Physical Functions and Comorbidity Affecting Collapse at 4 or More Weeks after Admission in Patients with Osteoporotic Vertebral Fractures: A Prospective Cohort Study

Lumbar radiculopathy and fracture risk: A Korean nationwide population-based cohort study

INTRODUCTION

Lumbar radiculopathy is a common disease with a high economic burden, and fractures in adults are a significant public health problem. However, studies of the relationship between lumbar radiculopathy and fractures are scarce. We investigated the fracture risk in patients with lumbar radiculopathy.

METHODS

This nationwide retrospective cohort study identified 815,101 patients with lumbar radiculopathy and randomly matched individuals without lumbar radiculopathy (1:1) who were included in the Korean National Health Insurance System in 2012. Cox proportional hazards regression analyses were performed to calculate the hazard ratio (HR) for fracture risk in patients with lumbar radiculopathy.

RESULTS

The study included 301,347 patients with lumbar radiculopathy and matched 289,618 individuals without lumbar radiculopathy. Compared to individuals without lumbar radiculopathy, patients with lumbar radiculopathy had a 27 % increased fracture risk (adjusted HR = 1.27, 95 % confidence interval = 1.24-1.31). The Kaplan-Meier plot showed a significantly higher fracture incidence in patients with lumbar radiculopathy than in individuals without lumbar radiculopathy at all times.

CONCLUSION

Lumbar radiculopathy is significantly associated with fracture risk.

Prediction of Progressive Collapse in Osteoporotic Vertebral Fractures Using Conventional Statistics and Machine Learning

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

The objective of this study was to determine prognostic factors for the progression of osteoporotic vertebral fracture (OVF) following conservative treatment.

SUMMARY OF BACKGROUND DATA

Few studies have evaluated factors associated with progressive collapse (PC) of OVFs. Furthermore, machine learning has not been applied in this context.

MATERIALS AND METHODS

The study involved the PC and non-PC groups based on a compression rate of 15%. Clinical data, fracture site, OVF shape, Cobb angle, and anterior wedge angle of the fractured vertebra were evaluated. The presence of intravertebral cleft and the type of bone marrow signal change were analyzed using magnetic resonance imaging. Multivariate logistic regression analysis was performed to identify prognostic factors. In machine learning methods, decision tree and random forest models were used.

RESULTS

There were no significant differences in clinical data between the groups. The proportion of fracture shape ( P <0.001) and bone marrow signal change ( P =0.01) were significantly different between the groups. Moderate wedge shape was frequently observed in the non-PC group (31.7%), whereas the normative shape was most common in the PC group (54.7%). The Cobb angle and anterior wedge angle at diagnosis of OVFs were higher in the non-PC group (13.2±10.9, P =0.001; 14.3±6.6, P <0.001) than in the PC group (10.3±11.8, 10.4±5.5). The bone marrow signal change at the superior aspect of the vertebra was more frequently found in the PC group (42.5%) than in the non-PC group (34.9%). Machine learning revealed that vertebral shape at initial diagnosis was a main predictor of progressive vertebral collapse.

CONCLUSION

The initial shape of the vertebra and bone edema pattern on magnetic resonance imaging appear to be useful prognostic factors for progressive collapse in osteoporotic vertebral fractures.

Underweight and risk of fractures in adults over 40 years using the nationwide claims database

We aimed to investigate how underweight affects the incidence of fractures, as well as the influence of cumulative, longitudinal periods of low body mass index (BMI) and changes in body weight on fracture development. Data on adults aged 40-year and over who had three health screenings between January 1, 2007, and December 31, 2009 were used to determine the incidence of new fractures. The hazard ratios (HRs) for new fractures depending on BMI, total cumulative number of underweight, and weight change over time were calculated using Cox proportional hazard analysis. In this study, 15,955 (2.8%) of the 561,779 adults were diagnosed with fractures more than once over three health examinations. The fully adjusted HR for fractures in underweight individuals was 1.173 (95% Confidence interval [CI] 1.093-1.259). Underweight individuals diagnosed only once, twice, or three times had an adjusted HR of 1.227 (95%CI 1.130-1.332), 1.174 (95%CI 1.045-1.319), and 1.255 (95%CI 1.143-1.379), respectively. Although the adjusted HR was higher in adults who consistently had underweight (HR; 1.250 [95%CI 1.146-1.363]), those with underweight had an increased risk of fractures regardless of weight change (HR; 1.171 [95%CI 1.045-1.312], and 1.203[95%CI 1.075-1.346]). Underweight is a risk factor for fractures in adults over the age of 40 years, even if they returned to normal weight.

The Impact of Persistent Low Weight Status on the Occurrence of Vertebral Fractures: A Nationwide Population-Based Cohort Study

BACKGROUND

Although, being underweight is commonly associated with osteoporosis and sarcopenia, its association with vertebral fractures (VFs), is less well researched. We investigated the influence of cumulative, chronic periods of low weight and changes in body weight on VF development.

METHODS

We used a nationwide, population-based database with data on people (> 40 years) who attended three health screenings between January 1, 2007, and December 31, 2009 to assess the incidence of new VFs. Cox proportional hazard analyses were used to establish the hazard ratios (HRs) for new VFs based on the degree of body mass index (BMI), the cumulative numbers of underweight participants, and temporal change in weight.

RESULTS

Of the 561,779 individuals in this analysis, 5,354 (1.0%) people were diagnosed three times, 3,672 (0.7%) were diagnosed twice, and 6,929 (1.2%) were diagnosed once. The fully adjusted HR for VFs in underweight individuals was 1.213. Underweight individuals diagnosed only once, twice, or three times had an adjusted HR of 0.904, 1.443, and 1.256, respectively. Although the adjusted HR was higher in adults who were consistently underweight, there was no difference in those who experienced a temporal change in body weight. BMI, age, sex, and household income were significantly associated with VF incidence.

CONCLUSION

Low weight is a risk factor for VFs in the general population. Given the significant correlation between cumulative periods of low weight and the risk of VFs, it is necessary to treat underweight patients before a VF to prevent its development and other osteoporotic fractures.

Operational Definition Identifying Osteoporotic Vertebral Fractures in the Claims Database

BACKGROUND

We analyzed the International Classification of Diseases, 10th edition (ICD-10) diagnostic codes, procedure codes, and radiographic image codes for vertebral fracture (VF) used in the database of Health Insurance Review and Assessment Service (HIRA) of Korea to establish a validated operational definition for identifying patients with osteoporotic VF in claims data.

METHODS

We developed three operational definitions for detecting VFs using 9 diagnostic codes, 5 procedure codes and 4 imaging codes. Medical records and radiographs of 2,819 patients, who had primary and subordinated codes of VF between January 2016 and December 2016 at two institutions, were reviewed to detect true vertebral fractures. We evaluated the sensitivity and positive predictive value (PPV) of the operational definition in detecting true osteoporotic VF and obtained the receiver operating characteristic (ROC) curve.

RESULTS

Among the 2,819 patients who had primary or secondary diagnosis codes for VF, 995 patients satisfied at least one of the criteria for the operational definition of osteoporotic VF. Of these patients, 594 were judged as having true fractures based on medical records and radiographic examinations. The sensitivity and PPV were 62.5 (95% confidence interval [CI], 59.4-65.6) and 59.7(95% CI, 56.6-62.8) respectively. In the receiver operating characteristic analysis, area under the curve (AUC) was 0.706 (95% CI, 0.688-0.724).

CONCLUSION

Our findings demonstrate the validity of our operational definitions to identify VFs more accurately using claims data. This algorithm to identify VF is likely to be useful in future studies for diagnosing osteoporotic VF.