Fogerty et al.4 conducted a very large retrospective case-control study reviewing admission and discharge data from over six million subjects in the Nationwide Inpatient Sample (NIS) to identify risk factors and demographic differences between those who developed PUs and those that did not. Some may describe their study as a nested case-control27 because they identified a cohort (inpatients in the NIS dataset), followed their records retrospectively from their hospital admission until hospital discharge (during 2003), and separated them into 2 groups: those who developed PUs (cases) and those that did not (controls). There were 94,758 incident PUs documented among a final discharge sample of 6,610,787 persons. Utilizing multivariate logistic regression analysis on 45 common diagnoses identified in persons with PUs, they reported odds ratios (estimate of relative risk) for the most significant risk factors associated with developing PUs. Analysis was also conducted stratifying the sample by age, race and gender. Age over 75 years was the strongest PU risk factor identified with an Odds Ratio (OR) of 12.63 (meaning people over 75 years are almost 13 times more likely to develop PUs than younger age groups). Other strong risk factors identified (listed in descending order) include: diagnosis of gangrene (OR 10.94, 95% CI 10.43-11.48), septicemia (OR 9.78, 95% CI 9.33- 10.26), osteomyelitis (OR 9.38, 95% CI 8.81-9.99), nutritional deficiencies (OR9.18, 95% CI 8.81-9.99), pneumonitis (OR 8.70, 95% CI 8.33-9.09), urinary tract infection (OR 7.17, 95% CI 6.96-7.38), paralysis (OR 10.30, 95% CI 9.69-10.96), age 59 to 75 years (OR 5.99, no CI reported), and African American race (OR 5.71, 95% CI 5.35-6.10). Fogerty et al. also reported a statistically significant interaction between race and age, such that as African Americans age, their risk of developing PUs increases faster than the risk Caucasians experience as they age, indicating noteworthy racial disparities. Other significant findings identified in their study highlight some of the strongest risk factors are non-modifiable (age, paralysis, race) while others are potentially modifiable (infection, nutritional deficiencies). Therefore, exploration is needed to determine when interventions are most effective in those persons with non-modifiable risk factors (such as age > 75, darker skin, or paralysis) or if interventions should be initiated in all persons over 75 years old, with darker skin, or with paralysis. Investigations should also examine the most effective interventions to reduce or eliminate the identified modifiable risk factors (infection and nutritional deficiencies) and ways to accurately identify them in patients.
The purpose of identifying a risk factor is to intervene and lower the associated risk. Identifying the strongest PU risk factors is important to be able to provide evidence-based interventions and thereby lower the likelihood of someone developing a PU or halting the progression of the PU. PU risk assessment tools provide a tangible way to quantify potential risk so that interventions may be reserved for those at highest risk and avoid unnecessary interventions and higher financial expenditures on those who may not need them.28
Interpreting Research Pearl: When reading results of research studies reporting Odds Ratios (OR), here are some tips to interpreting the data:
If the OR number is a number more than 1.0 (2, 3, 4, 13, etc.), this can be interpreted to say the associated risk is higher with the test group or exposure group than with the control group or comparison group. Example: An Odds Ratio (of developing lung cancer) of 7.0 associated with smokers versus non-smokers could indicate that those who smoke are 7.0 times more likely to develop lung cancer than those who do not smoke.
If the OR number is a number less than 1.0 (0.9, 0.7, 0.2, etc.) then you can subtract (in your head) this number from 1 and turn it into a percentage to say the associated risk is less in the test group than the control or comparison group. Example: An Odds Ratio of 0.25 of developing a Deep Vein Thrombosis (DVT) when you fly long distances by plane if you wear compression stockings versus if you do not wear compression stockings could indicate a 75% risk reduction in the compression stocking group. We got to this number by taking the OR of 0.25 and subtracting it from 1 = 0.75, then turned it into a percentage = 75%.
These OR interpretations are only worth something meaningful if the study was large enough (had enough subjects), was carried out with scientific rigor (done the best way to answer the question), and the 95% Confidence Interval (CI) does not contain numbers on both sides of 1.0. For instance, an OR of 7.0 with a 95% CI of 3.0 – 9.0 means that the estimated risk associated with whatever they are looking at is 7 times higher in the exposure or test group AND you can be 95% confident that the real risk is somewhere between 3 times higher risk and 9 times higher risk. If the OR was 7.0 with 95% CI of 0.5 to 8.0, then it is essentially worthless – you don’t get meaningful results if the risk might be 50% less or up to 8 times greater. It should be either less risk or more risk but if the real measure of risk could be either, don’t use these results as evidence to change practice! Therefore, anytime OR is reported, the authors should also report a 95% Confidence Interval (95% CI).
Cowan et al. (2012)22 conducted a retrospective analysis in a Veteran population to identify the strongest PU predictive model which demonstrated four medical factors (malnutrition, surgery, pneumonia, candidiasis) were more predictive of PU than total Braden Scale for Predicting Pressure Ulcer scores. The finding of a diagnosis of candidiasis as a risk factor for PUs may be related to medical conditions where candidiasis is most common (could it be a proxy variable for impaired immune function?). More research is needed to explore these relationships. Nevertheless, this research provides valuable information that may enhance current risk factor assessments. Identification of factors affecting the development of PUs is imperative in the present day population in order to select patients for effective prevention interventions. Furthermore, evaluation of the efficacy of existing preventive interventions must be ongoing, and new innovative interventions must be explored in order to impact PU incidence and prevalence significantly.4,29
One criticism of existing PU risk assessment tools is that “neither risk factors nor the weights attributed to them have been identified using adequate statistical techniques.”30 Risk factors are those factors or conditions that are noted to be most strongly associated with the outcome of interest. In order to provide evidence-based measures in the prevention of PUs, an effective means of identifying those at highest risk is imperative. Other criticism of PU risk assessment tools includes the lack of clear evidence that risk assessment tools have significant impact on clinical outcomes such as PU incidence rates,31-33 the subjective nature of some of the assessments,32,33 the lack of tools for specific settings such as the perioperative environment,34,35 and the fact that no one assessment tool could account for every risk factor.33 Current risk assessment tools may require further development, improved statistical evaluation, and possible modification in order to remain applicable to present-day populations.
Thomas25 posits an explanation for an unchanging incidence of PUs as “a failure of known effective prevention treatment to be applied, or the failure of prevention strategies to be effective despite being applied” (p.298). Effective preventive measures may not be applied if individuals are not appropriately identified as being at risk. Risk-screening tools are useless if they are not applicable to the population being screened, if they are used inconsistently, or scored incorrectly.25,32,36