OBJECTIVE: To identify the risk factors related to the timing of patients receiving extracorporeal membrane oxygenation (ECMO) initiation and construct a risk prediction model for ECMO initiation timing. METHODS: Patients who received ECMO admitted to the Second Xiangya Hospital of Central South University from January 2020 to January 2024 were retrospectively collected. The case data mainly included physiological and biochemical indicators 1 hour before ECMO initiation. According to the outcome of the patients, they were divided into survival group and death group. Univariate and multivariate Logistic regression analysis were used to analyze the predictors of mortality risk in patients with ECMO, and a nomogram prediction model was constructed. The discrimination, calibration accuracy, and goodness of the model were evaluated by the receiver operator characteristic curve (ROC curve), calibration curve, and the Hosmer-Lemeshow test, respectively. Decision curve analysis (DCA) evaluated the clinical net benefit rate of the model. RESULTS: A total of 81 ECMO patients were included, including 59 males and 22 females; age range from 16 to 61 years old, with a median age of 56.0 (39.5, 61.5) years old; 20 patients received veno-arterial (V-A) ECMO, and 61 patients received veno-venous (V-V) ECMO; 23 patients ultimately survived and 58 patients died. Univariate analysis showed that age, blood urea nitrogen, serum creatinine, D-dimer, arterial blood carbon dioxide partial pressure, and prothrombin time of the death group were all higher than those of the survival group, while albumin was slightly lower than that of the survival group. There was a statistically significant difference in the direct cause of ECMO initiation between the two groups. Multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.069, 95% confidence interval (95%CI) was 1.015-1.125, P = 0.012], direct cause of ECMO initiation [with heart failure as the reference, return of spontaneous circulation (ROSC) after cardiopulmonary support (OR = 30.672, 95%CI was 1.265-743.638, P = 0.035), novel coronavirus infection (OR = 8.666, 95%CI was 0.818-91.761, P = 0.073), other severe pneumonia (OR = 4.997, 95%CI was 0.558-44.765, P = 0.150)], pre-ECMO serum creatinine (OR = 1.008, 95%CI was 1.000-1.016, P = 0.044), prothrombin time (OR = 1.078, 95%CI was 0.948-1.226, P = 0.252), and D-dimer (OR = 1.135, 95%CI was 1.047-1.231, P = 0.002) were entered into the final regression equation. A nomogram prediction model was developed based on these five factors. The area under the ROC curve (AUC) of the model was 0.889 (95%CI was 0.819-0.959), higher than the AUC of the sequential organ failure assessment (SOFA; AUC = 0.604, 95%CI was 0.467-0.742). The calibration curve showed good consistency between the model predictions and the observed results. The Hosmer-Lemeshow goodness-of-fit test showed that χ ² = 4.668, P = 0.792. DCA analysis showed that when the risk threshold was 0-0.8, the net benefit rate was greater than 0, which was significantly better than that of SOFA score. CONCLUSIONS The risk prediction model for the timing of ECMO initiation, constructed using five factors (age, direct cause of ECMO initiation, thrombin time, serum creatinine, and D-dimer), demonstrated good discrimination and calibration. It can serve as a pre-initiation assessment tool to identify and predict post-initiation mortality risk in ECMO patients.
Humans ; Extracorporeal Membrane Oxygenation ; Middle Aged ; Male ; Female ; Retrospective Studies ; Adult ; Risk Factors ; Adolescent ; Young Adult ; Logistic Models ; Nomograms ; ROC Curve ; Time Factors ; Risk Assessment

OBJECTIVE: To observe change in serum growth differentiation factor 11 (GDF11) and killer cell lectin-like receptor B1 (KLRB1), to construct a nomogram model for 28-day death in patients with septic shock, and to explore its predictive value. METHODS: A prospective observational study was conducted. The patients with septic shock admitted to the emergency intensive care unit (ICU) of Cangzhou Central Hospital from September 2023 to March 2025 were selected as the septic shock group, the patients with sepsis admitted to the emergency general ward during the same period were selected as the sepsis group, and healthy individuals undergoing physical examination during the same period were selected as the control group. On the day of hospital admission or physical examination for the research subjects, the levels of serum GDF11 and KLRB1 were detected by enzyme-linked immunosorbent assay (ELISA). The patients with septic shock were divided into survival and death groups based on their 28-day survival status. The patients' gender, age, past medical history, infection site, severity of illness, mechanical ventilation, blood purification, infection indicators, biochemical indicators, coagulation function indicators, and blood lactic acid (Lac) were collected. The clinical data of the patients with septic shock between the two groups with different prognoses were compared. Multivariate Logistic regression analysis was used to screen the risk factors for 28-day death in patients with septic shock, and bivariate Pearson correlation analysis was conducted. A nomogram model was constructed based on the risk factors for 28-day death in patients with septic shock. The discrimination and calibration of the nomogram model were evaluated using the receiver operator characteristic curve (ROC curve), Hosmer-Lemeshow goodness-of-fit test, and calibration curve. The clinical utility of the model was evaluated using clinical decision curve analysis (DCA). RESULTS: A total of 168 patients in the emergency ICU were enrolled in the septic shock group, 40 patients in the emergency general ward were enrolled in the sepsis group, and 40 healthy individuals were enrolled in the control group. Compared with the healthy control group, the serum GDF11 levels in the sepsis and septic shock groups were significantly increased (μg/L: 13.09±3.51, 19.28±5.36 vs. 4.17±0.92, both P < 0.05), and the serum KLRB1 levels were significantly decreased (ng/L: 57.36±11.28, 45.52±9.07 vs. 84.19±17.16, both P < 0.05), with more significant changes in the septic shock group (both P < 0.05). Among the 168 patients with septic shock, 96 survived and 72 died within 28 days. Compared with the survival group, the serum GDF11 level in the death group was significantly increased (μg/L: 24.24±4.81 vs. 15.56±4.62, P < 0.05), and the serum KLRB1 level was significantly decreased (ng/L: 28.53±8.69 vs. 58.26±9.45, P < 0.05). There were also statistically significant differences in sequential organ failure assessment (SOFA) score, acute physiology and chronic health evaluation II (APACHEII) score, procalcitonin (PCT), activated partial thromboplastin time (APTT), D-dimer, and Lac between the two groups. Multivariate Logistic regression analysis showed that SOFA score [odds ratio (OR) = 1.96, 95% confidence interval (95%CI) was 1.38-3.65), Lac (OR = 1.38, 95%CI was 1.09-2.01), GDF11 (OR = 1.54, 95%CI was 1.21-2.33) and KLRB1 (OR = 0.64, 95%CI was 0.41-0.78) were independent risk factors for 28-day death in patients with septic shock (all P < 0.05). Bivariate Pearson correlation analysis showed that SOFA score was significantly positively correlated with Lac and GDF11 (r value was 0.37 and 0.58, respectively, both P < 0.05), and significantly negatively correlated with KLRB1 (r = -0.72, P < 0.05). A nomogram model was constructed based on the risk factors for 28-day death in patients with septic shock. ROC curve analysis showed that the area under the ROC curve (AUC) of the nomogram model for predicting 28-day death in patients with septic shock was 0.963 (95%CI was 0.929-0.990), indicating that the model had good discrimination and predictive ability. The Hosmer-Lemeshow goodness-of-fit test (χ ² = 9.578, P = 0.295) and calibration curve indicated that the predicted values of the model were in good agreement with the actual values. DCA indicated that the model provided a high net benefit for clinical decision-making. CONCLUSIONS The serum GDF11 level was significantly increased and the KLRB1 level was significantly decreased in patients with septic shock. The nomogram model based on GDF11 and KLRB1 could more accurately evaluate the 28-day death of patients with septic shock.
Humans ; Shock, Septic/blood* ; Nomograms ; Prospective Studies ; Prognosis ; Male ; Female ; Middle Aged ; Aged ; Intensive Care Units

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors

PURPOSE: To analyze risk factors for severe trauma and establish a nomogram for early risk prediction, to improve the early identification of severe trauma. METHODS: This study was conducted on the patients treated in 81 trauma treatment institutions in Gansu province from 2020 to 2022. Patients were grouped by year, with 5364 patients from 2020 to 2021 as the training set and 1094 newly admitted patients in 2020 as the external validation set. Based on the injury severity score (ISS), patients in the training set were classified into 2 subgroups of the severe trauma group (n = 478, ISS scores ≥25) and the non-severe trauma group (n = 4886, ISS scores <25). Univariate and binary logistic regression analyses were employed to identify independent risk factors for severe trauma. Subsequently, a predictive model was developed using the R software environment. Furthermore, the model was subjected to internal and external validation via the Hosmer-Lemeshow test and receiver operating characteristic curve analysis. RESULTS: In total, 6458 trauma patients were included in this study. Initially, this study identified several independent risk factors for severe trauma, including multiple traumatic injuries (polytrauma), external hemorrhage, elevated shock index, elevated respiratory rate, decreased peripheral oxygen saturation, and decreased Glasgow coma scale score (all p < 0.05). For internal validation, the area under the receiver operating characteristic curve was 0.914, with the sensitivity and specificity of 88.4% and 87.6%, respectively; while for external validation, the area under the receiver operating characteristic curve was 0.936, with the sensitivity and specificity of 84.6% and 93.7%, respectively. In addition, a good model fitting was observed through the Hosmer-Lemeshow test and calibration curve analysis (p > 0.05). CONCLUSION This study establishes a nomogram for early risk prediction of severe trauma, which is suitable for primary healthcare institutions in underdeveloped western China. It facilitates early triage and quantitative assessment of trauma severity by clinicians prior to clinical interventions.
Humans ; Nomograms ; Male ; Female ; Wounds and Injuries/diagnosis* ; Risk Factors ; Middle Aged ; Adult ; Injury Severity Score ; Risk Assessment ; ROC Curve ; Aged ; Logistic Models ; China ; Glasgow Coma Scale

OBJECTIVES: This study aimed to construct a risk prediction model for the occurrence of the demora-lization syndrome in patients with oral cancer and provide a scientific basis for the prevention of this syndrome in patients with oral cancer and the development of personalized care programs. METHODS: A total of 486 patients with oral cancer in West China Hospital of Stomatology of Sichuan University and Sun Yat-sen Memorial Hospital of Sun Yat-sen University from 2024 March to July were selected by convenience sampling. We integrated clinical data and evidence from previous studies to identify the key variables affecting the demoralization syndrome in patients with oral cancer. The 486 patients were divided into a training set and a validation set in an 8∶2 ratio. A clinical risk prediction model was established based on the individual data of 365 patients in the development cohort. Through least absolute shrinkage and selection operator (LASSO) regression, a moderate to severe risk prediction model of demoralization syndrome in oral cancer was constructed, and a clinical machine-learning nomogram was constructed. Bootstrap resampling was used for internal validation. The data of 121 patients in the validation cohort were externally validated. RESULTS: The incidence of the demoralization syndrome in patients with oral cancer was 405 cases (83.3%), of which 279 cases (57.4%) were mild, 176 cases (36.2%) were moderate, and 31 cases (6.4%) were severe. The core model, including patient education level, disease understanding, and MDASI-HN score, was used to predict the risk of outcome. Internal validation of the model yielded C statistic of 0.783 6 (95% CI: 0.78-0.87), beta of 0.843 4, and calibration intercept of -0.040 6. Through external validation, the validation set C statistic was 0.80 (95%CI: 0.71-0.87), beta was 0.80, and calibration intercept was -0.08. CONCLUSIONS Our risk prediction mo-del of the demoralization syndrome in patients with oral cancer performed robustly in validation cohorts of different nur-sing environments. The model has good correction and good discrimination and can be used as an evaluation and prediction item at admission.
Humans ; Mouth Neoplasms/complications* ; Male ; Female ; Nomograms ; Middle Aged ; Syndrome ; Aged ; Adult ; Risk Factors ; Risk Assessment ; Machine Learning

Objective To analyze the risk factors for prolonged length of hospital stay in patients with peritoneal dialysis associated peritonitis(PDAP)and construct a nomogram based on Logistic regression model.Methods A retrospective study was conducted on patients with PDAP who were hospitalized at the Affiliated Hospital of Jiangsu University from January 2013 to December 2023.Using the 75th percentile of hospitalization time as the cutoff(>21 days),the patients were divided into prolonged length of hospital stay group and normal length of hospital stay group.Clinical data were compared between the two groups.Logistic regression analysis was used to analyze the risk factors for prolonged hospital stay in PDAP patients and to construct a nomogram.Results A total of 131 PDAP patients were included in this study,including 40 cases in prolonged length of hospital stay group and 91 cases in normal length of hospital stay group.Multivariate Logistic regression analysis showed that Gram-negative bacteria detected in ascites(OR=6.012,95% CI=1.878-19.248,P=0.003)and elevated platelet count(OR=1.010,95% CI=1.005-1.015,P<0.001)were independent risk factors for prolonged length of hospital stay,while elevated serum chloride(OR=0.885,95% CI=0.802-0.978,P=0.016)was a protective factor.Based on the above three indicators,a nomogram was constructed.The multivariate Logistic regression model showed an area under the receiver operating characteristic curve(AUC)of 0.755,with an internal validation AUC of 0.727 using the Bootstrap method.The calibration curve indicated that the predicted probability was consistent with the actual probability.The decision curve showed that the model was clinically applicable when the threshold probabilities were 9%-10%,13% and 18%-92%.Conclusion A nomogram,based on the detection of gram-negative bacteria in ascites,platelet count and serum chloride,was helpful for clinical screening PADP patients at risk for prolonged length of hospital stay,and can provide a basis for optimizing clinical decision-making.
Humans ; Nomograms ; Risk Factors ; Peritoneal Dialysis/adverse effects* ; Retrospective Studies ; Length of Stay ; Peritonitis/etiology* ; Logistic Models ; Male ; Female ; Middle Aged ; Aged

Objective To identify disulfidptosis-related gene(DRG)in acute myocardial infarction(AMI)by bioinformatics,analyze the molecular pattern of DRGs in AMI,and construct a DRGs-related prediction model.Methods AMI-related datasets were downloaded from the Gene Expression Omnibus database,and DRGs with differential expression were screened in AMI.CIBERSORT method was used to analyze the immune infiltration.Based on the differentially expressed DRGs,the AMI patients were classified into distinct subtypes via consensus clustering,followed by immune infiltration analysis,differential expression analysis,gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis,and gene set variation analysis.Weighted gene co-expression network analysis(WGCNA)was then performed to construct subtype-associated modules and identify hub genes.Finally,least absolute shrinkage and selection operator,random forest,and support vector machine-recursive feature elimination were used to screen feature genes to construct a DRGs-related prediction model.The model's diagnostic efficacy was evaluated by nomogram and receiver operating characteristic(ROC)curve analysis,followed by external validation.Results Nine differentially expressed DRGs were identified between AMI patients and controls.Based on the expression levels of these nine DRGs,AMI patients were divided into two DRGs subtypes,C1 and C2.Increased infiltration of monocytes,M0 macrophages,and neutrophils was observed in AMI patients and C1 subtype(all P<0.05),indicating a close correlation between DRGs and immune cells.There were 257 differentially expressed genes between the C1 and C2 subtypes,which were related to biological processes such as myeloid leukocyte activation and positive regulation of cytokines.Fcγ receptor-mediated phagocytosis and NOD-like receptor signaling pathway activity were enhanced in C1 subtype.WGCNA analysis suggested that the brown module exhibited the strongest correlation with DRG subtypes(r=0.67),from which 23 differentially expressed genes were identified.The feature genes screened by three machine learning methods were interpolated to obtain a DRGs-related prediction model consisting of three genes(AQP9,F5 and PYGL).Nomogram and ROC curves(AUC_train=0.891,AUC_test=0.840)showed good diagnostic efficacy.Conclusions DRGs were closely related to the occurrence and progression of AMI.The DRGs-related prediction model consisting of AQP9,F5 and PYGL may provide targets for the diagnosis and personalized treatment of AMI.
Humans ; Myocardial Infarction/diagnosis* ; Transcriptome ; Computational Biology ; Gene Expression Profiling ; ROC Curve ; Gene Regulatory Networks ; Nomograms ; Disulfidptosis

Objective To investigate the value of ultrasonographic features combined with immunohistochemistry in predicting axillary lymph node metastasis in middle-aged women with breast cancer.Methods A retrospective analysis was conducted on 827 middle-aged female breast cancer patients who underwent surgical treatment at the Affiliated Hospital of Chengde Medical University from June 2017 to June 2023.Ultrasonographic and immunohistochemical information was collected,and the patients were randomly allocated into a training set(579 patients)and a validation set(248 patients).Univariate and multivariate Logistic regression analyses were performed to identify ultrasonographic and immunohistochemical risk factors associated with axillary lymph node metastasis in these patients,and a nomogram model was developed.Receiver operating characteristic curves and calibration curves were established to evaluate the performance of the nomogram model,and clinical decision curves were built to assess the clinical value of the model.Results The maximum diameter,morphology,boundary,calcification,and expression of human epidermal growth facor receptor 2 and Ki-67 in breast cancer lesions were identified as risk factors for predicting axillary lymph node metastasis in middle-aged women.The areas under the curve of the nomogram model on the training and validation sets were 0.747(0.707-0.787)and 0.714(0.647-0.780),respectively.Calibration curves and clinical decision curves indicated good consistency and performance of the model.Conclusion The nomogram model constructed based on ultrasonographic features and immunohistochemistry of the primary breast cancer lesion demonstrates high value in predicting axillary lymph node metastasis in middle-aged women with breast cancer.
Humans ; Female ; Breast Neoplasms/diagnostic imaging* ; Middle Aged ; Lymphatic Metastasis/diagnostic imaging* ; Axilla ; Retrospective Studies ; Nomograms ; Ultrasonography ; Immunohistochemistry ; Lymph Nodes/diagnostic imaging* ; Risk Factors ; Ki-67 Antigen

OBJECTIVES: To investigate the risk factors for plastic bronchitis (PB) in children with macrolide-unresponsive Mycoplasma pneumoniae pneumonia (MUMPP) and to establish a nomogram prediction model. METHODS: A retrospective analysis was conducted on 178 children with MUMPP who underwent bronchoscopy from January to December 2023. According to the presence or absence of PB, the children were divided into a PB group (49 children) and a non-PB group (129 children). The predictive factors for the development of PB in children with MUMPP were analyzed, and a nomogram prediction model was established. The model was assessed in terms of discriminatory ability, accuracy, and clinical effectiveness. RESULTS: The multivariate logistic regression analysis showed that older age and higher levels of lactate dehydrogenase and fibrinogen were closely associated with the development of PB in children with MUMPP (P<0.05). A nomogram model established based on these factors had an area under the receiver operating characteristic curve of 0.733 (95%CI: 0.651-0.816, P<0.001) and showed a good discriminatory ability. The Hosmer-Lemeshow goodness-of-fit test indicated that the predictive model had a good degree of fit (P>0.05), and the decision curve analysis showed that the model had a good clinical application value. CONCLUSIONS The risk nomogram model established based on age and lactate dehydrogenase and fibrinogen levels has good discriminatory ability, accuracy, and predictive efficacy for predicting the development of PB in children with MUMPP.
Retrospective Studies ; Risk Factors ; Nomograms ; Mycoplasma pneumoniae/isolation & purification* ; Pneumonia, Mycoplasma/microbiology* ; Bronchitis/microbiology* ; Macrolides/therapeutic use* ; Drug Resistance, Bacterial ; Bronchoscopy ; Area Under Curve ; ROC Curve ; Fibrinogen/analysis* ; Age Factors ; Humans ; Male ; Female ; Infant ; Child, Preschool ; Child ; Adolescent ; L-Lactate Dehydrogenase/blood*