Predicting Obstructive Sleep Apnea

Greg Maislin M.S., M.A.

Adjunct Associate Professor

of Biostatistics in Medicine

Director, Biostatistics and

Data Management Core

Center for Sleep and Respiratory Neurobiology

University of Pennsylvania School of Medicine

Brief Review of Statistical Concepts

Attia J. Diagnostic tests: Moving beyond

sensitivity and specificity: Using likelihood ratios

to help interpret diagnostic test. Australian

Prescriber 2003, 26:5 111-113.

Fig. 1: Estimating the sensitivity and specificity

of diagnostic tests

Fig. 2: Bayes nomogram

http://www.australianprescriber.com/magazine/26/5/111/13/

ROC Analysis for Diagnostic Tests with Continuous and Ordinal Values

False Positive Rate (%)

0 10 20 30 40 50 60 70 80 90 100

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MAP IndexBMI AloneIndex 1 Alone

Clinical Prediction Formulas for Predicting Obstructive Sleep Apnea

Rowley JA. Aboussouan LS, and Badr

MS. The use of clinical prediction

formulas in the evaluation of obstructive

sleep apnea. Sleep 2000, 23:7:929-937.

Evaluated and compared four relatively

simple prediction rules for AHI>=10 and

AHI>=20.

Variables Included in Models

Model 1: witnessed apnea, hypertension,

BMI, age

Model 2: snoring, BMI, age, sex

Model 3: snoring, gasping/choking,

hypertension, neck circumference

Model 4: loud snoring, snorting and

gasping, witnessed apneas, BMI, age,

sex

Receiver Operator Characteristic (ROC)Areas Under the Curves in New Cohort

0.7570.6110.801Model 4

0.7330.6480.707Model 3

0.7220.6260.801Model 2

0.7000.6330.761Model 1

All

AHI>=20

Females

AHI>=10

Males

AHI>=10

Predictive Capacity for ‘ Prioritizing’ Patients

859339Model 4 (0.85)

748934Model 3 (35)

728734Model 2 (0.95)

769033Model 1 (0.95)

PPVSpecificitySensitivityModel (cutpt.)

Model 4

A Survey Screen for Prediction of Apnea

Greg Maislin, Allan I. Pack, Nancy B. Kribbs,

Philip L. Smith, Alan R. Schwartz,

Lewis R. Kline, Richard J. Schwab,

and David F. Dinges

Sleep, 18(3): 158-166, 1995

Model 4

13 self-report symptom frequency questions abut

sleep apnea, difficulty sleeping, excessive daytime

sleepiness, and narcolepsy-like symptoms

“[ ] while sleeping, trying to sleep, or while feeling

sleepy. "During the last month, have you had, or

have been told about the following symptom (Show

the frequency): (0) Never; (1) Rarely, Less Than

Once a Week; (2) 1-2 Times Per Week; (3) 3-4 Times

Per Week; (4) 5-7 Times Per Week; (.) Don't Know".

Apnea items: Loud Snoring, Snorting or gasping,

Your breathing, stops or you struggle for breath

Equation for the Multivariable Apnea Prediction (MAP) Index

MAP = ex/(1 + ex)

Wherex = -8.160 + 1.299*I1 + 0.163*BMI -

0.028*I1*BMI + 0.032*Age + 1.278*Male

Male=1 if Male and 0 if female

MAP Values for 55 Year Old Male

BMI

20 25 30 35 40 45

Val

ue

of

MA

P

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0.6

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01234

Index 1

Likelihood Ratio Analysis of MAP Values

Older Males (Age = 55)

BMI

20 25 30 35 40 45

MA

P L

ikel

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od

Rat

io

012345678

Younger Females(Age = 35)

BMI

20 25 30 35 40 45

MA

P L

ikel

iho

od

Rat

io

012345678

Older Females(Age = 55)

BMI

20 25 30 35 40 45

MA

P L

ikel

iho

od

Rat

io

012345678

Younger Males (Age = 35)

BMI

20 25 30 35 40 45

MA

P L

ikel

iho

od

Rat

io

012345678

42.4%62.1%65.5%79.3%86.2%29>.9 - 1.0

33.6%61.6%70.6%77.4%86.3%146>.8 - .9

26.2%45.1%52.4%65.9%84.2%164>.7 - .8

19.3%38.7%47.1%51.3%66.4%119>.6 - .7

3.8%22.8%29.1%49.4%62.0%79>.5 - .6

3.0%13.4%17.9%38.8%49.3%67>.4 - .5

4.7%4.7%9.3%23.3%34.9%43>.3 - 4

5.7%5.7%14.3%22.9%40.0%35>.2 - .3

0.0%7.1%7.1%14.3%28.6%28>.1 -. 2

0.0%0.0%0.0%0.0%7.1%42 0 -.1

AHI≥40AHI≥20AHI≥15AHI≥10AHI≥5NDecile

Prospective Non-Parametric Calibration for YOUR Population (N=752 new)

Re-calibration inNon-Sleep Center Population

Figure 1Percentages with AHI>=15/hr in Two Populations

MAP Decile0-<.1 .1-<.2 .2-<.3 .3-<.4 .4-<.5 .5-<.6 .6-<.7 .7-<.8 .8-<.9 .9-1

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Elders with EDS CDL Holders (weighted)

Re-calibration inNon-Sleep Center Population AUC’s from ROC

AHI≥5, AHI≥15, AHI≥30

Elders 0.781, 0.759, 0.796

Truck Drivers 0.768, 0.746, 0.741

Conclusions

Overall predictive capacity is similar among

general populations compared to patients from the

sleep disorders clinics.

Symptoms are less important in the more obese

population of commercial drivers.

Obesity plays a smaller role in determining apnea

prevalence in older adults elevating the importance

of symptoms. Maislin G, Garubhagavatula I, Hachadoorian R, Pack F, O’Brien E, Staley B, Dinges DF, Pack

AI. Operating characteristics of the multivariable apnea prediction index in non-clinic

populations Sleep 26 Abstract Supplement 2003, 0618.J, page A247.

Beyond Symptoms, Demographics, and and Anthropometrics

Gurubhagavatula I, Maislin G, Pack AI. An

algorithm to stratify sleep apnea risk in a sleep

disorders clinic population. Am J Respir Crit Care

Med 2001 Nov 15;164(10 Pt 1):1904-9

Gurubhagavatula I, Maislin G, Nkwuo JE, Pack AI.

Occupational screening for obstructive sleep apnea in

commercial drivers. Am J Respir Crit Care Med

2004;170(4):371-6.

Beyond Symptoms, Demographics, and and Anthropometrics

Clinical prediction formula (MAP) + ambulatory

oximetry (ODI)

MAP > UB → confirmatory PSG

If LB<=MAP<=UB → nocturnal pulse oximetry If ODI > threshold → confirmatory PSG If ODI <= threshold → predict disease free

MAP < LB → predict disease free

Potential for improved AUC, SN, SP, PPV, NPV,

and LR in a cost effective fashion