Skip to content
BY 4.0 license Open Access Published by De Gruyter May 9, 2019

A high C-reactive protein/procalcitonin ratio predicts Mycoplasma pneumoniae infection

  • Olivia L. Neeser EMAIL logo , Tanja Vukajlovic , Laetitia Felder , Sebastian Haubitz , Angelika Hammerer-Lercher , Cornelia Ottiger , Beat Mueller , Philipp Schuetz and Christoph A. Fux EMAIL logo



Discriminating Mycoplasma pneumoniae (MP) from Streptococcus pneumoniae (SP) and viral etiologies of community-acquired pneumonia (CAP) is challenging but has important implications regarding empiric antibiotic therapy. We investigated patient parameters upon hospital admission to predict MP infection.


All patients hospitalized in a tertiary care hospital between 2013 and 2017 for CAP with a confirmed etiology were analyzed using logistic regression analyses and area under the receiver operator characteristics (ROC) curves (AUC) for associations between demographic, clinical and laboratory features and the causative pathogen.


We analyzed 568 patients with CAP, including 47 (8%) with MP; 152 (27%) with SP and 369 (65%) with influenza or other viruses. Comparing MP and SP by multivariate logistic regression analysis, younger age (odds ration [OR] 0.56 per 10 years, 95% CI 0.42–0.73), a lower neutrophil/lymphocyte ratio (OR 0.9, 0.82–0.99) and an elevated C-reactive protein/procalcitonin (CRP/PCT) ratio (OR 15.04 [5.23–43.26] for a 400 mg/μg cut-off) independently predicted MP. With a ROC curve AUC of 0.91 (0.80 for the >400 mg/μg cutoff), the CRP/PCT ratio was the strongest predictor of MP vs. SP. The discriminatory value resulted from significantly lower PCT values (p < 0.001) for MP, while CRP was high in both groups (p = 0.057). Comparing MP and viral infections showed similar results with again the CRP/PCT ratio providing the best information (AUC 0.83; OR 5.55 for the >400 mg/μg cutoff, 2.26–13.64).


In patients hospitalized with CAP, a high admission CRP/PCT ratio predicts M. pneumoniae infection and may improve empiric management.

Summary: In a retrospective analysis of 568 patients with community acquired pneumonia caused by Mycoplasma pneumoniae (MP), Streptococcus pneumoniae (SP) or viruses a serum CRP/PCT ratio >400 mg/μg strongly predicted MP (OR for MP vs. SP: 15, 95% CI 5–43).


Mycoplasma pneumoniae (MP) causes both upper and lower respiratory tract infections and accounts for 11%–15% of community acquired pneumonias (CAP) worldwide [1], [2]. Disease severity is often mild, but 1%–5% of cases require hospitalization [3]. The 0.5%–2% fulminant pneumonias primarily affect young adults with no underlying disease [4]. As MP are intracellular pathogens lacking a cell wall, betalactams are ineffective and macrolides, quinolones or tetracyclines are required for treatment [5]. The morbidity in severe cases has been attributed to delayed targeted antibiotic treatment [4]. In a multicenter CAP surveillance study with 227 MP infections, for example, severe cases requiring intensive care for respiratory failure were characterized by a significantly delayed (9.3 vs. 5.1 days) initiation of an appropriate antibiotic treatment [6]. Therefore, for cases requiring hospitalization, an early suspicion of MP allowing a targeted empirical treatment is essential.

Despite elaborated microbiological diagnostics the causative organisms of CAP remain unidentified in the majority of patients [7]. Even when combining culture, antigen tests and PCR in a research setting, the causative pathogen could not be identified in 38% of 2259 patients with radiographic evidence of pneumonia [8]. Similarly, recent studies have reported failures to detect a pathogen in 24%–44% of patients [9], [10], [11], [12], [13], [14]. Alternative diagnostic approaches are therefore needed to rapidly and accurately identify patients most or least likely to benefit from antibiotics [15]. Several studies have evaluated the utility of inflammatory markers to predict both the etiology and the outcome of CAP. Thereby, serum procalcitonin (PCT) levels have proven more effective than C-reactive protein (CRP) to distinguish typical bacterial from atypical bacterial or viral pneumonia [16], [17], [18], [19]. Also, higher PCT values indicated bacteremia [17], [20], [21] and a worse outcome of pneumonias, e.g. higher rates of ICU admission and mortality [22], [23], [24]. In a multicenter study of 645 CAP patients with an identified pathogen, higher PCT levels strongly correlated with bacterial pathogens, particularly typical bacteria, while PCT levels of atypical bacteria, mostly MP and Chlamydophila pneumoniae, were more similar to that of viruses and thus not suitable for the differentiation of the latter [16].

In our study we analyzed demographic, clinical and laboratory parameters upon hospital admission to discriminate MP pneumonias from pneumococcal as well as from viral etiologies. Based on clinical observations we hypothesized that the constellation of a high CRP with a relatively low PCT, i.e. a high CRP/PCT ratio, would predict MP.

Materials and methods

Study design and setting

We performed a retrospective single-center study with all adult patients hospitalized for CAP with a defined etiology between October 2013 and April 2017 in a Swiss tertiary care hospital. When a bacterial superinfection of a viral infection was detected, the patient was excluded from the analysis. Likewise, patients with Legionella infection were excluded due to the low number of detected cases. This was a secondary analysis of patients participating in the TRIAGE project, a prospective, observational, multicenter, multinational study that aimed to optimize triage and anticipate the post-acute care needs of patients seeking emergency medical care [25]. The study was continued as a quality control project approved by the local Ethical Committee (EKBB, Ethikkomission beider Basel: EK 2012/059) which waived the requirement for individual informed consent. The main study was registered at (NCT01768494).

We compared MP with SP as well as MP with influenza and other viruses in the absence of a bacterial superinfection in terms of demographic patient characteristics, symptoms, clinical signs and inflammatory markers measured within 48 h of hospitalization.

Clinical and laboratory data

Sociodemographic factors plus vital and clinical signs on admission were retrospectively extracted from the medical charts. Chest X-rays were analyzed by a radiologist. We collected laboratory parameters measured within 48 h of admission. In general, the first value was included. If two CRP or PCT values were available within 48 h, the higher was chosen. For CRP measurements a detection limit of <5 mg/L (EMIT, Merck Diagnostica, Zurich, Switzerland) was used. For PCT analyses we used a centralized time-resolved amplified cryptate emission technology-based assay with a 0.06 μg/L cutoff (Kryptor PCT, Thermo Scientific Biomarkers [Brahms AG], Henningsdorf, Germany) [26]. CRP/PCT ratios were calculated by dividing CRP in mg/L by PCT in μg/L.

Microbiological diagnostics

In clinical routine, microbiological analyses included blood cultures as well as urinary antigen testing for pneumococci and Legionella pneumophila serogroup 1a. Sputum cultures and influenza PCR or multiplex PCR testing of a nasopharyngeal swab were at the discretion of the treating physician. The Filmarray Respiratory Panel was designed by BioMérieux and tests for a comprehensive panel of 20 respiratory viruses and bacteria (adenovirus, coronavirus 229E, HKU1, OC43, NL63, human metapneumovirus, human rhinovirus/enterovirus, influenza A, A/H1, A/H1-2009, A/H3, influenza B, parainfluenza 1, 2, 3, 4, respiratory syncytial virus, Bordetella pertussis, C. pneumoniae and M. pneumoniae). It integrates sample preparation, amplification, detection and analysis into one simple system [27].

Statistical analysis

All statistical analyses were performed with STATA 11 (StataCorp, College Station, TX, USA). Patients with MP were compared to patients with SP, to patients with viral etiologies and to all non-MP infections combined. Discrete variables were presented as counts and percentage, continuous variables as median and interquartile ranges (IQR). Categorical data was analyzed with Fisher’s exact t-test. Two group comparisons of normally distributed non-parametric data were performed using Student’s t-test. For data not normally distributed, Mann-Whitney’s U-test was used.

The logarithmic values of the CRP/PCT ratio were used to identify discriminatory cutoffs between MP infections and other etiologies. To estimate the clinical potential of individual laboratory and clinical parameters to identify MP, we used univariate and multivariate logistic regression models. All parameters with a significant difference (p<0.05) between MP and non-MP etiologies (i.e. SP and viruses combined) and a univariate operating characteristics (ROC) curve area under the curve (AUC) >0.60 were considered for multivariate analyses. To account for colinearity of the several parameters describing pulmonary infiltrates and white blood cell (WBC) counts and distribution, only the most discriminative parameter was included in the multivariate analysis.


We included 568 patients with CAP and a defined etiology. Microbiological analyses included blood cultures in 361 patients, urine pneumococcal antigen tests in 326, sputum cultures in eight, serologies in 15, influenza PCR in 367 and multiplex PCR in 367. MP was the causative pathogen in 47 (8%), SP in 152 (27%) and influenza or other respiratory viruses in 369 (65%). Among the 152 patients with SP, there were 50 positive blood cultures and 122 positive urine antigen tests with some patients having both tests positive. In five patients, SP were identified from sputum. All MP infections were identified by PCR from nasopharyngeal swabs or consecutive antibody titers. Among the 369 patients with viral infection, PCR from nasopharyngeal swabs revealed 323 cases of influenza, 12 of corona, 16 of metapneumo, 20 of respiratory syncytial, 14 of parainfluenza and 23 of rhino viruses with again some patients showing positivity to more than one virus.

MP vs. SP

Differences between patients suffering from MP and SP are shown in Table 1. Patients with MP infections were significantly younger with 43 (IQR 29–65) vs. 72.5 (IQR 64–80) years (p<0.001) and less frequently had malignancies with 4.3% vs. 21.7% (p=0.006). Among clinical parameters, dry cough (36.2% vs. 17.1%, p=0.006), myalgias (29.8% vs. 13.8%, p=0.012) and longer duration of symptoms (7 days [IQR 3–11] vs. 5 days [IQR 2–7], p=0.007) predominated in MP CAP, while confusion was more frequent in SP infections (4.3% vs. 16.4%, p=0.033). An interstitial infiltrate in chest X-rays was seen in 12.8% of MP vs. 0% in SP pneumonia (p<0.001). Moreover, we observed significantly different laboratory parameters between the two groups: In SP pneumonia WBCs (12.7×10E9/L vs. 10.1×10E9/L, p=0.009), neutrophils (10.7×10E9/L vs. 8.1×10E9/L, p=0.011), the neutrophil/lymphocyte ratio (12.4 vs. 7.0, p<0.001) and PCT (3.47 μg/L vs. 0.19 μg/L, p<0.001) values were significantly higher than in MP pneumonia patients (Figure 1B); while the platelet count was lower (213×10E9/L vs. 247×10E9/L, p=0.012). The CRP value was high in both groups and not significantly different (SP: 190 mg/L vs. MP: 160 mg/L, p=0.057) (Figure 1A). As a consequence, the CRP/PCT ratio in mg/μg differed significantly between both groups (Figure 1C): 500 (IQR 380–1000) in MP and 53 (IQR 18–209) in SP infections (p<0.001). The CRP/PCT ratio was >400 mg/μg in 72.3% of patients with MP but only in 11.8% of patients with SP infection (p<0.001).

Table 1:

Comparison of patients with M. pneumoniae (MP) and S. pneumoniae (SP) CAP.

ParameterMP (n=47)SP (n=152)p-ValueUnivariate OR (95% CI)Univariate AUCMultivariate OR (95% CI)
Patient characteristics, n (%)
 Age, years, median (IQR)b43 (29, 65)72.5 (64, 80)<0.0010.93 (0.91–0.95)0.810.56 (0.42–0.73), p<0.001
 Male sex24 (51.1%)80 (52.6%)0.850.94 (0.49–1.81)0.51
 Neoplasia2 (4.3%)33 (21.7%)0.0060.16 (0.04–0.70)0.59
 Immunosuppressiona2 (4.3%)15 (9.9%)0.230.41 (0.09–1.84)0.53
Symptoms, n (%)
 Cough44 (93.6%)125 (82.2%)0.0573.17 (0.92–10.96)0.56
 Dry cough17 (36.2%)26 (17.1%)0.0062.75 (1.32–5.69)0.59
 Dyspnea29 (61.7%)101 (66.4%)0.550.81 (0.41–1.60)0.52
 Rhinitis4 (8.5%)21 (13.8%)0.340.58 (0.19–1.78)0.53
 Myalgia14 (29.8%)21 (13.8%)0.0122.65 (1.22–5.75)0.58
 Diarrhea5 (10.6%)19 (12.5%)0.730.83 (0.29–2.37)0.51
 Headache8 (17.0%)18 (11.8%)0.361.53 (0.62–3.78)0.53
 Confusion2 (4.3%)25 (16.4%)0.0330.23 (0.05–0.99)0.56
 Duration of symptoms, days7 (3, 11)5 (2, 7)0.0071.03 (0.99–1.08)0.631.02 (0.94–1.11), p=0.591
Vital signs, median (IQR)
 Body temperature, °C38.4 (37.5, 39.3)38.2 (37.2, 39)0.241.23 (0.91–1.67)0.56
 Oxygen saturation (%02)93 (90, 96)92 (88, 95)0.241.04 (0.97–1.11)0.56
 Heart rate, beats/min98 (20)102 (24)0.410.99 (0.98–1.01)0.56
 Systolic blood pressure, mmHg137 (128, 150)132 (115, 148)0.0461.02 (0.99–1.03)0.60
Chest X-ray, n (%)
 Infiltrate36 (76.6%)109 (71.7%)0.511.29 (0.60–2.77)0.52
 Lobar20 (42.6%)61 (40.1%)0.771.12 (0.57–2.14)0.51
 Interstitial6 (12.8%)0 (0.0%)<0.001NA0.56
Laboratory values, median (IQR)
 WBC, x10E9/L10.09 (7.66, 12.66)12.66 (8.25, 20.47)0.0090.92 (0.88–0.97)0.63
 Neutrophils, x10E9/L8.05 (6.01, 10.20)10.65 (6.16, 16.14)0.0110.91 (0.85–0.97)0.63
 Lymphocytes, x10E9/L0.94 (0.70, 1.38)0.85 (0.45, 1.29)0.120.94 (0.77–1.16)0.58
 NLR7.0 (5.7, 12.6)12.4 (6.9, 21.8)<0.0010.92 (0.87–0.97)0.680.90 (0.82–0.99), p=0.029
 Platelet count, G/Lc247 (200, 310)213 (168, 270)0.0121.00 (1.00–1.01)0.621.02 (0.96–1.08), p=0.473
 Plasma sodium, mmol/L136 (135, 138)136 (133, 139)0.831.02 (0.94–1.10)0.51
 Lactate dehydrogenase, IU/L235 (201, 308)228.5 (199, 280)0.421.02 (0.99–1.06)0.55
 CRP, mg/Ld160 (64, 220)190 (86, 300)0.0570.72 (0.54–0.95)0.59
 PCT, μg/L0.19 (0.11, 0.31)3.47 (0.4, 11)<0.0010.17 (0.06–0.51)0.85
 CRP/PCT ratio, mg/μg500 (380, 1000)53 (18, 209)<0.0011.36 (1.22–1.50)0.91
 CRP/PCT ratio, >400 mg/μg34 (72.3%)18 (11.8%)<0.00119.47 (8.69–43.62)0.8015.04 (5.23–43.26), p<0.001
  1. Continuous values as median and IQR, categorical values as absolute number and percentage. CRP, C-reactive protein; NLR, neutrophil-lymphocyte ratio; WBC, white blood cell count; PCT, procalcitonin; AUC, area under the curve, OR, odds ratio; aprednison equivalent >10 mg for >2 weeks. OR correspond to: ba 10 year increase of age, cto a 10 G/L increase, dto a 100 mg/L increase.

Figure 1: (A–C) CRP, PCT and the CRP/PCT ratio in pneumonia with S. pneumoniae, M. pneumoniae and viruses.(A) CRP, (B) PCT and (C) CRP/PCT ratio with the 400 mg/μg threshold.
Figure 1:

(A–C) CRP, PCT and the CRP/PCT ratio in pneumonia with S. pneumoniae, M. pneumoniae and viruses.

(A) CRP, (B) PCT and (C) CRP/PCT ratio with the 400 mg/μg threshold.

MP vs. viruses

Differences between patients suffering from MP and viral infections are shown in Table 2. Similar to the comparison between MP and SP, patients with MP showed a significantly younger age (43 [IQR 29–65] vs. 73 years [IQR 63–82], p<0.001) and less neoplastic diseases (4.3% vs. 16.8%, p=0.025) and a longer duration of symptoms (7 [IQR 3–11] vs. 4 days [IQR 2–7], p<0.001). More patients with MP showed an infiltrate on chest X-ray (76.6% vs. 34.1%, p<0.001), which more frequently was lobar (42.6% vs. 17.9%, p≤0.001) or interstitial (12.8% vs. 2.4%, p<0.001). Also, we observed significant differences in laboratory values between the two groups: MP pneumonia showed significantly higher WBCs (10.1×10E9/L vs. 7.6×10E9/L, p<0.001), neutrophils (8.1×10E9/L vs. 5.6×10E9/L, p<0.001) and platelet counts (247×10E9/L vs. 182×10E9/L p<0.001). In viral infections CRP values were significant lower (56 mg/L [IQR 26–110] vs. 160 mg/L [IQR 64–220], p<0.001), while PCT values did not differ significantly (0.19 μg/L [IQR 0.11–0.31] vs. 0.21 μg/L [IQR 0.14–0.41], p=0.13). The CRP/PCT ratio in mg/μg was 500 (IQR 380–1000) for MP and 188 (IQR 86–385) for viral infections (p<0.001). The CRP/PCT ratio was >400 mg/μg in 72.3% of patients with an MP, but only in 26.8% of the virus group (p<0.001).

Table 2:

Comparison of patients with Mycoplasma pneumoniae (MP) and viruses CAP.

ParameterMP (n=47)Viruses (n=369)p-ValueUnivariate OR (95% CI)Univariate AUCMultivariate OR (95% CI)
Patient characteristics, n (%)
 Age, yearsb43 (29, 65)73.0 (63.0, 82.0)<0.0010.93 (0.92–0.95)0.820.54 (0.43–0.67), p<0.001
 Male sex24 (51.1%)199 (53.9%)0.710.89 (0.49–1.64)0.51
 Neoplasia2 (4.3%)62 (16.8%)0.0250.22 (0.05–0.93)0.56
 Immunosuppressiona2 (4.3%)29 (7.9%)0.380.52 (0.12–2.26)0.52
Symptoms, n (%)
 Cough44 (93.6%)334 (90.5%)0.0571.54 (0.45–5.21)0.52
 Dry cough17 (36.2%)90 (24.4%)0.0821.76 (0.93–3.33)0.56
 Dyspnea29 (61.7%)193 (52.3%)0.221.47 (0.79–2.74)0.55
 Rhinitis4 (8.5%)58 (15.7%)0.190.5 (0.17–1.44)0.54
 Myalgia14 (29.8%)63 (17.1%)0.0352.06 (1.04–4.07)0.56
 Diarrhea5 (10.6%)54 (14.6%)0.460.69 (0.26–1.83)0.52
 Headache8 (17.0%)71 (19.2%)0.710.86 (0.39–1.92)0.51
 Confusion2 (4.3%)52 (14.1%)0.0590.27 (0.06–1.15)0.55
 Duration of symptoms, days7 (3, 11)4 (2, 7)<0.0011.05 (1.02–1.10)0.681.08 (1.02–1.15), p=0.008
Signs, median (IQR)
 Body temperature, °C38.4 (37.5, 39.3)38.3 (37.4, 39)0.361.16 (0.87–1.54)0.54
 Oxygen saturation (%02)93 (90, 96)94 (90, 97)0.310.99 (0.94–1.04)0.61
 Heart rate, beats/min98 (20)91 (23)0.0571.01 (1.0–1.03)0.61
 Systolic blood pressure, mmHg137 (128, 150)141 (123, 155)0.941 (0.99–1.01)0.5
Chest X-ray, n (%)
 Infiltrate36 (76.6%)126 (34.1%)<0.0016.31 (3.11–12.82)0.715.14 (2.09–12.62), p<0.001
 Lobar20 (42.6%)66 (17.9%)<0.0013.4 (1.8–6.43)0.62
 Interstitial6 (12.8%)9 (2.4%)<0.0015.85 (1.98–17.28)0.55
Laboratory values, median (IQR)
 WBC, x10E9/L10.09 (7.66, 12.66)7.56 (5.46, 10.02)<0.0011.02 (0.99–1.06)0.67
 Neutrophils, x10E9/L8.05 (6.01, 10.2)5.62 (3.8, 7.77)<0.0011.09 (1.02–1.17)0.680.98 (0.88–1.09), p=0.716
 Lymphocytes, x10E9/L0.94 (0.70, 1.38)0.87 (0.55 1.25)0.110.94 (0.77–1.16)0.58
 NLR7.0 (5.7, 12.6)6.6 (3.6, 11.4)0.131 (0.96–1.05)0.57
 Platelet count, G/Lc247 (200, 310)182 (144, 234)<0.0011.09 (1.06–1.13)0.731.06 (1.01–1.11), p=0.03
 Plasma sodium, mmol/L136 (135, 138)137 (134, 139)0.291 (0.96–1.03)0.55
 Lactate dehydrogenase, IU/L235 (201, 308)227 (197, 279)0.581 (0.99–1.00)0.54
 CRP, mg/Ld160 (64, 220)56 (26, 110)<0.0013.09 (2.13–4.47)0.77
 PCT, μg/L0.19 (0.11, 0.31)0.21 (0.14, 0.41)0.130.44 (0.17–1.16)0.59
 CRP/PCT ratio, mg/μg500 (380, 1000)188 (86, 385)<0.0011.24 (1.15–1.3)0.83
 CRP/PCT ratio, >400 mg/μg34 (72.3%)99 (26.8%)<0.0017.13 (3.62–14.07)0.735.55 (2.26–13.64), p<0.001
  1. Continuous values as median (IQR), categorical values as absolute number and percentage. CRP, C-reactive protein; NLR, neutrophil-lymphocyte ratio; WBC, white blood cell count; PCT, procalcitonin; AUC, area under the curve; OR, odds ratio; aprednison equivalent >10 mg for >2 weeks. OR correspond to: ba 10 year increase of age, cto a 10 G/L increase, dto a 100 mg/L increase.

Diagnostic reliability of clinical and laboratory parameters

We performed logistic regression analyses to assess the discriminatory power of symptoms, clinical signs and laboratory parameters to identify MP in CAP patients (Tables 1 and 2). Comparing MP and SP by multivariate logistic regression analysis, younger age (odds ration [OR] 0.56 per 10 years, 95% CI 0.42–0.73), a lower neutrophil/ lymphocyte ratio (OR 0.9, 0.82–0.99) and an elevated CRP/PCT ratio independently predicted MP. A CRP/PCT ratio >400 mg/μg thereby provided an OR of 15.04 (5.23–43.26) for MP. With a ROC AUC of 0.91 (0.80 for the >400 cutoff), the CRP/PCT ratio was the strongest predictor of MP vs. SP.

Comparing MP and viral infections by multivariate analysis, significant predictors for MP were younger age (OR 0.54 per 10 years, 0.43–0.67), longer duration of symptoms (OR 1.08 per day, 1.02–1.15), a pulmonary infiltrate on X-ray (OR 5.14, 2.09–12.62), higher platelet counts (OR 1.06 per increase of 10×10E9/L, 1.01–1.11), and a CRP/PCT ratio >400 (OR 5.55, 2.26–13.64). The ROC AUC for the CRP/PCT ratio to predict MP was 0.83 (0.73 for the >400 cutoff) vs. viral etiology.

MP vs. non-MP

When comparing MP infections with all non-MP infections combined, the CRP/PCT ratio still showed an excellent ROC AUC of 0.85 (Figure 2C).

Figure 2: (A–C) Discriminatory power of CRP, PCT and the CRP/PCT ratio for different pathogens.(A) MP vs. SP, (B) MP vs. viruses and (C) MP vs. non-MP (SP and viruses combined). CRP, C-reactive protein; PCT, procalcitonin; Ratio, CRP divided by PCT in mg/µg, Ratio>400, 400 mg/µg cutoff; ROC AUC, area under the receiver operator characteristic curve; MP, Mycoplasma pneumoniae, SP, Streptococcus pneumonia.
Figure 2:

(A–C) Discriminatory power of CRP, PCT and the CRP/PCT ratio for different pathogens.

(A) MP vs. SP, (B) MP vs. viruses and (C) MP vs. non-MP (SP and viruses combined). CRP, C-reactive protein; PCT, procalcitonin; Ratio, CRP divided by PCT in mg/µg, Ratio>400, 400 mg/µg cutoff; ROC AUC, area under the receiver operator characteristic curve; MP, Mycoplasma pneumoniae, SP, Streptococcus pneumonia.


Although many studies have proven the excellent performance of PCT in the differentiation of typical bacterial from viral pneumonia [16], [17], [18], [19], the differentiation between atypical bacterial from typical bacterial and viral pneumonia has remained difficult.

The aim of the current study was to identify parameters allowing to differentiate the atypical bacteria MP from the typical bacteria SP on the one hand, and from viral etiologies of pneumonia on the other hand, to allow targeted empirical treatment. Consistent with earlier reports we confirm the predictive value of a high PCT for typical vs. atypical bacteria [28], [29], [30], but also provide evidence for an even higher discriminatory power of the CRP/PCT ratio in serum. The CRP/PCT ratio predicted MP as compared to SP with an area under the ROC curve of 0.91 and an OR of 15.04, if a cutoff >400 mg/μg was chosen (Table 1). The ROC AUC of 0.85 we derived from our data for PCT values for the comparison of MP and SP compares well with the ROC AUC of 0.75 described by Jereb et al. for typical vs. atypical pneumonias [29]. Remarkably, CRP alone failed to discriminate MP and SP. On the other hand, CRP – but not PCT – was able to differentiate MP from exclusively viral pneumonia with an area under the ROC curve of 0.77 (Table 2). Again, the CRP/PCT ratio proved superior with a ROC AUC of 0.83 and an OR of 5.55 for a cutoff >400 mg/μg. As a consequence, the CRP/PCT ratio outperformed PCT and CRP each in the attempt to identify MP vs. non-MP pneumonia (Figure 2C). We found no other demographic or clinical parameter even close to the discriminatory power of the CRP/PCT ratio.

While we included the highest CRP and PCT values measured within 48 h of admission, a sensitivity analysis only considering the first values measured within 48 h did not reveal relevant differences (data not shown).

Considering patient characteristics, MP primarily affected younger individuals, which may explain the longer duration of symptoms before admission. For SP pneumonia, the earlier hospitalization can also be explained by its high virulence, which has been shown to directly correlate with high PCT values [22], [23], [24], high neutrophil counts or a high neutrophil/lymphocyte ratio (NLR) [31]. Consistently, we found higher WBCs counts and a higher NLR in pneumococcal and lower values in viral pneumonia as compared to mycoplasma in our data. Also, the later hospitalization of MP cases might be explained by lesser comorbidities, as suggested by the lower rate of neoplasias and immunosuppression.

Remarkably, dry cough and myalgias were a hallmark of MP, with significantly higher rates even when compared to viruses, while no clinical sign was helpful for the identification of the causative organism (Table 1). The presence of a pulmonary infiltrate differentiated bacterial from viral pneumonia with an interstitial pattern being suggestive for MP.

Some of these findings correlated with the score for atypical pneumonias suggested by the Japanese Respiratory Society by identifying six parameters [32]: age <60 years; no or only minor comorbidities; persistent cough; limited chest auscultatory findings; no sputum or negative SP urinary antigen testing and a WBC count <10×10E9/L. Macrolide use thereby was suggested for any score ≥4.

Surprisingly, the predictors of MP were almost identical for the comparison with SP or viruses: Younger age, longer duration of symptoms as well as a higher platelet count and a high CRP/PCT ratio all predicted MP infection in both comparisons, while NLR or neutrophil count were only predictive in the comparison with SP or viruses, respectively. The high resemblance of SP and viral pneumonia characteristics is surprising and may partially be due to unrecognized bacterial superinfection in the virus group. The good performance of the CRP/PCT ratio in multivariable analysis in an unselected real-world cohort emphasizes the robustness of this parameter for clinical use.

Due to the lack of cases, we did not include pneumonias due to L. pneumophila (LP). According to the literature, their CRP and PCT pattern resembles SP and thus is different from MP [16], [17], [29]. Still, the CRP/PCT ratio may also be helpful for the discrimination of pneumococci and Legionella. A study comparing 37 SP and 24 LP described that a log CRP/PCT ratio <0.5 was indicative of SP, whereas a log CRP/PCT ratio >1.25 suggested LP infection [33]. The discrepancy between MP and LP is further stressed by the observation, that the Legionella score suggested by Fiumefreddo et al. failed to identify MP infections in our analysis (data not shown) [34].

Acute respiratory tract infections are the most common outpatient indication to prescribe antibiotics, which often are misused for unrecognized viral infections [8]. On the other hand, a recent metaanalysis evaluating the proportion of atypical bacteria causing CAP considered MP pneumonias underdiagnosed and undertreated unless MP were systematically looked for [35]. Currently, rapid and accurate testing methods for MP are limited and only include IgM antibodies and PCR [5], [27]. The introduction of the CRP/PCT ratio and the Legionella score [34] in the diagnostic armamentarium of an emergency department may improve the detection of atypical pneumonias, enabling more specific – and more restrictive – antibiotic use.

The strengths of our study include the high number of included cases, comprehensive anamnestic, clinical and laboratory data as well as the real-world scenario with analyses based on diagnostic tests in clinical routine at the discretion of the treating physician. Of note, CRP and PCT values are measured systematically in our clinic and therefore did not introduce bias.

However, some limitations should be considered. First, we only included MP CAP requiring hospitalization. Outpatient MP infections mostly are oligosymptomatic and self-limiting and do not need specific diagnostic workup nor treatment. Second, multiplex PCR has not systematically been performed in all patients hospitalized for CAP. We will therefore have missed MP pneumonias, in particular cases with less typical presentation and no need of specific antibiotic treatment. Also, antibiotic pretreatment – possibly altering inflammatory markers on presentation – was not considered for our analysis. Fourth, we discovered several biomarker outliers within our patient categories: Low CRP values in a few patients with SP infections (Figure 1A) may be due to a duration of symptoms <24 h, while the high PCT values in a few patients with viral infections (Figure 1B) may be due to undetected bacterial superinfection. Finally, the retrospective study design based on medical chart reviews is subject to reporting bias. Anamnestic and clinical parameters may therefore be underestimated compared to laboratory data. As a consequence, our observations need confirmation in a prospective study.


In patients hospitalized with bacterial CAP, CRP is almost always elevated – whether the etiology is typical or atypical. On the other hand, PCT values are significantly lower in MP than SP infections. A high CRP/PCT ratio on admission thus predicted MP infection in our cohort. If validated in a prospective setting, this observation could improve the therapeutic management of patients hospitalized with CAP: For patients with a CRP/PCT ratio >400 mg/μg antibiotics covering atypical bacteria should be considered. The efficacy and safety of this targeted treatment approach and its potential to reduce betalactam consumption needs evaluation in a prospective study.


We thank the patients whose data are presented here for their participation in the main “TRIAGE” study.

  1. Author contributions: ON, SH and CF had complete access to all study data and take full responsibility for the integrity of the data and the accuracy of the analyses. ON, CF, SH and PS were involved in the conceptualization and design of the study. ON, TV, LF, SH, CF and PS performed the statistical analyses. All authors made substantive intellectual contributions to this study regarding conception and design of the study and were responsible for the acquisition, analysis and interpretation of the data. ON and CF drafted the manuscript. All authors approved the final version of the manuscript and the decision to submit the manuscript for publication.

  2. Research funding: PS is supported by the Swiss National Science Foundation (SNSF Professorship, Funder Id:, Grant Number: PP00P3_150531/1) and the Research Council of the Kantonsspital Aarau (1410.000.044). Drs. Schuetz and Mueller have received support from diagnostic companies including ROCHE, Abbott, Thermo-Fisher and BioMérieux. Dr Mueller has also served as a consultant to BioMérieux and Thermo-Fisher.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

  6. Declarations

  7. Consent for publication: Not applicable.


1. Nir-Paz R, Saraya T, Shimizu T, Van Rossum A, Bebear C. Editorial: Mycoplasma pneumoniae clinical manifestations, microbiology, and immunology. Front Microbiol 2017;8:1916.10.3389/fmicb.2017.01916Search in Google Scholar PubMed PubMed Central

2. Arnold FW, Summersgill JT, Lajoie AS, Peyrani P, Marrie TJ, Rossi P, et al. A worldwide perspective of atypical pathogens in community-acquired pneumonia. Am J Respir Crit Care Med 2007;175:1086–93.10.1164/rccm.200603-350OCSearch in Google Scholar PubMed

3. Lenglet A, Herrador Z, Magiorakos AP, Leitmeyer K, Coulombier D, European Working Group on Mycoplasma pneumoniae s.Surveillance status and recent data for Mycoplasma pneumoniaeinfections in the European Union and European Economic Area, January 2012. Euro Surveill 2012;17:2.10.2807/ese.17.05.20075-enSearch in Google Scholar PubMed

4. Izumikawa K. Clinical features of severe or fatal Mycoplasma pneumoniae pneumonia. Front Microbiol 2016;7:800.10.3389/fmicb.2016.00800Search in Google Scholar PubMed PubMed Central

5. Parrott GL, Kinjo T, Fujita J. A compendium for Mycoplasma pneumoniae. Front Microbiol 2016;7:513.10.3389/fmicb.2016.00513Search in Google Scholar PubMed PubMed Central

6. Miyashita N, Obase Y, Ouchi K, Kawasaki K, Kawai Y, Kobashi Y, et al. Clinical features of severe Mycoplasma pneumoniae pneumonia in adults admitted to an intensive care unit. J Med Microbiol 2007;56(Pt 12):1625–9.10.1099/jmm.0.47119-0Search in Google Scholar PubMed

7. Musher DM, Thorner AR. Community-acquired pneumonia. N Engl J Med 2014;371:1619–28.10.1056/NEJMra1312885Search in Google Scholar PubMed

8. Jain S, Self WH, Wunderink RG, Fakhran S, Balk R, Bramley AM, et al. Community-acquired pneumonia requiring hospitalization among U.S. Adults. N Engl J Med 2015;373:415–27.10.1056/NEJMoa1500245Search in Google Scholar PubMed PubMed Central

9. van Gageldonk-Lafeber AB, Wever PC, van der Lubben IM, de Jager CP, Meijer A, de Vries MC, et al. The aetiology of community-acquired pneumonia and implications for patient management. Neth J Med 2013;71:418–25.Search in Google Scholar

10. Capelastegui A, Espana PP, Bilbao A, Gamazo J, Medel F, Salgado J, et al. Etiology of community-acquired pneumonia in a population-based study: link between etiology and patients characteristics, process-of-care, clinical evolution and outcomes. BMC Infect Dis 2012;12:134.10.1186/1471-2334-12-134Search in Google Scholar PubMed PubMed Central

11. Templeton KE, Scheltinga SA, van den Eeden WC, Graffelman AW, van den Broek PJ, Claas EC. Improved diagnosis of the etiology of community-acquired pneumonia with real-time polymerase chain reaction. Clin Infect Dis 2005;41:345–51.10.1086/431588Search in Google Scholar PubMed PubMed Central

12. Huijskens EG, van Erkel AJ, Palmen FM, Buiting AG, Kluytmans JA, Rossen JW. Viral and bacterial aetiology of community-acquired pneumonia in adults. Influenza Other Respir Viruses 2013;7:567–73.10.1111/j.1750-2659.2012.00425.xSearch in Google Scholar PubMed PubMed Central

13. Johansson N, Kalin M, Tiveljung-Lindell A, Giske CG, Hedlund J. Etiology of community-acquired pneumonia: increased microbiological yield with new diagnostic methods. Clin Infect Dis 2010;50:202–9.10.1086/648678Search in Google Scholar PubMed PubMed Central

14. Bohte R, van Furth R, van den Broek PJ. Aetiology of community-acquired pneumonia: a prospective study among adults requiring admission to hospital. Thorax 1995;50:543–7.10.1136/thx.50.5.543Search in Google Scholar PubMed PubMed Central

15. Bergin SP, Tsalik EL. Procalcitonin: The right answer but to which question? Clin Infect Dis 2017;65:191–3.10.1093/cid/cix323Search in Google Scholar PubMed

16. Self WH, Balk RA, Grijalva CG, Williams DJ, Zhu Y, Anderson EJ, et al. Procalcitonin as a marker of etiology in adults hospitalized with community-acquired pneumonia. Clin Infect Dis 2017;65:183–90.10.1093/cid/cix317Search in Google Scholar PubMed PubMed Central

17. Menendez R, Sahuquillo-Arce JM, Reyes S, Martinez R, Polverino E, Cilloniz C, et al. Cytokine activation patterns and biomarkers are influenced by microorganisms in community-acquired pneumonia. Chest 2012;141:1537–45.10.1378/chest.11-1446Search in Google Scholar PubMed PubMed Central

18. Kruger S, Ewig S, Papassotiriou J, Kunde J, Marre R, von Baum H, et al. Inflammatory parameters predict etiologic patterns but do not allow for individual prediction of etiology in patients with CAP: results from the German competence network CAPNETZ. Respir Res 2009;10:65.10.1186/1465-9921-10-65Search in Google Scholar PubMed PubMed Central

19. Johansson N, Kalin M, Backman-Johansson C, Larsson A, Nilsson K, Hedlund J. Procalcitonin levels in community-acquired pneumonia – correlation with aetiology and severity. Scand J Infect Dis 2014;46:787–91.10.3109/00365548.2014.945955Search in Google Scholar PubMed

20. Muller F, Christ-Crain M, Bregenzer T, Krause M, Zimmerli W, Mueller B, et al. Procalcitonin levels predict bacteremia in patients with community-acquired pneumonia: a prospective cohort trial. Chest 2010;138:121–9.10.1378/chest.09-2920Search in Google Scholar PubMed

21. Julian-Jimenez A, Timon Zapata J, Laserna Mendieta EJ, Parejo Miguez R, Flores Chacartegui M, Gallardo Schall P. [Ability of procalcitonin to predict bacteremia in patients with community acquired pneumonia]. Med Clin (Barc) 2014;142:285–92.Search in Google Scholar

22. Zhydkov A, Christ-Crain M, Thomann R, Hoess C, Henzen C, Werner Z, et al. Utility of procalcitonin, C-reactive protein and white blood cells alone and in combination for the prediction of clinical outcomes in community-acquired pneumonia. Clin Chem Lab Med 2015;53:559–66.10.1515/cclm-2014-0456Search in Google Scholar PubMed

23. Jensen JU, Heslet L, Jensen TH, Espersen K, Steffensen P, Tvede M. Procalcitonin increase in early identification of critically ill patients at high risk of mortality. Crit Care Med 2006;34:2596–602.10.1097/01.CCM.0000239116.01855.61Search in Google Scholar

24. de Jager CP, de Wit NC, Weers-Pothoff G, van der Poll T, Wever PC. Procalcitonin kinetics in Legionella pneumophila pneumonia. Clin Microbiol Infect 2009;15:1020–5.10.1111/J.1469-0691.2009.02773.XSearch in Google Scholar

25. Schuetz P, Hausfater P, Amin D, Haubitz S, Fassler L, GrolimundE, et al. Optimizing triage and hospitalization in adult general medical emergency patients: the triage project. BMC Emerg Med 2013;13:12.10.1186/1471-227X-13-12Search in Google Scholar

26. Christ-Crain M, Jaccard-Stolz D, Bingisser R, Gencay MM, Huber PR, Tamm M, et al. Effect of procalcitonin-guided treatment on antibiotic use and outcome in lower respiratory tract infections: cluster-randomised, single-blinded intervention trial. Lancet 2004;363:600–7.10.1016/S0140-6736(04)15591-8Search in Google Scholar

27. Loens K, Ieven M. Mycoplasma pneumoniae: current knowledge on nucleic acid amplification techniques and serological diagnostics. Front Microbiol 2016;7:448.10.3389/fmicb.2016.00448Search in Google Scholar PubMed PubMed Central

28. Hedlund J, Hansson LO. Procalcitonin and C-reactive protein levels in community-acquired pneumonia: correlation with etiology and prognosis. Infection 2000;28:68–73.10.1007/s150100050049Search in Google Scholar PubMed

29. Jereb M, Kotar T. Usefulness of procalcitonin to differentiate typical from atypical community-acquired pneumonia. Wien Klin Wochenschr 2006;118:170–4.10.1007/s00508-006-0563-8Search in Google Scholar PubMed

30. Masia M, Padilla S, Ortiz de la Tabla V, Gonzalez M, Bas C, Gutierrez F. Procalcitonin for selecting the antibiotic regimen in outpatients with low-risk community-acquired pneumonia using a rapid point-of-care testing: a single-arm clinical trial. PLoS One 2017;12:e0175634.10.1371/journal.pone.0175634Search in Google Scholar PubMed PubMed Central

31. Curbelo J, Luquero Bueno S, Galvan-Roman JM, Ortega-Gomez M, Rajas O, Fernandez-Jimenez G, et al. Inflammation biomarkers in blood as mortality predictors in community-acquired pneumonia admitted patients: Importance of comparison with neutrophil count percentage or neutrophil-lymphocyte ratio. PLoS One 2017;12:e0173947.10.1371/journal.pone.0173947Search in Google Scholar PubMed PubMed Central

32. Ishida T, Miyashita N, Nakahama C. Clinical differentiation of atypical pneumonia using Japanese guidelines. Respirology 2007;12:104–10.10.1111/j.1440-1843.2006.00927.xSearch in Google Scholar PubMed

33. Bellmann-Weiler R, Ausserwinkler M, Kurz K, Theurl I, Weiss G. Clinical potential of C-reactive protein and procalcitonin serum concentrations to guide differential diagnosis and clinical management of pneumococcal and Legionella pneumonia. J Clin Microbiol 2010;48:1915–7.10.1128/JCM.01348-09Search in Google Scholar PubMed PubMed Central

34. Fiumefreddo R, Zaborsky R, Haeuptle J, Christ-Crain M, Trampuz A, Steffen I, et al. Clinical predictors for Legionella in patients presenting with community-acquired pneumonia to the emergency department. BMC Pulmonary Med 2009;9:4.10.1186/1471-2466-9-4Search in Google Scholar PubMed PubMed Central

35. Marchello C, Dale AP, Thai TN, Han DS, Ebell MH. Prevalence of atypical pathogens in patients with cough and community-acquired pneumonia: a meta-analysis. Ann Fam Med 2016;14:552–66.10.1370/afm.1993Search in Google Scholar PubMed PubMed Central

Received: 2019-02-18
Accepted: 2019-03-18
Published Online: 2019-05-09
Published in Print: 2019-09-25

©2019 Olivia L. Neeser, Christoph A. Fux et al., published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Downloaded on 20.3.2023 from
Scroll Up Arrow