Society for Technology in Anesthesia
Section Editor: Maxime Cannesson
The Accuracy, Precision and Reliability of Measuring
Ventilatory Rate and Detecting Ventilatory Pause by
Rainbow Acoustic Monitoring and Capnometry
Michael A. E. Ramsay, MD,* Mohammad Usman, PhD,† Elaine Lagow, RN,‡ Minerva Mendoza, RN,§
Emylene Untalan, RN,§ and Edward De Vol, PhD║
BACKGROUND: Current methods for monitoring ventilatory rate have limitations including poor
accuracy and precision and low patient tolerance. In this study, we evaluated a new acoustic
ventilatory rate monitoring technology for accuracy, precision, reliability, and the ability to detect
pauses in ventilation, relative to capnometry and a reference method in postsurgical patients.
METHODS: Adult patients presenting to the postanesthesia care unit were connected to a Pulse
CO-Oximeter with acoustic monitoring technology (Rad-87, version 7804, Masimo, Irvine, CA)
through an adhesive bioacoustic sensor (RAS-125, rev C) applied to the neck. Each subject also
wore a nasal cannula connected to a bedside capnometer (Capnostream20, version 4.5, Oridion,
Needham, MA). The acoustic monitor and capnometer were connected to a computer for continuous acoustic and expiratory carbon dioxide waveform recordings. Recordings were retrospectively
analyzed by a trained technician in a setting that allowed for the simultaneous viewing of both
waveforms while listening to the breathing sounds from the acoustic signal to determine inspiration and expiration reference markers within the ventilatory cycle without using the acoustic
monitor- or capnometer-calculated ventilatory rate. This allowed the automatic calculation of
a reference ventilatory rate for each device through a software program (TagEditor, Masimo).
Accuracy (relative to the respective reference) and precision of each device were estimated and
compared with each other. Sensitivity for detection of pauses in ventilation, defined as no inspiration or expiration activity in the reference ventilatory cycle for ≥30 seconds, was also determined.
The devices were also evaluated for their reliability, i.e., the percentage of the time when each
displayed a value and did not drop a measurement.
RESULTS: Thirty-three adults (73% female) with age of 45 ± 14 years and weight 117 ± 42
kg were enrolled. A total of 3712 minutes of monitoring time (average 112 minutes per subject) were analyzed across the 2 devices, reference ventilatory rates ranged from 1.9 to 49.1
bpm. Acoustic monitoring showed significantly greater accuracy (P = 0.0056) and precision
(P- = 0.0024) for respiratory rate as compared with capnometry. On average, both devices displayed data over 97% of the monitored time. The (0.95, 0.95) lower tolerance limits for the acoustic monitor and capnometer were 94% and 84%, respectively. Acoustic monitoring was marginally
more sensitive (P = 0.0461) to pauses in ventilation (81% vs 62%) in 21 apneic events.
CONCLUSIONS: In this study of a population of postsurgical patients, the acoustic monitor and
capnometer both reliably monitored ventilatory rate. The acoustic monitor was statistically more
accurate and more precise than the capnometer, but differences in performance were modest.
It is not known whether the observed differences are clinically significant. The acoustic monitor was more sensitive to detecting pauses in ventilation. Acoustic monitoring may provide an
effective and convenient means of monitoring ventilatory rate in postsurgical patients. (Anesth
Analg 2013;117:69–75)
From the *Department Of Anesthesiology and Pain Management, Baylor
Research Institute, Baylor University Medical Center, Dallas, Texas; †Masimo
Corporation, Irvine, California; ‡Baylor Research Institute and §Post Anesthesia Care Unit, Baylor University Medical Center; and ║Department of
Quantitative Sciences, Baylor Health Care System, Dallas, Texas.
Accepted for publication February 12, 2013.
Funding: Masimo provided equipment and sensors for the study and funded
the cost of research personnel who collected study data.
Conflict of Interest: See Disclosures at the end of the article.
Reprints will not be available from the authors.
Address correspondence to Michael A. E. Ramsay, MD, Department Of
Anesthesiology and Pain Management, Baylor University Medical Center, 3500 Gaston Ave., 2 Roberts, Dallas, Texas 75246. Address e-mail to
docram@baylorhealth.edu.
Copyright © 2013 International Anesthesia Research Society.
DOI: 10.1213/ANE.0b013e318290c798
July 2013 • Volume 117 • Number 1
T
here is an increasing realization in the health care
community of the importance of continuous patient
monitoring not only in the operating room but also
in procedure areas, the postanesthesia care unit (PACU)
and general care floor when patients are receiving sedative
drugs and postoperative opioids, all which may depress
breathing. The ability to detect and respond appropriately
Institute for Healthcare Improvement. Rapid Response Teams. Available at:
http://www.ihi.org/knowledge/Pages/Tools/HowtoGuideDeployRapidResponseTeams.aspx
a
b
The Joint Commission. 2009 National Patient Safety Goals. Available at:
http://www.jointcommission.org/patientsafety/nationalpatientsafetygoals
Anesthesia Patient Safety Foundation. Available at: http://www.apsf.org
c
d
American Society of Anesthesiologists. Available at: http://www.asahq.org
www.anesthesia-analgesia.org
69
Ventilatory Rate by Rainbow Acoustic Monitoring
to the early indicators of physiological deterioration in a
patient has become a major initiative for many nationally
recognized health care organizations such as the Institute
for Healthcare Improvement,a the Joint Commission,b the
Anesthesia Patient Safety Foundation,c and the American
Society of Anesthesiologists.d While early warning scoring
systems along with the notification of rapid response
teams are becoming widely implemented as a means of
identifying and intervening early in patients at risk for an
adverse event and readmission to the intensive care unit
(ICU) or death, they cannot replace continuous monitoring.
Changes in physiological variables such as ventilatory rate,
oxygen saturation, exhaled carbon dioxide (CO2), and heart
rate have been shown to be early indicators of a patient’s
deteriorating condition.1–4 Earlier activation of the rapid
response team may be possible if these variables could
be monitored continuously and alarms transmitted to the
caregiver.5
There is a need for a continuous monitor of ventilatory
rate in spontaneously breathing patients at risk for ventilatory depression. This is particularly important in patients
undergoing deep or moderate sedation and in patients at
risk for ventilatory depression secondary to the administration of postoperative opioids. There is a significant risk of
patient morbidity and mortality in the postoperative period
due to ventilatory depression after administration of neuraxial opioids, intermittent parenteral injections of opioids,
or IV patient-controlled analgesia (PCA), especially continuous infusions of opioids.6 Patients with obstructive sleep
apnea (OSA), morbid obesity, or the elderly appear to be at
higher risk for opioid-induced ventilatory depression.7,8 The
Anesthesia Patient Safety Foundationc and the American
Society of Anesthesiologistsd advocate the routine use of
continuous postoperative ventilatory monitoring in at-risk
patients receiving postoperative opioids including PCA or
neuraxial opioids. Manual methods of determining ventilatory rate such as intermittent auscultation are time consuming, unreliable, and often poorly documented.9 Continuous
methods of monitoring ventilatory rate such as capnometry
have known limitations that can affect accuracy and patient
tolerance.10 Capnometry measures the fractional CO2 in
patient exhalations by infrared light absorption, mass spectrometry, and other methods. Side-stream capnometry, the
method most commonly used in postoperative, nonventilated patients, uses a facemask or a nasal cannula for collection of exhaled gases, both of which may be bothersome
to patients and can become dislodged. The nasal cannula
has the further limitation of being inaccurate during mouth
breathing, airway obstruction, or when oxygen is administered through the cannula.8,10,11 Additionally, variation from
baseline of end-tidal carbon dioxide (EtCO2) is an important observation. Hypoventilation may cause the EtCO2 to
decrease, because there is inadequate mixing of alveolar
gas. Therefore, a low EtCO2 may be as important as a high
EtCO2 when assessing for hypoventilation.12
There has been a great deal of interest in the development of a noninvasive bioacoustic sensor for ventilatory
rate monitoring. Previous attempts to introduce the technology have been largely unsuccessful due to problems
with ambient noise and motion artifacts. Recently, a method
for the noninvasive measurement of ventilatory rate using
70 www.anesthesia-analgesia.org
a contact piezoelectric sensor received Food and Drug
Administration 510 (k) clearance and CE Mark and has
become commercially available. The technology detects,
amplifies, and processes the biological sounds and vibrations associated with ventilation through a disposable sensor placed on the neck.
The purpose of this study was to determine the accuracy
and reliability of this new bioacoustic sensor technology for
the measurement of ventilatory rate and detection of cessation of breathing (pause in ventilation) in adult postsurgical
patients when compared with side-stream capnometry, and
with a reference standard.
We are aware of 1 study that has systematically evaluated the use of continuous pulse oximetry monitoring in
postsurgical patients on the general floor for its ability to
affect clinical outcomes.13 This study used measure-through
motion and low perfusion pulse oximetry (Masimo SET®,
Masimo, Irvine, CA) to minimize false alarms and enhance
detection of true alarms. In a cohort evaluated before and
after the implementation of continuous pulse oximetry
monitoring, there was a decrease in rescue events from 3.4 to
1.2 per 1000 patient discharges and ICU transfers from 5.6 to
2.9 per 1000 patient days. In contrast, 2 other cohorts without implementation of pulse oximetry showed no difference
during the same time periods. While there was a dramatic
decrease in rescue events and ICU transfers, it is possible
that the addition of ventilatory rate monitoring could have
provided an earlier notification of ventilatory depression,
signaled by a decreasing ventilatory rate or pause in ventilation. Earlier notification might have allowed earlier or
additional intervention in many of the at-risk subjects and
further improved patient safety.
Overdyk et al.14 demonstrated that continuous monitoring of postoperative patients with pulse oximetry and
EtCO2 identified prolonged periods of hypoxia and apnea.
Of a total of 178 patients studied, 12% had incidences of
oxygen desaturation <90%, and 41% had periods of bradypnea <10 bpm for at least 3 minutes. One patient in the study
had to be rescued with bag-mask ventilation.
Abnormal ventilatory rate has been shown to be a common clinical feature in patients before a major clinical event
such as cardiac arrest, onset of sepsis, and in patients experiencing shock, pain, asthma, sepsis, and respiratory infection.15,16 Despite the strong correlation between ventilatory
rate changes and physiological deterioration, there is poor
compliance in ventilatory rate charting.17 Because of this,
ventilatory rate has been referred to as “the neglected vital
sign.”18 This is probably because, unlike arterial blood pressure and heart rate, this variable is not usually recorded
automatically.
METHODS
An observational study of adult patients presenting to
the PACU of an academic tertiary care facility (Baylor
University Medical Center, Dallas, TX) was conducted
after IRB approval and obtaining written informed consent.
On arrival in the PACU, a convenience sample of surgical patients was connected to a Pulse CO-Oximeter with
Rainbow® acoustic monitoring (RAM) technology (Rad87, version 7804, Masimo) through an adhesive bioacoustic
sensor (RAS-125, rev C) applied to the subject’s neck, lateral
anesthesia & analgesia
to the cricoid cartilage, following the manufacturer’s directions for use. Each subject was also fitted with a nasal cannula connected to a bedside capnometer (Smart Capnoline
adult CO2 oral/nasal sampling set with Capnostream20,
version 4.5, Oridion, Needham, MA). The oxygen flow rate
through the cannula was 2 L/min. We chose to use nasal
capnometry because that is what is typically used at our
institution. The acoustic monitor and capnometer were
connected to a computer with software (ADC, Masimo)
for continuous waveform and parameter recording. The
capnometer waveform data were collected at 20 Hz and
the ventilatory rate at 1 Hz; the acoustic monitor outputs
data at 2000 Hz and the ventilatory rate value at 0.5 Hz, so
ventilatory rate data at 1 Hz were used for comparison. The
ventilatory rate resolution for both monitors was 1 breath
per minute, i.e., integer values.
For the reference ventilatory rate, acoustic and EtCO2
waveform files were retrospectively analyzed by a trained
technician using a specialized LabVIEW-based software
program developed by Masimo (TagEditor, Masimo).
LabVIEW allows simultaneous viewing of both waveforms
while listening to the breathing sounds from the acoustic
signal to manually determine and annotate inspiration and
expiration reference markers within the ventilatory cycle
independent of the acoustic monitor- or capnometer-calculated ventilatory rates. An additional technician was used
to validate the reference markers, and if there was disagreement between technicians, a third technician was used to
adjudicate. This was very rarely necessary as there were
a small number of contentious events. Technicians were
respiratory therapists or medical doctors trained to use the
TagEditor program and contracted by Masimo. The reference ventilatory rate was then automatically calculated by
the software program based on 30 seconds averaged period
from the manual annotation of the data. The averaged ventilation period was calculated every 2 seconds using a sliding window and was equal to the mean period of all valid
inspiration to inspiration and expiration to expiration periods for about the last 30 seconds, based on manual annotation. Figure 1 shows a screen shot of the TagEditor program
patient file of the acoustic monitor and capnometry waveforms with manual markings during inspiration, expiration, and a respiratory pause.
Ventilatory rates from the acoustic monitor and capnometer were compared with their respective reference methods
at each time point, and absolute deviations (i.e., the absolute
value of the difference from reference) in breaths per minute
(bpm) were calculated. As a measure of accuracy, absolute
deviations were calculated as a percent of reference. The
process for calculation of accuracy is illustrated in Figure 2.
Percent absolute deviations were averaged for each subject yielding overall measures of accuracy. For comparison
of the accuracy of capnometry to that of RAM, the individual percent absolute deviations (RAM and capnometry)
were compared for each monitoring time point (when both
were present) by subtracting one from the other (capnometry minus RAM) and then averaged within subject. A 1-sample t test was used to evaluate significance across subjects.
To measure the precision of each device for each subject,
the standard deviation of percent deviations (in nonabsolute value) were calculated. The precisions of the 2 devices
were compared through a paired t test of the subject-specific standard deviations of the 2 devices’ percent deviations
from reference.
The ability of both methods to detect ventilatory
pauses was assessed. Ventilatory pause was defined as
Figure 1. Screen shot of patient file as
displayed by TagEditor program showing acoustic monitoring waveform (upper
trace, 1) and capnometry (lower trace, 2)
with manual markers for inspiration (A)
and expiration (B).
July 2013 • Volume 117 • Number 1
www.anesthesia-analgesia.org 71
Ventilatory Rate by Rainbow Acoustic Monitoring
Figure 2. Calculation of accuracy for each device. RAM = Rainbow® acoustic monitoring; EtCO2 = end-tidal carbon dioxide.
no inspiration or expiration activity in the ventilatory
cycle measured by the reference method for ≥30 seconds. For the purposes of the study, a true positive (TP)
was defined as no inspiration or expiration for ≥30 seconds by the reference method when the test method
detected no inspiration and expiration during the same
≥30-second period. A false negative (FN) was defined as
no inspiration or expiration for >15 seconds as determined by the reference method when the test method
showed at least 1 inspiration or expiration during the
same >15-second period. The sensitivity of each device
was calculated as TP/(TP+FN) (%). This calculation was
made across all the pauses for the 33 patients. Counts were
recorded in a matched sample table and sensitivities compared with a Fisher exact test. These type of data do not
lend themselves to an estimation of specificity, as distinct
periods of no ventilatory pauses cannot be defined.
Last, reliability of both devices was assessed by reviewing the number of time points each device displayed a
value during the total monitoring time, and calculating a
drop-out rate (percent of time points when the monitoring
device failed to provide data) and lower bound tolerance
intervals.19
RESULTS
Thirty-three adults (73% female) with age of 45 ± 14 years
and weight 117.4 ± 42.2 kg were enrolled. A total of 3712 minutes of monitoring time (average 112 minutes per subject)
were analyzed. The range of monitoring time for patients
was 20 to 259 minutes. Patient characteristics and surgical
procedures are shown in Table 1. All patients were recovering from major surgery requiring PCA pumps, intermittent
narcotics, or both. At least 35% of patients were diagnosed
with OSA before enrollment, but this group of patients was
72 www.anesthesia-analgesia.org
not analyzed separately. Ventilatory rate range by the reference method was 1.9 to 49.1 bpm for capnography and 4.0
to 41.5 bpm for RAM. On average, each of the devices displayed data over 97% of the monitored time (97.2% for capnometry and 98.4% for acoustic monitoring [P = 0.3750]).
There were 2 patients for whom the capnometer was not
reliably functioning, about 20% dropouts in one and 35%
dropouts in the other. The largest drop-out rate for acoustic monitoring across the 33 patients was 8.5%. The (0.95,
0.95) lower tolerance limits for the acoustic monitor and
capnometer were 94% and 84%, respectively. That is, with
95% confidence, 95% of acoustic monitor devices will return
a ventilatory rate at least 94% of the time within a patient.
Ninety-five percent of capnometers will return a rate at least
84% of the time within a patient.
Tables 2 and 3 show the accuracy and precision estimates
for both the capnometer and the RAM. On average, the capnometer deviated by 13% from its reference ventilation rate,
whereas the RAM deviated by 10%. Table 4 shows the comparative analysis of accuracy and precision between the 2
devices. Larger values for the average columns reflect less
accuracy and less precision. As the comparative analysis is
based on the capnometer value minus the RAM value, then
a positive value would indicate less accuracy/precision for
the capnometer relative to the RAM, and a negative value
would indicate less accuracy/precision for the RAM relative
to the capnometer. The results show the RAM to be statistically more accurate (Fig. 3, P = 0.0056) and precise (Fig. 4,
P = 0.0024).
Table 5 illustrates the results of the sensitivities of the
2 devices to detect the 21 occurrences of respiratory pause
that occurred across the 33 patients. Acoustic monitoring
trended a higher sensitivity (P = 0.0461), 17 detected (81%)
vs 13 detected (62%). Not all subjects had a ventilatory
anesthesia & analgesia
pause period of ≥30 seconds, and some subjects had several
pauses in ventilation ≥30 seconds.
DISCUSSION
We present results of 33 PACU patients exhibiting a wide
range of ventilation rates while being treated with intermittent narcotics, IV PCA pumps, or both. On average, the reliability of the 2 devices was high. The results suggest that
Table 1. Patient Characteristics, Postoperative
Analgesia, and Procedures
33
45 (±14)
24 (72)
118 ± 42
41 ± 13
112 ± 71
19.8–258.8
N
Age (y)
Female gender (%)
Weight (kg)
Body mass index
Monitoring time (min)
Range (min–max)
Diagnosed obstructive sleep apnea
Yes (%)
No (%)
Unknown (%)
Postsurgical analgesia
Prescribed PCA (%)
Prescribed narcotics PRN
Yes (%)
No (%)
Unknown (%)
Procedures
Laparoscopic gastric bypass
Laparoscopic gastric sleeve
Laparoscopic cholecystectomy
Herniorraphy
Cystoscopy and ureteroscopy
Other
12 (40)
18 (60)
3
15 (45)
30 (97)
1 (3)
2
11 (33)
5 (15)
5 (15)
2 (6)
2 (6)
8 (24)
Data are mean (±SD) or number (%).
PCA = patient-controlled analgesia; PRN = as needed.
the capnometer is about 3% less precise than the RAM (95%
confidence interval, 0.9%–4.8%). The study did not address
whether the better accuracy and precision of RAM was clinically significant.
An important issue in the monitoring of ventilation of such
patients is the ability to detect pauses in ventilation. Twentyone such events occurred among the 33 patients followed.
Acoustic monitoring showed a statistically higher sensitivity for detecting these events (81% vs 62% for capnometry).
Specificity was not assessed, because the nature of the monitoring (measured every 2 seconds) and the definition of a
pause in ventilation (no breathing for 30 seconds) precluded
counting true negatives (needed for estimating specificity).
There are several limitations to our study. First, although
both capnometry and acoustic monitoring provided data
during the vast majority of the monitoring time (drop-out
rates for both methods was <2%), we did not document
and analyze the factors associated with the loss of data. We
also did not analyze the main causes of inaccurate readings
that are likely to be different for capnometry and acoustic
monitoring. Perhaps the most important limitation to the
study is that we could only evaluate 21 respiratory pause
events, and the analyses involved the assumption that these
were independent events. Larger studies may increase the
understanding of factors associated with data loss, inaccurate readings, and false alarms. For example, observation of
what specific patient events (e.g., coughing, speaking, snoring) result in inaccurate readings, or data loss could lead to
a better understanding of the performance and limitations
of acoustic monitoring. We tested acoustic monitoring in the
PACU only, and since this technology is acoustic-based, it
may perform differently in care areas with higher or different types of ambient noise.
Ventilatory rate is an important vital sign. Current methods of continuous monitoring have significant drawbacks.
Table 2. Summary Estimates of Accuracy for Ventilatory Rate (Relative to the Respective Reference)
Across 33 Patients
Accuracy measure
|capnometer–reference|/reference
|RAM–reference|/reference
Average of patient averages
0.13
0.10
SD of patient averages
0.09
0.09
95% CI (N = 33)
0.10–0.16
0.07–0.13
CI = confidence interval; RAM = Rainbow acoustic monitoring
Table 3. Summary Estimates of Precision for Ventilatory Rate (Relative to the Respective Reference)
Across 33 Patients
Precision measure
SD ([capnometer–reference]/reference)
SD ([RAM–reference]/reference)
Average SD of percent bias
0.21
0.16
SD of SD of percent bias
0.14
0.12
95% CI (N = 33)
0.16–0.26
0.12–0.20
CI = confidence interval; RAM = Rainbow acoustic monitoring.
Table 4. Accuracy and Precision Comparisons Between Capnometry and Acoustic Monitoring
Measure
Accuracy
Precision
Capnometer percent absolute deviation–RAM
percent absolute deviation
SD(capnometer percent bias)–SD(RAM percent
bias)
Average of patient average
differences
0.028
SD of patient average
differences
0.055
95% CI
P
0.009–0.048
0.0056
0.050
0.088
0.019–0.081
0.0024
CI = confidence interval; RAM = Rainbow acoustic monitoring.
July 2013 • Volume 117 • Number 1
www.anesthesia-analgesia.org 73
Ventilatory Rate by Rainbow Acoustic Monitoring
Figure 3. Pairwise comparison of accuracy
for each of 33 patients. RAM = Rainbow®
acoustic monitoring.
correlates well with capnometry. This confirms an analysis
by Mimoz et al.22
The addition of a combination of continuous oxygenation monitoring with pulse oximetry and continuous
ventilatory rate monitoring may provide a safety net for
postsurgical patients receiving opioids. Therefore, a welltolerated patient monitoring technology that accurately,
automatically, and continuously tracks ventilatory rate has
the potential to greatly improve the timeliness of response to
a failing patient and improve patient safety and outcomes.
CONCLUSION
Figure 4. Pairwise comparison of precision for each of 33 patients.
RAM = Rainbow® acoustic monitoring.
The data from this study demonstrate that acoustic monitoring provides a very reliable noninvasive, continuous
method for estimating ventilatory rate in postsurgical
patients at risk for ventilatory compromise. Relative to
capnometry, it demonstrated greater accuracy, precision,
and sensitivity to pauses in ventilation. The clinical significance of this will need to be determined by a larger study. In
addition, the utility of these findings in patients with OSA
should be prospectively assessed. Acoustic monitoring may
provide a reliable means for tracking ventilation status in
patients at risk for ventilatory depression. E
DISCLOSURES
Table 5. Matched Sample Data for Comparison of
Sensitivities for Detection of 21 Respiratory Pauses
Across 33 Patients (P = 0.0461)
RAM
Positive
Negative
Positive
11
2
13
Capnometry
Negative
6
2
8
17
4
21
RAM = Rainbow acoustic monitoring
They are either not accurate or are not well tolerated by
patients.20,21 The development of an acoustic transducer that
can be integrated into an adhesive patch that is applied to
the patient’s neck may offer a good alternative for monitoring respiratory rate continuously. This study demonstrates that this technology monitors respiratory rate that
74 www.anesthesia-analgesia.org
Name: Michael A. E. Ramsay, MD.
Contribution: This author helped design and conduct the
study, has seen the original study data, reviewed the analysis
of the data and helped write the manuscript.
Affiliation: Michael A. E. Ramsay approved the final manuscript. He attests to the integrity of the original data and the
analysis reported in this manuscript. Michael A. E. Ramsay is
the archival author.
Conflict of Interest: This author receives research funds from
Masimo Corporation and is a Speaker Board member and
Advisory Board member for Masimo Corporation.
Name: Mohammad Usman, PhD.
Contribution: This author helped design the study, analyze the
data, and write the manuscript.
Affiliation: Mohammad Usman approved the final manuscript.
Conflict of Interest: This author is an employee of Masimo
Corporation.
anesthesia & analgesia
Name: Elaine Lagow, RN.
Contribution: This author helped conduct the study and
approved the final manuscript.
Conflict of Interest: This author is funded in part from research
funds from Masimo Corporation.
Name: Minerva Mendoza, RN.
Contribution: This author helped conduct the study and
approved the final manuscript.
Conflict of Interest: The author has no conflicts of interest to
declare.
Name: Emylene Untalan, RN.
Contribution: This author helped conduct the study and
approved the final manuscript.
Conflict of Interest: The author has no conflicts of interest to
declare.
Name: Edward De Vol, PhD.
Contribution: This author completed the review and revision
of the statistical analysis. He approved the final manuscript.
This author also attests to the integrity of the original data, having reviewed the original data and the analysis reported in this
manuscript.
Conflict of Interest: The author has no conflicts of interest to
declare.
This manuscript was handled by: Steven L. Shafer, MD.
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