Objective To investigate the risk factors for early neurological deterioration in mild acute ischemic stroke，to construct a clinical predictive model，and to perform internal validation of this model. Methods A retrospective analysis was performed for 739 patients with mild acute ischemic stroke who were admitted to Department of Neurology，Kuntong Hospital of Zunhua，from October 2020 to December 2023，and they were randomly divided into a training set with 534 patients （72.3%） and a validation set with 205 patients （27.7%） at a ratio of 7∶3. Univariate and multivariate logistic regression analyses were performed for the training set to determine the risk factors for early neurological deterioration in mild acute ischemic stroke. A clinical predictive model was constructed，and internal validation was performed in terms of discriminatory ability，calibration，and clinical decision making. A nomogram was plotted. Results The multivariate logistic regression analysis showed that female sex （OR=1.87，95% CI 1.14~3.09，P=0.014），time window ≤6 hours （OR=3.10，95%CI 1.56~6.19，P=0.001），a baseline NIHSS score of 2 points （OR=3.72，95%CI 1.30~10.61，P=0.014），a baseline NIHSS score of 3 points （OR=4.24，95%CI 1.45~12.35，P=0.008），a TOAST classification of large artery atherosclerosis （OR=3.88，95%CI 2.20~6.83，P<0.001），and the responsible arteries of the basilar artery，the middle cerebral artery，and the internal carotid artery （OR=8.39，95%CI 2.28~30.85，P=0.001； OR=6.22，95%CI 1.78~21.71，P=0.004； OR=5.38，95%CI 1.15~25.13，P=0.032） were independent risk factors for early neurological deterioration in mild acute ischemic stroke. The clinical predictive model constructed showed a moderate discriminatory ability （AUC>0.7），good calibration （P>0.05） in the Hosmer-Lemeshow goodness-of-fit test），and good clinical benefits in both the training set and the validation set. Conclusion This clinical predictive model can effectively predict the onset of early neurological deterioration in mild acute ischemic stroke and guide clinicians to make decisions，and therefore，it holds promise for clinical application.
Nomograms

OBJECTIVE: To analyze the risk factors of acute spinal cord injury complicated with respiratory dysfunction, and to construct the clinical prediction model of acute spinal cord injury complicated with respiratory dysfunction. METHODS: Continuous 170 cases of acute spinal cord injury treated from April 2019 to October 2022 were retrospectively collected, and clinical data were uniformly collected. Patients were divided into respiratory dysfunction group 30 cases and non-respiratory dysfunction group 140 cases according to whether they had respiratory dysfunction during treatment. The predictive factors of acute spinal cord injury complicated with respiratory dysfunction were screened by Lasso analysis, and the risk factors of acute spinal cord injury complicated with respiratory dysfunction were screened by multivariate Logistic regression analysis. R(R4.2.1) software was used to establish a nomogram risk warning model for predicting acute spinal cord injury complicated with respiratory dysfunction, and Hosmer-Lemeshow test was used to evaluate the model fit. Finally, area under receiver operating characteristic(ROC) curve (AUC), calibration curve, and decision curve analysis(DCA) were used to evaluate the differentiation, calibration and clinical impact of the model. RESULTS: The incidence of respiratory dysfunction in 170 patients was 17.65%. Lasso regression analysis selected age, residence, marital status, smoking, hypertension, degree of paralysis, spinal cord injury plane, multiple injuries, spinal cord fracture and dislocation, and ASIA grade as the influencing factors. Multivariate Logistic regression analysis showed that age, smoking, degree of paralysis, level of spinal cord injury, spinal cord injury of fracture and dislocation, and ASIA grade were risk factors for acute spinal cord injury complicated with respiratory dysfunction. The prediction model of acute spinal cord injury complicated with respiratory dysfunction was established by Hosmer-Lemeshow test, χ²=5.830, P=0.67. The AUC value of the model was 0.912. DCA analysis showed that the net benefit value of nomogram prediction of acute spinal cord injury complicated with respiratory dysfunction was higher when threshold probability ranged from 1% to 100%. CONCLUSION This column chart can help identify the risk of acute spinal cord injury complicated with respiratory dysfunction in early clinical stage, facilitate early clinical decision-making and intervention, and has important guiding significance for optimizing clinical efficacy and improving prognosis of patients. It is expected to improve and verify this model with larger samples and multi-center in the future.
Humans ; Spinal Cord Injuries/complications* ; Nomograms ; Male ; Female ; Middle Aged ; Adult ; Retrospective Studies ; Risk Factors ; Aged ; Respiration Disorders/etiology* ; Adolescent ; Logistic Models

OBJECTIVE: To analyze the factors influencing the occurrence of C5 nerve root palsy after posterior approach cervical single-door enlargement kyphoplasty and construct a Nomogram-related prediction model. METHODS: A total of 255 patients with cervical spondylotic myelopathy who underwent posterior cervical single-door laminoplasty between May 2019 and February 2023 were selected as the research subjects. They were divided into the occurrence group (45 patients) and the non-occurrence group (210 patients) based on whether C₅ nerve root palsy occurred after the operation. The general data of patients in the two groups were compared. The predictive value of statistically significant continuous variables was analyzed using receiver operating characteristic (ROC) curve analysis. The factors influencing patients' postoperative C₅ nerve root palsy were analyzed using Logistic regression analysis. And the clinical efficacy of Nomogram model was assessed using decision curve analysis. RESULTS: Compared with the non-occurrence group, the patients in the occurrence group had a shorter disease duration, higher preoperative cervical curvature and spinal cord posterior displacement distance, and higher percentage of positive pathological reflexes, foraminal stenosis, and ossification of the posterior longitudinal ligament.The difference was statistically significant P<0.05. The area under the curve (AUC) for cervical curvature and posterior displacement of the spinal cord prior to surgery were 0.699 and 0.697, respectively. The optimal cutoff values were determined to be 21° and 3 mm, with statistically significant differences (P<0.05). Logistic regression analysis showed that abnormal electromyography OR=6.693, 95%CI(2.754, 16.264), P<0.001;preoperative cervical curvature OR=2.254, 95%CI(1.215, 2.920), P=0.003;foraminal stenosis OR=3.049, 95%CI(1.234, 7.530), P=0.016;ossification of the posterior longitudinal ligament OR=2.646, 95% CI(1.015, 6.899), P=0.047;and the distance of spinal cord posterior displacement OR=0.298, 95% CI(0.173, 0.513), P<0.001;which were all related factors influencing postoperative C₅ nerve root palsy in patients with this disease. The C-index of the Nomogram model for predicting the risk of postoperative C₅ nerve root palsy in patients was 0.861, with a 95% confidence interval of (0.795, 0.927). The risk threshold of this model was determined to be greater than 0.17. CONCLUSION Abnormal electromyography, preoperative cervical curvature, intervertebral foramen stenosis, ossification of the posterior longitudinal ligament, and the degree of posterior displacement of the spinal cord are all significant contributing factors to C₅ nerve root palsy following posterior cervical single-door laminoplasty. A prediction model developed based on these factors demonstrates enhanced accuracy and substantial clinical application value.
Humans ; Female ; Nomograms ; Male ; Middle Aged ; Aged ; Kyphoplasty/adverse effects* ; Cervical Vertebrae/surgery* ; Spinal Nerve Roots ; Adult ; Postoperative Complications/etiology* ; Paralysis/etiology*

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: 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*

OBJECTIVES: To explore the predictive factors for hemodynamically significant patent ductus arteriosus (hsPDA) in preterm infants and to construct a nomogram prediction model for hsPDA occurrence in this population. METHODS: A retrospective analysis was conducted on the clinical data of preterm infants with gestational age <32 weeks diagnosed with patent ductus arteriosus (PDA) who were delivered at Nanjing Women and Children's Healthcare Hospital from January 2020 to December 2022. The subjects were divided into an hsPDA group (52 cases) and a non-hsPDA group (176 cases) based on the presence of hsPDA. Univariate analysis and multivariate logistic regression analysis were performed to screen predictive variables regarding the general information of the infants at birth, maternal pregnancy and delivery conditions, and relevant indicators during hospitalization. A nomogram prediction model for hsPDA occurrence was constructed using R software in preterm infants. Internal validation was performed using the Bootstrap method. Finally, the predictive model was evaluated for calibration, discrimination ability, and clinical utility. RESULTS: Multivariate regression analysis showed that the ratio of the left atrium to aorta diameter (LA/AO), mode of delivery (vaginal), and duration of mechanical ventilation were independent predictive factors for hsPDA in preterm infants (P<0.05). Based on the results of univariate analysis and multivariate logistic regression analysis, variables used to construct the nomogram prediction model for hsPDA risk included: LA/AO ratio, mode of delivery (vaginal), duration of mechanical ventilation, 5-minute Apgar score, and the presence of neonatal respiratory distress syndrome requiring surfactant therapy. The area under the receiver operating characteristic curve for this model was 0.876 (95%CI: 0.824-0.927), and the calibrated curve was close to the ideal reference line, indicating good calibration. The Hosmer-Lemeshow test demonstrated that the model fit well, and the clinical decision curve was above the extreme curves. CONCLUSIONS The nomogram prediction model, constructed using five variables (LA/AO ratio, vaginal delivery, duration of mechanical ventilation, 5-minute Apgar score, and the presence of neonatal respiratory distress syndrome requiring surfactant therapy), has reference significance for predicting the occurrence of hsPDA in preterm infants and provides valuable guidance for the early clinical identification of hsPDA.
Humans ; Ductus Arteriosus, Patent/etiology* ; Nomograms ; Female ; Infant, Newborn ; Infant, Premature ; Retrospective Studies ; Male ; Hemodynamics ; Logistic Models ; Pregnancy

OBJECTIVES: To explore the risk factors of feeding intolerance (FI) in critically ill children receiving enteral nutrition (EN) and to construct a prediction nomogram model for FI. METHODS: A retrospective study was conducted to collect data from critically ill children admitted to the Pediatric Intensive Care Unit of Xiangya Hospital, Central South University, between January 2015 and October 2020. The children were randomly divided into a training set (346 cases) and a validation set (147 cases). The training set was further divided into a tolerance group (216 cases) and an intolerance group (130 cases). Multivariate logistic regression analysis was used to screen for risk factors for FI in critically ill children receiving EN. A nomogram was constructed using R language, which was then validated on the validation set. The model's discrimination, calibration, and clinical net benefit were evaluated using receiver operating characteristic curves, calibration curves, and decision curves. RESULTS: Duration of bed rest, shock, gastrointestinal decompression, use of non-steroidal anti-inflammatory drugs, and combined parenteral nutrition were identified as independent risk factors for FI in critically ill children receiving EN (P<0.05). Based on these factors, a nomogram prediction model for FI in critically ill children receiving EN was developed. The area under the receiver operating characteristic curve for the training set and validation set was 0.934 (95%CI: 0.906-0.963) and 0.852 (95%CI: 0.787-0.917), respectively, indicating good discrimination of the model. The Hosmer-Lemeshow goodness-of-fit test showed that the model had a good fit (χ 2=12.559, P=0.128). Calibration curve and decision curve analyses suggested that the model has high predictive efficacy and clinical application value. CONCLUSIONS Duration of bed rest, shock, gastrointestinal decompression, use of non-steroidal anti-inflammatory drugs, and combined parenteral nutrition are independent risk factors for FI in critically ill children receiving EN. The nomogram model developed based on these factors exhibits high predictive efficacy and clinical application value.
Humans ; Critical Illness ; Enteral Nutrition/adverse effects* ; Male ; Risk Factors ; Female ; Child, Preschool ; Infant ; Nomograms ; Retrospective Studies ; Child ; Logistic Models

OBJECTIVES: To investigate the predictive factors for plastic bronchitis (PB) in children with Mycoplasma pneumoniae pneumonia (MPP) and to establish a nomogram prediction model for PB occurrence. METHODS: A retrospective analysis was conducted on children with MPP hospitalized at The Affiliated Hospital of Xuzhou Medical University from January 2023 to June 2024. The patients were randomly divided into a training set (n=562) and a validation set (n=240) at a ratio of 7:3 using simple random sampling. In the training set, patients were categorized into a PB group (n=70) and a non-PB group (n=492) based on the occurrence of PB. Spearman correlation analysis was performed to exclude collinearity among variables, followed by univariate analysis and LASSO regression to identify predictive factors. A nomogram prediction model for PB in children with MPP was constructed. The discriminative ability of the model was assessed using receiver operating characteristic (ROC) curve analysis, model calibration was evaluated with calibration curves, and clinical utility was appraised through decision curve analysis. RESULTS: Compared with the non-PB group, the PB group exhibited significantly longer disease duration prior to bronchoscopy, prolonged fever duration, higher fever peaks, higher proportions of patients with a family history of allergy and personal allergy history, and a higher proportion of patients with pleural effusion, as well as significantly elevated levels of white blood cell count, neutrophil percentage, C-reactive protein, procalcitonin, fibrinogen, D-dimer, aspartate aminotransferase, alanine aminotransferase, creatine kinase, lactate dehydrogenase, immunoglobulin A, and interleukin-6, along with a significantly lower lymphocyte percentage (all P<0.05). LASSO regression analysis identified pleural effusion, procalcitonin, D-dimer, and lactate dehydrogenase as major predictive factors for PB occurrence in children with MPP. The nomogram model based on these factors demonstrated good discriminative ability (area under the ROC curve: 0.852 in the training set and 0.830 in the validation set), with satisfactory calibration and clinical benefit. CONCLUSIONS The nomogram prediction model based on pleural effusion, procalcitonin, D-dimer, and lactate dehydrogenase provides effective predictive performance for the occurrence of PB in children with MPP.
Humans ; Pneumonia, Mycoplasma/complications* ; Nomograms ; Male ; Female ; Child ; Child, Preschool ; Retrospective Studies ; Bronchitis/etiology* ; Infant ; ROC Curve ; Adolescent

OBJECTIVES: To develop a risk prediction model for the transformation of chronic atrophic gastritis to high-grade intraepithelial neoplasia (HGIN) based on traditional Chinese medicine (TCM) syndrome patterns. METHODS: Clinical data of 201 chronic atrophic gastritis patients who visited the Second People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine and Dong'erhuan Branch between January 2022 and March 2023 were retrospectively analyzed, including 32 patients with HGIN (HGIN group) and 169 patients with moderate and severe chronic atrophic gastritis (non-HGIN group). The information of demographic characteristics, dietary habits, lifestyle factors, social and psychosocial factors, family history of tumors, medical history and comorbidities, long-term medication, endoscopic findings, histopathological examination results, as well as TCM syndrome types were collected. Potential HGIN risk factors were screened using LASSO regression, and the significant risk factors for establishing an HGIN risk prediction model were identified using logistic regression analysis. The final model was visually presented using a nomogram, and its diagnostic performance was evaluated through receiver operating characteristic curve analysis. RESULTS: Spleen-stomach Qi deficiency was the most common TCM syndrome in both HGIN and non-HGIN groups. LASSO-logistic regression model analysis showed that heavy alcohol consumption (X1), syndrome of static blood in stomach collaterals (X2), low-grade intraepithelial neoplasia (X3), high-salt diet (X4), and age (X5) were independent risk factors related to the occurrence of HGIN, and the predictive model was ln［P/(1－P)］=2.159X1+2.230X2+1.664X3+2.070X4+0.122X5- 11.096. The model demonstrated good discriminative ability, calibration, and goodness-of-fit, with area under the curve values of 0.940 and 0.891 in the training and validation sets, respectively. CONCLUSIONS The TCM syndrome of static blood in stomach collaterals shows correlation with the transformation from chronic atrophic gastritis to HGIN. The HGIN prediction model based on TCM syndrome patterns developed in the study demonstrates potential value in clinical application.
Humans ; Gastritis, Atrophic/diagnosis* ; Medicine, Chinese Traditional ; Retrospective Studies ; Female ; Male ; Middle Aged ; Stomach Neoplasms/diagnosis* ; Adult ; Risk Factors ; Carcinoma in Situ/diagnosis* ; Aged ; Nomograms ; Chronic Disease ; Logistic Models