The probability of readmission of patients with hospital-acquired pressure injuries (PI) is believed to be high. Since specific features of patients readmitted with PI are known, the predictive model can be developed to determine the probability of these individuals returning to a healthcare organization within one month after discharge. This model will be developed using retrospective data from hospitals to predict if specific patients with PI have a chance of readmission to prevent such situations in the future. The incoming patients will be classified as having a new condition or being admitted for a PI. These events will be used in determining an association between the risk factors and PI formation using linear regression analysis. The parameters that will be used to screen individuals who developed PI during a hospital stay are age and diabetes. Knowing the statistics and potential dangers for a specific patient population can help prevent readmissions through proper wound care and individual home visits by clinicians. Moreover, Lean and Six Sigma principles will be implemented to remove unnecessary data. Lastly, this predictive model will be tested in hospitals where data for the project is obtained.
Probability of Readmission among Patients with Pressure Injuries
Pressure injuries (PIs) are the most prevalent complications of extended hospitalization. PI can be described as localized skin and tissue damage caused by prolonged immobility, which causes shear stress on a particular body area, reducing blood flow to muscles and skin (McGee et al., 2019). Historically, pressure ulcers were first classified in 1975 by Shea, and in 1987 the International Association of Enterostomal Therapists released a guideline for caring for pressure wounds (Stewart et al., 2022). The classification was updated many times until the early 2000s when extensive clinical research allowed medical community to understand the pathogenesis of pressure ulcers and categorize PIs better (Stewart et al., 2022). Pressure wounds are difficult to treat; thus, the clinicians’ emphasis is placed on prevention. Since specific features of patients readmitted with PI are known, the predictive model can be developed to determine the probability of these individuals returning to a healthcare organization within one month after discharge. This model will be developed using retrospective data from hospitals and binomial distribution to predict if specific patients with PI have a chance of readmission to prevent such situations in the future.
Background of the Issue
Patients are often readmitted to healthcare facilities because of the worsening of PIs. The hospital readmission rate is 30% higher in those with pressure wounds than without PI (Park et al., 2019). Notably, the mortality rate and readmission to the intensive care units (ICUs) were found to be twice as high as in patients with PIs than without pressure ulcers (Figure C1). PI complications are often caused by such bacterial infections as Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa (Table B1). These complications are frequently associated with a more extended hospital stay of longer than three days and admission to the ICU (Chandra et al., 2019). The essential predictors of 30-day readmission in patients with pressure wounds are frequent hospital visits and comorbidities like diabetes, stage 5 chronic kidney disease, metastasized cancer, dementia, cerebrovascular condition, and cardiovascular diseases (Table A1). Moreover, such factors as limited mobility, moist skin, reduced sensory perception, and poor nutritional status were found to have a strong correlation with PI formation (McGee et al., 2019). Since many people who are readmitted to hospitals have various comorbidities, they are at an increased risk of developing pressure ulcers.
Since the prevalence of PIs is high, it causes significant damage to the budgets of healthcare institutions. More than 2.5 million hospitalized individuals in the United States develop PIs, and 60,000 of them die of complications (Padula et al., 2019). Moreover, PIs are relatively costly for healthcare organizations, reaching approximately $2 billion annually (Padula et al., 2019). Specifically, the average cost of treating for the first two stages of PI is about $8,450, while stages 3, 4, and unstageable require approximately $23,000 for one treatment cycle (Padula et al., 2019). Since it may take more than six months for pressure ulcers to heal, PIs place a substantial financial burden on hospitals (Padula et al., 2019). If the incidence of pressure ulcers is reduced, healthcare organizations will be able to save $12.7 billion per year (Padula et al., 2019). Thus, there is a high demand for preventive strategies to reduce the incidence of pressure ulcers and readmission rates due to PI-associated complications.
Current Preventive Strategies
Since the problem of PIs has existed for a long time, various preventive strategies have been developed. For example, when patients stay longer than three days in healthcare facilities, clinicians try to monitor them closely, cleaning their skin, repositioning, and balancing their nutrition (Citty et al., 2019). Indeed, malnutrition due to metabolic diseases like diabetes is the most significant contributor to the development of PIs and the poor healing of these wounds. Therefore, a protein-rich diet with the supplements of zinc, vitamin C, Arginine, and antioxidants is crucial in such cases (Citty et al., 2019). All these micro-and-macro-elements are essential for tissue regeneration, especially in those who are on prolonged mechanical ventilation support, cannot consume food, or have an endocrine issue (Citty et al., 2019). This nutrition should be maintained both in hospital settings and at home; hence, families and caregivers need to be adequately educated about the consequences of malnutrition.
A protective bandage is another efficacious preventive strategy for patients at the risk of pressure wounds. Specifically, placing five-layer silicone dressings on wounds or areas of the body exposed to pressure from medical devices can reduce the risk of complications (Padula et al., 2019). In fact, the BORDER clinical trial demonstrated the efficacy of these bandages in diminishing PI risk (Padula et al., 2019). Although silicone dressings may be more expensive than ordinary gauze, the long-term outcomes are much better with the former. It appears that all preventive protocols help diminish the occurrence of pressure wounds. For instance, according to Padula et al. (2019), patients who received prophylactic interventions were 67% less likely to develop superficial PIs and had a 47% lower risk of developing full-thickness pressure ulcers than the control groups. Since any patient admitted to a healthcare facility is at risk of developing pressure ulcers, prevention can improve health outcomes.
Available Tools for Predicting PI Risks
Different predictive models have been developed to assess patients for the risk of pressure ulcers. For example, such methods as the Braden scale, Scott Triggers tool, and laboratory-based Scott Triggers are often used in a clinical setting to assess patients for PIs (Park et al., 2019). Braden scale evaluates such variables as mobility, moisture, nutrition, sensory perception, and friction (Park et al., 2019). Scott Triggers tool assesses age, albumin level, and surgery time, while its laboratory-based version also includes blood parameters, type of anesthesia, and comorbid conditions (Park et al., 2019). It appears that the Braden scale is less sensitive than the other two models because of the increased chance of false-negative results, which means that many individuals at risk may be omitted. However, they are not specific enough because of a high rate of false positives (Table D1). Moreover, these methods are more applicable to individuals who underwent surgery and cannot effectively appraise non-surgical patients’ likelihood of PI formation.
Methodology for Developing a New Model
The incoming patients can be classified as having a new condition or being admitted for a PI. In this project, only individuals who were readmitted due to PI complications would be considered. These events will be used in calculating binomial distribution, which allows to “give the probability for the occurrence of any event that can have two outcomes” (Kros & Rosenthal, 2016, p. 168). This research will be a retrospective cohort study; hence, the data for statistical analysis will be obtained from the electronic health records of two local hospitals. Only two criteria will be used to screen individuals who developed PI during a hospital stay to reduce unnecessary processes and save time: age and diabetes status. Moreover, linear regression analysis will be implemented in this case to reveal if the statistically significant association between the selected variables and PI progression exists (Kros & Rosenthal, 2016). In fact, regression will help reduce the influence of the confounding factors to ensure that the true relationship between the variables is determined. Lastly, Lean and Six Sigma principles will be implemented to remove unnecessary data and improve the model.
Implementation of the Model in a Clinical Setting
The predictive model developed during this project can be applied to hospital settings to estimate a patient’s risk of PI and start preventive strategies. The local hospitals, where the patient data from electronic health records are obtained, will receive an offer to implement this tool to the admitted individuals. Notably, this method will be relevant both for surgical and non-surgical patients. The model’s performance will be evaluated monthly until the end of the year to estimate its usefulness in identifying patients at risk.
Pressure wounds are the most common complication of prolonged hospitalization; therefore, patients should frequently be assessed because timely prophylactic interventions can prevent PIs. Since the general picture of an individual at risk is known, elderly patients with comorbidities should be under the close supervision of nurses. Overall, frequent turning, early mobility, and protective dressing on the affected body surfaces are crucial elements of PI risk reduction.
In this project, three specific recommendations for improving patient outcomes were developed. Firstly, it is essential to examine the skin of hospitalized diabetic patients during the first 24 hours of admission (Stewart et al., 2022). Secondly, elderly and malnourished patients should receive additional nutritional support and frequent repositioning (Citty et al., 2019). Thirdly, Lean and Six Sigma principles suggest that, in this case, waiting for physicians’ orders should be minimized, and nurses need to implement the predictive model to identify patients at risk and take preventive measures. In fact, giving nursing staff more freedom and responsibility in solving this issue may empower them to formulate new suggestions based on their observations and experience.
Recommendation for Managers
Since it is in the hospital administrators’ best interest to have a low rate of pressure wounds and readmissions for PIs to reduce financial losses, they should try to introduce effective measures. Specifically, managers may consider purchasing silicone dressings to minimize shear stress from medical devices, primarily in old and malnourished patients with metabolic problems. This particular measure can be costly in the short term, but the long-term outcomes will benefit the organization and patients.
In summary, healthcare-associated pressure injuries frequently occur in immobilized patients with comorbidities who have a high probability of returning to a healthcare institution. According to the available literature, individuals who are at greater risk of PI formation are old, immobilized, and malnourished people who have comorbidities that affect their cardiovascular, endocrine, or immune systems. Although various models exist to assess a person for the risk of pressure ulcers, most of them have relatively low sensitivity and specificity. Furthermore, these tools only target surgical patients and hence cannot be applied to the cohort of people who were not operated. This project will develop the predictive model for these individuals using retrospective hospital data, binomial distribution model, and regression analysis to build strategies to prevent PI and readmission in the population at risk. The model may be implemented in hospitals to estimate hospitalized individuals at risk of PI and introduce preventive strategies. Overall, PI seems to be a relatively preventable condition that requires close monitoring and specific measures like turning, early mobility, protective bandages, and nutritional correction to minimize tissue damage.
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Citty, S. W., Cowan, L. J., Wingfield, Z., & Stechmiller, J. (2019). Optimizing Nutrition Care for Pressure Injuries in Hospitalized Patients. Advances in Wound Care, 8(7), 309-322. Web.
Kros, J. F., & Rosenthal, D. A. (2016). Statistics for health care management and administration: Working with Excel (3rd ed.). John Wiley & Sons, Inc.
McGee, W. T., Nathanson, B. H., Lederman, E., & Higgins, T. L. (2019). Pressure Injuries at Intensive Care Unit Admission as a Prognostic Indicator of Patient Outcomes. Critical Care Nurse, 39(3), 44–50. Web.
Padula, W. V., Chen, Y. H., & Santamaria, N. (2019). Five‐Layer Border Dressings as Part of a Quality Improvement Bundle to Prevent Pressure Injuries in US Skilled Nursing Facilities and Australian Nursing Homes: A Cost‐Effectiveness Analysis. International Wound Journal, 16(6), 1263-1272. Web.
Park, S. K., Park, H. A., & Hwang, H. (2019). Development and Comparison of Predictive Models for Pressure Injuries in Surgical Patients: A Retrospective Case-Control Study. Journal of Wound Ostomy & Continence Nursing, 46(4), 291-297. Web.
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Stewart, T. P., Black, J. M., & Alderden, J. (2022). The Past, Present, and Future of Deep-Tissue (Pressure) Injury. Advances in Skin and Wound Care, 78–80.
Table A1: Hospital Readmissions for PIs in Patients with Comorbidities
Table B1: Urine and Perineal Cultures Results in Hospitalized Patients with PI
Table D1: Comparison of 3 PI Predictive Models