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Development and validation of an obstetric early warning system model for use in low resource settings.

ABSTRACT: BACKGROUND:The use of obstetric early-warning-systems (EWS) has been recommended to improve timely recognition, management and early referral of women who have or are developing a critical illness. Development of such prediction models should involve a statistical combination of predictor clinical observations into a multivariable model which should be validated. No obstetric EWS has been developed and validated for low resource settings. We report on the development and validation of a simple prediction model for obstetric morbidity and mortality in resource-limited settings. METHODS:We performed a multivariate logistic regression analysis using a retrospective case-control analysis of secondary data with clinical indices predictive of severe maternal outcome (SMO). Cases for design and validation were randomly selected (n?=?500) from 4360 women diagnosed with SMO in 42 Nigerian tertiary-hospitals between June 2012 and mid-August 2013. Controls were 1000 obstetric admissions without SMO diagnosis. We used clinical observations collected within 24?h of SMO occurrence for cases, and normal births for controls. We created a combined dataset with two controls per case, split randomly into development (n?=?600) and validation (n?=?900) datasets. We assessed the model's validity using sensitivity and specificity measures and its overall performance in predicting SMO using receiver operator characteristic (ROC) curves. We then fitted the final developmental model on the validation dataset and assessed its performance. Using the reference range proposed in the United Kingdom Confidential-Enquiry-into-Maternal-and-Child-Health 2007-report, we converted the model into a simple score-based obstetric EWS algorithm. RESULTS:The final developmental model comprised abnormal systolic blood pressure-(SBP?>?140?mmHg or??90?mmHg), respiratory rate-(RR?>?40/min), temperature-(>?38?°C), pulse rate-(PR?>?120/min), caesarean-birth, and the number of previous caesarean-births. The model was 86% (95% CI 81-90) sensitive and 92%- (95% CI 89-94) specific in predicting SMO with area under ROC of 92% (95% CI 90-95%). All parameters were significant in the validation model except DBP. The model maintained good discriminatory power in the validation (n?=?900) dataset (AUC 92, 95% CI 88-94%) and had good screening characteristics. Low urine output (300mls/24?h) and conscious level (prolonged unconsciousness-GCS?

SUBMITTER: Umar A

PROVIDER: S-EPMC7488502 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Development and validation of an obstetric early warning system model for use in low resource settings.

Umar Aminu A Manu Alexander A Mathai Matthews M Ameh Charles C

BMC pregnancy and childbirth 20200911 1

<h4>Background</h4>The use of obstetric early-warning-systems (EWS) has been recommended to improve timely recognition, management and early referral of women who have or are developing a critical illness. Development of such prediction models should involve a statistical combination of predictor clinical observations into a multivariable model which should be validated. No obstetric EWS has been developed and validated for low resource settings. We report on the development and validation of a ...[more]

PMID: 32917151

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Project description:BackgroundThe need for palliative care in sub-Saharan Africa is staggering: this region shoulders over 67% of the global burden of HIV/AIDS and cancer. However, provisions for these essential services remain limited and poorly integrated with national health systems in most nations. Moreover, the evidence base for palliative care in the region remains scarce. This study chronicles the development and evaluation of DataPall, an open-source electronic medical records system that can be used to track patients, manage data, and generate reports for palliative care providers in these settings.DataPall was developed using design criteria encompassing both functional and technical objectives articulated by hospital leaders and palliative care staff at a leading palliative care center in Malawi. The database can be used with computers that run Windows XP SP 2 or newer, and does not require an internet connection for use. Subsequent to its development and implementation in two hospitals, DataPall was tested among both trained and untrained hospital staff populations on the basis of its usability with comparison to existing paper records systems as well as on the speed at which users could perform basic database functions. Additionally, all participants evaluated this program on a standard system usability scale.ResultsIn a study of health professionals in a Malawian hospital, DataPall enabled palliative care providers to find patients' appointments, on average, in less than half the time required to locate the same record in current paper records. Moreover, participants generated customizable reports documenting patient records and comprehensive reports on providers' activities with little training necessary. Participants affirmed this ease of use on the system usability scale.ConclusionsDataPall is a simple, effective electronic medical records system that can assist in developing an evidence base of clinical data for palliative care in low resource settings. The system is available at no cost, is specifically designed to chronicle care in the region, and is catered to meet the technical needs and user specifications of such facilities.

Dataset Information

Development and validation of an obstetric early warning system model for use in low resource settings.

Publications

Development and validation of an obstetric early warning system model for use in low resource settings.

Similar Datasets

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