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Original Research

Open Access

Predicting mid-term survival of patients during emergency department triage for resuscitation decision

  • Jae Yong Yu1,†
  • Hansol Chang1,2,†
  • Weon Jung3
  • Sejin Heo1,2
  • Gun Tak Lee2
  • Jong Eun Park2
  • Se Uk Lee2
  • Taerim Kim2
  • Sung Yeon Hwang2
  • Hee Yoon2
  • Tae Gun Shin2
  • Min Seob Sim2
  • Ik Joon Jo2
  • Won Chul Cha1,2,4,*,

1Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, 06355 Seoul, Republic of Korea

2Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 06355 Seoul, Republic of Korea

3Smart Health Lab, Research institute of Future Medicine, Samsung Medical Center, 06351 Seoul, Republic of Korea

4Digital Innovation Center, Samsung Medical Center, 06351 Seoul, Republic of Korea

DOI: 10.22514/sv.2022.018 Vol.19,Issue 2,March 2023 pp.28-38

Submitted: 11 December 2021 Accepted: 18 January 2022

Published: 08 March 2023

*Corresponding Author(s): Won Chul Cha E-mail: wc.cha@samsung.com

† These authors contributed equally.

Abstract

In patients with non-small cell lung cancer (NSCLC) visiting the emergency department (ED), clinical decisions must be made based on their disease prognosis. This study aims to predict the disease outcome of patients visiting the ED for the first time after NSCLC diagnosis. This study included patients who visited the ED in 2016–2020 after being diagnosed with NSCLC in study site or within 30 days before the first outpatient clinic visit after diagnosis. Primary outcome of prediction model was 3-month mortality from the initial ED visit. We analyzed the association between outcome and each variable as a risk factor and built a prediction model using these variables. Both oncologic factors and ED-associated factors were associated with the 3-month mortality of NSCLC from the first ED visit. We also visualized the treatment trace as a sequence and utilized it in prediction model building. The areas under the receiver operating curve (AUROCs) of the prediction model of 3-month mortality from the first ED visit ranged from 0.677 (95% Confidence Interval (CI), 0.640–0.708) to 0.729 (95% CI, 0.697–0.761). This study provides the prediction model about 3-month survival in first ED visit point and identified patient and disease-related factors to predict the prognosis of patients.


Keywords

Non-small cell lung cancer; Emergency department; Prognosis; Machine learning


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Jae Yong Yu,Hansol Chang,Weon Jung,Sejin Heo,Gun Tak Lee,Jong Eun Park,Se Uk Lee,Taerim Kim,Sung Yeon Hwang,Hee Yoon,Tae Gun Shin,Min Seob Sim,Ik Joon Jo,Won Chul Cha. Predicting mid-term survival of patients during emergency department triage for resuscitation decision. Signa Vitae. 2023. 19(2);28-38.

References

[1] Shin SH, Lee H, Kang HK, Park JH. Twenty-eight-day mortality in lung cancer patients with metastasis who initiated mechanical ventilation in the emergency department. Scientific Reports. 2019; 9: 1–7.

[2] Griffin JP, Nelson JE, Koch KA, Niell HB, Ackerman TF, Thompson M, et al. End-of-life care in patients with lung cancer. Chest. 2003; 123: 312S–331S.

[3] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA: A Cancer Journal for Clinicians. 2019; 69: 7–34.

[4] Nawar EW, Niska RW, Xu J. National hospital ambulatory medical care survey: 2005 emergency department summary. Advance Data. 2007; 386: 1–32.

[5] Rondeau DF, Schmidt TA. Treating cancer patients who are near the end of life in the emergency department. Emergency Medicine Clinics of North America. 2009; 27: 341–354.

[6] Kress J, Christenson J, Pohlman AS, Linkin DR, Hall JB. Outcomes of critically ill cancer patients in a university hospital setting. American Journal of Respiratory and Critical Care Medicine. 1999; 160: 1957–1961.

[7] Molina JR, Yang P, Cassivi SD, Schild SE, Adjei AA. Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. Mayo Clinic Proceedings. 2008; 83: 584–594.

[8] Lemjabbar-Alaoui H, Hassan OU, Yang YW, Buchanan P. Lung cancer: biology and treatment options. Biochimica et Biophysica Acta. 2015; 1856: 189–210.

[9] Sharma G, Freeman J, Zhang D, Goodwin JS. Trends in end-of-life ICU use among older adults with advanced lung cancer. Chest. 2008; 133: 72–78.

[10] Chapman AM, Sun KY, Ruestow P, Cowan DM, Madl AK. Lung cancer mutation profile of EGFR, ALK, and KRAS: meta-analysis and comparison of never and ever smokers. Lung Cancer. 2016; 102: 122–134.

[11] Lai YH, Chen WN, Hsu TC, Lin C, Tsao Y, Wu S. Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning. Scientific Reports. 2020; 10: 4679.

[12] She Y, Jin Z, Wu J, Deng J, Zhang L, Su H, et al. Development and validation of a deep learning model for non-small cell lung cancer survival. JAMA Network Open. 2020; 3: e205842.

[13] Iserson KV, Moskop JC. Triage in medicine, part I: concept, history, and types. Annals of Emergency Medicine. 2007; 49: 275–281.

[14] Christ M, Grossmann F, Winter D, Bingisser R, Platz E. Modern triage in the emergency department. Deutsches Ärzteblatt International. 2010; 107: 892–898.

[15] Wright AA, Keating NL, Ayanian JZ, Chrischilles EA, Kahn KL, Ritchie CS, et al. Family perspectives on aggressive cancer care near the end of life. JAMA. 2016; 315: 284–292.

[16] Miao M, Georgiou A, Dahm MR, Li J, Thomas J. Shared decision-making in emergency departments: context sensitivity through divergent discourses. Studies in Health Technology and Informatics. 2019; 265: 128–133.

[17] Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. British Journal of Surgery. 2015; 102: 148–158.

[18] Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. Journal of Medical Internet Research. 2016; 18: e323.

[19] Barfod C, Lauritzen MM, Danker JK, Sölétormos G, Forberg J, Berlac PA, et al. Abnormal vital signs are strong predictors for intensive care unit admission and in-hospital mortality in adults triaged in the emergency department—a prospective cohort study. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. 2012; 20: 1–10.

[20] Philip KEJ, Pack E, Cambiano V, Rollmann H, Weil S, O’Beirne J. The accuracy of respiratory rate assessment by doctors in a London teaching hospital: a cross-sectional study. Journal of Clinical Monitoring and Computing. 2015; 29: 455–460.

[21] Lundberg SM, Lee SI. ‘A unified approach to interpreting model predictions’, Proceedings of the 31st International Conference on Neural Information Processing Systems. Seattle, WA, USA. 2017.

[22] Detering KM, Hancock AD, Reade MC, Silvester W. The impact of advance care planning on end of life care in elderly patients: randomised controlled trial. The BMJ. 2010; 340: c1345.

[23] Heyland DK, Allan DE, Rocker G, Dodek P, Pichora D, Gafni A, et al. Discussing prognosis with patients and their families near the end of life: impact on satisfaction with end-of-life care. Open Medicine. 2009; 3: e101–e110.

[24] Schoenfeld EM, Kanzaria HK, Quigley DD, Marie PS, Nayyar N, Sabbagh SH, et al. Patient preferences regarding shared decision making in the emergency department: findings from a multisite survey. Academic Emergency Medicine. 2018; 25: 1118–1128.

[25] Wiener RS, Koppelman E, Bolton R, Lasser KE, Borrelli B, Au DH, et al. Patient and clinician perspectives on shared decision-making in early adopting lung cancer screening programs: a qualitative study. Journal of General Internal Medicine. 2018; 33: 1035–1042.

[26] Akgün KM. Palliative and end-of-life care for patients with malignancy. Clinics in Chest Medicine. 2017; 38: 363–376.

[27] Lim RB. End-of-life care in patients with advanced lung cancer. Therapeutic Advances in Respiratory Disease. 2016; 10: 455–467.


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