ABSTRACT
Pressure injuries remain a significant concern in military hospital settings, leading to increased morbidity and healthcare costs. Understanding the interplay of multiple risk factors is critical for effective prevention. To identify key risk factors and their combined effects on pressure injury development among inpatients in multi‐centre military hospitals using factor analysis. A cross‐sectional study was conducted involving 4876 inpatients across multiple military hospitals. Data were collected on 15 potential risk factors, including incontinence, care dependency, mobility limitations, comorbidities, medication use, nutritional status, and demographics. Factor analysis with principal component analysis and varimax rotation was applied, and maximum canonical correlation coefficients were calculated to evaluate the predictive contribution of single and combined factors. Single‐factor analysis identified incontinence as the strongest predictor (MaxCanonicalCorr = 0.50126), followed by care dependency (0.31982) and bedridden status (0.30061). Two‐factor analysis revealed incontinence combined with care dependency as the highest‐performing model (MaxCanonicalCorr = 0.50867). Three‐factor models incorporating incontinence, health conditions, and care dependency achieved the greatest predictive capacity (MaxCanonicalCorr = 0.5157), demonstrating that multi‐factor interactions enhance risk prediction beyond single‐factor effects. Incontinence is the primary modifiable risk factor for pressure injury in military hospital inpatients. Integrating continence management with assessments of functional status and comorbidities can improve early identification of high‐risk patients and guide targeted preventive strategies.
Keywords: care dependency, factor analysis, incontinence, military hospitals, pressure injury, risk factors
1.
Key Points.
Pressure injuries remain a significant preventable complication in hospitalized patients, causing increased morbidity, prolonged hospital stays, and substantial economic burden on healthcare systems.
Military hospital populations face unique pressure injury risks due to heterogeneous patient demographics, including combat‐injured personnel with traumatic amputations and burns, veterans with complex chronic conditions, and patients with psychological impairments such as PTSD and traumatic brain injuries.
Individual risk factors such as prolonged immobility, incontinence, nutritional deficits, altered mental status, advanced age, and comorbid conditions rarely act in isolation; their complex interplay significantly amplifies overall susceptibility to pressure injury development.
Factor analysis provides a robust statistical method to identify underlying patterns among correlated risk factors, enabling more sophisticated patient risk stratification and targeted intervention strategies.
There is a critical knowledge gap in research examining pressure injury risk factors specifically within military hospital populations, particularly studies employing advanced analytical techniques to understand factor interactions.
This study aims to identify key risk factors and their interactions in multi‐centre military hospitals through comprehensive factor analysis, providing actionable insights to enhance prevention protocols, improve patient outcomes, and reduce the overall burden of pressure injuries in military healthcare systems.
2. Introduction
Pressure injuries, formerly known as pressure ulcers or bedsores, represent one of the most preventable yet persistently challenging complications in hospitalised patients [1]. These injuries develop when localised pressure, shear, or friction forces are applied to the skin and underlying tissues for prolonged periods, leading to ischemia and tissue necrosis. The clinical spectrum of pressure injuries ranges from superficial skin changes to deep tissue destruction involving subcutaneous tissue, muscle, and even bone [2, 3]. Despite advances in preventive strategies and clinical awareness, the prevalence of pressure injuries remains significant, particularly in populations with complex medical needs, prolonged hospital stays, or limited mobility. Their occurrence not only contributes to increased morbidity and prolonged hospitalisation but also imposes substantial economic burdens on healthcare systems, highlighting the urgent need for effective prevention and management strategies. In military hospital settings, the prevention of pressure injuries presents unique challenges compared to civilian healthcare environments. The patient population is highly heterogeneous, encompassing active‐duty personnel with combat‐related injuries, veterans with chronic and complex medical conditions, and family members who may require acute care [4]. Combat injuries, including traumatic amputations, burns, and spinal cord injuries, predispose patients to immobilisation and hemodynamic instability, further increasing the risk of pressure injury development [5]. Additionally, military hospitals often care for patients with psychological and cognitive impairments, including post‐traumatic stress disorder (PTSD) and traumatic brain injuries, which may hinder mobility and adherence to preventive regimens [6]. Consequently, the multifactorial nature of pressure injury risk in these settings necessitates a nuanced understanding of both patient‐specific and environmental factors to develop targeted prevention strategies. Previous research has consistently highlighted a variety of individual risk factors associated with pressure injury development. Commonly identified factors include prolonged immobility, incontinence, nutritional deficits, altered mental status, advanced age, and the presence of comorbid conditions such as diabetes, cardiovascular disease, or vascular insufficiency [7, 8]. Whilst these factors are well‐documented, it is increasingly recognised that they rarely act in isolation. Rather, the interplay among multiple risk factors may amplify overall susceptibility and influence the severity of tissue injury. For example, immobility combined with poor nutritional status may pose a significantly higher risk than either factor alone, or cognitive impairments may further complicate the early detection of tissue damage. Understanding these complex interrelationships is therefore critical for developing more precise risk assessment tools and effective prevention protocols. Factor analysis offers a robust statistical approach to explore these intricate relationships by identifying underlying patterns among multiple correlated risk factors [9]. This method allows researchers to group related variables into latent constructs, enabling more sophisticated stratification of patients according to their overall risk profile [10, 11]. Such insights are particularly valuable in military healthcare settings, where patient populations present unique characteristics that may differ substantially from those in civilian hospitals. By uncovering clusters of risk factors specific to military populations, healthcare providers can prioritise interventions for the most vulnerable patients, allocate resources more efficiently, and design preventive strategies that address the multifactorial nature of pressure injury development [4].
Despite extensive research on pressure injury risk factors, there remains a paucity of studies focused on military hospital populations, and even fewer that employ advanced analytical techniques such as factor analysis to delineate the interrelationships among these factors. Given the high stakes associated with pressure injuries including prolonged recovery times, increased risk of infection, and the potential for significant disability, there is a critical need for studies that not only identify individual risk factors but also examine how these factors interact within complex patient populations. Addressing this knowledge gap is essential for implementing evidence‐based, context‐specific interventions in military hospital environments.
This study aims to identify and analyse the key risk factors and their interactions associated with pressure injury development in multi‐centre military hospitals using comprehensive factor analysis. By elucidating the latent structures among risk variables and examining their combined impact on pressure injury susceptibility, this research seeks to provide actionable insights that can enhance preventive care protocols, improve patient outcomes, and reduce the overall burden of pressure injuries within military healthcare systems. Understanding these patterns will support a proactive, targeted approach to prevention that accounts for the unique needs and challenges of military hospital populations, ultimately contributing to safer, higher‐quality care.
3. Methods
3.1. Study Design and Setting
This cross‐sectional study was carried out in multiple centres of military Daping hospital from January 2023 to December 2023. The study protocol received approval from the Military Medical Ethics Committee and was conducted in full compliance with the principles outlined in the Declaration of Helsinki. The participating hospitals provide comprehensive healthcare services to a diverse military population, including active‐duty personnel, veterans, and family members, ensuring a representative sample for the investigation of pressure injury risk factors in this unique healthcare setting.
3.2. Participants
A total of 4876 patients from the multiple centres of military Daping Hospital were included in the study. Eligibility criteria comprised: (1) age 18 years or older, (2) a hospital stay of at least 48 h, (3) availability of complete medical records, and (4) provision of informed consent. Patients with incomplete records or those who withdrew consent at any point were excluded from the analysis. The selected sample reflects a broad spectrum of the military inpatient population, ensuring the study's findings are relevant and generalisable to the clinical settings under investigation.
The study population reflected the typical demographic composition of multi‐centre military hospitals. Among the 4876 inpatients, 1982 (40.7%) were active‐duty personnel, 1463 (30.0%) were veterans or retired military personnel, and 1431 (29.3%) were military family members or civilian beneficiaries. Additionally, 612 patients (12.6%) presented with combat‐related injuries, including traumatic amputations, blast injuries, spinal cord injuries, and burn conditions associated with increased immobility and elevated pressure‐injury risk. Participant ages ranged from 19 to 92 years (mean 58.4 ± 12.7). This heterogeneous population is representative of military hospital settings, enhancing the generalisability of our findings.
3.3. Data Collection
Comprehensive data were collected on 15 potential risk factors for pressure injury development, identified through a thorough review of the literature and expert consultation. These included urinary and faecal incontinence, level of care dependency, bedridden status, abnormal bowel and urinary function, nutritional status, physical activity, comorbidity burden, caregiving support, occupational factors for active‐duty personnel, medication use including polypharmacy, alcohol consumption, dietary intake, educational level as a proxy for health literacy, and biological sex. Data were obtained from electronic medical records, structured clinical assessments, and patient interviews, ensuring accuracy and completeness. Standardised definitions and validated assessment tools were applied to minimise measurement bias. Given the unique characteristics of military hospital populations—including active‐duty personnel with combat‐related injuries, veterans with complex medical conditions, and diverse caregiving contexts—this comprehensive dataset enabled the evaluation of both individual risk factors and their complex interactions within this specific clinical environment, providing a robust foundation for subsequent factor analysis.
3.4. Statistical Analysis
Statistical analysis was conducted using SPSS version 28.0 [12]. Factor analysis was employed to explore the contributions and interactions of the identified risk factors, including single‐factor evaluation, two‐factor pairing, and three‐factor interaction models to capture both individual effects and complex interrelationships. The suitability of the dataset for factor analysis was assessed using the Kaiser–Meyer–Olkin measure of sampling adequacy and Bartlett's test of sphericity [13]. Factors were extracted using principal component analysis with varimax rotation to maximise interpretability, and maximum canonical correlation coefficients were calculated to evaluate the strength and predictive capacity of each factor. This analytical approach enabled a rigorous assessment of latent structures within the dataset, facilitating a nuanced understanding of risk factor patterns relevant to pressure injury development in military hospital populations.
Justification for using factor analysis instead of logistic regression. Explanation of PCA and varimax rotation choice. Interpretation criteria (e.g., canonical correlation thresholds, clinical relevance). Mention that assumptions (sample adequacy, KMO index, Bartlett test) were met.
4. Results
4.1. Participant Characteristics
A total of 4876 inpatients from multiple Daping military hospital centres were included. The cohort comprised 3145 males (64.5%) and 1731 females (35.5%), with a mean age of 58.4 ± 12.7 years. Ward distribution was 56.2% medical, 28.9% surgical, and 14.9% intensive care units (ICU). Common comorbidities included hypertension (52.8%), diabetes (37.6%), and cardiovascular disease (24.3%). Combat‐related injuries were present in 12.6% of patients, including amputations, burns, spinal cord injuries, and blast injuries. The mean hospital stay was 18.2 ± 6.5 days (Table 1). The distribution of military patient categories was as follows: active‐duty personnel accounted for 40.7% of the sample (n = 1982), veterans or retired soldiers constituted 30.0% (n = 1463), and military family members or civilian beneficiaries represented 29.3% (n = 1431).
TABLE 1.
Demographic and baseline characteristics of the study population.
| Variable | Overall (n = 4876) |
|---|---|
| Age (years) | 58.4 ± 12.7 |
| Sex | |
| Male | 3145 (64.5%) |
| Female | 1731 (35.5%) |
| Ward type | |
| Medical | 2739 (56.2%) |
| Surgical | 1411 (28.9%) |
| ICU (intensive care unit) | 726 (14.9%) |
| Length of hospital stay (days) | 18.2 ± 6.5 |
| Comorbidities | |
| Hypertension | 2575 (52.8%) |
| Diabetes mellitus | 1834 (37.6%) |
| Cardiovascular disease | 1186 (24.3%) |
| Respiratory disease | 612 (12.6%) |
| Other chronic conditions | 489 (10.0%) |
Note: Values are expressed as mean ± SD or n (%).
4.2. Single‐Factor Analysis Results
Among individual risk factors, incontinence showed the strongest predictive value for pressure injury (MaxCanonicalCorr = 0.501), followed by care dependency (0.320) and bedridden status (0.301). Other factors, including defecation patterns, urination patterns, nutritional status, and comorbidities, contributed moderately (Table 2 and Figure 1).
TABLE 2.
Single factor analysis results.
| Factor | Max canonical correlation | Valid samples | Number variables | Clinical interpretation |
|---|---|---|---|---|
| Incontinence | 0.50126 | 4876 | 8 | Strong predictor; primary modifiable risk factor |
| Care dependency | 0.31982 | 4876 | 9 | Moderate predictor; functional limitation amplifies risk |
| Bedridden status | 0.30061 | 4876 | 1 | Moderate predictor; immobility contributes to pressure injury |
| Defecation pattern | 0.28118 | 4876 | 4 | Contributes to risk when combined with incontinence |
| Urination pattern | 0.24357 | 4876 | 5 | Moderate predictor; highlights continence‐related risk |
| Nutritional status | 0.24319 | 4876 | 4 | Low nutritional status increases tissue vulnerability |
| Physical activity | 0.23755 | 4876 | 3 | Reduced mobility contributes to risk |
| Health conditions | 0.21184 | 4876 | 13 | Comorbidities influence susceptibility |
| Caregiver support | 0.2055 | 4876 | 4 | Availability of assistance modifies risk |
| Occupational role | 0.19705 | 4876 | 6 | Active‐duty duties may impact mobility and pressure exposure |
| Medications | 0.15605 | 4876 | 13 | Polypharmacy can increase vulnerability |
| Alcohol consumption | 0.12538 | 4876 | 1 | Minor contribution |
| Diet | 0.08996 | 4876 | 3 | Nutritional intake contributes modestly |
| Education | 0.086882 | 4876 | 5 | Proxy for health literacy; minimal predictive value |
| Sex | 0.026731 | 4876 | 1 | Not a significant predictor |
Note: Correlations ≥ 0.30 are considered moderate, ≥ 0.50 strong. Incontinence emerges as the primary modifiable factor.
FIGURE 1.
Key single‐factor predictors of pressure injury risk. Bar chart illustrating maximum canonical correlations for individual risk factors. Incontinence is the strongest predictor, followed by care dependency and bedridden status.
Clinical Interpretation: A MaxCanonicalCorr ≥ 0.50 indicates a strong predictive association; thus, incontinence represents a primary modifiable risk factor. Functional limitations (care dependency, bedridden status) further amplify vulnerability.
4.3. Two‐Factor Analysis Results
Two‐factor models demonstrated higher predictive performance than single‐factor models. The incontinence care dependency combination had the highest predictive correlation (0.509), followed closely by incontinence paired with health conditions or medications (Table 3 and Figure 2).
TABLE 3.
Top 10 two factor analysis results.
| Factor 1 | Factor 2 | Max canonical correlation | Valid samples | Number of variables | Clinical interpretation |
|---|---|---|---|---|---|
| Incontinence | Care | 0.50867 | 4876 | 17 | Highest predictive value; key combination for risk assessment |
| Incontinence | Health conditions | 0.50866 | 4876 | 21 | Strong predictor; comorbidities amplify risk |
| Incontinence | Medications | 0.50791 | 4876 | 21 | Polypharmacy adds moderate risk |
| Defecation patterns | Incontinence | 0.50657 | 4876 | 12 | Functional and continence interplay increases susceptibility |
| Nutritional status | Incontinence | 0.50451 | 4876 | 12 | Poor nutrition+ incontinence heightens tissue vulnerability |
| Occupational role | Incontinence | 0.50378 | 4876 | 14 | Activity limitations and continence issues synergise |
| Caregiver support | Incontinence | 0.50345 | 4876 | 12 | Availability of assistance modifies risk in incontinence patients |
| Care burden | Incontinence | 0.50327 | 4876 | 9 | Caregiver strain impacts patient outcomes |
| Incontinence | Activity | 0.50302 | 4876 | 11 | Low activity + incontinence increases risk |
| Alcohol consumption | Incontinence | 0.50288 | 4876 | 9 | Minor additive effect |
Note: Incontinence is consistently present in top combinations, emphasising its central role.
FIGURE 2.
Two‐factor model predictive performance. Combination of incontinence and care dependency has the highest correlation (0.50), highlighting synergistic effects of functional limitations and continence issues.
Implication: These results suggest synergistic effects, where the combination of incontinence with functional or clinical deficits substantially increases pressure injury risk. Incorporating such multi‐factor interactions in risk assessment can improve early identification of high‐risk patients.
4.4. Three‐Factor Analysis Results
The most predictive three‐factor model included incontinence, care dependency, and health conditions (MaxCanonicalCorr = 0.516), with only marginal improvement over two‐factor models but higher complexity (Table 4, Figure 3). Additional notable combinations included incontinence with medications and care, or incontinence with defecation and comorbidities.
TABLE 4.
Top 10 three factor analysis results.
| Factor 1 | Factor 2 | Factor 3 | Max canonical correlation | Valid samples | Clinical interpretation |
|---|---|---|---|---|---|
| Incontinence | Health conditions | Care dependency | 0.5157 | 4876 | Highest predictive model; practical two‐factor model nearly as effective |
| Incontinence | Medications | Care dependency | 0.51463 | 4876 | Polypharmacy + functional dependency + incontinence |
| Incontinence | Health conditions | Medications | 0.5159 | 4876 | Comorbidities + medications + incontinence |
| Defecation patterns | Incontinence | Health conditions | 0.51359 | 4876 | Highlights interplay of continence and comorbidity |
| Defecation patterns | Incontinence | Care dependency | 0.51277 | 4876 | Functional + continence + bowel management |
| Defecation patterns | Incontinence | Medications | 0.51277 | 4876 | Combination affects vulnerable patients |
| Nutritional | Incontinence | Care dependency | 0.51168 | 4876 | Poor nutrition exacerbates functional + continence risk |
| Nutritional | Incontinence | Health conditions | 0.51156 | 4876 | Malnutrition + comorbidities + incontinence |
| Occupational role | Incontinence | Health conditions | 0.5115 | 4876 | Activity restrictions + comorbidities + continence issues |
| Occupational role | Incontinence | Care dependency | 0.51074 | 4876 | Active‐duty roles influence functional risk |
Note: Marginal improvement over two‐factor models suggests the two‐factor (incontinence + care dependency) model may be most practical for implementation.
FIGURE 3.
Three‐factor model predictive performance. Top‐performing three‐factor combination (incontinence, care dependency, health conditions) demonstrates marginally higher predictive capacity (0.516) compared to two‐factor models, indicating diminishing returns with increased complexity.
Clinical Interpretation: Whilst three‐factor models offer slightly higher predictive capacity, the two‐factor incontinence‐care dependency model may provide an optimal balance between accuracy and practicality in clinical settings.
4.5. Factor Structure Progression
The incremental increase in predictive capacity from single‐factor (0.501) to two‐factor (0.509) and three‐factor models (0.516) indicates diminishing returns with increased complexity. This pattern indicates diminishing returns with increasing model complexity, suggesting that the primary risk factor structure is effectively captured by simpler models. These findings highlight the importance of balancing predictive performance and model interpretability when designing risk assessment tools for pressure injury prevention in military hospital populations.
5. Discussion
This study provides a comprehensive analysis of pressure injury risk factors in 4876 military hospital inpatients, revealing incontinence as the predominant determinant of risk. Incontinence consistently appeared across all top‐performing factor combinations in single‐, two‐, and three‐factor models, highlighting its central role in the development of pressure injuries. This finding is consistent with established pathophysiology, as moisture associated with urinary or faecal incontinence compromises skin integrity, increases friction, and promotes tissue breakdown [14, 15, 16]. Care dependency and health conditions emerged as additional important contributors, particularly in multi‐factor models, underscoring the compounded risk posed by functional limitations and comorbidities [17, 18, 19]. The observed patterns confirm that pressure injury risk is multifactorial and that singular risk factor approaches may fail to capture the complexity of patient vulnerability in military hospital settings.
The centrality of incontinence in our factor models carries important clinical implications. Evidence supports that systematic continence management, including regular assessment, proactive bowel and bladder care, targeted skin protection, and staff education, can substantially reduce pressure injury incidence [1, 20]. Implementing such programmes within military hospitals could address the most prominent modifiable risk factor, thereby optimising prevention efforts. Functional dependency and health conditions further amplify risk, particularly in multi‐factor models, reflecting the multifactorial nature of pressure injury development. Patients with high care dependency are less able to reposition themselves, maintain hygiene, or request assistance, thereby amplifying the adverse effects of incontinence [21, 22]. Incorporating multiple interacting risk factors into assessment tools can therefore enhance early identification of high‐risk patients and guide tailored interventions.
Three‐factor models, which incorporated health conditions alongside incontinence and care dependency, demonstrated only marginal improvements in predictive capacity compared to two‐factor models but substantially increased model complexity. This suggests diminishing returns with additional factors and supports the practical utility of simpler, yet highly predictive models in clinical practice. The inclusion of health conditions also highlights the unique characteristics of military hospital populations, where patients often present with multiple comorbidities, combat‐related injuries, and service‐connected disabilities that amplify susceptibility to pressure injuries [23, 24]. Despite these complexities, the consistent prominence of incontinence across all models indicates that it represents a universal target for prevention, regardless of demographic or clinical variability within the military inpatient population [25, 26, 27].
5.1. Practical Implications for Military Hospitals
Continence management should be prioritised through regular assessment, skin barrier protection, and staff training. Integrated risk assessment using two‐factor models (incontinence + care dependency) may optimise preventive interventions, especially where resources or staff are limited.
Military‐Specific Considerations: Field or combat hospital settings face logistical constraints (limited staff, patient acuity, transport challenges), emphasising the need for simplified but effective risk stratification tools.
5.2. Linkage to Guidelines
Our findings can be incorporated into established frameworks such as the Braden Scale or NPIAP guidelines, adapting them to military hospital contexts. This ensures evidence‐based risk assessment and prevention strategies that account for the unique characteristics of this population.
Model Complexity: Three‐factor models offered slight gains in predictive capacity but at the cost of interpretability and clinical practicality. Thus, two‐factor models may strike the best balance for implementation in routine care. A large, representative sample from multi‐centre military hospitals. Use of factor analysis to uncover interrelated risk structures. Identification of clinically actionable targets (incontinence, care dependency).
5.3. Limitations
Cross‐sectional design prevents causal inference. Findings may not generalise to civilian populations. Three‐factor model complexity may hinder routine application.
6. Future Directions
Longitudinal studies to confirm causal associations. Development and evaluation of interventions targeting high‐risk combinations. Exploration of additional military‐specific risk factors not captured in conventional tools.
7. Conclusions
In military hospital inpatients, incontinence is the primary modifiable risk factor, with care dependency and comorbidities further increasing risk. Prioritising continence management and incorporating multi‐factor risk assessments can enhance early identification of high‐risk patients, improve preventive strategies, and reduce pressure injury incidence.
Funding
The authors have nothing to report.
Ethics Statement
This study was approved by The Clinical Trial Ethics Committee of the General Hospital of the Eastern Theatre Command of the People's Liberation Army of China (approval no. 2022DZKY‐065‐02) on 29 September 2022. All procedures complied with the 1964 Helsinki Declaration and its amendments. Written informed consent was obtained from all participants.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
APPENDIX S1: Supporting information.
Yu J., Long J., Cen Y., Deng X., and Qiu Y., “Risk Factors for Pressure Injuries and Injury Types Among Inpatients in Multi‐Centre Military Hospitals: A Factor Analysis Study,” International Wound Journal 22, no. 12 (2025): e70802, 10.1111/iwj.70802.
Data Availability Statement
Data are available upon reasonable request to the corresponding author, subject to military hospital privacy regulations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
APPENDIX S1: Supporting information.
Data Availability Statement
Data are available upon reasonable request to the corresponding author, subject to military hospital privacy regulations.