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1.
Hum Reprod ; 37(5): 936-946, 2022 05 03.
Artigo em Inglês | MEDLINE | ID: mdl-35333346

RESUMO

STUDY QUESTION: What are clinical predictors for successful medical treatment in case of early pregnancy loss (EPL)? SUMMARY ANSWER: Use of mifepristone, BMI, number of previous uterine aspirations and the presence of minor clinical symptoms (slight vaginal bleeding or some abdominal cramps) at treatment start are predictors for successful medical treatment in case of EPL. WHAT IS KNOWN ALREADY: Success rates of medical treatment for EPL vary strongly, between but also within different treatment regimens. Up until now, although some predictors have been identified, no clinical prediction model has been developed yet. STUDY DESIGN, SIZE, DURATION: Secondary analysis of a multicentre randomized controlled trial in 17 Dutch hospitals, executed between 28 June 2018 and 8 January 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS: Women with a non-viable pregnancy between 6 and 14 weeks of gestational age, who opted for medical treatment after a minimum of 1 week of unsuccessful expectant management. Potential predictors for successful medical treatment of EPL were chosen based on literature and expert opinions. We internally validated the prediction model using bootstrapping techniques. MAIN RESULTS AND THE ROLE OF CHANCE: 237 out of 344 women had a successful medical EPL treatment (68.9%). The model includes the following variables: use of mifepristone, BMI, number of previous uterine aspirations and the presence of minor clinical symptoms (slight vaginal bleeding or some abdominal cramps) at treatment start. The model shows a moderate capacity to discriminate between success and failure of treatment, with an AUC of 67.6% (95% CI = 64.9-70.3%). The model had a good fit comparing predicted to observed probabilities of success but might underestimate treatment success in women with a predicted probability of success of ∼70%. LIMITATIONS, REASONS FOR CAUTION: The vast majority (90.4%) of women were Caucasian, potentially leading to less optimal model performance in a non-Caucasian population. Limitations of our model are that we have not yet been able to externally validate its performance and clinical impact, and the moderate accuracy of the prediction model of 0.67. WIDER IMPLICATIONS OF THE FINDINGS: We developed a prediction model, aimed to improve and personalize counselling for medical treatment of EPL by providing a woman with her individual chance of complete evacuation. STUDY FUNDING/COMPETING INTEREST(S): The Triple M Trial, upon which this secondary analysis was performed, was funded by the Healthcare Insurers Innovation Foundation (project number 3080 B15-191). TRIAL REGISTRATION NUMBER: Clinicaltrials.gov: NCT03212352.


Assuntos
Aborto Espontâneo , Cólica , Aborto Espontâneo/tratamento farmacológico , Cólica/tratamento farmacológico , Feminino , Humanos , Masculino , Mifepristona/uso terapêutico , Modelos Estatísticos , Gravidez , Probabilidade , Prognóstico , Hemorragia Uterina/tratamento farmacológico
2.
Ned Tijdschr Geneeskd ; 160: A9883, 2016.
Artigo em Holandês | MEDLINE | ID: mdl-27405565

RESUMO

BACKGROUND: Endometriosis is a common disease, especially in subfertile women. The most common location is in the pelvis, but extragenital locations are also possible. This far less common condition has been described in almost all tissues in the body. Symptoms occurring cyclically are characteristic of endometriosis. CASE DESCRIPTION: A 37-year-old woman was discovered by chance to have ascites and pleural effusion. She had no symptoms of this. Thoracoscopy showed an image consistent with thoracic endometriosis. After initial drug therapy was unsuccessful, surgical intervention was performed. CONCLUSION: Thoracic endometriosis is a rare disease, in which the cyclical nature of the symptoms often leads to correct diagnosis. Drug therapy is the preferred treatment for patients.


Assuntos
Ascite/etiologia , Endometriose/complicações , Derrame Pleural/etiologia , Doenças Torácicas/complicações , Adulto , Endometriose/cirurgia , Feminino , Humanos , Doenças Torácicas/cirurgia , Toracoscopia
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