Procalcitonin Levels in COVID-19 Patients Are Strongly Associated with Mortality and ICU Acceptance in an Underserved, Inner City Population
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Participants
2.2. Predictor Variables
2.3. Outcomes
2.4. Measurement of Covariates
2.5. Statistical Analysis
3. Results
3.1. Patient Demographics
3.2. Bivariate Analyses
3.3. Multivariate Analyses
3.4. Assessment of Bacterial Co-Infection
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Johns Hopkins Coronavirus Resource Center. Covid-19 Dashboard. 2020. Available online: https://coronavirus.jhu.edu/map.html (accessed on 21 December 2020).
- Price-Haywood, E.G.; Burton, J.; Fort, D.; Seoane, L. Hospitalization and Mortality among Black Patients and White Patients with Covid-19. N. Engl. J. Med. 2020, 382, 2534–2543. [Google Scholar] [CrossRef] [PubMed]
- Gold, J.A.W.; Wong, K.K.; Szablewski, C.M.; Patel, P.R.; Rossow, J.; Da Silva, J.; Natarajan, P.; Morris, S.B.; Fanfair, R.N.; Rogers-Brown, J.; et al. Characteristics and Clinical Outcomes of Adult Patients Hospitalized with COVID-19—Georgia, March 2020. MMWR. Morb. Mortal. Wkly. Rep. 2020, 69, 545–550. [Google Scholar] [CrossRef]
- Suleyman, G.; Fadel, R.A.; Malette, K.M.; Hammond, C.; Abdulla, H.; Entz, A.; Demertzis, Z.; Hanna, Z.; Failla, A.; Dagher, C.; et al. Clinical Characteristics and Morbidity Associated with Coronavirus Disease 2019 in a Series of Patients in Metropolitan Detroit. JAMA Netw. Open 2020, 3, e2012270. [Google Scholar] [CrossRef] [PubMed]
- Cheng, K.J.G.; Sun, Y.; Monnat, S.M. COVID-19 Death Rates Are Higher in Rural Counties with Larger Shares of Blacks and Hispanics. J. Rural. Health 2020, 36, 602–608. [Google Scholar] [CrossRef] [PubMed]
- Huang, I.; Pranata, R.; Lim, M.A.; Oehadian, A.; Alisjahbana, B. C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: A meta-analysis. Ther. Adv. Respir. Dis. 2020, 14, 1753466620937175. [Google Scholar] [CrossRef] [PubMed]
- Merad, M.; Martin, J.C. Pathological inflammation in patients with COVID-19: A key role for monocytes and macrophages. Nat. Rev. Immunol. 2020, 20, 355–362. [Google Scholar] [CrossRef] [PubMed]
- Rubinson, L. Intensive Care Unit Strain and Mortality Risk Among Critically Ill Patients With COVID-19—There Is No “Me” in COVID. JAMA Netw. Open 2021, 4, e2035041. [Google Scholar] [CrossRef]
- Bhatraju, P.K.; Ghassemieh, B.J.; Nichols, M.; Kim, R.; Jerome, K.R.; Nalla, A.K.; Greninger, A.L.; Pipavath, S.; Wurfel, M.M.; Evans, L.; et al. Covid-19 in Critically Ill Patients in the Seattle Region—Case Series. N. Engl. J. Med. 2020, 382, 2012–2022. [Google Scholar] [CrossRef]
- Richardson, S.; Hirsch, J.S.; Narasimhan, M.; Crawford, J.M.; McGinn, T.; Davidson, K.W.; Northwell COVID-19 Research Consortium. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized with COVID-19 in the New York City Area. JAMA 2020, 323, 2052–2059. [Google Scholar] [CrossRef]
- Armstrong, R.A.; Kane, A.D.; Cook, T.M. Outcomes from intensive care in patients with COVID-19: A systematic review and meta-analysis of observational studies. Anaesthesia 2020, 75, 1340–1349. [Google Scholar] [CrossRef]
- Tomazini, B.M.; Maia, I.S.; Cavalcanti, A.B.; Berwanger, O.; Rosa, R.G.; Veiga, V.C.; Avezum, A.; Lopes, R.D.; Bueno, F.R.; Silva, M.V.A.O.; et al. Effect of Dexamethasone on Days Alive and Ventilator-Free in Patients with Moderate or Severe Acute Respiratory Distress Syndrome and COVID-19: The CoDEX Randomized Clinical Trial. JAMA 2020, 324, 1307–1316. [Google Scholar] [CrossRef]
- Hermine, O.; Mariette, X.; Tharaux, P.-L.; Resche-Rigon, M.; Porcher, R.; Ravaud, P.; Bureau, S.; Dougados, M.; Tibi, A.; CORIMUNO-19 Collaborative Group; et al. Effect of Tocilizumab vs Usual Care in Adults Hospitalized With COVID-19 and Moderate or Severe Pneumonia. JAMA Intern. Med. 2021, 181, 32–40. [Google Scholar] [CrossRef] [PubMed]
- Tang, J.; Lin, J.; Zhang, E.; Zhong, M.; Luo, Y.; Fu, Y.; Yang, Y. Serum IL-6 and procalcitonin are two promising novel biomarkers for evaluating the severity of COVID-19 patients. Medicine 2021, 100, e26131. [Google Scholar] [CrossRef] [PubMed]
- Sarfaraz, S.; Shaikh, Q.; Saleem, S.G.; Rahim, A.; Herekar, F.F.; Junejo, S.; Hussain, A. Determinants of in-hospital mortality in COVID-19: A prospective cohort study from Pakistan. PLoS ONE 2021, 16, e0251754. [Google Scholar] [CrossRef]
- Müller, B.; White, J.C.; Nylen, E.S.; Snider, R.H.; Becker, K.L.; Habener, J.F. Ubiquitous Expression of the Calcitonin-I Gene in Multiple Tissues in Response to Sepsis 1. J. Clin. Endocrinol. Metab. 2001, 86, 396–404. [Google Scholar] [CrossRef]
- Gilbert, D.N. Procalcitonin as a Biomarker in Respiratory Tract Infection. Clin. Infect. Dis. 2011, 52, S346–S350. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Self, W.H.; Wunderink, R.G.; Jain, S.; Edwards, K.M.; Grijalva, C.G.; Etiology of Pneumonia in the Community (EPIC) Study Investigators. Procalcitonin as a Marker of Etiology in Adults Hospitalized with Community-Acquired Pneumonia. Clin. Infect. Dis. 2018, 66, 1640–1641. [Google Scholar] [CrossRef] [PubMed]
- Delevaux, I.; André, M.; Colombier, M.; Albuisson, E.; Meylheuc, F.; Bégue, R.-J.; Piette, J.-C.; Aumaître, O. Can procalcitonin measurement help in differentiating between bacterial infection and other kinds of inflammatory processes? Ann. Rheum. Dis. 2003, 62, 337–340. [Google Scholar] [CrossRef] [PubMed]
- Self, W.H.; Balk, R.A.; Grijalva, C.; Williams, D.J.; Zhu, Y.; Anderson, E.J.; Waterer, G.W.; Courtney, D.M.; Bramley, A.M.; Trabue, C.; et al. Procalcitonin as a Marker of Etiology in Adults Hospitalized with Community-Acquired Pneumonia. Clin. Infect. Dis. 2017, 65, 183–190. [Google Scholar] [CrossRef]
- Christ-Crain, M.; Jaccard-Stolz, D.; Bingisser, R.; Gencay, M.M.; Huber, P.R.; Tamm, M.; Müller, B. Effect of procalcitonin-guided treatment on antibiotic use and outcome in lower respiratory tract infections: Cluster-randomised, single-blinded intervention trial. Lancet 2004, 363, 600–607. [Google Scholar] [CrossRef]
- Lippi, G.; Plebani, M. Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. Clin. Chim. Acta 2020, 505, 190–191. [Google Scholar] [CrossRef]
- Del Sole, F.; Farcomeni, A.; Loffredo, L.; Carnevale, R.; Menichelli, D.; Vicario, T.; Pignatelli, P.; Pastori, D. Features of severe COVID-19: A systematic review and meta-analysis. Eur. J. Clin. Investig. 2020, 50, 13378. [Google Scholar] [CrossRef] [PubMed]
- Hu, R.; Han, C.; Pei, S.; Yin, M.; Chen, X. Procalcitonin levels in COVID-19 patients. Int. J. Antimicrob. Agents 2020, 56, 106051. [Google Scholar] [CrossRef] [PubMed]
- Yoo, J.-K.; Kim, T.S.; Hufford, M.M.; Braciale, T.J. Viral infection of the lung: Host response and sequelae. J. Allergy Clin. Immunol. 2013, 132, 1263–1276. [Google Scholar] [CrossRef] [PubMed]
- Talbot, T.R.; Poehling, K.A.; Hartert, T.V.; Arbogast, P.G.; Halasa, N.B.; Edwards, K.M.; Schaffner, W.; Craig, A.S.; Griffin, M.R. Seasonality of invasive pneumococcal disease: Temporal relation to documented influenza and respiratory syncytial viral circulation. Am. J. Med. 2005, 118, 285–291. [Google Scholar] [CrossRef] [PubMed]
- Morens, D.M.; Taubenberger, J.K.; Fauci, A.S. Predominant Role of Bacterial Pneumonia as a Cause of Death in Pandemic Influenza: Implications for Pandemic Influenza Preparedness. J. Infect. Dis. 2008, 198, 962–970. [Google Scholar] [CrossRef]
- Langford, B.J.; So, M.; Raybardhan, S.; Leung, V.; Westwood, D.; MacFadden, D.R.; Soucy, J.-P.R.; Daneman, N. Bacterial co-infection and secondary infection in patients with COVID-19: A living rapid review and meta-analysis. Clin. Microbiol. Infect. 2020, 26, 1622–1629. [Google Scholar] [CrossRef]
- Liu, J.; Liu, Y.; Xiang, P.; Pu, L.; Xiong, H.; Li, C.; Zhang, M.; Tan, J.; Xu, Y.; Song, R.; et al. Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. J. Transl. Med. 2020, 18, 206. [Google Scholar] [CrossRef]
- Cummings, M.J.; Baldwin, M.R.; Abrams, D.; Jacobson, S.D.; Meyer, B.J.; Balough, E.M.; Aaron, J.G.; Claassen, J.; Rabbani, L.E.; Hastie, J.; et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: A prospective cohort study. medRxiv 2020. [Google Scholar] [CrossRef]
- Wang, D.; Hu, B.; Hu, C.; Zhu, F.; Liu, X.; Zhang, J.; Wang, B.; Xiang, H.; Cheng, Z.; Xiong, Y.; et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA 2020, 323, 1061–1069. [Google Scholar] [CrossRef]
- Parveen, R.; Sehar, N.; Bajpai, R.; Agarwal, N.B. Association of diabetes and hypertension with disease severity in covid-19 patients: A systematic literature review and exploratory meta-analysis. Diabetes Res. Clin. Pract. 2020, 166, 108295. [Google Scholar] [CrossRef]
- Singh, A.K.; Gupta, R.; Ghosh, A.; Misra, A. Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 303–310. [Google Scholar] [CrossRef] [PubMed]
- Nédélec, Y.; Sanz, J.; Baharian, G.; Szpiech, Z.A.; Pacis, A.; Dumaine, A.; Grenier, J.-C.; Freiman, A.; Sams, A.J.; Hebert, S.; et al. Genetic Ancestry and Natural Selection Drive Population Differences in Immune Responses to Pathogens. Cell 2016, 167, 657–669.e21. [Google Scholar] [CrossRef] [Green Version]
- Qu, Y.; Olonisakin, T.; Bain, W.; Zupetic, J.; Brown, R.; Hulver, M.; Xiong, Z.; Tejero, J.; Shanks, R.M.; Bomberger, J.M.; et al. Thrombospondin-1 protects against pathogen-induced lung injury by limiting extracellular matrix proteolysis. JCI Insight 2018, 3, e96914. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhu, Y.; Zhang, J.; Li, Y.; Liu, F.; Zhou, Q.; Peng, Z. Association between thrombocytopenia and 180-day prognosis of COVID-19 patients in intensive care units: A two-center observational study. PLoS ONE 2021, 16, e0248671. [Google Scholar] [CrossRef]
- Xu, J.; Yang, X.; Yang, L.; Zou, X.; Wang, Y.; Wu, Y.; Zhou, T.; Yuan, Y.; Qi, H.; Fu, S.; et al. Clinical course and predictors of 60-day mortality in 239 critically ill patients with COVID-19: A multicenter retrospective study from Wuhan, China. Crit. Care 2020, 24, 394. [Google Scholar] [CrossRef]
- Fan, X.; Zhu, B.; Nouri-Vaskeh, M.; Jiang, C.; Feng, X.; Poulsen, K.; Baradaran, B.; Fang, J.; Ade, E.A.; Sharifi, A.; et al. Scores based on neutrophil percentage and lactate dehydrogenase with or without oxygen saturation predict hospital mortality risk in severe COVID-19 patients. Virol. J. 2021, 18, 67. [Google Scholar] [CrossRef] [PubMed]
- Ali, A.; Noman, M.; Guo, Y.; Liu, X.; Zhang, R.; Zhou, J.; Zheng, Y.; Zhang, X.-E.; Qi, Y.; Chen, X.; et al. Myoglobin and C-reactive protein are efficient and reliable early predictors of COVID-19 associated mortality. Sci. Rep. 2021, 11, 1–13. [Google Scholar] [CrossRef]
- Turan, O.; Turgut, D.; Gunay, T.; Yilmaz, E.; Turan, A.; Akkoclu, A. The contribution of clinical assessments to the diagnostic algorithm of pulmonary embolism. Adv. Clin. Exp. Med. 2017, 26, 303–309. [Google Scholar] [CrossRef]
- Spindler, C.; Ortqvist, A. Prognostic score systems and community-acquired bacteraemic pneumococcal pneumonia. Eur. Respir. J. 2006, 28, 816–823. [Google Scholar] [CrossRef] [Green Version]
Characteristics | Survivor n (%) | Non-Survivor n (%) | Total | p-Value |
---|---|---|---|---|
n = 89 | n = 182 | n = 271 | ||
Age (mean, SD) | 61.62 ± 1.50 | 68.98 ± 0.85 | ||
Sex | ||||
Female | 45 (50.6) | 70 (38.5) | 115 (42.4) | 0.06 |
Male | 44 (49.4) | 112 (61.5) | 156 (57.6) | |
Race/Ethnicity | ||||
Asian | 2 (2.25) | 0 (0) | 2 (0.74) | 0.10 |
Black | 74 (83.2) | 165 (90.7) | 239 (88.2) | |
Hispanic | 4 (4.49) | 2 (1.10) | 6 (2.21) | |
White | 4 (4.49) | 7 (3.85) | 11 (4.06) | |
Unknown | 5 (5.62) | 8 (4.40) | 13 (4.80) | |
Comorbidities | ||||
Asthma | 8 (9) | 17 (9.34) | 25 (9.23) | 0.93 |
COPD | 6 (6.74) | 16 (8.79) | 22 (8.12) | 0.56 |
Diabetes | 48 (53.9) | 104 (57.1) | 152 (56.1) | 0.62 |
Hypertension | 63 (70.8) | 148 (81.3) | 211 (77.9) | 0.05 |
HIV | 1 (1.1) | 7 (3.85) | 8 (2.95) | 0.21 |
CKD | 12 (13.5) | 29 (15.9) | 41 (15.1) | 0.60 |
Factors | Survivor | Non-Survivor | p-Value | ||
---|---|---|---|---|---|
n | Mean (SD) | n | Mean (SD) | ||
Age | 89 | 61.62 ± 1.50 | 182 | 68.98 ± 0.85 | <0.0001 |
HCO3− | 88 | 21.95 ± 0.76 | 181 | 20.66 ± 0.45 | <0.02 |
BUN | 88 | 36.02 ± 3.27 | 182 | 47.87 ± 3.34 | <0.004 |
Cr | 88 | 2.43 ± 0.30 | 182 | 2.91 ± 0.24 | <0.02 |
AST | 85 | 82.44 ± 16.43 | 165 | 241.4 ± 106.6 | <0.02 |
LDH | 47 | 636.3 ± 84.10 | 88 | 740 ± 55.14 | <0.05 |
CRP | 49 | 161.7 ± 16.04 | 94 | 230.4 ± 12.56 | <0.002 |
PLT | 85 | 292.2 ± 14.82 | 179 | 227.6 ± 8.36 | <0.0001 |
Neutrophil % | 73 | 79.98 ± 1.12 | 155 | 83.57 ± 0.63 | <0.004 |
Lymphocyte % | 73 | 11.81 ± 0.97 | 154 | 9.06 ± 0.45 | <0.009 |
PT | 46 | 13.83 ± 0.32 | 69 | 18.13 ± 2.11 | <0.05 |
Procalcitonin | 47 | 3.155 ± 2.02 | 77 | 10.18 ± 3.01 | <0.0001 |
Troponin | 44 | 0.40 ± 2.02 | 84 | 2.60 ± 2.02 | <0.03 |
Factors | Not Accepted | Accepted | p-Value | ||
---|---|---|---|---|---|
n | Mean (SD) | n | Mean (SD) | ||
Age | 115 | 67.99 ± 0.92 | 156 | 65.46 ± 1.03 | <0.05 |
HCO3− | 113 | 21.88 ± 0.44 | 156 | 20.39 ± 0.53 | <0.03 |
BUN | 114 | 42.56 ± 3.24 | 156 | 44.96 ± 3.06 | 0.32 |
Cr | 114 | 2.35 ± 0.22 | 156 | 2.91 ± 0.24 | <0.03 |
AST | 102 | 300.0 ± 148.3 | 148 | 134.4 ± 33.60 | 0.09 |
LDH | 50 | 647.5 ± 52.37 | 85 | 758.5 ± 61.41 | 0.17 |
CRP | 58 | 212.4 ± 12.61 | 85 | 210.7 ± 13.96 | 0.10 |
PLT | 110 | 252.4 ± 9.39 | 154 | 241.1 ± 9.53 | 0.37 |
PMN% | 94 | 82.18 ± 1.13 | 134 | 82.53 ± 0.69 | 0.33 |
Lymphocyte% | 94 | 9.09 ± 0.54 | 133 | 1.00 ± 0.56 | 0.10 |
PT | 39 | 17.72 ± 2.39 | 76 | 15.07 ± 0.69 | 0.42 |
Procalcitonin | 45 | 2.84 ± 0.67 | 79 | 10.50 ± 3.13 | <0.009 |
Troponin | 41 | 0.56 ± 0.17 | 87 | 2.50 ± 1.72 | 0.24 |
Characteristic | Q1 (Referent) | Q2 | Q3 | Q4 | p-Trend |
---|---|---|---|---|---|
OR | OR (95% CI) | OR (95% CI) | OR (95% CI) | ||
Procalcitonin | |||||
Model a | 1.00 | 1.07 (0.51–2.21) | 1.58 (0.74–3.37) | 5.92 (2.25–15.6) | <0.001 |
Model b | 1.00 | 1.12 (0.53–2.36) | 1.75 (0.79–3.84) | 5.65 (2.14–14.9) | <0.001 |
CRP | |||||
Model a | 1.00 | 1.80 (0.87–3.74) | 2.89 (1.32–6.32) | 2.25 (1.04–4.87) | 0.034 |
Model b | 1.00 | 1.91 (0.90–4.04) | 2.92 (1.32–6.48) | 2.38 (1.08–5.25) | 0.049 |
BUN | |||||
Model a | 1.00 | 1.36 (0.65–2.85) | 1.94 (0.88–4.25) | 1.72 (0.81–3.65) | 0.22 |
Model b | 1.00 | 1.42 (0.67–2.99) | 2.08 (0.93–4.65) | 1.64 (0.77–3.49) | 0.31 |
PLT | |||||
Model a | 1.00 | 1.41 (0.63–3.16) | 0.96 (0.44–2.07) | 0.46 (0.22–0.97) | 0.010 |
Model b | 1.00 | 1.58 (0.69–3.63) | 0.96 (0.44–2.09) | 0.47 (0.22–0.998) | 0.010 |
PMN% (neutrophils) | |||||
Model a | 1.00 | 1.58 (0.76–3.29) | 1.06 (0.52–2.15) | 2.50 (1.14–5.51) | 0.12 |
Model b | 1.00 | 1.57 (0.75–3.29) | 1.07 (0.52–2.19) | 2.64 (1.17–5.93) | 0.10 |
Lym% | |||||
Model a | 1.00 | 1.20 (0.56–2.57) | 1.06 (0.49–2.28) | 0.57 (0.27–1.19) | 0.039 |
Model b | 1.00 | 1.22 (0.56–2.66) | 1.02 (0.47–2.22) | 0.55 (0.26–1.15) | 0.029 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Feng, T.; James, A.; Doumlele, K.; White, S.; Twardzik, W.; Zahid, K.; Sattar, Z.; Ukponmwan, O.; Nakeshbandi, M.; Chow, L.; et al. Procalcitonin Levels in COVID-19 Patients Are Strongly Associated with Mortality and ICU Acceptance in an Underserved, Inner City Population. Medicina 2021, 57, 1070. https://doi.org/10.3390/medicina57101070
Feng T, James A, Doumlele K, White S, Twardzik W, Zahid K, Sattar Z, Ukponmwan O, Nakeshbandi M, Chow L, et al. Procalcitonin Levels in COVID-19 Patients Are Strongly Associated with Mortality and ICU Acceptance in an Underserved, Inner City Population. Medicina. 2021; 57(10):1070. https://doi.org/10.3390/medicina57101070
Chicago/Turabian StyleFeng, Theresa, Alecia James, Kyra Doumlele, Seth White, Wendy Twardzik, Kanza Zahid, Zeeshan Sattar, Osato Ukponmwan, Mohamd Nakeshbandi, Lillian Chow, and et al. 2021. "Procalcitonin Levels in COVID-19 Patients Are Strongly Associated with Mortality and ICU Acceptance in an Underserved, Inner City Population" Medicina 57, no. 10: 1070. https://doi.org/10.3390/medicina57101070