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Collected data included patients\u2019 demographics, smoking status, comorbidities, immunosuppressive diseases, blood laboratory tests, in-hospital treatment, and adverse clinical outcomes. Predictor selection was performed using the least absolute shrinkage and selection operator (LASSO) and logistic regression, with selected features incorporated into a nomogram. Internal validation was conducted using a 500-bootstrap resampling method to ensure model robustness. Model performance was assessed via the area under the receiver operating curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curve (CIC).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Among the 431 patients, 243 (56.4%) experienced adverse clinical outcomes. LASSO regression screened 21 variables, selecting 9 predictors with non-zero coefficients through 10-fold cross-validation at lambda.1se\u2009=\u20090.0453 (log(lambda.1se) = -3.092). Multivariate logistic regression identified 7 independent risk factors for adverse clinical outcomes: smoking status, cytomegalovirus infection, diabetes, neutrophil-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), PaO<jats:sub>2<\/jats:sub>\/FiO<jats:sub>2<\/jats:sub> (PFR), and lymphocyte subset. These factors were incorporated into a nomogram, achieving an AUC of 0.89 (95% CI: 0.86\u20130.92), with the Hosmer\u2013Lemeshow test (<jats:italic>p<\/jats:italic>\u2009=\u20090.134) and calibration curves showing strong agreement between predicted and observed outcomes. Internal validation via 500-bootstrap resampling yielded a bias-corrected AUC of 0.83 (95% CI: 0.80\u20130.86). DCA demonstrated strong clinical decision-making utility, while the CIC confirmed its practical reliability.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Regression analysis identified smoking status, CMV infection, diabetes, NLR, LDH, PFR, and lymphocyte subset as independent risk factors for adverse clinical outcomes in non-HIV PJP patients. 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