Advertisement

Perioperative Ischemic Optic Neuropathy after Cardiac Surgery: Development and Validation of a Preoperative Risk Prediction Model

Published:August 11, 2022DOI:https://doi.org/10.1053/j.jvca.2022.08.005

      Abstract

      Objective

      Previous studies identified risk factors for ischemic optic neuropathy (ION) after cardiac surgery; however, there is no easy-to-use risk calculator for the physician to identify high-risk patients for ION before cardiac surgery. The authors sought to develop and validate a simple-to-use predictive model and calculator to assist with preoperative identification of risk and informed consent for this rare but serious complication.

      Design

      Retrospective case-control study.

      Setting

      Hospital discharge records.

      Patients

      A total of 5,561,177 discharges in the National Inpatient Sample >18 years of age, with procedure codes for coronary artery bypass grafting, heart valve repair/replacement, or left ventricular assist device insertion.

      Interventions

      All patients had undergone cardiac surgery.

      Measurements and Main Results

      Known preoperative risk factors for ION after cardiac surgery were assessed to develop a risk score and prediction model. This model was validated internally using the split-sample method. There were 771 cases of ION among 5,561,177 patients in the National Inpatient Sample. The risk factors for ION used in the model were carotid artery stenosis, cataract, diabetic retinopathy, macular degeneration, glaucoma, male sex, and prior stroke; whereas uncomplicated diabetes decreased risk. With the internal validation, the predictive model had an area under the receiver operating characteristic curve of 0.66. A risk score cutoff ≥3 had 98.4% specificity.

      Conclusions

      This predictive model, based on previously identified preoperative factors, predicted risk of perioperative ION with a fair area under the receiver operating characteristic curve. This predictive model could enable screening to provide a more accurate risk assessment for ION, and consent process for cardiac surgery.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of Cardiothoracic and Vascular Anesthesia
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Scott AW
        • Bressler NM
        • Folkes S
        • et al.
        Public attitudes about eye and vision health.
        JAMA Ophthalmol. 2016; 134: 1111-1118
        • Kolli A
        • Seiler K
        • Kamdar N
        • et al.
        Longitudinal associations between vision impairment and the incidence of neuropsychiatric, musculoskeletal, and cardiometabolic chronic diseases.
        Am J Ophthalmol. 2021; 235: 163-171
        • Nuttall GA
        • Garrity JA
        • Dearani JA
        • et al.
        Risk factors for ischemic optic neuropathy after cardiopulmonary bypass: A matched case/control study.
        Anesth Analg. 2001; 93: 1410-1416
        • Rubin DS
        • Matsumoto MM
        • Moss HE
        • et al.
        Ischemic optic neuropathy in cardiac surgery: Incidence and risk factors in the United States from the National Inpatient Sample 1998 to 2013.
        Anesthesiology. 2017; 126: 810-821
        • Shapira OM
        • Kimmel WA
        • Lindsey PS
        • et al.
        Anterior ischemic optic neuropathy after open heart operations.
        Ann Thorac Surg. 1996; 61: 660-666
        • Shen Y
        • Drum M
        • Roth S.
        The prevalence of perioperative visual loss in the United States: A 10-year study from 1996 to 2005 of spinal, orthopedic, cardiac, and general surgery.
        Anesth Analg. 2009; 109: 1534-1545
        • Rubin DS
        • Parakati I
        • Lee LA
        • et al.
        Perioperative visual Loss in spine fusion surgery: Ischemic optic neuropathy in the United States from 1998 to 2012 in the Nationwide Inpatient Sample.
        Anesthesiology. 2016; 125: 457-464
        • Assawakawintip C
        • Nuttall GA
        • Garrity JA
        • et al.
        Incidence of ischemic optic neuropathy after cardiopulmonary bypass: 20-year experience.
        J Cardiothorac Vasc Anesth. 2021; 35: 35-38
        • Moss HE
        • Xiao L
        • Shah SH
        • et al.
        Predictive model of ischemic optic neuropathy in spinal fusion surgery using a longitudinal medical claims database.
        Spine J. 2021; 21: 377-386
        • Shah SH
        • Chen YF
        • Moss HE
        • et al.
        Predicting risk of perioperative ischemic optic neuropathy in spine fusion surgery: A cohort study using the National Inpatient Sample.
        Anesth Analg. 2020; 130: 967-974
      1. Agency for Healthcare Research and Quality. Overview of the National (Nationwide) Inpatient Sample (NIS). vol 2016. Rockville, MD, 2015. https://www.hcup-us.ahrq.gov/db/nation/nis/nisdbdocumentation.jsp Available at: Accessed September 8, 2022.

        • Fry CL
        • Carter JE
        • Kanter MC
        • et al.
        Anterior ischemic optic neuropathy is not associated with carotid artery atherosclerosis.
        Stroke. 1993; 24: 539-542
        • Khera R
        • Angraal S
        • Couch T
        • et al.
        Adherence to methodological standards in research using the National Inpatient Sample.
        JAMA. 2017; 318: 2011-2018
      2. Houchens DR, Elixhauser A. Final report on calculating national inpatient sample (NIS) variances for data years 2012 and later: HCUP Methods Series Report. vol 2018. U.S. Agency for Healthcare Research and Quality, 2015. http://www.hcup-us.ahrq.gov/reports/methods/methods.jsp Available at:. Accessed September 8, 2022.

        • Mazumdar M
        • Smith A
        • Bacik J.
        Methods for categorizing a prognostic variable in a multivariable setting.
        Stat Med. 2003; 22: 559-571
        • Bang H
        • Vupputuri S
        • Shoham DA
        • et al.
        SCreening for Occult REnal Disease (SCORED): A simple prediction model for chronic kidney disease.
        Arch Int Med. 2007; 167: 374-381
        • Hanley JA
        • McNeil BJ.
        The meaning and use of the area under a receiver operating characteristic (ROC) curve.
        Radiology. 1982; 143: 29-36
        • Russotto V
        • Sabaté S
        • Canet J.
        Development of a prediction model for postoperative pneumonia: A multicentre prospective observational study.
        Eur J Anaesthesiology. 2019; 36: 93-104
        • Glance LG
        • Dutton RP
        • Feng C
        • et al.
        Variability in case durations for common surgical procedures.
        Anesth Analg. 2018; 126: 2017-2024
        • Spatz ES
        • Krumholz HM
        • Moulton BW.
        The new era of informed consent: Getting to a reasonable-patient standard through shared decision making.
        JAMA. 2016; 315: 2063-2064
        • Corda DM
        • Dexter F
        • Pasternak JJ
        • et al.
        Patients' perspective on full disclosure and informed consent regarding postoperative visual loss associated with spinal surgery in the prone position.
        Mayo Clin Proc. 2011; 86: 865-868
        • Saydah SH
        • Gerzoff RB
        • Saaddine JB
        • et al.
        Eye care among US adults at high risk for vision loss in the United States in 2002 and 2017.
        JAMA Ophthalmol. 2020; 138: 479-489
        • Johnson LN
        • Arnold AC.
        Incidence of nonarteritic and arteritic anterior ischemic optic neuropathy. Population-based study in the state of Missouri and Los Angeles County.
        California. J Neuro-ophthalmol. 1994; 14: 38-44
        • Glaser J
        • Nouri S
        • Fernandez A
        • et al.
        Interventions to improve patient comprehension in informed consent for medical and surgical procedures: An updated systematic review.
        Med Decis Making. 2020; 40: 119-143
        • Marchesi N
        • Fahmideh F
        • Boschi F
        • et al.
        Ocular neurodegenerative diseases: Interconnection between retina and cortical areas.
        Cells. 2021; 10: 2394
        • Friedman DS
        • Wolfs RC
        • O'Colmain BJ
        • et al.
        Prevalence of open-angle glaucoma among adults in the United States.
        Arch Ophthalmol. 2004; 122: 532-538
        • Trott M
        • Smith L
        • Veronese N
        • et al.
        Eye disease and mortality, cognition, disease, and modifiable risk factors: An umbrella review of meta-analyses of observational studies.
        Eye. 2022; 36: 369-378
        • Hashemi H
        • Pakzad R
        • Yekta A
        • et al.
        Global and regional prevalence of age-related cataract: A comprehensive systematic review and meta-analysis.
        Eye. 2020; 34: 1357-1370
      3. Houchens R, Ross D, Elixhauser A, et al. Nationwide inpatient sample redesign final report. HCUP NIS Related Reports 2014. https://www.hcup-us.ahrq.gov/db/nation/nis/nisrelatedreports.jsp Available at:. Accessed September 8, 2022.