Factors Influencing Letrozole Response in Ovulation Induction among Polycystic Ovary Syndrome Women

Factors Influencing Letrozole Response in Ovulation Induction among Polycystic Ovary Syndrome Women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Factors Influencing Letrozole Response in Ovulation Induction among Polycystic Ovary Syndrome Women Sholen Acharya, Sheila Balakrishnan, Deepak Rath This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7553292/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Letrozole is the first-line ovulation induction agent in PCOS, but some women do not respond. This study aimed to evaluate ovulatory response to Letrozole and identify predictors of non-response to individualise treatment. Methods: This retrospective observational study included women with PCOS (Rotterdam criteria) undergoing their first Letrozole ovulation induction cycle between January 2019 and December 2020. Exclusions were severe male factor infertility, pelvic pathologies, or repeat cycles. Baseline assessments included clinical evaluation, day 3 hormonal profiling (LH, FSH, TSH, Prolactin), and transvaginal ultrasound for antral follicle count (AFC). Letrozole (5 mg/day) was given from day 3 to 7, with follicular monitoring from day 9 to 27. Responders developed ≥1 dominant follicle (≥18 mm), non-responders did not. Data were analysed with Jamovi; logistic regression identified non-response predictors. ROC analysis determined optimal cut-offs (p<0.05 significant). Results: Of 327 women, 186 met criteria. The response rate was 83.3% (155 responders, 31 non-responders). Non-responders had higher BMI, AFC, LH, and LH/FSH ratios. Multivariable regression showed AFC as the only independent predictor of non-response (OR 1.039, 95% CI: 1.003–1.076, p=0.033). ROC analysis identified AFC ≥35 as the optimal cut-off with 61.3% sensitivity, 83.9% specificity, 43.2% PPV, and 91.5% NPV. This indicates women with AFC <35 are more likely to respond to Letrozole. Conclusion: Letrozole induces ovulation in 83.3% of PCOS women. AFC is a key predictor, with <35 indicating better response. Using AFC can reduce failed cycles and guide timely alternative treatments like gonadotropins or IVF. Letrozole polycystic ovary syndrome ovulation induction antral follicle count body mass index LH/FSH ratio Figures Figure 1 Introduction Polycystic Ovary Syndrome (PCOS) is one of the most common endocrine disorders affecting women of reproductive age and is a leading cause of anovulatory infertility [ 1 ]. Ovulation induction remains a cornerstone in the management of infertility associated with PCOS. Letrozole, an aromatase inhibitor, has emerged as a superior option for ovulation induction in women with PCOS compared to the traditional use of clomiphene citrate (CC) [ 2 ]. Studies have consistently shown that letrozole leads to higher ovulation and pregnancy rates, making it a more effective treatment for infertility associated with PCOS [ 3 ]. However, a subset of women with PCOS fails to respond to Letrozole. Non-response to letrozole in ovulation induction can be attributed to various clinical and hormonal factors. Those having resistance to clomifene have a significantly higher likelihood of non-response to letrozole [ 4 ]. But since now Letrozole is considered the first-line ovulation induction regimen, it is important to evaluate the baseline characteristics and hormonal parameters that lead to non-response to Letrozole. Understanding these predictors is crucial for optimising treatment strategies. While an extended letrozole regimen may slightly improve ovulation rates, it does not significantly outperform the conventional regimen [ 5 ], emphasising the need to identify these cases before guiding individualised therapy. This study aims to assess the ovulatory response to Letrozole in women with PCOS diagnosed using the Rotterdam criteria and to explore the baseline clinical and hormonal characteristics that distinguish responders from non-responders. The analysis focuses on identifying predictors of non-response to help refine and personalise ovulation induction strategies. Material and Methods Study Design and Setting This retrospective observational study was conducted at the Department of Reproductive Medicine and Surgery, Government Medical College, Thiruvananthapuram. The study included a review of medical records of women treated for infertility due to polycystic ovarian syndrome (PCOS) between January 1, 2019, and December 31, 2020. Ethical approval was obtained from the institutional ethics committee, and the requirement for informed consent was waived due to the retrospective nature of the study. Study Population and Inclusion Criteria Records of women diagnosed with PCOS and treated with Letrozole for ovulation induction were screened. Only the first ovulation induction cycle was included for each participant, as hormonal profiling (LH, FSH, TSH, and Prolactin) was performed during the same cycle in which ovulation induction began. Exclusion Criteria The following participants were excluded from the study: Those with severe male factor infertility Women with associated endometriosis, uterine fibroids, unilateral tubal block, or a history of prior pelvic surgeries Individuals undergoing subsequent ovulation induction cycles beyond their first cycle at our centre Clinical Evaluation and Investigations All participants underwent baseline evaluations including: History and clinical examination: height, weight, BMI, and assessment for hirsutism Laboratory tests: hemogram, liver and kidney function tests, and day 3 hormone profile (LH, FSH, TSH, Prolactin) Transvaginal ultrasonography: performed on day 3 to assess for uterine or adnexal masses and to determine antral follicle count (AFC) For male partners: history and semen analysis were reviewed Menstruation in the subjects could be either spontaneous or progestogen-induced. Day 3 hormone profiling and transvaginal scans were scheduled simultaneously to exclude hormonally active ovarian cysts. Diagnosis of PCOS The diagnosis of PCOS was made based on the Rotterdam criteria, where at least two of the following three features were required: Oligo/anovulation (irregular cycles) Clinical hyperandrogenism (biochemical testing was not routinely performed except in virilizing cases) Polycystic ovarian morphology (AFC ≥ 12 or ovarian volume > 10 mL in either ovary) Ovulation Induction Protocol Letrozole 2.5 mg was administered twice daily (total 5 mg/day) from day 3 to day 7 of the menstrual cycle. Participants were monitored from day 9 onward, every alternate day, up to day 27 using transvaginal ultrasonography to detect follicular growth. A dominant follicle was defined as one with a diameter ≥ 18 mm. When such follicles were observed, Inj. hCG 5000 IU was administered at approximately 6 PM on the same day. Patients were advised to have timed intercourse, or if mild oligospermia was present, intrauterine insemination (IUI) was performed. Outcome Classification Responders: Those who developed one or more dominant follicles (≥ 18 mm) by day 27 of the cycle Non-responders: Those who did not develop any dominant folli cle by day 27 Ovulation was assumed to occur 36 hours after hCG administration, two days after identifying the dominant follicle. Sample Size Calculation The required sample size was calculated using the formula for prevalence studies: $$\:n=\frac{{Z}^{2}.\:p(1-p)}{{d}^{2}}$$ Assuming a Letrozole response rate of 76% from previous studies [ 6 ] and a precision of 7%, the minimum required sample size was 144. Data Analysis All collected data were compiled in Microsoft Excel and analysed using Jamovi software [ 7 ]. Response rate was calculated as the proportion of participants who developed a dominant follicle after Letrozole induction. Categorical variables were expressed as frequencies and percentages and analysed using Fisher’s exact test. Continuous variables were tested for normality using the Shapiro-Wilk test and presented as median with interquartile range (IQR) due to non-normal distribution. Differences in continuous variables between responders and non-responders were assessed using the Mann–Whitney U test. Univariate analysis was done, and those whose p-value was < 0.25 were entered into multivariable logistic regression analyses [ 8 ] to identify factors independently associated with non-response to Letrozole. Receiver Operating Characteristic (ROC) curve analysis was performed to determine optimal cut-off values for significant predictors. A p-value < 0.05 was considered statistically significant. Results A total of 327 women underwent ovulation induction during the study period. After applying the inclusion and exclusion criteria, 186 subjects were included in the final analysis. Specifically, 32 women were excluded due to associated endometriosis, fibroids, single tubal block, or prior pelvic surgeries. An additional 109 participants were excluded because they were undergoing subsequent ovulation induction cycles, and hormonal profiling was not repeated for subsequent cycles at our centre. Response Rate to Letrozole Among the 186 women included, 155 developed at least one dominant follicle (defined as follicle diameter ≥ 18 mm) between Day 9 and Day 23, indicating a response rate of 83.3%. The remaining 31 women did not respond to Letrozole, resulting in a non-response rate of 16.7%. Baseline Characteristics The demographic and hormonal baseline characteristics are summarized in Table 1 . Most continuous variables, including age, BMI, AFC, and hormonal parameters, were found to be non-normally distributed based on the Shapiro-Wilk test. Table 1 Baseline characteristics of the study population with normality test (Shapiro-Wilk) Shapiro-Wilk Class Count (%) (N = 186) Mean Median SD IQR W p Age (in years) > 25 50 (26.9) 27.4 27 3.93 6 0.966 30 43 (23.1) BMI (in kg/m 2 ) Underweight (< 18.5) 12 (6.5) 24.16 24 4.147 5 0.977 0.003 Normal (18.5- 22.99) 57 (30.6) Overweight (23- 24.99) 38 (20.4) Obese (≥ 25) 79 (42.5) AFC - - 27.92 26 11.437 12.75 0.937 < .001 FSH - - 5.99 5.585 2.766 2.5 0.623 < .001 LH - - 6.47 5.2 4.376 4.597 0.859 < .001 TSH - - 2.19 2 1.17 1.6 0.943 < .001 Prolactin - - 14.34 13.7 6.47 8.807 0.973 0.001 LH/FSH ratio - - 1.15 0.902 0.767 0.923 0.881 < .001 Table presents descriptive statistics and results of the Shapiro-Wilk test for normality for baseline clinical and hormonal parameters. Variables with p < 0.05 indicate significant deviation from normal distribution, suggesting non-parametric methods may be appropriate for further analysis. Day of Ovulation Ovulation occurred in the majority (74.2%) of responders between Day 11 and Day 17 of the menstrual cycle. A delayed ovulation response was seen in 9.1% of participants, occurring between Day 19 and Day 25. Table 2 Distribution of ovulation day among responders Day of ovulation Counts % of Total Cumulative % 11 9 4.8% 4.8% 13 58 31.2% 36.0% 15 42 22.6% 58.6% 17 29 15.6% 74.2% 19 9 4.8% 79.0% 21 4 2.2% 81.2% 23 2 1.1% 82.3% 25 2 1.1% 83.3% no 31 16.7% 100.0% Factors Associated with Non-response A comparison between responders and non-responders revealed that higher BMI, increased AFC, elevated LH levels, and a higher LH/FSH ratio were significantly associated with non-response to Letrozole. These findings are detailed in Table 3 . Table 3 Comparison of baseline parameters between responders and non-responders Parameter Class Responder (n = 155) Non-responder (n = 31) Fisher’s exact test Mann-Whitney U test Count Median Count Median p-value Statistic p-value Age (in years) 30 39 4 BMI (in kg/m 2 ) Underweight (< 18.5) 10 24 2 25.8 0.049 1819 0.033 Normal (18.5- 22.99) 51 6 Overweight (23- 24.99) 35 3 Obese (≥ 25) 59 20 AFC - - 24 - 36 - 1336 < .001 FSH - - 5.7 - 5 - 1993 0.086 LH - - 5 - 7 - 1775 0.022 TSH - - 2 - 1.8 - 2259 0.601 Prolactin - - 13.2 - 16 - 2065 0/217 LH/FSH ratio - - 0.867 - 1.47 - 1562 0.002 Fisher’s exact test was used to compare categorical variables such as age class and BMI class between responders and non-responders. The Mann–Whitney U test was applied to continuous variables (e.g., AFC, FSH, LH, TSH, Prolactin, LH/FSH ratio) as they were found to be non-normally distributed based on Shapiro–Wilk test results; Abbreviations: BMI – Body Mass Index; AFC – Antral Follicle Count; FSH – Follicle-Stimulating Hormone; LH – Luteinizing Hormone; TSH – Thyroid-Stimulating Hormone. Logistic Regression Analysis Univariable Analysis: (Table 4 ) In univariable logistic regression, significant predictors of non-response included: BMI class : Obese vs Normal (p = 0.035), Obese vs Overweight (p = 0.036) AFC : Higher AFC associated with increased odds of non-response (p = 0.001) LH levels : Higher LH correlated with non-response (p = 0.016) LH/FSH ratio : Elevated ratio associated with non-response (p = 0.003) Table 4 Univariate logistic regression analysis evaluating baseline clinical and hormonal predictors of Letrozole response among PCOS patients Predictor Model fit Dummy variables Model coefficients AIC R 2 Estimate SE Z p-value Age 170 0.009 - -0.0654 0.0534 -1.224 0.221 Age class 171 0.017 30 0.619 0.651 0.950 0.342 25–30 vs > 30 0.918 0.585 1.570 0.116 BMI 169 0.017 - 0.0789 0.0461 1.71 0.087 BMI class 168 0.048 Underweight vs normal 0.531 0.887 0.598 0.550 Overweight vs normal -0.317 0.740 -0.428 0.669 Obese vs normal 1.058 0.503 2.103 0.035 Obese vs underweight 0.528 0.817 0.646 0.518 Overweight vs underweight -0.847 0.981 -0.864 0.388 Obese vs overweight 1.375 0.655 2.1 0.036 AFC 161 0.065 - 0.0534 0.0164 3.26 0.001 FSH 169 0.018 - -0.190 0.121 -1.57 0.116 LH 166 0.034 - 0.0968 0.04 2.42 0.016 TSH 171 9.36e-4 - -0.068 0.173 -0.392 0.695 Prolactin 170 0.0089 - 0.0366 0.0297 1.23 0.218 LH/FSH ratio 163 0.0537 - 0.703 0.233 3.01 0.003 Table displays model fit parameters (AIC, R-squared), coefficients, standard errors, and p-values for univariate logistic regression models. Dummy variables represent categorical comparisons where applicable. Statistically significant predictors (p < 0.05) are highlighted, notably obesity class and elevated AFC and LH/FSH ratio. Multivariable Analysis: (Table 5) Variables with p < 0.25 in the univariable model were included in the multivariable logistic regression. After adjusting for potential confounders, AFC remained the only independent and statistically significant predictor of non-response to Letrozole. The adjusted odds ratio was 1.0389 (95% CI: 1.003–1.076; p = 0.033), indicating that for each unit increase in AFC was associated with a modest increase in the odds of non-response. While the odds ratio suggests only a small effect size, AFC still demonstrated predictive value. To better determine its clinical utility, a ROC curve analysis was conducted to establish a suitable threshold. Table 5 Multivariable logistic regression analysis to determine significant predictors Model fit AIC 159 R 2 0.137 Predictor Dummy variables Model coefficients OR 95% CI Estimate SE Z p-value Lower Upper BMI class (reference- obese) underweight vs obese -0.0290 0.8591 -0.034 0.973 0.9714 0.1804 5.232 normal vs obese -0.9890 0.5379 -1.838 0.066 0.3720 0.1296 1.068 overweight vs obese -1.2340 0.6758 -1.826 0.068 0.2911 0.0774 1.095 AFC 0.0382 0.0179 2.128 0.033 1.0389 1.0030 1.076 LH -0.0728 0.1019 -0.714 0.475 0.9298 0.7614 1.135 LH/FSH ratio 1.0140 0.6013 1.686 0.092 2.7566 0.8484 8.957 Table displays model fit parameters (AIC, R-squared), coefficients, standard errors, and p-values for univariate logistic regression models. Dummy variables represent categorical comparisons where applicable. Statistically significant predictors (p < 0.05) are highlighted, notably obesity class and elevated AFC and LH/FSH ratio. ROC analysis Receiver operating characteristic (ROC) curve analysis was conducted to identify the optimal cut-off value for AFC. Using Youden’s Index, an AFC ≥ 35 was determined to be the optimal threshold for predicting non-response. The diagnostic performance of this cut-off was as follows: Sensitivity: 61.3% Specificity: 83.9% Positive Predictive Value (PPV): 43.2% Negative Predictive Value (NPV): 91.5% These findings indicate that an AFC value below 35 is strongly associated with a favourable response to Letrozole. Discussion This study assessed the ovulatory response to Letrozole in women with PCOS, as diagnosed by the Rotterdam criteria. The overall response rate was 83.3%, aligning with prior studies utilizing a 5 mg/day dose of Letrozole [9]. Most women ovulated between Days 11 and 17 of the menstrual cycle, with a minority (9.1%) exhibiting later ovulation between Days 19 and 25. When comparing responders to non-responders, significant differences emerged in BMI, BMI classification, AFC, day 3 LH levels, and LH/FSH ratio. These findings are consistent with earlier studies, including a prospective cohort that identified BMI as a differentiating factor [10], and a retrospective study where baseline LH and LH/FSH ratios were found to be significantly different between the two groups [11]. A case-control study also reported differences in AFC and day 2 LH among Letrozole responders and non-responders [4]. Upon conducting univariable logistic regression, several factors appeared predictive of non-response: BMI class, AFC, LH levels, and LH/FSH ratio. However, multivariable logistic regression analysis revealed that only AFC remained a statistically significant predictor. This is noteworthy, as previous studies, such as one conducted in China, found LH/FSH ratio and AMH to be predictive, though they did not include AFC in their models [11]. At our centre, AMH is not routinely measured before ovulation induction, limiting its utility in our analysis. However, AFC is a reliable ovarian reserve marker with a strong correlation to AMH [12]. To further refine the predictive value of AFC, ROC curve analysis was performed. An AFC threshold of 35 was found to optimally predict non-response, offering a sensitivity of 61.3%, specificity of 83.9%, PPV of 43.2%, and a high NPV of 91.5%. Clinically, this suggests that women with AFC <35 are significantly more likely to respond to Letrozole. Despite moderate sensitivity and a lower PPV, the high NPV underscores AFC’s utility as a screening tool to anticipate treatment success. No previous studies have proposed a specific AFC cut-off to predict Letrozole response in PCOS. However, a small prospective study did suggest an AMH cut-off of 16.43 ng/mL as predictive of Letrozole responsiveness [10]. Interestingly, both AMH and AFC appear to be more effective in identifying patients who are likely to respond to treatment rather than those who will fail, as Letrozole has a high response rate. Nonetheless, for the minority who do not respond, a higher AFC (≥35) or elevated AMH may help identify patients who may require alternative induction strategies. Implications for Practice and/or Policy AFC offers a practical and accessible biomarker for predicting response to Letrozole in women with PCOS. While it should not be the sole criterion for clinical decision-making, its predictive value, especially when used in conjunction with other clinical parameters, can aid in developing more tailored and effective ovulation induction protocols. Conclusion Letrozole achieves an ovulation response rate of 83.3% in women with PCOS when administered at a dose of 5 mg/day, encompassing all BMI categories. Among various baseline parameters, Antral Follicle Count (AFC) emerged as the sole significant predictor of response. A threshold AFC of 35 demonstrated clinical value: women with AFC <35 had a substantially higher likelihood of responding to treatment. Although an AFC ≥35 was associated with an increased probability of non-response, the high negative predictive value suggests that the cut-off can better predict treatment success than treatment failure. Given its routine use in clinical settings, AFC can be effectively employed to individualise ovulation induction strategies, helping to minimise unsuccessful cycles and inform timely progression to alternative therapies such as gonadotropin regimens or assisted reproductive technologies like IVF. Glossary Polycystic Ovary Syndrome (PCOS): An endocrine disorder affecting women of reproductive age, characterised by irregular menstrual cycles, hyperandrogenism (excess male hormones), and polycystic ovaries. Letrozole: An aromatase inhibitor used as a first-line agent for ovulation induction in women with PCOS. It works by reducing estrogen levels, thereby promoting the release of follicle-stimulating hormone (FSH). Ovulation Induction: A medical treatment aimed at stimulating the ovaries to produce and release eggs in women who do not ovulate regularly or at all. Antral Follicle Count (AFC): A transvaginal ultrasound measurement of the number of small (2–10 mm) follicles in the ovaries, used to assess ovarian reserve and predict response to fertility treatments. Body Mass Index (BMI): A measure of body fat based on height and weight, calculated as weight (kg) divided by height (m²). Luteinizing Hormone (LH): A hormone secreted by the pituitary gland that plays a key role in triggering ovulation and supporting reproductive function. Follicle-Stimulating Hormone (FSH): A hormone produced by the pituitary gland that stimulates the growth and maturation of ovarian follicles in women. Sensitivity: The ability of a test to correctly identify individuals who have a condition (true positive rate). Specificity: The ability of a test to correctly identify individuals who do not have a condition (true negative rate). Positive Predictive Value (PPV): The probability that individuals with a positive test result actually have the condition being tested for. Negative Predictive Value (NPV): The probability that individuals with a negative test result truly do not have the condition. Declarations Ethics approval and consent to participate This study was conducted following the principles outlined in the Declaration of Helsinki. The study protocol was approved by the Institutional Review Committee (IRC) and Human Ethics Committee (HEC) of Government Medical College, Thiruvananthapuram. As this was a retrospective observational study using anonymised data from patient records, the requirement for informed consent was waived by the ethics committee. All data were handled with strict confidentiality, and no personally identifiable information was used in the analysis. Consent for publication Informed consent for publication was provided by the participants. Availability of data and material The dataset generated and analyzed during the current study is available in the Mendeley Data repository and can be accessed at https://data.mendeley.com [13]. Competing interests The authors declare that they have no competing interests. Funding None Authors' contributions SA was responsible for conceptualisation, data curation, and formal analysis, and also contributed to methodology design, software development, and data visualisation. SA drafted the original manuscript and participated in subsequent review and editing. SB contributed to the conceptualisation and methodological framework, and was responsible for investigation, project administration, and resource management. SB provided supervision, supported validation, assisted with visualisation, and took part in reviewing and editing the manuscript. DR contributed to conceptualisation and took the lead in data curation, formal analysis, and software development. DR also assisted with methodology and validation, contributed to visualisation, and was actively involved in drafting the original manuscript as well as reviewing and editing. Acknowledgements Not applicable Author’s information : Dr Sholen Acharya holds a prestigious MCh degree in Reproductive Medicine and Surgery from Government Medical College, Thiruvananthapuram, with research interests focused on the clinical and basic sciences of infertility, assisted reproductive technology (ART), and women's reproductive health. Dr Sheila Balakrishnan is a senior clinician with several years of experience in fertility care and currently leads the Department of Reproductive Medicine and Surgery. Her research focuses on improving clinical practices in infertility and in vitro fertilisation (IVF) for enhanced patient outcomes. Dr Deepak Rath is a seasoned physician in the Department of Internal Medicine with several years of clinical experience. His research interests center on improving outcomes in patients with chronic diseases, with a strong focus on evidence-based interventions that enhance quality of life. References E. Thornton, “Polycystic ovarian syndrome,” InnovAiT: Education and inspiration for general practice , vol. 16, no. 5, pp. 249–253, May 2023, doi: 10.1177/17557380231154668 . R. S. Legro et al. , “Letrozole versus Clomiphene for Infertility in the Polycystic Ovary Syndrome,” N Engl J Med , vol. 371, no. 2, pp. 119–129, July 2014, doi: 10.1056/NEJMoa1313517 . M. R. Pandya and K. Patel, “P-596 Comparative efficacy of Letrozole (5 mg) versus Clomiphene citrate (100 mg) for ovulation induction among infertile women,” Human Reproduction , vol. 37, no. 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Yu, “Predictors of response to ovulation induction using letrozole in women with polycystic ovary syndrome,” BMC Endocr Disord , vol. 23, no. 1, p. 90, Apr. 2023, doi: 10.1186/s12902-023-01336-z . K. L. Mounika, S. Jayashree, and M. M. Rangaswamy, “Correlation of Anti-Mullerian Hormone with Antral Follicle Count among South Indian Infertile Women in a Tertiary Care Center,” Indian Journal of Medical Specialities , Apr. 2024, doi: 10.4103/injms.injms_138_23 . S. Acharya, “Factors Influencing Letrozole Response in Ovulation Induction among Polycystic Ovary Syndrome Women dataset.” Mendeley Data, June 09, 2025. doi: 10.17632/NG4XYTPDFJ.1 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7553292","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511286032,"identity":"1c76ff54-4e5f-4fc8-8384-85cc1010d2ad","order_by":0,"name":"Sholen Acharya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCQbGAyDaAMyrAGJm5gZCWhiQtJwBaWEkRQtjG5jEr0V+dvOBAx932Mmbs599JvFzXm00fztQy4+KbTi1GNw5lnBw5plkw5096WaSvduO5844zNjA2HPmNm4tEjkGh3nbmBMMDqSxSfBuO5bbANTCzNiGW4v8DKCWv231CQbnn7FJ/p1zLHc+IS0MN4BaGNsOJxjcSGOT5m2oyd1ASAtQZcLB3rbjhhtuPGO2ljl2IHcjUMtBfH6Rn5F88MHPtmp5g/NpjDff1NTlzjt/+OCDHxV4HIYEWIBxdBjMOkCUeiBg/sDAUEes4lEwCkbBKBhBAADK8GEl1mKpgwAAAABJRU5ErkJggg==","orcid":"","institution":"SAT Hospital, Government Medical College","correspondingAuthor":true,"prefix":"","firstName":"Sholen","middleName":"","lastName":"Acharya","suffix":""},{"id":511286033,"identity":"4691e8b9-1b75-4bf7-9d6a-27c0ad153ec7","order_by":1,"name":"Sheila Balakrishnan","email":"","orcid":"","institution":"SAT Hospital, Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Sheila","middleName":"","lastName":"Balakrishnan","suffix":""},{"id":511286034,"identity":"137c3cf6-d0be-4ea1-becb-e58d80adaea1","order_by":2,"name":"Deepak Rath","email":"","orcid":"","institution":"John H. Stroger, Jr. Hospital of Cook County","correspondingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Rath","suffix":""}],"badges":[],"createdAt":"2025-09-06 23:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7553292/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7553292/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90892306,"identity":"6f17a09a-bf04-46c0-b15a-d0d8cd1b4bac","added_by":"auto","created_at":"2025-09-09 11:17:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77436,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of AFC as a predictor of Letrozole non-response\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure Legend: ROC curve analysis showing the diagnostic performance of Antral Follicle Count (AFC) in predicting non-response to Letrozole therapy. The area under the curve (AUC) demonstrates moderate discriminative ability, and a threshold AFC value of 35 was identified as the optimal cut-off for predicting non-responsiveness.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7553292/v1/04f4b7dee444e07a2dffd163.png"},{"id":96913941,"identity":"680abd2e-eb10-4f30-a836-b5f62f819de1","added_by":"auto","created_at":"2025-11-27 14:04:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1112611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7553292/v1/ebb6b1a1-199c-4503-93cc-84a237cf64ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Influencing Letrozole Response in Ovulation Induction among Polycystic Ovary Syndrome Women","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePolycystic Ovary Syndrome (PCOS) is one of the most common endocrine disorders affecting women of reproductive age and is a leading cause of anovulatory infertility [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Ovulation induction remains a cornerstone in the management of infertility associated with PCOS. Letrozole, an aromatase inhibitor, has emerged as a superior option for ovulation induction in women with PCOS compared to the traditional use of clomiphene citrate (CC) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Studies have consistently shown that letrozole leads to higher ovulation and pregnancy rates, making it a more effective treatment for infertility associated with PCOS [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, a subset of women with PCOS fails to respond to Letrozole.\u003c/p\u003e\u003cp\u003eNon-response to letrozole in ovulation induction can be attributed to various clinical and hormonal factors. Those having resistance to clomifene have a significantly higher likelihood of non-response to letrozole [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. But since now Letrozole is considered the first-line ovulation induction regimen, it is important to evaluate the baseline characteristics and hormonal parameters that lead to non-response to Letrozole. Understanding these predictors is crucial for optimising treatment strategies. While an extended letrozole regimen may slightly improve ovulation rates, it does not significantly outperform the conventional regimen [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], emphasising the need to identify these cases before guiding individualised therapy.\u003c/p\u003e\u003cp\u003eThis study aims to assess the ovulatory response to Letrozole in women with PCOS diagnosed using the Rotterdam criteria and to explore the baseline clinical and hormonal characteristics that distinguish responders from non-responders. The analysis focuses on identifying predictors of non-response to help refine and personalise ovulation induction strategies.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eStudy Design and Setting\u003c/p\u003e\u003cp\u003eThis retrospective observational study was conducted at the Department of Reproductive Medicine and Surgery, Government Medical College, Thiruvananthapuram. The study included a review of medical records of women treated for infertility due to polycystic ovarian syndrome (PCOS) between January 1, 2019, and December 31, 2020. Ethical approval was obtained from the institutional ethics committee, and the requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\u003cp\u003eStudy Population and Inclusion Criteria\u003c/p\u003e\u003cp\u003eRecords of women diagnosed with PCOS and treated with Letrozole for ovulation induction were screened. Only the first ovulation induction cycle was included for each participant, as hormonal profiling (LH, FSH, TSH, and Prolactin) was performed during the same cycle in which ovulation induction began.\u003c/p\u003e\u003cp\u003eExclusion Criteria\u003c/p\u003e\u003cp\u003eThe following participants were excluded from the study:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThose with severe male factor infertility\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWomen with associated endometriosis, uterine fibroids, unilateral tubal block, or a history of prior pelvic surgeries\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIndividuals undergoing subsequent ovulation induction cycles beyond their first cycle at our centre\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eClinical Evaluation and Investigations\u003c/p\u003e\u003cp\u003eAll participants underwent baseline evaluations including:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHistory and clinical examination: height, weight, BMI, and assessment for hirsutism\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLaboratory tests: hemogram, liver and kidney function tests, and day 3 hormone profile (LH, FSH, TSH, Prolactin)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTransvaginal ultrasonography: performed on day 3 to assess for uterine or adnexal masses and to determine antral follicle count (AFC)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFor male partners: history and semen analysis were reviewed\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eMenstruation in the subjects could be either spontaneous or progestogen-induced. Day 3 hormone profiling and transvaginal scans were scheduled simultaneously to exclude hormonally active ovarian cysts.\u003c/p\u003e\u003cp\u003eDiagnosis of PCOS\u003c/p\u003e\u003cp\u003eThe diagnosis of PCOS was made based on the Rotterdam criteria, where at least two of the following three features were required:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eOligo/anovulation (irregular cycles)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eClinical hyperandrogenism (biochemical testing was not routinely performed except in virilizing cases)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePolycystic ovarian morphology (AFC\u0026thinsp;\u0026ge;\u0026thinsp;12 or ovarian volume\u0026thinsp;\u0026gt;\u0026thinsp;10 mL in either ovary)\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eOvulation Induction Protocol\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eLetrozole 2.5 mg was administered twice daily (total 5 mg/day) from day 3 to day 7 of the menstrual cycle.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eParticipants were monitored from day 9 onward, every alternate day, up to day 27 using transvaginal ultrasonography to detect follicular growth.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eA dominant follicle was defined as one with a diameter\u0026thinsp;\u0026ge;\u0026thinsp;18 mm. When such follicles were observed, Inj. hCG 5000 IU was administered at approximately 6 PM on the same day.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePatients were advised to have timed intercourse, or if mild oligospermia was present, intrauterine insemination (IUI) was performed.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eOutcome Classification\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eResponders: Those who developed one or more dominant follicles (\u0026ge;\u0026thinsp;18 mm) by day 27 of the cycle\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNon-responders: Those who did not develop any dominant folli\u003cb\u003ecle\u003c/b\u003e by day 27\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eOvulation was assumed to occur 36 hours after hCG administration, two days after identifying the dominant follicle.\u003c/p\u003e\u003cp\u003eSample Size Calculation\u003c/p\u003e\u003cp\u003eThe required sample size was calculated using the formula for prevalence studies:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:n=\\frac{{Z}^{2}.\\:p(1-p)}{{d}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAssuming a Letrozole response rate of 76% from previous studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and a precision of 7%, the minimum required sample size was 144.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eAll collected data were compiled in Microsoft Excel and analysed using Jamovi software [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Response rate was calculated as the proportion of participants who developed a dominant follicle after Letrozole induction. Categorical variables were expressed as frequencies and percentages and analysed using Fisher\u0026rsquo;s exact test. Continuous variables were tested for normality using the Shapiro-Wilk test and presented as median with interquartile range (IQR) due to non-normal distribution. Differences in continuous variables between responders and non-responders were assessed using the Mann\u0026ndash;Whitney U test. Univariate analysis was done, and those whose p-value was \u0026lt;\u0026thinsp;0.25 were entered into multivariable logistic regression analyses [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] to identify factors independently associated with non-response to Letrozole. Receiver Operating Characteristic (ROC) curve analysis was performed to determine optimal cut-off values for significant predictors. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 327 women underwent ovulation induction during the study period. After applying the inclusion and exclusion criteria, 186 subjects were included in the final analysis. Specifically, 32 women were excluded due to associated endometriosis, fibroids, single tubal block, or prior pelvic surgeries. An additional 109 participants were excluded because they were undergoing subsequent ovulation induction cycles, and hormonal profiling was not repeated for subsequent cycles at our centre.\u003c/p\u003e\n\u003cp\u003eResponse Rate to Letrozole\u003c/p\u003e\n\u003cp\u003eAmong the 186 women included, 155 developed at least one dominant follicle (defined as follicle diameter\u0026thinsp;\u0026ge;\u0026thinsp;18 mm) between Day 9 and Day 23, indicating a response rate of 83.3%. The remaining 31 women did not respond to Letrozole, resulting in a non-response rate of 16.7%.\u003c/p\u003e\n\u003cp\u003eBaseline Characteristics\u003c/p\u003e\n\u003cp\u003eThe demographic and hormonal baseline characteristics are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Most continuous variables, including age, BMI, AFC, and hormonal parameters, were found to be non-normally distributed based on the Shapiro-Wilk test.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline characteristics of the study population with normality test (Shapiro-Wilk)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eShapiro-Wilk\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClass\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCount (%) (N\u0026thinsp;=\u0026thinsp;186)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIQR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eW\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge (in years)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (26.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e27.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e3.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u0026ndash;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (50)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43 (23.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBMI (in kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (6.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e24.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e4.147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.977\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal (18.5- 22.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57 (30.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOverweight (23- 24.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38 (20.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObese (\u0026ge;\u0026thinsp;25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79 (42.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAFC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.437\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.937\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFSH\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.766\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.623\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLH\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.597\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.859\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTSH\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.943\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eProlactin\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.973\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLH/FSH ratio\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.902\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.767\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.923\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.881\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eTable presents descriptive statistics and results of the Shapiro-Wilk test for normality for baseline clinical and hormonal parameters. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicate significant deviation from normal distribution, suggesting non-parametric methods may be appropriate for further analysis.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eDay of Ovulation\u003c/h2\u003e\n\u003cp\u003eOvulation occurred in the majority (74.2%) of responders between Day 11 and Day 17 of the menstrual cycle. A delayed ovulation response was seen in 9.1% of participants, occurring between Day 19 and Day 25.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDistribution of ovulation day among responders\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDay of ovulation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCounts\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e% of Total\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCumulative %\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.8%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.8%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.8%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.3%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83.3%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eno\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.7%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eFactors Associated with Non-response\u003c/h3\u003e\n\u003cp\u003eA comparison between responders and non-responders revealed that higher BMI, increased AFC, elevated LH levels, and a higher LH/FSH ratio were significantly associated with non-response to Letrozole. These findings are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of baseline parameters between responders and non-responders\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParameter\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eClass\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eResponder (n\u0026thinsp;=\u0026thinsp;155)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNon-responder (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFisher\u0026rsquo;s exact test\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMann-Whitney U test\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCount\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCount\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStatistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge (in years)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e2139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.334\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u0026ndash;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBMI (in kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e25.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e1819\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal (18.5- 22.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOverweight (23- 24.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObese (\u0026ge;\u0026thinsp;25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAFC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1336\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFSH\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1993\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.086\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLH\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTSH\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.601\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eProlactin\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2065\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0/217\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLH/FSH ratio\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.867\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1562\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eFisher\u0026rsquo;s exact test was used to compare categorical variables such as age class and BMI class between responders and non-responders. The Mann\u0026ndash;Whitney U test was applied to continuous variables (e.g., AFC, FSH, LH, TSH, Prolactin, LH/FSH ratio) as they were found to be non-normally distributed based on Shapiro\u0026ndash;Wilk test results; Abbreviations: BMI \u0026ndash; Body Mass Index; AFC \u0026ndash; Antral Follicle Count; FSH \u0026ndash; Follicle-Stimulating Hormone; LH \u0026ndash; Luteinizing Hormone; TSH \u0026ndash; Thyroid-Stimulating Hormone.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eLogistic Regression Analysis\u003c/h2\u003e\n\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n\u003cp\u003eUnivariable Analysis: (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eIn univariable logistic regression, significant predictors of non-response included:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eBMI class\u003c/strong\u003e: Obese vs Normal (p\u0026thinsp;=\u0026thinsp;0.035), Obese vs Overweight (p\u0026thinsp;=\u0026thinsp;0.036)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAFC\u003c/strong\u003e: Higher AFC associated with increased odds of non-response (p\u0026thinsp;=\u0026thinsp;0.001)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eLH levels\u003c/strong\u003e: Higher LH correlated with non-response (p\u0026thinsp;=\u0026thinsp;0.016)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eLH/FSH ratio\u003c/strong\u003e: Elevated ratio associated with non-response (p\u0026thinsp;=\u0026thinsp;0.003)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eUnivariate logistic regression analysis evaluating baseline clinical and hormonal predictors of Letrozole response among PCOS patients\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePredictor\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel fit\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDummy variables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eModel coefficients\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAIC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0534\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.224\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.221\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;25 vs\u0026thinsp;\u0026gt;\u0026thinsp;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.950\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.342\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u0026ndash;30 vs\u0026thinsp;\u0026gt;\u0026thinsp;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.918\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.570\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.116\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0789\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0461\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.087\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI class\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnderweight vs normal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.531\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.887\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.598\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.550\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOverweight vs normal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.317\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.740\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.428\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.669\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObese vs normal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.503\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObese vs underweight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.817\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.518\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOverweight vs underweight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.847\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.981\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.864\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.388\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObese vs overweight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.655\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAFC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.065\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0534\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFSH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.116\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTSH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.36e-4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.695\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProlactin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0366\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLH/FSH ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0537\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.233\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eTable displays model fit parameters (AIC, R-squared), coefficients, standard errors, and p-values for univariate logistic regression models. Dummy variables represent categorical comparisons where applicable. Statistically significant predictors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are highlighted, notably obesity class and elevated AFC and LH/FSH ratio.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eMultivariable Analysis: (Table 5)\u003c/h3\u003e\n\u003cp\u003eVariables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.25 in the univariable model were included in the multivariable logistic regression. After adjusting for potential confounders, AFC remained the only independent and statistically significant predictor of non-response to Letrozole. The adjusted odds ratio was 1.0389 (95% CI: 1.003\u0026ndash;1.076; p\u0026thinsp;=\u0026thinsp;0.033), indicating that for each unit increase in AFC was associated with a modest increase in the odds of non-response. While the odds ratio suggests only a small effect size, AFC still demonstrated predictive value. To better determine its clinical utility, a ROC curve analysis was conducted to establish a suitable threshold.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultivariable logistic regression analysis to determine significant predictors\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel fit\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAIC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e159\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePredictor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDummy variables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eModel coefficients\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLower\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUpper\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI class (reference- obese)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eunderweight vs obese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0290\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8591\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.973\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9714\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1804\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.232\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003enormal vs obese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.9890\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5379\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3720\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1296\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.068\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eoverweight vs obese\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.2340\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6758\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.826\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2911\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.095\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAFC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0382\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.076\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.714\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.475\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9298\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7614\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.135\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLH/FSH ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.686\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.7566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8484\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.957\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eTable displays model fit parameters (AIC, R-squared), coefficients, standard errors, and p-values for univariate logistic regression models. Dummy variables represent categorical comparisons where applicable. Statistically significant predictors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are highlighted, notably obesity class and elevated AFC and LH/FSH ratio.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eROC analysis\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curve analysis was conducted to identify the optimal cut-off value for AFC. Using Youden\u0026rsquo;s Index, an AFC\u0026thinsp;\u0026ge;\u0026thinsp;35 was determined to be the optimal threshold for predicting non-response. The diagnostic performance of this cut-off was as follows:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eSensitivity: 61.3%\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSpecificity: 83.9%\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePositive Predictive Value (PPV): 43.2%\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNegative Predictive Value (NPV): 91.5%\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese findings indicate that an AFC value below 35 is strongly associated with a favourable response to Letrozole.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study assessed the ovulatory response to Letrozole in women with PCOS, as diagnosed by the Rotterdam criteria. The overall response rate was 83.3%, aligning with prior studies utilizing a 5 mg/day dose of Letrozole\u0026nbsp;[9]. Most women ovulated between Days 11 and 17 of the menstrual cycle, with a minority (9.1%) exhibiting later ovulation between Days 19 and 25.\u003c/p\u003e\n\u003cp\u003eWhen comparing responders to non-responders, significant differences emerged in BMI, BMI classification, AFC, day 3 LH levels, and LH/FSH ratio. These findings are consistent with earlier studies, including a prospective cohort that identified BMI as a differentiating factor\u0026nbsp;[10], and a retrospective study where baseline LH and LH/FSH ratios were found to be significantly different between the two groups\u0026nbsp;[11]. A case-control study also reported differences in AFC and day 2 LH among Letrozole responders and non-responders\u0026nbsp;[4].\u003c/p\u003e\n\u003cp\u003eUpon conducting univariable logistic regression, several factors appeared predictive of non-response: BMI class, AFC, LH levels, and LH/FSH ratio. However, multivariable logistic regression analysis revealed that only AFC remained a statistically significant predictor. This is noteworthy, as previous studies, such as one conducted in China, found LH/FSH ratio and AMH to be predictive, though they did not include AFC in their models\u0026nbsp;[11]. At our centre, AMH is not routinely measured before ovulation induction, limiting its utility in our analysis. However, AFC is a reliable ovarian reserve marker with a strong correlation to AMH\u0026nbsp;[12].\u003c/p\u003e\n\u003cp\u003eTo further refine the predictive value of AFC, ROC curve analysis was performed. An AFC threshold of 35 was found to optimally predict non-response, offering a sensitivity of 61.3%, specificity of 83.9%, PPV of 43.2%, and a high NPV of 91.5%. Clinically, this suggests that women with AFC \u0026lt;35 are significantly more likely to respond to Letrozole. Despite moderate sensitivity and a lower PPV, the high NPV underscores AFC’s utility as a screening tool to anticipate treatment success.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo previous studies have proposed a specific AFC cut-off to predict Letrozole response in PCOS. However, a small prospective study did suggest an AMH cut-off of 16.43 ng/mL as predictive of Letrozole responsiveness\u0026nbsp;[10]. Interestingly, both AMH and AFC appear to be more effective in identifying patients who are likely to respond to treatment rather than those who will fail, as Letrozole has a high response rate. Nonetheless, for the minority who do not respond, a higher AFC (≥35) or elevated AMH may help identify patients who may require alternative induction strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for Practice and/or Policy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAFC offers a practical and accessible biomarker for predicting response to Letrozole in women with PCOS. While it should not be the sole criterion for clinical decision-making, its predictive value, especially when used in conjunction with other clinical parameters, can aid in developing more tailored and effective ovulation induction protocols.\u003c/p\u003e\n\n"},{"header":"Conclusion","content":"\u003cp\u003eLetrozole achieves an ovulation response rate of 83.3% in women with PCOS when administered at a dose of 5 mg/day, encompassing all BMI categories. Among various baseline parameters, Antral Follicle Count (AFC) emerged as the sole significant predictor of response. A threshold AFC of 35 demonstrated clinical value: women with AFC \u0026lt;35 had a substantially higher likelihood of responding to treatment. Although an AFC ≥35 was associated with an increased probability of non-response, the high negative predictive value suggests that the cut-off can better predict treatment success than treatment failure. Given its routine use in clinical settings, AFC can be effectively employed to individualise ovulation induction strategies, helping to minimise unsuccessful cycles and inform timely progression to alternative therapies such as gonadotropin regimens or assisted reproductive technologies like IVF.\u003c/p\u003e"},{"header":"Glossary","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003ePolycystic Ovary Syndrome (PCOS):\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;An endocrine disorder affecting women of reproductive age, characterised by irregular menstrual cycles, hyperandrogenism (excess male hormones), and polycystic ovaries.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLetrozole:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;An aromatase inhibitor used as a first-line agent for ovulation induction in women with PCOS. It works by reducing estrogen levels, thereby promoting the release of follicle-stimulating hormone (FSH).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOvulation Induction:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A medical treatment aimed at stimulating the ovaries to produce and release eggs in women who do not ovulate regularly or at all.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAntral Follicle Count (AFC):\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A transvaginal ultrasound measurement of the number of small (2–10 mm) follicles in the ovaries, used to assess ovarian reserve and predict response to fertility treatments.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBody Mass Index (BMI):\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A measure of body fat based on height and weight, calculated as weight (kg) divided by height (m²).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLuteinizing Hormone (LH):\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A hormone secreted by the pituitary gland that plays a key role in triggering ovulation and supporting reproductive function.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFollicle-Stimulating Hormone (FSH):\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A hormone produced by the pituitary gland that stimulates the growth and maturation of ovarian follicles in women.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSensitivity:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The ability of a test to correctly identify individuals who have a condition (true positive rate).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSpecificity:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The ability of a test to correctly identify individuals who do not have a condition (true negative rate).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePositive Predictive Value (PPV):\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The probability that individuals with a positive test result actually have the condition being tested for.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNegative Predictive Value (NPV):\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The probability that individuals with a negative test result truly do not have the condition.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003eEthics approval and consent to participate\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis study was conducted following the principles outlined in the Declaration of Helsinki. The study protocol was approved by the Institutional Review Committee (IRC) and Human Ethics Committee (HEC) of Government Medical College, Thiruvananthapuram. As this was a retrospective observational study using anonymised data from patient records, the requirement for informed consent was waived by the ethics committee. All data were handled with strict confidentiality, and no personally identifiable information was used in the analysis.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eConsent for publication\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInformed consent for publication was provided by the participants.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAvailability of data and material\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe dataset generated and analyzed during the current study is available in the Mendeley Data repository and can be accessed at\u0026nbsp;\u003ca href=\"https://data.mendeley.com\"\u003ehttps://data.mendeley.com\u003c/a\u003e [13].\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCompeting interests\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFunding\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAuthors' contributions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSA was responsible for conceptualisation, data curation, and formal analysis, and also contributed to methodology design, software development, and data visualisation. SA drafted the original manuscript and participated in subsequent review and editing.\u003c/p\u003e\n\u003cp\u003eSB contributed to the conceptualisation and methodological framework, and was responsible for investigation, project administration, and resource management. SB provided supervision, supported validation, assisted with visualisation, and took part in reviewing and editing the manuscript.\u003c/p\u003e\n\u003cp\u003eDR contributed to conceptualisation and took the lead in data curation, formal analysis, and software development. DR also assisted with methodology and validation, contributed to visualisation, and was actively involved in drafting the original manuscript as well as reviewing and editing.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAcknowledgements\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s information\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eDr Sholen Acharya holds a prestigious MCh degree in Reproductive Medicine and Surgery from Government Medical College, Thiruvananthapuram, with research interests focused on the clinical and basic sciences of infertility, assisted reproductive technology (ART), and women's reproductive health.\u003c/p\u003e\n\u003cp\u003eDr Sheila Balakrishnan is a senior clinician with several years of experience in fertility care and currently leads the Department of Reproductive Medicine and Surgery. Her research focuses on improving clinical practices in infertility and in vitro fertilisation (IVF) for enhanced patient outcomes.\u003c/p\u003e\n\u003cp\u003eDr Deepak Rath is a seasoned physician in the Department of Internal Medicine with several years of clinical experience. His research interests center on improving outcomes in patients with chronic diseases, with a strong focus on evidence-based interventions that enhance quality of life.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eE. 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Yu, \u0026ldquo;Predictors of response to ovulation induction using letrozole in women with polycystic ovary syndrome,\u0026rdquo; \u003cem\u003eBMC Endocr Disord\u003c/em\u003e, vol. 23, no. 1, p. 90, Apr. 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12902-023-01336-z\u003c/span\u003e\u003cspan address=\"10.1186/s12902-023-01336-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK. L. Mounika, S. Jayashree, and M. M. Rangaswamy, \u0026ldquo;Correlation of Anti-Mullerian Hormone with Antral Follicle Count among South Indian Infertile Women in a Tertiary Care Center,\u0026rdquo; \u003cem\u003eIndian Journal of Medical Specialities\u003c/em\u003e, Apr. 2024, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/injms.injms_138_23\u003c/span\u003e\u003cspan address=\"10.4103/injms.injms_138_23\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eS. Acharya, \u0026ldquo;Factors Influencing Letrozole Response in Ovulation Induction among Polycystic Ovary Syndrome Women dataset.\u0026rdquo; Mendeley Data, June 09, 2025. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17632/NG4XYTPDFJ.1\u003c/span\u003e\u003cspan address=\"10.17632/NG4XYTPDFJ.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Letrozole, polycystic ovary syndrome, ovulation induction, antral follicle count, body mass index, LH/FSH ratio","lastPublishedDoi":"10.21203/rs.3.rs-7553292/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7553292/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Letrozole is the first-line ovulation induction agent in PCOS, but some women do not respond. This study aimed to evaluate ovulatory response to Letrozole and identify predictors of non-response to individualise treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This retrospective observational study included women with PCOS (Rotterdam criteria) undergoing their first Letrozole ovulation induction cycle between January 2019 and December 2020. Exclusions were severe male factor infertility, pelvic pathologies, or repeat cycles. Baseline assessments included clinical evaluation, day 3 hormonal profiling (LH, FSH, TSH, Prolactin), and transvaginal ultrasound for antral follicle count (AFC). Letrozole (5 mg/day) was given from day 3 to 7, with follicular monitoring from day 9 to 27. Responders developed ≥1 dominant follicle (≥18 mm), non-responders did not. Data were analysed with Jamovi; logistic regression identified non-response predictors. ROC analysis determined optimal cut-offs (p\u0026lt;0.05 significant).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Of 327 women, 186 met criteria. The response rate was 83.3% (155 responders, 31 non-responders). Non-responders had higher BMI, AFC, LH, and LH/FSH ratios. Multivariable regression showed AFC as the only independent predictor of non-response (OR 1.039, 95% CI: 1.003–1.076, p=0.033). ROC analysis identified AFC ≥35 as the optimal cut-off with 61.3% sensitivity, 83.9% specificity, 43.2% PPV, and 91.5% NPV. This indicates women with AFC \u0026lt;35 are more likely to respond to Letrozole.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Letrozole induces ovulation in 83.3% of PCOS women. AFC is a key predictor, with \u0026lt;35 indicating better response. Using AFC can reduce failed cycles and guide timely alternative treatments like gonadotropins or IVF.\u003c/p\u003e","manuscriptTitle":"Factors Influencing Letrozole Response in Ovulation Induction among Polycystic Ovary Syndrome Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 11:17:15","doi":"10.21203/rs.3.rs-7553292/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"25024a1a-ebc3-4dc6-bc71-149dd6c6ae35","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T11:08:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 11:17:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7553292","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7553292","identity":"rs-7553292","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}