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Adherence to treatment for polycystic ovarian syndrome: A systematic review.

ABSTRACT: BACKGROUND:Polycystic ovarian syndrome (PCOS) is one of the most prevalent endocrine disorders of women of reproductive age. Treatment plans for this chronic condition frequently include long-term use of a combination of medication and lifestyle interventions. However, treatment outcomes are dependent on adherence to treatment regimens. This study aimed to systematically review the literature for reported adherence to treatments for PCOS. METHODS:A systematic search of Embase, Cochrane, PubMed, CINAHL, PsychINFO, SCOPUS, and International Pharmaceutical Abstracts from inception until January 2019 utilizing the terms PCOS, adherence, and patient compliance was conducted. A total of 179 possible articles were identified. RESULTS:Fourteen articles reporting adherence data were included in the review. Self-report was the most commonly reported method of measuring adherence. Adherence to lifestyle interventions, such as prescribed diets and physical activity, was reported in ten studies and adherence to medications was reported in seven studies, with some reporting both. CONCLUSIONS:Minimal data are available regarding factors associated with adherence in patients with PCOS. Diverse methods of adherence assessment are utilized. Future studies of PCOS treatments should effectively assess and report adherence data as it is essential to evaluating the effectiveness of PCOS treatments and is critically needed to guide clinician efforts to facilitate optimal outcomes for patients.

SUBMITTER: Parker M

PROVIDER: S-EPMC7017995 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Adherence to treatment for polycystic ovarian syndrome: A systematic review.

Parker Madison M Warren Anna A Nair Sonam S Barnard Marie M

PloS one 20200213 2

<h4>Background</h4>Polycystic ovarian syndrome (PCOS) is one of the most prevalent endocrine disorders of women of reproductive age. Treatment plans for this chronic condition frequently include long-term use of a combination of medication and lifestyle interventions. However, treatment outcomes are dependent on adherence to treatment regimens. This study aimed to systematically review the literature for reported adherence to treatments for PCOS.<h4>Methods</h4>A systematic search of Embase, Coc ...[more]

PMID: 32053629

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Project description:Polycystic ovarian syndrome (PCOS) is a kind of common gynecological endocrine disorder. And the mutations of melatonin receptor (MTNR) genes are related to the occurrence of PCOS. But previous researches have shown opposite results. So, the object of our systematic review and meta-analysis is to investigate the relationship between MTNR 1A/B polymorphisms and PCOS. PubMed, Embase, Ovid, the Cochrane Library, Web of Science and three Chinese databases (VIP, CNKI and Wanfang) were used to retrieve eligible articles published between January 1980 and February 2020. And we used the odds ratio (OR) and its 95% confidence interval (CI) to investigate the strength of the association by six genetic models, allelic, codominant (homozygous and heterozygous), dominant, recessive and superdominant models. Review Manager 5.3, IBM SPSS statistics 25 and Stata MP 16.0 software were used to do this meta-analysis. Our meta-analysis involved 2553 PCOS patients and 3152 controls, for two single nucleotide polymorphisms (rs10830963 C> G in MTNR1B and rs2119882 T> C in MTNR1A) and significant associations were found in some genetic models of these single nucleotide polymorphisms (SNPs). For rs10830963, strongly significant was found in the heterozygote model (GC vs. CC, P=0.02). Additionally, a slight trend was detected in the allelic (G vs. C), homozygote (GG vs. CC) and dominant (GG+GC vs. CC) model of rs10830963 (P=0.05). And after further sensitivity analysis, a study with high heterogeneity was removed. In the allelic (P=0.000), homozygote (P=0.001), dominant (P=0.000) and recessive (GG vs. GC+CC, P=0.001) model, strong associations between rs10830963 and PCOS were found. Moreover, for rs2119882, five genetic models, allelic (C vs. T, P=0.000), codominant (the homozygote (CC vs. TT, P=0.000) and heterozygote model (CT vs. TT, P=0.02), dominant (CC + CT vs. TT, P=0.03) and recessive model (CC vs. CT + TT, P=0.000) showed significant statistical associations with PCOS. MTNR1B rs10830963 and MTNR1B rs2119882 polymorphisms are associated with PCOS risk. However, the above conclusions still require being confirmed by much larger multi-ethnic studies.

Project description:To evaluate in vitro maturation (IVM) in sub-fertile women with polycystic ovarian syndrome (PCOS) undergoing in vitro fertilisation (IVF), by comparing outcomes with a control group of non-PCOS.A search strategy was developed for PubMed and studies reporting rates of the following outcomes (live birth; clinical pregnancy; implantation; cycle cancellation; oocyte maturation; oocyte fertilization; miscarriage) between patients with PCOS, PCO and controls undergoing IVM were deemed eligible. The review was conducted in accordance to the PRISMA guidelines and included studies quality was assessed through the Newcastle-Ottawa Quality scale. ORs with their corresponding 95% CIs were calculated for the main analysis and subgroup analyses were performed for PCOS cases vs. controls and PCOS vs. PCO cases. Alternative analyses were performed for live birth and clinical pregnancy, based on cycles and on women. Subgroup analyses for FSH stimulation, hCG priming and type of procedure (IVF/ICSI) were undertaken for all meta-analyses encompassing at least four study arms. Random effects models were used to calculate pooled effect estimates.Eleven studies were identified. A total of 268 PCOS patients (328 cycles), 100 PCO patients (110 cycles) and 440 controls (480 cycles) were included in the meta-analysis. A borderline trend towards higher birth rates among PCOS patients emerged (pooled OR = 1.74, 95%CI: 0.99-3.04) mainly reflected at the subgroup analysis vs. controls. Clinical pregnancy (pooled OR = 2.37, 95%CI: 1.53-3.68) and implantation rates (pooled OR = 1.73, 95%CI: 1.06-2.81) were higher, while cancellation rates lower (pooled OR = 0.18, 95%CI: 0.06-0.47) among PCOS vs. non-PCOS subjects; maturation and miscarriage rates did not differ between groups, while a borderline trend towards lower fertilization rates among PCOS patients was observed.The present meta-analysis provides preliminary evidence on the effectiveness of IVM as a treatment option when offered in sub-fertile PCOS women, as the latter present at least as high outcome rates as those in non-PCOS.

Project description:IntroductionPolycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS.MethodsWe applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022.Results135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies).ConclusionArtificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022295287.

Dataset Information

Adherence to treatment for polycystic ovarian syndrome: A systematic review.

Publications

Adherence to treatment for polycystic ovarian syndrome: A systematic review.

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