Dataset Information

A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit.

ABSTRACT: Early diagnosis of sepsis and septic shock has been unambiguously linked to lower mortality and better patient outcomes. Despite this, there is a strong unmet need for a reliable clinical tool that can be used for large-scale automated screening to identify high-risk patients. We addressed the following questions: Can a novel algorithm to identify patients at high risk of septic shock 24?hours before diagnosis be discovered using available clinical data? What are performance characteristics of this predictive algorithm? Can current metrics for evaluation of sepsis be improved using novel algorithm? Publicly available data from the intensive care unit setting was used to build septic shock and control patient cohorts. Using Bayesian networks, causal relationships between diagnosis groups, procedure groups, laboratory results, and demographic data were inferred. Predictive model for septic shock 24?hours prior to digital diagnosis was built based on inferred causal networks. Sepsis risk scores were augmented by de novo inferred model and performance was evaluated. A novel predictive model to identify high-risk patients 24?hours ahead of time, with area under curve of 0.81, negative predictive value of 0.87, and a positive predictive value as high as 0.65 was built. The specificity of quick sequential organ failure assessment, systemic inflammatory response syndrome, and modified early warning score was improved when augmented with the novel model, whereas no improvements were made to the sequential organ failure assessment score. We used a data-driven, expert knowledge agnostic method to build a screening algorithm for early detection of septic shock. The model demonstrates strong performance in the data set used and provides a basis for expanding this work toward building an algorithm that is used to screen patients based on electronic medical record data in real time.

SUBMITTER: Yee CR

PROVIDER: S-EPMC6829643 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit.

Yee Christopher R CR Narain Niven R NR Akmaev Viatcheslav R VR Vemulapalli Vijetha V

Biomedical informatics insights 20191104

Early diagnosis of sepsis and septic shock has been unambiguously linked to lower mortality and better patient outcomes. Despite this, there is a strong unmet need for a reliable clinical tool that can be used for large-scale automated screening to identify high-risk patients. We addressed the following questions: Can a novel algorithm to identify patients at high risk of septic shock 24 hours before diagnosis be discovered using available clinical data? What are performance characteristics of t ...[more]

PMID: 31700248

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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:Background: The impact of intensive care unit (ICU) admission during life-threatening critical illness on survival of patients with advanced cancer remains unknown. Methods: We identified incident stage IV cancer patients from Taiwan Cancer Registry during 2009-2013 and ascertained the first episode of septic shock after cancer diagnosis. Patient was classified as ICU admission and no ICU admission during the index hospitalization. Primary outcome of interest was overall survival. Propensity score (PS) and proportional hazards regression were used to control potential confounders. Results: A total of 11,825 stage IV cancer patients with septic shock were identified. Among them, 6,089 (51.5%) patients were admitted to ICU during the index hospitalization and 3,626 (30.7%) patients survived the index hospitalization. A 1:1 propensity score (PS)-matched cohort of 7,186 patients were created for patients with/without ICU admission among the total study population. Both the PS-stratified analysis among the overall population (pooled hazard ratio [HR]: 0.78, 95% confidence interval [CI]: 0.74-0.81) and analysis among the PS-matched population (HR: 0.76, 95% CI: 0.72-0.79) showed association between ICU admission and better overall survival. ICU admission was also associated with a lower risk of in-hospital mortality in both PS-stratified analysis (pooled odds ratio [OR]: 0.69, 95% CI: 0.63-0.75) and PS-matched analysis (OR: 0.61, 95% CI: 0.55-0.68). In PS-stratified analysis for long-term survival after discharge among hospital survivors, ICU admission was associated with improved long-term survival after discharge (pooled HR: 0.73, 95% CI: 0.68-0.80). Also ICU admission was associated with better long-term survival after discharge (HR: 0.77, 95% CI: 0.70-0.85) in PS-matched analysis. Conclusions: Though ICU admission with aggressive treatment may be associated with improved survival, the majority (70%) of stage IV cancer patients with septic shock were unable to survive until hospital discharge.

Dataset Information

A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit.

Publications

A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit.

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