Development and validation of a simple-to-use nomogram for predicting the delayed radiographic recovery in children with mycoplasma pneumoniae pneumonia complicated with atelectasis.

This study aimed to develop and validate a simple-to-use nomogram for predicting the delayed radiographic recovery in children with mycoplasma pneumoniae pneumonia (MPP) complicated with atelectasis. A retrospective study of 306 children with MPP complicated with atelectasis was performed at the Children's Hospital of Chongqing Medical University from February 2017 to March 2020.The patients were divided into recovery group and delayed recovery group based on chest CT scan 1 month after discharge. A least absolute shrinkage and selection operator (LASSO) regression model was used to identify the optimal predictors, and the predictive nomogram was plotted by multivariable logistic regression. The nomogram was assessed by calibration, discrimination, and clinical utility. LASSO regression analysis identified that lactate dehydrogenase (LDH), duration of illness prior to bronchoalveolar lavage (BAL), systemic glucocorticoid use and extrapulmonary complications were the optimal predictors for delayed radiographic recovery. The nomogram was plotted by the four predictors. The area under the Receiver Operating Characteristic (ROC) curve of the nomogram was 0.840 (95% CI = 784 ∼ 0.896) in the training set and 0.833 (95% CI = 0.8737 ∼ 0.930) in the testing set. The calibration curve demonstrated that the nomogram was well-fitted, and decision curve analysis (DCA) showed that the nomogram was clinically beneficial. This study developed and validated a simple-to-use nomogram for predicting delayed radiographic recovery in children with MPP complicated with atelectasis. This might be generally applied in clinical practice.

Clinical Characteristics of Necrotizing Pneumonia Caused by Different Pathogens.

Objective: To explore the clinical characteristics of necrotizing pneumonia (NP) caused by different pathogens. Methods: A total of 282 children with NP admitted to Kunming Children's Hospital from January 2014 to November 2022 were enrolled. The clinical data of all children was collected. According to the different pathogens causing NP, the children were divided into three groups: the Mycoplasma pneumoniae necrotizing pneumonia (MPNP) group, the bacterial necrotizing pneumonia (BNP) group, and necrotizing pneumonia with no pathogen detected (NNP) group. The basic information, symptoms, signs, laboratory tests, radiological features, treatment, and prognosis of the three groups were compared. Results: Among the 282 cases of NP, there were 62 (22.0%) cases of MPNP, 98 (34.75%) cases of BNP, and 142 (50.35%) cases of NNP. The most common bacteria causing NP were Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, and Acinetobacter baumannii, respectively. Most clinical features of the three groups were statistically significant. The area under the concentration curve of white blood cell, C-reactive protein, albumin, neutrophil percentage, and fibrinogen in differentiating MPNP from BNP were 0.743 (0.638-0.849), 0.797 (0.711-0.883), 0.766 (0.671-0.861), 0.616 (0.509-0.724), and 0.634 (0.523-0.744), respectively. The decision curve showed that white blood cells, albumin, and C-reactive protein had good clinical application in differentiating MPNP from BNP. All patients were improved and discharged without death. Conclusion: Bacteria are the most common cause of NP, and the most common bacteria are Streptococcus pneumoniae, Staphylococcus aureus, and Haemophilus influenzae. NP with no pathogen detected accounted for a large proportion. white blood, C-reactive protein, and albumin can identify the pathogens of NP. Patients with BNP were more severe, had a longer hospital stay, and were more likely to undergo closed drainage and surgery.

Risk prediction model for long-term atelectasis in children with pneumonia.

BACKGROUND: This study aimed to develop a risk prediction model for long-term atelectasis in children with pneumonia. METHODS: A retrospective study of 532 children with atelectasis was performed at the Children's Hospital of Chongqing Medical University from February 2017 to March 2020. The predictive variables were screened by LASSO regression analysis and the nomogram was drawn by R software. The area under the Receiver Operating Characteristic (ROC) curve, calibration chart and decision curve were used to evaluate the predictive accuracy and clinical utility. 1000 Bootstrap resampling was used for internal verification. RESULTS: Multivariate logistic regression analysis showed that clinical course before bronchoscopy, length of stay, bronchial mucus plug formation, age were independent risk factors for long-term atelectasis in children. The area under the ROC curve of nomogram was 0.857(95% CI = 0.8136 ~ 0.9006) in training set and 0.849(95% CI = 0.7848-0.9132) in the testing set. The calibration curve demonstrated that the nomogram was well-fitted, and decision curve analysis (DCA) showed that the nomogram had good clinical utility. CONCLUSIONS: The model based on the risk factors of long-term atelectasis in children with pneumonia has good predictive accuracy and consistency, which can provide a certain reference value for clinical prevention and treatment of long-term atelectasis in children.

Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia.

Objective: To analyze the predictive factors for necrotizing pneumonia (NP) in children with Mycoplasma pneumoniae pneumonia (MPP) and construct a prediction model. Methods: The clinical data with MPP at the Children's Hospital of Kunming Medical University from January 2014 to November 2022 were retrospectively analyzed. Eighty-four children with MPP who developed NP were divided into the necrotizing group, and 168 children who did not develop NP were divided into the non-necrotizing group by propensity-score matching. LASSO regression was used to select the optimal factors, and multivariate logistic regression analysis was used to establish a clinical prediction model. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the nomogram. Clinical decision curve analysis was used to evaluate the clinical predictive value. Results: LASSO regression analysis showed that bacterial co-infection, chest pain, LDH, CRP, duration of fever, and D-dimer were the influencing factors for NP in children with MPP (P < 0.05). The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting necrotizing MPP was 0.870 (95% CI: 0.813-0.927, P < 0.001) in the training set and 0.843 (95% CI: 0.757-0.930, P < 0.001) in the validation set. The Bootstrap repeated sampling for 1000 times was used for internal validation, and the calibration curve showed that the model had good consistency. The Hosmer-Lemeshow test showed that the predicted probability of the model had a good fit with the actual probability in the training set and the validation set (P values of 0.366 and 0.667, respectively). The clinical decision curve showed that the model had good clinical application value. Conclusion: The prediction model based on bacterial co-infection, chest pain, LDH, CRP, fever duration, and D-dimer has a good predictive value for necrotizing MPP.

Development of a Nomogram for Predicting Massive Necrotizing Pneumonia in Children.

Objective: This study aimed to develop a nomogram model for predicting massive necrotizing pneumonia (NP) in children. Methods: A total of 282 children with NP admitted to Kunming Children's Hospital from January 2014 to November 2022 were enrolled. The children with NP were divided into massive necrotizing pneumonia (MNP) group and non-MNP group according to the severity of the lung necrosis. The clinical data of the children were collected, and least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression models were used to analyze the influencing factors of MNP. A nomogram model was constructed, and its predictive efficacy was evaluated. Results: The predictors selected by LASSO regression analysis were: haematogenous spread, white blood cell (WBC), hemoglobin (Hb), C-reactive protein (CRP), lactate dehydrogenase (LDH), and activated partial thromboplastin time (APTT) (P < 0.05). Based on the above independent influencing factors, a nomogram model for MNP was constructed. The bootstrap method was used to repeat sampling 1000 times. The results showed that the consistency index of the nomogram model in predicting MNP was 0.833 in the training set and 0.810 in the validation set. The results of ROC curve analysis showed that the area under the receiver-operating-characteristic curve (AUC) of the nomogram model for predicting MNP was 0.889 [95% CI (0.818, 0.959)] in the training set and 0.814 [95% CI (0.754, 0.874)] in the validation set. The calibration curve of the nomogram predicting MNP was basically close to the actual curve. The decision curve showed that the nomogram had good clinical utility. Conclusion: We developed a nomogram for predicting MNP, which can help clinicians identify the severity of lung necrosis early.

Consistency between nasopharyngeal aspirates and bronchoalveolar lavage fluid in pathogen detection in children with pneumonia: an analysis of 533 cases. / 533ä¾‹è‚ºç‚Žå„¿ç«¥é¼»å’½æŠ½å¸ç‰©å’Œæ”¯æ°”ç®¡è‚ºæ³¡çŒæ´—æ¶²çš„ç— åŽŸæ£€å‡ºä¸€è‡´æ€§åˆ†æž.

OBJECTIVES: To study the consistency between nasopharyngeal aspirates (NPA) and bronchoalveolar lavage fluid (BALF) in pathogen detection in children with pneumonia. METHODS: A retrospective analysis was performed on the data of pathogens detected in 533 children with pneumonia from February 2017 to March 2020. The paired McNemar's test was used to compare the difference in pathogen detection between NPA and BALF groups. The Kappa coefficient was used to analyze the consistency in pathogen detection between the two groups. RESULTS: NPA had a sensitivity of 28%, a specificity of 74%, a positive predictive value of 14%, and a negative predictive value of 91% in detecting bacteria, and a Kappa coefficient of 0.013 suggested poor consistency between NPA and BALF. NPA had a sensitivity of 52%, a specificity of 81%, a positive predictive value of 24%, and a negative predictive value of 94% in detecting viruses, and a Kappa coefficient of 0.213 suggested poor consistency between NPA and BALF. NPA had a sensitivity of 78%, a specificity of 71%, a positive predictive value of 49%, and a negative predictive value of 90% in detecting Mycoplasma pneumoniae, and a Kappa coefficient of 0.407 suggested moderate consistency between NPA and BALF. CONCLUSIONS: There is poor consistency between NPA and BALF in the detection of bacteria and viruses, and clinicians should be cautious in diagnosing lower respiratory tract infection based on bacteria or viruses detected in NPA. There is moderate consistency between NPA and BALF in the detection of Mycoplasma pneumoniae, suggesting that it may be reliable to diagnose lower respiratory tract infection based on Mycoplasma pneumoniae detected in NPA, while comprehensive judgment in combination with clinical conditions is needed.