Predicting the Prognosis of Patients with Pneumocystis Pneumonia by the (1,3)-β-D-Glucan Test Change

doi:10.21203/rs.2.13406/v1

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Predicting the Prognosis of Patients with Pneumocystis Pneumonia by the (1,3)-β-D-Glucan Test Change

https://doi.org/10.21203/rs.2.13406/v1

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Background: To investigate the effects of the (1, 3)-β-D-glucan test (G test) to predict the outcomes of the patients with Pneumocystis pneumonia ( PCP ).

Methods: The clinical data of PCP patients diagnosed from 2010 to 2018 in Peking Union Hospital collected. The imaging changes, the independent risk factors of death, changes of the G test and the patients’ outcomes were used as a receiver operating the characteristic (ROC) curves, and the area under the curve (AUC) calculated.

Results: A total number of 104 patients enrolled in. The independent risk factors of death for PCP included pulmonary interstitial fibrosis (P=0.003), Cytomegalovirus co-infection (P=0.006), severe complication during treatment (P=0.001). The reduction of the G test was less than 9.13% of the initial G test (P=0.005), and the second imaging was not better than the first imaging (P=0.001). The efficiency of predicting the prognosis by the declined G test (AUC=0.740) is similar (P=0.893) to that of improving the imaging (AUC=0.731). The combination of the risk factors was the most effective predictor of the patients’ outcomes (AUC=0.938, CI 0.873 - 0.976, P < 0.001).

Conclusion: The changes of G test can predict patient prognosis, and their efficiency is comparable to that of the changed imaging.

Infectious Diseases

3)-β-D-glucan test

Pneumocystis pneumonia

Prognosis

As early as in 1996, researchers have proposed that the (1, 3)-β-D-glucan test (G test) could diagnose Pneumocystis pneumonia (PCP) through some animal experiments. Moreover, the changes of the G test could be used to monitor the effects of the treatment (1, 2). Later, the researchers have confirmed the role of the G test in the diagnosis of PCP in the clinical studies (3–5), but the G test was considered to be independent of the severity of the disease (6), and it doesn’t reflect the patients’ response to the anti-PCP drugs (6,7). A prospective study of 31 patients with PCP in 2011 suggested that the G test could serve as a marker for monitoring the effects of the treatment (8). Another study conducted showed a dynamic review of the results of the G test in 18 patients with PCP, and found that patients with a dynamically reduced results of the G test were mostly improved, but the dynamically elevated results of the G test didn’t mean a worse clinical performance (9). One study in 2012 indicated that changes of the G test could not predict clinical mortality in 6 or 12 weeks (10). In conclusion, the G test can diagnose PCP, however, whether it can judge the prognosis of patients is still controversial. This study was designed to analyze whether the changes of the G test can be used as a predictor for the patients’ outcomes, and how is its efficiency comparable to the other clinical indicators.

Research design: This study is a retrospective, single-center, clinical study. The protocol was approved by our institutional review board(IRB)(Peking Union Medical College Hospital(PUMCH), IRB S-K 643).
Population*: (1) The diagnosis of PCP was confirmed on the first page of the medical record in Peking Union Hospital from 2010 to 2018, with or without fungal infections. (2) There were at least 2 results of G test (Endosafe Glucan Assay Cartridges) during hospitalization. The interval between the first G test (G1) and the second G test (G2) was ≥ 5 days. The results of theG1 ≥105pg/ml (11). (3) There are at least 2 results of the CT imaging or the X-Ray imaging during the hospitalization. The interval between the first imaging and the second imaging was ≥ 5 days. (4) The patient didn’t receive anti-PCP treatment outside the hospital.
Data collection: see figure–1

*Considering that period of the anti-PCP treatment may be too short to affect the results of the G test, the interval of the 2 G tests was required ≥ 5 days. According to the literature, 105pg/ml as a cutoff value is able to distinguish between Pneumocystis jirovecii colonization and P. jirovecii infection. Therefore, the result of the G1 was required ≥ 105pg/ml (11).

Data processing and statistical methods used

The study used the IBM@ SPSS@ Statistics: Version 23 and the MedCalc Statistical Software: version 15.8 for the statistical analysis
We compared the baseline data using the t-test, the Mann–Whitney U test and the chi-squared test to determine whether the data differences were significant between the survival group and the death group, based on the data type and its distribution.
The binary logistic regression was used for multivariate analysis to derive the independent risk factors of death. (1) The clinical characters were analyzed by a single factor analysis, and the characters whose P value <0.05 was included in the candidate variables. (2) The selected candidate varied according to the identified risk factors from literature. (3) Tested the collinearity of the above candidate variables. (4) Due to the limitation of a sample size, 5 variables were selected, and they were submitted to the binary logistic regression as the covariates with a prognosis as dependent (Method is Forward: Conditional). (5) According to the P value of Hosmer and Lemeshow Test, the fitness of the model was observed. Moreover, the ability of model predicting patients’ outcomes was observed by the Predict Correct Overall Percentage. (6) Calculated the maximum of the Yoden index and took the corresponding (G1-G2)/G1 as the cut-off point
The capacity of different indicators to predict prognosis by MedCalc considered. (1) The changes of the G test predicted the outcomes (method 1): Calculated the changes of the G test by a formula (G1-G2)/G1, took the ROC curve analysis and got an area under the curve 1 (AUC1) by submitting (G1-G2)/G1 as a variable and the prognosis (0 death, 1 survival) as the classification variable. (2) The changes of the imaging predicted the outcomes (method 2): Whether the imaging improved (0 improved, 1 not improved) and prognosis (0 death, 1 survival) were taking into the ROC curve analysis, and AUC2 was calculated. (3) Established the logistic regression model by the independent risk factors of death that predicted the outcomes (method 3): Whether imaging improved after the anti-PCP treatment (0 improved, 1 not improved), whether there were complications during the treatment (0 no, 1 yes), whether the patient had a pulmonary fibrosis (0 no, 1 yes), whether the CMV was infected (0 no, 1 yes), and the changes of the G test were regarded as X1, X2, X3, X4, X5. The binary logistic regression was applied with them to obtain a regression model. At the meantime, the new variable Pred1 showed that the prediction probability of each factors was generated. The Pred1 and the prognosis (0 death, 1 survival) were submitted to calculate AUC3 (12).

1. The Baseline data

A total of 104 patients were enrolled in the study, with a male to female ratio of 43/61, with 70 patients survived and 34 patients died. 63 of 104 (60.58%) patients were treated in the emergency department and ICU. Autoimmune disease or chronic glomerulonephritis requiring glucocorticoids as the main cause of immune system damage occurred in 72 of 104 patients (69.23%) (Figure 2). 84 of 104 (80.77%) patients were infected with the other pathogens, mainly the CMV and the A. baumannii (Figure 3A). In total, 38 of 104 (36%) patients used antifungal drugs, mainly the Voriconazole (Figure 3B); 54 of 104 (51.92%) patients were treated with the Sulfamethoxazole Compound(SMZ-TMP) alone, and 34 of 104 (32.69%) were treated with the SMZ-TMP and Caspofungin (Figure 3C). The most common complication over the treatment course was bleeding in various sites (12 of 104 [1.54%]), followed by accumulation of a gas in the chest and the impaired acute kidney function (Figure 3D).
The patients with the different prognoses had significant differences in the APACHE II score, the pulmonary fibrosis, the CMV infection, the A. baumannii infection and many complications. There were no significant differences in age, sex, length of hospital stay and hypertension (Table 1).

2. The independent risk factors of death and the effects of the different methods judging the prognosis

The independent risk factors of death: Pulmonary interstitial fibrosis, diabetes, co-infection with the CMV, the complication occurred during the treatment, and the imaging didn’t improve: are the 5 risk factors of the PCP. The risk of death in the patients with (G1-G2)/G1≤9.13% was 1.50 (CI 1.13–1.993, P = 0.005) times the risk of death in the patients with (G1-G2)/G1＞9.13%. The risk of death in the patients with the pulmonary interstitial fibrosis was 26.11 (CI 3.06–222.86, P = 0.003) times that in the patients without the pulmonary fibrosis. The risk of death in patients with the CMV infection was 9.71 (CI 1.89–49.78, P = 0.006) times that in the patients without the CMV infection. The risk of death in the patients occurred complication was 10.38 (CI 2.51–42.82, P = 0.001) times that in the patients without the complication occurred. The risk of death in the patients with the unimproved imaging was 9.86(CI 2.46–39.51, P = 0.001) times that in the patients whose imaging was improved. See Table 2 for details.
The efficiency of the different methods judging the prognosis: The results are shown in Table 3 and Figure 4. The AUCs corresponding to the 3 methods is 0.74, 0.73 and 0.938, respectively, which are statistically significant. Among them, the AUC value of the third method is the largest. There was no significant difference (P = 0.893) between the efficacy(efficiency) of predicting by the changes of the G test and that by the changes of imaging. But there was a significant difference (P = 0.001) between the AUC of the G test and that of the independent risk factors of death. The efficacy of predicting by risk factors is the most effective one.

The efficacy of the G test predicting the outcomes

The aim of the study is exploring the clinical value of the changes of the G test in predicting the outcomes in the PCP patients. The reference system was established by being compared with the changes of imaging, the APACHE II score, and the independent risk factors of death, for it is well-known that the changes of the imaging was the most common way used to judge the patients’ condition, the APACHE score is related to mortality, and the risk factors are the most closely related to the prognosis. The multivariate logistic regression showed that the APACHE II score was not one of the independent risk factors of death. However, the changes of the G test was included in the 5 risk factors. Moreover, it is suggested by the ROC analysis that the efficacy of predicting outcomes by changes of the G test is comparable to that by the changes of imaging, lower than that of the combination of the risk factors.

The independent risk factors of death in this study is different from that of other studies. The other risk factors including low FiO2 (13–18), high level of serum LDH (15, 16, 19–21), co-infection of other pathogens (15, 19), admission to the ICU (14, 22, 23), mechanical ventilation (19, 24, 25), low level of serum albumin (15–17, 24), high level of serum CRP (18, 22), high APACHE score (26), elder (15, 22), female (21), high ECOG score (22), elevated neutrophils in alveolar lavage fluid (28), underlying disease (22), failure of initial anti-PCP treatment (29), basic lung disease (30), and high P_(A-a)O₂ (30). CMV co-infection and pulmonary fibrosis are consistent with the factors mentioned above. Especially, the changes of the G test, the changes of imaging, and the complication were proposed as the new risk factors, which may be related to the facts that the previous researchers did not collect those types of data and heterogeneity of the population.

The authors believe that independent risk factors of death in the PCP patients can be divided into 2 categories: the one is disease-specific. For examples, low FiO2, CMV co-infection and pulmonary interstitial fibrosis are the risk factors of the PCP rather than the risk factors of gastrointestinal bleeding. The other one is common for all diseases, like low level of serum albumin, elder, and underlying disease, etc. For instances, the higher the APACHE II score is, the higher probability of death of the patient admitted to the ICU, regardless of what kind of disease the patient suffered. The changes of the G test reflect whether the P. jirovecii were decreased in lung after anti-PCP treatment was received. It is a disease-specific risk factor, and it could not completely predict patients’ outcomes because there are interferences of other disease-common risk factors.

The comparison of the AUCs showed that capacity of predicting outcomes by the changes of the G test was similar to that of the lung imaging. However, the G test and the lung imaging have different characteristics and uses. The G test reflects the concentration of (1, 3)-β-D-glucan in patient’s blood, and the pulmonary imaging provides more information from the distribution and the morphology of the lesions. When the G test is declining but the lung imaging is not improved, the clinicians need to consider whether the patient is infected by other pathogens. When the G test and the lung imaging both were not improved, the clinicians need to consider whether the P. jirovecii is resistant to drugs. The G test and the pulmonary imaging could be mutually assisted in the clinic.

Possible reason of complication

The data showed that bleeding in different parts was the most common complication, including gastrointestinal bleeding, hemoptysis, cerebral hemorrhage, diffuse alveolar hemorrhage, and abdominal hematoma in 12 (12/104, 11.54%) cases. The second complication is gas accumulation in chest, including mediastinal emphysema, subcutaneous emphysema, and pneumothorax, accounting for 10/104 (9.62%). Since the patients’ severe respiratory failure required mechanical ventilation, it’s easily understand that gas accumulation in chest is the second common complication. But the reason of that bleeding is the most common complication remains unknown.

There are 2 possible reasons as it follows: one is a deficiency of the vitamin K - it is a case report that the patient had bleeding caused by a deficiency of the vitamin K after taking sulfonamide, PT and APTT returned to normal value after oral administration of sufficient vitamin K (31). Approximately 100% of the PCP patients received the SMZ-TMP. Is it truly the reason of bleeding complication? Another is of the anticoagulant drugs: the patients lying on bed for long time are prone to have deep vein thrombosis and need to use anticoagulant drugs. Was such a bleeding induced by the anticoagulant drugs? Because the study did not collect serum concentration of the Vitamin K and the incident rate of deep vein thrombosis, the real reason needs some further study. It’s suggested that occurring complication is one of independent risk factors of death, so reduction of complication may benefit for the PCP patients.

Limitation

The study is a retrospective study and unable to fix the interval between the 2 results of the G tests and pulmonary imaging. The APACHE II score was scored retrospectively, which may bring a little bias.
There is a heterogeneity in the sample population collected in the study, for a significant difference of the APACHE II score showed between the death group and the survival group.
Because the diagnosis of the fungal infection is difficult, the study did not analyze whether the fungal infection is the independent risk factor of death.
The study did not collect the data of serum levels of albumin and the LDH that was mentioned as an independent risk factors in other literature, and did not assess their capacity of predicting prognosis compared with the G test.
Only the 5 variables were chosen and submitted to the binary logistic regression, due to the limitation of the sample size (104). If the sample size was expanded, the other variables might have been more meaningful.

The changes of G test and the changes of imaging are both independent risk factors of PCP, and their efficiency are comparable. Combined application of them could improve the accuracy of predicting prognosis. Clinical doctors could appropriately refer to those results.

Ethics approval and consent to participate

The protocol was approved by our institutional review board(IRB)(Peking Union Medical College Hospital(PUMCH), IRB S-K 643).

Consent for publication

Yes.

Availability of data and material

Yes.

Competing interests

Not applicable.

Funding

Not applicable.

Authors’ contributions

Xu Jun put forward the idea and designed the experimental framework;

Yao Hui collected data, processed data, and drafted the manuscript;

Sun Feng reviewed the experimental framework and manuscript.

Acknowledgements

Not applicable.

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Predicting the Prognosis of Patients with Pneumocystis Pneumonia by the (1,3)-β-D-Glucan Test Change

Status:

Version 1

Abstract

Figures

Introduction

Methods

Data processing and statistical methods used

Results

Discussion

The efficacy of the G test predicting the outcomes

Possible reason of complication

Limitation

Conclusion

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and material

Competing interests

Funding

Authors’ contributions

Acknowledgements

References

Tables

Supplementary Files

Status:

Version 1