Dataset Information

Development and validation of a protocol for documentation of obstetric perineal lacerations.

ABSTRACT: INTRODUCTION AND HYPOTHESIS:The aim of this study was to develop a new protocol for documentation of perineal lacerations and to validate the latter against the most common obstetric record system in Sweden. The hypothesis was that the new protocol would render more complete data on perineal lacerations than the current documentation method. METHODS:A protocol for documentation of perineal lacerations was developed to be sufficiently comprehensive to serve research purposes. All women delivering their first child vaginally from 13 October 2015 to 1 February 2016 at Örebro University Hospital were eligible for the validation study. Perineal lacerations were documented using the protocol in parallel with the regular obstetric record system (ObstetriX). Cross tabulations were used to compare the coverage regarding perineal lacerations between the two documentation methods. McNemar's test was used to evaluate systematic differences between the methods. RESULTS:A total of 187 women were included. The coverage of documentation regarding perineal laceration was significantly higher (p?

SUBMITTER: Jansson MH

PROVIDER: S-EPMC6861348 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Development and validation of a protocol for documentation of obstetric perineal lacerations.

Jansson Markus Harry MH Nilsson Kerstin K Franzén Karin K

International urogynecology journal 20190319 12

<h4>Introduction and hypothesis</h4>The aim of this study was to develop a new protocol for documentation of perineal lacerations and to validate the latter against the most common obstetric record system in Sweden. The hypothesis was that the new protocol would render more complete data on perineal lacerations than the current documentation method.<h4>Methods</h4>A protocol for documentation of perineal lacerations was developed to be sufficiently comprehensive to serve research purposes. All w ...[more]

PMID: 30888454

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Project description:OBJECTIVE:Sexuality is an important aspect of human identity and contributes significantly to the quality of life in women as well as in men. Impairment in sexual health after vaginal delivery is a major concern for many women. We aimed to examine the association between degree of perineal tear and sexual function 12 months postpartum. DESIGN:A prospective cohort study SETTING: Four Danish hospitals between July 2015 and January 2019 PARTICIPANTS: A total of 554 primiparous women: 191 with no/labia/first-degree tears, 189 with second-degree tears and 174 with third-degree/fourth-degree tears. Baseline data were obtained 2 weeks postpartum by a questionnaire and a clinical examination. Sexual function was evaluated 12 months postpartum by an electronic questionnaire (Pelvic Organ Prolapse/Urinary Incontinence Sexual Function Questionnaire (PISQ-12)) and a clinical examination. PRIMARY OUTCOME MEASURES:Total PISQ-12 score and dyspareunia RESULTS: Episiotomy was performed in 54 cases and 95 women had an operative vaginal delivery. The proportion of women with dyspareunia was 25%, 38% and 53% of women with no/labia/first-degree, second-degree or third-degree/fourth-degree tears, respectively.Compared with women with no/labia/first-degree tears, women with second-degree or third-degree/fourth-degree tears had a higher risk of dyspareunia (adjusted relative risk (aRR) 2.05; 95%?CI 1.51 to 2.78 and aRR 2.09; 95%?CI 1.55 to 2.81, respectively). Women with third-degree/fourth-degree tears had a higher mean PISQ-12 score (12.2) than women with no/labia/first-degree tears (10.4). CONCLUSIONS:Impairment of sexual health is common among primiparous women after vaginal delivery. At 12 months postpartum, more than half of the women with a third-degree/fourth-degree tear experienced dyspareunia. Women delivering with no/labia/first-degree tears reported the best outcomes overall. Thus, it is important to minimise the extent of perineal trauma and to counsel about sexuality during and after pregnancy.

Project description:BackgroundResidents receive infrequent feedback on their clinical reasoning (CR) documentation. While machine learning (ML) and natural language processing (NLP) have been used to assess CR documentation in standardized cases, no studies have described similar use in the clinical environment.ObjectiveThe authors developed and validated using Kane's framework a ML model for automated assessment of CR documentation quality in residents' admission notes.Design, participants, main measuresInternal medicine residents' and subspecialty fellows' admission notes at one medical center from July 2014 to March 2020 were extracted from the electronic health record. Using a validated CR documentation rubric, the authors rated 414 notes for the ML development dataset. Notes were truncated to isolate the relevant portion; an NLP software (cTAKES) extracted disease/disorder named entities and human review generated CR terms. The final model had three input variables and classified notes as demonstrating low- or high-quality CR documentation. The ML model was applied to a retrospective dataset (9591 notes) for human validation and data analysis. Reliability between human and ML ratings was assessed on 205 of these notes with Cohen's kappa. CR documentation quality by post-graduate year (PGY) was evaluated by the Mantel-Haenszel test of trend.Key resultsThe top-performing logistic regression model had an area under the receiver operating characteristic curve of 0.88, a positive predictive value of 0.68, and an accuracy of 0.79. Cohen's kappa was 0.67. Of the 9591 notes, 31.1% demonstrated high-quality CR documentation; quality increased from 27.0% (PGY1) to 31.0% (PGY2) to 39.0% (PGY3) (p < .001 for trend). Validity evidence was collected in each domain of Kane's framework (scoring, generalization, extrapolation, and implications).ConclusionsThe authors developed and validated a high-performing ML model that classifies CR documentation quality in resident admission notes in the clinical environment-a novel application of ML and NLP with many potential use cases.

Project description:BACKGROUND:A number of first-trimester prediction models addressing important obstetric outcomes have been published. However, most models have not been externally validated. External validation is essential before implementing a prediction model in clinical practice. OBJECTIVE:The objective of this paper is to describe the design of a study to externally validate existing first trimester obstetric prediction models, based upon maternal characteristics and standard measurements (eg, blood pressure), for the risk of pre-eclampsia (PE), gestational diabetes mellitus (GDM), spontaneous preterm birth (PTB), small-for-gestational-age (SGA) infants, and large-for-gestational-age (LGA) infants among Dutch pregnant women (Expect Study I). The results of a pilot study on the feasibility and acceptability of the recruitment process and the comprehensibility of the Pregnancy Questionnaire 1 are also reported. METHODS:A multicenter prospective cohort study was performed in The Netherlands between July 1, 2013 and December 31, 2015. First trimester obstetric prediction models were systematically selected from the literature. Predictor variables were measured by the Web-based Pregnancy Questionnaire 1 and pregnancy outcomes were established using the Postpartum Questionnaire 1 and medical records. Information about maternal health-related quality of life, costs, and satisfaction with Dutch obstetric care was collected from a subsample of women. A pilot study was carried out before the official start of inclusion. External validity of the models will be evaluated by assessing discrimination and calibration. RESULTS:Based on the pilot study, minor improvements were made to the recruitment process and online Pregnancy Questionnaire 1. The validation cohort consists of 2614 women. Data analysis of the external validation study is in progress. CONCLUSIONS:This study will offer insight into the generalizability of existing, non-invasive first trimester prediction models for various obstetric outcomes in a Dutch obstetric population. An impact study for the evaluation of the best obstetric prediction models in the Dutch setting with respect to their effect on clinical outcomes, costs, and quality of life-Expect Study II-is being planned. TRIAL REGISTRATION:Netherlands Trial Registry (NTR): NTR4143; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4143 (Archived by WebCite at http://www.webcitation.org/6t8ijtpd9).

Dataset Information

Development and validation of a protocol for documentation of obstetric perineal lacerations.

Publications

Development and validation of a protocol for documentation of obstetric perineal lacerations.

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