Abstract
ObjectivePneumocystis jirovecii pneumonia (PCP) is a life-threatening disease associated with a high mortality rate among immunocompromised patient populations. Invasive mechanical ventilation (IMV) is a crucial component of treatment for PCP patients with progressive hypoxemia. This study explored the risk factors for IMV and established a model for early predicting the risk of IMV among patients with PCP.MethodsA multicenter, observational cohort study was conducted in 10 hospitals in China. Patients diagnosed with PCP were included, and their baseline clinical characteristics were collected. A Boruta analysis was performed to identify potentially important clinical features associated with the use of IMV during hospitalization. Selected variables were further analyzed using univariate and multivariable logistic regression. A logistic regression model was established based on independent risk factors for IMV and visualized using a nomogram.ResultsIn total, 103 patients comprised the training cohort for model development, and 45 comprised the validation cohort to confirm the model’s performance. No significant differences were observed in baseline clinical characteristics between the training and validation cohorts. Boruta analysis identified eight clinical features associated with IMV, three of which were further confirmed to be independent risk factors for IMV, including age (odds ratio [OR] 2.615 [95% confidence interval (CI) 1.110–6.159]; p = 0.028), oxygenation index (OR 0.217 [95% CI 0.078–0.604]; p = 0.003), and serum lactate dehydrogenase level (OR 1.864 [95% CI 1.040–3.341]; p = 0.037). Incorporating these three variables, the nomogram achieved good concordance indices of 0.829 (95% CI 0.752–0.906) and 0.818 (95% CI 0.686–0.950) in predicting IMV in the training and validation cohorts, respectively, and had well-fitted calibration curves.ConclusionsThe nomogram demonstrated accurate prediction of IMV in patients with PCP. Clinical application of this model enables early identification of patients with PCP who require IMV, which, in turn, may lead to rational therapeutic choices and improved clinical outcomes.
Highlights
Pneumocystis jirovecii pneumonia (PCP) is an opportunistic fungal infection that commonly affects immunocompromised patient populations
(odds ratio [OR] 2.615 [95% confidence interval (CI) 1.110–6.159]; p = 0.028), oxygenation index, and serum lactate dehydrogenase level
Incorporating these three variables, the nomogram achieved good concordance indices of 0.829 and 0.818 in predicting invasive mechanical ventilation (IMV) in the training and validation cohorts, respectively, and had well-fitted calibration curves
Summary
Pneumocystis jirovecii pneumonia (PCP) is an opportunistic fungal infection that commonly affects immunocompromised patient populations. It remains a severe disease associated with a high mortality rate among patients either positive or negative for human immunodeficiency virus (HIV) infection (Roux et al, 2014; Alanio et al, 2016). According to data from previous studies, 16% of HIVinfected and 50%–64% of non-HIV-infected PCP patients require mechanical ventilation during hospitalization (Nüesch et al, 1999; Kim et al, 2014). It has been found that failure of non-invasive mechanical ventilation is significantly associated with hospital survival among PCP patients, with a mortality rate as high as 88.9% (Wang et al, 2020). While prognostic factors for mortality have been well established (Walzer et al, 2008; Fei et al, 2009; Wang et al, 2011; Weng et al, 2016; Gaborit et al, 2019), little is known about the early prediction of requirements for IMV in patients with PCP so far
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