Maternal Early Warning Systems

Friedman, Alexander M.

doi:10.1016/j.ogc.2015.01.006

Cited by 44 publications

(49 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The puerperal were evaluated by the physician, and were verified by the time that was approximately five minutes between the trigger and the call for medical care, and the same timing was verified between the trigger and the medical care. These findings are in agreement with the recommendations of various early warning systems, 18,19,22 where rapid response in providing medical care is the key to avoid clinical decompensation.…”

Section: Tablesupporting

confidence: 89%

“…MEOWS was developed to provide a specific early warning system for the obstetric population, identifying patients at risk on severe obstetric complications and permit intervention in being made at an early stage. 9,19 Nevertheless, despite the large number of triggers found, there were no cases of maternal near miss or death. We believe that adverse outcomes were not detected because the sample size was too small to show these outcomes.…”

Section: Discussionmentioning

confidence: 94%

See 1 more Smart Citation

The use of the Modified Early Obstetric Warning System (MEOWS) in women after pregnancies: a descriptive study

Schuler

Katz

Melo

et al. 2019

Rev. Bras. Saude Mater. Infant.

View full text Add to dashboard Cite

Objectives: to evaluate the modified early obstetric warning system (MEOWS) in women after pregnancies in a tertiary hospital in Brazil. Methods: a descriptive study was conducted with 705 hospitalized women. Vital signs (systolic and diastolic blood pressure, heart rate, respiratory rate, temperature) and lochia were registered on medical records and transcribed into the MEOWS chart of physiological parameters. On this graphic chart, yellow alerts were used to present moderate abnormalities in vital signs, while severe abnormalities were presented in red. The presence of at least one red alert or two yellow alerts were triggered to indicate the need for medical evaluation. Results: although abnormalities were found in the physiological parameters of 49.8% of the women identified from MEOWS triggers, medical evaluation was only requested for three patients (0.8%). Conclusions: in a retrospective application of the use of MEOWS showed a significant number of patients had triggered in which the nursing team did not recognize 99.2% of cases. This finding could be attributed to the fact that MEOWS has not been yet adopted in this service as part of the nursing care. The application of this tool would result in a better care because critical situations would be recognized and corrected quickly, avoiding unfavorable outcomes.

show abstract

Section: Tablesupporting

confidence: 89%

Section: Discussionmentioning

confidence: 94%

The use of the Modified Early Obstetric Warning System (MEOWS) in women after pregnancies: a descriptive study

Schuler

Katz

Melo

et al. 2019

Rev. Bras. Saude Mater. Infant.

View full text Add to dashboard Cite

show abstract

“…Early-warning systems are tools used by health-care providers to recognize the early signs of a serious and potentially life-threatening clinical deterioration in order to initiate mitigating interventions and management [26][27][28][29][30] . The key to a successful EWS is to accurately identify highrisk patients for which mitigating interventions exist or can be developed with a high degree of efficacy.…”

Section: The Suicide Attempt Early-warning System (Ews)mentioning

confidence: 99%

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records

Zheng

Wang²,

Hao

et al. 2020

Transl Psychiatry

View full text Add to dashboard Cite

Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to the risk of eventual death by suicide. In this study we sought to develop an EWS for high-risk suicide attempt patients through the development of a population-based risk stratification surveillance system. Advanced machine-learning algorithms and deep neural networks were utilized to build models with the data from electronic health records (EHRs). A final risk score was calculated for each individual and calibrated to indicate the probability of a suicide attempt in the following 1-year time period. Risk scores were subjected to individual-level analysis in order to aid in the interpretation of the results for health-care providers managing the at-risk cohorts. The 1-year suicide attempt risk model attained an area under the curve (AUC ROC) of 0.792 and 0.769 in the retrospective and prospective cohorts, respectively. The suicide attempt rate in the "very high risk" category was 60 times greater than the population baseline when tested in the prospective cohorts. Mental health disorders including depression, bipolar disorders and anxiety, along with substance abuse, impulse control disorders, clinical utilization indicators, and socioeconomic determinants were recognized as significant features associated with incident suicide attempt.

show abstract

“…Obstetric patients with infections may present with nonspecific symptoms and early investigation is necessary to exclude severe infection . Maternity units should ensure observations are recorded on maternity‐specific charts for all obstetric and postpartum patients . Utilization of these charts has been shown to promote earlier detection and therefore treatment of pregnant or early postpartum women who are developing a critical illness…”

Section: Methodsmentioning

confidence: 99%