4.7 Article

Predictors of Prolonged Hospitalizations in Pediatric Complicated Pneumonia

Journal

CHEST
Volume 153, Issue 1, Pages 172-180

Publisher

ELSEVIER
DOI: 10.1016/j.chest.2017.09.021

Keywords

empyema; infectious disease; pediatric infections; pleural effusion; pneumonia

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BACKGROUND: Pediatric community-acquired complicated pneumonia (PCACP) is characterized by a prolonged clinical course, but this may be highly variable. METHODS: A multicenter observational study was conducted to develop and validate a clinical prediction tool for prolonged hospitalizations in PCACP. The derivation and validation cohorts consisted of 144 and 169 patients with PCACP, respectively, hospitalized between the years 1997 and 2017 in three tertiary care hospitals. Logistic regression analyses were used to identify parameters associated with a prolonged hospitalization and to develop and validate a prediction model for constructing a useful clinical tool. RESULTS: Higher levels of lactate dehydrogenase (LDH) (P < .026) and lower levels of glucose (P = .018) in pleural fluid were significantly associated with prolonged hospitalization. A predictive stepwise logistic regression model was developed and applied to the validation cohort. The area under the receiver operating characteristic curve (AUROC) constructed indicated that the model retained good predictive value (AUROC for the derivation vs validation data, [ 0.77 (95% CI, 0.66-0.87) vs 0.82 (95% CI, 0.72-0.91)], respectively). From these data, a clinical tool was derived; the combination of pleural LDH > 1,000 units/L and pleural glucose levels < 1 mmol/L or pleural LDH levels > 2,000 units/L and pleural glucose levels < 2 mmol/L or pleural LDH levels > 3,000 units/L and pleural glucose < 3 mmol/L predict prolonged hospitalization with positive and negative predictive values of 78% (95% CI, 0.71-0.85) and 73% (95% CI, 0.59-0.85), respectively. CONCLUSIONS: In children, pleural fluid LDH and glucose levels are useful parameters for assessing the severity of PCACP. The model developed in this study accurately predicts patients who will have prolonged hospitalization.

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