ChanScore Predicts Presence And Severity Of Sleep Apnea-Hypopnea At The Bedside Before A Polysomnogram Sleep Test Michael P. Chan, MD, MS, RPSGT Yale New Haven Hospital, Yale University School of Medicine Nicholas D. Ty Lim, Allyn Ly, B.S. Antonio Q. Chan, MD, MBA, FACC, FAHA, RPSGT, FAASM Chanwell Clinic Institute for Heart & Sleep Disorders Milpitas, California World Congress of Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013
ChanScore Predicts the Presence and Severity of Obstructive Sleep Apnea Hypopnea at the Bedside Before Polysomnogram Sleep Test Michael P. Chan, MD, MS, RPSGT, Yale University School of Medicine, Yale New Haven Hospital; Nicholas D. Lim; Allyn Ly, BS; Antonio Q. Chan, MD, MBA, FACC, FAHA, RPSGT, FAASM Chanwell Clinic Institute for Heart & Sleep Disorders, Milpitas, California Background: Obstructive sleep apnea-hypopnea (OSAH) increases the risk for metabolic syndrome, heart failure, myocardial infarction, stroke, sudden cardiac death, pulmonary arterial hypertension, vehicular accidents, cancer, depression etc.. In spite of the wide prevalence of OSAH estimated at 24% of the American adult s population and much higher rate among Asians, physicians often miss the signs and symptoms of OSAH, thus a large number of undiagnosed patients remain at great risks. Several bedside history-taking methodologies were developed to assess the daytime sleepiness of patients that come into the clinic. Both the Stanford Sleepiness Scale (SSS) and the Epworth Sleepiness Scale (ESS) were found to be lacking in predicting the presence and severity of OSAH. In the clinical population the Berlin Questionnaire (BQ) was found to have a low level of sensitivity and specificity for prediction of OSAH. Thus there is an urgent need to train physicians in accurate methodologies to screen patients that may suffer from OSAH. Given the scarcity of sleep specialists; primary care physicians and physician assistants (PAS) should be at the forefront of addressing the task of early detection of patients that may have OSAH. To date, there is no combined qualitative and quantitative measure that takes into account the history, age, body mass index (BMI), ESS score, and upper airway structure (Mallampati classification) of a patient. It is our contention that the preceding risk variables when taken individually are either weakly or not strongly correlated with the presence and severity of OSAH; however, when all these risk variables are weighted and measured collectively, the predictability at the bedside for the probable presence and severity of OSAH is further enhanced. Furthermore, most effective methods of diagnosing OSAH require an overnight in-sleep laboratory polysomnogram (SLPSG), and/or a portable home sleep-monitor which can be both costly and time consuming. To address the issue of the accuracy between bedside screening and actual diagnosis, we devised a method of predicting the presence and severity of OSAH by assigning weighted numerical values that consider a patient s age, history of snoring, Epworth Sleepiness Scale (ESS), body mass index (BMI), and upper airway structure (Mallampati classification).
Purpose To predict the presence and severity of OSAH (in the form of an AHI category) with a multilateral bedside scoring system wherein physicians or PAS with no training in sleep medicine may be able to enhance the awareness of OSAH, weed out unnecessary referral of low probability OSAH to SLPSG, and expedite referrals to sleep medicine specialists and SLPSG for those at high probability for mild moderate, moderate to severe OSAH. Methods: Given known correlations of age, snoring, body mass index (BMI), Epworth Sleepiness Scale (ESS), and Mallampati classification with OSAH, we assigned simplified weighted values to the listed variables. The total ChanScore (0 to 10) is the sum of the weighted values that corresponds to each variable. Analyses of 315 patients (Male: Female 175:140), picked at random with scored SLPSG, were retrospectively correlated to individual variables and used to optimize the weighting of the ChanScore. Ordinal regression analyses were executed using AHI-categories as defined by the following: 1 AHI <5, 2 AHI 5-14.99, 3 AHI 15-29.99, 4 AHI 30-49.99, 5 AHI 50. Findings We found that age, snoring, body mass index (BMI), Epworth Sleepiness Scale (ESS), and Mallampati classification when analyzed individually proved to have lesser statistical significance in ordinal regression using AHI-Categories (Age: p-value <.00001, Snoring: p-value.02, BMI: p-value <.00001, ESS: p-value.10, Mallampati: p-value.007) than the multilateral ChanScore (p-value <.0000000001) which accurately diagnosed over 80% of the population s OSAH (AHI 5 threshold for OSAH as defined by the American Academy of Sleep Medicine). Conclusion Our scoring system, ChanScore, predicts the presence and severity of OSAH at the bedside and correlated remarkably well with SLPSG results. It is a simple, valuable clinical tool for physicians, or PA's who may have no training in Sleep Medicine, to quickly identify patients who may have OSAH and predict its severity. World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore Background Obstructive sleep apnea-hypopnea (OSAH) increases the risk for: Metabolic syndrome Heart failure Myocardial infarction (up to 300%) Stroke (up to 300%) Sudden cardiac death (up to 300%) Pulmonary arterial hypertension Vehicular accidents (up to 700%) Cancer Depression
ChanScore Background Prevalence of OSAH, estimated at 24% of the American adult s population Much higher rate among Asians, physicians often miss the signs and symptoms of OSAH, thus a large number of undiagnosed patents remain at great risks World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore Background Diagnosing OSAH: The observation of a sleep technician An overnight in-lab polysomnogram (PSG) A portable home sleep-monitor All of the above can be time-consuming and costly & may not be available in the community World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore Background Assess the daytime sleepiness of patients: Stanford Sleepiness Scale (SSS) Epworth Sleepiness Scale (ESS) Berlin Questionnaire (BQ) Limitation: SSS, ESS, BQ are exclusively subjective and may result in error. Insomnia + OSAH = may have low ESS, hence ESS is not reliable World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore Background Stanford Sleepiness Scale (SSS) Epworth Sleepiness Scale (ESS) Degree of Sleepiness Scale Rating Situation Chance of Dozing Feeling active, vital, alert, or wide awake 1 Functioning at high levels, but not at peak; able to concentrate Awake, but relaxed; responsive but not 3 fully alert Somewhat foggy, let down 4 Foggy, losing interest in remaining awake, slowed down Sleepy, woozy, fighting sleep; prefer to lie down 2 5 6 Sitting and Reading 0 1 2 3 Watching TV 0 1 2 3 Sitting inactive in a public place 0 1 2 3 As a passenger in a car for one hour without a break Lying down to rest in the afternoon 0 1 2 3 0 1 2 3 Sitting and talking to someone 0 1 2 3 No longer fighting sleep, sleep onset soon; having dream-like thoughts Asleep Rate your alertness at different times during the day. If you go below a three when you should be feeling alert, this is an indication that you have a serious sleep debt 7 X Sitting quietly after lunch (without alcohol) In a car, stopped for a few minutes in traffic Total Score 0 1 2 3 0 1 2 3
ChanScore Background Berlin Questionnaire (BQ) considered other factors of OSAH besides general sleepiness such as: BMI Blood pressure Snoring history Limitation: BQ approach considers single threshold based interpretations of qualitative information World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore Background Berlin Questionnaire (BQ) Annals of Internal Medicine 1999
ChanScore Background Current history-taking methodologies (ESS, SSS, BQ) lack quantitative multi-threshold (non-binary) consideration and apnea-hypopnea related factors (besides snoring) Given these limitations, Chanwell Clinic devised a method of predicting OSAH & its severity by assigning a score that considers a patient s age, history of snoring, ESS score, BMI, and upper airway structure (Mallampati classification) World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
Physical Exam Examine the Upper Airway! World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013 Mallampati Score
Children & Young Adults World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScorePurpose To Predict the Presence and Severity of OSAH with a Multilateral Bedside Scoring System Before Polysomnogram Sleep Test. Provide simplified tool for healthcare workers to identify patients who may have OSAH World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScorePurpose Weed out unnecessary referral of low probability OSAH to sleep lab PSG Expedite referrals to sleep medicine specialistsand sleep lab PSG for those at high probability for mild moderate, moderate to severe OSAH World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore Study Patient Population Characteristics Overall (n = 315) <5 (n = 44) 5-14.99 (n = 75) 15-29.99 (n = 73) 30-49.99 (n = 67) 50 (n = 56) P Value* Male, % 55.56 54.55 42.67 53.42 58.21 73.21 0.005 Snorer, % 97.14 90.91 96 98.63 98.51 100 0.02 Age <70 y, % 60.54 20.45 46.67 41.1 44.78 55.36 0.007 Ethnicity, % Caucasian 10.79 13.64 2.67 10.96 16.42 12.5 African-American 1.9 2.27 4 0 1.49 1.79 Middle Eastern 2.86 2.27 1.33 2.74 5.97 1.79 Asian 74.6 70.45 82.67 80.82 65.67 69.64 0.4 Hispanic 9.84 11.36 9.33 5.48 10.45 14.29 Other 0 0 0 0 0 0 BMI, kg/m^2, % <25 47.94 65.91 54.67 53.42 38.81 28.57 25-29.99 35.56 25 38.67 32.88 34.33 44.64 <.001 >30 16.51 9.09 6.67 13.7 26.87 26.79 Epworth, % <10 10.48 18.18 5.33 15.07 11.94 3.57 10-15 77.78 75 86.67 72.6 71.64 82.14 0.1 >15 11.75 6.82 8 12.33 16.42 14.29 Mallampati, % Class I 0 0 0 0 0 0 Class II 1.27 6.82 1.33 0 0 0 Class III 32.06 38.64 32 38.36 23.88 28.57 0.007 Class IV 66.67 54.55 66.67 61.64 76.12 71.43 ChanScore, % 0-2 0 0 0 0 0 0 3-4 6.03 25 5.33 5.48 0 0 5-6 34.29 47.73 37.33 41.1 26.87 19.64 <.0001 7-8 51.43 25 50.67 47.95 61.19 66.07 9-10 8.25 2.27 6.67 5.48 11.94 14.29
ChanScore Breakdown Mallampati Classification: Class I 0 Class II 1 Class III 2 Class IV 3 Age (Years): <40 0 40-69 1 70 2 BMI (kgm -2 ) <25 0 25-29.99 1 30 2 Epworth Sleepiness Scale: <10 0 10-15 1 >15 2 Snoring: No 0 Yes 1 Minimum ChanScore: 0; Maximum ChanScore: 10 Total Weighted Value
ChanScore Breakdown Given the known correlations of snoring, BMI, ESS score, Mallampati classification, and age with OSAH, we assigned simplified values to the listed factors as shown above In essence, the ChanScore combines quantitative assessment with qualitative historybased methodologies and craniofacial structure to generate a more precise prediction of the presence and severity of Sleep Disordered Breathing (SDB)
ChanScore Methods Probabilities were generated using ordinal regression analysis chiefly executed using the apnea hypopnea index (AHI)-Categories as follows: 1 AHI <5 2 AHI 5 14.99 3 AHI 15 29.99 4 AHI 30 49.99 5 AHI 50 Regression model used was: ln(py/(1-py)) = Constant + (-0.526852) * ChanScore Py represents the probability of event Y occurring, the constant is different for each Y(AHI- Category)
ChanScore Methods During the initial analyses of the smaller data sample, ordinal regression and retrospective usage of the regression model (with solved constants) displayed a low percentage (below 30%) of correctly predicted AHI-Categories (1-5) This phenomenon likely occurred given the distinction created in non-binary probabilities between a plurality (highest probability) and majority (over 50% probability and naturally indicative of plurality)
ChanScore Methods Binary Logistic Regression Model: ln(py/(1-py)) = (-4.537) + (0.593317) * TOTAL Logit Link Response Summary: SEVERE(>30) Value Count Proportion Event 0 84 0.579310345 1 61 0.420689655 X Total 145 Py represents the probability of event Y occurring, the constant is different for each Y(AHI-Category)
ChanScore Methods Parameter Estimate Term Coefficie nt SE Coefficient Z P Odds Ratio Lower 95% Odds Ratio Upper 95% Odds Ratio Constant -4.537 1.106742243-4.0990.0000 TOTAL 0.593317 0.151948 3.9050.0001 1.810 1.344 2.438 World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
Model Summary of Goodness Fit Statistics Log-Likelihood -89.309 Test that all slope coefficients are equal to zero: Likelihood Ratio Chi-Square (G) 18.731 DF 1 P-Value 0.0000 McFadden's Pseudo R-Square 9.49% (P-Value <.05 indicates Lack-of-Fit): Pearson Residuals Chi-Square 5.263 DF 6 P-Value 0.5106 Deviance Residuals Chi-Square 5.750 DF 6 P-Value 0.4517 Hosmer-Lemeshow Chi-Square 5.131 DF 4 P-Value 0.2742 Measures of Association Concordant 3062 Discordant 1074 Ties 988 Total 5124 Concordant Percent 59.76 Discordant Percent 20.96 Ties Percent 19.28 Goodman-Kruskal Gamma 0.480658 Somers' D 0.387978 Kendall's Tau-a 0.190421
Initial Observed & Predicted Outcome Observed Outcome Predicted Outcome Row Total Ŷ = 0 Ŷ = 1 Y = 0 65 19 84 Y = 1 27 34 61 Column Total 92 53 145 Percent Correctly Predicte 68.28%
ChanScore Methods 315 patients (M:F 175:140), picked at random with scored in-lab PSGs, were retrospectively correlated to individual factors and used to optimize the weighting of the ChanScore The data generated from the analyses of the study base were used to generate equations to predict the likelihood of each patient falling into a specific apnea hypopnea index(ahi) Category. World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore Methods Calculating a more precise ChanScore methodology: Version 1: Range: 0-8; Mallampati classification worth maximum of 2 points (Class <2 0, Class 3 1, Class 4 2); Snoring not considered Version 2: Range: 0-9; Mallampati classification worth maximum of 3 points (Class 1 0, Class 2 1, Class 3 2, Class 4 3); Snoring not considered Version 3: Range: 0-10; Mallampati classification worth maximum of 3 points (Class 1 0, Class 2 1, Class 3 2, Class 4 3); Snoring considered
ChanScore Methods Consider a multi-party first-past-the-post election There are 5 parties in total sharing 100 votes To win a minority government, a party must only obtain the most votes among the other parties this is a plurality To win a majority government, a party must obtain more votes than all other parties combined this is a majority To create a coalition government, two or more parties may combine their votes to form a majority
ChanScore Methods In a majority event, the probability (associated with said event) must overweigh the sum of the probabilities of all other events In a plurality event, the probability (associated with said event) need only overweigh each of the other probabilities individually In a coalition event, the probabilities of two or more separate events can be combined to form a unified majority event.
ChanScore Methods Applying the coalition principle, the predictions were recalculated to include the two most likely AHI-Categories (in this case with any ChanScore, the two highest scores in a plurality generated a likelihood of over 50%) Note: this system was only applied in the case where there was no majority present Successful implementation resulted in over 50% correctly predicted AHI-Categories (final study, n = 315) & over 80% accuracy in predicted presence of OSAH.
ChanScore Methods Upon further examination of the ordinal logistic study, it was found that over 50% of the error was constituted by predictions that correctly diagnosed the presence of OSAH (AHI >5), but ended up under-predicting the severity This discovery then prompted the usage of binary logistic regression to predict a Y/N situation based on the AHI being greater than certain values (>5, >15, >30) All 3 models resulted in over 65% accuracy with Version 3 over 86% accuracy.
ChanScore Methods ChanScore was eventually optimized to Version 3 based on analyses of the PSG data With the same data selection and ordinal logistic regression, Version 3 had the lowest p-value or most statistical significance (p <.0000000001) compared to Version 1 (p <.00004) and Version 2 (p <.00001) World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
Observed and Predicted Outcomes Observed Outcome Predicted Outcome Column1 Row Total Ŷ = 0 Ŷ = 1 Y = 0 4 40 44 Y = 1 1 270 271 Column Total 5 310 315 Percent Correctly Predicte 86.98%
Fig 1.2: ChanScoreInterval Correlation with Severity Obstructive Sleep Apnea-Hypopnea % Distributions ChanScore Interval Apnea-Hypopnea Index CaOverall (n=315)0-2 (n=0) 3-4 (n=19) 5-6 (n=108) 7-8 (n=162) 9-10 (n <5 (n=44) 13.97 0 57.9 19.4 6.79 3.85 5-14.99 (n=75) 23.81 0 21.1 25.9 23.46 19.23 15-29.99 (n=73) 23.17 0 21.1 27.8 21.6 15.38 30-49.99 (n=67) 21.27 0 0 16.7 25.31 30.77 50 (n=56) 17.78 0 0 10.2 22.84 30.77 World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
Fig 1.3: ChanScoreAHI Category Probability Ordinal Logistic Regression with Retrospective Study Apnea Hypopnea Index Category, % ChanScore <5 5-14.99 15-29.99 30-49.99 50 0 82.9 12.5 3 1.2 1 1 74.1 18.3 4.8 2 1 2 62.8 24.9 7.6 3.2 1.5 3 49.9 30.9 11.4 5.2 2.5 4 37.1 34.3 16.2 8.3 4.2 5 25.8 33.7 21.1 12.5 6.9 6 17 29.4 24.6 17.8 11 7 10.8 23.1 25.3 23.4 17.5 8 6.7 16.5 22.9 27.5 26.4 9 4.1 11.1 18.4 28.7 37.8 10 2.4 7.1 13.4 26.3 50.7
ChanScore 1- Predicted OSAH Severity Outcome World Congress on Sleep Medicine Valencia, Sp Enter Predicted Event Predictors Settings: Probability Journal TOTAL 1 2% 2% probability of OSAH World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore 5- Predicted OSAH Severity Outcome Predicted Predictors Enter Settings: Event Probability TOTAL 5 17% 17% probability of OSAH World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore 10- Predicted OSAH Severity Outcome Predicted Enter Event Predictors Settings: Probability TOTAL 10 80% 80% probability of OSAH World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
AHI Category Distribution ChanScore Range 100% 0 90% 80% 17.78 21.05 10.19 16.67 22.84 30.77 Percentage Distribu ution 70% 60% 50% 40% 30% 20% 10% 0% 21.27 21.05 25.31 27.78 30.77 23.17 21.6 25.93 15.38 57.89 23.81 23.46 19.23 19.44 13.97 6.79 3.85 Overall (n=315) 3-4 (n=19) 5-6 (n=108) 7-8 (n=162) 9-10 (n=26) ChanScore Range 50 (n=56) 30-49.99 (n=67) 15-29.99 (n=73) 5-14.99 (n=75)
ChanScore 10 9 8 7 6 5 4 3 2 1 0 2.4 4.1 6.7 7.1 10.8 11.1 17 ChanScore RangeProbability 25.8 13.4 16.5 37.1 23.1 18.4 49.9 29.4 62.8 26.3 22.9 74.1 33.7 82.9 Probability of AHI Categories 28.7 25.3 34.3 27.5 24.6 30.9 23.4 21.1 50.7 24.9 37.8 17.8 16.2 18.3 26.4 12.5 11.4 12.5 17.5 8.3 7.6 11 5.2 4.8 6.9 4.2 2.5 3.21.5 2 1 3 1.2 1 <5 5-14.99 15-29.99 30-49.99 50 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Probability
PSG Decision Tree Based on ChanScore When to refer to Sleep Lab? World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore AHI Probability Decision 0 80% probability no OSAH. No need to do anything. 1 74% probability no OSAH. Overnight sleep pulse oximetry If 4% DE is <10, do nothing, reevaluate in 1 year If 4% DE is >10, may recommend PSG sleep test 2 63% probability no OSAH May recommend PSG 18% mild OSAH 3 41% probability no OSAH PSG sleep test 25% mild OSAH 4 37% probability no OSAH PSG sleep test 31% mild OSAH 11% moderate OSAH 5 26% probability no OSAH PSG sleep test 34% mild OSAH 16% moderate OSAH 8% severe OSAH
ChanScore AHI Probability Decision 6 34% mild OSAH PSG sleep test 21% moderate OSAH 13% severe OSAH 11% unusually severe OSAH 7 11% probability no OSAH PSG sleep test 23% mild OSAH 25% moderate OSAH 23% severe OSAH 18% unusually severe OSAH 8 7% probability no OSAH PSG sleep test 17% mild OSAH 23% moderate OSAH 28% severe OSAH 26% unusually severe OSAH 9 4% probability no OSAH PSG sleep test 11% mild OSAH 18% moderate OSAH 29% severe OSAH 38% unusually severe OSAH MP Chan, AQ Chan et al ChanScore Predicts Presence & Severity of Sleep Apnea Hypopnea Journal of Sleep Supplement October 2013 10 2% probability no OSAH PSG sleep test 7% mild OSAH 13% moderate OSAH 26% severe OSAH 51% unusually severe OSAH
ChanScore Conclusions ChanScorepredicts the presence and severity of OSAH at the bedside and correlated remarkably well with sleep lab PSG results. World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013
ChanScore Conclusions ChanScoreis a simple, valuable clinical tool for physicians, or Physician Assistant's or Nurses who may have no training in Sleep Medicine, to quickly identify patients who may have OSAH and predict its severity. World Congress on Sleep Medicine Valencia, Spain Sept 28 Oct 2, 2013 Journal of Sleep Supplement Oct. 2013