# Evolving a Model for Cochlear Implant Outcome

^{1}

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## Abstract

**:**

## 1. Introduction

_{HL}(dB hearing loss), the variability of the aided speech recognition is substantial [8,9,10,11,12,13,14,15,16,17]. Nevertheless, in individual cases the speech recognition with HA can be assessed preoperatively. However, the large variability in CI outcome as assessed by word recognition scores with CI [18,19,20,21,22] represents a major obstacle: for the patient population with benefit from HAs, the individual prediction is of major importance, as the patient and the professional have to balance the residual aided word recognition with the HA, the expected word recognition with CI, the expected improvement in quality of life, and the impact of CI surgery. Some studies have also included subjects with lesser hearing loss (e.g., <80 dB

_{HL}) who were considered likely to benefit from cochlear implantation [5,6,17,23,24,25,26,27,28,29]. A retrospective analysis [22] of 312 postlingually deafened adult CI recipients yielded the preoperative maximum word recognition score (WRS

_{max}) as a predictor for the minimum WRS with CI at conversation level, WRS

_{65}(CI). The importance of this preoperative measure was confirmed by two studies including, respectively, 128 [28] and 664 [17] cases. In an earlier study we addressed explicitly the prediction of WRS

_{65}(CI) in a population with hearing losses of less than 80 dB

_{HL}only [6]. This retrospective analysis led to a generalised linear model (GLM) that provides an estimated prediction of WRS

_{65}(CI) six months after implantation on the basis of three preoperatively known factors: WRS

_{max}, the patient’s age at implantation, and the aided WRS at conversation level, WRS

_{65}(HA), according to Equation (1).

_{0}= 0.84 ± 0.18, β

_{1}= 0.012 ± 0.0015, β

_{2}= −0.0094 ± 0.0025 year

^{−1}, and β

_{3}= 0.0059 ± 0.0026; all WRS expressed in %.

_{max}accounts for up to 27 percentage points (pp) in WRS

_{65}(CI) differences. WRS

_{65}(HA) influences the prediction by up to 9 pp, while age at implantation is associated with a deterioration of up to 17 pp. The GLM resulted from an analysis based on a population of 128 postlingually deafened adult CI recipients, all with a preoperative hearing loss equal to or less than 80 dB

_{HL}as measured by the hearing loss at 0.5, 1, 2, and 4 kHz (four-frequency pure-tone average, 4FPTA).

_{max}greater than zero, a prediction error of 11.5 pp was found. Only 6% (5/85) of the recipients missed the predicted score by more than 20 pp within one year after implantation. As shown in Figure 1, the output range is limited to scores between 49% and 90%. This is due to the fact that patients with significant residual hearing are most likely to perform in this range [6,17,22,28,29]. This is not the case for the application of the model in a population with preoperative WRS

_{max}= 0%, which, as expected, resulted in a higher prediction error of 23.2 pp. If both WRS

_{max}and WRS

_{65}(HA) are zero, the prediction from Equation (1) is based solely on the patient’s age, represented by β

_{2}, and the population mean outcome, represented by β

_{0}.

_{HL}were included in the previous study [6]. Holden et al. [20] showed that the duration of hearing impairment (DHI) is a factor that contributes to speech recognition with CI. Additionally, DHI is applicable for subjects with residual hearing, regardless of the degree of hearing loss.

_{max}of zero and all degrees of hearing loss. The design requirements for the model were defined as follows: Since Equation (1) has proved its applicability [29,30], the coefficients β

_{0–3}remained fixed. Only preoperative measures were to be included in the model. Additionally, these measures were to be subsets of clinical routine measurements within the CI candidate assessment according to the German CI Guidelines [3] and the German white book CI provision [4].

## 2. Materials and Methods

#### 2.1. Patients

_{65}(CI) for a period of at least six months after surgery and CI fitting were available for 165 patients. The patient population consisted of 90 men and 75 women. Their mean age at the time of surgery was 66 ± 14 years. The hearing loss for air conduction was determined as the mean value over the four octave frequencies 0.5, 1, 2, and 4 kHz (4FPTA). For hearing thresholds beyond the maximum possible presentation levels of the audiometers, a value of 130 dB

_{HL}was imputed. The resulting mean preoperative hearing loss was 94 ± 21 dB

_{HL}. The 165 CI recipients used either the behind-the-ear processor CP1000 (or later) or the off-the-ear processor CP950 (or later). CI-aided listeners were divided into two groups according to their preoperative WRS

_{max}. Group 1 (n = 109) comprised individuals with WRS

_{max}> 0%, while group 2 (n = 56) comprised those with WRS

_{max}= 0%. While there were no significant differences between these groups in age or in duration of hearing impairment, audiometric data differed owing to the group definitions. Demographic details are summarised in Table 1. Figure 2 complements the characteristics in Table 1 by representing the individual data for age, duration of hearing loss, and duration of unaided hearing loss. Age was not correlated with either duration of hearing impairment (DHI) or duration of unaided hearing impairment (DuHI), while DHI and DuHI were strongly correlated (R

_{Spearman}= 0.68 with p = 5 × 10

^{−24}).

#### 2.2. Speech Audiometry

_{max}), i.e., the word recognition score at the greatest just-tolerable sound pressure level or, in case of 100%, at lower levels. Additionally, WRS

_{65}(HA) was defined as the word recognition score with hearing aid measured at 65 dB

_{SPL}. The hearing aids were checked technically in advance. In particular, in situ measurements were performed to ensure that the settings yielded the necessary gains [16].

_{SPL}in 5 dB steps in order to find the sound pressure level for 50% recognition (SRT

_{num}). For SRTs above the maximum possible presentation levels of the audiometers, a value of 120 dB

_{HL}was imputed. All audiometric measurements were performed monaurally with the ear that was intended for the implant, while the contralateral ear was masked appropriately when necessary.

_{SPL}, WRS

_{65}(CI) was assessed. The free-field measurements were conducted in a soundproof cabin measuring 6 × 6 m. The loudspeaker was placed 1.5 m in front of the patient (0° azimuth). The contralateral ear was masked appropriately with broadband noise introduced through headphones, if necessary.

#### 2.3. Data Analysis

_{65}(CI); this model represented a further development of our earlier model (see Section 1) and is described below. Significant differences in word recognition scores were determined according to the characteristics of the Freiburg monosyllable test [36].

## 3. Results

#### 3.1. Preoperative Measurements

_{65}(HA) and WRS

_{max}, and the average pure-tone hearing loss, 4FPTA. Figure 3D–F show how speech recognition thresholds for numbers in quiet (SRT

_{num}) are related to the 4FPTA and the two WRS. The curves in Figure 3A,B represent WRS as a function of 4FPTA in a population of HA users from previous studies [8,14]. In all cases the preoperative WRS

_{65}(HA) was within the current German CI guidelines [3], which recommend a cut-off at 60% for preoperative WRS

_{65}(HA). Figure 3D–F illustrate the relationship between the audiometric measures WRS

_{65}(HA), WRS

_{max}, 4FPTA, and SRT

_{num}. Even for cases where WRS

_{65}(HA) and WRS

_{max}are zero, SRT

_{num}can still be measured: there were 95 of 165 cases with WRS

_{65}(HA) = 0%, of which 53 (56%) had a measurable SRT

_{num}. Among the 56 cases in group 2 (preoperative WRS

_{max}= 0%), a measurable SRT

_{num}was still found in 12 cases (21%). All speech recognition measures were highly correlated: WRS

_{max}with SRT

_{num}(R

_{Spearman}= −0.72 with p = 6 × 10

^{−28}), WRS

_{65}(HA) with SRT

_{num}(R

_{Spearman}= −0.58 with p = 2 × 10

^{−16}), and WRS

_{max}with WRS

_{65}(HA) (R

_{Spearman}= −0.62 with p = 1 × 10

^{−18}).

#### 3.2. Postoperative Measurements

_{65}(CI) six months after surgery. The two groups with WRS

_{max}above zero (group 1, black) or equal to zero (group 2, blue) show WRS

_{65}(CI) ranging from 0 to 100%. Both groups have their peak in Figure 4C at a WRS

_{65}(CI) of 70%, and the median for WRS

_{65}(CI) is 70% for both groups. However, the variabilities differed considerably: the standard deviation of WRS

_{65}(CI) was 19 pp for group 1 and 30 pp for group 2. Postoperative results are summarised in Table 2.

_{65}(CI), this study yielded the following results for group 1: Six months after surgery, 6 cases (5.5%) did not reach WRS

_{max}, while in 64 cases (58.7%) WRS

_{max}was significantly [36] exceeded. The remaining 39 (35.8%) cases reached WRS

_{max}within the confidence intervals yielded by the Freiburg test [36].

#### 3.3. Model Expansion

_{num}were considered. The strong correlations between SRT

_{num}and WRS

_{max}and between SRT

_{num}and WRS

_{65}(HA) (see Section 3.1) indicate that the linear equation system is over-determined.

_{num}included resulted in a GLM with a corresponding positive β

_{i}. Such an equation would result in a poorer prediction for WRS

_{65}(CI) with better preoperative SRT

_{num}. The ablation analysis [37] did not yield an improvement with respect to the overall MAE with SRT

_{num}included (12 pp) compared with the final GLM (12 pp). Consequently, the regression analysis was continued with DHI and DuHI only. The ablation analysis yielded the best results applying the two predictors with an interaction term [38]: The overall MAE was 12.3 pp, while for group 1 it was 11.1 pp and for group 2 it was 17.0 pp. Table 3 summarises the results of the regression analysis.

^{−1}, ${\mathsf{\beta}}_{3}$ = 0.0059 ± 0.0026, ${\mathsf{\beta}}_{0}^{\prime}$ = 0.35 ± 0.04, and ${\mathsf{\beta}}_{4}$ = −0.0171 ± 0.0056 year

^{−1}(all WRS expressed in %), as shown in Table 3.

^{−1}, ${\mathsf{\beta}}_{3}$ = 0.0059 ± 0.0026, ${\mathsf{\beta}}_{0}^{\prime}$ = 0.41 ± 0.04, and ${\mathsf{\beta}}_{4}$ = −0.0125 ± 0.0013 year

^{−1}(all WRSs expressed in %), as shown in Table 4.

## 4. Discussion

_{max}larger than zero, group 1, and patients with preoperative WRS

_{max}equal to zero, group 2) showed a median WRS

_{65}(CI) of 70%. However, as illustrated by Figure 4, the variability of the outcome was greater for group 2, and the mean WRS

_{65}(CI) was smaller: 59% in group 2, compared with 68% in group 1. Additionally, group 2 includes seven subjects (13%) with WRS

_{65}(CI) = 0, while in group 1, only one subject (1%) scored 0%. These subjects clearly indicate the demand for future studies dealing with unexpected low speech perception.

_{max}= 0 is feasible. It was shown that for group 2 an improved prediction is possible without impairment of the prediction for group 1. Most remarkably, the inclusion of just one additional input variable (the duration of unaided hearing impairment, DuHI) in the previous prediction model for the WRS

_{65}(CI) [6] resulted in a decreased prediction error for group 2: the new GLM (Equation (2)) resulted in a decreased MAE of 17.0, compared with the MAE of the previous model (Equation (1)) of 23.7 pp. The prediction error for group 1 remained almost unchanged: the new model indicates a slightly decreased MAE of 11.1 pp, compared with 11.4 obtained from the previous model [6].

_{Spearman}= 0.7). Hence, they may provide similar information on the CI outcome. The ablation analysis showed that the MAE was not greatly increased when DHI or DuHI was omitted. We decided to retain the latter because DHI was found as not significant (p = 0.16) in the presence of DuHI. Additionally, the MAE was smaller for both groups when DuHI was used instead of DHI (Equation (2)). However, the DHI offers some advantages. The DHI is just defined by one time point, the time of onset of hearing loss, while determination of DuHI requires knowledge of two time points: HA provision and onset of hearing loss. Yet both factors depend on the patient’s ability to remember or reconstruct events which may well have occurred decades earlier. In summary, the model according to Equation (3) inherits larger MAE. However, Equation (3) and therefore the DHI may be used in cases where DuHI is not available.

_{HL}for the grade “profound impairment including deafness”. A more recent classification from WHO [40] defines “Complete or total hearing loss/deafness” as hearing threshold in the better ear of 95 dB

_{HL}or greater. Those authors explicitly explain that the PTA should not be used as the “sole determinant for rehabilitation” and that “the classification and grades are for epidemiological use” [40]. For prediction models and clinical process management, to our knowledge, there is a lack of applicable, defined criteria for cut-off relating to the duration of deafness and hearing impairment. Additionally, in the presence of a decentralised hearing health care system (e.g., in Germany), the chance of obtaining all necessary data retrospectively is rather low. In our population of consecutive Nucleus CI provisions in adults within a period of 2.5 years, the majority is not deaf using this definition, so a broader application of DoD in a regression model is not relevant. In addition, about one-third of the patients defined as “deaf” using the above WHO criterion [40] had a measurable ipsilateral maximum recognition score for Freiburg words, and slightly under one-half had a measurable speech recognition threshold for Freiburg numbers in quiet. This supports the preference for functional, speech-related variables instead of DoD.

_{max}> 0). This can be interpreted as giving strong support to the use of WRS

_{max}for predictive purposes [6,17,22,28], as it accumulates the detrimental effects of long DHI (or DuHI). The situation is different in group 2 (preoperative WRS

_{max}= 0), where such functional assessment with the established test WRS

_{max}and WRS

_{65}(HA) is not possible. Here, the additional information of DuHI or DHI considerably reduces the prediction error.

_{num}did not decrease MAE. Hence, SRT

_{num}was not taken into account any longer, which however does not necessarily mean that this variable is unimportant. Together with the strong correlation with WRS

_{max}and WRS

_{65}(HA), this indicates an over-determined equation system. Nevertheless, especially for cases with no preoperative monosyllable speech perception, it might be a useful addition. In our population only about one-quarter of group 2 had a measurable SRT

_{num}. Perhaps an additional split beyond groups 1 and 2 will improve the prediction with the help of SRT

_{num}in a clearly and more narrowly defined population. On the other hand, other model approaches—such as random forest regression—would induce such a split per se. However, more data would be needed for such an approach. In a recent study, Rieck et al. [17] used the Freiburg numbers and found a predictive value in a population of nearly 500 recipients. Two characteristics of their study population would support the assertion of a positive impact of SRT

_{num}on prediction error in a population with low preoperative speech perception in general. The mean values obtained in their study represent the characteristics of an established patient population with a preoperative mean WRS

_{65}(HA) of 4.2% compared with 9.7% and a WRS

_{max}of 11.8 compared with 27.6% in this population. Rieck et al. [17] included clinical data with implantations dating from 2002 to 2019, while the inclusion period of the present study was from 2020 to 2022. Consequently, this relationship should be reconsidered in future studies that include more CI candidates who are in group 2 but who have measurable SRT

_{num}.

## 5. Conclusions

_{HL}. The preoperative prediction of expected word recognition after CI provision is possible within clinically relevant limits.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Output characteristics of the generalised linear model as a function of the three input variables within a reasonable input range. The predicted word recognition score WRS

_{65}(CI) after six months is shown as a function of (

**A**) preoperative maximum word score WRS

_{max}, (

**B**) preoperative aided score WRS

_{65}(HA), and (

**C**) age at implantation. In each panel the remaining two factors are kept constant at the selected values indicated, covering the observed range, and the thin black curves show the variation in WRS

_{65}(CI). The thick grey curves represent the model’s results for the most recent population means at our clinic: WRS

_{max}= 50%, WRS

_{65}(HA) = 9%, and age = 66 years. Dotted lines indicate a rather unlikely combination of input factors, namely a high WRS

_{65}(HA) in the presence of much lower WRS

_{max}.

**Figure 2.**Distribution of age, duration of hearing impairment, and duration of unaided hearing impairment in the two patient groups with preoperative maximum word recognition (WRS

_{max}) of zero (

**left**) or above zero (

**right**).

**Figure 3.**Preoperative audiometry of the 165 cases: (

**A**) The aided word recognition score, WRS

_{65}(HA), as a function of average pure-tone hearing loss, 4FPTA; (

**B**) the maximum word recognition score, WRS

_{max}, as a function of 4FPTA; (

**C**) relation between WRS

_{65}(HA) and WRS

_{max}. The black curves in panels (

**A**,

**B**) represent the average relation between WRS values and 4FPTA in a population of HA users [8,14]. The lower panels (

**D**–

**F**) show the relationship between SRT

_{num}and 4FPTA, WRS

_{65}(HA), and WRS

_{max}, respectively.

**Figure 4.**Relationship between preoperative and postoperative word recognition scores for the 165 cases: (

**A**) word recognition score with CI after six months (WRS

_{65}(CI) vs. the preoperative aided score, WRS

_{65}(HA)); (

**B**) WRS

_{65}(CI) vs. the maximum preoperative word recognition score, WRS

_{max}; (

**C**) distribution of WRS

_{65}(CI) for the two patient groups (black, group 1 with a preoperative WRS

_{max}> 0%; blue, group 2 with WRS

_{max}= 0%). This colour code applies to panels (

**A**,

**B**) as well.

**Figure 5.**Distribution of differences between predicted and measured word recognition with CI and WRS

_{65}(CI), six months after surgery: (

**A**) prediction errors in percentage points (pp) for group 1 (preoperative WRS

_{max}> 0); (

**B**) prediction errors for group 2 (WRS

_{max}= 0). In both panels the dotted line indicates the prediction error resulting from the application of the previous model [6] (Equation (1)). The solid line indicates the prediction error resulting from the advanced model (Table 3). The MAE was 11.1 pp in group 1 and 17.1 pp in group 2 according to Equation (2). The previous model resulted in MAEs of 11.3 pp for group 1 and 23.7 pp for group 2.

Size | Age [Years] | 4FPTA [dB _{HL}] | WRS_{max}[%] | WRS_{65}(HA) | Duration of Hearing Impairment [Years] | Duration of Unaided Hearing Impairment [Years] | SRT_{num}[dB _{SPL}] | |
---|---|---|---|---|---|---|---|---|

Group 1 WRS _{max} > 0% | 109 | 67 ± 14 | 83 ± 14 | 42 ± 23 | 15 ± 16 | 24 ± 18 | 9 ± 13 | 85 ± 15 |

Group 2 WRS _{max} = 0% | 56 | 64 ± 14 | 114 ± 17 | 0 | 0 ± 1 | 20 ± 22 | 10 ± 16 | 124 ± 10 |

total | 165 | 66 ± 14 | 94 ± 21 | 27 ± 27 | 10 ± 15 | 22 ± 20 | 9 ± 14 | 98 ± 23 |

_{num}, speech recognition threshold for 50% number recognition; SPL, sound pressure level; for other abbreviations, see text above. Means ± standard deviations are shown.

**Table 2.**Variability of word recognition with CI six months after surgery with respect to preoperative maximum word recognition.

Group | Size | WRS_{65}(CI) [%] | No. of Cases with a Score of … | |||
---|---|---|---|---|---|---|

Mean ± SD | Median | WRS_{65}(CI) = 0% | WRS_{65}(CI): >0–<50% | WRS_{65}(CI): 50–100% | ||

Group 1 WRS _{max} > 0% | 109 | 68 ± 19 | 70 | 1 (1%) | 12 (11%) | 96 (88%) |

Group 2 WRS _{max} = 0% | 56 | 59 ± 30 | 70 | 7 (13%) | 9 (16%) | 40 (71%) |

total | 165 | 65 ± 24 | 70 | 8 (5%) | 21 (13%) | 136 (82%) |

**Table 3.**Results of regression analysis with two additional predicting variables and their interaction term, duration of hearing impairment (DHI), and duration of unaided hearing impairment (DuHI). The coefficients from the previous model [6] were fixed.

Estimate | Standard Error | t Statistic | p | |
---|---|---|---|---|

Constant, ${\mathsf{\beta}}_{0}^{\prime}$. | 0.35 | 0.04 | 8.44 | 3 × 10^{−17} |

DHI [year^{−1}] | −0.0027 | 0.0019 | −1.41 | 0.16 |

DuHI, ${\mathsf{\beta}}_{4}$ [year^{−1}] | −0.0171 | 0.0056 | −3.05 | 0.002 |

DHI:DuHI [year^{−2}] | −4.20 | 0.0001 | −0.41 | 0.68 |

^{2}statistic vs. constant model: 152, p = 1 × 10

^{−32}.

**Table 4.**Results of regression analysis with one additional predicting variable only: duration of hearing impairment (DHI). The coefficients from the previous model [6] were fixed.

Estimate | Standard Error | t Statistic | p | |
---|---|---|---|---|

Constant, ${\mathsf{\beta}}_{0}^{\prime}$. | 0.41 | 0.04 | 10.75 | 6 × 10^{−27} |

DHI, ${\mathsf{\beta}}_{4}$ [year^{−1}] | −0.0125 | 0.0013 | −9.73 | 2 × 10^{−22} |

^{2}-statistic vs. constant model: 96.4, p = 1 × 10

^{−22}.

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## Share and Cite

**MDPI and ACS Style**

Hoppe, U.; Hast, A.; Hornung, J.; Hocke, T.
Evolving a Model for Cochlear Implant Outcome. *J. Clin. Med.* **2023**, *12*, 6215.
https://doi.org/10.3390/jcm12196215

**AMA Style**

Hoppe U, Hast A, Hornung J, Hocke T.
Evolving a Model for Cochlear Implant Outcome. *Journal of Clinical Medicine*. 2023; 12(19):6215.
https://doi.org/10.3390/jcm12196215

**Chicago/Turabian Style**

Hoppe, Ulrich, Anne Hast, Joachim Hornung, and Thomas Hocke.
2023. "Evolving a Model for Cochlear Implant Outcome" *Journal of Clinical Medicine* 12, no. 19: 6215.
https://doi.org/10.3390/jcm12196215