Decision Tree Analysis: A Retrospective Analysis of Postoperative Recurrence of Adhesions in Patients with Moderate-to-Severe Intrauterine.
Objective. To establish and validate a decision tree model to predict the recurrence of intrauterine adhesions (IUAs) in patients after separation of moderate-to-severe IUAs. Design. A retrospective study. Setting. A tertiary hysteroscopic center at a teaching hospital. Population. Patients were ret...
| Publicado en: | BioMed Research International pp. 1 - 9 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Wiley-Blackwell
12/12/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=140303324&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140303324 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/12/2019 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 140303324 140303324 140303324 10.1155/2019/7391965 140303324 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Decision Tree Analysis: A Retrospective Analysis of Postoperative Recurrence of Adhesions in Patients with Moderate-to-Severe Intrauterine. aug: au: Zhu, Ru Duan, Hua Wang, Sha Gan, Lu Xu, Qian Li, Jinjiao affil: Department of Minimally Invasive Gynecology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100006, China sug: subj: Severity of Illness Uterus Pathology Uterus Surgery Adhesions Surgery Postoperative Complications Risk Factors Recurrence Risk Factors Adhesions Risk Factors Risk Assessment Decision Trees Human Retrospective Design Randomized Controlled Trials Tertiary Health Care Academic Medical Centers Record Review Hysteroscopy Methods Multiple Logistic Regression Algorithms Organizations, Nonprofit United States United States Endometrium Anatomy and Histology Uterus Anatomy and Histology Validity ROC Curve Confidence Intervals Physicians Decision Making, Clinical ab: Objective. To establish and validate a decision tree model to predict the recurrence of intrauterine adhesions (IUAs) in patients after separation of moderate-to-severe IUAs. Design. A retrospective study. Setting. A tertiary hysteroscopic center at a teaching hospital. Population. Patients were retrospectively selected who had undergone hysteroscopic adhesion separation surgery for treatment of moderate-to-severe IUAs. Interventions. Hysteroscopic adhesion separation surgery and second-look hysteroscopy 3 months later. Measurements and Main Results. Patients' demographics, clinical indicators, and hysteroscopy data were collected from the electronic database of the hospital. The patients were randomly apportioned to either a training or testing set (332 and 142 patients, respectively). A decision tree model of adhesion recurrence was established with a classification and regression tree algorithm and validated with reference to a multivariate logistic regression model. The decision tree model was constructed based on the training set. The classification node variables were the risk factors for recurrence of IUAs: American Fertility Society score (root node variable), isolation barrier, endometrial thickness, tubal opening, uterine volume, and menstrual volume. The accuracies of the decision tree model and multivariate logistic regression analysis model were 75.35% and 76.06%, respectively, and areas under the receiver operating characteristic curve were 0.763 (95% CI 0.681–0.846) and 0.785 (95% CI 0.702–0.868). Conclusions. The decision tree model can readily predict the recurrence of IUAs and provides a new theoretical basis upon which clinicians can make appropriate clinical decisions. pubtype: Academic Journal doctype: research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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