Clinical features and risk factors analysis of bronchitis obliterans due to refractory Mycoplasma pneumoniae pneumonia in children: a nomogram prediction model

Cheng, Qi; Zhang, Han; Shang, Yunxiao; Zhao, Yuetong; Zhang, Ye; Zhuang, Donglin; Cai, Xuxu; Chen, Ning

doi:10.1186/s12879-021-06783-4

Cited by 26 publications

(18 citation statements)

References 49 publications

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“…Our study confirmed that consolidation size corrected for BSA remained an independent risk factor for RMPP and entered the prediction model. Several studies confirmed 7,12,26 that unilateral pulmonary solidity or atelectasis was the most critical risk factor for developing RMPP, which was consistent with our findings. Musolino et al 35 showed that 63.6% of patients with complicated pneumonia had a lung consolidation size ≥5 cm in LUS.…”

Section: Dovepresssupporting

confidence: 92%

“…Among predictive models, including laboratory indicators, recent studies have found that the levels of LDH and CRP 9 in the blood are the best predictors of RMPP. Some studies have also found inflammatory indicators in the blood, such as DD, 10 SF, IL-18, 11 ALB, 12 the neutrophil-to-lymphocyte ratio (NLR) and the mean platelet volume-to-lymphocyte ratio (MPVLR), 13 are suitable for predicting RMPP. Increased IL-17A in bronchoalveolar lavage fluid is an independent risk factor for RMPP.…”

Section: Introductionmentioning

confidence: 99%

“…15 Therefore, some researchers have added CXR findings to the design of RMPP prediction models. Bi et al 7 found that large-area lung consolidation was a risk factor for developing RMPP; Cheng et al 12 found that CXR showed that consolidation in more than two-thirds of the lung was predictive of bronchiolitis obliterans (BO) in children with RMPP. The findings of these studies indicate that pulmonary imaging assessment is vital for the early diagnosis and prognosis determination of RMPP.…”

Section: Introductionmentioning

confidence: 99%

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A Lung Ultrasound-Based Nomogram for the Prediction of Refractory Mycoplasma pneumoniae Pneumonia in Hospitalized Children

Liu¹,

Wang²,

Zhan³

et al. 2022

IDR

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Early diagnosis of refractory Mycoplasma pneumoniae pneumonia (RMPP) is challenging because of the lack of practical diagnostic imaging tools. Lung ultrasound (LUS) is an emerging tool for diagnosing childhood pneumonia. Hence, we evaluated the role of a nomogram combining LUS findings, clinical features, and laboratory indices in the early prediction of RMPP in children. Patients and Methods: We retrospectively analyzed 225 children with Mycoplasma pneumoniae pneumonia (MPP) admitted to our hospital between Dec 2018 and Aug 2021. Logistic regression analysis incorporated LUS findings and clinical predictors into the nomogram. Ninety patients hospitalized from Sep 2021 to Dec 2021 were used for external validation of the prediction model. Receiver operating characteristics (ROC) and calibration curves were used to evaluate the performance of the nomogram in the early diagnosis of RMPP.Results: Ultimately, Consolidation size /BSA (odds ratio (OR) 1.015, 95% confidence interval (CI) 1.536-2.446), Pleural Effusion (OR 3.551, 95% CI 1.921-15.600), LDH (OR 1.044, 95% CI 1.006-1. 021) and CRP (OR 3.293, 95% CI 1.019-1.098) were independent risk factors for the development of RMPP. The prediction model was represented visually as a nomogram. The area under the ROC curve for the predictive nomogram was 0.955 (95% CI 0.919-0.978) in the training cohort and 0.916 (95% CI 0.838-0.964) in the validation cohort. The calibration curve is close to the diagonal. Conclusion: This is the first-time lung ultrasound was added to the predicted nomogram, which can more comprehensively assess the condition and more accurately predict the occurrence of RMPP early. Therefore, this nomogram can be widely used in the early diagnosis of RMPP, especially in primary care hospitals.

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Section: Dovepresssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Lung Ultrasound-Based Nomogram for the Prediction of Refractory Mycoplasma pneumoniae Pneumonia in Hospitalized Children

Liu¹,

Wang²,

Zhan³

et al. 2022

IDR

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“…Zhang et al retrospectively analyzed the clinical baseline characteristics of 64 children in China and further created a recurrence risk model of children Neurophyllitis optica spectrum disorders using a nomogram [ 17 ]. Cheng et al analyzed the clinical characteristics and risk factors for the development of PIBO in children (0–14 years old) with refractory Mycoplasma pneumoniae pneumonia (MPP) and constructed a nomogram to predict the risk ratio for the development of PIBO [ 18 ]. Similarly, the current study analyzed the risk factors for the development of PIBO in 66 children (0–36 months) with ADVP after IMV retrospectively and constructed a nomogram.…”

Section: Discussionmentioning

confidence: 99%

“…More importantly, our study further analyzed the occurrence of PIBO in children with ADVP after IMV and constructed a nomogram to predict the risk ratio for PIBO. Cheng et al constructed a nomogram model for the occurrence of PIBO by analyzing the data of 141 children with MPP (aged 1–14 years) [ 18 ]. Unlike the study by Cheng et al [ 18 ], our study was conducted on children with ADVP aged 0–3 years and not on children with MPP aged 1–14 years.…”

Section: Discussionmentioning

confidence: 99%

Construction and analysis of a nomogram prediction model for post-infectious bronchiolitis obliterans in children with adenovirus pneumonia after invasive mechanical ventilation

Liu

Xie

et al. 2023

BMC Pediatr

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Background Post-infectious bronchiolitis obliterans (PIBO) is the most common sequelae in children with adenovirus pneumonia (ADVP). However, there are few studies on the risk factors for PIBO occurrence. This study aims to investigate the risk factors for PIBO in pediatric patients with severe ADVP, especially after invasive mechanical ventilation (IMV), as well as to build a nomogram prediction model. Methods The clinical data, laboratory and imaging features, and treatment of 863 children with ADVP under 3 years old who were admitted to our hospital from January to December 2019 were retrospectively analyzed. Among them, 66 children with severe ADVP received IMV treatment. The situation and the influencing factors of PIBO in children with severe ADVP were explored, and a nomogram prediction model was constructed. Results Among the 863 cases of ADVP, 46 cases (5.33%) developed PIBO. Duration of fever, IMV, complications, and neutrophil percentage were independent risk factors for PIBO in children with ADVP. Among the 66 patients with ADVP who underwent IMV, 33 patients (50.0%) developed PIBO. Gender, duration of fever, adenovirus (ADV) load, and mixed fungal coinfections were independent risk factors for PIBO. In the nomogram prediction model analysis, the area under the curve (AUC) was 0.857; in addition, Hosmer‒Lemeshow (H–L) detection reflected good alignment (χ2 = 68.75, P < 0.01). Conclusions A nomogram prediction model, which can be utilized to predict PIBO occurrence in pediatric patients with ADVP after IMV at an early time period, was successfully built.

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Nomogram for predicting fulminant necrotizing enterocolitis

Zhang

et al. 2023

Pediatr Surg Int

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Background Fulminant necrotizing enterocolitis (FNEC) is the most serious subtype of NEC and has a high mortality rate and a high incidence of sequelae. Onset prediction can help in the establishment of a customized treatment strategy. This study aimed to develop and evaluate a predictive nomogram for FNEC. Methods We conducted a retrospective observation to study the clinical data of neonates diagnosed with NEC (Bell stage ≥ IIB). Neonates were divided into the FNEC and NEC groups. A multivariate logistic regression model was used to construct the nomogram model. The performance of the nomogram was assessed using area under the curve, calibration analysis, and decision curve analysis. Results A total of 206 neonate cases were included, among which 40 (19.4%) fulfilled the definition of FNEC. The identified predictors were assisted ventilation after NEC onset; shock at NEC onset; feeding volumes before NEC onset; neutrophil counts on the day of NEC onset; and neutrophil, lymphocyte, and monocyte counts on day 1 after NEC onset. The nomogram exhibited good discrimination, with an area under the receiver operating characteristic curve of 0.884 (95% CI 0.825–0.943). The predictive model was well calibrated. Decision curve analysis confirmed the clinical usefulness of this nomogram. Conclusion A nomogram with a potentially effective application was developed to facilitate the individualized prediction of FNEC, with the hope of providing further direction for the early diagnosis of FNEC and timing of intervention.

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Clinical features and risk factors analysis of bronchitis obliterans due to refractory Mycoplasma pneumoniae pneumonia in children: a nomogram prediction model

Cited by 26 publications

References 49 publications

A Lung Ultrasound-Based Nomogram for the Prediction of Refractory Mycoplasma pneumoniae Pneumonia in Hospitalized Children

A Lung Ultrasound-Based Nomogram for the Prediction of Refractory Mycoplasma pneumoniae Pneumonia in Hospitalized Children

Construction and analysis of a nomogram prediction model for post-infectious bronchiolitis obliterans in children with adenovirus pneumonia after invasive mechanical ventilation

Nomogram for predicting fulminant necrotizing enterocolitis

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