Best practices and standards dictate that prevention strategies be implemented to reduce the occurrence of pressure injuries (PIs).1–4 Heels are the second most common anatomical PI location.5–10 Further, the heel is the most frequently occurring location for deep tissue pressure injuries (DTPIs; 41%) compared with the sacrum (19%) and the buttocks (13%).11 Comparing the distribution of all patients with PIs, there is a significantly lower than expected number of DTPIs at the sacrum, coccyx, buttocks, and ischial tuberosities, whereas the heel, ankle, and foot have significantly more than expected.
Despite global efforts to raise awareness and reduce their occurrence, heel PIs (HPIs) persist. Hospital-acquired HPIs can pose a substantial financial burden from a regulatory and liability perspective. Pressure injuries can cause reimbursement losses up to $100,000, with liability losses quoted in the millions.12–16
Hospitalized patients face many risk factors, making them more vulnerable to HPIs.17–24 Because of this vulnerability, it becomes paramount to recognize patients who are more susceptible to an HPI and apply prevention strategies in a timely manner. There are many references such as position papers, guideline recommendations, and research studies that speak to HPI prevention strategies.4,25,26
Determining the risk factors that precipitate an HPI occurrence identifies who is at risk and allows prevention strategies to be applied to preserve costs. However, little evidence of HPI risk factors has been reported in the literature to substantiate the strength of their association.
Risk assessment tools can guide the clinician in determining which patients are at risk of PIs so that timely and appropriate prevention measures can be used.27 However, HPI risk assessment tools are limited in the literature and not reflective of recent HPI research.28–31 Studying risk factors is an important part of the tool creation process because these factors serve as alerts to clinicians for potential conditions and encourage action.
A previous study from these authors revealed four significant and independent predictors of HPI formation in hospitalized patients: diabetes mellitus (DM), vascular disease, immobility, and an admission Braden Scale score of 18 or less.32 However, that research was confined to one hospital. In this study, the primary aim was to replicate this research using a dataset from multiple hospitals to examine a larger and more diverse patient population. That said, the simple knowledge of risk factors is not enough and must be translated into everyday practice. Therefore, the secondary aim was to create a clinical enabler for assessing patients at risk of HPIs to help alert clinicians when they should apply PI prevention strategies.
The research design was a retrospective, case-control study using validation data to confirm the model from the main analysis. Data were extracted from the New York Statewide Planning and Research Cooperative System (SPARCS), which is a comprehensive all-payer data reporting system that collects patient-level details on patient characteristics, diagnoses and treatments, services, and charges for each hospital inpatient stay and outpatient visit (ambulatory surgery, ED, and outpatient services). It is governed by the New York State Department of Health.
To ensure that cases were deidentified to the research group, an intermediary department sanctioned by the New York State Department of Health removed any patient identifiers linked to each case. A unique identifier was assigned to each case to ensure that investigators did not select the same patient twice. The institutional review board assigned the research proposal a determination of Not Human Research.
Data from January 1, 2014 to June 30, 2015 were extracted from the SPARCS database. Many patients had multiple hospital admissions. For these analyses, study authors examined only each patient’s first hospital encounter during the 2014 to 2015 period. The inclusion criteria included all hospitalized adult patients (18 years or older) with and without HPIs. The exclusion criteria were:
- Patients with heel vascular wounds as designated by the associated International Classification of Diseases, Ninth Revision (ICD-9) code. Rationale: The etiology of these wounds stems from a disease process rather than from pressure.
- Obstetric and psychiatric patients. Rationale: Low risk of PI formation based on their admission reasons.
- Children younger than 18 years. Rationale: Younger children (<8 years old) are more prone to device-related PIs,33–35 and previous research identified only 0.1% of patients who were between 8 and 17 years old had an HPI.32
Information from the extracted sample was collected on ICD-9 diagnosis codes or dichotomous options for diseases and conditions reflecting those factors associated with HPIs to select eligible patients for the study. The following variables are associated or have shown to be predictive of an HPI in hospitalized patients and were studied initially as dichotomous variables:
- impaired nutrition, as defined by codes that indicate severe protein-calorie malnutrition, moderate-degree malnutrition, malnutrition of mild degree, other protein-calorie malnutrition, unspecified protein-calorie malnutrition, and cachexia;
- obesity conditions;
- vascular disease—unspecified, atherosclerosis of native arteries of the extremities;
- end-stage renal disease;
- neuropathy, other specified idiopathic peripheral neuropathy, unspecified, hereditary sensory neuropathy, idiopathic progressive polyneuropathy, and hereditary peripheral neuropathy;
- perfusion issues evidenced by documentation of at least one of the following: cardiovascular disease class IV, dehydration, edema, hereditary edema of legs, heparin-induced thrombocytopenia, myocardial infarction diagnosed during admission, severe anemia (hemoglobin <7 g/dL), cardiac arrest sustained during current admission, prolonged hypotension–unspecified, postoperative, due to shock from injury, cardiogenic shock, hypovolemic shock, and hemorrhagic shock (under hypovolemic shock);
- DM type 1 or 2 diagnosis;
- surgery–cardiovascular, vascular, orthopedic, neurosurgery, intestinal, genitourinary, gynecology, and transplant (liver, kidney, lung, heart, combined heart-lung);
- immobility defined by cerebral vascular accident, plegia defined by quadriplegia and quadriparesis, and/or other specified paralytic syndromes. This variable was also defined by states that would make a patient immobile, such as hemodynamic insatiability, shock states, and encephalopathy;
- mechanical ventilation;
- ICU stay;
- lower extremity fractures; and
- age (18–64 or 65 years or older).
All patient characteristics were analyzed as dichotomous variables unless their distributions precluded studying them in this manner. In addition, demographic variables included sex, race, age (specific year), specific admitting diagnosis, total hospital length of stay, and type of surgery. In patients who developed another PI, the intent was to collect the stage and location.
After the initial selection of patients from SPARCS, a total of 2,301 patient cases qualified as a case or control subject. From the group with HPIs, researchers then removed:
- patient cases that had community-acquired pressure injuries (CAPIs; this was because study authors could not account for variables that may have led to the precipitation of the HPI before their hospital admission);
- cases that had a mix of CAPIs and HAPIs for which it could not be determined if the PI was a CAPI or HAPI;
- cases with incomplete HPI data.
There were 364 excluded cases in total. The final sample for both the main and validation analyses consisted of 403 cases with an HAPI and 1,534 cases without any PIs, for a total sample of 1,937. Within this sample, researchers also checked for data missing critical variables to remove the entire patient case from the final analyses. Fortunately, no cases were removed for this reason. However, the variables for obesity and neuropathy could not be used in the final analyses because the obesity variable was flawed, and the neuropathy variable had no data.
For the main analysis, researchers used all patients who satisfied the inclusion and exclusion criteria with the exception of 240 patients—80 patients with HPIs, 160 patients without—who were randomly selected for the validation analysis. This left a total of 1,697 patients for the main analysis: 323 patients who developed HPIs and 1,374 who did not, which was larger than statistically necessary. For the main analysis, researchers estimated conservatively that the variable or variables that significantly predict the occurrence of HPI have an odds ratio (OR) of 2 or more in favor of developing HPI. Based on these parameters, study authors estimated that only 672 patients were required to detect an OR of 2 or higher for at least one variable. Therefore, 141 patients with HPIs and 531 patients without HPIs would be more than sufficient to detect an OR of 2.0 at a level of .05 with 80% power (PASS 2008; NCSS Statistical Software, Kaysville, Utah). That said, the larger sample required no additional time or cost to obtain and provided investigators with smaller confidence intervals for the ORs.
First, a set of univariate χ2 analyses was used to select predictor variables associated with development of HPIs with an OR (in favor of development of an HPI) of 2 or more. These predictor variables were then used in a series of forward stepwise logistic regression analyses to select variables that were significantly and independently associated with the development of an HPI in a multivariable setting. Each series of stepwise procedures studied a distinct category of variables, representing disease status (renal disease, diabetes, vascular disease), physical conditions (impaired nutrition, age, lower extremity fractures, immobility, perfusion issues), and conditions of hospitalization (mechanical ventilation, ICU stay, surgery). All of the variables that were significantly associated with the occurrence of an HPI were used in a final stepwise logistic regression model to select the final set of predictor variables from all categories. A receiver operating characteristic (ROC) curve was used to assess the adequacy of the model. A score derived from the final logistic model can be used to identify the patients at high risk of HPIs and for any hospital patients based on their data.
Researchers randomly selected data from the entire sample to validate the model. These data were randomly removed from the main analysis. A classification table was used to estimate overall classification accuracy of the main model by comparing the estimated and actual status of validation patients (HPI, no HPI). A cutoff point (ie, the value to be used in the estimate of which group a patient belonged) was set to approximately 0.3, the proportion of the general population that develops HPI in hospitals.
Table 1 displays various patient characteristics for the cases and controls in the main and validation groups. Among both samples, cases had a mean age of 74.4 years and a median length of stay 18 days or longer. By contrast, the mean age of controls was no more than 55.8 years, and the median length of stay was 3 days.
Across the main and validation samples, the majority of patients were female (54.2%–61.0%). The admitting diagnoses were predominantly related to cardiac, gastrointestinal, and respiratory conditions. In the main analysis, the top two most common HPI stages were stage 2 (n = 99) and unstageable (n = 92). In the validation analysis, the top two most common HPI stages were unstageable (n = 25) and stage 1 (n = 22; Table 2).
Table 3 presents the final logistic regression model developed using the main dataset. Seven variables were significantly and independently associated with HPI: DM, vascular disease, perfusion issues, impaired nutrition, age, mechanical ventilation, and surgery. These variables are displayed with their regression coefficients with standard errors and their adjusted ORs with 95% confidence intervals. Note that the significant variables, DM and surgery, were included in the analysis despite ORs of less than 2 because of their importance to HPIs. Figure 1 displays the ROC curve that assesses the overall utility of the regression model to predict the development of HPIs. The area under the curve (84.2) shows an impressive predictive accuracy of the model.
The validation analysis used 240 subjects randomly selected from the original sample (80 patients with HPIs; 160 patients without). Researchers used the variables and regression coefficients obtained using the main model to predict HPIs status for the validation patients. The overall percentage correct for this validation sample was 74%, with 91% of patients who developed HPIs correctly identified and 48.3% of patients who did not develop HPIs correctly identified.
Table 4 shows the final model variables for the main and validation analyses categorized by cases and controls that have ICD-9 subcategorizations or dichotomous options. Table 5 shows the types of surgeries by ICD-9 codes. Some surgical categories were so large that they had to be collapsed.
In this study, investigators confirmed risk factors from their previous research32 that were significant and independent predictors of an HPI during a patient’s hospital stay (DM and vascular disease). Study authors also confirmed new factors that were significant and independent predictors of HPI: perfusion issues, impaired nutrition, age 65 years or older, mechanical ventilation, and surgery.
The authors could not confirm that an admission Braden Scale score of 18 or less and/or immobility were significant and independent predictors.32 The previous study was a retrospective chart review at a single site that allowed access to Braden Scale scores and information regarding the patient’s mobility status and associated conditions while in the hospital.32 However, the intent in this research was to study a larger and more diverse population using mostly diagnosis codes to define the variables. Unfortunately, Braden Scale scores could not be obtained from SPARCS. For the variable of immobility, there were only four patients (<1%) in the main analysis as defined by ICD-9 codes. This may be attributable to the low sample size for this condition (<1%) and/or the manner in which this variable was defined for this research.
Previously published research has found similar risk factors as in this study. Diabetes32,36,37 and vascular disease32,36 have been associated with HPIs. The following risk factors have also been associated with PIs in general: age 65 years or older,6,38 perfusion issues,19,38–40 impaired nutrition,40,41 mechanical ventilation,19,38 and surgery.17,22,42 In a previous study by these authors, neither impaired nutrition nor malnutrition were significant.32 The criteria for malnutrition were different at that time and have since been redefined, which may explain the significant finding in the current study.
Patients with impaired nutrition were found to be almost seven times more likely to develop an HPI. Consistent with the literature,43–45 this research demonstrated an association between impaired nutrition status and risk of PIs (in this case, HPIs). Poor dietary intake, unintentional weight loss, underweight, and malnutrition are associated with an increased risk of PIs in general.46
It is interesting that the most common or second most common HPI stage in the main and validation analyses was unstageable. An unstageable PI is a full-thickness wound47 requiring diligent care and increased resources to heal. In addition, as stated previously, the heels are a frequent location for DTPI.11 This study examined ICD-9 codes that did not have a specific code for the DTPI stage, so it is plausible that DTPIs were subsumed under the unstageable or unspecified code.
Future work, retrospective or prospective, may study other diagnoses such as cognitive disorders. Muntlin Athlin et al7 found a statistically significant difference between patients who developed HPIs and those who did not in terms of the patient’s mental condition (using the Modified Norton Pressure Sore Risk-Assessment Scale Scoring System), as well as patients with mobility issues.
Heel Pressure Injury Clinical Enabler
As part of the prevention process, it has been recommended that patient risk factors be assessed to determine their risk of developing the condition.25 Once a patient’s risk factors have been identified, prevention strategies should be initiated and evaluated for effectiveness, and all observations and actions documented.25 Prevention strategies considered best practice for preventing HPIs have been well described.4,25,48–50 Targeted and consistent prevention practices have demonstrated effectiveness in reducing HPIs. Hanna-Bull10 reported that a 4-year hospital quality improvement effort targeting HPIs reduced their prevalence from 5.8% (preimplementation) to 1.6% by the end of year 4. This reinforces that preventing HPI is a focused and ongoing process.
There has been research conducted to determine which interventions for offloading or preventing pressure on the heels, such as heel-lift devices and pillows, are most effective. Generally, heel-lift devices are favored not only for their off-loading ability but also for maintaining the foot in a neutral position and avoiding unnecessary pressure on other parts of the foot, foot drop, and contracture formation.4,30,48,51 Research has also been performed on prophylactic dressings, usually a foam composition. However, the use of such dressings should be considered as part of the HPI prevention strategy, but they are not a standalone option.4,25,26,52–55
Based on best practice recommendations and findings from this and previous research,32 the authors created an educational enabler that combines HPI risk factors and prevention strategies to provide clinicians with a more focused clinical assessment process (Figure 2). The clinical enabler displays a mnemonic of the seven risk factors found to be significant and independent predictors of HPIs, as well as the Braden Scale admission score and immobility variables.32 The authors felt strongly about including immobility based on the results from this and other research7,32 regarding HPI formation and PI formation in general.18,40
One of the limitations of this study is its retrospective design. Data were collected via a large dataset, limiting available clinical information. Study authors could not verify at what time or where during the hospital stay a patient developed an HPI, only that the patient had one.
Unfortunately, databases are generally imperfect, creating data quality issues. In SPARCS, the flaws encountered included issues surrounding poor data entry (miscoding) or lack of data entry. For example, the category of obesity had invalid data and could not be used in the analysis with confidence. However, this variable was examined in the authors’ previous research32 and found nonsignificant. The researchers thought that defining the variables with ICD codes would change the results. Although obesity has been tied to PI formation,6 it does not necessarily place a patient at a higher risk of HPI formation.37 However, further research should be conducted.
As a function of the data points collected for this database, study authors did not have the ability to analyze the number of PIs that each patient had; it was only possible to ascertain that some of the patients in the main and validation analyses had more than one PI and the possible location(s). The same issue was encountered in patients with both HAPIs and CAPIs because investigators could not determine the origin of their HPIs. The inability to tie the number of PIs to individual patients may have resulted in an underestimation of the number of HPIs.
Because of their frequency, HPIs remain a priority to prevent. Identifying risk factors helps to alert a clinician to apply appropriate and timely prevention strategies. This research identified seven risk factors that were independent and significant predictors for HPI formation in the hospitalized patient: DM, vascular disease, perfusion issues, impaired nutrition, age 65 years or older, mechanical ventilation, and surgery. Using these risk factors and risk factors from earlier research, study authors formulated specific cues for HPIs in the form of a clinical enabler to provide a more focused clinical assessment process to inform care.
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Keywords:Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.
heel pressure injuries; heel pressure ulcers; hospitalized patients; incidence; prevalence; risk assessment; risk factors