Key points:
This study included 120 late-implanted cochlear implantation users with
prelingual deafness, the most extensive sample size research in China so
far.
In this retrospective study, 97 (81%) exhibited rehabilitation benefits
assessed by CAP scores before and after implantation, and 23 showed no
perceivable improvement.
We developed and validated an easy-to-use nomogram to illustrate a model
for predicting rehabilitation benefits after cochlear implantation.
The novel nomogram included three elements: age, residual hearing and
regular cochlear implant use.
The predictive performance of the nomogram was satisfactory, as verified
by the calibration curve and ROC curve.
Keywords : cochlear implant, prelingually deafened late
implanted cochlear implant user, nomogram, rehabilitation
1. Introduction
The Second National Sample Survey on Disability in China reported 27.8
million people with hearing loss in 2006 1. More than
half of a million people are < 18 years of age, and 6.77
million are 18-60 years old. Although cochlear implants, as a
sophisticated device for auditory stimulation, have been introduced into
China for more than 20 years, everyone has yet to accept them as a
medical device. It continues to present a financial challenge for many
due to potentially prohibitive prices. As a result, some
prelingually-deaf individuals become late-implanted users. This
particular cochlear implant user population in China is yet to be
studied.
Previous results in the literature both refute and support late cochlear
implantation in prelingually deaf adults. Poor speech discrimination is
reported firstly, which may be related to long-term auditory deprivation
and lack of auditory memory. Some encouraging results have been reported
as surgery techniques and cochlear implant technologies have improved,
including improving speech recognition and quality of life after
cochlear implantation. Some scholars have attempted to summarize
potential predictive factors for functional benefits, such as age at
implantation, communication mode, residual hearing, education, and so
on. However, results from one
study are not consistently confirmed by other studies, probably for
reasons such as characteristics of the study population, variable
inclusion and evaluation criteria across medical centers, and relatively
small subject sample sizes 2.
To mitigate the impacts mentioned above, we collected a relatively large
sample (n=120) from our medical center. We aimed to assess
rehabilitation
outcomes
in late-implanted prelingually deafened adolescents and adults, identify
predictive factors, and develop a nomogram incorporating CI to predict
rehabilitation outcomes.
2. Methods
2.1 Participants
Data of patients aged 7 to 30 years who received cochlear implants at
the *** Hospital, Beijing, China, between January 1, 2003, and December
31, 2017, were retrospectively analyzed. All subjects had finished a
series of preoperative audiological and imaging studies, including
tympanometry, auditory brainstem responses, temporal bone computerized
tomography, and magnetic resonance imaging. The subjects were selected
based on the following criteria: binaural severe to profound hearing
loss with onset before the age of 3 years, late implantation (at 6 years
or older), realistic expectations, and willingness to participate in
follow-up visits.
Medical records were reviewed for gender, age at diagnosis of
sensorineural hearing loss (SNHL), age at implantation, residence
address, body mass index (BMI), side of the operation, usage of hearing
aid, and preoperative hearing tests. Information
on rehabilitation training, the CI
use pattern (regularly or not), and the time of CI use were obtained
during follow-up visits. Our study defined regular CI use at ≥8 hours
per day. According to the implantation age, subjects were divided into 3
groups: <10 years, 10 – 20 years, and
20 – 30 years. To gauge the
subject’s living environment, the residence address was used to classify
the set into three categories: rural, small town, and metropolitan.
Based on the age at diagnosis of SNHL and caregivers’ medical history,
deafness was classified as
congenital or acquired. Based on
hearing test results, subjects were categorized as having complete
deafness (pure tone threshold>120 dB HL or ASSR
threshold>120 dB nHL)
or residual hearing (hearing
threshold ≤120 dB HL).
2.2 Categories of Auditory Performance (CAP) Scales
The CAP, developed by Archbold, was used to rate auditory ability before
and after cochlear implantation 3. The score was
categorized into eight levels from 0 to 7 (Table1). Two audiologists
collected and scored CAP data in a face-to-face fashion. Another
supervising audiologist would review the assessment to resolve any
disagreements. Subjects were divided into two groups based on
therapeutic outcomes assessed by CAP scores: “improved”
(post-operative score better than pre-operative score) and
“ineffective” (no improvement in CAP score or subject refusing to use
the implant).
2.3 Statistical Analysis
Statistical analyses were conducted using the SPSS vision 25 software
(IBM, Armonk, NY). Single-factor analyses were done first. Student’s t
test or Rank sum test (Mann–Whitney U test) was performed to detect
differences in body mass index and time of CI use between the improved
and ineffective groups. Chi-square tests were used for categorical
variables, such as gender, age group, address, side of the operation,
classification of deafness, use of hearing aid, residual hearing, and
rehabilitation training. A P-value of <0.05 was considered
statistically significant. Multivariate analysis was done secondly.
Logistics regression analysis was used in this step. Receiver operating
characteristic (ROC) analysis was used to illustrate model performance
(larger area under the curve, AUC, indicating more accurate
predictions). Hosmer-Lemeshow test
was used to test the goodness of fit for the logistic model
(P>0.05, no evidence of lack of fit.).
2.4 Nomogram
Based on logistic regression analysis, a nomogram model was developed
using the “rms” package in the R Version 2.14.1
(http://www.R-project.org) and
EmpowerStats (X&Y Solutions Inc, Boston MA) software. Validation of the
nomogram was assessed by discrimination and calibration. The ROC curve
evaluated discrimination of the nomogram by comparing nomogram-predicted
vs. observed probability. The calibration curve was derived based on
regression analysis, and 500 bootstraps resamples were applied to the
activities.
3. Results
3.1 Patient Characteristics and Follow-up
Of the 120 subjects with prelingual deafness (who received cochlear
implantation at our medical center) included
in this retrospective study, 97
(81%) exhibited rehabilitation benefits assessed by CAP scores before
and after implantation, and 23 showed no perceivable improvement. Before
implantation, the effective group socred 1.1±1.0 and the ineffective
group scored 0.9±1.1. During follow-up visit, the effecvive socred
5.3±1.5 and the ineffective group scored 0.9±1.1. The rate of
effectiveness gradually dropped along with age increase and was 94.3%
for patients under 10 years old, 80.3% for those between 10 and 20
years old, and 57.9% for those between 20 and 30 years old,
respectively (P<0.01). The residual hearing was present in
87.6% of patients demonstrating improvements and in only 52.2% of
those without improvements (P<0.01). Patients showing
improvements also reported more regular cochlear implant use than those
without improvements (77.3% vs. 30.4%,
P<0.01)
with longer CI usage time (5.7±3.0 vs. 3.6±4.5 years, P<0.05).
No statistically significant difference (P>0.05) was
identified for gender, BMI, residence location, side of the operation,
history of deafness, use of hearing AID, or structured rehabilitation
training (Table 2).
There was no distinct correlation between residual hearing and regular
CI use (r=0.12, P=0.18), nor between residual hearing and age (r=-0.13,
P=0.15) or between regular CI use and age (r=-0.14, P=0.13).
3.2 Predictive factors
Multivariate analysis showed that residual hearing (Hazard Ratio, 6.11;
95% Confidence Interval, 1.83-20.41; P<0.01), age at
implantation (Hazard Ratio, 0.31; 95% Confidence Interval, 0.14-0.83;
P=0.02) and regular CI use (Hazard Ratio, 7.79; 95% Confidence
Interval, 2.50-24.20; P<0.01) were independent predictors for
improvement. Time of CI use (P=0.08) was a non-significant factor, as
the outcome of logistics regression analysis showed.
Omnibus Tests of Model Coefficients confirmed a difference, indicating
statistically significant differences between at least two tested
parameters (P<0.01). Hosmer-Lemeshow test showed the goodness
of fit for the logistic model (χ2 =0.53, P=0.97). The area under the ROC
curve was 0.84 (95% Confidence
Interval, 0.74-0.94), indicating a satisfactory accuracy.
3.3 Nomogram
The novel nomogram incorporating the predictive factors (Figure1)
illustrated that age at implantation and regular CI use shared the
largest contribution to prediction, followed by residual hearing. The
nomogram can be used in a few steps: firstly take the points for each
factor, then summarize the total points, then predict the value and
probability. For example, a patient at 11(38 points) with preoperative
residual hearing (84 points) who uses CI regularly (92.5 points) will
have a total score of 214.5 points, and the probability of benefits is
>90%.
The calibration curve of the nomogram is depicted in Figure 2 and ROC in
Figure 3. The AUC was 0.859 (95% Confidence Interval, 0.761-0.957,
sensitivity=94.9%, specificity=69.6%, accuracy=90.0%, positive
predictive value=92.9%, negative predictive Value=76.2%, positive
likelihood ratio=3.12, negative likelihood ratio=0.07, diagnostic odds
ratio=42.05).
4. Discussion
To our knowledge, this study included the largest number of
late-implanted adolescents and
young adults with prelingual deafness at a single medical center in
China to this date. What’s more, multivariate analysis in the present
study illustrated three independent factors predicting rehabilitation
results: i.e., residual hearing, age group, and regular CI use. A
simple-to-use nomogram model was developed for late-implanted
adolescents and young adults (<30 years old) with prelingual
deafness, with satisfactory prediction performance.
Residual hearing is regarded as an essential predictor as it may offer
acoustic stimulus for the development and maintenance of central
auditory pathways. Some scholars have reported positive effects of
residual hearing. For example, Carlson reported a 63-percentage-point
improvement of open-set speech recognition in pediatric implantees with
residual hearing 4. Gordon demonstrated that children
with more residual hearing showed higher speech perception scores over
the first year of implant use than children with poorer hearing5. A more recent study showed the same6. Consistent with these positive findings, our
results also showed that residual hearing was an independent predictor
correlated with rehabilitation benefits in our late-implanted prelingual
deafened young patient population. However, we could not illustrate the
mechanism directly, although it is possible, as mentioned above, that
residual hearing provided some acoustic stimulation benefitting the
development and maintenance of auditory cortical centers and central
auditory representation integrity 7. Of course, this
mechanism needs to be further investigated.
Our study showed an inverse correlation between age and
post-implantation auditory performance improvement. The rate of benefits
dropped from 94.3% to 57.9% as age increased from under 10 to between
20 and 30. For those younger subjects who got used to implant usage, it
can be inferred that their central auditory system remained relatively
functional. Other scholars have also stressed the importance of age.
Harrison used three different speech recognition tasks to evaluated 82
congenitally deaf children ranging from 1.9 to 15.4 years old8. He reported that the age of operation was a more
prominent factor when using complex measurement. There were hypotheses
such as reduced plasticity, delayed development of frameworks involved
in communication, and decoupling of auditory pathways.
For postoperative factors, our multivariate analysis demonstrated the
correlation between rehabilitation outcomes and regular use of cochlear
implants, with regular CI use found more commonly in patients showing
benefits than those without benefits. Our study defined regular CI use
at ≥8 hours per day. Regular CI use involves two elements: i.e., time
and frequency, both of which essential for adaption and practice.
Likewise, Nava revealed that some sound localization ability could
emerge in the condition of a long time and extensive CI use for late
implanted adults 9. As Strelinikov indicated in a
review of PET neuroimaging articles, reorganization of the brain
functional architecture needed a long-term period 10.
Regular CI use is necessary for good practice to restore auditory
cognitive processing in implanted subjects.
Nomograms have been widely used in cancer research, especially for
predicting disease, for example, survival in patients with resected lung
cancer or recurrence of hepatic carcinoma 11, 12. Some
cardiologists and radiologists expanded nomogram usage to predict
survival in coronary artery calcium in recent years13. However, no nomogram can predict the effect of
CI.
We
developed and validated an
easy-to-use nomogram to illustrate a model for predicting rehabilitation
benefits after cochlear implantation. This novel nomogram contains three
routinely collected clinical variables and should be easy to understand
for both patients and caregivers during the consultation.
Both otologists and patients can
estimate the probability of benefits after cochlear implantation using
this scoring system.
This study had several limitations. First, the sample size was not
sufficiently large in our study, which might lead to statistical biases
and reduce results’ reliability. Second, our study used a subjective
measurement, although two audiologists were involved in scoring to
ensure objectivity. Third, our study’s rate of improvement cannot be
directly compared with other studies using different measurements, such
as closed-set auditory discrimination and open-set word recognition. The
results of the CAP can be affected to some extent by the ceiling effect.
Therefore, an objective measurement with a detailed classification or
point-scoring system needs to be used in future studies. Fourth, the
real-world application of this nomogram to late-implanted prelingually
deafened adults remains to be determined.
In conclusion, to our knowledge, this study demonstrated for the first
time that age, residual hearing, and regular CI use are associated with
rehabilitation benefits in
late-implanted young patients with prelingual deafness through
multivariate logic analysis. The proposed nomogram based on statistical
analysis is a novel and easy-to-use tool for otologists and
late-implanted patients less than 30 years old.