A Prediction Model for Osteoporosis Risk Using a Machine-Learning Approach and Its Validation in a Large Cohort

Prediction of Osteoporotic Hip Fracture Outcome: Comparative Accuracy of 27 Immune-Inflammatory-Metabolic Markers and Related Conceptual Issues

Objectives: This study, based on the concept of immuno-inflammatory-metabolic (IIM) dysregulation, investigated and compared the prognostic impact of 27 indices at admission for prediction of postoperative myocardial injury (PMI) and/or hospital death in hip fracture (HF) patients. Methods: In consecutive HF patient (n = 1273, mean age 82.9 ± 8.7 years, 73.5% females) demographics, medical history, laboratory parameters, and outcomes were recorded prospectively. Multiple logistic regression and receiver-operating characteristic analyses (the area under the curve, AUC) were used to establish the predictive role for each biomarker. Results: Among 27 IIM biomarkers, 10 indices were significantly associated with development of PMI and 16 were indicative of a fatal outcome; in the subset of patients aged >80 years with ischaemic heart disease (IHD, the highest risk group: 90.2% of all deaths), the corresponding figures were 26 and 20. In the latter group, the five strongest preoperative predictors for PMI were anaemia (AUC 0.7879), monocyte/eosinophil ratio > 13.0 (AUC 0.7814), neutrophil/lymphocyte ratio > 7.5 (AUC 0.7784), eosinophil count < 1.1 × 109/L (AUC 0.7780), and neutrophil/albumin × 10 > 2.4 (AUC 0.7732); additionally, sensitivity was 83.1-75.4% and specificity was 82.1-75.0%. The highest predictors of in-hospital death were platelet/lymphocyte ratio > 280.0 (AUC 0.8390), lymphocyte/monocyte ratio < 1.1 (AUC 0.8375), albumin < 33 g/L (AUC 0.7889), red cell distribution width > 14.5% (AUC 0.7739), and anaemia (AUC 0.7604), sensitivity 88.2% and above, and specificity 85.1-79.3%. Internal validation confirmed the predictive value of the models. Conclusions: Comparison of 27 IIM indices in HF patients identified several simple, widely available, and inexpensive parameters highly predictive for PMI and/or in-hospital death. The applicability of IIM biomarkers to diagnose and predict risks for chronic diseases, including OP/OF, in the preclinical stages is discussed.

Prediction of subsequent fragility fractures: application of machine learning

BACKGROUND

Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients.

METHODS

We retrospectively analyzed data from patients presented with fragility fractures at our Fracture Liaison Service, categorizing them into index fragility fracture (n = 905) and subsequent fragility fracture groups (n = 195). We independently trained ML models using 27 features for both male and female cohorts. The algorithms tested include Random Forest, XGBoost, CatBoost, Logistic Regression, LightGBM, AdaBoost, Multi-Layer Perceptron, and Support Vector Machine. Model performance was evaluated through 10-fold cross-validation.

RESULTS

The CatBoost model outperformed other models, achieving 87% accuracy and an AUC of 0.951 for females, and 93.4% accuracy with an AUC of 0.990 for males. The most significant predictors for females included age, serum C-reactive protein (CRP), 25(OH)D, creatinine, blood urea nitrogen (BUN), parathyroid hormone (PTH), femoral neck Z-score, menopause age, number of pregnancies, phosphorus, calcium, and body mass index (BMI); for males, the predictors were serum CRP, femoral neck T-score, PTH, hip T-score, BMI, BUN, creatinine, alkaline phosphatase, and spinal Z-score.

CONCLUSION

ML models, especially CatBoost, offer a valuable approach for predicting subsequent fragility fractures in osteoporotic patients. These models hold the potential to enhance clinical decision-making by supporting the development of personalized preventative strategies.

Knowledge framework of intravenous immunoglobulin resistance in the field of Kawasaki disease: A bibliometric analysis (1997-2023)

BACKGROUND

Kawasaki disease (KD) is an autoimmune disease with cardiovascular disease as its main complication, mainly affecting children under 5 years old. KD treatment has made tremendous progress in recent years, but intravenous immunoglobulin (IVIG) resistance remains a major dilemma. Bibliometric analysis had not been used previously to summarize and analyze publications related to IVIG resistance in KD. This study aimed to provide an overview of the knowledge framework and research hotspots in this field through bibliometrics, and provide references for future basic and clinical research.

METHODS

Through bibliometric analysis of relevant literature published on the Web of Science Core Collection (WoSCC) database between 1997 and 2023, we investigated the cooccurrence and collaboration relationships among countries, institutions, journals, and authors and summarized key research topics and hotspots.

RESULTS

Following screening, a total of 364 publications were downloaded, comprising 328 articles and 36 reviews. The number of articles on IVIG resistance increased year on year and the top three most productive countries were China, Japan, and the United States. Frontiers in Pediatrics had the most published articles, and the Journal of Pediatrics had the most citations. IVIG resistance had been studied by 1889 authors, of whom Kuo Ho Chang had published the most papers.

CONCLUSION

Research in the field was focused on risk factors, therapy (atorvastatin, tumor necrosis factor-alpha inhibitors), pathogenesis (gene expression), and similar diseases (multisystem inflammatory syndrome in children, MIS-C). "Treatment," "risk factor," and "prediction" were important keywords, providing a valuable reference for scholars studying this field. We suggest that, in the future, more active international collaborations are carried out to study the pathogenesis of IVIG insensitivity, using high-throughput sequencing technology. We also recommend that machine learning techniques are applied to explore the predictive variables of IVIG resistance.

Development and Validation of a Nomogram Model for Individualizing the Risk of Osteopenia in Abdominal Obesity

OBJECTIVE

This study was aimed to create and validate a risk prediction model for the incidence of osteopenia in individuals with abdominal obesity.

METHODS

Survey data from the National Health and Nutrition Examination Survey (NHANES) database for the years 2013-2014 and 2017-2018 was selected and included those with waist circumferences ≥102 m in men and ≥88 cm in women, which were defined as abdominal obesity. A multifactor logistic regression model was constructed using LASSO regression analysis to identify the best predictor variables, followed by the creation of a nomogram model. The model was then verified and evaluated using the consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Results Screening based on LASSO regression analysis revealed that sex, age, race, body mass index (BMI), alkaline phosphatase (ALP) and Triglycerides (TG) were significant predictors of osteopenia development in individuals with abdominal obesity (P < 0.05). These six variables were included in the nomogram. In the training and validation sets, the C indices were 0.714 (95 % CI: 0.689-0.738) and 0.701 (95 % CI: 0.662-0.739), respectively, with corresponding AUCs of 0.714 and 0.701. The nomogram model exhibited good consistency with actual observations, as demonstrated by the calibration curve. The DCA nomogram showed that early intervention for at-risk populations has a net positive impact.

CONCLUSION

Sex, age, race, BMI, ALP and TG are predictive factors for osteopenia in individuals with abdominal obesity. The constructed nomogram model can be utilized to predict the clinical risk of osteopenia in the population with abdominal obesity.

Demystifying the Risk Factors and Preventive Measures for Osteoporosis

Background

Osteoporosis is a major health problem, globally. It is characterized by structural bone weakness leading to an increased risk of fragility fractures. These fractures commonly affect the spine, hip and wrist bones. Consequently, Osteoporosis related proximal femur and vertebral fractures represent a substantial, growing social and economic burden on healthcare systems worldwide. Indentification of the risk factors, clinical risk assessment, utilization of risk assessment tools and appropriate management that play a crucial role in reducing the burden of Osteoporosis by tackling modifiable risk factors.

Methods

This chapter explores various risk factors that are associated with Osteoporosis and provides an overview of various clinical and diagnostic risk assessment tools with a particular emphasis on evidence-based strategies for their prevention.

Conclusion

The role of emerging technologies such as Artificial Intelligence (AI) and perspectives such as newer diagnostic modalities, monitoring and surveillance approaches in prevention of risk factors in the pathogenesis of Osteoporosis is highlighted.

Construction of a predictive model for osteoporosis risk in men: using the IOF 1-min osteoporosis test

OBJECTIVE

To construct a clinical prediction nomogram model using the 1-min IOF osteoporosis risk test as an evaluation tool for male osteoporosis.

METHODS

The 1-min test results and the incidence of osteoporosis were collected from 354 patients in the osteoporotic clinic of our hospital. LASSO regression model and multi-factor logistic regression were used to analyze the risk factors of osteoporosis in patients, and the risk prediction model of osteoporosis was established. Verify with an additional 140 objects.

RESULTS

We used logistic regression to construct a nomogram model. According to the model, the AUC value of the training set was 0.760 (0.704-0.817). The validation set has an AUC value of 0.806 (0.733-0.879). The test set AUC value is 0.714 (0.609-0.818). The calibration curve shows that its advantage is that the deviation correction curve of the nomogram model can maintain a good consistency with the ideal curve. In terms of clinical applicability, compared with the "total intervention" and "no intervention" schemes, the clinical net return rate of the nomogram model showed certain advantages.

CONCLUSION

Using the 1-min osteoporosis risk test provided by IOF, we built a male osteoporosis risk prediction model with good prediction effect, which can provide greater reference and help for clinicians.

Prediction of the Risk of Bone Mineral Density Decrease in Type 2 Diabetes Mellitus Patients Based on Traditional Multivariate Logistic Regression and Machine Learning: A Preliminary Study

Purpose

There remains a lack of a machine learning (ML) model incorporating body composition to assess the risk of bone mineral density (BMD) decreases in type 2 diabetes mellitus (T2DM) patients. We aimed to use ML algorithms and the traditional multivariate logistic regression to establish prediction models for BMD decreases in T2DM patients over 50 years of age, and compare the performance of the two methods.

Patients and Methods

This cross-sectional study was conducted among 450 patients with T2DM from 1 August 2016 to 31 December 2022. The participants were divided into a normal BMD group and a decreased BMD group. Traditional multivariate logistic regression and six ML algorithms were selected to construct male and female models. Two nomograms were constructed to evaluate the risk of BMD decreases in the male and female T2DM patients, respectively. The ML models with the highest area under the curve (AUC) were compared with the traditional multivariate logistic regression models in terms of discriminant ability and clinical applicability.

Results

The optimal ML model was the extreme gradient boost (XGBoost) model. The AUCs of the traditional multivariate logistic regression and the XGBoost models were 0.722 and 0.800 in the male testing dataset, respectively, and 0.876 and 0.880 in the female testing dataset, respectively. The decision curve analysis results suggested that using the XGBoost models to predict the risk of BMD decreases obtained more net benefits compared with the traditional models in both sexes.

Conclusion

We preliminarily proved that the XGBoost models outperformed most other ML models in both sexes and achieved higher accuracy than traditional analyses. Due to the limited sample size in the study, it is necessary to validate our findings in larger prospective cohort studies.