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A framework to characterize the performance of early warning index alarm systems for patient monitoring.

ABSTRACT: In [Scully, C.G., and Daluwatte, C., Evaluating performance of early warning indices to predict physiological instabilities. J Biomed Inform. 75 (2017) 14-21], a framework was presented to characterize the performance of warning indices to provide information on the 1) probability a critical health event will occur when a warning is given (analogous to positive predictive value) and 2) proportion of warned events to all events (analogous to sensitivity). This framework also provides information about the timeliness of the warnings with respect to event occurrence and the warning burden of the system. •In the current work, we provide information on how this framework can be used when cases without events are present in a dataset to examine the proportion of warned non-events to all non-events (analogous to false positive rate).•Information on steps to apply the method, software, data and results for the case study are also provided to enable implementation of the framework.•Application and extension of the framework is demonstrated and discussed by adding non-event records to our previous case study comparing two warning strategies to predict physiologic instabilities.

SUBMITTER: Daluwatte C

PROVIDER: S-EPMC6660561 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A framework to characterize the performance of early warning index alarm systems for patient monitoring.

Daluwatte Chathuri C Yaghouby Farid F Scully Christopher C

MethodsX 20190708

In [Scully, C.G., and Daluwatte, C., Evaluating performance of early warning indices to predict physiological instabilities. J Biomed Inform. 75 (2017) 14-21], a framework was presented to characterize the performance of warning indices to provide information on the 1) probability a critical health event will occur when a warning is given (analogous to positive predictive value) and 2) proportion of warned events to all events (analogous to sensitivity). This framework also provides information ...[more]

PMID: 31372354

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Project description:IntroductionSeveral versions of Early Warning Systems (EWS) are used in obstetrics to detect and treat early clinical deterioration to avert morbidity and mortality. EWS can potentially be useful to improve the quality of care and reduce the risk of maternal mortality in resource-limited settings. We conducted a systematic literature review of published obstetric early warning systems, define their predictive accuracy for morbidity and mortality, and their effectiveness in triggering corrective actions and improving health outcomes.MethodsWe systematically searched for primary research articles on obstetric EWS published in peer-reviewed journals between January 1997 and March 2018 in Medline, CINAHL, SCOPUS, Science Direct, and Science Citation Index. We also searched reference lists of relevant articles and websites of professional societies. We included studies that assessed the predictive accuracy of EWS to detect clinical deterioration, or/and their effectiveness in improving clinical outcomes in obstetric inpatients. We excluded studies with a paediatric or non-obstetric adult population. Cross-sectional and qualitative studies were also excluded. We performed a narrative synthesis since the outcomes reported were heterogeneous.ResultsA total of 381 papers were identified, 17 of which met the inclusion criteria. Eleven of the included studies evaluated the predictive accuracy of EWS for obstetric morbidity and mortality, 5 studies assessed the effectiveness of EWS in improving clinical outcomes, while one study addressed both. Sixteen published EWS versions were reviewed, 14 of which included five basic clinical observations (pulse rate, respiratory rate, temperature, blood pressure, and consciousness level). The obstetric EWS identified had very high median (inter-quartile range) sensitivity-89% (72% to 97%) and specificity-85% (67% to 98%) but low median (inter-quartile range) positive predictive values-41% (25% to 74%) for predicting morbidity or ICU admission. Obstetric EWS had a very high accuracy in predicting death (AUROC >0.80) among critically ill obstetric patients. Obstetric EWS improves the frequency of routine vital sign observation, reduces the interval between the recording of specifically defined abnormal clinical observations and corrective clinical actions, and can potentially reduce the severity of obstetric morbidity.ConclusionObstetric EWS are effective in predicting severe morbidity (in general obstetric population) and mortality (in critically ill obstetric patients). EWS can contribute to improved quality of care, prevent progressive obstetric morbidity and improve health outcomes. There is limited evidence of the effectiveness of EWS in reducing maternal death across all settings. Clinical parameters in most obstetric EWS versions are routinely collected in resource-limited settings, therefore implementing EWS may be feasible in such settings.

Project description:BackgroundEarly warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. While climate-based EWS have been developed for malaria, no standard protocol to evaluate and compare EWS has been proposed. Additionally, there are several neglected tropical diseases whose transmission is sensitive to environmental conditions, for which no EWS have been proposed, though they represent a large burden for the affected populations.Methodology/principal findingsIn the present paper, an overview of the available linear and non-linear tools to predict seasonal time series of diseases is presented. Also, a general methodology to compare and evaluate models for prediction is presented and illustrated using American cutaneous leishmaniasis, a neglected tropical disease, as an example. The comparison of the different models using the predictive R(2) for forecasts of "out-of-fit" data (data that has not been used to fit the models) shows that for the several linear and non-linear models tested, the best results were obtained for seasonal autoregressive (SAR) models that incorporate climatic covariates. An additional bootstrapping experiment shows that the relationship of the disease time series with the climatic covariates is strong and consistent for the SAR modeling approach. While the autoregressive part of the model is not significant, the exogenous forcing due to climate is always statistically significant. Prediction accuracy can vary from 50% to over 80% for disease burden at time scales of one year or shorter.Conclusions/significancethis study illustrates a protocol for the development of EWS that includes three main steps: (i) the fitting of different models using several methodologies, (ii) the comparison of models based on the predictability of "out-of-fit" data, and (iii) the assessment of the robustness of the relationship between the disease and the variables in the model selected as best with an objective criterion.

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A framework to characterize the performance of early warning index alarm systems for patient monitoring.

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A framework to characterize the performance of early warning index alarm systems for patient monitoring.

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