features to aid in early neurodegenerative disease detection.
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Affiliation(s)
- Brandon Frank
- University of Florida, Gainesville, USA
- Boston University, MA, USA
| | | | | | | | | | - Dana Penney
- Lahey Clinic Medical Center, Burlington, MA, USA
| | - Randall Davis
- Massachusetts Institute of Technology, Cambridge, USA
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Wiggins ME, Dion C, Formanski E, Davoudi A, Amini S, Heilman KM, Penney D, Davis R, Garvan CW, Arnaoutakis GJ, Tighe P, Libon DJ, Price CC. Proof of concept: digital clock drawing behaviors prior to transcatheter aortic valve replacement may predict length of hospital stay and cost of care. Explor Med 2021; 2:110-121. [PMID: 34263257 PMCID: PMC8276939 DOI: 10.37349/emed.2021.00036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Aims Reduced pre-operative cognitive functioning in older adults is a risk factor for postoperative complications, but it is unknown if preoperative digitally-acquired clock drawing test (CDT) cognitive screening variables, which allow for more nuanced examination of patient performance, may predict lengthier hospital stay and greater cost of hospital care. This issue is particularly relevant for older adults undergoing transcatheter aortic valve replacement (TAVR), as this surgical procedure is chosen for intermediate-risk older adults needing aortic replacement. This proof of concept research explored if specific latency and graphomotor variables indicative of planning from digitally-acquired command and copy clock drawing would predict post-TAVR duration and cost of hospitalization, over and above age, education, American Society of Anesthesiologists (ASA) physical status classification score, and frailty. Methods Form January 2018 to December 2019, 162 out of 190 individuals electing TAVR completed digital clock drawing as part of a hospital wide cognitive screening program. Separate hierarchical regressions were computed for the command and copy conditions of the CDT and assessed how a-priori selected clock drawing metrics (total time to completion, ideal digit placement difference, and hour hand distance from center; included within the same block) incrementally predicted outcome, as measured by R2 change significance values. Results Above and beyond age, education, ASA physical status classification score, and frailty, only digitally-acquired CDT copy performance explained significant variance for length of hospital stay (9.5%) and cost of care (8.9%). Conclusions Digital variables from clock copy condition provided predictive value over common demographic and comorbidity variables. We hypothesize this is due to the sensitivity of the copy condition to executive dysfunction, as has been shown in previous studies for subtypes of cognitive impairment. Individuals undergoing TAVR procedures are often frail and executively compromised due to their cerebrovascular disease. We encourage additional research on the value of digitally-acquired clock drawing within different surgery types. Type of cognitive impairment and the value of digitally-acquired CDT command and copy parameters in other surgeries remain unknown.
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Affiliation(s)
- Margaret Ellenora Wiggins
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL 32610, USA.,Perioperative Cognitive Anesthesia Network (PeCAN), University of Florida, Gainesville, FL 32610, USA
| | - Catherine Dion
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL 32610, USA.,Perioperative Cognitive Anesthesia Network (PeCAN), University of Florida, Gainesville, FL 32610, USA
| | - Erin Formanski
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL 32610, USA.,Perioperative Cognitive Anesthesia Network (PeCAN), University of Florida, Gainesville, FL 32610, USA
| | - Anis Davoudi
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Shawna Amini
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL 32610, USA.,Perioperative Cognitive Anesthesia Network (PeCAN), University of Florida, Gainesville, FL 32610, USA
| | - Kenneth M Heilman
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL 32610, USA
| | - Dana Penney
- Department of Neurology, Lahey Hospital and Medical Center, Boston, Mass 02421, USA
| | - Randall Davis
- Department of Electronical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Mass 02139, USA
| | - Cynthia W Garvan
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL 32610, USA.,Perioperative Cognitive Anesthesia Network (PeCAN), University of Florida, Gainesville, FL 32610, USA
| | - George J Arnaoutakis
- Department of Surgery, University of Florida College of Medicine, Gainesville, FL 32610, USA
| | - Patrick Tighe
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL 32610, USA.,Perioperative Cognitive Anesthesia Network (PeCAN), University of Florida, Gainesville, FL 32610, USA
| | - David J Libon
- Department of Geriatrics and Gerontology, New Jersey Institute for Successful Aging, Rowan University School of Osteopathic Medicine, Stratford, NJ 08084, USA
| | - Catherine C Price
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL 32610, USA.,Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL 32610, USA.,Perioperative Cognitive Anesthesia Network (PeCAN), University of Florida, Gainesville, FL 32610, USA
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Davoudi A, Dion C, Formanski E, Frank BE, Amini S, Matusz EF, Wasserman V, Penney D, Davis R, Rashidi P, Tighe PJ, Heilman KM, Au R, Libon DJ, Price CC. Normative References for Graphomotor and Latency Digital Clock Drawing Metrics for Adults Age 55 and Older: Operationalizing the Production of a Normal Appearing Clock. J Alzheimers Dis 2021; 82:59-70. [PMID: 34219739 PMCID: PMC8379638 DOI: 10.3233/jad-201249] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Relative to the abundance of publications on dementia and clock drawing, there is limited literature operationalizing 'normal' clock production. OBJECTIVE To operationalize subtle behavioral patterns seen in normal digital clock drawing to command and copy conditions. METHODS From two research cohorts of cognitively-well participants age 55 plus who completed digital clock drawing to command and copy conditions (n = 430), we examined variables operationalizing clock face construction, digit placement, clock hand construction, and a variety of time-based, latency measures. Data are stratified by age, education, handedness, and number anchoring. RESULTS Normative data are provided in supplementary tables. Typical errors reported in clock research with dementia were largely absent. Adults age 55 plus produce symmetric clock faces with one stroke, with minimal overshoot and digit misplacement, and hands with expected hour hand to minute hand ratio. Data suggest digitally acquired graphomotor and latency differences based on handedness, age, education, and anchoring. CONCLUSION Data provide useful benchmarks from which to assess digital clock drawing performance in Alzheimer's disease and related dementias.
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Affiliation(s)
- Anis Davoudi
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Catherine Dion
- Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Erin Formanski
- Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Brandon E Frank
- Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Shawna Amini
- Department of Anesthesiology, University of Florida, Gainesville, FL, USA
| | - Emily F Matusz
- Department of Geriatrics and Gerontology, New Jersey Institute for Successful Aging, School of Osteopathic Medicine, Rowan University, NJ, USA
| | | | - Dana Penney
- Department of Neurology, Lahey Clinic Medical Center, Burlington, MA, USA
| | - Randall Davis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Patrick J Tighe
- Department of Anesthesiology, University of Florida, Gainesville, FL, USA
| | - Kenneth M Heilman
- Department of Neurology, Veterans Affairs Medical Center, University of Florida, Gainesville, FL, USA
| | - Rhoda Au
- Framingham Heart Study, Boston University School of Medicine, Boston, MA, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - David J Libon
- Department of Geriatrics and Gerontology, New Jersey Institute for Successful Aging, School of Osteopathic Medicine, Rowan University, NJ, USA
- Department of Psychology, Rowan University, NJ, USA
| | - Catherine C Price
- Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
- Department of Anesthesiology, University of Florida, Gainesville, FL, USA
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