C-reactive protein testing to reduce antibiotic prescribing for acute respiratory infections in adults: a systematic review and meta-analysis

Introduction

Antimicrobial resistance (AMR) has become one of the major public health problems because of antibiotic abuse (1-4). AMR not only increases the mortality of infectious diseases, but also brings some social problems and economic burden (5,6). The prescription of inappropriate antimicrobials is directly related to the AMR (7). The available evidences indicate that some biomarkers guide the antibiotic prescribing can reduce mortality and antibiotic prescribing rate (8-11). However, this remains controversial (12).

Acute respiratory tract infections (ARI) is one of the most common acute diseases that promotes the general practitioner (GP) to prescribe antibiotic in primary care. However, the pathogens of ARI are most virals and bacterias with mild self-limited (13,14). Therefore, the antibiotic prescribing for ARI need appropriate guidance. C-reactive protein (CRP) is a biomarker of inflammatory process (15,16). CRP activates the classical complement pathway to stimulate bacterial phagocytosis in bacterial infection. When the bacterial inflammatory factors are eliminated, the level of CRP decreases rapidly (17-19).

At present, several guidelines recommend CRP testing to guide antibiotic prescribing (20-22). Therefore, it is necessary to design reasonable meta-analysis for high-quality clinical studies. A Cochrane review confirmed that CRP could reduce the antibiotic prescription in ARI patients (23). However, this review was published in 2014, and many high-quality clinical studies have been published recently. A review includes intervention studies and observational studies, and the participants were adults and children, resulting in greater heterogeneity (24). A systematic review focused on acute infections in ambulatory care of adults and children, including randomized controlled trials (RCTs) and non-RCTs (25). A recent review included RCTs and cluster RCTs, while the participants were adults and children (26). In summary, the four related reviews (23-26) conducted the meta-analysis of CRP testing to guide antibiotic prescribing, but none of those reviews analysed adults separately. Moreover, the quality of the studies was varied, which affected the reliability of the conclusion. Therefore, we conducted a meta-analysis based on RCTs instead of cluster trials and only adults were chosen, to provide high-quality clinical evidence for CRP testing to reduce antibiotic prescribing in adult ARI.

We present the following article in accordance with the PRISMA reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-21-705/rc).

Methods

Search strategy

We searched databases of PubMed, Cochrane clinical trial database and Embase using search terms comprising medical subject headings (MeSH) and free-text terms from their inception to January 16, 2021 without language restrictions. The key search terms as following: C-reactive protein, anti-bacterial agents, RCT. We also checked references of the previous reviews to identify additional potentially eligible studies. The retrieval strategy is shown in Appendix 1.

Eligibility criteria

(I) Participants: adults (≥18 years) were diagnosed with ARI. (II) Intervention: the intervention was CRP testing; the comparator was routine care. (III) Outcomes: the primary outcomes were antibiotic prescribing rate at the index consultation and during 28 days follow-up. The secondary outcome measures were patient clinical recovery within 7 days and the adverse events. (IV) Studies type: RCTs.

Exclusion criteria

(I) Conference abstracts with no corresponding full article published in journal. (II) Duplicate publications. (III) Study protocol. (IV) Cluster RCTs.

Study selection

First of all, duplicated and non-relevant studies were excluded, then non-ARI studies, non-adult related studies, non-RCTs and cluster-RCTs were excluded through examining titles and abstracts. And literatures that satisfactory with the enrolling criteria were screened out by reading the full text finally.

Data extraction

Two reviewers (YL and ZC) extracted the data, assessed the quality and content of the data independently. Disagreements were solved by consultation with the third reviewers (KZ). The contents of information were extracted as follow: first author, years of publication, country, characteristics of participants, CRP level as the recommended threshold, treatment duration, follow-up duration and outcomes.

Quality assessment

Three reviewers (KX, KZ and CZ) independently assessed the quality of the included studies. We used the Cochrane Collaboration’s tool to assess risk of bias (27). The assessment details included sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessors, incomplete outcome data, selective reporting, and other sources of bias. Each domain was assessed as “low risk”, “high risk” or “unclear risk”.

Statistical analysis

The data analyses were accomplished by Review Manager 5.3 software. All the outcomes are consistent with dichotomous outcomes, so we use risk ratio (RR) with 95% confidence interval (CI) to calculate the data. The heterogeneity was high among the studies (I²>50%) of antibiotic prescribing rate at the index consultation, and the random effect model was chosen. The heterogeneity was low among the studies (I² <50%) of the others outcomes, and the fixed effect model was selected for data analysis. The factors that affect the heterogeneity were found out by sensitivity analysis and subgroup analysis.

Evidence quality assessment

We use the GRADE system (28) to evaluate the evidence quality for the outcome measures, and the evidence quality was divided into four levels: high, moderate, low, or very low. Evidence of RCTs is regarded as high quality, but the credibility would be decreased if there were inconsistency of results.

Results

Studies retrieved

A total of 2,028 studies were identified. After removing duplicates and screening of the titles and abstracts, 34 studies were deemed potentially eligible. After reviewing the full-text articles, 7 trials (29-35) were included in the final analysis. The screening process was summarized in a flow diagram (Figure 1).

Figure 1 Study identification and process for selection of studies included in the review. ARI, acute respiratory tract infections; RCTs, randomized controlled trials.

Characteristics of included studies

A total of 3,614 patients were included, and 1,868 patients were in the CRP testing group, while the others were in the routine care group. Six of seven studies included only adults (29-31,33-35), while one study included both adults and children (32). We only extracted data about adults. Patients in three studies were low ARI, included only acute exacerbation of chronic obstructive pulmonary disease (AECOPD) (29,30,35), while in four studies were upper ARI, included rhinosinusitis, rhinitis, pharyngitis and acute cough (31-34). The detailed information of included studies is shown in Table 1.

Assessment of risk of bias

Six of seven studies (29,30,32-35) reported funding or conflicts of interest which showed there were no interest-related and conflicts among researchers. All of these seven studies were RCTs. Moreover, specific randomized methods and specific random hidden assignments were mentioned. The blind method was not used in all studies. The detailed assessment was provided in Figures 2,3.

Figure 2 Risk of bias graph.

Figure 3 Risk of bias graph.

Antibiotic prescribing rate at the index consultation

Antibiotic prescribing rate at the index consultation was reported in seven studies (29-35). There was significant heterogeneity (I² =63%), hence, the random effect model was used. The antibiotic prescribing rate in the CRP group was lower compared with the routine care significantly (RR =0.76; 95% CI: 0.68–0.85; P<0.00001) (Figure 4). The factors affecting the heterogeneity were found out by sensitivity analysis. We found that the heterogeneity disappeared when one studies was excluded (31). Therefore, we infered that heterogeneity mentioned above might come from this study (Table S1).

Figure 4 CRP testing group versus routine care group, antibiotic prescribing rate at the index consultation. CRP, C-reactive protein; M-H, Mantel-Haenszel; CI, confidence interval.

Antibiotic prescribing rate during 28 days

Antibiotic prescribing rate during 28 days follow-up was reported in four studies (30,32,34,35). The between-study heterogeneity was low (I² =0%), therefore, the fixed effect model was used. It showed that CRP testing significantly decreased the antibiotic prescribing rate during 28 days follow-up compared with the routine care (RR =0.77; 95% CI: 0.73–0.81; P<0.00001) (Figure 5).

Figure 5 CRP testing group versus routine care group, antibiotic prescribing rate during 28 days follow-up. CRP, C-reactive protein; M-H, Mantel-Haenszel; CI, confidence interval.

The clinical recovery of patients within 7 days

Two studies (31,33) reported the clinical recovery of patients within 7 days. There was no significant heterogeneity (I² =0%), therefore, the fixed effect model was chosen. It showed that there were no significant differences about the recovery of patients within 7 days between the CRP testing and routine care (RR =0.95; 95% CI: 0.90–1.01; P=0.08) (Figure 6).

Figure 6 CRP testing group versus routine care group, the recovery of patients within 7 days. CRP, C-reactive protein; M-H, Mantel-Haenszel; CI, confidence interval.

Adverse events

Five studies (29,30,32,33,35) reported the adverse events. Trails-related adverse events were found in three (29,30,32) studies. We gave up the meta-analysis of adverse events and only made a descriptive analysis because of great heterogeneity. All of the studies showed that there were no differences between the two groups about adverse events. Detailed adverse events were showed in Table S2.

Subgroup analysis

We made the subgroup analyses of antibiotic prescribing rate at the index consultation. The subgroup analysis was conducted by different types of ARI and different CRP levels as recommended of antibiotic prescribing. It showed that CRP testing reduce the antibiotic prescribing rate compared with the routine care in low ARI significantly (RR =0.71; 95% CI: 0.65–0.78; P<0.00001), but not in upper ARI (RR =0.83; 95% CI: 0.66–1.03; P=0.09). Subgroup analyses by different CRP levels as recommended threshold showed that using of 40 mg/L as the recommended threshold was the most obvious to reduce the antibiotic prescribing compared with the routine care. However, there were no significant differences between CRP testing and routine care using 50 mg/L as the recommended (Table S3).

Quality of evidence

According to the outcome’s measures, the quality of antibiotic prescribing rate at the index consultation was moderate, and the quality of antibiotic prescribing rate during 28 days follow-up, patient clinical recovery within 7 days and adverse events were high. The GRADE evidence profiles of the primary outcomes are shown in Table 2.

Discussion

Main findings

This review included seven trials about CRP testing reducing antibiotic prescribing for ARI. The participants were adults, and all the included studies were RCTs. We assessed the outcomes of antibiotic prescribing rate at the index consultation and during 28 days follow-up, patient clinical recovery within 7 days and the adverse events. We concluded that CRP testing could reduce antibiotic prescribing in adult ARI.

At present, the prescribing of antibiotic is unreasonable seriously (36,37). Antibiotic abuse is the main reason of drug resistance and makes adverse reaction risk increase (38-40). Therefore, rational reduction of antibiotic prescriptions is worthwhile and meaningful. The results of this study showed that CRP testing can reduce the antibiotic prescribing in ARI. Subgroup analysis by different types of ARI showed that CRP testing significantly reduce the antibiotic prescribing rate compared with the routine care in low ARI (Table S2).

Inappropriate antibiotics prescription is not only abuse of antibiotics, but also lack of antibiotics prescription, which makes the infection uncontrollable and increases the mortality of infectious diseases. Meta-analysis of the clinical recovery of patients within 7 days and adverse events found that CRP testing guide the antibiotic prescribing in ARI did not affect patient clinical recovery, and there was no evidence of serious adverse events associated with CRP testing. It showed that CRP testing was safe to guide the use of antibiotics for ARI, and would not affect the therapeutic effects.

Previous similar reviews (23-26) found that CRP testing might reduce the antibiotic prescribing, but showed uncertain degree of antibiotic reduction. The Cochrane review (23) showed that CRP testing could reduce antibiotic prescribing, but might increase hospital admissions. Subgroup analysis found that individual RCTs showed non-significant relative reduction of antibiotic prescribing. Petel et al. (24) found that CRP testing can reduce antibiotic prescribing in newborns and adults, but the numbers of studies were small relatively, including interventional and observational studies, with high heterogeneity. A review (25) showed that CRP testing could reduce the antibiotic prescribing combined with clinical guidance, but the differences disappeared in two groups when absence of clinical guidance. A recent study (26) observed that CRP testing could reduce immediate antibiotic prescribing in primary care, but might increase re-consultations. Our review showed that CRP testing could reduce the antibiotic prescribing in adult ARI based on the individual RCTs. We excluded deviations due to enrolled population and study type, and the conclusion had high credibility.

CRP and COVID-19

COVID-19 is the highly pathogenic SARS coronavirus pneumonia that infects human. Inflammatory reaction plays a critical role in COVID-19. Inflammatory storm can increase the severity of COVID-19 and leads to serious complications and death (41-43). CRP is a biomarker of inflammatory response, which can predict the severity and prognosis of COVID-19 (44,45). A retrospective study conducted in China found that patients with CRP level >41.8 mg/L in COVID-19 were more vulnerable to develop severe disease (46). A study on COVID-19 patients who need mechanical ventilation shows that CRP testing can be used to guide escalation of treatment in patients with COVID-19–related hyperinflammatory syndrome (47). However, the pathogen of COVID-19 is coronavirus, while antibiotics are aimed at bacterial infection. Therefore, CRP testing cannot be used as the guide to antibiotic prescribing for patients with COVID-19. For patients with overactivated inflammatory response in COVID-19, recent research recommended glucocorticoid for anti-inflammatory treatment (48,49).

Suggestions for future research

There is no unified standard for using antibiotic according to the level of CRP at present. One guidance suggested that antibiotic prescribing was beneficial when CRP level was higher than 40 mg/L (50). DCGP guidelines suggest that antibiotic should not be used when the CRP level is lower than 20 mg/L, while should be used immediately when CRP level was higher than 100 mg/L (51). One trial conducted in Thailand and Myanmar (52) using CRP testing with a threshold of 20 and 40 mg/L to guide antibiotic prescribing in febrile patients, and found that CRP testing with a threshold of 40 mg/L could reduce antibiotic prescribing significantly. Therefore, it is meaningful to choose different CRP level to guide the use of antibiotic, and further experiments can be designed to verify the best recommended threshold of CRP. Our review showed that CRP testing did not affect therapeutic effects. But the Cochrane review (23) found that CRP testing may increase the hospital admissions, and a recent study (26) showed that CRP testing may increase re-consultations. We infer that CRP testing does not affect the short-term efficacy of ARI, but it is necessary to evaluate the long-term effects of CRP testing.

Strengths and limitations

Due to CRP testing did not have a high predictive value for severe infections in children and newborn infants (53,54), only adults were included in this review, which reduced the heterogeneity caused by age. Considering that this type of studies would increase study bias risks, we did not include cluster-RCTs. We assessed the bias risk of the included studies and found that the methodological quality of the included literatures was high. Seven studies were included from five countries, reducing regional bias. But there are also inadequacies in our research. The CRP level as the recommended threshold of antibiotic were different in including studies (Table S2). Two studies used 40 mg/L as the recommended threshold of antibiotic, while two studies used 50 mg/L as the recommended threshold, and three studies used 100 mg/L as the recommended threshold. The subgroup analysis showed that 40 mg/L as the recommended threshold could reduce the antibiotic prescribing great significantly. However, considering the small number of references and large heterogeneity, reasonable researches should be designed in further researches to verify the best recommended threshold of CRP.

Conclusions

CRP testing can reduce the antibiotic prescribing in adult ARI, which is safe and would not affect therapeutic effects. However, the CRP level as the recommended threshold of antibiotic prescribing is not consistent. Considering the individuals difference of patients, physicians should make clinical decisions combined with patient’s preferences, best available evidence and experience of professionals.

Acknowledgments

Funding: This work was supported by the National Natural Science Foundation of China (No. 81774222; No. 82074411); the Chinese Medicine Top-notch Talents Training Project of Henan Province (No. 2019ZYBJ05); the Program for Key Subjects of Henan Province (No. STS-ZYX2017002).

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-21-705/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-21-705/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-21-705/coif). All authors report that this work is supported by the National Natural Science Foundation of China (No. 81774222; No. 82074411); the Chinese Medicine Top-notch Talents Training Project of Henan Province (No. 2019ZYBJ05); the Program for Key Subjects of Henan Province (No. STS-ZYX2017002). This work has no relationship with any manufacturers of antibiotic medication. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy and integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Eurosurveillance editorial team. WHO member states adopt global action plan on antimicrobial resistance. Euro Surveill 2015;20:21137. [PubMed]
2. Prestinaci F, Pezzotti P, Pantosti A. Antimicrobial resistance: a global multifaceted phenomenon. Pathog Glob Health 2015;109:309-18. [Crossref] [PubMed]
3. Okomo U, Akpalu ENK, Le Doare K, et al. Aetiology of invasive bacterial infection and antimicrobial resistance in neonates in sub-Saharan Africa: a systematic review and meta-analysis in line with the STROBE-NI reporting guidelines. Lancet Infect Dis 2019;19:1219-34. [Crossref] [PubMed]
4. Borisov S, Danila E, Maryandyshev A, et al. Surveillance of adverse events in the treatment of drug-resistant tuberculosis: first global report. Eur Respir J 2019;54:1901522. [Crossref] [PubMed]
5. Smith R, Coast J. The true cost of antimicrobial resistance. BMJ 2013;346:f1493. [Crossref] [PubMed]
6. Hughes D. Selection and evolution of resistance to antimicrobial drugs. IUBMB Life 2014;66:521-9. [Crossref] [PubMed]
7. Goossens H, Ferech M, Vander Stichele R, et al. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. Lancet 2005;365:579-87. [Crossref] [PubMed]
8. Claxton AN, Dark PM. Biomarker-guided antibiotic cessation in sepsis: evidence and future challenges. Br J Hosp Med (Lond) 2018;79:136-41. [Crossref] [PubMed]
9. Schuetz P, Wirz Y, Sager R, et al. Procalcitonin to initiate or discontinue antibiotics in acute respiratory tract infections. Cochrane Database Syst Rev 2017;10:CD007498. [Crossref] [PubMed]
10. Pepper DJ, Sun J, Rhee C, et al. Procalcitonin-Guided Antibiotic Discontinuation and Mortality in Critically Ill Adults: A Systematic Review and Meta-analysis. Chest 2019;155:1109-18. [Crossref] [PubMed]
11. Heilmann E, Gregoriano C, Schuetz P. Biomarkers of Infection: Are They Useful in the ICU? Semin Respir Crit Care Med 2019;40:465-75. [Crossref] [PubMed]
12. Hellyer TP, McAuley DF, Walsh TS, et al. Biomarker-guided antibiotic stewardship in suspected ventilator-associated pneumonia (VAPrapid2): a randomised controlled trial and process evaluation. Lancet Respir Med 2020;8:182-91. [Crossref] [PubMed]
13. Donnelly JP, Baddley JW, Wang HE. Antibiotic utilization for acute respiratory tract infections in U.S. emergency departments. Antimicrob Agents Chemother 2014;58:1451-7. [Crossref] [PubMed]
14. Aabenhus R, Siersma V, Hansen MP, et al. Antibiotic prescribing in Danish general practice 2004-13. J Antimicrob Chemother 2016;71:2286-94. [Crossref] [PubMed]
15. Simon L, Gauvin F, Amre DK, et al. Serum procalcitonin and C-reactive protein levels as markers of bacterial infection: a systematic review and meta-analysis. Clin Infect Dis 2004;39:206-17. [Crossref] [PubMed]
16. Lubell Y, Blacksell SD, Dunachie S, et al. Performance of C-reactive protein and procalcitonin to distinguish viral from bacterial and malarial causes of fever in Southeast Asia. BMC Infect Dis 2015;15:511. [Crossref] [PubMed]
17. Ansar W, Ghosh S. C-reactive protein and the biology of disease. Immunol Res 2013;56:131-42. [Crossref] [PubMed]
18. Luna CM. C-reactive protein in pneumonia: let me try again. Chest 2004;125:1192-5. [Crossref] [PubMed]
19. Al-Zwaini EJ. C-reactive protein: a useful marker for guiding duration of antibiotic therapy in suspected neonatal septicaemia? East Mediterr Health J 2009;15:269-75. [Crossref] [PubMed]
20. Minnaard MC, van de Pol AC, Hopstaken RM, et al. C-reactive protein point-of-care testing and associated antibiotic prescribing. Fam Pract 2016;33:408-13. [Crossref] [PubMed]
21. Woodhead M, Blasi F, Ewig S, et al. Guidelines for the management of adult lower respiratory tract infections--full version. Clin Microbiol Infect 2011;17:E1-59. [Crossref] [PubMed]
22. Verlee L, Verheij TJ, Hopstaken RM, et al. Summary of NHG practice guideline 'Acute cough'. Ned Tijdschr Geneeskd 2012;156:A4188. [PubMed]
23. Aabenhus R, Jensen JU, Jørgensen KJ, et al. Biomarkers as point-of-care tests to guide prescription of antibiotics in patients with acute respiratory infections in primary care. Cochrane Database Syst Rev 2014;CD010130. [Crossref] [PubMed]
24. Petel D, Winters N, Gore GC, et al. Use of C-reactive protein to tailor antibiotic use: a systematic review and meta-analysis. BMJ Open 2018;8:e022133. [Crossref] [PubMed]
25. Verbakel JY, Lee JJ, Goyder C, et al. Impact of point-of-care C reactive protein in ambulatory care: a systematic review and meta-analysis. BMJ Open 2019;9:e025036. [Crossref] [PubMed]
26. Martínez-González NA, Keizer E, Plate A, et al. Point-of-Care C-Reactive Protein Testing to Reduce Antibiotic Prescribing for Respiratory Tract Infections in Primary Care: Systematic Review and Meta-Analysis of Randomised Controlled Trials. Antibiotics (Basel) 2020;9:610. [Crossref] [PubMed]
27. Higgins J, Green SE. Cochrane Handbook for Systematic Reviews of Interventions, Version 5.1.0. The Cochrane Collaboration, 2011. Available online: http://www.handbook.cochrane.org/
28. Guyatt GH, Oxman AD, Vist GE, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 2008;336:924-6. [Crossref] [PubMed]
29. Prins HJ, Duijkers R, van der Valk P, et al. CRP-guided antibiotic treatment in acute exacerbations of COPD in hospital admissions. Eur Respir J 2019;53:1802014. [Crossref] [PubMed]
30. Butler CC, Gillespie D, White P, et al. C-Reactive Protein Testing to Guide Antibiotic Prescribing for COPD Exacerbations. N Engl J Med 2019;381:111-20. [Crossref] [PubMed]
31. Diederichsen HZ, Skamling M, Diederichsen A, et al. Randomised controlled trial of CRP rapid test as a guide to treatment of respiratory infections in general practice. Scand J Prim Health Care 2000;18:39-43. [Crossref] [PubMed]
32. Do NT, Ta NT, Tran NT, et al. Point-of-care C-reactive protein testing to reduce inappropriate use of antibiotics for non-severe acute respiratory infections in Vietnamese primary health care: a randomised controlled trial. Lancet Glob Health 2016;4:e633-41. [Crossref] [PubMed]
33. Cals JW, Schot MJ, de Jong SA, et al. Point-of-care C-reactive protein testing and antibiotic prescribing for respiratory tract infections: a randomized controlled trial. Ann Fam Med 2010;8:124-33. [Crossref] [PubMed]
34. Gonzales R, Aagaard EM, Camargo CA Jr, et al. C-reactive protein testing does not decrease antibiotic use for acute cough illness when compared to a clinical algorithm. J Emerg Med 2011;41:1-7. [Crossref] [PubMed]
35. Francis NA, Gillespie D, White P, et al. C-reactive protein point-of-care testing for safely reducing antibiotics for acute exacerbations of chronic obstructive pulmonary disease: the PACE RCT. Health Technol Assess 2020;24:1-108. [Crossref] [PubMed]
36. Bell BG, Schellevis F, Stobberingh E, et al. A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance. BMC Infect Dis 2014;14:13. [Crossref] [PubMed]
37. Fleming-Dutra KE, Hersh AL, Shapiro DJ, et al. Prevalence of Inappropriate Antibiotic Prescriptions Among US Ambulatory Care Visits, 2010-2011. JAMA 2016;315:1864-73. [Crossref] [PubMed]
38. Chatterjee A, Modarai M, Naylor NR, et al. Quantifying drivers of antibiotic resistance in humans: a systematic review. Lancet Infect Dis 2018;18:e368-78. [Crossref] [PubMed]
39. Fuchs BB, Tharmalingam N, Mylonakis E. Vulnerability of long-term care facility residents to Clostridium difficile infection due to microbiome disruptions. Future Microbiol 2018;13:1537-47. [Crossref] [PubMed]
40. Shehab N, Lovegrove MC, Geller AI, et al. US Emergency Department Visits for Outpatient Adverse Drug Events, 2013-2014. JAMA 2016;316:2115-25. [Crossref] [PubMed]
41. YangPDingYXuZEpidemiological and clinical features of COVID-19 patients with and without pneumonia in Beijing, China.medRxiv 2020. doi: .10.1101/2020.02.28.20028068
42. Zumla A, Hui DS, Azhar EI, et al. Reducing mortality from 2019-nCoV: host-directed therapies should be an option. Lancet 2020;395:e35-6. [Crossref] [PubMed]
43. WanSYiQFanSCharacteristics of lymphocyte subsets and cytokines in peripheral blood of 123 hospitalized patients with 2019 novel coronavirus pneumonia (NCP).medRxiv 2020. doi: .10.1101/2020.02.10.20021832
44. Zhang T, Huang WS, Guan W, et al. Risk factors and predictors associated with the severity of COVID-19 in China: a systematic review, meta-analysis, and meta-regression. J Thorac Dis 2020;12:7429-41. [Crossref] [PubMed]
45. Liu Y, Yang Y, Zhang C, et al. Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci 2020;63:364-74. [Crossref] [PubMed]
46. Liu F, Li L, Xu M, et al. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol 2020;127:104370. [Crossref] [PubMed]
47. Herold T, Jurinovic V, Arnreich C, et al. Elevated levels of IL-6 and CRP predict the need for mechanical ventilation in COVID-19. J Allergy Clin Immunol 2020;146:128-36.e4. [Crossref] [PubMed]
48. Berlin DA, Gulick RM, Martinez FJ. Severe Covid-19. N Engl J Med 2020;383:2451-60. [Crossref] [PubMed]
49. WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group. Association Between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients With COVID-19: A Meta-analysis. JAMA 2020;324:1330-41. [Crossref] [PubMed]
50. Miravitlles M, Moragas A, Hernández S, et al. Is it possible to identify exacerbations of mild to moderate COPD that do not require antibiotic treatment? Chest 2013;144:1571-7. [Crossref] [PubMed]
51. Verheij T, Hopstaken RM, Prins JM, et al. NHG-standaard Acuut hoesten. Huisarts En Wetenschap 2011;54:68-92.
52. Althaus T, Greer RC, Swe MMM, et al. Effect of point-of-care C-reactive protein testing on antibiotic prescription in febrile patients attending primary care in Thailand and Myanmar: an open-label, randomised, controlled trial. Lancet Glob Health 2019;7:e119-31. [Crossref] [PubMed]
53. Koster MJ, Broekhuizen BD, Minnaard MC, et al. Diagnostic properties of C-reactive protein for detecting pneumonia in children. Respir Med 2013;107:1087-93. [Crossref] [PubMed]
54. Brown JVE, Meader N, Wright K, et al. Assessment of C-Reactive Protein Diagnostic Test Accuracy for Late-Onset Infection in Newborn Infants: A Systematic Review and Meta-analysis. JAMA Pediatr 2020;174:260-8. [Crossref] [PubMed]