Prophylaxis and Treatment of <i>Pneumocystis jiroveci</i> Pneumonia in Lymphoma Patients Subjected to Rituximab-Contained Therapy: A Systemic Review and Meta-Analysis

<div><p><i>Pneumocystis jiroveci</i> pneumonia (PCP) is frequently reported in lymphoma patients treated with rituximab-contained regimens. There is a trend toward a difference in PCP risk between bi- and tri-weekly regimens. The aims of this systemic review and meta-analysis were to estimate the risk for PCP in these patients, compare the impact of different regimens on the risk, and evaluate the efficacy of prophylaxis. The cohort studies with incept up to January 2014 were retrieved from the Cochrane Library, Medline, Embase, and Web of Science databases. Studies that compared the incidence of PCP in patients with and without rituximab treatment were conducted. Studies that reported the results of prophylaxis were concentrated to evaluate the efficacy of prophylaxis. Fixed effect Mantel-Haenszel model was chosen as the main analysis method. Funnel plots were examined to estimate the potential selection bias. Egger’s test and Begg’s test were used for the determination of possible small study bias. Eleven cohort studies that met the inclusion criteria were finally included. Results indicated that rituximab was associated with a significantly increased risk for PCP (28/942 vs 5/977; risk ratio: 3.65; 95% confidence interval 1.65 to 8.07; <i>P</i>=0.001), and no heterogeneity existed between different studies (<i>I²</i>=0%). Little significant difference in PCP risk was found between bi-weekly and tri-weekly regimens (risk ratio: 3.11; 95% confidence interval 0.92 to 10.52, <i>P</i>=0.068). PCP risk was inversely associated with prophylaxis in patients treated with rituximab (0/222 vs 26/986; risk ratio: 0.28; 95% confidence interval 0.09 to 0.94; <i>P</i>=0.039). In conclusion, PCP risk was increased significantly in lymphoma patients subjected to rituximab-contained chemotherapies. Difference in PCP risk between bi-weekly and tri-weekly regimens was not significant. Additionally, prophylaxis was dramatically effective in preventing PCP in rituximab-received lymphoma patients, suggesting that rituximab should be recommended for these patients.</p></div

Flow diagram of identification process for eligible studies.

<p>Flow diagram of identification process for eligible studies.</p

Effect of rituximab treatment on PCP risk.

<p>M-H pooled risk ratio = 3.65, fixed effect model method. R: rituximab. Rituximab increased the risk for PCP in lymphoma patients significantly.</p

PCP risk in bi-weekly and tri-weekly regimens.

<p>M-H pooled risk ratio = 3.11; fixed effect model method. R-C-14: rituximab-added chemotherapy bi-weekly; R-C-21: rituximab-added chemotherapy tri-weekly. Patients treated with bi-weekly regimen seemed to have a higher risk for PCP but the difference between the two regimens was not statistically significant.</p

Effect of prophylaxis on PCP risk in rituximab-received lymphoma patients.

<p>M-H pooled risk ratio = 0.28, fixed effect model method. Prophylaxis dramatically reduced PCP risk in rituximab-received patients.</p

Image_1_A Novel Strategy for Predicting 72-h Mortality After Admission in Patients With Polytrauma: A Study on the Development and Validation of a Web-Based Calculator.tif

BackgroundEarly and accessible screening of patients with polytrauma at a high risk of hospital death is essential. The purpose of this research was to seek an accurate and convenient solution to predict deaths occurring within 72 h after admission of these patients.MethodsA secondary analysis was conducted on 3,075 patients with polytrauma from the Dryad database. We imputed missing values in eligible individuals with the k-nearest neighbor algorithm and then randomly stratified them into the training group (n = 2,461) and the validation group (n = 614) based on a proportion of 8:2. The restricted cubic spline, univariate, backward stepwise, and multivariate logistic regression methods were employed to determine the suitable predictors. Calibration and receiver operating characteristic (ROC) curves were applied to assess the calibration and discrimination of the obtained model. The decision curve analysis was then chosen as the measure to examine the clinical usage.ResultsAge, the Glasgow Coma Scale score, the Injury Severity Score, base excess, and the initial lactate level were inferred as independent prognostic factors related to mortality. These factors were then integrated and applied to construct a model. The performance of calibration plots, ROC curves, and decision curve analysis indicated that the model had satisfactory predictive power for 72-h mortality after admission of patients with polytrauma. Moreover, we developed a nomogram for visualization and a web-based calculator for convenient application (https://songandwen.shinyapps.io/DynNomapp/).ConclusionsA convenient web-based calculator was constructed to robustly estimate the risk of death in patients with polytrauma within 72 h after admission, which may aid in further rationalization of clinical decision-making and accurate individual treatment.</p

Funnel plot (with pseudo 95% confidence limits) for rituximab on PCP risk.

<p>Funnel plot (with pseudo 95% confidence limits) for rituximab on PCP risk.</p

Data_Sheet_1_A Novel Strategy for Predicting 72-h Mortality After Admission in Patients With Polytrauma: A Study on the Development and Validation of a Web-Based Calculator.docx

BackgroundEarly and accessible screening of patients with polytrauma at a high risk of hospital death is essential. The purpose of this research was to seek an accurate and convenient solution to predict deaths occurring within 72 h after admission of these patients.MethodsA secondary analysis was conducted on 3,075 patients with polytrauma from the Dryad database. We imputed missing values in eligible individuals with the k-nearest neighbor algorithm and then randomly stratified them into the training group (n = 2,461) and the validation group (n = 614) based on a proportion of 8:2. The restricted cubic spline, univariate, backward stepwise, and multivariate logistic regression methods were employed to determine the suitable predictors. Calibration and receiver operating characteristic (ROC) curves were applied to assess the calibration and discrimination of the obtained model. The decision curve analysis was then chosen as the measure to examine the clinical usage.ResultsAge, the Glasgow Coma Scale score, the Injury Severity Score, base excess, and the initial lactate level were inferred as independent prognostic factors related to mortality. These factors were then integrated and applied to construct a model. The performance of calibration plots, ROC curves, and decision curve analysis indicated that the model had satisfactory predictive power for 72-h mortality after admission of patients with polytrauma. Moreover, we developed a nomogram for visualization and a web-based calculator for convenient application (https://songandwen.shinyapps.io/DynNomapp/).ConclusionsA convenient web-based calculator was constructed to robustly estimate the risk of death in patients with polytrauma within 72 h after admission, which may aid in further rationalization of clinical decision-making and accurate individual treatment.</p

Image_2_A Novel Strategy for Predicting 72-h Mortality After Admission in Patients With Polytrauma: A Study on the Development and Validation of a Web-Based Calculator.tif

BackgroundEarly and accessible screening of patients with polytrauma at a high risk of hospital death is essential. The purpose of this research was to seek an accurate and convenient solution to predict deaths occurring within 72 h after admission of these patients.MethodsA secondary analysis was conducted on 3,075 patients with polytrauma from the Dryad database. We imputed missing values in eligible individuals with the k-nearest neighbor algorithm and then randomly stratified them into the training group (n = 2,461) and the validation group (n = 614) based on a proportion of 8:2. The restricted cubic spline, univariate, backward stepwise, and multivariate logistic regression methods were employed to determine the suitable predictors. Calibration and receiver operating characteristic (ROC) curves were applied to assess the calibration and discrimination of the obtained model. The decision curve analysis was then chosen as the measure to examine the clinical usage.ResultsAge, the Glasgow Coma Scale score, the Injury Severity Score, base excess, and the initial lactate level were inferred as independent prognostic factors related to mortality. These factors were then integrated and applied to construct a model. The performance of calibration plots, ROC curves, and decision curve analysis indicated that the model had satisfactory predictive power for 72-h mortality after admission of patients with polytrauma. Moreover, we developed a nomogram for visualization and a web-based calculator for convenient application (https://songandwen.shinyapps.io/DynNomapp/).ConclusionsA convenient web-based calculator was constructed to robustly estimate the risk of death in patients with polytrauma within 72 h after admission, which may aid in further rationalization of clinical decision-making and accurate individual treatment.</p

Additional file 2 of Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome

Additional file 2: Supplementary Table 1. Gene expression data from the Gene Expression Omnibus (GEO) database