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A decision tree prediction model for a short-term outcome of delirium in patients with advanced cancer receiving pharmacological interventions: A secondary analysis of a multicenter and prospective observational study (Phase-R)

Published online by Cambridge University Press:  30 September 2021

Ken Kurisu ,
Shuji Inada ,
Isseki Maeda ,
Asao Ogawa ,
Satoru Iwase ,
Tatsuo Akechi ,
Tatsuya Morita ,
Shunsuke Oyamada ,
Takuhiro Yamaguchi  and
Kengo Imai
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Ken Kurisu
Affiliation:
Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
Shuji Inada
Affiliation:
Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
Isseki Maeda
Affiliation:
Department of Palliative Care, Senri-Chuo Hospital, Toyonaka, Osaka, Japan
Asao Ogawa
Affiliation:
Department of Psycho-Oncology Service, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
Satoru Iwase
Affiliation:
Department of Palliative Medicine, Saitama Medical University, Iruma, Saitama, Japan
Tatsuo Akechi
Affiliation:
Center for Psycho-Oncology and Palliative Care, Nagoya City University Hospital, Nagoya, Aichi, Japan Department of Psychiatry and Cognitive-Behavioral Medicine, Nagoya City University, Graduate School of Medical Sciences, Nagoya, Aichi, Japan
Tatsuya Morita
Affiliation:
Department of Palliative and Supportive Care, Palliative Care Team, Seirei Mikatahara General Hospital, Hamamatsu, Shizuoka, Japan Seirei Hospice, Seirei Mikatahara General Hospital, Hamamatsu, Shizuoka, Japan
Shunsuke Oyamada
Affiliation:
Department of Biostatistics, JORTC Data Center, Tokyo, Japan
Takuhiro Yamaguchi
Affiliation:
Division of Biostatistics, Tohoku University School of Medicine, Sendai, Japan
Kengo Imai
Affiliation:
Seirei Hospice, Seirei Mikatahara General Hospital, Hamamatsu, Shizuoka, Japan
Rika Nakahara
Affiliation:
Department of Psycho-Oncology, National Cancer Center Hospital, Tokyo, Japan
Keisuke Kaneishi
Affiliation:
Department of Palliative Care Unit, JCHO Tokyo Shinjuku Medical Center, Tokyo, Japan
Nobuhisa Nakajima
Affiliation:
Division of Community Medicine and Internal Medicine, University of the Ryukyus Hospital, Okinawa, Japan
Masahiko Sumitani
Affiliation:
Department of Pain and Palliative Medicine, The University of Tokyo Hospital, Tokyo, Japan
Kazuhiro Yoshiuchi*
Affiliation:
Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
*
Author for correspondence: Kazuhiro Yoshiuchi, Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. E-mail: [email protected]
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Abstract

Objective

There is no widely used prognostic model for delirium in patients with advanced cancer. The present study aimed to develop a decision tree prediction model for a short-term outcome.

Method

This is a secondary analysis of a multicenter and prospective observational study conducted at 9 psycho-oncology consultation services and 14 inpatient palliative care units in Japan. We used records of patients with advanced cancer receiving pharmacological interventions with a baseline Delirium Rating Scale Revised-98 (DRS-R98) severity score of ≥10. A DRS-R98 severity score of <10 on day 3 was defined as the study outcome. The dataset was randomly split into the training and test dataset. A decision tree model was developed using the training dataset and potential predictors. The area under the curve (AUC) of the receiver operating characteristic curve was measured both in 5-fold cross-validation and in the independent test dataset. Finally, the model was visualized using the whole dataset.

Results

Altogether, 668 records were included, of which 141 had a DRS-R98 severity score of <10 on day 3. The model achieved an average AUC of 0.698 in 5-fold cross-validation and 0.718 (95% confidence interval, 0.627–0.810) in the test dataset. The baseline DRS-R98 severity score (cutoff of 15), hypoxia, and dehydration were the important predictors, in this order.

Significance of results

We developed an easy-to-use prediction model for the short-term outcome of delirium in patients with advanced cancer receiving pharmacological interventions. The baseline severity of delirium and precipitating factors of delirium were important for prediction.

Keywords

CancerDeliriumMachine learningPalliative carePsycho-oncology
Type
Original Article
Information
Palliative & Supportive Care , Volume 20 , Issue 2 , April 2022 , pp. 153 - 158
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

REFERENCES

American Geriatrics Society Expert Panel on Postoperative Delirium in Older Adults (2015) American Geriatrics Society abstracted clinical practice guideline for postoperative delirium in older adults. Journal of the American Geriatrics Society 63(1), 142–150.CrossRefGoogle Scholar
American Psychiatric Association (2013) Diagnostic Statistical Manual of Mental Disorders, 5th ed. Washington, DC: American Psychiatric Association Publishing.Google Scholar
Blonde, L, Khunti, K, Harris, SB, et al. (2018) Interpretation and impact of real-world clinical data for the practicing clinician. Advances in Therapy 35(11), 1763–1774.CrossRefGoogle ScholarPubMed
Breitbart, W, Gibson, C and Tremblay, A (2002) The delirium experience: Delirium recall and delirium-related distress in hospitalized patients with cancer, their spouses/caregivers, and their nurses. Psychosomatics 43(3), 183–194.CrossRefGoogle ScholarPubMed
Brims, FJ, Meniawy, TM, Duffus, I, et al. (2016) A novel clinical prediction model for prognosis in malignant pleural mesothelioma using decision tree analysis. Journal of Thoracic Oncology 11(4), 573–582.CrossRefGoogle ScholarPubMed
Burry, L, Mehta, S, Perreault, MM, et al. (2018) Antipsychotics for treatment of delirium in hospitalised non-ICU patients. The Cochrane Database of Systematic Reviews 6(6), CD005594.Google ScholarPubMed
Bush, SH, Lawlor, PG, Ryan, K, et al. (2018) Delirium in adult cancer patients: ESMO clinical practice guidelines. Annals of Oncology 29(Suppl 4), iv143–iv165.CrossRefGoogle ScholarPubMed
Centeno, C, Sanz, A and Bruera, E (2004) Delirium in advanced cancer patients. Palliative Medicine 18(3), 184–194.CrossRefGoogle ScholarPubMed
Dasgupta, M and Hillier, LM (2010) Factors associated with prolonged delirium: A systematic review. International Psychogeriatrics 22(3), 373–394.CrossRefGoogle ScholarPubMed
Elsayem, A, Bush, SH, Munsell, MF, et al. (2010) Subcutaneous olanzapine for hyperactive or mixed delirium in patients with advanced cancer: A preliminary study. Journal of Pain and Symptom Management 40(5), 774–782.CrossRefGoogle ScholarPubMed
Esteban, C, Arostegui, I, Garcia-Gutierrez, S, et al. (2015) A decision tree to assess short-term mortality after an emergency department visit for an exacerbation of COPD: A cohort study. Respiratory Research 16, 151.CrossRefGoogle ScholarPubMed
Goodman, KE, Lessler, J, Cosgrove, SE, et al. (2016) A clinical decision tree to predict whether a bacteremic patient is infected with an extended-spectrum β-lactamase-producing organism. Clinical Infectious Diseases 63(7), 896–903.CrossRefGoogle ScholarPubMed
Inouye, SK, Westendorp, RG and Saczynski, JS (2014) Delirium in elderly people. Lancet 383(9920), 911–922.CrossRefGoogle ScholarPubMed
Kato, M, Kishi, Y, Okuyama, T, et al. (2010) Japanese version of the delirium rating scale, revised-98 (DRS-R98-J): Reliability and validity. Psychosomatics 51(5), 425–431.Google ScholarPubMed
Kurisu, K, Yoshiuchi, K, Ogino, K, et al. (2019) Machine learning analysis to identify the association between risk factors and onset of nosocomial diarrhea: A retrospective cohort study. PeerJ 7, e7969.CrossRefGoogle ScholarPubMed
Lawlor, PG, Gagnon, B, Mancini, IL, et al. (2000) Occurrence, causes, and outcome of delirium in patients with advanced cancer: A prospective study. Archives of Internal Medicine 160(6), 786–794.CrossRefGoogle ScholarPubMed
Ligthelm, RJ, Borzì, V, Gumprecht, J, et al. (2007) Importance of observational studies in clinical practice. Clinical Therapeutics 29(6 Pt1), 1284–1292.CrossRefGoogle ScholarPubMed
Maeda, I, Ogawa, A, Yoshiuchi, K, et al. (2020) Safety and effectiveness of antipsychotic medication for delirium in patients with advanced cancer: A large-scale multicenter prospective observational study in real-world palliative care settings. General Hospital Psychiatry 67, 35–41.CrossRefGoogle Scholar
Maeda, I, Inoue, S, Uemura, K, et al. (2021) Low-dose trazodone for delirium in patients with cancer who received specialist palliative care: A multicenter prospective study. Journal of Palliative Medicine 24(6), 914–918.CrossRefGoogle ScholarPubMed
Marcantonio, ER (2017) Delirium in hospitalized older adults. The New England Journal of Medicine 377(15), 1456–1466.CrossRefGoogle ScholarPubMed
Matsuda, Y, Maeda, I, Morita, T, et al. (2020) Reversibility of delirium in ill-hospitalized cancer patients: Does underlying etiology matter? Cancer Medicine 9(1), 19–26.CrossRefGoogle ScholarPubMed
Meagher, D, Moran, M, Raju, B, et al. (2008) A new data-based motor subtype schema for delirium. The Journal of Neuropsychiatry and Clinical Neurosciences 20(2), 185–193.CrossRefGoogle ScholarPubMed
Meagher, DJ, McLoughlin, L, Leonard, M, et al. (2013) What do we really know about the treatment of delirium with antipsychotics? Ten key issues for delirium pharmacotherapy. The American Journal of Geriatric Psychiatry 21(12), 1223–1238.CrossRefGoogle ScholarPubMed
Ministry of Health, Labor, and Welfare (2008) The guideline by the Ministry of Health, Labor, and Welfare (in Japanese). Available at: https://www.mhlw.go.jp/general/seido/kousei/i-kenkyu/ekigaku/0504sisin.html (accessed July 29, 2021).Google Scholar
Molnar, C (2019) Interpretable machine learning. A guide for making black box models explainable. Available at: https://christophm.github.io/interpretable-ml-book/ (accessed 29 July 2021).Google Scholar
Morita, T, Tei, Y, Tsunoda, J, et al. (2001) Underlying pathologies and their associations with clinical features in terminal delirium of cancer patients. Journal of Pain and Symptom Management 22(6), 997–1006.CrossRefGoogle ScholarPubMed
Morita, T, Hirai, K, Sakaguchi, Y, et al. (2004) Family-perceived distress from delirium-related symptoms of terminally ill cancer patients. Psychosomatics 45(2), 107–113.CrossRefGoogle ScholarPubMed
National Institute for Health and Care Excellence (UK) (2019) Delirium: prevention, diagnosis and management. Available at: https://www.nice.org.uk/guidance/cg103. Updated March 14, 2019 (accessed July 29, 2021).Google Scholar
Neufeld, KJ, Yue, J, Robinson, TN, et al. (2016) Antipsychotic medication for prevention and treatment of delirium in hospitalized adults: A systematic review and meta-analysis. Journal of the American Geriatrics Society 64(4), 705–714.CrossRefGoogle ScholarPubMed
Oh, ES, Fong, TG, Hshieh, TT, et al. (2017) Delirium in older persons: Advances in diagnosis and treatment. JAMA 318(12), 1161–1174.CrossRefGoogle ScholarPubMed
Oken, MM, Creech, RH, Tormey, DC, et al. (1982) Toxicity and response criteria of the Eastern Cooperative Oncology Group. American Journal of Clinical Oncology 5(6), 649–655.CrossRefGoogle ScholarPubMed
Okuyama, T, Yoshiuchi, K, Ogawa, A, et al. (2019) Current pharmacotherapy does not improve severity of hypoactive delirium in patients with advanced cancer: Pharmacological Audit Study of Safety and Efficacy in Real World (Phase-R). The Oncologist 24(7), e574–e582.CrossRefGoogle Scholar
Roger, E, Torlay, L, Gardette, J, et al. (2020) A machine learning approach to explore cognitive signatures in patients with temporo-mesial epilepsy. Neuropsychologia 142, 107455.CrossRefGoogle ScholarPubMed
Swets, JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857), 1285–1293.CrossRefGoogle ScholarPubMed
Tahir, TA, Eeles, E, Karapareddy, V, et al. (2010) A randomized controlled trial of quetiapine versus placebo in the treatment of delirium. Journal of Psychosomatic Research 69(5), 485–490.CrossRefGoogle ScholarPubMed
Tamune, H, Ukita, J, Hamamoto, Y, et al. (2020) Efficient prediction of vitamin B deficiencies via machine-learning using routine blood test results in patients with intense psychiatric episode. Frontiers in Psychiatry 10, 1029.CrossRefGoogle ScholarPubMed
Trzepacz, PT, Mittal, D, Torres, R, et al. (2001) Validation of the delirium rating scale-revised-98: Comparison with the delirium rating scale and the cognitive test for delirium. The Journal of Neuropsychiatry and Clinical Neurosciences 13(2), 229–242.CrossRefGoogle ScholarPubMed
Uchida, M, Morita, T, Akechi, T, et al. (2020) Are common delirium assessment tools appropriate for evaluating delirium at the end of life in cancer patients? Psycho-Oncology 29(11), 1842–1849.CrossRefGoogle ScholarPubMed
U.S. Food and Drug Administration (2018) Framework for FDA's real-world evidence program. Available at: https://www.fda.gov/media/120060/download (accessed July 29, 2021).Google Scholar
Wada, K, Morita, Y, Iwamoto, T, et al. (2018) First- and second-line pharmacological treatment for delirium in general hospital setting-retrospective analysis. Asian Journal of Psychiatry 32, 50–53.CrossRefGoogle ScholarPubMed
Witlox, J, Eurelings, LS, de Jonghe, JF, et al. (2010) Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: A meta-analysis. JAMA 304(4), 443–451.CrossRefGoogle ScholarPubMed