Highlights
Chronic obstructive pulmonary disease (COPD) is increasingly recognized as a systemic condition where fall risk is significantly elevated, yet accurate prediction remains a challenge. A recent study has established a clinical prediction model with the following key findings:
- Approximately 42% of individuals with COPD experienced at least one fall over a 12-month prospective period.
- The final predictive model identified three robust factors: a history of two or more falls in the preceding year, a higher number of chronic conditions, and poor performance on the Timed Up and Go Dual-Task (TUG-DT) test.
- A history of recurrent falls (≥2) was the strongest predictor, increasing the odds of future falls by more than 3.5 times.
- The model demonstrated acceptable discrimination (c-statistic 0.69) and excellent calibration, providing a foundation for clinical screening.
Background: The Systemic Burden of COPD and Fall Risk
While COPD is primarily defined by airflow limitation, its systemic manifestations—including skeletal muscle dysfunction, nutritional depletion, and cardiovascular comorbidities—profoundly impact physical function. Among these manifestations, falls represent a critical but often overlooked complication. Falls in the COPD population lead to a devastating cascade: hip fractures, head injuries, increased hospitalization, and a heightened fear of falling that subsequently drives physical inactivity and further respiratory decline.
Despite the high prevalence of falls in this demographic, clinicians have lacked a validated, specific tool to identify which patients are at the highest risk. General geriatric fall risk assessments may not fully capture the unique physiological stressors of COPD, such as exertional dyspnea and the cognitive-motor interference associated with increased work of breathing. This study, led by Nguyen et al., sought to fill this gap by developing and internally validating a targeted clinical prediction model.
Study Design and Methodology
The researchers conducted a secondary analysis using data from a recent fall prevention trial (NCT02995681). The study population consisted of 178 participants with a confirmed diagnosis of COPD. To be included, participants had to report either a 12-month history of falls, concerns regarding their balance, or a history of recent near-falls. This cohort was then tracked prospectively for 12 months to record fall incidents.
Baseline assessments were comprehensive, covering demographics, balance, mobility, and overall health status. The researchers initially considered 17 candidate predictors. To refine the model, they employed backward-selected multivariate logistic regression. The dependent variable was fall status (categorized as no falls versus one or more falls).
To ensure the reliability of the model, internal validation was performed using the bootstrap resampling method. The performance was assessed based on:
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Discrimination:
Measured by the concordance (c) statistic, which indicates the model’s ability to differentiate between those who fall and those who do not.
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Calibration:
Assessed via the expected-to-observed (E:O) ratio, calibration in the large (CITL), and the calibration slope, ensuring the predicted probabilities align with actual observed outcomes.
Key Findings: Identifying the High-Risk Profile
The study cohort had a mean age of 73 (±9) years, with a nearly even gender distribution (83 females). Over the 12-month follow-up, 74 participants (42%) reported at least one fall, with a staggering total of 188 falls recorded across the group. This high incidence underscores the vulnerability of this patient population.
The multivariate analysis narrowed the 17 candidate predictors down to three statistically significant factors:
1. Recurrent Fall History (The Power of Two)
Participants who reported a history of two or more falls in the 12 months prior to baseline were at the highest risk. The Odds Ratio (OR) was 3.59 (95% CI 1.65 to 7.82). This suggests that recurrent falling is not merely an isolated event but a marker of persistent physiological or environmental instability.
2. Multimorbidity (Number of Chronic Conditions)
The number of comorbid chronic conditions was also a significant predictor (OR 1.14, 95% CI 1.01 to 1.28). This reflects the cumulative impact of systemic illness on frailty. In COPD, comorbidities such as diabetes, cardiovascular disease, and osteoporosis often coexist, each contributing to a decline in postural control and compensatory mechanisms.
3. Timed Up and Go Dual-Task (TUG-DT) Scores
The TUG-DT test, which requires participants to perform a cognitive task (such as counting backward) while walking, was a crucial predictor (OR 1.04, 95% CI 1.00 to 1.09). This finding highlights the role of cognitive-motor interference. Patients with COPD often require more conscious attention to maintain balance and manage dyspnea; when a secondary cognitive task is introduced, their ability to maintain gait stability is compromised.
Expert Commentary: Why Dual-Tasking and Comorbidities Matter
The inclusion of the TUG-DT score in the final model provides significant mechanistic insight. In clinical practice, we often assess mobility in a vacuum, but real-world walking involves simultaneous cognitive demands—navigating a crowd, reading signs, or talking. In COPD, the ‘capacity-sharing’ model of attention is often strained. The brain must prioritize the high mechanical work of breathing and the sensory processing of dyspnea, leaving fewer neural resources for postural stability. The TUG-DT effectively captures this ‘frailty of attention.’
Furthermore, the emphasis on multimorbidity aligns with the evolving view of COPD as a component of a multi-organ frailty syndrome. Clinicians should not view a COPD patient’s fall risk solely through the lens of lung function (FEV1), as traditional pulmonary metrics often correlate poorly with fall incidents. Instead, the total burden of disease and the history of prior instability are far more predictive.
Model Performance and Validation
The model’s internal validation results were encouraging. The c-statistic of 0.69 (95% CI 0.61 to 0.78) is considered ‘acceptable’ for clinical prediction models, especially in complex populations like those with COPD. The calibration metrics were excellent: an E:O ratio of 1.01 and a CITL of -0.01 indicate that the model does not systematically over- or under-predict risk. The calibration slope of 0.93, after adjusting for optimism with a bootstrap shrinkage factor, suggests the model is robust and likely to perform well in similar cohorts.
Clinical Implications and Practice Gaps
For the practicing clinician, these findings suggest a shift in how we screen for falls in pulmonary rehabilitation and outpatient clinics. Rather than exhaustive balance testing for every patient, a tiered approach may be more efficient:
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Screening Question:
Ask specifically about the number of falls in the last year. A ‘yes’ to two or more falls should immediately trigger a high-risk classification.
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Comorbidity Audit:
Review the total number of chronic conditions. Patients with high multimorbidity require integrated care pathways that include physical therapy.
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The Dual-Task Challenge:
Incorporate a simple cognitive challenge into the standard TUG test. If a patient’s mobility significantly degrades under cognitive load, they require specific dual-task training interventions.
The primary limitation of the study is the need for external validation. While the internal validation was successful, the model must be tested in diverse geographical and clinical settings (e.g., primary care vs. tertiary pulmonary centers) to confirm its generalizability.
Conclusion
The development of this clinical prediction model marks a significant step toward personalized medicine in COPD care. By identifying that a history of recurrent falls, the burden of comorbidities, and impaired dual-task mobility are the primary drivers of fall risk, healthcare providers can better allocate resources to those most in need. Implementing these three simple assessments—recurrent fall history, comorbidity count, and TUG-DT—into routine clinical practice could significantly reduce the incidence of falls and improve the quality of life for individuals living with COPD.
Funding and Registration
This study was supported by various health research institutes and conducted as a secondary analysis of a trial registered at clinicaltrials.gov (NCT02995681). Detailed funding information can be found in the original publication.
References
Nguyen KT, Brooks D, Macedo LG, et al. Development of a clinical prediction model for falls in individuals with COPD. BMJ Open Respir Res. 2025 Dec 25;12(1):e002556. doi: 10.1136/bmjresp-2024-002556. PMID: 41448796.
