Surprisingly low C-reactive protein levels in the statistics of sudden death among patients in the Regional Hospital in Racibórz, Poland
, 2, 3, 4, 5
Abstract
Introduction: Identifying patients at high risk of in-hospital mortality within early days of admission is crucial for guiding medical decisions and allocating resources effectively. The aim of this study was to explore whether changes in routinely conducted Emergency Department (ED) and in-hospital admission tests were associated with sudden death among patients (all causes) in the Regional Hospital in Racibórz, Poland. Methods: The first laboratory tests of biomarkers from 7,827 unique patients were examined, from January 1 to December 31, 2023. Associations between risk factors and all-cause sudden death outcomes were estimated using the Cox regression. Based on the estimated concordance statistic, the most fitting hematological biomarker was selected. Its values were categorized following the interquartile ranges and death rates for each range were modeled using Poisson regression. Results: The highest and statistically significant (p<0.05) mortality rate was recorded in the Intensive Care Unit and Internal Medicine Department, five and three times higher than in the ED, respectively. In the Trauma and Orthopedic Surgery, Geriatrics, and Observation and Infectious Diseases departments, the risk of patient death was lower compared to that in the ED. In other hospital departments, the differences were incalculable due to the absence of deaths. Following a concordance statistic, C-reactive protein (CRP) among ten biomarkers shows the best fit in the Cox regression model. Surprisingly, the very low threshold level of CRP=7.4 mg/L was differentiating 30-day mortality in patients. Conclusion: Compared to other scientific reports, the results obtained in this study are difficult for the authors to explain and require verification.
Keywords: sudden deathemergency departmentin-hospital admissionC-reactive protein
References
- Stoessel D, Fa R, Artemova S, et al. Early prediction of in-hospital mortality utilizing multivariate predictive modelling of electronic medical records and socio-determinants of health of the first day of hospitalization. BMC Med Inform Decis Mak. 2023; 23(1): 259.
- Murillo-Zamora E, Mendoza-Cano O, Delgado-Enciso I, et al. National retrospective cohort study to identify risk factors for in-hospital 30-day lethality in laboratory-confirmed cases of influenza. Rev Clin Esp. 2021; 221(2): 76–85.
- Mo C, Cheng Y, Pan J, et al. Association between hospital characteristics and 30-day mortality of patients hospitalized for acute myocardial infarction in Sichuan, China. J Evid Based Med. 2022; 15(3): 236–244.
- Saw J, Starovoytov A, Aymong E, et al. Canadian spontaneous coronary artery dissection cohort study: in-hospital and 30-day outcomes. Eur Heart J. 2019; 40(15): 1188–1197.
- Adielsson A, Djärv T, Rawshani A, et al. Changes over time in 30-day survival and the incidence of shockable rhythms after in-hospital cardiac arrest - A population-based registry study of nearly 24,000 cases. Resuscitation. 2020; 157: 135–140.
- Alkhamis MA, Al Jarallah M, Attur S, et al. Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait. Sci Rep. 2024; 14(1): 1243.
- Álvarez-Kailis S, Paniagua-Arribas E, Paderne-Díaz B, et al. Value of soluble urokinase plasminogen activator receptor levels in predicting adverse events at 30 days in hospital emergency departments. Emergencias. 2021; 33(6): 477–479.
- Atkinson E, Crowson C, Pedersen R, et al. Poisson models for person-years and expected rates. Mayo Foundation 2008; Technical Report. ; 81.
- Kamarulariffin Kamarudin M, Tan Jen Ai C, Lisa Zaharan N, et al. Predicting acute myocardial infarction (AMI) 30-days mortality: Using standardised mortality ratio (SMR) as the hospital performance measure. Int J Med Inform. 2022; 168: 104865.
- Therneau TA. package for survival analysis in R. R package version 3. 7-0. : CRAN.
- R Core Team. R: A language and environment for statistical computing. Version 4.4.0. R Foundation for Statistical Computing. ; 2024: Vienna.
- Bhandarkar P, Patil P, Soni KD, et al. Australia-India Trauma System Collaboration. An Analysis of 30-Day in-Hospital Trauma Mortality in Four Urban University Hospitals Using the Australia India Trauma Registry. World J Surg. 2021; 45(2): 380–389.
- Saw J, Starovoytov A, Aymong E, et al. Canadian spontaneous coronary artery dissection cohort study: in-hospital and 30-day outcomes. Eur Heart J. 2019; 40(15): 1188–1197.
- Zhou R, Xu J, Luan J, et al. Predictive role of C-reactive protein in sudden death: a meta-analysis of prospective studies. J Int Med Res. 2022; 50(2): 3000605221079547.
- Mazurkiewicz M, Lizurej W, Mazurkiewicz L, et al. C-Reactive Protein and Albumin Levels as Useful Tools for Predicting In-Hospital Outcomes in Polish Nonagenarians. Clin Interv Aging. 2023; 18: 1547–1554.
- Ziakas A, Gavrilidis S, Giannoglou G, et al. In-hospital and long-term prognostic value of fibrinogen, CRP, and IL-6 levels in patients with acute myocardial infarction treated with thrombolysis. Angiology. 2006; 57(3): 283–293.
- Luo X, Zhou W, Yan X, et al. Prognostic Value of C-Reactive Protein in Patients With Coronavirus 2019. Clin Infect Dis. 2020; 71(16): 2174–2179.
- Milenkovic M, Hadzibegovic A, Kovac M, et al. D-dimer, CRP, PCT, and IL-6 Levels at Admission to ICU Can Predict In-Hospital Mortality in Patients with COVID-19 Pneumonia. Oxid Med Cell Longev. 2022; 2022: 8997709.