Original Article

There Is a Third Condition Aside from Survival and Death that Affects Outcome Statistics: Terminal Discharge

Abstract

Background: Some patients discharged automatically are classified as terminal discharge, while their clinical outcome is survival, disrupting the results of clinical research.

Methods: The data of this study were taken from inpatients admitted to the ICU of the First Medical Center of the People's Liberation Army General Hospital, Beijing, China from 2008-2017. We collected the data regarding medications used over the three days before discharge from the group of patients who survived and the group of patients who died, and the outcomes of all patients were recalculated by three classification algorithms (AdaBoosting, Pearson correlation coefficient, observed to expected ratio-weighted cosine similarity). Our basic assumption is that if the classification result is death but the actual in-hospital outcome is survival, the associated patient was likely terminally discharged.

Results: The coincidence rate of the outcomes calculated by the AdaBoosting algorithm was 98.1%, the coincidence rate calculated by the Pearson correlation coefficient was 61.1%, and the coincidence rate calculated by the observed to expected ratio-weighted cosine similarity was 93.4%. When the three classification methods were combined, the accuracy reached 98.56%.

Conclusion: The combination of clinical rules and classification methods has a synergistic effect on judgments of patients’ discharge outcomes, greatly saving time on manual retrieval and reducing the negative influence of statistics or rules.

1. Radha Krishna LK, Murugam V, Quah DSC (2018). The practice of terminal dis-charge: Is it euthanasia by stealth? Nurs Ethics, 25(8): 1030-1040.
2. Onukwugha E, Saunders E, Mullins C D, et al (2010). Reasons for discharges against medical advice: a qualitative study. Qual Saf Health Care, 19(5): 420-424.
3. Green P, Watts D, Poole S, et al (2004). Why patients sign out against medical advice (AMA): factors motivating patients to sign out AMA. Am J Drug Alcohol Abuse, 30(2): 489-493.
4. Parchure P, Joshi H, Dharmarajan K, et al (2020). Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19. BMJ Support Palliat Care, 22: bmjspcare-2020-002602.
5. Ngiam KY, Khor IW (2019). Big data and machine learning algorithms for health-care delivery. Lancet Oncol, 20(5): e262-e273.
6. Ragab DA, Sharkas M, Attallah O (2019). Breast Cancer Diagnosis Using an Effi-cient CAD System Based on Multiple Classifiers. Diagnostics (Basel), 9(4): 165.
7. Saccenti E, Hendriks MHWB, Smilde AK (2020). Corruption of the Pearson corre-lation coefficient by measurement error and its estimation, bias, and correction under different error models. Sci Rep, 10(1): 438.
8. Bate A, Lindquist M, Edwards IR, et al (2002). A data mining approach for signal detection and analysis. Drug Saf, 25(6): 393-397.
9. Doupe P, Faghmous J, Basu S (2019). Ma-chine Learning for Health Services Re-searchers. Value Health, 22(7): 808-815.
10. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G (2018). Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA In-tern Med, 178(11): 1544-1547.
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IssueVol 50 No 8 (2021) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/ijph.v50i8.6809
Keywords
Intensive care units Terminal discharge Machine learning
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Xu J, Chen Y, Wang J, Yang G, Yan P, Duan Z, Xie L, Mo G. There Is a Third Condition Aside from Survival and Death that Affects Outcome Statistics: Terminal Discharge. Iran J Public Health. 2021;50(8):1635-1641.