As suggested by the title, the article titled by Meehan et al. (2019), the main aim is to revalidate the six-term perioperative risk assessment measure for skin (PRAMS) in a larger sample. The research was conducted to identify the risk factors for hospital-acquired pressure injuries. The initial study was conducted in a community hospital with positive findings. The following is a critical appraisal of the research article.
From a personal perspective, the study utilized the most rigorous possible design, given the research's purpose. The study design used is a retrospective cohort study, a type of observational research that looks back in time at archived or self-reported data to determine whether the risk of disease was different between exposed and non-exposed patients. In this particular research, the researchers looked at the initial PRAMS study that had generated positive findings to determine whether the results are consistent within a larger population. The research made appropriate comparisons to enhance the interpretability of the findings. For instance, the study took the six variables used in the initial research and classified them into three categories i.e., low risk, moderate risk, and high risk (Meehan, Beinlich, Bena & Mangira, 2019). The risk categories did a good job of guiding the selection of prevention interventions based on the frequency of occurrence in patients who developed postsurgical PI.
The study utilized one data collection point. However, the collection point was appropriate because it included a larger and more diverse population. The design minimized biases and threats of validity by focusing on only adult patients i.e., 18 years and above. The study included all the adult patients, and attrition was minimized by using professionals from the wound, ostomy, and continence (WOC) nurses who had specialized training in the identification and prevention of PIs (Meehan, Beinlich, Bena & Mangira, 2019). The reason for the inclusion of professionals was to build trust between the patients and the researchers.
Population and Sample Size
The research identifies its population and describes it in sufficient detail. The study sample included all adult in-patients who underwent surgery in the supine and lateral decubitus position during the two quarters of 2013. The research focused on patients who underwent surgical procedures such as cardiovascular, orthopedic, neurological, urological, organ transplantation, and general surgery. Patients below 18 years were excluded because they missed documentation of the Braden risk assessment score. The study utilizes a random sampling design, as all adult patients were to be examined. The study used an event per variable (EPV) ratio of 20, which is preferable considering the research used six indicators to minimize bias (Meehan, Beinlich, Bena & Mangira, 2019). From a personal perspective, the sample size used i.e., n= 1,526, is adequate compared to that of the initial study i.e., n= 350. No power analysis was used to determine the sample size. The justification of the size was based on the number and diversity of the patients and surgical procedures (Meehan, Beinlich, Bena & Mangira, 2019). Therefore, the sample was likely to give an accurate validation of the previous study’s results.
Data Collection and Measurement
The authors meticulously describe the data collection and measurement method used by the researchers. They say that a data collection template was developed that replicated the one used in the initial study. In addition, data was extracted electronically by a nursing informatics specialist (Meehan, Beinlich, Bena & Mangira, 2019). From a personal perspective, the key variables were operationalized using the best possible method because all the researchers needed were the patient records upon admission. The researchers did not need to interview the patients since they did not require intricate information to make the observations. The only variables that needed to be verified are age and any other preexisting conditions.
The specific instruments needed for the research are described clearly. For instance, the Braden score was the most recent before surgery and surgical time was recorded as anesthesia started to stop. The authors break down how each variable was observed within the sample population from start to end. The report also provides enough evidence that the data collection methods yielded data that were high on reliability. For instance, they indicate that the nurses used the standardized National Pressure Ulcer Advisory Panel definitions to validate all documented HAPIs. In addition, an open chart audit was conducted to confirm the findings (Meehan, Beinlich, Bena & Mangira, 2019). What this means is that the methods used have been tested and proved to be of high quality.
The intervention offered during the research was proactive screening for the possible risk factors for PI. The authors indicate that all the 1,526 participants were offered the same care to prevent the onset of PIs. Out of all the participants, only 12% i.e., n=182, developed a HAPI (Meehan, Beinlich, Bena & Mangira, 2019). Looking at the report, one can arguably say that data was collected in a manner that minimized bias. All the participants were analyzed using the same set of variables using a standardized procedure approved by the national pressure ulcer advisory panel. In addition, the staff involved consisted of nurses who had specialized training in the identification of PIs (Meehan, Beinlich, Bena & Mangira, 2019). Therefore, it is no doubt that the process followed in data collection was appropriate.
Results and Data Analysis
The authors highlight an elaborate statistical method that was used to analyze the variables. For instance, the categorical variables were described using frequencies and percentages. The comparisons were made using p values obtained using Pearson chi-square tests or Fisher’s exact tests. The analytic method used was powerful as it had control for confounding variables. The researcher’s used a multiple regression model using the previously established predictor, and it was a fit. In addition, the researcher’s used SAS version 9.4, which is a leading software in data analytics. Finally, the report shows that Type I and Type II errors were minimized using indices such as sensitivity and negative predictive value, which increase when the numbers of false negatives are low (Meehan, Beinlich, Bena & Mangira, 2019). Therefore, the results arrived at are accurate and consistent.
Findings and Interpretation
According to the authors, the significance levels used for the research were exact to those used in the previous study. The research assumed a significance level of .05 for all tests. For this reason, the results showed that the negative predictive values remained high, which means that patients who had no indicators were unlikely to develop postsurgical PI (Meehan, Beinlich, Bena & Mangira, 2019). However, the authors did not present enough information regarding the effect of estimates' size and precision. Nevertheless, they clearly explain the correlation between the indicators and the chances of developing PIs. Finally, the authors explicitly discuss the clinical significance of the findings, which is to reduce the burden and severity of PI on patients and the healthcare system. The significance is expressed in terms of the costs of preventing PIs and the cost of treatment. The research concludes that investing in preventative care would ultimately cut the cost of the HAPI burden.
Despite the limitations such as hospitals being skeptical of the costs involved in offering preventative care and sample size representation, I have confidence in the results' truth value. Postsurgical PIs are a real burden in the health sector, and there is a close correlation between the stated variables and the risk of developing one. In addition, the study offers critical information that can be used in nursing practice to prevent the onset of postsurgical PIs. The research concludes that nurses should know how to initiate preventative interventions to at-risk patients during surgery to prevent complications that occur afterward.
Meehan, A. J., Beinlich, N. R., Bena, J. F., & Mangira, C. (2019). Revalidation of a perioperative risk assessment measure for skin. Nursing Research, 68(5), 398-404.
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