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The Vitals Risk Index-Retrospective Performance Analysis of an Automated and Objective Pediatric Early Warning System.

ABSTRACT: Introduction:Pediatric in-hospital cardiac arrests and emergent transfers to the pediatric intensive care unit (ICU) represent a serious patient safety concern with associated increased morbidity and mortality. Some institutions have turned to the electronic health record and predictive analytics in search of earlier and more accurate detection of patients at risk for decompensation. Methods:Objective electronic health record data from 2011 to 2017 was utilized to develop an automated early warning system score aimed at identifying hospitalized children at risk of clinical deterioration. Five vital sign measurements and supplemental oxygen requirement data were used to build the Vitals Risk Index (VRI) model, using multivariate logistic regression. We compared the VRI to the hospital's existing early warning system, an adaptation of Monaghan's Pediatric Early Warning Score system (PEWS). The patient population included hospitalized children 18 years of age and younger while being cared for outside of the ICU. This dataset included 158 case hospitalizations (102 emergent transfers to the ICU and 56 "code blue" events) and 135,597 control hospitalizations. Results:When identifying deteriorating patients 2 hours before an event, there was no significant difference between Pediatric Early Warning Score and VRI's areas under the receiver operating characteristic curve at false-positive rates ? 10% (pAUC10 of 0.065 and 0.064, respectively; P = 0.74), a threshold chosen to compare the 2 approaches under clinically tolerable false-positive rates. Conclusions:The VRI represents an objective, simple, and automated predictive analytics tool for identifying hospitalized pediatric patients at risk of deteriorating outside of the ICU setting.

SUBMITTER: Gorham TJ

PROVIDER: S-EPMC7190256 | biostudies-literature | 2020 Mar-Apr

REPOSITORIES: biostudies-literature

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The Vitals Risk Index-Retrospective Performance Analysis of an Automated and Objective Pediatric Early Warning System.

Gorham Tyler J TJ Rust Steve S Rust Laura L Kuehn Stacy S Yang Jing J Lin James Shuhan JS Hoffman Jeffrey J Huang Yungui Y Lin Simon S McClead Richard R Brilli Richard R Bode Ryan R Maa Tensing T

Pediatric quality & safety 20200320 2

<h4>Introduction</h4>Pediatric in-hospital cardiac arrests and emergent transfers to the pediatric intensive care unit (ICU) represent a serious patient safety concern with associated increased morbidity and mortality. Some institutions have turned to the electronic health record and predictive analytics in search of earlier and more accurate detection of patients at risk for decompensation.<h4>Methods</h4>Objective electronic health record data from 2011 to 2017 was utilized to develop an autom ...[more]

PMID: 32426637

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Project description:Early recognition of critically ill patients is of paramount importance to reduce pediatric mortality and morbidity. We created a risk stratification system combining vital parameters and predefined risk factors aimed at reducing the risk of unrecognized clinical deterioration compared with conventional Pediatric Early Warning Systems (PEWS). This single-center retrospective case cohort study included infants (gestational age ≥ 37 weeks) to adolescents (aged <18 years) with unplanned pediatric intensive care unit (PICU) admission between April 01, 2014, and February 28, 2018. The sensitivity in the 24 h prior to endpoint of the Pediatric Risk Evaluation and Stratification System (PRESS) was compared with that of the conventional PEWS and calculated as the proportion of study patients who received a high-risk score. Seventy-four PICU admissions were included. PRESS and PEWS sensitivities at 2 h prior to endpoint were 0.70 (95%CI 0.59 to 0.80) and 0.30 (95%CI 0.20 to 0.42) respectively (p < 0.001). Excluding patients with seizures, PRESS sensitivity increased to 0.75 (95%CI 0.64 to 0.85). Forty-nine patients (66%) scored positive on at least one high-risk factor, and "worried sign" was scored in 31 patients (42%).Conclusion: Risk stratification seems advantageous for a faster detection of clinical deterioration, providing opportunity for earlier intervention. What is Known: • Prompt detection of clinical deterioration is of essential importance to reduce morbidity and mortality. • Conventional Pediatric Early Warning Systems (PEWS) have limited sensitivity and a short window of detection of 1 to 2 h. What is New: • Risk stratification based on context factors allows earlier identification of patients at risk, well before deviation of vital signs. • Risk stratification combined with continuous monitoring of deteriorating trends in vital signs could lead to the development of next-generation warning systems achieving true patient safety.

Project description:ImportanceThere is limited evidence that the use of severity of illness scores in pediatric patients can facilitate timely admission to the intensive care unit or improve patient outcomes.ObjectiveTo determine the effect of the Bedside Paediatric Early Warning System (BedsidePEWS) on all-cause hospital mortality and late admission to the intensive care unit (ICU), cardiac arrest, and ICU resource use.Design, setting, and participantsA multicenter cluster randomized trial of 21 hospitals located in 7 countries (Belgium, Canada, England, Ireland, Italy, New Zealand, and the Netherlands) that provided inpatient pediatric care for infants (gestational age ≥37 weeks) to teenagers (aged ≤18 years). Participating hospitals had continuous physician staffing and subspecialized pediatric services. Patient enrollment began on February 28, 2011, and ended on June 21, 2015. Follow-up ended on July 19, 2015.InterventionsThe BedsidePEWS intervention (10 hospitals) was compared with usual care (no severity of illness score; 11 hospitals).Main outcomes and measuresThe primary outcome was all-cause hospital mortality. The secondary outcome was a significant clinical deterioration event, which was defined as a composite outcome reflecting late ICU admission. Regression analyses accounted for hospital-level clustering and baseline rates.ResultsAmong 144 539 patient discharges at 21 randomized hospitals, there were 559 443 patient-days and 144 539 patients (100%) completed the trial. All-cause hospital mortality was 1.93 per 1000 patient discharges at hospitals with BedsidePEWS and 1.56 per 1000 patient discharges at hospitals with usual care (adjusted between-group rate difference, 0.01 [95% CI, -0.80 to 0.81 per 1000 patient discharges]; adjusted odds ratio, 1.01 [95% CI, 0.61 to 1.69]; P = .96). Significant clinical deterioration events occurred during 0.50 per 1000 patient-days at hospitals with BedsidePEWS vs 0.84 per 1000 patient-days at hospitals with usual care (adjusted between-group rate difference, -0.34 [95% CI, -0.73 to 0.05 per 1000 patient-days]; adjusted rate ratio, 0.77 [95% CI, 0.61 to 0.97]; P = .03).Conclusions and relevanceImplementation of the Bedside Paediatric Early Warning System compared with usual care did not significantly decrease all-cause mortality among hospitalized pediatric patients. These findings do not support the use of this system to reduce mortality.Trial registrationclinicaltrials.gov Identifier: NCT01260831.

Project description:Pediatric early warning systems using expert-derived vital sign parameters demonstrate limited sensitivity and specificity in identifying deterioration. We hypothesized that modified tools using data-driven vital sign parameters would improve the performance of a validated tool.Retrospective case control.Quaternary-care children's hospital.Hospitalized, noncritically ill patients less than 18 years old. Cases were defined as patients who experienced an emergent transfer to an ICU or out-of-ICU cardiac arrest. Controls were patients who never required intensive care. Cases and controls were split into training and testing groups.The Bedside Pediatric Early Warning System was modified by integrating data-driven heart rate and respiratory rate parameters (modified Bedside Pediatric Early Warning System 1 and 2). Modified Bedside Pediatric Early Warning System 1 used the 10th and 90th percentiles as normal parameters, whereas modified Bedside Pediatric Early Warning System 2 used fifth and 95th percentiles.The training set consisted of 358 case events and 1,830 controls; the testing set had 331 case events and 1,215 controls. In the sensitivity analysis, 207 of the 331 testing set cases (62.5%) were predicted by the original tool versus 206 (62.2%; p = 0.54) with modified Bedside Pediatric Early Warning System 1 and 191 (57.7%; p < 0.001) with modified Bedside Pediatric Early Warning System 2. For specificity, 1,005 of the 1,215 testing set control patients (82.7%) were identified by original Bedside Pediatric Early Warning System versus 1,013 (83.1%; p = 0.54) with modified Bedside Pediatric Early Warning System 1 and 1,055 (86.8%; p < 0.001) with modified Bedside Pediatric Early Warning System 2. There was no net gain in sensitivity and specificity using either of the modified Bedside Pediatric Early Warning System tools.Integration of data-driven vital sign parameters into a validated pediatric early warning system did not significantly impact sensitivity or specificity, and all the tools showed lower than desired sensitivity and specificity at a single cutoff point. Future work is needed to develop an objective tool that can more accurately predict pediatric decompensation.

Project description:OBJECTIVES:Develop and test the performance of electronic version of the Children's Hospital of Pittsburgh Pediatric Risk of Mortality-IV and electronic version of the Children's Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 scores. DESIGN:Retrospective, single-center cohort derived from structured electronic health record data. SETTING:Large, quaternary PICU at a freestanding, university-affiliated children's hospital. PATIENTS:All encounters with a PICU admission between January 1, 2009, and December 31, 2017, identified using electronic definitions of inpatient encounter. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:The main outcome was predictive validity of each score for hospital mortality, assessed as model discrimination and calibration. Discrimination was examined with the area under the receiver operating characteristics curve and the area under the precision-recall curve. Calibration was assessed with the Hosmer-Lemeshow goodness of fit test and calculation of a standardized mortality ratio. Models were recalibrated with new regression coefficients in a training subset of 75% of encounters selected randomly from all years of the cohort and the calibrated models were tested in the remaining 25% of the cohort. Content validity was assessed by examining correlation between electronic versions of the scores and prospectively calculated data (electronic version of the Children's Hospital of Pittsburgh Pediatric Risk of Mortality-IV) and an alternative informatics approach (Children's Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 score). The cohort included 21,335 encounters. Correlation coefficients indicated strong agreement between different methods of score calculation. Uncalibrated area under the receiver operating characteristics curves were 0.96 (95% CI, 0.95-0.97) for electronic version of the Children's Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 score and 0.87 (95% CI, 0.85-0.89) for electronic version of the Children's Hospital of Pittsburgh Pediatric Risk of Mortality-IV for inpatient mortality. The uncalibrated electronic version of the Children's Hospital of Pittsburgh Pediatric Risk of Mortality-IV standardized mortality ratio was 0.63 (0.59-0.66), demonstrating strong agreement with previous, prospective evaluation at the study center. The uncalibrated electronic version of the Children's Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 score standardized mortality ratio was 0.20 (0.18-0.21). All models required recalibrating (all Hosmer-Lemeshow goodness-of-fit, p < 0.001) and subsequently demonstrated acceptable goodness-of-fit when examined in a test subset (n = 5,334) of the cohort. CONCLUSIONS:Electronically derived intensive care acuity scores demonstrate very good to excellent discrimination and can be calibrated to institutional outcomes. This approach can facilitate both performance improvement and research initiatives and may offer a scalable strategy for comparison of interinstitutional PICU outcomes.

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The Vitals Risk Index-Retrospective Performance Analysis of an Automated and Objective Pediatric Early Warning System.

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The Vitals Risk Index-Retrospective Performance Analysis of an Automated and Objective Pediatric Early Warning System.

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