A Study on Factors Impacting Length of Hospital Stay of COVID-19 Inpatients

Öz

Knowing the typical length of hospital stay of COVID-19 patients and which factors affecting the stay time is important for hospital management. 3184 COVID-19 patients from the Tokat State Hospital collected from were examined on arrival to the hospital and were either treated as inpatients, or as outpatients. By using simple, conditional and cause-specific Cox proportional-hazard regressions for competing risk, we examined factors impacting hospital stay time, both overall and by taking into account patient’s age or survival status and contribution from a factor to the rate of event of mortality, and to the event of discharge. Surviving ICU patients have longer hospital stay time than non-surviving ICU patients, which is longer than non-ICU patients. Older age is correlated with a longer hospital stay. Increased C-reactive protein (CRP), decreased hemoglobin (HGB) and calcium levels are associated with longer hospital stay, independent from the contribution from surviving status. Almost all factors we collected contribute to a faster/slower mortality or discharge rate. We also observed that glucose is more important than HbA1C or diabetes status in its influence on hospital stay time. This information could be used for a better hospital bed management.

Anahtar Kelimeler

• COVID-19
• ;demographic and clinical factors
• Cox proportional-hazard regressions

COVID-19 Tanısı ile Hastanede Yatan Hastalarda Hastanede Kalış Süresini Etkileyen Faktörler Üzerine Bir Araştırma

Öz

COVID-19 hastalarının hastanede kalış süreleri ve kalış sürelerini etkileyen faktörlerin belirlenmesi hastane yönetimi için önem arz etmektedir. Tokat Devlet Hastanesin’e başvuran 3184 COVID-19 hastası hastaneye gelişlerinde muayene edilip ayakta veya yatarak tedavi edilmelerine göre kategorize edildiler. Basit, koşullu ve yarışan riskler için Cox orantılı hazard modeli kullanılarak, hem genel olarak ve hem de hastanın yaşı veya hayatta kalma süresi bir faktörün ölüm oranına ve taburcu olma durumuna katkısı dikkate alınarak, hastanede kalış sürelerini etkileyen faktörler incelendi. Hayatta kalan Yoğun Bakım Ünitesi hastalarının, hayatta kalmayan Yoğun Bakım Ünitesi hastalarına ve Yoğun Bakım Ünitesi olmayan hastalara göre daha uzun hastanede kalış süresine sahip olduğu bulgulandı. Artan yaşın, hastanede daha uzun kalış süresiyle ilişkili olduğu gözlemlendi. Yüksek C-reaktif protein (CRP), düşük hemoglobin (HGB) ve kalsiyum seviyelerinin, hayatta kalma durumunun katkısından bağımsız olarak, hastanede daha uzun kalma süresiyle ilişkili olduğu belirlendi. Çalışmamızda hemen hemen bütün faktörlerin daha hızlı / daha yavaş ölüm veya taburcu olma oranına katkıda bulunduğu gözlemlenmiştir. Ayrıca, glukozun, hastanede kalış süresi ile ilgili olarak, HbA1C veya diyabet durumundan daha önemli olduğu gözlemlendi. Bu bulguların, daha iyi bir hastane yatak yönetimi için kullanılabileceğini düşünmekteyiz.

Anahtar Kelimeler

• COVID-19
• Demografik ve Klinik Faktörler
• Cox orantılı hazard modeli

Kaynakça

1. (1) Brown TM. The COVID-19 pandemic in historical perspective: an AJPH dossier, Am. J. Pub. Health 2021; 111:402-4.
2. (2) Allison P. Event History Analysis: Regression for Longitudinal Event Data, 2nd edition, (SAGE Publications); 2014.
3. (3) Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks, Circulation 2016;133:601-9.
4. (4) Colquhoun D. The reproducibility of research and the misinterpretation of p-values, Royal Soc. Open Sci. 2017; 4:171085.
5. (5) Ioannidis JPA. The proposal to lower P value thresholds to. 005, JAMA 2018;319:1429-30.
6. (6) Li W, Shih A, Freudenberg-Hua Y, Fury W, Yang Y. Beyond standard pipeline and p < 0.05 in pathway enrichment analyses, Comp. Biol. and Chem. 2021; 92:107455.
7. (7) Latouche A, Allignol A, Beyersmann J, Labopin M, Fine JP. A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions, J. Clin. Epid. 2013; 66:648-53.
8. (8) Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data, Am. J. Epid. 2009; 170:244-56.

9. (9) Putter H, Schmacher M, Van Houwelingen HC. On the relation between the causespecific hazard and the subdistribution rate for competing risks data: The Fine-Gray model revisited, Biometrical J. 2020; 62:790-807.
10. (10) Fine JP and Gray RJ. A proportional hazards model for the subdistribution of a competing risk, J. Am. Stat. Asso. 1999; 94:496-509.
11. (11) McInnes L, Healy J, Saul N, Grossberger L. UMAP: uniform manifold approximation
12. and projection, J. Open Source Software 2018; 3:861.
13. (12) Singh AK, Gupta R, Ghosh A, Misra A. Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations, Clinc. Res. Rev.2020; 14:303-10.
14. (13) Skwieresky S, Rosengarten S, Change M, Thomson A, Meisel T, Macaluso F, Da Silva B, Oommen A, Banerji MA. Sugar is not always sweet: exploring the relationhsip between hyperglycemia and COVID- 19 in a predominantly African American population, abstract ENDO2021 (Mar 20-23, 2021, Endocrine Society), 2021.
15. (14) Sharifpour M, Rangaraju S, Liu M, Alabyad D, Nahab FB, Creel-Bulos CM, Jabaley CS, on behalf of the Emory COVID-19 Quality & Clinical Research Collaborative. C-Reactive protein as a prognostic indicator in hospitalized patients with COVID-19, PLoS ONE 2020; 15:e0242400.
16. (15) Nemer DM, Wilner BR, Burkle A, Aguilera J, Adewumi J, Gillombardo C, Wazni O, Menon V, Pengel S, Foxx M, Petre M, Hamilton AC, Cantillon DJ. Clinical characteristics and outcomes of Non-ICU hospitalization for COVID-19 in a nonepicenter, centrally monitored healthcare system, J. Hosp. Med. 2021; 16:7-14.
17. (16) Bergamaschi G, de Andreis FB, Aronico N, Lenti MV, Barteselli C, Merli S, Pellegrino I, Coppola L, Cremonte EM, Croce G, Morda´ F, Lapia F, Ferrari S, Ballesio A, Parodi A, Calabretta F, Ferrari MG, Fumoso F, Gentile A, Melazzini F, Di Sabatino A, on behalf of the Internal Medicine Covid-19 Collaborators. Anemia in patients with Covid-19: pathogenesis and clinical significance, Clin. Exp. Med. 2021; to appear. doi: 10.1007/s10238-020-00679-4
18. (17) Lippi G and Mattiuzzi C. Hemoglobin value may be decreased in patients with severe coronavirus disease 2019, Hematol. Transfus. Cell Ther.2020; 42:116-7. (18) Zhang L, Yan X, Fan Q, Liu H, Liu X, Liu Z, Zhang Z. D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19, J. Thromb. Haemost. 2020; 18:1324-9.
19. (19) Hachim MY, Hachim IY, Naeem KB, Hannawi H, Al Salmi I, Hannawi S. D-dimer, troponin, and urea level at presentation with COVID-19 can predict ICU admission: a single centered study, Front. Med. 2020; 7:949.
20. (20) Ceriello A. Hyperglycemia and COVID-19: What was known and what is really new? Diabetes Res. Clin. Pract.2020; 167:108383.
21. (21) Sun JK, Zhang WH, Zou L, Liu Y, Li JJ, Kan XH, Dai L, Shi QK, Yuan ST, Yu WK, Xu HY, Gu W, Qi JW. Serum calcium as a biomarker of clinical severity and prognosis in patients with coronavirus disease 2019, Aging 2020; 12:11287-95.
22. (22) Di Filippo L, Formenti AM, Rovere-Querini P, Carlucci M, Conte C, Ciceri F, Zangrillo A, Giustina A. Hypocalcemia is highly prevalent and predicts hospitalization in patients with COVID-19, Endocrine 2020; 68:475- 8.
23. (23) Benedetti C, Waldman M, Zaza G, Riella LV, Cravedi P. COVID-19 and the kidneys: an update, Front. Med. 2020; 7:423.
24. (24) Chen D, Li X, Song Q, Hu C, Su F, Dai J, Ye Y, Huang J, Zhang X. Assessment of hypokalemia and clinical characteristics in patients with coronavirus disease 2019 in Wenzhou, China, JAMA Netw. Open 2020; 3:e2011122.
25. (25) Tzoulis P, Waung JA, Bagkeris E, Hussein Z, Biddanda A, Cousins J, et al. Dysnatremia is a predictor for morbidity and mortality in hospitalized patients with COVID-19, J. Clin. Endo. Metab. 2021; to appear. doi: 10.1210/clinem/dgab107
26. (26) Anurag A, Jha PK, Kumar A. Differential white blood cell count in the COVID-19: A cross-sectional study of 148 patients, Diabetes Metab. Syndr. 2020; 14:2099-2102. (27) Aschenbrenner AC, Mouktaroudi M, Krmer B, Oestreich M, Antonakos N, Nuesch-Germano M, et al. German COVID-19 Omics Initiative (DeCOI). Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients, Genome Biol. 2020; 13:7.
27. (28) Huang I and Pranata R. Lymphopenia in severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis, J. Intensive Care 2020; 8:36. (29) Liu J, Li H, Luo M, Liu J, Wu L, Lin X, et al. Lymphopenia predicted illness severity and recovery in patients with COVID-19: a single-center, retrospective study, PLoS ONE 2020; 15:e0241659.
28. (30) Li X, Liu C, Mao Z, Xiao M, Wang L, Qi S. Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis, Crtical Care 2020; 24:647.
29. (31) Simadibrata DM, Pandhita BAW, Ananta ME, Tango T. Platelet-to-lymphocyte ratio, a novel biomarker to predict the severity of COVID-19 patients: a systematic review and meta-analysis, J. Intensive Care Soc. 2020; to appear. doi: 10.1177/1751143720969587
30. (32) Leclerc QJ, Fuller NM, Keogh RH, Diaz-Ordaz K, Sekula R, Semple MG, ISARIC4C Investigators, CMMID COVID-19 Working Group, Atkins KE, Procter SR, Knight GM. Importance of patient bed pathways and length of stay differences in predicting COVID-19 bed occupancy in England, medRxiv preprint 2021; doi: 10.1101/2021.01.14.21249791
31. (33) Rees EM, Nightingale ES, Jafari Y, Waterlow NR, Clifford S, Pearson CA, CMMID Working Group, Jombart T, Procter SR, Knight GM. COVID-19 length of hospital stay: a systematic review and data synthesis, BMC Med. 2020; 18:270.
32. (34) Lane EA, Barrett DJ, Casey M, McAloon CG, Collins B, Hunt K, et al. Country differences in hospitalisation, length of stay, admission to Intensive Care Units, and mortality due to SARS-CoV-2 infection at the end of the first wave in Europe: a rapid review of available literature, MedRxiv preprint 2020; doi:10.1101/2020.05.12.20099473
33. (35) Wu S, Xue L, Legido-Quigley H, Khan M, Wu H, Peng X, et al. Understanding factors influencing the length of hospital stay among non-severe COVID-19 patients: A retrospective cohort study in a Fangcang shelter hospital, PLoS ONE 2020; 15:e0240959.
34. (36) Hong Y, Wu X, Qu J, Gao Y, Chen H, Zhang Z. Clinical characteristics of Coronavirus Disease 2019 and development of a prediction model for prolonged hospital length of stay, Ann. Transl. Med. 2020; 8:443.
35. (37) Liu X, Zhou H, Zhou Y, Wu X, Zhao Y, Lu Y. Risk factors associated with disease severity and length of hospital stay in COVID-19 patients, J. Infection 2020; 81:e95-e97.
36. (38) Vekaria B, Overton C, Wisniowski A, Ahmad S, Aparicio-Castro A, Curran-Sebastian J, et al. Hospital length of stay For COVID-19 patients: data-driven methods for forward planning, Res. Square preprint 2020; doi: 10.21203/rs.3.rs-56855/v1
37. (39) Thai PQ, Toan DTT, Son DT, Van HTH, Minh LN, Hung LX, et al. Factors associated with the duration of hospitalisation among COVID-19 patients in Vietnam: A survival analysis, Epid. & Infec. 2020; 148:e114.
38. (40) Lopez-Cheda A, J´acome MA, Cao R, De Salazar PM. Estimating lengths-of-stay of hospitalized COVID-19 patients using a non-parametric model: a case study in Galicia (Spain), medRxiv preprint 2021; doi: 10.1101/2020.09.04.20187963
39. (41) Moriconi D, Masi S, Rebelos E, Virdis A, Manca ML, De Marco S, et al. Obesity prolongs the hospital stay in patients affected by COVID-19, and may impact on SARS-COV-2 shedding, Obesity Res. Clin. Pract.2020; 14:205-9.
40. (42) Kompaniyets L, Goodman AB, Belay B, Freedman DS, Sucosky MS, Lange SJ et al. Body mass index and risk for COVID-19related hospitalization, intensive care unit admission, invasive mechanical ventilation, and death United States, MarchDecember 2020, Morb. Mortality Wkly. Rep., ePub 2021; doi: 10.15585/mmwr.mm7010e4
41. (43) Braude P, Carter B, Short R, Vilches-Moraga A, Verduri A, Pearce L, et al. The influence of ACE inhibitors and ARBs on hospital length of stay and survival in people with COVID-19, Int. J. Cardio. Heart Vasc. 2020; 31:100660.