Development and Evaluation of a Nomogram for Predicting Pulmonary Embolism in Children With Severe Mycoplasma pneumoniae Pneumonia.

To construct a nomogram utilizing pediatric severe Mycoplasma pneumoniae pneumonia (SMPP) risk factors for pulmonary embolism (PE), facilitating the clinical identification and management of high-risk patients and reducing the excessive use of CT pulmonary angiography (CTPA). This was a retrospective analysis conducted between August 2021 and March 2024. We identified 35 children with SMPP complicated by PE, forming the PE group. A control group of 70 age- and sex-matched children with SMPP without PE was randomly selected at a 1:2 ratio. Clinical, laboratory, and CT findings were compared between the groups. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to develop a scoring model using a nomogram. The model's performance was assessed via the receiver operating characteristic curve (ROC), fivefold cross-validation, calibration curve, and clinical decision curve analysis. LASSO regression and multivariate logistic regression analyses revealed that D-dimer, neutrophil ratio, time to admission, pleural effusion, and necrotizing pneumonia were independent risk factors for PE in patients with SMPP. A nomogram prediction model was established based on the aforementioned independent risk factors. The area under ROC curve was 0.900. Fivefold cross-validation results further confirmed the model's stability. The calibration curve revealed good agreement between the predicted and actual probabilities of PE caused by SMPP, and the decision curve demonstrated that the nomogram model had a higher clinical net benefit. The nomogram serves as a predictive tool to aid in early intervention for pediatric patients with SMPP at high risk for PE, while minimizing unnecessary CTPA and overtreatment in low-risk patients.

Guan Y ，Zhao B ，Song C ，Hou Q ，Tong T ，Xu S ... -  《-》

Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study.

The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman's correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model's discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer-Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.

Zhuo LY ，Hao JW ，Song ZJ ，Meng H ，Wang TD ，Yang LL ，Yang ZM ，Ma JM ，Shen D ，Cui JJ ，Chen WJ ，Yang W ，Zang LL ，Wang JN ，Yin XP ... -  《-》

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. Level III, diagnostic study.

Lee CC ，Chen CW ，Yen HK ，Lin YP ，Lai CY ，Wang JL ，Groot OQ ，Janssen SJ ，Schwab JH ，Hsu FM ，Lin WH ... -  《-》

Development of a predictive nomogram for early identification of pulmonary embolism in hospitalized patients: a retrospective cohort study.

Hospitalized patients often present with complex clinical conditions, but there is a lack of effective tools to assess their risk of pulmonary embolism (PE). Therefore, our study aimed to develop a nomogram model for better predicting PE in hospitalized populations. Data from hospitalized patients (aged ≥ 15 years) who underwent computed tomography pulmonary angiography (CTPA) to confirm PE and non-PE were collected from December 2013 to April 2023. Univariate and multivariate stepwise logistic regression analyses were conducted to identify independent predictors of PE, followed by the construction of a predictive nomogram and internal validation. The efficiency and clinical utility of the nomogram model were assessed using receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve (CIC). The study included 313 PE and 339 non-PE hospitalized patients. Male gender, dyspnea or shortness of breath, interstitial lung disease, lower limb deep vein thrombosis, elevated fibrin degradation product (FDP), pulmonary arterial hypertension, and tricuspid regurgitation were identified as independent risk factors. The AUC of the predictive nomogram model was 0.956 (95% CI: 0.939-0.974), demonstrating superior performance compared with the simplified Wells score of 0.698 (95% CI: 0.654-0.741) and the modified Geneva score of 0.758 (95% CI: 0.717-0.799). Our study demonstrated that challenges remain in the accuracy of the Wells score and revised Geneva score in assessing PE in hospitalized patients. Fortunately, the nomogram we developed has shown a favorable ability to discriminate PE cases, providing high reference value for clinical practice. However, given that this was a single-center study, we plan to expand efforts to collect data from additional centers to further validate our model.

Cao Z ，Yang L ，Han J ，Lv X ，Wang X ，Zhang B ，Ye X ，Ye H ... -  《BMC Pulmonary Medicine》

Building and Verifying a Prediction Model for Deep Vein Thrombosis Among Spinal Cord Injury Patients Undergoing Inpatient Rehabilitation.

To explore the relevant variables that contribute to deep vein thrombosis (DVT) among spinal cord injury (SCI) patients undergoing inpatient rehabilitation and to build and validate a nomogram model that predicts DVT risk. By convenience sampling, 558 SCI patients who were hospitalized at a tertiary-level Grade A general hospital in Anhui Province, China between January 2017 and March 2022 were chosen as the study subjects. They were split into 2 groups at random, one for training (n = 446) and the other for validation (n = 112). The ratio was 8:2. The clinical information of patients was gathered, including sociodemographic characteristics, data about disease characteristics, and examinations pertaining to laboratories. The related factors of DVT among SCI patients undergoing inpatient rehabilitation were analyzed using both univariate and multivariate logistic regression. Using the variables identified by the multivariate logistic regression analysis, we constructed a predictive nomogram model with the aid of the R software. The model's predictive accuracy for assessing the risk of DVT was validated through the use of receiver operating characteristic curves and calibration plots. Prothrombin time, D-dimer, age, and Caprini score were independent related factors for DVT among SCI patients undergoing inpatient rehabilitation, according to multivariate logistic regression analysis (odds ratio > 1, P < 0.05). These 4 variables selected by the multivariate logistic regression analysis were used to build a nomogram risk model, which was found to have strong predictive capacity for predicting the risk of DVT among SCI patients undergoing inpatient rehabilitation. The nomogram model's area under the receiver operating characteristic curve in the training group and validation group was 0.793 and 0.905, and the 95% confidence intervals were 0.750∼0.837 and 0.830∼0.980, separately, indicating good discrimination of the nomogram model. A good calibration of the model was shown by the calibration curve, which was well consistent between the model's predicted probability and the actual frequency of DVT in both the training and validation groups. Prothrombin time, D-dimer level, age, and Caprini score are independent related factors for DVT among SCI patients undergoing inpatient rehabilitation. According to the variables mentioned previously, a nomogram model was constructed that can accurately and easily predict DVT risk among SCI patients undergoing inpatient rehabilitation. This facilitates the early identification of high-risk groups and the timely implementation of prevention, treatment, rehabilitation, and nursing strategies by clinical medical staff.

Zhao F ，Zhang L ，Chen X ，Huang C ，Sun L ，Ma L ，Wang C ... -  《-》