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Original Article| Volume 64, ISSUE 4, P400-409, October 2022

Natural Language Processing for Computer-Assisted Chart Review to Assess Documentation of Substance use and Psychopathology in Heart Failure Patients Awaiting Cardiac Resynchronization Therapy

      Highlights

      • This article describes a study of 965 patients with heart failure who were awaiting cardiac resynchronization therapy.
      • Using natural language processing, a branch of artificial intelligence, we found that clinicians are under documenting assessments and plans pertaining to mental health and substance use.
      • Despite clinical recommendations.

      Abstract

      Context

      Advanced heart failure (HF) patients often experience distressing psychological symptoms, frequently meeting diagnostic criteria for psychological disorders, including anxiety, depression, and substance use disorder. Patients with device-based HF therapies have added risk for psychological disorders, with consequences for their physiological functioning, including adverse cardiac outcomes.

      Objectives

      This study used natural language processing (NLP) for computer-assisted chart review to assess documentation of mental health and substance use in HF patients awaiting cardiac resynchronization therapy (CRT), a device-based HF therapy.

      Methods

      We applied NLP to clinical notes from electronic health records (EHR) of 965 consecutive patients, with 9821 total clinical notes, at two academic medical centers between 2004 and 2015. We developed and validated a keyword library capturing terms related to mental health and substance use, while balancing specificity and sensitivity.

      Results

      Mean age was 71.6 years (SD = 11.8), 78% male, and 87% non-Hispanic White. Of the 544 patients (56.4%) with documentation of mental health history, 9.7% had their mental health assessed and 6.6% had a plan documented. Of the 773 patients (80.1%) with documentation of substance use history, 10 (1.0%) had an assessment, and 3 (0.3%) had a plan.

      Conclusion

      Despite clinical recommendations and standards of care, clinicians are under documenting assessments and plans prior to CRT. Future research should develop an algorithm to prompt clinicians to document this content. Such quality improvement efforts may ensure adherence to standards of care and clinical guidelines.

      Key Words

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      References

        • Ahmedani BK
        • Solberg LI
        • Copeland LA
        • et al.
        Psychiatric comorbidity and 30-day readmissions after hospitalization for heart failure, AMI, and pneumonia.
        Psychiatr Serv. 2015; (Published online)https://doi.org/10.1176/appi.ps.201300518
        • Chamberlain AM
        • Sauver JLS
        • Gerber Y
        • et al.
        Multimorbidity in heart failure: a community perspective.
        Am J Med. 2015; (Published online)https://doi.org/10.1016/j.amjmed.2014.08.024
        • Dekker RL
        • Lennie TA
        • Doering LV.
        • Chung ML
        • Wu JR
        • Moser DK.
        Coexisting anxiety and depressive symptoms in patients with heart failure.
        Eur J Cardiovasc Nurs. 2014; (Published online)https://doi.org/10.1177/1474515113519520
        • Gottlieb SS
        • Khatta M
        • Friedmann E
        • et al.
        The influence of age, gender, and race on the prevalence of depression in heart failure patients.
        J Am Coll Cardiol. 2004; (Published online)https://doi.org/10.1016/j.jacc.2003.10.064
        • Djoussé L
        • Gaziano JM.
        Alcohol consumption and heart failure: a systematic review.
        Curr Atheroscler Rep. 2008; (Published online)https://doi.org/10.1007/s11883-008-0017-z
        • Ebinger J.
        • Wiley B.
        • Devendra G.
        • et al.
        Stimulant associated heart failure with methamphetamine and cocaine use: survival and resource utilization at a safety-net hospital.
        J Am Coll Cardiol. 2018; : A790
        • Havakuk O
        • Rezkalla SH
        • Kloner RA.
        The cardiovascular effects of cocaine.
        J Am Coll Cardiol. 2017; (Published online)https://doi.org/10.1016/j.jacc.2017.05.014
        • Laonigro I
        • Correale M
        • Di Biase M
        • Altomare E
        Alcohol abuse and heart failure.
        Eur J Heart Fail. 2009; (Published online)https://doi.org/10.1093/eurjhf/hfp037
        • Bosworth HB
        • Steinhauser KE
        • Orr M
        • Lindquist JH
        • Grambow SC
        • Oddone EZ.
        Congestive heart failure patients’ perceptions of quality of life: the integration of physical and psychosocial factors.
        Aging Ment Heal. 2004; (Published online)https://doi.org/10.1080/13607860310001613374
        • Whooley MA
        • De Jonge P
        • Vittinghoff E
        • et al.
        Depressive symptoms, health behaviors, and risk of cardiovascular events in patients with coronary heart disease.
        JAMA - J Am Med Assoc. 2008; (Published online)https://doi.org/10.1001/jama.2008.711
        • Alhurani AS
        • Dekker RL
        • Abed MA
        • et al.
        The association of co-morbid symptoms of depression and anxiety with all-cause mortality and cardiac rehospitalization in patients with heart failure.
        Psychosomatics. 2015; (Published online)https://doi.org/10.1016/j.psym.2014.05.022
        • Vaccarino V
        • Kasl SV.
        • Abramson J
        • Krumholz HM.
        Depressive symptoms and risk of functional decline and death in patients with heart failure.
        J Am Coll Cardiol. 2001; (Published online)https://doi.org/10.1016/S0735-1097(01)01334-1
        • Rutledge T
        • Reis VA
        • Linke SE
        • Greenberg BH
        • Mills PJ.
        Depression in heart failure. A meta-analytic review of prevalence, intervention effects, and associations with clinical outcomes.
        J Am Coll Cardiol. 2006; (Published online)https://doi.org/10.1016/j.jacc.2006.06.055
        • Sokoreli I
        • de Vries JJG
        • Pauws SC
        • Steyerberg EW.
        Depression and anxiety as predictors of mortality among heart failure patients: systematic review and meta-analysis.
        Heart Fail Rev. 2016; (Published online)https://doi.org/10.1007/s10741-015-9517-4
        • Celano CM
        • Villegas AC
        • Albanese AM
        • Gaggin HK
        • Huffman JC.
        Depression and anxiety in heart failure: a review.
        Harv Rev Psychiatry. 2018; (Published online)https://doi.org/10.1097/HRP.0000000000000162
        • Kapa S
        • Rotondi-Trevisan D
        • Mariano Z
        • et al.
        Psychopathology in patients with icds over time: Results of a prospective study.
        PACE - Pacing Clin Electrophysiol. 2010; (Published online)https://doi.org/10.1111/j.1540-8159.2009.02599.x
        • Carroll SL
        • Markle-Reid M
        • Ciliska D
        • Connolly SJ
        • Arthur HM.
        Age and mental health predict early device-specific quality of life in patients receiving prophylactic implantable efibrillators.
        Can J Cardiol. 2012; (Published online)https://doi.org/10.1016/j.cjca.2012.01.008
        • de Ornelas Maia ACC
        • Soares-Filho G
        • Pereira V
        • Nardi AE
        • Silva AC.
        Psychiatric disorders and quality of life in patients with implantable cardioverter defibrillators: A systematic review.
        Prim Care Companion J Clin Psychiatry. 2013; (Published online)https://doi.org/10.4088/PCC.12r01456
        • Pauli P
        • Wiedemann G
        • Dengler W
        • Blaumann-Benninghoff G
        • Kühlkamp V.
        Anxiety in patients with an automatic implantable cardioverter defibrillator: What differentiates them from panic patients?.
        Psychosom Med. 1999; (Published online)https://doi.org/10.1097/00006842-199901000-00012
        • Yancy CW
        • Jessup M
        • Bozkurt B
        • et al.
        2013 ACCF/AHA guideline for the management of heart failure: A report of the american college of cardiology foundation/american heart association task force on practice guidelines.
        Circulation. 2013; (Published online)https://doi.org/10.1161/CIR.0b013e31829e8776
        • Lache B
        • Meyer T
        • Herrmann-Lingen C.
        Social support predicts hemodynamic recovery from mental stress in patients with implanted defibrillators.
        J Psychosom Res. 2007; 63: 515-523https://doi.org/10.1016/j.jpsychores.2007.06.024
        • Kuijpers PMJC
        • Honig A
        • Wellens HJJ.
        Effect of treatment of panic disorder in patients with frequent ICD discharges: A pilot study.
        Gen Hosp Psychiatry. 2002; (Published online)https://doi.org/10.1016/S0163-8343(02)00176-7
        • Kim JM
        • Stewart R
        • Lee YS
        • et al.
        Effect of escitalopram vs. placebo treatment for depression on long-term cardiac outcomes in patients with acute coronary syndrome: A randomized clinical trial.
        JAMA - J Am Med Assoc. 2018; (Published online)https://doi.org/10.1001/jama.2018.9422
        • Murdoch TB
        • Detsky AS.
        The inevitable application of big data to health care.
        JAMA - J Am Med Assoc. 2013; (Published online)https://doi.org/10.1001/jama.2013.393
        • Chekroud AM
        • Zotti RJ
        • Shehzad Z
        • et al.
        Cross-trial prediction of treatment outcome in depression: A machine learning approach.
        The Lancet Psychiatry. 2016; (Published online)https://doi.org/10.1016/S2215-0366(15)00471-X
        • Zhong QY
        • Karlson EW
        • Gelaye B
        • et al.
        Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing.
        BMC Med Inform Decis Mak. 2018; (Published online)https://doi.org/10.1186/s12911-018-0617-7
        • Patel R
        • Lloyd T
        • Jackson R
        • et al.
        Mood instability and clinical outcomes in mental health disorders: a natural language processing (NLP) study.
        Eur Psychiatry. 2016; (Published online)https://doi.org/10.1016/j.eurpsy.2016.01.551
        • Althoff T
        • Clark K
        • Leskovec J.
        Large-scale analysis of counseling conversations: an application of natural language processing to mental health.
        Trans Assoc Comput Linguist. 2016; (Published online)https://doi.org/10.1162/tacl_a_00111
        • Weiss ST
        • Shin MS.
        Infrastructure for personalized medicine at partners healthcare.
        J Pers Med. 2016; (Published online)https://doi.org/10.3390/jpm6010013
        • Lindvall C
        • Deng C-Y
        • Moseley E
        • et al.
        Natural language processing to identify advance care planning documentation in a multisite pragmatic clinical trial.
        J Pain Symptom Manage. 2021; (Published online)https://doi.org/10.1016/j.jpainsymman.2021.06.025
        • Agaronnik ND
        • Lindvall C
        • El-Jawahri A
        • He W
        • Iezzoni LI.
        Challenges of developing a natural language processing method with electronic health records to identify persons with chronic mobility disability.
        Arch Phys Med Rehabil. 2020; 101: 1739-1746https://doi.org/10.1016/j.apmr.2020.04.024
        • Marziliano A
        • Burns E
        • Chauhan L
        • et al.
        Patient factors and hospital outcomes associated with atypical presentation in hospitalized older adults with COVID-19 during the first surge of the pandemic.
        J Gerontol Ser A. 2021; (Published online): glab171https://doi.org/10.1093/gerona/glab171
        • Lindvall C
        • Lilley EJ
        • Zupanc SN
        • et al.
        Natural language processing to assess end-of-life quality indicators in cancer patients receiving palliative surgery.
        J Palliat Med. 2019; 22: 183-187https://doi.org/10.1089/jpm.2018.0326
        • Lee KC
        • Udelsman B V
        • Streid J
        • et al.
        Natural Language processing accurately measures adherence to best practice guidelines for palliative care in trauma.
        J Pain Symptom Manage. 2020; 59 (225-232.e2)https://doi.org/10.1016/j.jpainsymman.2019.09.017
        • Valencia VA
        • Dols JD.
        Evaluating depressive symptoms in advanced heart failure.
        J Nurse Pract. 2019; 15: e17-e21https://doi.org/10.1016/j.nurpra.2018.07.018
        • Velupillai S
        • Suominen H
        • Liakata M
        • et al.
        Using clinical natural language processing for health outcomes research: overview and actionable suggestions for future advances.
        J Biomed Inform. 2018; 88: 11-19https://doi.org/10.1016/j.jbi.2018.10.005
        • Tannenbaum C
        • Lexchin J
        • Tamblyn R
        • Romans S.
        Indicators for measuring mental health: towards better surveillance.
        Healthc Policy. 2009; 5: e177-e186
        • Nguyen TQ
        • Simpson PM
        • Braaf SC
        • Cameron PA
        • Judson R
        • Gabbe BJ
        Level of agreement between medical record and ICD-10-AM coding of mental health, alcohol and drug conditions in trauma patients.
        Heal Inf Manag J. 2018; 48: 127-134https://doi.org/10.1177/1833358318769482
        • Digan W
        • Névéol A
        • Neuraz A
        • et al.
        Can reproducibility be improved in clinical natural language processing? A study of 7 clinical NLP suites.
        J Am Med Informatics Assoc. 2021; 28: 504-515https://doi.org/10.1093/jamia/ocaa261
        • Cohen KB
        • Xia J
        • Zweigenbaum P
        • et al.
        Three dimensions of reproducibility in natural language processing.
        Lr . Int Conf Lang Resour Eval [proceedings] Int Conf Lang Resour Eval. 2018; 2018: 156-165
        • Liu L
        • Bustamante R
        • Earles A
        • Demb J
        • Messer K
        • Gupta S.
        A strategy for validation of variables derived from large-scale electronic health record data.
        J Biomed Inform. 2021; 121103879https://doi.org/10.1016/j.jbi.2021.103879
        • Canales L
        • Menke S
        • Marchesseau S
        • et al.
        Assessing the performance of clinical natural language processing systems: development of an evaluation methodology.
        JMIR Med informatics. 2021; 9: e20492https://doi.org/10.2196/20492
        • Sperry BW
        • Ikram A
        • Alvarez PA
        • et al.
        Standardized psychosocial assessment before left ventricular assist device implantation.
        Circ Heart Fail. 2019; (Published online)https://doi.org/10.1161/CIRCHEARTFAILURE.118.005377