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All Journal Prominentia Medical Journal The International Journal of Medical Science and Health Research
Santoso, Rosalyn Devina
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Dentistry Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

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Analisis Korelasi Antara Indeks Massa Tubuh (BMI), Homa-Ir dan Insulin pada Wanita dengan Sindrom Ovarium Polikistik Zaman, Thomy Al Jabbari; Siahaan, Salmon Charles Pardomuan Tua; Santoso, Rosalyn Devina; Adipranoto, Gladys; Arisanti, Raden Roro Shinta
Bahasa Indonesia Vol 6 No 1 (2025): Prominentia Medical Journal
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/pmj.v6i1.5747

Abstract

Abstrak Sindrom Ovarium Polikistik (PCOS) merupakan gangguan endokrin yang sering dijumpai pada wanita usia reproduktif, ditandai oleh anovulasi, hiperandrogenisme, dan morfologi ovarium polikistik. Kondisi ini juga sering berkaitan dengan resistensi insulin dan peningkatan indeks massa tubuh (BMI), sehingga penting untuk mengevaluasi hubungan antara BMI, HOMA-IR, dan kadar insulin. Penelitian ini bertujuan untuk membandingkan karakteristik klinis dan metabolik antara wanita sehat dan penderita PCOS, serta menganalisis hubungan antara BMI, HOMA-IR, dan kadar insulin pada kelompok PCOS. Penelitian observasional analitik ini menggunakan desain potong lintang dengan total 24 subjek (12 sehat, 12 PCOS). Data dikumpulkan melalui pemeriksaan klinis dan laboratorium, dan dianalisis menggunakan uji Chi-Square untuk data kategorik, serta uji T dan korelasi Pearson untuk data numerik. Hasil menunjukkan perbedaan signifikan antara kelompok sehat dan PCOS dalam hal anovulasi, hiperandrogenisme, dan morfologi ovarium polikistik (p = 0,000). Selain itu, kelompok PCOS menunjukkan BMI dan HOMA-IR yang lebih tinggi secara bermakna (p = 0,003 dan p = 0,000). Pada kelompok PCOS, terdapat korelasi positif yang signifikan antara BMI dan HOMA-IR (r = 0,711; p = 0,010), serta antara BMI dan insulin, maupun HOMA-IR dan insulin. Disimpulkan bahwa wanita dengan PCOS memiliki profil metabolik yang lebih buruk, dengan hubungan yang kuat antara peningkatan BMI dan resistensi insulin.   Kata kunci: Sindrom Ovarium Polikistik, Indeks Massa Tubuh, Resistensi Insulin   Abstract Polycystic Ovary Syndrome (PCOS) is an endocrine disorder that affects women of reproductive age. It is often associated with insulin resistance and increases in body mass index (BMI). This study aims to analyze the correlation between BMI, HOMA-IR, and insulin levels in women with PCOS. This study also compares clinical and metabolic characteristics of these women with those of healthy women. This cross-sectional observational study involved 24 participants consisting of 12 women with PCOS and 12 health women. Clinical and laboratory data were analyzed using Chi-Square test, t-test, and Pearson correlation. The PCOS group showed significantly higher rates of anovulation, hyperandrogenism, and polycystic ovarian morphology (p = 0.000), as well as increased BMI and HOMA-IR (p = 0.003 and p = 0.000). Significant positive correlations were found between BMI, HOMA-IR, and insulin levels in the PCOS group. Women with PCOS tend to exhibit a poorer metabolic profile, characterized by elevated BMI and insulin resistance.   Keywords: Polycystic Ovary Syndrome, Body Mass Index, Insulin Resistan  
Prognostic Performance Of Artificial Intelligence Models In Predicting 12-Week Healing of Chronic Wounds: A Systematic Review And Meta-Analysis Jeany Thalia Hartono; Fanny Evasari Lesmanawati; Rosalyn Devina Santoso
The International Journal of Medical Science and Health Research Vol. 21 No. 4 (2025): The International Journal of Medical Science and Health Research
Publisher : International Medical Journal Corp. Ltd

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70070/ewjesj02

Abstract

Introduction Chronic wounds pose a substantial health burden, requiring intensive, long-term management and carrying a high risk of debilitating complications. Accurate prognosis regarding the probability and rate of healing within the critical 12-week timeframe is essential for optimizing specific care strategies and ensuring the effective allocation of scarce medical resources. Artificial Intelligence (AI), particularly through its foundation in Machine Learning (ML), offers significant potential to enhance prognostic accuracy by rigorously processing vast quantities of Electronic Medical Record (EMR) data and advanced wound imagery. Methods This systematic review and meta-analysis was conducted in strict adherence to the PRISMA 2020 reporting guidelines. Included studies specifically evaluated AI models designed to predict chronic wound healing outcomes within 12 weeks. The methodological quality of these studies was critically assessed using the specialized Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence (PROBAST+AI). Quantitative synthesis was executed to determine the pooled discrimination performance metric, the Area Under the Curve (AUC), and to measure the independent effects of key predictors using pooled Hazard Ratios (HR). Results The analysis incorporated two large-scale primary studies boasting high data volumes, alongside several supporting methodological studies. The resultant pooled AUC for AI models reached 0.805 (95% CI: 0.778–0.832), definitively confirming significant prognostic capability. Specifically, models utilizing Gradient-Boosted Decision Tree (GBDT) algorithms achieved an AUC of 0.853, a performance level that substantially outperformed conventional Logistic Regression models (AUC 0.712). Assessment utilizing PROBAST+AI consistently highlighted systemic methodological quality issues, predominantly stemming from weak internal validation within the Analysis Domain, which consequently elevated the Overall Risk of Bias. The pooled HR analysis, synthesizing data for 10 critical prognostic predictors, confirmed that local wound characteristics are the paramount determinants of prognosis. High-Grade Wound Depth (Stage 3/4) was identified as the single strongest inhibitor of healing (HR 0.65 (95% CI: 0.59–0.71)), whereas Normal/Good Vascularization Status represented the strongest accelerator (HR 1.30 (95% CI: 1.22–1.39)). Discussion and Conclusion The prognostic performance demonstrated by AI models is statistically significant and definitively exceeds that of conventional statistical methods. This heightened accuracy is attributed to the inherent capacity of non-linear models to effectively capture complex multi-variable interactions central to wound healing pathophysiology. Notwithstanding the encouraging performance metrics, the documented high risk of overfitting due to analytical bias necessitates strict and rigorous external validation prior to any extensive clinical implementation.
Co-Authors Adipranoto, Gladys Fanny Evasari Lesmanawati Jeany Thalia Hartono Raden Roro Shinta Arisanti Salmon Charles Pardomuan Tua Siahaan Zaman, Thomy Al Jabbari