Identifying Obstructive Sleep Apnea in Hospitalized Patients Using the STOP-Bang Questionnaire

Background of the Problem

Obstructive sleep apnea (OSA) is a common sleep disorder affecting 2-26% of the population (Yang & Chung, 2013). As many as 25 percent of the adult population is thought to be high risk for OSA (history of hypertension, coronary artery disease, obese and male) and 90 percent of patients are undiagnosed. While there are evidence-based recommendations to support diagnosis and management of OSA, exactly how to screen or approach high risk patients remains unclear (Epstein et al., 2009). Several factors contribute to the difficulty in diagnosing OSA. Poor awareness of the disease by patient and primary care provider, lack of routine screening in the primary care and acute care environment, cumbersome diagnostics and limited number of sleep study centers contribute to the high number of patients who are undiagnosed. Despite the lack of screening, the treatment of OSA is relatively straightforward. Interventions such as continuous positive airway pressure (CPAP), weight loss and blood pressure management are all effective strategies to manage OSA (Epstein et al., 2009).

OSA is characterized by nighttime sleep disturbances such as loud snoring, restless legs and awakenings due to gasping or choking in the presence of at least five obstructive respiratory events (apneas, hypoapneas) per hour of sleep (American Academy of Sleep Medicine, 2005; Epstein et al. 2009; Finkel et al. 2009). Risk factors for OSA include adult and child obesity, craniofacial abnormalities, upper airway soft tissue abnormalities, male gender, smoking and age greater than 40 (Lawati, Patel, & Ayas, 2009; Madani & Madani, 2009). Mild OSA patients typically have apnea hypopnea index (AHI) of 5 to 15 respiratory events per hour of sleep, have mild daytime sleepiness and may be asymptomatic. The AHI score is calculated during a sleep study and is the number of apnea or hypopnea episodes that occur during one hour of sleep. Moderate OSA is classified as those patients having an AHI between 15 and 30 respiratory events per hour of sleep, experiencing excessive daytime drowsiness, and having difficulty with concentration and typically avoiding driving long distances. Severe OSA is classified as those patients with an AHI of greater than 30 respiratory events per hour of sleep and experience a desaturation of oxygen below 90% (American Academy of Sleep Medicine, 2005).

OSA is directly correlated with the development of hypertension and pulmonary hypertension (Levy et al., 2009; Pack & Gislason, 2009) and is associated with all-cause mortality (Rich, Raviv, Raviv, & Brietzke, 2012), coronary artery disease, congestive heart failure, atrial fibrillation (Caples & Somers, 2009) and stroke (Loke, Brown, Kwok, Niruban, & Myint, 2012). Failure to diagnose OSA in patients with moderate to severe OSA have a higher mortality and cardiovascular disorders and events than compared to patients without OSA or treated OSA patients (Rich et al., 2012).

Direct and indirect costs of undiagnosed OSA lead to disproportionate use of costly healthcare resources (Alghanim, Comondore, Fleetham, Marra, & Ayas, 2008). Direct costs include diagnosis and management and costs from associated conditions. Indirect health costs consist of work-related injuries, motor vehicle collisions and decreased quality of life. Researchers have demonstrated that early identification of OSA substantially reduces healthcare costs and decreases related morbidity and mortality (Alghanim et al., 2008; Banno, Ramsey, Walld, & Kryger, 2009; Kapur, 2010). It is anticipated that by implementing a screening process within a hospital unit with high risk patients, referrals to the onsite pulmonary clinic will increase and further diagnostic testing to confirm the diagnosis will commence. The purpose of this scholarly project is to assess provider compliance in using the STOP Bang screening tool for OSA in high risk patients following an educational in-service in an in-patient setting.

Theoretical Framework

In Rogers’ Diffusion of Innovation (2003) for organizations, the five-step diffusion process includes the following concepts: agenda setting, matching, redefining/restructuring, clarifying and routinizing. There are three-time related concepts: initiation, decision and implementation. The time related concepts are presented as a linear process in the model and each has its own tasks. The DOI framework can be used to guide a QI project such as screening for OSA in high risk patients. Setting a priority for patient safety and preventing further harm is example of agenda setting and choosing an evidenced base screening tool for OSA defines the matching process. In redefining and restructuring, the plan for implementation is modified and reinvented for the organization. Additionally, the redefining and restructuring in the QI initiative within the organization can be a period of critical review to ensure that the initiative is in alignment with organization’s strategic plan. The organization’s vision and mission come together with the planned initiative and often times it is a reflective time in which the initiative may be modified. By introducing this QI process from within the organization, individuals may embrace the initiative and adopt it more easily. Clarifying occurs as the initiative is implemented and its widespread effects are recognized.

Rapid implementation of the screening process without proper planning can produce disastrous results. Implementing OSA screening within a hospital unit with high risk patients as a pilot trial should be used to slowly integrate the screening process. Additionally, provider champions need to be active and available. Participants in the screening process should have vested interest in the sustainability of the QI process. Reportable data such as rates of screening that lead to successful referral and diagnosis will continue the staff motivation as well as promote staff satisfaction. Finally, financial data, patient satisfaction and feasibility are outcomes to examine to determine if the innovation is sustainable (Rogers, 2003).

Literature Review

In a hospital setting—especially when surgery is planned—a variety of preoperative assessments are undertaken. However, while the literature reveals significant details related to such assessments in the context of heart and lung diseases, information on screening for OSA is not nearly as common (Finkel, Searleman & Tymkew, et al., 2009). This can be considered troubling, since patients diagnosed with OSA often experience surgery-related issues, especially related to anesthesia. In fact, the medications typically utilized in surgery pose a significant risk for patients with OSA and could certainly increase the risk for prolonged periods of apnea and/or respiratory arrest. Moreover, anesthesia may also result in other problems related to the patient’s airway functioning. Especially are these potential detrimental outcomes more likely if physicians or other providers are unaware of the patient’s OSA (Finkel et al., 2009).

Realistically, since the evaluation of patients prior to surgery takes place several days before the procedure, there should be sufficient time for OSA assessments to be conducted. In view of that, the American Society of Anesthesiologists (ASA)—in 2006—established some basic guidelines for OSA screening prior to most surgeries (Gross, Bachenberg & Benumof et al., 2006). The original assessments were based on questionnaires, and this has not changed. But, the earliest questionnaires were only tested in a controlled setting—a sleep laboratory. However, as explained by Abrishami, Khajehdehi and Chung (2010), the assessments used in recent years have been modified to be used specifically in the preoperative setting (Abrishami, Khajehdehi & Chung, 2010).

There are a number of assessment tools utilized by different hospitals across the country, and each one differs regarding the number of questions as well as what questions are asked the patients. For example, the Berlin questionnaire is a self-reported questionnaire consisting of ten total questions, addressing snoring, excessive daytime sleepiness, sleepiness while driving, how sleep affects the body, and hypertension (Loo, Hein & Tai, et al., 2013; Malish & Gay, 2012). According to the literature, the ability of the Berlin questionnaire to identify OSA is largely connected to patient population. In other words, high-risk patients are much more likely to be diagnosed with OSA. More precisely, some studies indicate that nearly one quarter of high-risk patients assessed before surgery display symptoms of OSA (Chung, Ward & Ho et al., 2007; Loo et al., 2013). Moreover, these results are even higher in other studies, finding OSA is well over half of surgical candidates (Chung, Yegneswaran & Liao et al., 2008). Conversely, in spite of the ability of the Berlin Questionnaire to properly identify OSA preoperatively, Chung at al. (2008) concluded that the process itself may be too complex for many anesthesiologists as well as patients.

One OSA screening tool that initially showed some promise was the Sleep Apnea Clinical Score (SACS). However, this assessment tool has primarily only been tested in a controlled environment and not used extensively in hospital settings. Still, the predictive value of the tool was impressive in comparison to others that are being used (Flemons, Whitelaw, Brant & Remmers, 1994; Gali, Whalen & Gay, 2007; Gali, Whalen, Schroeder, Gay & Plevak, 2009). For some reason, this tool has yet to be widely tested in hospitals for pre-surgical assessments of OSA. On the other hand, it has been found effective in some postoperative hospital ward episodes of respiratory complications. However, the consensus of the researchers who have studied the SACS questionnaire is that it has limited value in predicting complications or an extended hospital stay (Flemons et al., 1994; Gali et al., 2007; Gali et al., 2009).

Attempts to improve upon the Berlin tool include the STOP questionnaire, which limits the number of questions asked of patients prior to surgery. The STOP questionnaire, which is a greatly simplified version of earlier assessment tools, addresses four primary issues: Snoring, Tiredness (in the daytime), Observed (breathing cessation), and (high blood) Pressure (Malish & Gay, 2012). Studies determining the effectiveness of the STOP assessment tool indicate that it is more effective than the Berlin questionnaire, as well as being much simpler to administer (Chung et al., 2008). However, there are continuous efforts to find and even better method or tool for assessing patients with OSA prior to surgery.

One of the more promising newer tools is a modification of the STOP assessment. This model, called the STOP-Bang questionnaire, contains eight questions based on the acronym STOP-Bang (Snoring; Tired; Observed; [Blood] Pressure; BMI; Age; Neck [Circumference]; and Gender) and is scored based on Yes or No answers with a possible score of either 1 or 0 for each question (and a total score ranging from 0-8) (Chung et al., 2011). While previous assessment questionnaires have been successful, at best, in the range of 70-80% accuracy, the STOP-Bang questionnaire tests in the range of 93-100% in detecting OSA in a patient. In fact, this questionnaire is also considered a valuable tool for determining that a patient is not a good candidate for anesthesia, which could prevent later problems in surgery (Chung et al., 2012). In conclusion, a higher score on the STOP-Bang questionnaire provides a very good indication that a patient has OSA—in the range of moderate to severe.

The STOP-Bang tool is currently in use at a number of hospitals in the country, including UPMC Shadyside Hospital, Pittsburgh (Lakdawala, 2011). That facility has effectively used STOP-Bang to determine whether or not a patient with OSA should undergo anesthesia. In addition, the real-world results identified at UPMC Shadyside confirmed earlier studies which found a high rate of assessment of OSA with the STOP-Bang tool. Comparatively, while the best-known results from the Berlin questionnaire is 89% predictive of OSA; STOP-Bang scoring model predicted OSA with 95% confidence in multiple studies (Chung et al., 2008; Silva et al., 2011; Yang & Chung, 2013).

This brief review of the literature indicated that the ultimate use of a consistent tool for the assessment of OSA in hospital patients remains undetermined (Malish & Gay, 2012). There is little doubt that the use of some type of OSA assessment tool or questionnaire is valuable and desirable for improved patient outcomes. Nevertheless, hospitals across the country continue to select a wide variety of available assessment tools. Many hospitals have adopted their own approaches in screening and monitoring OSA in patients, especially prior to surgery (Malish & Gay, 2012). While the literature reviewed here indicates that the STOP-Bang questionnaire is likely superior in its ability to predict OSA in hospital settings, further studies and research is required to fully confirm these findings.

References

Abrishami, A., Khajehdehi, A., & Chung, F. (2010). A systematic review of screening questionnaires for obstructive sleep apnea. Canadian Journal of Anesthesiology, 57, 423-438.

Alghanim, N., Comondore, V. R., Fleetham, J., Marra, C. A., & Ayas, N. T. (2008). The economic impact of obstructive sleep apnea. Lung, 186, 7-12.

American Academy of Sleep Medicine (2005). The International Classification of Sleep Disorders: Diagnostic and Coding Manual (2nd ed., Rev.). Westchester, IL: American Academy of Sleep Medicine.

Banno, K., Ramsey, C., Walld, R., & Kryger, M. (2009). Expenditure on healthcare in obese women with and without sleep apnea. Sleep, 32(2), 247-252.

Caples, S. M., & Somers, V. K. (2009). Sleep-disordered breathing and atrial fibrillation. Progress in Cardiovascular Diseases, 51(5), 411-415.

Chung, F., Subramanyam, R., Liao, P., Sasaki, E., Shapiro, C., & Sun, Y. (2012). High STOP-Bang score indicates a high probability of obstructive sleep apnoea. British Journal of Anesthesiology, 108 (5), 768-775.

Chung, F., Ward, B., Ho, J., et al. (2007). Preoperative identification of sleep apnea risk in elective surgical patients using the Berlin Questionnaire. Journal of Clinical Anesthesiology, 19, 130-4.

Chung, F., Yegneswaran, B., Liao, P., et al. (2008). STOP questionnaire: A tool to screen patients for obstructive sleep apnea. Anesthesiology, 108, 812-821.

Epstein, L. J., Kristo, D., Strollo, P. J. et al. (2009). Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. Journal of Clinical Sleep Medicine, 5(3), 263-276.

Finkel, K. J., Searleman, A. C., Tymkew, H. et al.. (2009). Prevalence of undiagnosed obstructive sleep apnea among adult surgical patients in an academic medical center. Sleep Medicine, 10, 753-758.

Flemons, W.W., Whitelaw, W.A., Brant, R., & Remmers, J.E. (14994). Likelihood ratios for a sleep apnea clinical prediction rule. American Journal of Respiratory and Critical Care Medicine, 150 (5), 1279-85.

Gali, B., Whalen, F.X., Gay, P.C. et al. (2007). Management plan to reduce risks in perioperative care of patients with presumed obstructive sleep apnea syndrome. Journal of Clinical Sleep Medicine, 3 (6), 582-8.

Gali, B., Whalen, F.X., Schroeder, D.R., Gay, P.C., & Plevak, D.J. (2009). Identification of patients at risk for postoperative respiratory complications using a preoperative obstructive sleep apnea screening tool and postanesthesia care assessment. Anesthesiology, 110 (4), 869-77.

Gross, J.B., Bachenberg, K.L., Benumof, J.L., et al. (2006). Practice guidelines for the perioperative management of patients with obstructive sleep apnea: a report by the American Society of Anesthesiologists Task Force on perioperative management of patients with obstructive sleep apnea. Anesthesiology, 104, 1081-93.

Lakdawala, L. (2011). Creating a safer perioperative environment with an obstructive sleep apnea screening tool. Journal of PeriAnesthesia Nursing, 26(1), 15-24.

Lawati, N. M., Patel, S. R., & Ayas, N. T. (2009). Epidemiology, risk factors, and consequences of obstructive sleep apnea and short sleep duration. Progress in Cardiovascular Disease, 51(4), 285-293.

Levy, P., Pepin, J., Arnaud, C., Baguet, J., Dematteis, M., & Mach, F. (2009). Obstructive sleep apnea and atherosclerosis. Progress in Cardiovascular Diseases, 51(5), 400-410.

Loke, Y. K., Brown, J. W., Kwok, C. S., Niruban, A., & Myint, P. K. (2012). Association of obstructive sleep apnea with risk of serious cardiovascular events: A systematic review and meta-analysis. Circulation and Cardiovascular Quality and Outcomes, 5, 720-728.

Loo, G., Hein, T., Tai, B., et al. (2103). Screening of hospitalized patients at high risk of obstructive sleep apnea in general cardiology service. International Journal of Cardiology, 164, 368-377.

Madani, M., & Madani, F. (2009). Epidemiology, pathophysiology and clinical features of obstructive sleep apnea. Oral Maxillofacial Surgical Clinics of North America, 21, 369-375.

Malish, H.R. & Gay, P.C. (2012). Perioperative Care of Patients with Obstructive Sleep Apnea Syndrome (pp. 371-394). In Safwan Badr (ed.) Essentials of Sleep Medicine: An Approach for Clinical Pulmonology. New York: Springer.

Pack, A. I., & Gislason, T. (2009). Obstructive sleep apnea and cardiovascular disease: a perspective and future directions. Progress in Cardiovascular Diseases, 51(5), 434-451.

Rich, J., Raviv, A., Raviv, N., & Brietzke, S. E. (2012). All-cause mortality and obstructive sleep apnea severity revisited. Otolaryngology Head and Neck Surgery, 147(3), 583-587.

Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York, NY: Free Press.

Silva, G. E., Vana, K. D., Goodwin, J. L., Sherrill, D. L., & Quan, S. F. (2011). Identification of patients with sleep disordered breathing: Comparing the Four-Variable Screening Tool, Stop, Stop-Bang, and Epworth Sleepiness Scales. Journal of Clinical Sleep Medicine, 7(5), 467-472.

Yang, Y., & Chung, F. (2013). A screening tool of obstructive sleep apnea: STOP-Bang questionnaire. Sleep Medicine Clinics, 8, 65-72.

St. Louis, E. K., & Morgenthaler, T. I. (2012). Sleep Disorders. In E.T. Bope & R.D. Kellerman (Eds.), Bope & Kellerman: Conn's Current Therapy 2013, 1st ed. Philadelphia, PA: Elsevier Health Sciences.