Hospital-acquired Pressure Injuries Research Articles

Implementing a Hospital-Acquired Pressure Injury Prevention Bundle in Critical Care.

The emerging field of implementation science (IS) facilitates the sustainment of evidence-based practice in clinical care. This article, the third in a series on applying IS, describes how a nurse-led team at a multisite health system used IS concepts, methods, and tools to implement a hospital-acquired pressure injury (HAPI) prevention bundle on six critical care units, with the aim of decreasing HAPI incidence.

Implementing a Hospital-Wide Programme Using iPARiHS to Prevent and Manage Incontinence-Associated Dermatitis and Improve Hospital-Acquired Pressure Injuries.

To outline the implementation of a hospital-wide programme using the Integrated Promoting Action on Research Implementation in Health Services framework to prevent and manage incontinence-associated dermatitis and improve hospital-acquired pressure injuries. This is an empirical research study using mixed methods. The study, conducted across surgical, medical and critical care wards between June and October 2023, aimed to address knowledge gaps, enhance clinical practice and evaluate the effectiveness of interventions. The implementation strategy included education modules, engagement of staff through focus groups and targeted interventions such as individualised toileting plans and structured skin care regimens. Data collection involved audits, incident reporting and clinician knowledge surveys. Findings indicate a reduction in hospital-acquired incontinence-associated dermatitis and pressure injuries postimplementation, with observed improvements in clinician knowledge. However, challenges including workload, skill mix and resource limitations were identified as barriers to implementation. The sustainability and scalability of the programme were emphasised, with ongoing monitoring and evaluation essential for long-term success. This study underscores the importance of evidence-based interventions, interdisciplinary collaboration and leadership support in improving patient outcomes and reducing healthcare costs associated with preventable skin injuries. Further research is needed to assess implementation in community settings and scale up interventions across healthcare networks. Analysing a hospital-wide programme using the Integrated Promoting Action on Research Implementation in Health Service framework to prevent and manage incontinence-associated dermatitis and improve hospital-acquired pressure injuries, could help identify the challenges for delivering patient-centred care. No patient or public involvement. To describe the implementation study, we referred to the StaRI Guideline. This intervention study was applied to the whole population and was therefore not a trial and did not require trial registration. The study was considered low risk and the Human Research Ethics Application (HREA) was approved.

Mixed-variable graphical modeling framework towards risk prediction of hospital-acquired pressure injury in spinal cord injury individuals

Developing machine learning (ML) methods for healthcare predictive modeling requires absolute explainability and transparency to build trust and accountability. Graphical models (GM) are key tools for this but face challenges like small sample sizes, mixed variables, and latent confounders. This paper presents a novel learning framework addressing these challenges by integrating latent variables using fast causal inference (FCI), accommodating mixed variables with predictive permutation conditional independence tests (PPCIT), and employing a systematic graphical embedding approach leveraging expert knowledge. This method ensures a transparent model structure and an explainable feature selection and modeling approach, achieving competitive prediction performance. For real-world validation, data of hospital-acquired pressure injuries (HAPI) among individuals with spinal cord injury (SCI) were used, where the approach achieved a balanced accuracy of 0.941 and an AUC of 0.983, outperforming most benchmarks. The PPCIT method also demonstrated superior accuracy and scalability over other benchmarks in causal discovery validation on synthetic datasets that closely resemble our real dataset. This holistic framework effectively addresses the challenges of mixed variables and explainable predictive modeling for disease onset, which is crucial for enabling transparency and interpretability in ML-based healthcare.

The Use of a Single Risk Assessment Tool for Mortality and Numerous Hospital-Acquired Conditions.

Quality assessment organizations leverage numerous patient safety measures to evaluate hospital performance, resulting in significant financial, administrative, and operational burdens on health systems. Low-intensity approaches that allow for reliable risk stratification of patients can reduce the required investment. The Braden score is a routinely performed bedside nursing evaluation validated to assess risk for hospital-acquired pressure injury. We hypothesized that the tool can be used to evaluate risk for other hospital-related adverse outcomes, including mortality, catheter-associated urinary tract infection (CAUTI), and central line-associated blood stream infection (CLABSI). We found that abnormal Braden scores have significant association with numerous adverse outcome measures, including mortality, CLABSI, CAUTI, and iatrogenic hypoglycemia. Because of its frequency of reevaluation, we have found preliminary evidence that leveraging this tool can reduce harm by quickly identifying the most at-risk patients for various types of iatrogenic harm. We conclude that in the face of increasing automation and technical applications, for example, artificial intelligence-driven tools, highly reliable clinician bedside physical examination and evaluation can still have significant, low-cost, and high-value impact in improving patient safety.

Skin injury: Associations with variables related to perfusion and pressure.

Skin injuries are a major healthcare problem that are not well understood or prevented in the critically ill, suggesting that underappreciated variables are contributing. This pilot study tested the hypothesis that perfusion-related factors contribute to skin injuries diagnosed as hospital-acquired pressure injuries (HAPIs). A total of 533 adult patients were followed over 2574 critical care days (mean age 62.4, standard deviation (SD) 14.3 years, mean body mass index 30.4 (SD 7.4) kg/m2, 36.4% female). This was a secondary analysis of prospective, non-randomised clinical data from an intensive care unit at a large urban teaching hospital. Factors related to perfusion, specifically two or more infusions of vasopressors/inotropes, temporary mechanical circulatory support (MCS), extracorporeal membrane oxygenation, and durable MCS, were analysed to determine whether they were more strongly associated with HAPIs than immobility due to prolonged mechanical ventilation (>72 h) or operating room time (>6 h). Patients diagnosed with a HAPI had a statistically significant higher risk of being exposed to variables related to perfusion and immobility (P < 0.05 for each variable). Perfusion-related variables, except durable MCS, had a larger effect on skin breakdown (number needed to harm (NNH) 4-10) than immobility-associated variables (NNH 12-17). The finding that perfusion-related variables predicted HAPIs may warrant consideration of alternative diagnoses, such as skin failure due to impaired perfusion as a pathophysiological process that occurs concurrently with multisystem organ failure. Differentiation of skin injuries primarily from circulatory malfunction, rather than external pressure, may guide the development of more effective treatment and prevention protocols. This pilot study suggests that the contribution of perfusion to skin injuries should be explored further.

The pressure injury prevalence and practice improvements (PIPPI) study: A multiple methods evaluation of pressure injury prevention practices in an acute-care hospital.

Pressure injuries are a significant problem for immobile patients in acute care and can have a profound impact on patients' health and well-being, putting pressure on healthcare systems and strain on the healthcare economy. Nurses play a pivotal role in preventing pressure injuries. A study using multiple methods was conducted to explore pressure injury prevention practices in four inpatient units within a tertiary-level Australian Hospital. Quantitative and qualitative methods were used to gather data across a 9-month period. Observations, audits, surveys and interviews were used to collect data across five time points. Statistical analysis of the quantitative data was undertaken, and thematic analysis was used to analyse qualitative data. Data were integrated using a realist evaluation framework. Ethical approval for the study was granted. The quantitative results demonstrated significant reductions in pressure injury prevalence from 11.5% at commencement to 4.8% at completion of the study. Hospital-acquired pressure injuries also reduced from 4.6% to 1.9%. These results were achieved even though nursing knowledge and attitudes did not increase during the study period. Three qualitative themes were identified: Making Nursing Care Visible, Understanding the 'Why' and Engagement is Key. This study demonstrates that pressure injuries can be prevented with improvements in nursing care processes. Nurses' knowledge and attitudes towards pressure injury prevention did not change throughout this study and further research is required on how nurses' knowledge and attitudes contribute towards pressure injury prevention practices.

Impact of the degree of synergy between patient and nurse perceptions on the clinical outcome of pressure injury prevention: a mixed-methods systematic review protocol

IntroductionPressure injuries are a common and significant concern in clinical practice, often serving as a vital quality indicator. While (clinical) practice guidelines have been established to offer recommendations for mitigating...

Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk.

Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden Scale, often fail to capture ICU-specific factors, limiting their predictive accuracy. Although artificial intelligence models offer improved accuracy, their "black box" nature poses a barrier to clinical adoption. To develop an artificial intelligence-based HAPI risk-assessment model enhanced with an explainable artificial intelligence dashboard to improve interpretability at both the global and individual patient levels. An explainable artificial intelligence approach was used to analyze ICU patient data from the Medical Information Mart for Intensive Care. Predictor variables were restricted to the first 48 hours after ICU admission. Various machine-learning algorithms were evaluated, culminating in an ensemble "super learner" model. The model's performance was quantified using the area under the receiver operating characteristic curve through 5-fold cross-validation. An explainer dashboard was developed (using synthetic data for patient privacy), featuring interactive visualizations for in-depth model interpretation at the global and local levels. The final sample comprised 28 395 patients with a 4.9% incidence of HAPIs. The ensemble super learner model performed well (area under curve = 0.80). The explainer dashboard provided global and patient-level interactive visualizations of model predictions, showing each variable's influence on the risk-assessment outcome. The model and its dashboard provide clinicians with a transparent, interpretable artificial intelligence-based risk-assessment system for HAPIs that may enable more effective and timely preventive interventions.

Early Identification, Intervention, and Prevention of Hospital-Acquired Pressure Injuries Using a Nurse-Driven Pressure Injury Prevention Program.

The purpose of this quality improvement project was to reduce the hospital-acquired pressure injury (HAPI) rate to less than 1.177 per 1000 patient-days, increase staff competency and care in pressure injury prevention best practices through implementation of a nurse-driven pressure injury prevention program, to engage patients in pressure injury prevention through implementation of skin rounds, and improve staff adherence to documentation requirements for pressure injury interventions on an amputee/stroke unit. HAPIs can lead to negative patient outcomes including pain, infection, extended hospitalization, and morbidity. Using an evidence-based education strategy, the Agency for Healthcare Research and Quality pressure ulcer prevention clinical pathway and skin rounds were implemented. Focused education for nursing, staff competency, daily audits, HAPI rates, and documentation compliance were evaluated pre and post intervention. The HAPI rate reduced from 1.177 to 0.272 per 1000 patient-days. After completion, the unit maintained zero pressure injuries, daily patient care for pressure injuries improved, documentation compliance increased, and staffs' knowledge and skill set in early identification, intervention, and prevention of pressure injuries heightened. A nurse-driven pressure injury prevention program was successful in the reduction of the HAPI rate.

Evaluation of Machine Learning Algorithms for Pressure Injury Risk Assessment in a Hospital with Limited IT Resources.

Clinical decision support systems for Nursing Process (NP-CDSSs) help resolve a critical challenge in nursing decision-making through automating the Nursing Process. NP-CDSSs are more effective when they are linked to Electronic Medical Record (EMR) Data allowing for the computation of Risk Assessment Scores. Braden scale (BS) is a well-known scale used to identify the risk of Hospital-Acquired Pressure Injuries (HAPIs). While BS is widely used, its specificity for identifying high-risk patients is limited. This study develops and evaluates a Machine Learning (ML) model to predict the HAPI risk, leveraging EMR readily available data. Various ML algorithms demonstrated superior performance compared to BS (pooled model AUC/F1-score of 0.85/0.8 vs. AUC of 0.63 for BS). Integrating ML into NP-CDSSs holds promise for enhancing nursing assessments and automating risk analyses even in hospitals with limited IT resources, aiming for better patient safety.

COVID-19 Pandemic Increases in Nursing-Sensitive Quality Indicators.

The pandemic profoundly stressed practicing nurses and could have thereby affected trends in nursing-sensitive quality indicators (NSIs), measures that detect changes in patient health status directly affected by nursing care. The aim of the study was to determine if NSIs have worsened in response to the pandemic and then returned to prepandemic levels using data from 2019 through 2022. We conducted a cross-sectional descriptive study of annual trends, examining unit data from the National Database of Nursing Quality Indicators (NDNQI) from 2019 through 2022 for five indicators: rates of falls, central line-associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), hospital-acquired pressure injuries (HAPI), and ventilator-associated events (VAE). The NDNQI is the largest repository of nursing quality indicators, which are derived from patient-level events, reported at the nursing unit level, and submitted quarterly by over 2,000 member hospitals. Adult medical-surgical or critical care inpatient nursing units with complete data for the 4 years were included, with samples ranging from 456 to 5,818 nursing units in 2,346 hospitals. Analysis of variance was conducted by comparing the 2019 rates to each subsequent year. In decreasing order of prevalence, the mean prepandemic rates were 6.58 VAE per 1,000 ventilator days (critical care only), 2.41 HAPI per 1,000 device days, 2.20 falls per 1,000 patient days, 0.96 CAUTI per 1,000 catheter days, and 0.68 CLABSI per 1,000 central line days for medical-surgical and critical care units combined. The rates for all five nurse-sensitive indicators increased significantly beginning in 2020 and have begun to decline but have not returned to baseline by 2022. The maximum rate was observed in 2020 for falls and 2021 for the remaining indicators. These increases to the maximum ranged from a 12% increase in CAUTI to 49% for CLABSI. NSIs increased during the pandemic and are now returning to baseline. The pandemic underscored the importance of nursing practice. The pandemic's enduring negative effects on the nursing workforce must be addressed to preserve patient safety.

Pediatric pressure injuries: A systematic review of interventions to prevent hospital-acquired pressure injuries in the pediatric population

Pediatric pressure injuries: A systematic review of interventions to prevent hospital-acquired pressure injuries in the pediatric population

Changes in Interprofessional Practice During the COVID-19 Pandemic and the Effect on Patient Outcomes: A Retrospective Observational Study.

Changes in healthcare delivery were required during the first year of the COVID-19 pandemic. The purpose of this study was to determine the impact of the approach to care of the COVID-19 patient on nursing sensitive indicators and nutrition therapy and the utilization of rehabilitation services during the first year of the pandemic in the acute care setting. A retrospective study of 894 patients admitted with a COVID-19 diagnosis was conducted between March 2020 and February 2021 in 3-month cohorts. All charts were reviewed for general demographics and hospital data, nursing quality indicators, and nutritional and rehabilitation services for the first 30 days of admission. Differences in patient characteristics were noted among the cohorts. Variations were observed between time points in hospital-acquired pressure injury occurrence, with mechanical ventilation and proning being independent predictors of hospital-acquired pressure injuries. There were differences noted in the percentage of patients with a central line-associated bloodstream infection among the time points (P < .001), but there were no differences noted in catheter-associated urinary tract infections (P = .20). Overall, 15.5% had a malnutrition diagnosis, with most patients receiving 50% of prescribed calorie and protein needs. Rehabilitation services increased over time with these services being initiated earlier in the later cohorts (P < .001). The results of this study demonstrated the impact of the pandemic on outcomes in the areas of nursing, nutrition, and rehabilitation, which varied across quarterly cohorts as we learned and developed new practices and adapted to a novel pandemic.

Nurse-Sensitive Indicators as Predictors of Trauma Patient Discharge Disposition.

About 3.5 million trauma patients are hospitalized every year, but 35%-40% require further care after discharge. Nurses' ability to affect discharge disposition by minimizing the occurrence of nurse-sensitive indicators (catheter-associated urinary tract infection [CAUTI], central line-associated bloodstream infection [CLABSI], and hospital-acquired pressure injury [HAPI]) is unknown. These indicators may serve as surrogate measures of quality nursing care. The purpose of this study was to determine whether nursing care, as represented by three nurse-sensitive indicators (CAUTI, CLABSI, and HAPI), predicts discharge disposition in trauma patients. This study was a secondary analysis of the 2021 National Trauma Data Bank. We performed logistic regression analyses to determine the predictive effects of CAUTI, CLABSI, and HAPI on discharge disposition, controlling for participant characteristics. A total of n =29,642 patients were included, of which n =21,469 (72%) were male, n =16,404 (64%) were White, with a mean (SD) age of 44 (14.5) and mean (SD) Injury Severity Score of 23.2 (12.5). We created four models to test nurse-sensitive indicators, both individually and compositely, as predictors. While CAUTI and HAPI increased the odds of discharge to further care by 1.4-1.5 and 2.1 times, respectively, CLABSI was not a statistically significant predictor. Both CAUTI and HAPI are statistically significant predictors of discharge to further care for patients after traumatic injury. High-quality nursing care to prevent iatrogenic complications can improve trauma patients' long-term outcomes.

The effects of a combined occupational therapy and nursing preventative approach to reduce hospital-acquired pressure injuries in an acute inpatient hospital setting: a case control study

The effects of a combined occupational therapy and nursing preventative approach to reduce hospital-acquired pressure injuries in an acute inpatient hospital setting: a case control study

The Changing Prevalence of Pressure Injury among Ontarians with SCI/D at Rehabilitation Admission: Opportunities for Improvement.

Despite preventability, 20-50% of patients with acute spinal cord injury/disease (SCI/D) develop hospital-acquired pressure injuries (PIs). The Spinal Cord Injury Implementation and Evaluation Quality Care Consortium (SCI IEQCC) aimed to mitigate PI risk through patient-reported daily skin checks alongside usual care. This quality improvement initiative utilized an interrupted time series design, encompassing adults ≥ 18 years admitted for inpatient rehabilitation across five Ontario sites from 2020 to 2023. Patient demographics, etiology, and impairment data were obtained from a national registry, while participating sites gathered data on PI onset, location, and severity. Run charts depicted temporal trends, and statistical analyses, including chi-square and logistic regression, compared patients with and without PIs. Data from 1767 discharged SCI/D patients revealed that 26% had ≥1 PI, with 59% being prevalent and 41% incident. Most severe PIs (stages III and IV and unstageable) were acquired prior to admission. Process indicator fidelity was reasonable at 68%. Patients with PIs experienced longer hospital stays, lower Functional Independence Measure (FIM) changes, and FIM efficiency during rehabilitation. PI prevalence is increasing, particularly sacral injuries at admission, while incident cases have decreased since 2021 due to regular skin checks. This trend calls for proactive health system interventions to reduce costs and improve patient outcomes.

Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events.

To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI). This was a retrospective observational study from a single academic hospital over six calendar years (2016-2021). Machine learning was used to examine patients with an NSI compared to those without. Inclusion criteria: all adult inpatient admissions (2016-2021). Three approaches were used to analyze the NSI group compared to the No-NSI group. In the univariate analysis, descriptive statistics, and absolute standardized differences (ASDs) were employed to compare the demographics and clinical variables of patients who experienced a NSI and those who did not experience any NSIs. For the multivariate analysis, a light grading boosting machine (LightGBM) model was utilized to comprehensively examine the relationships associated with the development of an NSI. Lastly, a simulation study was conducted to quantify the strength of associations obtained from the machine learning model. From 163,507 admissions, 4643 (2.8%) were associated with at least one NSI. The mean, standard deviation (SD) age was 59.5 (18.2) years, males comprised 82,397 (50.4%). Non-Hispanic White 84,760 (51.8%), non-Hispanic Black 8703 (5.3%), non-Hispanic Asian 23,368 (14.3%), non-Hispanic Other 14,284 (8.7%), and Hispanic 30,271 (18.5%). Race and ethnicity alone were not associated with occurrence of an NSI. The NSI group had a statistically significant longer length of stay (LOS), longer intensive care unit (ICU) LOS, and was more likely to have an emergency admission compared to the group without an NSI. The simulation study results demonstrated that likelihood of NSI was higher in patients admitted under the major diagnostic categories (MDC) associated with circulatory, digestive, kidney/urinary tract, nervous, and infectious and parasitic disease diagnoses. In this study, race/ethnicity was not associated with the risk of an NSI event. The risk of an NSI event was associated with emergency admission, longer LOS, longer ICU-LOS and certain MDCs (circulatory, digestive, kidney/urinary, nervous, infectious, and parasitic diagnoses). Machine learning methodologies provide a new mechanism to investigate NSI events through the lens of health equity/disparity. Understanding which patients are at higher risk for adverse outcomes can help hospitals improve nursing care and prevent NSI injury and harm.

Enhancing Wound, Ostomy, and Continence (WOC) Nurse Verification: Utilizing the Electronic Medical Record for a Globalized View of Hospital-Acquired Pressure Injuries

Enhancing Wound, Ostomy, and Continence (WOC) Nurse Verification: Utilizing the Electronic Medical Record for a Globalized View of Hospital-Acquired Pressure Injuries

Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.

Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care. To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale). We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort. A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803). We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes.

Regional Transformation: An Integrated System's Approach to Magnet Designation Utilizing High-Reliability Organization Implementation Strategies.

Synergistic opportunity exists between Magnet-aspiring environments and High Reliability Organization (HRO) cultures to elevate safety, quality, new knowledge, continuous improvement, hierarchical flattening, and frontline empowerment. Variation existed across a region's 15 hospitals regarding Magnet-readiness, leadership engagement, journey strategies, and resource capacity. Quality improvement (QI) methodology and a hybrid-hub-spoke-model (HHSM) were used to support region-wide Magnet designation, improve patient outcomes, and advance nursing research. HRO strategies were used region-wide to strengthen implementation and enculturation of Magnet excellence infrastructure. Nine facilities achieved Magnet recognition. Magnet sites demonstrated significantly lower falls with injury ( P =.012), lower health care-associated infections and hospital-acquired pressure injuries than non-Magnet sites. Nursing research studies increased 223%. Utilizing a HHSM and HRO strategies represent effective methods for supporting region-wide Magnet designation and continuous QI.