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Developing and validating a multivariable prediction model for in-hospital mortality of pneumonia with advanced chronic kidney disease patients: a retrospective analysis using a nationwide database in Japan

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Abstract

Background

The prognosis of pneumonia in patients with advanced stage chronic kidney disease (CKD) remains unimproved for years. We attempt to develop a simple and more useful scoring system for predicting in-hospital mortality for advanced CKD patients with pneumonia.

Methods

Using the Diagnosis Procedure Combination database, we identified the in-hospital adult patients both with a record of pneumonia and stage 5 or 5D CKD as a comorbidity on admission between April 1, 2012 and March 31, 2016. Predictive variable selection was analyzed by multivariable logistic regression analysis, stepwise method, LASSO method and random forest method, and then develop a new simple scoring system seeking for highest c-statistics combination of variables in one sample data set for model development. Finally, we compared c-statistics of univariate logistic regression about new scoring system with c-statistics about “A-DROP” in the other sample data set.

Result

We identified 8402 patients in 707 hospitals, and the total in-hospital mortality was 11.0% (437 patients) in development data set. Seven variables were selected, which includes age (male ≥ 70 years, female ≥ 75 years), respiratory failure, orientation disturbance, low blood pressure, the need of assistance in feeding or bowel control, severe or moderate thinness and CRP 200 mg/L or extent of consolidation on chest X-ray ≥ 2/3 of one lung. The c-statistics of univariate logistic regression was 0.8017 using seven variables, while that was 0.7372 using “A-DROP”

Conclusion

In advanced CKD patients, if we select appropriate variables for predicting in-hospital mortality, simple scoring system may have better discrimination than “A-DROP”.

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References

  1. James MT, Quan H, Tonelli M, Manns BJ, Faris P, Laupland KB, et al. CKD and risk of hospitalization and death with pneumonia. Am J Kidney Dis. 2009;54(1):24–322. https://doi.org/10.1053/j.ajkd.2009.04.005.

    Article  PubMed  Google Scholar 

  2. Wakasugi M, Kazama JJ, Narita I. Mortality trends among Japanese dialysis patients, 1988–2013: a joinpoint regression analysis. Nephrol Dial Transplant. 2016;31(9):1501–7. https://doi.org/10.1093/ndt/gfw249.

    Article  PubMed  Google Scholar 

  3. Wetmore JB, Li S, Molony JT, Guo H, Herzog CA, Gilbertson DT, et al. Insights from the 2016 peer kidney care initiative report: still a ways to go to improve care for dialysis patients. Am J Kidney Dis. 2018;71(1):123–32. https://doi.org/10.1053/j.ajkd.2017.08.023.

    Article  PubMed  Google Scholar 

  4. Wakasugi M, Kawamura K, Yamamoto S, Kazama JJ, Narita I. High mortality rate of infectious diseases in dialysis patients: a comparison with the general population in Japan. Ther Apher Dial. 2012;16(3):226–31. https://doi.org/10.1111/j.1744-9987.2012.01062.x.

    Article  PubMed  Google Scholar 

  5. Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58(5):377–82.

    Article  CAS  Google Scholar 

  6. Shindo Y, Sato S, Maruyama E, Ohashi T, Ogawa M, Imaizumi K, et al. Comparison of severity scoring systems A-DROP and CURB-65 for community-acquired pneumonia. Respirology (Carlton, Vic). 2008;13(5):731–5. https://doi.org/10.1111/j.1440-1843.2008.01329.x.

    Article  Google Scholar 

  7. Uematsu H, Kunisawa S, Sasaki N, Ikai H, Imanaka Y. Development of a risk-adjusted in-hospital mortality prediction model for community-acquired pneumonia: a retrospective analysis using a Japanese administrative database. BMC Pulm Med. 2014;14:203. https://doi.org/10.1186/1471-2466-14-203.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kunisawa S, Fushimi K, Imanaka Y. Reducing length of hospital stay does not increase readmission rates in early-stage gastric, colon, and lung cancer surgical cases in japanese acute care hospitals. PLoS ONE. 2016;11(11):e0166269. https://doi.org/10.1371/journal.pone.0166269.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Matsunuma R, Asai N, Ohkuni Y, Nakashima K, Iwasaki T, Misawa M, et al. I-ROAD could be efficient in predicting severity of community-acquired pneumonia or healthcare-associated pneumonia. Singapore Med J. 2014;55(6):318–24.

    Article  CAS  Google Scholar 

  10. World Health Organization. Global database on body mass index. https://www.assessmentpsychology.com/icbmi.htm. Accessed 3 Sep 2019.

  11. Mahoney FI, Barthel DW. Functional evaluation: the Barthel index. Md State Med J. 1965;14:61–5.

    CAS  PubMed  Google Scholar 

  12. Takada D, Kunisawa S, Fushimi K, Imanaka Y. Previously-initiated hemodialysis as prognostic factor for in-hospital mortality in pneumonia patients with stage 5 chronic kidney disease: retrospective database study of Japanese hospitals. PLoS ONE. 2019;14(2):e0213105. https://doi.org/10.1371/journal.pone.0213105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ambler G, Brady AR, Royston P. Simplifying a prognostic model: a simulation study based on clinical data. Stat Med. 2002;21(24):3803–22. https://doi.org/10.1002/sim.1422.

    Article  PubMed  Google Scholar 

  14. Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385–95.

    Article  CAS  Google Scholar 

  15. Pavlou M, Ambler G, Seaman SR, Guttmann O, Elliott P, King M, et al. How to develop a more accurate risk prediction model when there are few events. BMJ (Clin Res ed). 2015;351:h3868. https://doi.org/10.1136/bmj.h3868.

    Article  CAS  Google Scholar 

  16. Kruppa J, Liu Y, Biau G, Kohler M, Konig IR, Malley JD, et al. Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory. Biom J. 2014;56(4):534–63. https://doi.org/10.1002/bimj.201300068.

    Article  PubMed  Google Scholar 

  17. Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.

    Article  Google Scholar 

  18. Speiser JL, Durkalski VL, Lee WM. Random forest classification of etiologies for an orphan disease. Stat Med. 2015;34(5):887–99. https://doi.org/10.1002/sim.6351.

    Article  PubMed  Google Scholar 

  19. Calle ML, Urrea V. Letter to the editor: stability of random forest importance measures. Brief Bioinform. 2011;12(1):86–9. https://doi.org/10.1093/bib/bbq011.

    Article  PubMed  Google Scholar 

  20. Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48(12):1503–10. https://doi.org/10.1016/0895-4356(95)00048-8.

    Article  CAS  PubMed  Google Scholar 

  21. Hosmer DW. Applied Logistic Regression Third Edition. Wiley-Blackwell Online Books. 2013.

  22. Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Trans Evol Comput. 1997;1(1):67–82. https://doi.org/10.1109/4235.585893.

    Article  Google Scholar 

  23. Ramos LR, Simoes EJ, Albert MS. Dependence in activities of daily living and cognitive impairment strongly predicted mortality in older urban residents in Brazil: a 2-year follow-up. J Am Geriatr Soc. 2001;49(9):1168–75.

    Article  CAS  Google Scholar 

  24. Scott WK, Macera CA, Cornman CB, Sharpe PA. Functional health status as a predictor of mortality in men and women over 65. J Clin Epidemiol. 1997;50(3):291–6.

    Article  CAS  Google Scholar 

  25. Mlinac ME, Feng MC. Assessment of Activities of Daily Living, Self-Care, and Independence. Arch Clin Neuropsychol. 2016;31(6):506–16. https://doi.org/10.1093/arclin/acw049.

    Article  Google Scholar 

  26. Giebel CM, Sutcliffe C, Challis D. Activities of daily living and quality of life across different stages of dementia: a UK study. Aging Ment Health. 2015;19(1):63–71. https://doi.org/10.1080/13607863.2014.915920.

    Article  PubMed  Google Scholar 

  27. Nie W, Zhang Y, Jee SH, Jung KJ, Li B, Xiu Q. Obesity survival paradox in pneumonia: a meta-analysis. BMC Med. 2014;12:61. https://doi.org/10.1186/1741-7015-12-61.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Atamna A, Elis A, Gilady E, Gitter-Azulay L, Bishara J. How obesity impacts outcomes of infectious diseases. Eur J Clin Microbiol Infect Dis. 2017;36(3):585–91. https://doi.org/10.1007/s10096-016-2835-1.

    Article  CAS  PubMed  Google Scholar 

  29. Ma L, Zhao S. Risk factors for mortality in patients undergoing hemodialysis: a systematic review and meta-analysis. Int J Cardiol. 2017;238:151–8. https://doi.org/10.1016/j.ijcard.2017.02.095.

    Article  PubMed  Google Scholar 

  30. American Thoracic Society, et al. Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia. Am J Respir Crit Care Med. 2005;171(4):388–416. https://doi.org/10.1164/rccm.200405-644ST.

    Article  Google Scholar 

  31. Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40–9. https://doi.org/10.1002/mpr.329.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all the staff members at all the participating acute care hospitals.

Funding

This work was funded by the Ministry of Health, Labour and Welfare (Grant number H27-iryo-ippan-001, H30-seisaku-shitei-004); Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (Grant number 16H02634, 19H01075).

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Authors and Affiliations

  1. Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto City, Kyoto, Japan

    Daisuke Takada, Susumu Kunisawa & Yuichi Imanaka

  2. Department of Nephrology, Graduate School of Medicine, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto City, Kyoto, Japan

    Daisuke Takada, Takeshi Matsubara & Motoko Yanagita

  3. Department of Health Policy and Informatics, Graduate School of Medicine, Tokyo Medical and Dental University, Yushima, Bunkyo-ku, Tokyo, Japan

    Kiyohide Fushimi

Authors
  1. Daisuke Takada
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  2. Susumu Kunisawa
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  3. Takeshi Matsubara
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  4. Kiyohide Fushimi
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  5. Motoko Yanagita
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  6. Yuichi Imanaka
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Contributions

Research idea and study design: DT, SK, TM, MY, and YI; data analysis/interpretation: DT, SK, and YI; data acquisition: DT, SK, KF, and YI; statistical analysis: DT and YI. Each author contributed important intellectual content during manuscript drafting or revision, accepts personal accountability for the author’s own contributions, and agrees to ensure that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Yuichi Imanaka.

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Conflict of interest

Potential financial conflicts of interest, Honoraria: Motoko Yanagita (Chugai Pharmaceutical, Kyowa Kirin).

Ethical approval

The study protocol was approved by the ethics committee of Kyoto University Graduate School and the Faculty of Medicine (approval number: R0135). This study was conducted in accordance with the ethical guidelines for medical and health research involving human participants issued by the Japanese National Government. These guidelines include a stipulation for the protection of patient anonymity.

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The data were anonymized, and the requirement for informed consent was waived.

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Takada, D., Kunisawa, S., Matsubara, T. et al. Developing and validating a multivariable prediction model for in-hospital mortality of pneumonia with advanced chronic kidney disease patients: a retrospective analysis using a nationwide database in Japan. Clin Exp Nephrol 24, 715–724 (2020). https://doi.org/10.1007/s10157-020-01887-8

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  • DOI: https://doi.org/10.1007/s10157-020-01887-8

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