**Purpose**:
To investigate whether preoperative corneal topographic and biomechanical parameters (CTBPs) predict postoperative residual refractive error (RRE).

**Methods**:
We retrospectively included 151 eyes from 151 patients of small-incision lenticule extraction (SMILE) with target RRE of plano and 3-month measurements of refractive error from Tianjin Eye Hospital. Multivariate linear/logistic regressions were performed to associate age, gender, preoperative refractive error, lenticule thickness, and CTBPs with postoperative RRE/the occurrence of myopic RRE ≤ −0.25 diopter (D). Stepwise regression was used for feature selection. Leave-one-cross-validation was used for model evaluation by the area under the receiver operating characteristic curve (AUC).

**Results**:
From linear regression, more myopic RRE was associated with higher preoperative myopia, intraocular pressure (IOP), flattest curvature of anterior cornea (AC), and highest concavity deformation (HCD), and was associated with lower anterior elevation, anterior asphericity, steepest curvature of AC, and second applanation velocity. The occurrence of ≤ −0.25 D RRE was associated with higher myopia, IOP, posterior elevation and asphericity, flattest curvature of AC, first applanation velocity and HCD, and was associated with lower first applanation stiffness parameter, central corneal thickness, anterior elevation and asphericity, steepest curvature of AC, and second applanation velocity as well as thinner lenticule thickness. Compared to the baseline model using age, gender, and preoperative refractive error, adding CTBPs significantly (*P* < 0.001) improved the AUC performance to 0.771 from 0.615.

**Conclusions**:
Postoperative outcomes of SMILE can be predicted by individual CTBPs.

**Translational Relevance**:
Our findings could be used to customize a refractive nomogram based on individual corneal properties improving outcomes and patient satisfaction.

^{1}In SMILE, a femtosecond laser is used to cut an intrastromal lenticule, which is then extracted manually through a peripheral corneal tunnel incision.

^{2}Compared to conventional laser-assisted in situ keratomileusis (LASIK), SMILE promises to reduce a number of potential LASIK side effects including flap dislocation,

^{3,4}reduced corneal sensitivity,

^{5,6}corneal ectasia,

^{7,8}dry eye,

^{9,10}epithelial ingrowth,

^{11,12}etc.

^{13–15}; however, the factors associated with the postoperative visual and refractive outcomes of SMILE remain largely unexplored in comparison to LASIK. For example, in LASIK, higher preoperative myopia, residual astigmatism, and older age were identified as risk factors for retreatment.

^{16,17}In addition, environmental factors such as procedure room humidity, 2-week preoperative mean outdoor humidity, outdoor temperature, and room temperature were associated with enhancement after LASIK.

^{18}For SMILE, steeper corneal curvature and increasing age have been associated with undercorrection of myopia,

^{19}while higher preoperative myopia and greater intraoperative suction loss were associated with enhancement after SMILE.

^{20}To date, the postoperative visual and refractive outcomes of LASIK and SMILE have not been associated with corneal biomechanical properties, which have been shown to be related to refractive error and to be weaker after LASIK/SMILE in previous studies.

^{21–25}Furthermore, no studies have been performed associating the extensive available topographic parameters apart from the simple summary index of corneal curvature with the outcome of refractive surgery, while it is known that corneal topography is related to refractive error

^{26}and is altered by refractive surgery.

^{27,28}More importantly, the corneal biomechanical properties are also interactively related to the relevant topographic parameters.

^{29}All measurements of CTBPs for each eye were repeated three times by the same technician. The measurements with signal quality that passed the machine threshold were used for statistical analysis. In addition, preoperative manifest refractions were extracted. Manifest refractive error measured at 3-month follow-up was considered to be stable and was used to analyze the efficacy of the SMILE.

^{30}Multivariate linear regression was performed to associate postoperative 3-month spherical equivalent (SE) with the CTBPs in addition to demographics, preoperative refraction, and lenticule thickness. Variance inflation factor was calculated to detect potential multicollinearity issue. To remove the redundant features that might cause the multicollinearity issue, stepwise regression was used to select the optimal feature combination that predicts the postoperative RRE based on Akaike information criterion.

^{31}In addition, logistic regression was applied to predict the occurrence of myopic RRE after SMILE for myopia correction. Specifically, we are particularly interested in predicting the occurrence of myopic RRE ≤ −0.25 D. Similarly, stepwise regression was used for feature selection. Leave-one-cross-validation

^{32}was used to evaluate the model on testing data by the area under the receiver operating characteristic curve (AUC).

^{33}Jackknife resampling

^{34}was applied to obtain the confidence interval (CI) of the AUC performance.

^{30,35}

*r*= 0.29,

*P*< 0.001) correlated to the postoperative SE (−0.07 ± 0.21 D) as shown in Figure 1(a). As shown in Figure 1(b), 94 eyes achieved the target fraction of plano, while 43 and 14 eyes had myopic and hyperopic RRE, respectively. For eyes with myopic and hyperopic RRE, −0.25 and 0.25 D were the most frequent (32 and 10 eyes) values of RRE, respectively.

*P*values) between aforementioned features and postoperative SE, and (3) the summary of multivariate linear regression (

*r*

^{2}: 0.31) from aforementioned features to predict postoperative SE. In particular, the preoperative sphere (

*r*= 0.29,

*P*< 0.001), lenticule thickness (

*r*= −0.29,

*P*< 0.001), and HCD (

*r*= −0.25,

*P*= 0.01) were significantly correlated to postoperative SE after

*P*value adjustment for multiple comparisons. Second applanation velocity (

*r*= −0.17,

*P*= 0.03) and anterior asphericity (

*r*= 0.17,

*P*= 0.03) were also significantly correlated to postoperative SE without multiple comparison adjustment. From the multivariate linear regression, it was observed that the postoperative SE was positively and significantly associated with anterior elevation (

*P*= 0.049) and anterior asphericity (

*P*= 0.008), and was negatively and significantly associated with HCD (

*P*< 0.001). The multicollinearity was high as there were seven parameters with variance inflation factor > 10 including sphere,

^{36}lenticule thickness, bIOP, IOP, anterior astigmatism, flattest curvature, and steepest curvature.

*r*

^{2}: 0.29) with the optimal feature combination to predict the postoperative SE. Redundant features were removed by stepwise regression to resolve the multicollinearity issue. More myopic RRE was associated with higher preoperative myopia, IOP, flattest curvature of anterior cornea (AC) and HCD, and was associated with lower anterior elevation and asphericity, steepest curvature of AC, and second applanation velocity.

*P*< 0.001) than the baseline model (0.771 [95% CI: 0.770, 0.772]) compared to 0.615 (95% CI: 0.614, 0.615). The AUC to predict the occurrence of RRE < 0 D with respective optimal model selected by stepwise regression was 0.790 (95% CI: 0.788, 0.793).

^{16,19,20}Beyond that, we are the first to show that corneal dynamic biomechanical parameters are significantly correlated to postoperative RRE. Specifically, higher HCD and lower second applanation velocity were correlated to more myopic RRE. In following multivariate linear regression study including 20 available features, the postoperative RRE was positively associated with anterior elevation and asphericity significantly, and was negatively associated with HCD significantly. Note that while the impact of preoperative myopia was dampened to become insignificant due to the strong correlations between features, the impact of the dynamic biomechanical parameter of HCD still remained to be significant, which might suggest that HCD covers a substantially different proportion of variance to predict postoperative RRE compared to preoperative myopia related parameters. We further applied step regression to select the optimal feature combination to predict the postoperative RRE based on Akaike information criterion.

^{31}In the optimal feature set, more myopic RRE was associated with higher preoperative myopia, IOP, flattest curvature of AC, and HCD, and was associated with lower anterior elevation and asphericity, steepest curvature of the AC, and second applanation velocity. Since intuitively higher IOP, flattest curvature of AC and HCD are all related to weaker corneal biomechanical condition, our results solidly confirmed the speculation in previous studies

^{19,20}that the age-related corneal biomechanical properties might be related to the outcome of refractive surgery. In addition, from the perspective of biomechanics, higher preoperative myopia also implies greater disturbance of the refractive surgery to the corneal structure, which imposes more difficulty and unpredictability to the postoperative remodeling process and can be also considered as weak biomechanical condition.

*P*< 0.001) higher than the AUC (0.615) of the baseline model with risk factors of age, gender, preoperative sphere, and cylinder that were previously identified in the literature.

^{16,19,20}In the optimal model, the occurrence of myopic RRE ≤ −0.25 D was associated with higher myopia, higher IOP, lower stiffness parameter at first applanation, lower CCT, higher first applanation velocity, and higher HCD, which are all clearly associated with weaker corneal biomechanical conditions. In addition, the occurrence of myopic RRE ≤ −0.25 D was associated with lower anterior elevation and asphericity of cornea but with higher posterior elevation and asphericity of cornea. The different association of the anterior and posterior properties of elevation and asphericity might be related to their respective different impacts on the cornea biomechanical property. Patient-specific finite element modeling might be useful to analyze the sensitivity of those parameters to the corneal mechanical response.

^{19}steeper mean corneal curvature was associated with the undercorrection of 0.25 D for myopia. In our results, we demonstrated that the flattest and steepest curvature of AC were differently associated with postoperative RRE. Our results suggest that it might be not sufficient to only use mean corneal curvature to characterize postoperative RRE. In previous studies,

^{16,19}older age was also shown to be related to greater myopic RRE after LASIK/SMILE, while we did not find that age was associated with postoperative RRE. The discrepancy between our results and previous findings is likely owing to the younger age of our patients (23.2 ± 5.4 years) compared to the age of 38.3 ± 8.3 years in the work by Hjortdal et al.

^{19}and the age of 42.8 years in the work by Hersh et al.

^{16}

^{37}which are all related to weaker biomechanical conditions. However, when accounting the variance explained by other CTBPs in addition to age, gender, and refraction, it became reversely that lower second applanation velocity was associated with more myopic RRE. We suspect that second applanation velocity might be related to viscoelastic properties of the cornea and therefore represents different aspects of the biomechanical properties of the cornea compared to other CTBPs.

^{16–18,20}the major clinical relevance and importance of this work is that we are the first to demonstrate that the postoperative refractive outcome after refractive surgery (herein, SMILE) is predictable by CTBPs in addition to the previously known risk factors of age and preoperative SE, such that the refractive surgery nomogram can be adapted based on individual corneal parameters to finally improve the precision of refractive surgery. Note that although most of our patients with myopic RRE were only slightly undercorrected, which typically is not considered to have significant clinical consequence of visual function, it is still strongly valuable to improve the refractive surgery precision and make the outcome more predictable. More importantly, unexpected postoperative RRE (especially myopic RRE) for the patients with target refraction of plano typically reduces the satisfaction of patients and sometimes causes anxiety of patients. Moreover, the purpose of some patients to take the refractive surgery is occupationally relevant, for example, to qualify the physical examination of the recruitment of airline pilot or military service. In such case, perfect postoperative vision is even more demanded by patients. Therefore, it is necessary to improve the precision of current refractive surgery based on individual corneal anatomy and properties. We recognized that the measurements of −0.25 D undercorrection can be disturbed by measurement noise; however, the systematical associations between the occurrence of −0.25 D undercorrection and CTBPs were not likely to be due to the measurement bias. Whatsoever, our results suggest that the preoperative CTBPs are predictive of postoperative refractive outcomes of SMILE, which can be further explored to test whether those CTBPs are also predictive of clinically significant myopic outcomes (postoperative RRE ≤ −0.5 D) in future studies.

*P*= 0.35, paired

*t*-test) and the RREs of 12-month (mean ± standard deviation: −0.08 ± 0.22 D,

*P*= 0.33, paired

*t*-test) follow-ups. Moreover, at least two previous studies

^{38,39}have reported that there was no significant difference of postoperative SE between 3-month and 12-month follow-ups.

^{40,41}Second, the model performance of

*r*

^{2}and AUCs to predict RRE and the occurrence of myopic RRE ≤ −0.25 D were not very high (0.29 and 0.771). This is partly because our data sample size was not large (151 eyes from 151 patients) with a relatively large candidate feature size (20 features) to be considered to select the optimal relevant feature combination. Small sample size with large feature size can cause substantial overfitting problem, which can deteriorate the model performance. We envision that our model performance will be improved with more data collected. Though the currently model has not reached the accuracy level that can be directly used to personalize individual nomogram yet, we anticipate that those corneal parameters that are associated with postoperative refractive outcome could be combined with other possible related parameters together to improve the nomogram at individual level ultimately. More investigations will be needed. Lastly, with our limited data sample size, we do not have the capacity to further investigate whether the CTBPs are systematically associated with undercorrection (postoperative RRE ≤ −0.5 D) with severe clinical consequence, since we had so few such cases (8 cases out of 151 eyes) that did not allow us to attain meaningful statistical modeling and results. More data will be needed to investigate this valuable aspect.

**M. Wang**, None;

**Y. Zhang**, None;

**W. Wu**, None;

**J.A. Young**, None;

**K.M. Hatch**, Avedro, Johnson & Johnson, Shire and Checked up (C);

**R. Pineda II**, Beaver Visitec International, Amgen and Sanofi/Genzyme (C), Royalties from Elsevier (I);

**T. Elze**, Patent (WO2015027225 A1; P);

**Y. Wang**, None

*J Cataract Refract Surg*. 2015; 41: 652–665.

*J Cataract Refract Surg*. 2011; 37: 127–137.

*Ophthalmology*. 2000; 107: 2136–2139.

*J Cataract Refract Surg*. 2001; 27: 1111–1114.

*Ophthalmology*. 2003; 110: 497–502.

*Cornea*. 2001; 20: 30–32.

*Ophthalmology*. 2003; 110: 267–275.

*Ophthalmology*. 2008; 115: 37–50.

*Am J Ophthalmol*. 2001; 132: 1–7.

*Am J Ophthalmol*. 2006; 141: 438–445.

*Am J Ophthalmol*. 2000; 129: 746–751.

*Am J Ophthalmol*. 2002; 134: 801–807.

*J Cataract Refract Surg*. 2012; 38: 2003–2010.

*Am J Ophthalmol*. 2014; 157: 128–134.

*Ophthalmology*. 2014; 121: 822–828.

*Ophthalmology*. 2003; 110: 748–754.

*J Cataract Refract Surg*. 2004; 30: 363–368.

*J Cataract Refract Surg*. 2004; 30: 798–803.

*J Refract Surg*. 2012; 28: 865–871.

*Ophthalmology*. 2017; 124: 813–821.

*Invest Ophthalmol Vis Sci*. 2008; 49: 3852–3857.

*Vision Res*. 2008; 48: 2167–2171.

*Invest Ophthalmol Vis Sci*. 2011; 52: 4923–4928.

*Graefe's Arch Clin Exp Ophthalmol*. 2014; 252: 1329–1335.

*Cornea*. 2009; 28: 765–769.

*Invest Ophthalmol Vis Sci*. 1997; 38: 311–320.

*Ophthalmology*. 2002; 109: 1991–1995.

*J Cataract Refract Surg*. 1999; 25: 663–669.

*Rev Bras Oftalmol*. 2013; 72: 99–102.

*International Encyclopedia of Statistical Science*. New York, NY: Springer; 2011: 25.

*Pattern Recognit*. 2003; 36: 2585–2592.

*Pattern Recognit*. 1997; 30: 1145–1159.

*The Jackknife, the Bootstrap and Other Resampling Plans*. SIAM; 1982.

*Behav Res Methods*. 2007; 39: 175–191.

*Qual Quant*. 2007; 41: 673–690.

*Biomed Res Int*. 2014; 2014: 748671.

*J Refract Surg*. 2014; 30: 812–818.

*BMJ Open*. 2016; 6: e010993.

*Optom Vis Sci*. 2006; 83: 512–515.

*Ophthalmology*. 2004; 111: 2211–2219.