Using Routine Laboratory Markers and Immunological Indicators for Predicting Pneumocystis jiroveci Pneumonia in Immunocompromised Patients

Clinical course and prognostic factors of Pneumocystis pneumonia with respiratory failure in non-HIV patients

Background

Compared with Human Immunodeficiency Virus (HIV) patients, non-HIV patients with Pneumocystis pneumonia (PCP) have more rapid onset, more rapid progression, and higher mortality.

Objectives

To investigate the predictive value of variables obtained upon hospital admission for in-hospital death and 90-day outcomes in non-HIV-PCP patients with respiratory failure (RF).

Methods

This was a single center retrospective study in a tertiary care institution over 15 years. It included all adults inpatients (≥18 years old) with laboratory confirmed non-HIV-PCP with RF who were discharged or died from Peking University First Hospital between April 1st, 2007 and November 1st, 2022. Epidemiological, clinical, laboratory, imaging and outcome data were collected from patient records.

Results

In this study, a total of 146 non-HIV-PCP patients with RF were included. There were 57 patients (39%) died during hospitalization, 44 patients (53%) died in Intensive care unit (ICU). A total of 137 patients completed 90 days of follow-up, of which 58 (42.3%) died. The multivariable regression analysis revealed that a CD8+ T cell count <115/μl (P=0.009), bronchoalveolar lavage fluid (BALF)-neutrophil percentage ≥50% (P=0.047), the time from corticosteroids withdrawal to symptom onset ≤5 days (P=0.012), and the time from visit to initiation of sulfonamides ≥2 days (P=0.011) were independent risk factors for in-hospital death. Furthermore, a CD8+ T cell count < 115/μl (P=0.001) and the time from visit to initiation of sulfonamides therapy ≥2 days (P=0.033) was independently associated with 90-day all-cause death.

Conclusions

A low CD8+ T cell count in peripheral blood, a high percentage of BALF-neutrophils, a short time from corticosteroids withdrawal to symptom onset, and a long time from visit to initiation of sulfonamides are associated with poor prognosis in non-HIV-PCP patients with RF.

A combined immune and inflammatory indicator predict the prognosis of severe Pneumocystis jirovecii pneumonia patients: a 12-year, retrospective, observational cohort

Persistent inflammatory damage and suppressed immune function play a crucial role in the pathogenesis and progression of the pneumocystis jirovecii pneumonia (PjP). Therefore, we aimed to investigate the correlation between the combined immune and inflammatory indicator: the neutrophil-to-lymphocyte ratio (NLR) and prognosis of non-human immunodeficiency virus (non-HIV) PjP.In the retrospective analysis conducted in ICUs at Beijing Chao-Yang Hospital, we examined data from 157 patients diagnosed with non-HIV PjP. Our findings reveal a concerning hospital mortality rate of 43.3%, with the 28-day mortality rate reaching 47.8%.Through multivariable logistic and Cox regression analyses, we established a significant association between elevated NLR levels and hospital mortality (adjusted odd ratio, 1.025; 95% CI, 1.008-1.043; p = 0.004) or 28-day mortality (adjusted hazard ratio, 1.026; 95% CI, 1.008-1.045; p = 0.005). Specifically, patients with an NLR exceeding 20.3 demonstrated markedly lower overall survival rates, underscoring the biomarker's predictive value for both hospital and 28-day mortality.In conclusion, non-HIV PjP patients in the ICU still have a high rate of mortality and a poor short-term prognosis after discharge. A high level of NLR was associated with an increased risk of hospital mortality and 28-day mortality.

Study on mNGS Technique in Diagnosing Pneumocystis jirovecii Pneumonia in Non-HIV-Infected Patients

Objective

To investigate the value of metagenomic Next-Generation Sequencing (mNGS) in diagnosing Pneumocystis jirovecii pneumonia (PJP) in non-human immunodeficiency virus (HIV)-infected patients.

Methods

In this retrospective study, non-HIV-infected patients with PJP and those diagnosed with non-PJP from August 2022 to December 2024 were selected as subjects. The presence of Pneumocystis jirovecii (PJ) and other co-pathogens in bronchoalveolar lavage fluid (BALF) was analyzed, and the diagnostic efficacy of NGS, polymerase chain reaction (PCR) and serum 1,3-β-D-glucan (BDG) in PJP was compared with the reference standard of clinical compound diagnosis.

Results

Eighty-nine non-HIV-infected patients were recruited, with dyspnea as the primary symptom (69.66%) and solid malignant tumor as the most common underlying disease (20.22%). Taking clinical compound diagnosis as the reference standard, the sensitivity, specificity, negative predictive value and positive predictive value of mNGS were higher than those detected by PCR and serum BDG. Among 42 non-HIV-infected patients with PJP who underwent mNGS and conventional pathogen detection of BALF, 6 had simple PJ infection and 36 had combined PJ infection. The detection rate of mNGS in mixed infections was significantly higher than that of conventional pathogen detection (85.71 vs 61.70%, P = 0.012). A total of 127 pathogens were detected in BALF using mNGS, among which fungi had the highest detection rate (46.46%). The fungi, viruses and bacteria detected were mainly Pneumocystis jirovecii, human gammaherpesvirus 4 and Acinetobacter baumannii.

Conclusion

mNGS is highly effective in diagnosing non-HIV-infected patients with PJP and exhibits ideal performance in the detection of co-pathogens. In addition, it has certain value for clinical diagnosis and guidance of targeted anti-infective drug treatment.

Circulating TIGIT±PD1+TPH, TIGIT ± PD1+TFH cells are elevated and their predicting role in systemic lupus erythematosus

It is well established that increased peripheral helper T cells (TPH) and follicular helper T cells (TFH) was found in systemic lupus erythematosus (SLE) patients. However, the expression patterns and immunomodulatory roles of TIGIT and PD1 on TPH/TFH in SLE are poorly understood. The expression patterns of TIGIT and PD1 on TPH and TFH cells were examined using flow cytometry and their expression patterns in SLE patients were then further evaluated for their correlation with auto-antibodies, disease activity and severity, B cell differentiation. Logistic regression was used to analyze the risk factors. And the receiver operating characteristic curves and logistic regression model were created to evaluate the predicting role in SLE. TIGIT±PD1+TPH, TIGIT±PD1+TFH cells in the peripheral blood of SLE patients were upregulated, whereas TIGIT+PD1-TFH was downregulated. TIGIT ± PD1+TPH, TIGIT ± PD1+TFH cells positively correlated with auto-antibodies production, disease activity and severity, whereas TIGIT+PD1-TFH cells negatively correlated. TIGIT ± PD1+TPH, TIGIT-PD1+TFH were positively correlated with the frequency of plasmablasts. Furthermore, higher TIGIT+PD1+TPH and TIGIT+PD1+TFH were shown to be risk factors for SLE, whereas TIGIT+PD1-TFH was found to be a protective factor, according to logistic regression analysis. A further logistic regression model showed that combination of TPH/TFH and routine blood indicators may has potential predicting value for SLE, with AUC of 0.957. The increased TIGIT ± PD1+TPH, increased TIGIT ± PD1+TFH, decreased TIGIT+PD1-TFH correlates with disease severity and activity, may boost our comprehending of the role of TIGIT and PD1 on TPH/TFH in SLE, and a logistic regression model based on combination of TPH/TFH and routine blood indicators shows prominent value for predicting SLE.

Alterations of lung microbiota in lung transplant recipients with pneumocystis jirovecii pneumonia

BACKGROUND

Increasing evidence revealed that lung microbiota dysbiosis was associated with pulmonary infection in lung transplant recipients (LTRs). Pneumocystis jirovecii (P. jirovecii) is an opportunistic fungal pathogen that frequently causes lethal pneumonia in LTRs. However, the lung microbiota in LTRs with P. jirovecii pneumonia (PJP) remains unknow.

METHODS

In this prospective observational study, we performed metagenomic next-generation sequencing (mNGS) on 72 bronchoalveolar lavage fluid (BALF) samples from 61 LTRs (20 with PJP, 22 with PJC, 19 time-matched stable LTRs, and 11 from LTRs after PJP recovery). We compared the lung microbiota composition of LTRs with and without P. jirovecii, and analyzed the related clinical variables.

RESULTS

BALFs collected at the episode of PJP showed a more discrete distribution with a lower species diversity, and microbiota composition differed significantly compared to P. jirovecii colonization (PJC) and control group. Human gammaherpesvirus 4, Phreatobacter oligotrophus, and Pseudomonas balearica were the differential microbiota species between the PJP and the other two groups. The network analysis revealed that most species had a positive correlation, while P. jirovecii was correlated negatively with 10 species including Acinetobacter venetianus, Pseudomonas guariconensis, Paracandidimonas soli, Acinetobacter colistiniresistens, and Castellaniella defragrans, which were enriched in the control group. The microbiota composition and diversity of BALF after PJP recovery were also different from the PJP and control groups, while the main components of the PJP recovery similar to control group. Clinical variables including age, creatinine, total protein, albumin, IgG, neutrophil, lymphocyte, CD3+CD45+, CD3+CD4+ and CD3+CD8+ T cells were deeply implicated in the alterations of lung microbiota in LTRs.

CONCLUSIONS

This study suggests that LTRs with PJP had altered lung microbiota compared to PJC, control, and after recovery groups. Furthermore, lung microbiota is related to age, renal function, nutritional and immune status in LTRs.

Decreased expression of NAT10 in peripheral blood mononuclear cells from new-onset ankylosing spondylitis and its clinical significance

BACKGROUND

NAT10 is the firstly recognized RNA acetyltransferase that participates in multiple cellular biological processes and human disease. However, the role of N-acetyltransferase 10 (NAT10) in ankylosing spondylitis (AS) is still poorly elaborated.

METHODS

Fifty-six patients with New-Onset AS, 52 healthy controls (HC), 20 patients with rheumatoid arthritis (RA) and 16 patients with systemic lupus erythematosus (SLE) were recruited from The First Afliated Hospital of Nanchang University, and their clinical characteristics were recorded. The expression level of NAT10 in peripheral blood mononuclear cell (PBMC) was examined using reverse transcription-quantitative PCR analysis. The correlations between the expression level of NAT10 in the New-Onset AS patients and disease activity of AS were examined, and receiver operating characteristic (ROC) curves were built to evaluate predictive value in AS. Univariate analysis and multivariate regression analysis were used to analyze the risk factors and construct predictive model.

RESULTS

The mRNA expressions of NAT10 in PBMC from new-onset AS patients were significantly low and there were negative correlation between mRNA NAT10 and ASDAS-CRP, BASDIA in new-onset AS patients. ROC analysis suggested that mRNA NAT10 has value in distinguishing new-onset AS patients from HC, RA and SLE. Furthermore, a novel predictive model based on mRNA NAT10 and neutrophil percentages (N%) was constructed for distinguishing new-onset AS patients from HC (AUC = 0.880, sensitivity = 84.62%, specificity = 76.92%) and the predictive model correlated with the activity of new-onset AS. Furthermore, the predictive model could distinguish new-onset AS patients from RA and SLE (AUC = 0.661, sensitivity = 90.38%, specificity = 47.22%). Moreover, the potential predictive value of the combination of predictive model-HLA-B27 for AS vs. HC with a sensitivity of 92.86% (39/42), a specificity of 100.00% (52/52) and an accuracy of 96.81% (91/94) was superior to that of HLA-B27, which in turn had a sensitivity of 84.44% (38/45), a specificity of 100.00% (52/52) and an accuracy of 92.78% (90/97).

CONCLUSION

The present study suggested that the decreased mRNA NAT10 may play a role in AS pathogenesis and predictive model based on mRNA NAT10 and N% act as bioindicator for forecast and progression of diseases.

Relationship between clinical features and droplet digital PCR copy number in non-HIV patients with pneumocystis pneumonia

OBJECTIVE

Droplet digital PCR (ddPCR) is a novel assay to detect pneumocystis jjrovecii (Pj) which has been defined to be more sensitive than qPCR in recent studies. We aimed to explore whether clinical features of pneumocystis pneumonia (PCP) were associated with ddPCR copy numbers of Pj.

METHODS

A total of 48 PCP patients were retrospectively included. Pj detection was implemented by ddPCR assay within 4 h. Bronchoalveolar fluid (BALF) samples were collected from 48 patients with molecular diagnosis as PCP via metagenomic next generation sequencing (mNGS) or quantitative PCR detection. Univariate and multivariate logistic regression were performed to screen out possible indicators for the severity of PCP. The patients were divided into two groups according to ddPCR copy numbers, and their clinical features were further analyzed.

RESULTS

Pj loading was a pro rata increase with serum (1,3)-beta-D glucan, D-dimmer, neutrophil percentage, procalcitonin and BALF polymorphonuclear leucocyte percentage, while negative correlation with albumin, PaO2/FiO2, BALF cell count, and BALF lymphocyte percentage. D-dimmer and ddPCR copy number of Pj were independent indicators for moderate/severe PCP patients with PaO2/FiO2 lower than 300. We made a ROC analysis of ddPCR copy number of Pj for PaO2/FiO2 index and grouped the patients according to the cut-off value (2.75). The high copy numbers group was characterized by higher level of inflammatory markers. Compared to low copy number group, there was lower level of the total cell count while higher level of polymorphonuclear leucocyte percentage in BALF in the high copy numbers group. Different from patients with high copy numbers, those with high copy numbers had a tendency to develop more severe complications and required advanced respiratory support.

CONCLUSION

The scenarios of patients infected with high ddPCR copy numbers of Pj showed more adverse clinical conditions. Pj loading could reflect the severity of PCP to some extent.

Negative serum (1,3) -β-D-glucan has a low power to exclude Pneumocystis jirovecii pneumonia (PJP) in HIV-uninfected patients with positive qPCR

OBJECTIVE

The current study evaluated the diagnostic performance of serum (1,3)-beta-D Glucan (BDG) in differentiating PJP from P. jirovecii-colonization in HIV-uninfected patients with P. jirovecii PCR-positive results.

METHODS

This was a single-center retrospective study between 2019 and 2021. The diagnosis of PJP was based on the following criteria: detection of P. jirovecii in sputum or BAL specimen by qPCR or microscopy; Meet at least two of the three criteria: (1) have respiratory symptoms of cough and/or dyspnea, hypoxia; (2) typical radiological picture findings; (3) receiving a complete PJP treatment. After exclusion, the participants were divided into derivation and validation cohorts. The derivation cohort defined the cut-off value of serum BDG. Then, it was verified using the validation cohort.

RESULTS

Two hundred and thirteen HIV-uninfected patients were enrolled, with 159 PJP and 54 P. jirovecii-colonized patients. BDG had outstanding specificity, LR, and PPV for PJP in both the derivation (90.00%, 8.900, and 96.43%) and the validation (91.67%, 9.176, and 96.30%) cohorts at ≥ 117.7 pg/mL. However, it had lower sensitivity and NPV in the derivation cohort (89.01% and 72.97%), which was even lower in the validation cohort (76.47% and 57.89%). Of note, BDG ≥ 117.7 pg/mL has insufficient diagnostic efficacy for PJP in patients with lung cancer, interstitial lung disease (ILD) and nephrotic syndrome. And although lymphocytes, B cells, and CD4+ T cells in PJP patients were significantly lower than those in P. jirovecii-colonized patients, the number and proportion of peripheral blood lymphocytes did not affect the diagnostic efficacy of serum BDG.

CONCLUSIONS

Serum BDG ≥ 117.7 pg/mL could effectively distinguish P. jirovecii-colonization from infection in qPCR-positive HIV-uninfected patients with infectious diseases, solid tumors (excluding lung cancer), autoimmune or inflammatory disorders, and hematological malignancies. Of note, for patients with lung cancer, ILD, and nephrotic diseases, PJP should be cautiously excluded at BDG < 117.7 pg/mL.

Machine Learning Models for Prediction of Severe Pneumocystis carinii Pneumonia after Kidney Transplantation: A Single-Center Retrospective Study

BACKGROUND

The objective of this study was to formulate and validate a prognostic model for postoperative severe Pneumocystis carinii pneumonia (SPCP) in kidney transplant recipients utilizing machine learning algorithms, and to compare the performance of various models.

METHODS

Clinical manifestations and laboratory test results upon admission were gathered as variables for 88 patients who experienced PCP following kidney transplantation. The most discriminative variables were identified, and subsequently, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (XGB) models were constructed. Finally, the models' predictive capabilities were assessed through ROC curves, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1-scores. The Shapley additive explanations (SHAP) algorithm was employed to elucidate the contributions of the most effective model's variables.

RESULTS

Through lasso regression, five features-hemoglobin (Hb), Procalcitonin (PCT), C-reactive protein (CRP), progressive dyspnea, and Albumin (ALB)-were identified, and six machine learning models were developed using these variables after evaluating their correlation and multicollinearity. In the validation cohort, the RF model demonstrated the highest AUC (0.920 (0.810-1.000), F1-Score (0.8), accuracy (0.885), sensitivity (0.818), PPV (0.667), and NPV (0.913) among the six models, while the XGB and KNN models exhibited the highest specificity (0.909) among the six models. Notably, CRP exerted a significant influence on the models, as revealed by SHAP and feature importance rankings.

CONCLUSIONS

Machine learning algorithms offer a viable approach for constructing prognostic models to predict the development of severe disease following PCP in kidney transplant recipients, with potential practical applications.

Clinical characteristics and risk factor analysis of Pneumocystis jirovecii pneumonia in patients with CKD: a machine learning-based approach

Patients with chronic kidney disease (CKD) who are being treated with immunosuppressive medications are at risk for developing Pneumocystis jirovecii pneumonia (PCP). We attempted to characterize the clinical aspects of PCP in CKD patients in order to alert high-risk patients with bad prognosis. A retrospective study of CKD patients was conducted from June 2018 to June 2022. Based on PCP diagnostic criteria, these patients were divided into PCP and non-PCP groups. Using univariate and multivariate logistic regression analysis, risk indicators were evaluated, and nomogram and decision tree were developed. Of the CKD patients screened for Pneumocystis carinii nucleic acid, 1512 were included. Two-hundred forty four (16.14%) were diagnosed with PCP. Of the PCP, 88.5% was receiving glucocorticoid (GC) therapy, of which 66.3% received more than 0.5 mg/kg GC. Multivariate analysis showed that membranous nephropathy (OR 2.35, 95% CI 1.45-3.80), immunosuppressive therapy (OR 1.94, 95% CI 1.06-3.69), and ground glass opacity of CT scanning (OR 1.71, 95% CI 1.10-2.65) were associated with increased risk of Pneumocystis carinii infection. The AUC of nomogram based on logistics regression was 0.78 (0.75-0.81). The mortality in patients with PCP was 32.40%. Univariate analysis and decision tree showed that pulmonary insufficiency (PO₂: OR 0.98, 95% CI 0.96-1.00), elevated APTT (OR 1.07, 95% CI 1.04-1.11), and reduced hemoglobin (OR 0.97, 95% CI 0.96-0.98) were associated with poor prognosis. PCP is not rare in CKD patients, particularly in those treated with immunosuppressive therapy. Considering the high mortality of the cases, further studies on the prevention and management of these patients are needed.

Risk factors associated with Pneumocystis jirovecii pneumonia in non-HIV immunocompromised patients and co-pathogens analysis by metagenomic next-generation sequencing

BACKGROUND

Pneumocystis jirovecii pneumonia (PJP) is one of the most common opportunistic infections in immunocompromised patients. However, the accurate prediction of the development of PJP in non-HIV immunocompromised patients is still unclear.

METHODS

Non-HIV immunocompromised patients confirmed diagnosis of PJP by the clinical symptoms, chest computed tomography and etiological results of metagenomic next-generation sequencing (mNGS) were enrolled as observation group. Another group of matched non-HIV immunocompromised patients with non-PJP pneumonia were enrolled to control group. The risk factors for the development of PJP and the co-pathogens in the bronchoalveolar lavage fluid (BALF) detected by mNGS were analyzed.  RESULTS: A total of 67 (33 PJP, 34 non-PJP) participants were enrolled from Fujian Provincial Hospital. The ages, males and underlying illnesses were not significantly different between the two groups. Compared to non-PJP patients, PJP patients were more tends to have the symptoms of fever and dyspnea. The LYM and ALB were significantly lower in PJP patients than in non-PJP patients. Conversely, LDH and serum BDG in PJP patients were significantly higher than in non-PJP controls. For immunological indicators, the levels of immunoglobulin A, G, M and complement C3, C4, the numbers of T, B, and NK cells, had no statistical difference between these two groups. Logistic multivariate analysis showed that concomitant use of corticosteroids and immunosuppressant (OR 14.146, P = 0.004) and the lymphocyte counts < 0.7 × 10⁹/L (OR 6.882, P = 0.011) were risk factors for the development of PJP in non-HIV immunocompromised patients. 81.82% (27/33) and 64.71% (22/34) mixed infections were identified by mNGS in the PJP group and non-PJP group separately. CMV, EBV and Candida were the leading co-pathogens in PJP patients. The percentages of CMV and EBV identified by mNGS in PJP group were significantly higher than those in the control group(p < 0.005).  CONCLUSIONS: Clinicians should pay close attention to the development of PJP in non-HIV immunocompromised patients who possess the risk factors of concomitant use of corticosteroids and immunosuppressant and the lymphocyte counts < 0.7 × 10⁹/L. Prophylaxis for PJP cannot rely solely on CD4⁺ T counts in non-HIV immunocompromised patients. Whether CMV infection increases the risk of PJP remains to be further investigated.

Development and Validation of a Diagnostic Nomogram for Pneumocystis jirovecii Pneumonia in Non-HIV-Infected Pneumonia Patients Undergoing Oral Glucocorticoid Treatment

Purpose

Pneumocystis jirovecii pneumonia (PJP) is an opportunistic but potentially fatal infection with increasing prevalence in HIV-free patients. Glucocorticoid therapy is one of the most important risk factors for PJP. The delay in diagnosis contributes to poor outcomes. Hence, the aim of this study was to develop and validate a nomogram for the diagnosis of PJP in patients with non-HIV-infected pneumonia who are undergoing oral glucocorticoid treatment.

Patients and Methods

This study was a retrospective, cross-sectional research. The development group included 434 patients who were admitted with pneumonia from 6 hospitals. Demographics, symptomatic features, laboratory and computed tomography data were analyzed using the least absolute shrinkage and selection operator (LASSO) to select potential diagnostic indicators. Binary logistic regression was used to develop a diagnostic nomogram. Another 119 patients with pneumonia admitted at Sichuan Provincial People's Hospital was used as the validation group. The diagnostic performance of the nomogram was measured by area under the receiver-operating-characteristics curve (AUC), calibration curves, and the net benefit by decision curve.

Results

PJP prevalence was 25.3% in the development group. LASSO regression revealed that age, lymphocyte count, fever, dry cough, respiratory failure, ground-glass opacity in lungs, glucocorticoid therapy duration, and immunosuppressive therapy were indicators of PJP. The nomogram showed robust discrimination, with an AUC of 0.82 (95% CI 0.77-0.86) in the development group and an AUC of 0.87 (95% CI 0.80-0.94) in the validation group, both showing acceptable calibration. In the decision curve analysis, our model consistently achieved a greater net benefit across almost all ranges of clinical thresholds.

Conclusion

We developed a nomogram with good diagnostic power for PJP diagnosis in pneumonia patients receiving oral glucocorticoids. This nomogram may help promote timely treatment of PJP and thus reduce the mortality rate in these patients.

Metagenomic next-generation sequencing for the diagnosis of Pneumocystis jirovecii Pneumonia in critically pediatric patients

OBJECTIVE

The aim of this study was to evaluate the effectiveness of metagenomic next-generation sequencing (mNGS) for the diagnosis of Pneumocystis jirovecii Pneumonia (PCP) in critically pediatric patients.

METHODS

Seventeen critically pediatric patients with PCP and sixty patients diagnosed with non-PCP pneumonia who were admitted in pediatric intensive care unit between June 2018 and July 2021 were enrolled. Conventional methods and mNGS for detecting Pneumocystis jirovecii (P. jirovecii) were compared. The patients' demographics, comorbidities, laboratory test results, antibiotic treatment response and 30 day mortality were analyzed.

RESULT

The mNGS showed a satisfying diagnostic performance with a sensitivity of 100% in detecting P. jirovecii compared with Gomori methenamine silver staining (5.9%), serum (1,3)-β-D-glucan (86.7%) and and LDH (55.6%). The diagnostic specificity of mNGS for PCP was higher than that of serum BDG (56.7%) and LDH (71.4%). In PCP group, over one thirds' cases had mixed infections. Compared with survivors, non-survivors had higher stringently mapped read numbers (SMRNs) in bronchoalveolar lavage fluid (BALF) sample (P < 0.05), suggesting SMRNs were closely associated with the severity of response. The detection for P. jirovecii by mNGS both in BALF and blood samples reached a concordance rate of 100%, and the SMRNs in the BALF were remarkably higher than that in blood samples. Initial antimicrobial treatment was modified in 88.2% of PCP patients based on the mNGS results.

CONCLUSION

The mNGS is a potential and efficient technology in diagnosing PCP and shows a satisfying performance in the detection of co-pathogens. Both blood and BALF samples for mNGS are suggested for the presumptive diagnosis of PCP.

Development and validation of tools for predicting the risk of death and ICU admission of non-HIV-infected patients with Pneumocystis jirovecii pneumonia

Introduction

The mortality rate of non-HIV-infected Pneumocystis jirovecii pneumonia (PCP) is high. This research aimed to develop and validate two clinical tools for predicting the risk of death and intensive care unit (ICU) admission in non-HIV-infected patients with PCP to reduce mortality.

Methods

A retrospective study was conducted at Peking Union Medical College Hospital between 2012 and 2021. All proven and probable non-HIV-infected patients with PCP were included. The least absolute shrinkage and selection operator method and multivariable logistic regression analysis were used to select the high-risk prognostic parameters. In the validation, the receiver operating characteristic curve and concordance index were used to quantify the discrimination performance. Calibration curves were constructed to assess the predictive consistency compared with the actual observations. A likelihood ratio test was used to compare the tool and CURB-65 score.

Results

In total, 508 patients were enrolled in the study. The tool for predicting death included eight factors: age, chronic lung disease, respiratory rate, blood urea nitrogen (BUN), lactate dehydrogenase (LDH), cytomegalovirus infection, shock, and invasive mechanical ventilation. The tool for predicting ICU admission composed of the following factors: respiratory rate, dyspnea, lung moist rales, LDH, BUN, C-reactive protein/albumin ratio, and pleural effusion. In external validation, the two clinical models performed well, showing good AUCs (0.915 and 0.880) and fit calibration plots. Compared with the CURB-65 score, our tool was more informative and had a higher predictive ability (AUC: 0.880 vs. 0.557) for predicting the risk of ICU admission.

Conclusion

In conclusion, we developed and validated tools to predict death and ICU admission risks of non-HIV patients with PCP. Based on the information from the tools, clinicians can tailor appropriate therapy plans and use appropriate monitoring levels for high-risk patients, eventually reducing the mortality of those with PCP.

Supplementary Role of Immunological Indicators in the Diagnosis and Prognosis of Pneumocystis Pneumonia in Non-HIV Immunocompromised Patients

Background

Pneumocystis pneumonia (PCP) has a high mortality in HIV-negative immunocompromised patients. The occurrence and development of PCP are believed to be correlated with the level of lymphocytes and their subsets. The aim of this study was to determine if the levels of lymphocyte subpopulations and immunoglobulin are associated with PCP.

Methods

A total of 74 immunocompromised patients were enrolled in this single-center cohort study. Diagnosis of PCP was based on the relevant pulmonary symptoms and radiological imaging, and the detection of Pneumocystis jirovecii in BAL fluid or biopsy tissue by metagenomic next-generation sequencing (mNGS). All patients were divided into two groups (PCP group and non-PCP group) and the patients in PCP group were then divided into two groupsbased on the outcome of the disease during the hospitalization.

Results

We observed a significant lower level of IgG (p=0.000) and B lymphocyte counts (p=0.017) in the PCP group comparing to that in the non-PCP group. CD4+ T cell counts, as well as the ratio of CD4+/CD8+ T cells in circulation and BAL fluid were also lower in the PCP group comparing to those in the non-PCP group. Lactate dehydrogenase (LDH) in the PCP group was significantly higher than that in the non-PCP group (p=0.029). In the PCP group, a lower level of total lymphocytes (p=0.004), T cells (p=0.001), CD4+ cells (p=0.001), and CD8+ cells (p=0.007), as well as the proportion of lymphocytes in BAL fluid (p=0.000) were found in deceased patients comparing to those in the survived group.

Conclusion

Our study revealed an important role of humoral immunity in the infection of Pneumocystis jirovecii. The level of B cells and IgG could be used as a supplement to predict the occurrence of PCP. The level of CD4+ and CD8+ lymphocytes was significantly correlated with the outcome of PCP.

Expression and Clinical Significance of the m6A RNA-Binding Proteins YTHDF2 in Peripheral Blood Mononuclear Cells From New-Onset Ankylosing Spondylitis

This study has focused on determining the association of m6A methyltransferase [methyltransferase-like 3 (METTL3), methyltransferase-like 14 (METTL14), and Wilms tumor 1-associating protein (WTAP)], demethylase [fat mass and obesity-associated protein (FTO) and alkylation repair homolog protein 5 (ALKBH5)], RNA-binding proteins [YT521-B homology domains 2 (YTHDF2)], and ankylosing spondylitis (AS). A total of 154 specimens, containing 79 patients with new-onset AS and 75 healthy controls (HCs), participated in the study. The mRNA expressions of these m6A methyltransferase, demethylase, and RNA-binding protein in peripheral blood mononuclear cells (PBMCs) were detected by quantitative real-time PCR (qRT-PCR). The data showed that the mRNA expressions of YTHDF2 and ALKBH5 in PBMC from patients with new-onset AS were significantly decreased, and there was a positive correlation between RNA-binding proteins (YTHDF2) and demethylase (ALKBH5) in patients with new-onset AS. Logistic regression analysis demonstrated that the expression of YTHDF2 mRNA in PBMC is a risk factor of AS. Receiver operating characteristic (ROC) analysis of the area under the curve (AUC) for mRNA YTHDF2 in new-onset AS and HC was 0.692, with a cutoff value of &lt;0.8724, a sensitivity of 67%, and a specificity of 63%. Moreover, we constructed a novel predictive model based on a combination of mRNA YTHDF2 and systemic immune-inflammation index (SII) for AS diagnosis (AUC = 0.865, sensitivity = 79.45%, specificity = 84.00%), and the predictive model correlated with the activity and severity of AS. This study indicates that the mRNA expression of YTHDF2 in PBMC may be involved in AS pathogenesis and a predictive model based on a combination of mRNA YTHDF2 and SII acts as a marker for diagnosis and progression of diseases.

Pneumocystis Jirovecii Pneumonia Diagnosis via Metagenomic Next-Generation Sequencing

The incidence of non-HIV-infected Pneumocystis Jirovecii Pneumonia (PJP) is increasing. The prognosis for non-HIV PJP is poor and diagnostic tests are of lower sensitivity in non-HIV patients. Metagenomic next-generation sequencing (mNGS) was compared with routine detection assays, including Gomori methenamine silver (GMS) stain and polymerase chain reaction (PCR) technique. Specimens of 4 bronchoalveolar lavages (BAL) and 1 lung tissue samples were obtained from 4 non-HIV patients from our hospitals. Although both GMS and mNGS were positive for P. jirovecii with PCR as positive control, the testing time of mNGS was obviously shorter than GMS. Compared with the traditional GMS method, mNGS has absolute advantages. However, the issue with PJP presentations having atypical symptoms and ambiguous imaging features persists. Hence, the disease can easily be ignored. Secondly, PJP progresses rapidly in non-HIV-infected patients and can cause severe respiratory failure with unfavorable prognosis. This study affirms that mNGS can be used to quickly and accurately diagnose PJP, but a combination of clinical judgement of symptoms, laboratory testing, and imaging examination is required to make a comprehensive judgment along with mNGS test results.

Characterization and associated risk factors of Pneumocystis Jirovecii Pneumonia in patients with AIRD: A retrospective study

OBJECTIVES

To explore clinical features of autoimmune inflammatory rheumatic disease (AIRD) patients with Pneumocystis jirovecii pneumonia (PJP) and identify potential risk factors and prognostic factors.

METHODS

AIRD patients who had respiratory symptoms and underwent Pneumocystis jirovecii detection were identified from the database in our department from November 2018 to October 2020. These patients were categorized into PJP and non-PJP groups according to the diagnostic criteria of PJP. Univariate and multivariate analysis were conducted.

RESULTS

173 patients were enrolled and 46 of them had laboratory confirmed PJP. Glucocorticoid (GC) increased the risk of PJP in a dose-dependent manner. In addition, shorter duration of immunosuppressive therapy (IST), combination therapy with CsA, and chronic pulmonary comorbidities were also strongly associated with a higher risk of PJP. Combination of IgM and IgA could well identify AIRD patients with PJP from other AIRD patients with respiratory symptoms, with the optimal cutoff value of -0.96 g/L. 7 of 46 AIRD patients with PJP died (15.2%). Higher level of serum LDH, dyspnea, ARDS, and the presence of extensive ground glass opacity (GGO) in radiologic examinations were more common in deceased patients.

CONCLUSIONS

AIRD patients with high-dose GC treatment, recent initiation of IST, combination therapy with CsA, and history of chronic pulmonary diseases were associated with greater risks of PJP infection. PJP patients with a higher level of serum LDH, dyspnea, moderate and severe ARDS, and the presence of extensive GGO in radiologic examinations had poorer prognosis.

TRIAL REGISTRATION

Chinese Clinical Trial Register; https://www.chictr.org.cn/; ChiCTR2100044095.