Showing papers by "Lisa Shieh published in 2016"

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach.

Abstract: Background: Sepsis is one of the leading causes of mortality in hospitalized patients. Despite this fact, a reliable means of predicting sepsis onset remains elusive. Early and accurate sepsis onset predictions could allow more aggressive and targeted therapy while maintaining antimicrobial stewardship. Existing detection methods suffer from low performance and often require time-consuming laboratory test results. Objective: To study and validate a sepsis prediction method, InSight, for the new Sepsis-3 definitions in retrospective data, make predictions using a minimal set of variables from within the electronic health record data, compare the performance of this approach with existing scoring systems, and investigate the effects of data sparsity on InSight performance. Methods: We apply InSight, a machine learning classification system that uses multivariable combinations of easily obtained patient data (vitals, peripheral capillary oxygen saturation, Glasgow Coma Score, and age), to predict sepsis using the retrospective Multiparameter Intelligent Monitoring in Intensive Care (MIMIC)-III dataset, restricted to intensive care unit (ICU) patients aged 15 years or more. Following the Sepsis-3 definitions of the sepsis syndrome, we compare the classification performance of InSight versus quick sequential organ failure assessment (qSOFA), modified early warning score (MEWS), systemic inflammatory response syndrome (SIRS), simplified acute physiology score (SAPS) II, and sequential organ failure assessment (SOFA) to determine whether or not patients will become septic at a fixed period of time before onset. We also test the robustness of the InSight system to random deletion of individual input observations. Results: In a test dataset with 11.3% sepsis prevalence, InSight produced superior classification performance compared with the alternative scores as measured by area under the receiver operating characteristic curves (AUROC) and area under precision-recall curves (APR). In detection of sepsis onset, InSight attains AUROC = 0.880 (SD 0.006) at onset time and APR = 0.595 (SD 0.016), both of which are superior to the performance attained by SIRS (AUROC: 0.609; APR: 0.160), qSOFA (AUROC: 0.772; APR: 0.277), and MEWS (AUROC: 0.803; APR: 0.327) computed concurrently, as well as SAPS II (AUROC: 0.700; APR: 0.225) and SOFA (AUROC: 0.725; APR: 0.284) computed at admission (P<.001 for all comparisons). Similar results are observed for 1-4 hours preceding sepsis onset. In experiments where approximately 60% of input data are deleted at random, InSight attains an AUROC of 0.781 (SD 0.013) and APR of 0.401 (SD 0.015) at sepsis onset time. Even with 60% of data missing, InSight remains superior to the corresponding SIRS scores (AUROC and APR, P<.001), qSOFA scores (P=.0095; P<.001) and superior to SOFA and SAPS II computed at admission (AUROC and APR, P<.001), where all of these comparison scores (except InSight) are computed without data deletion. Conclusions: Despite using little more than vitals, InSight is an effective tool for predicting sepsis onset and performs well even with randomly missing data. [JMIR Med Inform 2016;4(3):e28]

Hand hygiene of medical students and resident physicians: predictors of attitudes and behaviour

Abstract: Objectives We measured medical students’ and resident trainees’ hand hygiene behaviour, knowledge and attitudes in order to identify important predictors of hand hygiene behaviour in this population. Methods An anonymous, web-based questionnaire was distributed to medical students and residents at Stanford University School of Medicine in August of 2012. The questionnaire included questions regarding participants’ behaviour, knowledge, attitude and experiences about hand hygiene. Behaviour, knowledge and attitude indices were scaled from 0 to 1, with 1 representing superior responses. Using multivariate regression, we identified positive and negative predictors of superior hand hygiene behaviour. We investigated effectiveness of interventions, barriers and comfort reminding others. Results 280 participants (111 students and 169 residents) completed the questionnaire (response rate 27.8%). Residents and medical students reported hand hygiene behaviour compliance of 0.45 and 0.55, respectively (p=0.02). Resident and medical student knowledge was 0.80 and 0.73, respectively (p=0.001). The attitude index for residents was 0.56 and 0.55 for medical students. Regression analysis identified experiences as predictors of hand hygiene behaviour (both positive and negative influence). Knowledge was not a significant predictor of behaviour, but a working gel dispenser and observing attending physicians with good hand hygiene practices were reported by both groups as the most effective strategy in influencing trainees. Conclusions Medical students and residents have similar attitudes about hand hygiene, but differ in their level of knowledge and compliance. Concerns about hierarchy may have a significant negative impact on hand hygiene advocacy.

Validation of Test Performance and Clinical Time Zero for an Electronic Health Record Embedded Severe Sepsis Alert.

Abstract: Bachground: Increasing use of EHRs has generated interest in the potential of computerized clinical decision support to improve treatment of sepsis. Electronic sepsis alerts have had mixed results due to poor test characteristics, the inability to detect sepsis in a timely fashion and the use of outside software limiting widespread adoption. We describe the development, evaluation and validation of an accurate and timely severe sepsis alert with the potential to impact sepsis management. Objective: To develop, evaluate, and validate an accurate and timely severe sepsis alert embedded in a commercial EHR. Methods: The sepsis alert was developed by identifying the most common severe sepsis criteria among a cohort of patients with ICD 9 codes indicating a diagnosis of sepsis. This alert requires criteria in three categories: indicators of a systemic inflammatory response, evidence of suspected infection from physician orders, and markers of organ dysfunction. Chart review was used to evaluate test performance and the ability to detect clinical time zero, the point in time when a patient develops severe sepsis. Results: Two physicians reviewed 100 positive cases and 75 negative cases. Based on this review, sensitivity was 74.5%, specificity was 86.0%, the positive predictive value was 50.3%, and the negative predictive value was 94.7%. The most common source of end-organ dysfunction was MAP less than 70 mm/Hg (59%). The alert was triggered at clinical time zero in 41% of cases and within three hours in 53.6% of cases. 96% of alerts triggered before a manual nurse screen. Conclusion: We are the first to report the time between a sepsis alert and physician chart-review clinical time zero. Incorporating physician orders in the alert criteria improves specificity while maintaining sensitivity, which is important to reduce alert fatigue. By leveraging standard EHR functionality, this alert could be implemented by other healthcare systems.