Prediction Model for Severe Mycoplasma Pneumoniae Pneumonia in Pediatric Patients by Admission Laboratory Indicators

Abstract Objective The purpose of this study was to develop a model for predicting severe mycoplasma pneumoniae pneumonia (SMMP) in pediatric patients with MMP on admission by laboratory indicators. Methods Pediatric patients with MPP from January 2019 to December 2020 in our hospital were enrolled in this study. SMMP was diagnosed according to guideline for diagnosis and treatment of community acquired pneumonia in children (2019 version). Prediction model was developed according to the admission laboratory indicators. ROC curve and Goodness of fit test were analyzed for the predictive value. Results A total of 233 MMP patients were included in the study, with 121 males and 112 females, aged 4.541 (1–14) years. Among them, 84 (36.1%, 95% CI 29.9%-42.6%) pediatric patients were diagnosed as SMPP. Some admission laboratory indicators (IgM, eosinophil proportion, eosinophil count, hemoglobin, ESR, total protein, albumin and prealbumin) were found statistically different (P < 0.05) between non-SMMP group and SMMP group. Logistic regress analysis showed IgM, eosinophil proportion, eosinophil count, ESR, and prealbumin were independent risk factors for SMMP. According to these five admission laboratory indicators, Nomograph prediction model was developed. The AUC of the Nomograph prediction model was 0.777, and the goodness of fit test showed that the predicted incidence of the model was consistent with the actual incidence (χ2 = 244.51, P = 0.203). Conclusion We developed a model for predicting SMMP in pediatric patients by admission laboratory indicators. This model has good discrimination and calibration, which provides a basis for the early identification SMMP on admission.

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Sequential Mycoplasma pneumoniae pneumonia and Chromobacterium violaceum skin abscess in a pediatric patient

The Journal of Infection in Developing Countries ◽

10.3855/jidc.8878 ◽

2017 ◽

Vol 11 (08) ◽

pp. 656-661 ◽

Cited By ~ 1

Author(s):

Weizhen Guo ◽

Iris Wai Sum Li ◽

Xi Li ◽

Hua Xu ◽

Dongrong Lu ◽

...

Keyword(s):

Mycoplasma Pneumoniae ◽

Pediatric Patients ◽

Community Acquired Pneumonia ◽

Chromobacterium Violaceum ◽

Respiratory Pathogen ◽

First Case ◽

History Of ◽

The Right ◽

Super Infection ◽

Mycoplasma Pneumoniae Pneumonia

Mycoplasma pneumoniae is a common atypical respiratory pathogen causing community-acquired pneumonia in children. Co-infection with other respiratory viruses is common in pediatric patients but super-infection with bacteria other than Streptococcus pneumoniae and Haemophilus influenzae is rare. The first case of Chromobacterium violaceum infection incubated during and manifested after pneumonia caused by Mycoplasma pneumoniae in a 12-month old girl without any known history of immunodeficiency is here reported. The patient developed fever with redness and swelling over the middle phalanx of the right hand index finger which progressed to the formation of skin abscess. Following a course of intravenous meropenem and surgical drainage of the skin abscess, the patient fully recovered and was discharged.

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A Prediction Model of Mortality in COVID-19 Pneumonia Based on CT Score and Lymphocyte Count

Austin Journal of Pathology & Laboratory Medicine ◽

10.26420/austinjpathollabmed.2021.1030 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Xie Y ◽

◽

Dong H ◽

Liao Y ◽

Zhang J ◽

...

Keyword(s):

Risk Factors ◽

Prediction Model ◽

Lymphocyte Count ◽

Goodness Of Fit ◽

False Positive Rate ◽

Laboratory Data ◽

Absolute Lymphocyte Count ◽

Youden Index ◽

Goodness Of Fit Test ◽

Independent Risk Factors

Background: COVID-19 nucleic acid swab tests have a high false positive rate; therefore, diagnosing COVID-19 pneumonia and predicting prognosis by CT scan are very important. Methods: In this retrospective single-centre study, we included consecutive suspected critical COVID-19 pneumonia cases in the intensive care unit of Wuhan Third Hospital from January 31, 2020, to March 16, 2020. 204 cases were confirmed by real-time RT-PCR, and all patients were evaluated with CT, cut-off values were obtained according to the Youden index and were divided into a high CT score group and a low CT score group. Epidemiological, demographic, clinical, and laboratory data were collected. Finally, Through multi-factor logistic regression model, a prediction model based on multiple prediction indicators was formed, and new joint predictive factors were calculated. The prediction model of mortality in COVID-19 pneumonia based on CT score and lymphocyte count was constructed through data processing analysis. Results: The major imaging feature of COVID-19 pneumonia is Ground Glass Opacities (GGOs). Multivariate regression analysis found that the CT score and absolute lymphocyte count were independent risk factors for death and that the CT score predicted mortality (AUC-ROC =0.7, cut-off=1.45). When the absolute lymphocyte count was lower, the patient’s CT score was also lower. Based on this, a prediction model was established. The prediction model was: In [P/(1-P)]=0.667*gender+0.057*age-0.086CT score-0.831 lymphocyte count-3.91, the goodness of fit test of the model was P=0.041, and the area under the curve of the ROC curve of the model was 0.779. Conclusion: CT score and absolute lymphocyte count are independent risk factors for mortality, and patients with a high CT score may have a worse prognosis. A lower absolute lymphocyte count may indicate that the patient’s CT score is also reduced. The model established by combining CT scores and lymphocyte count showed a good degree of calibration and differentiation.

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Risk factors for peripheral venous catheter failure: A prospective cohort study of 5345 patients

The Journal of Vascular Access ◽

10.1177/11297298211015035 ◽

2021 ◽

pp. 112972982110150

Author(s):

Ya-mei Chen ◽

Xiao-wen Fan ◽

Ming-hong Liu ◽

Jie Wang ◽

Yi-qun Yang ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Cohort Study ◽

Goodness Of Fit ◽

Insertion Site ◽

Healthcare Providers ◽

Venous Catheter ◽

Goodness Of Fit Test ◽

Peripheral Venous Catheter ◽

Independent Risk Factors

Purpose: The objective of this study was to determine the independent risk factors associated with peripheral venous catheter (PVC) failure and develop a model that can predict PVC failure. Methods: This prospective, multicenter cohort study was carried out in nine tertiary hospitals in Suzhou, China between December 2017 and February 2018. Adult patients undergoing first-time insertion of a PVC were observed from catheter insertion to removal. Logistic regression was used to identify the independent risk factors predicting PVC failure. Results: This study included 5345 patients. The PVC failure rate was 54.05% ( n = 2889/5345), and the most common causes of PVC failure were phlebitis (16.3%) and infiltration/extravasation (13.8%). On multivariate analysis, age (45–59 years: OR, 1.295; 95% CI, 1.074–1.561; 60–74 years: OR, 1.375; 95% CI, 1.143–1.654; ⩾75 years: OR, 1.676; 95% CI, 1.355–2.073); department (surgery OR, 1.229; 95% CI, 1.062–1.423; emergency internal/surgical ward OR, 1.451; 95% CI, 1.082–1.945); history of venous puncture in the last week (OR, 1.298, 95% CI 1.130–1.491); insertion site, number of puncture attempts, irritant fluid infusion, daily infusion time, daily infusion volume, and type of sealing liquid were independent predictors of PVC failure. Receiver operating characteristic curve analysis indicated that a logistic regression model constructed using these variables had moderate accuracy for the prediction of PVC failure (area under the curve, 0.781). The Hosmer-Lemeshow goodness of fit test demonstrated that the model was correctly specified (χ2 = 2.514, p = 0.961). Conclusion: This study should raise awareness among healthcare providers of the risk factors for PVC failure. We recommend that healthcare providers use vascular access device selection tools to select a clinically appropriate device and for the timely detection of complications, and have a list of drugs classified as irritants or vesicants so they can monitor patients receiving fluid infusions containing these drugs more frequently.

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Assessment of levels of D‑dimer and interferon‑γ in pediatric patients with Mycoplasma pneumoniae pneumonia and its clinical implication

Experimental and Therapeutic Medicine ◽

10.3892/etm.2018.6873 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xiaoqiu Jin ◽

Ying Zhu ◽

Yingchun Zhang ◽

Jing Chen ◽

Li Rong ◽

...

Keyword(s):

Mycoplasma Pneumoniae ◽

Clinical Implication ◽

Pediatric Patients ◽

Interferon Γ ◽

Mycoplasma Pneumoniae Pneumonia ◽

D Dimer

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Viruses and Mycoplasma pneumoniae are the main etiological agents of community‐acquired pneumonia in hospitalized pediatric patients in Spain.

Pediatric Pulmonology ◽

10.1002/ppul.25721 ◽

2021 ◽

Author(s):

Enrique Otheo ◽

Mario Rodríguez ◽

Cinta Moraleda ◽

Sara Domínguez‐Rodríguez ◽

María Dolores Martín ◽

...

Keyword(s):

Mycoplasma Pneumoniae ◽

Pediatric Patients ◽

Community Acquired Pneumonia ◽

Etiological Agents

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External validation of the Pediatric Extracorporeal Membrane Oxygenation Prediction model for risk adjusting mortality

Perfusion ◽

10.1177/0267659120952979 ◽

2020 ◽

pp. 026765912095297

Author(s):

David K Bailly ◽

Jamie M Furlong-Dillard ◽

Melissa Winder ◽

Mark Lavering ◽

Ryan P Barbaro ◽

...

Keyword(s):

Extracorporeal Membrane Oxygenation ◽

Prediction Model ◽

Goodness Of Fit ◽

Pediatric Patients ◽

Life Support ◽

Extracorporeal Life Support ◽

Characteristic Curve ◽

External Validation ◽

Group Model ◽

Membrane Oxygenation

Introduction: The Pediatric Extracorporeal Membrane Oxygenation Prediction (PEP) model was created to provide risk stratification for all pediatric patients requiring extracorporeal life support (ECLS). Our purpose was to externally validate the model using contemporaneous cases submitted to the Extracorporeal Life Support Organization (ELSO) registry. Methods: This multicenter, retrospective analysis included pediatric patients (<19 years) during their initial ECLS run for all indications between January 2012 and September 2014. Median values from the BATE dataset for activated partial thromboplastin time and internationalized normalized ratio were used as surrogates as these were missing in the ELSO group. Model discrimination was evaluated using area under the receiver operating characteristic curve (AUC), and goodness-of-fit was evaluated using the Hosmer-Lemeshow test. Results: A total of 4,342 patients in the ELSO registry were compared to 514 subjects from the bleeding and thrombosis on extracorporeal membrane oxygenation (BATE) dataset used to develop the PEP model. Overall mortality was similar (42% ELSO vs. 45% BATE). The c-statistic after external validation decreased from 0.75 to 0.64 and model calibration decreases most in the highest risk deciles. Conclusion: Discrimination of the PEP model remains modest after external validation using the largest pediatric ECLS cohort. While the model overestimates mortality for the highest risk patients, it remains the only prediction model applicable to both neonates and pediatric patients who require ECLS for any indication and thus maintains potential for application in research and quality benchmarking.

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Validation and update of the thoracic surgery scoring system (Thoracoscore) risk model

European Journal of Cardio-Thoracic Surgery ◽

10.1093/ejcts/ezaa056 ◽

2020 ◽

Vol 58 (2) ◽

pp. 350-356

Author(s):

Julien Die Loucou ◽

Pierre-Benoit Pagès ◽

Pierre-Emmanuel Falcoz ◽

Pascal-Alexandre Thomas ◽

Caroline Rivera ◽

...

Keyword(s):

Thoracic Surgery ◽

Prediction Model ◽

Goodness Of Fit ◽

Prediction Models ◽

Risk Model ◽

Age Distribution ◽

Original Data ◽

Validation Group ◽

Goodness Of Fit Test ◽

Validation Data

Abstract OBJECTIVES The performance of prediction models tends to deteriorate over time. The purpose of this study was to update the Thoracoscore risk prediction model with recent data from the Epithor nationwide thoracic surgery database. METHODS From January 2016 to December 2017, a total of 56 279 patients were operated on for mediastinal, pleural, chest wall or lung disease. We used 3 recommended methods to update the Thoracoscore prediction model and then proceeded to develop a new risk model. Thirty-day hospital mortality included patients who died within the first 30 days of the operation and those who died later during the same hospital stay. RESULTS We compared the baseline patient characteristics in the original data used to develop the Thoracoscore prediction model and the validation data. The age distribution was different, with specifically more patients older than 65 years in the validation group. Video-assisted thoracoscopy accounted for 47% of surgeries in the validation group compared but only 18% in the original data. The calibration curve used to update the Thoracoscore confirmed the overfitting of the 3 methods. The Hosmer–Lemeshow goodness-of-fit test was significant for the 3 updated models. Some coefficients were overfitted (American Society of Anesthesiologists score, performance status and procedure class) in the validation data. The new risk model has a correct calibration as indicated by the Hosmer–Lemeshow goodness-of-fit test, which was non-significant. The C-index was strong for the new risk model (0.84), confirming the ability of the new risk model to differentiate patients with and without the outcome. Internal validation shows no overfitting for the new model CONCLUSIONS The new Thoracoscore risk model has improved performance and good calibration, making it appropriate for use in current clinical practice.

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84 Development of a Risk Prediction Model and Comparison with Clinical Severity Scores in Burn Patients

Journal of Burn Care & Research ◽

10.1093/jbcr/iraa024.088 ◽

2020 ◽

Vol 41 (Supplement_1) ◽

pp. S54-S55

Author(s):

Dohern Kym

Keyword(s):

Prediction Model ◽

Goodness Of Fit ◽

Scoring Systems ◽

Clinical Severity ◽

Brier Score ◽

P Value ◽

Burn Patients ◽

Goodness Of Fit Test ◽

Prediction Of Mortality ◽

Severity Scores

Abstract Introduction The purpose of this study was to develop a new prediction model to reflect the risk of mortality and severity of disease and to evaluate the ability of the developed model to predict mortality among adult burn patients. Methods This study included 2009 patients aged more than 18 years who were admitted to the intensive care unit (ICU) within 24 hours after a burn. We divided the patients into two groups; those admitted from January 2007 to December 2013 were included in the derivation group and those admitted from January 2014 to September 2017 were included in the validation group. Shrinkage methods with 10-folds cross-validation were performed to identify variables and limit overfitting of the model. The discrimination was analyzed using the area under the curve (AUC) of the receiver operating characteristic curve. The Brier score, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were also calculated. The calibration was analyzed using the Hosmer-Lemeshow goodness-of-fit test (HL test). The clinical usefulness was evaluated using a decision-curve analysis. Results The new prediction model showed good calibration with the HL test (χ2=8.785, p=0.361); the highest AUC and the lowest Brier score were 0.943 and 0.068, respectively. The NRI and IDI were 0.124 (p-value = 0.003) and 0.079 (p-value < 0.001) when compared with FLAMES, respectively. Conclusions This model reflects the current risk factors of mortality among adult burn patients. Furthermore, it was a highly discriminatory and well-calibrated model for the prediction of mortality in this cohort. Applicability of Research to Practice There are many severity scoring systems widely used in the ICU to predict outcomes and characterize the severity of the disease. All of these scoring systems have been developed for the mixed population in the ICU. Their accuracy among subgroups, such as burn patients, is questionable and therefore, burn-specific scoring systems are required for accurate prediction.

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