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All Journal Folia Medica Indonesiana Majalah Obstetri dan Ginekologi Indonesian Journal of Obstetrics and Gynecology (Majalah Obstetri dan Ginekologi Indonesia) Indian Journal of Forensic Medicine & Toxicology International Islamic Medical Journal Indonesian Journal of Perinatology
Khanisyah Erza Gumilar
Department Of Obstetrics & Gynecology, Faculty Of Medicine, Airlangga University, Dr. Soetomo Hospital, Surabaya
Religion Humanities Health Professions Medicine & Pharmacology Nursing Public Health Other

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Journal : Majalah Obstetri dan Ginekologi

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Characteristics of Peripartum Cardiomyopathy (PPCM) pregnancy and preeclampsia in Dr Soetomo Hospital, Surabaya, Indonesia, 2014-2016 Dibya Arfianda; Budi Wicaksono; Khanisyah Erza Gumilar; Andrianto Andrianto
Majalah Obstetri dan Ginekologi Vol. 27 No. 1 (2019): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (334.898 KB) | DOI: 10.20473/mog.V27I12019.40-44

Abstract

Objectives: to present data on the characteristics of pregnancy with PPCM and PE. Management of patients with PPCM is almost the same as for patients with acute or chronic heart failure, which uses drug therapy. PPCM and preeclampsia (PE) are two related diseases, although not directly. Both have similar pathophysiological mechanisms.Case Report: We present 25 pregnancy cases with PPCM at Dr. Soetomo Hospital within 3 years. Data were collected from January 2014 to December 2016, consisting of 5 PPCM cases and the other 20 cases were PPCM with PE cases.Conclusion: Pregnancy with PPCM-PE has higher morbidity than PPCM only. The diagnosis of PPCM should be established immediately if heart failure symptoms are found in the third trimester and the patient has risk factors, such as age >30 years, multigravida, obesity, and multiple pregnancy.
Peripartum cardiomyopathy and its relationship with preeclampsia Christina Meilani Susanto; Khanisyah Erza Gumilar
Majalah Obstetri dan Ginekologi Vol. 28 No. 2 (2020): August
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/mog.V28I22020.52-58

Abstract

Objectives: To know the characteristic of PPCM in RSUD Dr. Soetomo Hospital Surabaya and to know the relationship between PPCM and PE.Materials and Methods: This was a case control study. Data was obtained from medical record of 2843 patients within 2014-2015, divided into 2 groups, 19 patients with PPCM in a case group, and 2824 patients in control group. The statistical analysis used was Fisher exact test.Results: Peripartum cardiomyopathy patients were older compared to control group (32.21 ± 6.83 y.o vs 29.26 ± 6.45 y.o). The incidence of PPCM in our study was about 1 per 149 live births. Most cases were diagnosed antepartum (52.63%), and about 84.2% PPCM cases were also complicating with preeclampsia. The statistical analysis revealed that there was increase risk of PPCM if the pregnant women complicates PE during pregnancy, with Odds Ratio (OR) 20.679, p<0.05. The most common perinatal outcomes was Small for Gestational Age (SGA) babies (81.8%), whereas case fatality rate (CFR) in maternal was 15.7%.Conclusion: Although diagnosis of PPCM is still an exclusion diagnosis, we have to pay more attention to pregnant women complicating with preeclampsia, since preeclampsia can increase the risk of PPCM.
The promise and challenges of Artificial Intelligence-Large Language Models (AI-LLMs) in obstetric and gynecology Gumilar, Khanisyah Erza; Tan, Ming
Majalah Obstetri & Ginekologi Vol. 32 No. 2 (2024): August
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/mog.V32I22024.128-135

Abstract

HIGHLIGHTS 1. The article highlights how Artificial Intelligence with Large Language Models (AI-LLMs) greatly improves diagnosis and treatment personalization in obstetrics & gynecology, and also enhances medical education through interactive simulations and up-to-date learning materials.2. The article also discusses the ethical issues linked to AI, emphasizing the need for cooperation among different stakeholders to use AI responsibly in medicine, focusing on protecting data privacy and minimizing reliance on technology.   ABSTRACT The introduction of Artificial Intelligence through Large Language Models (AI-LLM) into medicine holds great promise for improving patient care and medical education, especially in obstetrics and gynecology. AI-LLM can significantly improve diagnostic accuracy and treatment efficiency by utilizing large medical databases, which is especially useful for dealing with rare diseases that are difficult to document or understand by human practitioners alone. In addition, AI-LLM can provide informed patient care recommendations by analyzing large amounts of data and providing insights based on unique patient profiles, with the added benefit of being accessible 24/7 via the internet. This constant availability ensures that patients receive prompt information and assistance as needed. In the field of education, AI-LLMs enhance the learning experience by incorporating interactive simulations into the curriculum, improving medical students' and professionals' practical knowledge. They also ensure that educational materials are always up-to-date reflecting the most recent research and worldwide medical standards. This access latest information from global resources helps to bridge the educational gap, making advanced knowledge more accessible to learners regardless of their geographic location. However, the introduction of AI-LLMs is not without challenges. Ethical issues, such as data privacy and the risk of overreliance on technology, must be addressed. Effective management of these concerns necessitates collaboration among medical professionals, technological experts, academics, hospital committees, and representatives of patients. This multidisciplinary teamwork is vital for upholding ethical norms and preserving patient dignity and respect. AI-LLMs can considerably improve both patient care and medical education in obstetrics and gynecology provided they are appropriately balanced with innovation and ethics.
Co-Authors Abd Rauf, Khairunnisa binti Aldika Akbar, Muhammad Ilham Andrianto Askandar Tjokroprawiro Bachnaas, Muhammad Adrianes Baktiar, Muhammad Priza Budi Wicaksono Christina Meilani Susanto Christina Meilani Susanto Dibya Arfianda Ecccita Raheestyningtyas Ernawati Ernawati Erry Gumilar Dachlan, Hermanto Tri Joewono Jimmy Yanuar Anas Laksana, Muhammad Ardian Cahya Muhammad Adrianes Bachnas Pratama, Nur Aprilianita PUNGKY MULAWARDHANA, PUNGKY Rahayu, Ana Puji Tan, Ming Virdayanti, Dian Puspita Wardhana, Manggala Pasca