Data-driven approach to Early Warning Score-based alert management

Capan, Müge; Hoover, Stephen; Miller, Kristen; Pal, Carmen; Glasgow, Justin; Jackson, Eric; Arnold, Ryan

doi:10.1136/bmjoq-2017-000088

Cited by 12 publications

(8 citation statements)

References 29 publications

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“…Prestructured fields in the EHR may be pertinent tools for preventing omissions and could provide an alert in real time, prompting such discussion. Alerts of this type have already proved effective for preventing drug interactions and are currently used in this context ( 30 , 31 ). A similar reminder could be issued for all women of childbearing age receiving gonadotoxic treatment, to improve oncofertility practices in cancer care.…”

Section: Discussionmentioning

confidence: 99%

Factors Associated With the Discussion of Fertility Preservation in a Cohort of 1,357 Young Breast Cancer Patients Receiving Chemotherapy

et al. 2021

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PurposeFemale breast cancer (BC) patients exposed to gonadotoxic chemotherapy are at risk of future infertility. There is evidence of disparities in the discussion of fertility preservation for these patients. The aim of the study was to identify factors influencing the discussion of fertility preservation (FP).Material and MethodsWe analyzed consecutive BC patients treated by chemotherapy at Institut Curie from 2011-2017 and aged 18-43 years at BC diagnosis. The discussion of FP was classified in a binary manner (discussion/no discussion), based on mentions present in the patient’s electronic health record (EHR) before the initiation of chemotherapy. The associations between FP discussion and the characteristics of patients/tumors and healthcare practitioners were investigated by logistic regression analysis.ResultsThe median age of the 1357 patients included in the cohort was 38.7 years, and median tumor size was 30.3 mm. The distribution of BC subtypes was as follows: 702 luminal BCs (58%), 241 triple-negative breast cancers (TNBCs) (20%), 193 HER2+/HR+ (16%) and 81 HER2+/HR- (6%). All patients received chemotherapy in a neoadjuvant (n=611, 45%) or adjuvant (n= 744, 55%) setting. A discussion of FP was mentioned for 447 patients (33%). Earlier age at diagnosis (discussion: 34.4 years versus no discussion: 40.5 years), nulliparity (discussion: 62% versus no discussion: 38%), and year of BC diagnosis were the patient characteristics significantly associated with the mention of FP discussion. Surgeons and female physicians were the most likely to mention FP during the consultation before the initiation of chemotherapy (discussion: 22% and 21%, respectively). The likelihood of FP discussion increased significantly over time, from 15% in 2011 to 45% in 2017. After multivariate analysis, FP discussion was significantly associated with younger age, number of children before BC diagnosis, physicians’ gender and physicians’ specialty.ConclusionFP discussion rates are low and are influenced by patient and physician characteristics. There is therefore room for improvement in the promotion and systematization of FP discussion.

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Section: Discussionmentioning

confidence: 99%

Factors Associated With the Discussion of Fertility Preservation in a Cohort of 1,357 Young Breast Cancer Patients Receiving Chemotherapy

et al. 2021

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“…Accuracy and false alarm rate are important issues to consider in the practical implementation of EWS in ICU settings. Because sensitivity and specificity mutually interact, the performance of EWS and alarm fatigue should be weighed and optimized [50]. Notably, PROMPT consistently provided higher specificity than PIM 3 and other algorithms against which it was tested.…”

Section: Discussionmentioning

confidence: 99%

A deep learning model for real-time mortality prediction in critically ill children

Kim

Kim²,

Cho

et al. 2019

Crit Care

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Background The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units. Methods Utilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center. Results Using seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89–0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts. Conclusions PROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients. Electronic supplementary material The online version of this article (10.1186/s13054-019-2561-z) contains supplementary material, which is available to authorized users.

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“…After investigating those recovered patients, we found that most of them (25/31) received diagnoses of either cardiovascular diseases, renal disease, cancer, lung disease, or acute cerebrovascular disease, which implied severe acute or chronic disease conditions as well as the requirement of more intense care during their hospital stay. In such an early warning context, it is demonstrated elsewhere that sensitivity and PPV are always considered important indicators; however, these patients who were alerted as high risk yet later recovered may not necessarily be treated as falsely alarmed individuals, as caregivers could always provide clinical intervention or treatment to these high-risk patients during their deterioration process and potentially prevent their death events from occurring [34]. Thus, from the perspective of inpatient mortality reduction and clinical care promotion, it would be valuable to track and summarize those efficient interventions or treatments provided to these high-risk but recovered patients, facilitating evidence-based clinical decision making and individualized care planning for other high-risk patients.…”

Section: Discussionmentioning

confidence: 99%

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data

Ye¹,

Wang²,

Liu³

et al. 2019

J Med Internet Res

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Background The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs. Objective The aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes. Methods Data were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality. Results The EWS algorithm scored patients’ daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS. Conclusions In this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients’ better health outcomes in target medical facilities.

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Data-driven approach to Early Warning Score-based alert management

Cited by 12 publications

References 29 publications

Factors Associated With the Discussion of Fertility Preservation in a Cohort of 1,357 Young Breast Cancer Patients Receiving Chemotherapy

Factors Associated With the Discussion of Fertility Preservation in a Cohort of 1,357 Young Breast Cancer Patients Receiving Chemotherapy

A deep learning model for real-time mortality prediction in critically ill children

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data

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