Background

In 2017, the Centers for Disease Control and Prevention (CDC) revealed 23 million cases of Type II diabetes. Among adults in the United States 18 years of age or older between 2013 and 2016, 88 million (34.5%) were diagnosed with prediabetes. Of those diagnosed, only 15.3% reported awareness (CDC, 2020). The prevalence of undiagnosed diabetes continues to increase, given the asymptomatic nature of the early stages of diabetes. To systematically address diabetes as a growing public health concern, early identification through screening is imperative.

The preoperative assessment is an opportune time to screen for diabetes, particularly because surgical complications related to diabetes can be minimized when the diagnosis is known and optimization strategies can be implemented (Shohat et al., 2018). An estimated 25% of patients with diabetes will require surgery at some point, and 5%-10% of all surgical patients who present for surgery are found to have previously undiagnosed diabetes (Setji et al., 2017). Clearly identifying patients with prediabetes and diabetes prior to surgery allows the clinical team to appropriately target interventions to reduce the risks of complications. Yet, preoperative diabetes screening protocols appear to vary throughout a variety of clinical settings across the United States. The American Diabetes Association (ADA) Risk Test is one avenue that can be taken to evaluate diabetes risk. On the basis of patient responses, points are provided for each item and totaled. A score of 5 or greater denotes that the individual is at increased risk for having or developing Type II diabetes and correlates with a need for further evaluation and/or testing.

Purpose

The purpose of this article is to present the effects of implementing a preoperative diabetes screening process in an elective total joint patient population. Prior to project commencement, submission of the project specifications to the institutional review board was completed and approved.

Methods

Setting and Sample

The evidence-based practice (EBP) project was implemented among a cohort of elective total joint replacement patients within a preoperative clinic, located in an academic medical center in the southeastern part of the United States. The medical center consists of 600+ inpatient beds and averages 100 surgical cases per day. Approximately 33%-37% of daily surgical cases are orthopaedic in nature and under the umbrella of the orthopaedic department. Although numerous orthopaedic clinics are situated across the medical center campus, one particular clinic focuses on the management of patients with hip and knee conditions and serves as the entry point for patients undergoing elective total joint replacement surgery. Once patients are seen and evaluated here, they then are referred to the preoperative clinic, which funnels most surgical patients for comprehensive, preoperative screenings prior to their scheduled surgical procedures. The walk-in clinic, which operates Monday through Friday, consists of seven patient screening rooms and three laboratory stations. On average, the clinic services between five and 10 total joint replacement patients per day. More than 75% of these patients are older than 65 years of age. All adults 19 years of age and older scheduled for elective total joint (knee or hip) replacement surgery were screened as part of their preoperative assessment appointment. Most visit the clinic to undergo screening no later than 3-5 days in advance of surgery. Patients are not seen the day immediately prior to surgery. Patients receiving preoperative screening at any location other than the preoperative clinic were excluded. In addition, patients who had a preexisting diagnosis of prediabetes or diabetes were noted in data collection but separated from the targeted, at-risk population.

Measures

To verify the validity of the ADA Risk Test, Bang et al. (2009) conducted a study that evaluated and compared it against other available screening instruments. As a result, all defined criteria were evaluated and found to be statistically significant in their association with identifying undiagnosed diabetes. Those characteristics included age, sex, a family history of diabetes, a history of hypertension, obesity, and physical activity, all of which are included as items on the ADA Risk Test. As part of Bang et al.'s study, separate analyses were completed to assess the impact of the individual risk factors when determining diabetes risk. In particular, one analysis evaluated patients 45 years of age and older, the age threshold recommended by the ADA for universal screening, and found 88% sensitivity and 40% specificity for detecting diabetes. Furthermore, in an earlier pilot study conducted by the project lead, 35 of the 36 patients (97.23%) who screened at risk were 45 years of age or older, supporting the ADA guidelines for universal screening based on age (Smith, 2020). In addition, Bang et al. (2009) used fasting blood glucose (FBG) to define diabetes, which reflected 88% sensitivity and 40% specificity for participants 45 years of age or older. The study also evaluated the use of glycated hemoglobin (HbA_1c) in detecting diabetes in a separate analysis, which revealed 80% sensitivity and 63% specificity.

Bang et al. (2009) evaluated sensitivity and specificity for patients 45 years of age and older. Using a cut point of 4 rather than 5, the instrument yielded a higher sensitivity (97%) but a significantly lower specificity (20%). This reduced cut point was also evaluated when utilizing HbA_1c and revealed a higher sensitivity (91%) but a slightly lower specificity (47%) (Bang et al., 2009). In future research, the consideration could be made to adjust the at-risk score to 4, as a lower threshold score could increase identification of prediabetes in certain populations.

The self-administered, ADA Risk Test consists of seven items that address age, gender, history of gestational diabetes (as applicable), family history of diabetes, history of or current diagnosis of hypertension, physical activity, and weight. On the basis of applicable patient responses to the ADA Risk Test, points were assigned to each item and totaled at the bottom of the instrument. A score of 5 or greater denoted that the individual was at increased risk for having or developing Type II diabetes mellitus and correlated with a need for further evaluation and/or testing. Within this EBP project, patients who received a score of 5 or greater were scheduled to have an FBG test in the form of a point-of-care (POC) test drawn on arrival to the preoperative suite on the day of surgery, prior to the administration of any steroids that could skew the result. A score of 4 or lower on the ADA screening instrument indicated a lower risk for diabetes, and additional testing was not ordered. Although the ADA recommends either HbA_1c or FBG, for the purpose of this project, FBG was the primary test utilized when screening at-risk patients for prediabetes/diabetes. For clarity, fasting is defined as not having anything to eat or drink with the exception of water for at least 8 hours before the test. ADA criteria were used to measure FBG results and categorized as appropriate. Normal FBG was indicated with a measurement of less than 100 mg/dl, but considered in the prediabetes range if measured between 100 and 125 mg/dl. Any FBG measurement of 126 mg/dl or greater was considered diabetes (ADA, 2020).

HbA_1c measurements were also utilized to assess undiagnosed, at-risk patients who had received an HbA_1c test within the last 90 days. HbA_1c is a venous blood sample that can be drawn at any point in the day and does not require fasting. Hemoglobin is the oxygen-carrying pigment that gives blood its red color and is the predominant protein in red blood cells. About 92% of hemoglobin is labeled hemoglobin A, and the remaining 8% of hemoglobin A is made up of minor components, one of which is HbA_1c (E-medicine Health, 2020). HbA_1c is the component of hemoglobin to which glucose is bound.

Procedures

The Iowa EBP Model was used to anchor this project. Prior to implementation, a face-to-face education session was provided to the preoperative clinic staff to ensure a clear understanding of the project intent and to define their roles throughout implementation. Additional education sessions were not required, but available if needed. Printed screening forms were provided and kept at the patient check-in desk and maintained by the administrative staff responsible for providing the screening and additional paperwork to the patients. Data collection occurred from September 10 through October 30, 2020. Before the first week of the pilot, the project lead was provided a list of total joint orthopaedic patients expected to be seen during the upcoming week by the orthopaedic physician assistant (PA) involved in the project. Each week, the project lead was sent a new list for the upcoming week. The list was then provided to the preoperative clinic staff as a projection of how many total joint orthopaedic patients would be coming in for their preoperative assessments. It is important to note that patients had the option to complete preoperative screening elsewhere.

Each day, clinic staff greeted total joint orthopaedic patients upon arrival to the preoperative clinic and were provided with the routine preoperative screening instruments and health assessment forms. The ADA Risk Test (see Figure 1) was included and patients were asked to complete it while in the clinic waiting room. The first page of the screening (see Figure 2) included the determination of whether or not the patient had a preexisting prediabetes or diabetes diagnosis, as well as item selections for race and ethnicity. Demographic information was also available for capture in the electronic health record (EHR). Report of a previous diagnosis of prediabetes/diabetes prompted the individual to not continue with the risk questionnaire. The intent was to identify only individuals at risk and/or currently undiagnosed. FBG samples were not collected at this time because many patients did not anticipate this screening and therefore were not fasting. Patients with a preexisting prediabetes/diabetes diagnosis were noted in data collection but were excluded from further testing. Patients who reported no previous prediabetes/diabetes diagnosis were prompted to continue with the diabetes risk questionnaire and complete the screening. Once the completed screening instruments were returned to the clinic staff, the staff confirmed they had been completed correctly. If the staff determined that the screening instruments were not fully completed, appropriate instructions were provided or clarification obtained from the patient.

Figure 1. American Diabetes Association risk screening instrument. ADA = American Diabetes Association. Reprinted with permission from the American Diabetes Association. Copyright 2022 by the American Diabetes Association.

Figure 2. Determination for diabetes screening questionnaire.

All screening forms were marked with numeric identifiers prior to dissemination to patients. To match the screening instruments with the POC FBG measurements, a linking document was created. The linking document was kept at the check-in desk of the preoperative clinic in a confidential and secured file. The linking document contained two columns: one with the numeric identifier and the other left open for patient identification stickers to be placed as the screening instruments were completed and returned to the front desk. This ensured de-identification of any patient data on the screening instrument.

Screening instruments revealing no previous prediabetes/diabetes diagnosis with a score of 5 or greater indicated that the patient screened at risk and needed a POC FBG test on the day of surgery. The patient identification stickers of those total joint orthopaedic patients who screened at risk were marked with a colored sticker, which indicated further testing was needed as part of their day of surgery preoperative requirements. Each day, the project lead would inform the orthopaedic PA which patients required a one-time order for a POC FBG test to be completed in the presurgical suite. This order was placed by the PA to be visible in the patient's EHR until performed. FBG measurements were then collected from the EHR and recorded for further analysis in conjunction with the screening instrument and individual items.

Data Collection and Analysis

EBP project implementation and data collection occurred over an 8-week period. The project lead was regularly present in the clinic to oversee the process along with continuous collaboration from the Doctorate of Nursing Practice (DNP) project team. In addition, contact information for both the project lead and the institution's diabetes clinical nurse specialist (CNS) was made available to all preoperative clinic and surgical suite staff as an immediate resource for patient or staff concerns or questions that arose regarding the process or calculated ADA risk scores. Chart audits were conducted no less than every 2-3 days to ensure at-risk patients were not overlooked. Confirmation of risk score calculation and verification of patient data were also consistently monitored. Screening forms and linking documents were collected, recorded daily, and securely stored.

As with many new processes, a transition period was established. The transition phase occurred during the first 2 weeks of implementation, allowing for evaluation, real-time adjustments, and feedback from staff. The process itself contained several steps, all of which were critical to appropriately screen and further test the intended population (see Figure 3). For example, during the transition period, the need to increase the time frame of data collection in the clinic was increased from 5 to 8 weeks in order to support the implementation of this project and capture the full complement of necessary data. At the completion of the pilot, after consulting with a statistician, descriptive and inferential statistics were computed using raw numbers and percentages to display results and identify relationships between the measured variables and the data collected.

Figure 3. Diabetes screening process. Note. This image represents the process developed from the time patients entered the preoperative clinic to the time the patient received an FBG test on their scheduled day of surgery if indicated. The end of this process was intended to result in the facilitation of increased screening of an at-risk population as well as improved pre-, intra-, and postoperative blood glucose management, with the goal of increasing positive patient outcomes. FBG = fasting blood glucose.

Results

Characteristics of Sample

During the 8-week implementation period, 121 total joint orthopaedic patients were screened in the preoperative clinic. The ADA Risk Test scores were calculated and analyzed in comparison with demographic data as well as the results of the day-of-surgery FBG and HbA_1c measurements that were documented in the EHR within the previous 90 days. Of the 121 patients, 27 (22.31%) had a self-reported or confirmed diagnosis of prediabetes or diabetes and 17 (14.05%) screened at low risk, which was designated by a score of less than 5 on the ADA Risk Test. For the focus of the project, 77 of 121 (63.64%) previously undiagnosed patients screened at risk (designated by a score of 5 or higher; see Table 1).

Table 1. At-Risk (Undiagnosed) Sample Demographics: Elevated Measurements

Screening Evaluation

After further analysis of the 77 at-risk patients, 23 had a documented HbA_1c measurement in the EHR within the previous 90 days. Twenty of those were within normal limits and three were elevated. In addition, among patients who received an FBG test on their scheduled day of surgery, nine were found to be elevated and 23 were normal. It is important to note that an additional 22 patients did not receive their ordered FBG test or received it after dexamethasone was administered, leading to a reduction in the analyzable sample size from 77 to 55 patients. Of those 55 undiagnosed, at-risk patients, 12 (21.8%) were identified as having prediabetes. In a detailed analysis of the 12 at-risk patients who were found to have elevated FBG or HbA_1c levels within the past 90 days, the vast majority were "White, non-Hispanic/Latino." Two patients self-identified as "Black, non-Hispanic/Latino." The mean body mass index (BMI) was 28.95, with a maximum of 34.45 and a minimum of 24.25, indicating that the majority had elevated BMIs. Fifty-eight percent of the patients were male and 75% were 60 years of age or older and had a history of hypertension. Eight of the 12 patients reported no family history of diabetes. Ten of the 12 patients self-reported being physically active. All but one of the patients had some level of risk based on weight. One of the 12 patients failed to complete the screening, but with close tracking by the clinic staff and a chart evaluation with audit, patient data were able to be extracted from the EHR to answer four out of the seven questionnaire items, which alone provided a score of 5. This patient's history and physical activity were unknown but did not prohibit the project lead from being able to determine risk level and need for further diagnostic testing. In the immediate postoperative period, patients were followed by the diabetes CNS as well as the inpatient diabetes management team. Glucose management was provided as needed, as were appropriate discharge instructions and guidance for primary care follow-up were given.

Discussion

Strengths and Limitations

The successful implementation of the current clinical practice recommendations was the most impactful aspect of this project. Diabetes screening did not require a drastic adjustment in the current standard work or administrative flow; the addition of the screening instrument did not delay patient care, nor did it increase the need for clinical or personnel resources. Its continued use stands to strengthen the preoperative clinical assessment by creating awareness within the interprofessional care team and promoting early recognition of chronic disease. The validated instrument is utilized by the ADA as a tool to screen and promote awareness of diabetes risk factors as well as a means to support further diagnostic testing. The project revealed the potential to both improve individual health and increase positive patient outcomes, such as decreased length of stay and hyperglycemic episodes in the postoperative period. The project was an initial step to identify diabetes risk in total joint orthopaedic patients and integrate strategies to optimize not only their surgical care but also overall wellness. Because diabetes prevalence is drastically high and continues to increase despite comprehensive efforts within the healthcare system, early recognition and intervention could impact a significant number of patients by simply increasing awareness, as the first step toward change.

Limitations of this project begin with the understanding that the target population was the total joint patient population within the orthopaedic department. Because of this, the results may not be generalizable to other surgical populations. In addition, the sample from the earlier pilot consisted of patients mostly 60 years of age or older, revealing that there was minimal variance in the age of the cohort. This does not completely stand as a shortcoming as the majority of total joint arthroplasties are performed in this aging population, which is also reflected in this project as nearly 80% of the at-risk patients were 60 years of age or older. Lack of age variance may be more applicable in different surgical categories and less of a concern in the total joint patient population as discussed. One clearly notable challenge is that not all patients who receive an elective surgery at this facility complete preoperative assessments at a single location, but instead do so at various outlying clinics. This was a recognized obstacle due to the inability to capture all elective total joint replacement patients along with a high likelihood of preoperative diabetes screenings not being conducted at all.

Implications for Practice

Diabetes is a chronic illness that often remains asymptomatic for most patients until the patient undergoes screening, develops symptoms, or experiences a complication of diabetes. Diabetes screening is imperative for providing early recognition and increases awareness of associated risk factors. Although implementation opens yet another avenue for diabetes health promotion, this screening also serves as an opportunity to streamline perioperative glucose management, decrease postoperative complications, and increase cost savings. In addition to routine costs associated with diabetes management, treatment of surgical complications as a result of glycemic instability generates increased costs to both healthcare facilities and patients. The burden of undiagnosed diabetes and prediabetes, which are $4,030 and $510 per case, respectively, is significant when comparing the cost of a single HbA_1c blood test that typically ranges between $22 and $65, depending on insurance coverage (Capozzi et al., 2017). Capozzi et al. (2017) also pointed out that patients with diabetes have a significantly higher risk of arthroplasty revision at the 1- and 5-year marks. Prediabetic patients exhibit a similar risk, even if they do not progress to a full diabetes diagnosis. In addition, the same study discussed the presence of increased wound complications with postoperative glucose levels greater than 200 mg/dl and that postoperative hyperglycemia was associated with an increased wound infection rate even in patients without a prior diagnosis of diabetes. From a behavioral perspective, an increased awareness of diabetes risk may serve as a motivating force for patients to take personal responsibility in their health choices and minimize disease progression. Adoption of the ADA Risk Test as part of the preoperative assessment could serve as a baseline for perioperative glucose management, reducing fluctuations and instability of blood glucose throughout the surgical process.

Conclusion

Regardless of clinical setting, implementation of standard workflows allows for processes to be followed by taking steps to avoid gaps in care, errors, and suboptimal patient outcomes. Although many frameworks are available, the Iowa Model supported this EBP initiative in the effort to adopt diabetes screening as part of the preoperative process for total joint orthopaedic patients. The primary objective was to screen and identify all patients at risk for diabetes and conduct further diagnostic testing for confirmation. As a two-part process, the ADA Risk Test was effective in evaluating all total joint orthopaedic patients seen in the preoperative clinic. However, challenges were presented when conducting further diagnostic testing in the surgical suite. Based on the standard workflow in place prior to project implementation, patients who had a documented diagnosis of prediabetes/diabetes in the EHR automatically received POC FBG testing immediately prior to surgery. It was not the surgical suite's process to conduct POC testing on patients without a confirmed diagnosis. As a result, 22 patients did not receive the intended POC test to confirm or refute their risk score of 5 or greater. This accounted for 40% of the sample being undetermined with regard to their glycemic status after determining they were at risk. If captured, these results could have revealed a vast difference in potentially elevated FBG measurements. Continuous chart audits provided awareness of missed POC tests and allowed the project lead to conduct additional education sessions to the surgical suite staff, ensuring clarity of their roles and need for the POC test. Despite several face-to-face education sessions conducted with the surgical suite staff along with shared electronic tracking for patients needing an FBG POC test, constant challenges with obtaining the ordered laboratory test results remained. This highlighted the importance of garnering support from key stakeholders to fully adopt the shift in practice.

The first of three decision points embedded within the Iowa Model asks: "Is this topic a priority?" (Buckwalter et al., 2017). Engaging and collaborating with the surgical suite staff at the inception of the project may have provided insights into the staff's perception of diabetes screening and thoughts about prioritizing POC FBG tests. The challenge in adopting this new practice change leads to the discussion of alternative methods of diagnostic testing for diabetes. Although HbA_1c would have been the preferred biomarker for evaluating glycemic status in this project, lack of insurance coverage for the test required FBG to be utilized for further diagnostic testing of at-risk patients. According to Medicare coverage guidelines, Part B covers laboratory test screenings if the patient is determined to be at risk for developing diabetes. Furthermore, laboratory screenings are covered if a patient has any of the following risk factors: hypertension, dyslipidemia, obesity, or a history of hyperglycemia levels. More specifically, if two or more of the following apply to the patient, the screenings are covered: age 65 years of age or older, overweight, a family history of diabetes, or gestational diabetes (Medicare Coverage, 2020). Despite Medicare coverage information, HbA_1c coverage still remained a challenge when evaluating its use in the pilot. According to the ADA (2020), HbA_1c testing has several advantages compared with FBG and oral glucose tolerance test (OGTT) such as greater convenience (fasting not required) and less day-to-day influences during stress, diet, or illness, all of which can influence FBG accuracy. Shohat et al. (2018) revealed this very point in their study that conducted diabetes screening in patients undergoing total joint arthroplasty surgery when a statistically significant difference between patients with diagnosed and undiagnosed diabetes was found when using HbA_1c (p = .001) as a diagnostic tool compared with FBG (p = .91). As there is less variability associated with HbA_1c than with FBG, it is not unreasonable to suggest that HbA_1c to be the gold standard for diabetes screening.

Although it is clear that there are advantages and disadvantages with using both FBG and HbA_1c for diabetes screening and diagnosis, there are also challenges to include them both in the preoperative screening process. Specific to practice sites and their partner institutions, multiple pathways of preoperative screening may exist and this variety creates challenges of its own. Of the 187 total joint orthopaedic patients who were seen by the PA at the orthopaedic clinic, the preoperative clinic captured and screened 121 of them, which left 66 patients who may or may not have been appropriately screened for diabetes prior to surgery. This gap proves the importance of implementing universal diabetes screening regardless of the pathway taken for preoperative patient evaluation and screening. A clear challenge in the pilot was capturing POC tests in the surgical suite immediately prior to surgery. Based on identified gaps in the pilot, it is reasonable to suggest screening and testing occur earlier in the preoperative process, which contributes to patient optimization with decreased potential for missed screenings. The adoption of a diabetes screening based on risk factor assessment and further diagnostic testing (if indicated) could be added to the institution's preoperative assessment, ensuring all patients are screened regardless of their preoperative pathway and choice of clinic.

Regarding specific diabetes risk factors, it is appropriate to discuss the ADA Risk Test and its individual items. First and foremost, the tool was self-administered, which alone is subject to variance with regard to accuracy. Three of the seven instrument items have the potential to be subject to more personal bias than the others. Gestational diabetes is one item that could have presented some discrepancy simply because it is assumed that most people understand the term "gestational," which may not have been the case. Pilot data reflected that not one patient selected "yes" to having a history of gestational diabetes. If it is assumed that "gestational" was understood across the sample, this item would not be one in question for potentially skewing the risk score. The second item that could present a discrepancy with scoring is identifying if the patient has an immediate family member with diabetes. Many patients struggle to remain fully informed about their own health; expecting that they would know the health history of family members could be a stretch. This understanding makes it fair to believe that this item could be reported inaccurately. Of the seven items, the most subjective topic addresses whether or not the patient is physically active. The definition of being physically active can vary dramatically, but age alone is a primary example of the variance that could be present when answering this question. Although many patients in the sample answered "yes" to being physically active, it is important to consider the personal bias present in this question. To adequately counter the potential for self-bias from the instrument, incorporating the screening items into the EHR to be asked by healthcare personnel could significantly reduce the discrepancy and variance that comes with self-administration. This would also allow for clarifications and explanations to be made and provided as needed when answering each item.

Despite the complexities of implementation, continued use of the validated screening instrument requires minimal effort when compared with the level of impact it can have on health promotion, disease prevention, and perioperative patient outcomes. The addition of the screening instrument requires minimal change to staffing resources, does not impact the current established patient flow processes, and serves as a means to guide perioperative care. Adjusting for the challenges discussed earlier would be crucial in completely capturing glucose measurements to confirm or refute a patient's risk for a diabetes diagnosis and support clinically appropriate measures to improve overall patient health. Specifically, embedding screening into the EHR for an expedited and more accurate process is a modification that should be considered in the future. Streamlining implementation of the ADA Risk Test will facilitate sustainability of the practice change by hardwiring it into structured work. Mandatory diabetes screening would help ensure a reduction of gaps in patient-centered care across not only the total joint orthopaedic patients but other surgical populations as well. Overall, the findings of this project support adoption of the ADA Risk Test in the preoperative setting and emphasize the feasibility of integrating the instrument to obtain valuable patient information.

References