Dataset Information

Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data.

ABSTRACT:

Background

Accurate, timely and automated identification of patients at high risk for severe clinical deterioration using readily available clinical information in the electronic medical record (EMR) could inform health systems to target scarce resources and save lives.

Methods

We identified 7,466 patients admitted to a large, public, urban academic hospital between May 2009 and March 2010. An automated clinical prediction model for out of intensive care unit (ICU) cardiopulmonary arrest and unexpected death was created in the derivation sample (50% randomly selected from total cohort) using multivariable logistic regression. The automated model was then validated in the remaining 50% from the total cohort (validation sample). The primary outcome was a composite of resuscitation events, and death (RED). RED included cardiopulmonary arrest, acute respiratory compromise and unexpected death. Predictors were measured using data from the previous 24 hours. Candidate variables included vital signs, laboratory data, physician orders, medications, floor assignment, and the Modified Early Warning Score (MEWS), among other treatment variables.

Results

RED rates were 1.2% of patient-days for the total cohort. Fourteen variables were independent predictors of RED and included age, oxygenation, diastolic blood pressure, arterial blood gas and laboratory values, emergent orders, and assignment to a high risk floor. The automated model had excellent discrimination (c-statistic=0.85) and calibration and was more sensitive (51.6% and 42.2%) and specific (94.3% and 91.3%) than the MEWS alone. The automated model predicted RED 15.9 hours before they occurred and earlier than Rapid Response Team (RRT) activation (5.7 hours prior to an event, p=0.003)

Conclusion

An automated model harnessing EMR data offers great potential for identifying RED and was superior to both a prior risk model and the human judgment-driven RRT.

SUBMITTER: Alvarez CA

PROVIDER: S-EPMC3599266 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Publications

Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data.

Alvarez Carlos A CA Clark Christopher A CA Zhang Song S Halm Ethan A EA Shannon John J JJ Girod Carlos E CE Cooper Lauren L Amarasingham Ruben R

BMC medical informatics and decision making 20130227

<h4>Background</h4>Accurate, timely and automated identification of patients at high risk for severe clinical deterioration using readily available clinical information in the electronic medical record (EMR) could inform health systems to target scarce resources and save lives.<h4>Methods</h4>We identified 7,466 patients admitted to a large, public, urban academic hospital between May 2009 and March 2010. An automated clinical prediction model for out of intensive care unit (ICU) cardiopulmonary ...[more]

PMID: 23442316

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Project description:RationalePatients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients.ObjectivesWe aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms.MethodsThis observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total of 24,885 intensive care unit transfers to the wards were included, with 14,962 transfers (60%) in the training cohort and 9,923 transfers (40%) in the internal validation cohort. Patient characteristics, nursing assessments, International Classification of Diseases, Ninth Revision codes from prior admissions, medications, intensive care unit interventions, diagnostic tests, vital signs, and laboratory results were extracted from the electronic health record and used as predictor variables in a gradient-boosted machine model. Accuracy for predicting intensive care unit readmission was compared with the Stability and Workload Index for Transfer score and Modified Early Warning Score in the internal validation cohort and also externally using the Medical Information Mart for Intensive Care database (n = 42,303 intensive care unit transfers).ResultsEleven percent (2,834) of discharges to the wards were later readmitted to the intensive care unit. The machine-learning-derived model had significantly better performance (area under the receiver operating curve, 0.76) than either the Stability and Workload Index for Transfer score (area under the receiver operating curve, 0.65), or Modified Early Warning Score (area under the receiver operating curve, 0.58; P value < 0.0001 for all comparisons). At a specificity of 95%, the derived model had a sensitivity of 28% compared with 15% for Stability and Workload Index for Transfer score and 7% for the Modified Early Warning Score. Accuracy improvements with the derived model over Modified Early Warning Score and Stability and Workload Index for Transfer were similar in the Medical Information Mart for Intensive Care-III cohort.ConclusionsA machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.

Project description:ObjectiveTo establish the role of high-fidelity simulation training to test the efficacy and safety of the electronic health record (EHR)-user interface within the intensive care unit (ICU) environment.DesignProspective pilot study.SettingMedical ICU in an academic medical centre.ParticipantsPostgraduate medical trainees.InterventionsA 5-day-simulated ICU patient was developed in the EHR including labs, hourly vitals, medication administration, ventilator settings, nursing and notes. Fourteen medical issues requiring recognition and subsequent changes in management were included. Issues were chosen based on their frequency of occurrence within the ICU and their ability to test different aspects of the EHR-user interface. ICU residents, blinded to the presence of medical errors within the case, were provided a sign-out and given 10 min to review the case in the EHR. They then presented the case with their management suggestions to an attending physician. Participants were graded on the number of issues identified. All participants were provided with immediate feedback upon completion of the simulation.Primary and secondary outcomesTo determine the frequency of error recognition in an EHR simulation. To determine factors associated with improved performance in the simulation.Results38 participants including 9 interns, 10 residents and 19 fellows were tested. The average error recognition rate was 41% (range 6-73%), which increased slightly with the level of training (35%, 41% and 50% for interns, residents, and fellows, respectively). Over-sedation was the least-recognised error (16%); poor glycemic control was most often recognised (68%). Only 32% of the participants recognised inappropriate antibiotic dosing. Performance correlated with the total number of screens used (p=0.03).ConclusionsDespite development of comprehensive EHRs, there remain significant gaps in identifying dangerous medical management issues. This gap remains despite high levels of medical training, suggesting that EHR-specific training may be beneficial. Simulation provides a novel tool in order to both identify these gaps as well as foster EHR-specific training.

Dataset Information

Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data.

Background

Methods

Results

Conclusion

Publications

Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data.

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