Aetna considers the use of intermittent or continuous computerized or electronic wheeze detectors for the diagnostic evaluation of lung sounds (e.g., wheezing) or cough and all other indications, experimental and investigational due to insufficient evidence of their effectiveness.

Auscultation of the chest using a stethoscope is an integral part of a respiratory examination.  Sounds heard at auscultation have been simplified into breath sounds (normal or bronchial), adventitious sounds (crackles, wheezes and rhonchi) and voice sounds (normal and bronchophony).  Other sounds such as pleural rubs, clicks and squawks may also be heard (Ceresa and Johnston, 2008).  However, this practice has always been vulnerable to poor audibility, inter-observer variability, the type of stethoscope used and poor reproducibility.  Thus, computerized or electronic wheeze detectors have been proposed as a diagnostic tool in the evaluation of lung sounds.

The PulmoTrack, or WIM-PC (KarmelSonix, Ltd), received 510(k) marketing clearance from the U.S. Food and Drug Administration (FDA) in 1998 as a computer-based electronic stethoscope.  It utilizes up to 5 contact sensors simultaneously to acquire, amplify, filter, record and analyze pulmonary sounds from the trachea and thorax.  The device provides high fidelity audio outputs, visual displays and printed reports.  It has been proposed as a diagnostic tool in the interpretation of lung sounds during (i) broncho-provocation testing, (ii) broncho-dilator administration, (iii) pulmonary function testing to quantify the presence of wheezing, and (iv) when there is a need to listen to amplified and filtered breath sounds. The PulmoTrack’s wheeze detection software uses a fast Fournier transform algorithm with a reported sensitivity of 91 % and a specificity of 89 % in wheeze detection when compared to consensus assessment by a panel of pulmonary experts (Beck et al, 2007).  The algorithm identifies continuous adventitious breath sounds in the frequency range of 80 to 4,800 Hz in the tracheal channel, and 80 to 2,400 Hz in the chest-wall channels.  The system identifies and discards speech, crying, and other vocal-cord sounds.  It is intended for use by or under the supervision of a physician.

The Personal Wheezometer (KarmelSonix, Ltd) received 510(k) marketing clearance from the FDA in 2009 as a home use version of the PulmoTrack.  It is a hand-held electronic device that utilizes an acoustic contact sensor to acquire, amplify, filter, record and analyze pulmonary sounds from the trachea for the presence of wheezes.  The device is pressed against the base of the neck and takes 30 seconds to output a wheeze rate score referred to as Wz%.

The VRlxp (Deep Breeze, Ltd) received 510(k) marketing clearance from the FDA in 2010 as an electronic stethoscope.  The device consists of sound sensors that are designed to collect lung sounds, a digital collection module for the conversion of analog data to digital data, and a mobile computer workstation to assist in processing, displaying, and/or storing information.  The device produces images based on vibration response generated by the air passing through the lungs.  The system records these vibrations and then uses an algorithm to convert the data into images.  The images can be viewed via a personal computer monitor and stored for future review.  Lung sounds can be viewed collectively as a grayscale image, as well as audibly by sensor.  The VRIxp is intended for monitoring and recording lung sounds and automatic detection of crackles and wheezes. 

Bentur et al (2004) evaluated the use of automatic computerized wheeze detection (CWD) in determining bronchial hyper-reactivity (BHR) in young infants with prolonged cough, and its correlation with the subsequent development of wheezing.  Infants aged less than 24 months with prolonged cough (i.e., greater than 2 months) underwent acoustic bronchial provocation tests (BPTs) with the response determined by CWD and auscultation by a physician.  Telephone interviews with parents were conducted after 1 month and yearly for the next 3 years.  A total of 28 infants who were 4 to 24 months old with prolonged cough were included in the study.  Twenty of these infants (71.4 %) had BHR as determined by a positive acoustic BPT result.  In 11 of these 20 tests, the CWD occurred earlier, and in 9 tests it occurred at the same step as auscultation by a physician.  Rhonchi or whistles often preceded wheezes.  Seventeen of the 20 patients with BHR completed 3 years of follow-up.  Of these, 14 had recurrent episodes of wheezing and shortness of breath, and 3 were healthy.  Six of the 8 adenosine-negative patients completed 3 years of follow-up and had no symptoms of BHR.  The authors concluded that acoustic BPT is a technically feasible test for the detection of BHR in young infants and that CWD provides an earlier detection of wheeze than stethoscope auscultation.  The authors reported that in their group of infants, a positive acoustic BPT result had high correlation with symptoms compatible with BHR over the next 3 years.

Beck et al (2007) evaluated the feasibility of using the PulmoTrack’s computerized quantification of wheezing and crackles compared to a clinical score in assessing the effect of inhaled albuterol or inhaled epinephrine in infants (n = 27) with respiratory syncytial virus (RSV) bronchiolitis.  The authors noted that the recording is susceptible to outside noise and interference and requires a relatively quiet environment.  Occasional motion artifact and the infant’s own crying interfered with the recording.  The researchers reported complete agreement between clinician and PulmoTrack results in all sound segments, in off-line auditory analysis of the data, and no significant difference in wheezing and crackles by computerized lung sounds analysis was noted.  These investigators concluded that computerized lung sound analysis is feasible in young infants with RSV bronchiolitis and provides a non-invasive, quantitative measure of wheezing and crackles; however, further studies are needed to clarify its potential role as a clinical tool for assessing and following infants with acute respiratory illnesses. 

Prodhan et al (2008) studied wheeze detection among staff members in the intensive care unit (ICU) compared to digital recordings from the PulmoTrack.  Pediatric patients (n = 11) were prospectively studied in the ICU.  Eight of the 11 subjects had acute asthma, and 3 of the subjects were admitted for conditions other than asthma.  A physician, nurses, and respiratory therapists auscultated the patients and recorded their opinions about the presence of wheeze at baseline and then every hour for 6 hours.  The data were analyzed by a technician trained in interpretation of acoustic data and by a panel of experts blinded to the source of the recorded data.  The determinations of the expert panel were taken as the accepted standard.  The PulmoTrack and expert panel were in agreement on detection of wheeze during inspiration, expiration, and the whole breath cycle.  The PulmoTrack was significantly more sensitive but not more specific than that of the health care professionals in their detection of wheeze.  The authors stated that “[f]urther studies should address whether characterizing wheeze by intensity, pitch, or duration could improve detection of the degree of airway obstruction and have clinical implications.”

Guntupalli et al (2008) evaluated the accuracy of the VRIxp, a multi-sensor, computer-based device with an automated technique of wheeze detection.  The method was validated in 100 sound files from 7 subjects with asthma or chronic obstructive pulmonary disease and 7 healthy subjects by comparison of auscultation findings, examination of audio files, and computer detection of wheezes.  Three blinded physicians identified 40 sound files with wheezes and 60 sound files without wheezes.  Sensitivity and specificity were 83 % and 85 %, respectively.  Negative-predictive value and positive-predictive value were 89 % and 79 %, respectively.  Overall inter-rater agreement was 84 %.  False-positive cases were found to contain sounds that simulate wheezes, such as background noises with high frequencies or strong noises from the throat that could be heard and identified without a stethoscope.  The authors concluded that the wheeze detection algorithm had good accuracy, sensitivity, specificity, negative-predictive value and positive-predictive value for wheeze detection in regional analyses with a single sensor and multiple sensors.  The device is user-friendly, requires minimal patient effort, and, distinct from other devices, it provides a dynamic image of breath sound distribution with wheeze detection output in less than 1 minute.

Vizel et al (2010) described the validation process of a novel cough detecting and counting technology (PulmoTrack-CC, KarmelSonix, Haifa, Israel).  Tracheal and chest wall sounds, ambient sounds and chest motion were digitally recorded, using the PulmoTrack(R) hardware, from healthy volunteers coughing voluntarily while (i) laying supine, (ii) sitting, (iii) sitting with strong ambient noise, (iv) walking, and (v) climbing stairs, a total of 25 minutes per subject.  The cough monitoring algorithm was applied to the recorded data to detect and count coughs.  The detection algorithm first searches for cough “candidates” by identifying loud sounds with a cough pattern, followed by a secondary verification process based on detection of specific characteristics of cough.  The recorded data were independently and blindly evaluated by trained experts who listened to the sounds and visually reviewed them on a sonogram display.  The validation process was based on 2 methods: (i) Referring to an expert consensus as gold standard, and comparing each cough detected by the algorithm to the expert marking, these researchers marked true and false, positive and negative detections.  These values were used to evaluate the specificity and sensitivity of the cough monitoring system; (ii) Counting the number of coughs in longer segments (t = 60 sec, n = 300) and plotting the cough count versus the corresponding experts' count whereby the linear regression equation, the regression coefficient (R2) and the joint-distribution density Bland-Altman plots could be determined.  Data were recorded from 12 volunteers undergoing the complete protocol.  The overall specificity for cough events was 94 % and the sensitivity was 96 %, with similar values found for all conditions, except for the stair climbing stage where the specificity was 87 % with sensitivity of 97 %.  The regression equation between the PulmoTrack-CC cough event counts and the experts' determination was with R2 of 0.94.  The authors concluded that this validation scheme provided an objective and quantitative assessment method of a cough counting algorithm in a range of realistic situations that simulate ambulatory monitoring of cough.  The ability to detect voluntary coughs under acoustically challenging ambient conditions may represent a useful step towards a clinically applicable automatic cough detector.

Furthermore, an UpToDate review on “Approach to wheezing in children” (Fakhoury, 2013) does not mention the use of computerized or electronic wheeze detectors as management tools.

There is insufficient evidence of the effectiveness of intermittent or continuous computerized or electronic wheeze detectors for the diagnostic evaluation of lung sounds or cough compared to auscultation and standard pulmonary function testing.  Randomized controlled studies are needed to determine their effectiveness.  Furthermore, no professional medical society has recommended their use.