A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children.
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ABSTRACT: Objectives
Clinical deterioration in hospitalized children is associated with increased risk of mortality and morbidity. A prediction model capable of accurate and early identification of pediatric patients at risk of deterioration can facilitate timely assessment and intervention, potentially improving survival and long-term outcomes. The objective of this study was to develop a model utilizing vital signs from electronic health record data for predicting clinical deterioration in pediatric ward patients.Design
Observational cohort study.Setting
An urban, tertiary-care medical center.Patients
Patients less than 18 years admitted to the general ward during years 2009-2018.Interventions
None.Measurements and main results
The primary outcome of clinical deterioration was defined as a direct ward-to-ICU transfer. A discrete-time logistic regression model utilizing six vital signs along with patient characteristics was developed to predict ICU transfers several hours in advance. Among 31,899 pediatric admissions, 1,375 (3.7%) experienced the outcome. Data were split into independent derivation (yr 2009-2014) and prospective validation (yr 2015-2018) cohorts. In the prospective validation cohort, the vital sign model significantly outperformed a modified version of the Bedside Pediatric Early Warning System score in predicting ICU transfers 12 hours prior to the event (C-statistic 0.78 vs 0.72; p < 0.01).Conclusions
We developed a model utilizing six commonly used vital signs to predict risk of deterioration in hospitalized children. Our model demonstrated greater accuracy in predicting ICU transfers than the modified Bedside Pediatric Early Warning System. Our model may promote opportunities for timelier intervention and risk mitigation, thereby decreasing preventable death and improving long-term health.
SUBMITTER: Mayampurath A
PROVIDER: S-EPMC7483876 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient's current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends.Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC).A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC -0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05).Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores.
| S-EPMC4834231 | biostudies-literature
Project description:BackgroundVital signs, i.e. respiratory rate, oxygen saturation, pulse, blood pressure and temperature, are regarded as an essential part of monitoring hospitalized patients. Changes in vital signs prior to clinical deterioration are well documented and early detection of preventable outcomes is key to timely intervention. Despite their role in clinical practice, how to best monitor and interpret them is still unclear.ObjectiveTo evaluate the ability of vital sign trends to predict clinical deterioration in patients hospitalized with acute illness.Data sourcesPubMed, Embase, Cochrane Library and CINAHL were searched in December 2017.Study selectionStudies examining intermittently monitored vital sign trends in acutely ill adult patients on hospital wards and in emergency departments. Outcomes representing clinical deterioration were of interest.Data extractionPerformed separately by two authors using a preformed extraction sheet.ResultsOf 7,366 references screened, only two were eligible for inclusion. Both were retrospective cohort studies without controls. One examined the accuracy of different vital sign trend models using discrete-time survival analysis in 269,999 admissions. One included 44,531 medical admissions examining trend in Vitalpac Early Warning Score weighted vital signs. They stated that vital sign trends increased detection of clinical deterioration. Critical appraisal was performed using evaluation tools. The studies had moderate risk of bias, and a low certainty of evidence. Additionally, four studies examining trends in early warning scores, otherwise eligible for inclusion, were evaluated.ConclusionsThis review illustrates a lack of research in intermittently monitored vital sign trends. The included studies, although heterogeneous and imprecise, indicates an added value of trend analysis. This highlights the need for well-controlled trials to thoroughly assess the research question.
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Project description:Introduction: Hospitalized pediatric hematology-oncology and post-hematopoietic cell transplant (HCT) patients have frequent deterioration requiring Pediatric Intensive Care Unit (PICU) care. Critical deterioration (CD), defined as unplanned PICU transfer requiring life-sustaining interventions within 12 h, is a pragmatic metric to evaluate emergency response systems (ERS) in pediatrics, however, it has not been investigated in these patients. The goal of this study was to evaluate if CD is an appropriate metric to assess effectiveness of ERS in pediatric hematology-oncology and post-HCT patients and if it is preceded by an actionable period of vital sign changes. Methods: A retrospective review of all unplanned PICU transfers and floor cardiopulmonary arrests in a dedicated pediatric hematology-oncology hospital between August 2014 and July 2016. Vital signs and physical exam findings 48 h prior to events were converted to Pediatric Early Warning System-Like Scores (PEWS-LS) using cardiovascular, respiratory, and neurologic criteria. Results: There were 220 deterioration events, with 107 (48.6%) meeting criteria for CD, representing a rate of 2.98 per 1,000-inpatient-days. Using the first event per hospitalization (n = 184), patients with CD had higher mortality (17.4 vs. 7.6%, p = 0.045), fewer median ICU-free-days (21 vs. 24, p = 0.011), ventilator-free-days (25 vs. 28, p < 0.001), and vasoactive-free-days (27 vs. 28, p < 0.001). Using vital sign data 48 h prior to deterioration events, those with CD had higher PEWS-LS on PICU admission (p < 0.001), spent more time with elevated PEWS-LS prior to PICU transfer (p = 0.008 to 0.023) and had a longer time from first abnormal PEWS-LS (p = 0.007 to 0.043). Significant difference between the two groups was observed as early as 4 h prior to the event (p = 0.047). Conclusion: Hospitalized pediatric hematology-oncology and post-HCT patients have frequent deterioration resulting in a high mortality. In these patients, CD is over 13 times more common than floor cardiopulmonary arrests and associated with higher mortality and fewer event-free days, making it a useful metric in these patients. CD is preceded by a long duration of abnormal vital signs, making it potentially preventable through earlier recognition.
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Project description:Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.
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Project description:IntroductionAmbulatory monitoring systems (AMS) can facilitate early detection of clinical deterioration, and have the potential to improve hospitalised patient outcomes. The objective of this systematic review is to assess the impact of vital signs monitoring on detection of deterioration and related outcomes in hospitalised patients using AMS, in comparison with standard care.Methods and analysisA systematic search was conducted on 27 August 2020 in MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, CENTRAL and Health Technology Assessment databases, as well as grey literature. Search results will be reviewed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis checklist for systematic reviews. Studies comparing the use of ambulatory monitoring devices against standard care for deterioration detection and related clinical outcomes in hospitalised patients will be included and further clinical and other outcomes will also be explored. Deterioration-related outcomes may include (but not limited to) unplanned intensive care admissions, rapid response team activation and unscheduled emergency interventions, as defined by the included studies. Two reviewers will independently extract study data and assess the quality and risk of bias of included studies. Where possible, a meta-analysis will be conducted and quantitative results presented. Alternatively, a narrative synthesis will be reported.Ethics and disseminationEthical approval is not required for this study as no primary data will be collected. This study is part of our virtual High Dependency Unit project and will be disseminated through peer-reviewed publications, public and scientific conference presentations.Prospero registration numberCRD42020188633.
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Project description:BackgroundTimely recognition of the deteriorating inpatient remains challenging. Wearable monitoring systems (WMS) may augment current monitoring practices. However, there are many barriers to implementation in the hospital environment, and evidence describing the clinical impact of WMS on deterioration detection and patient outcome remains unclear.ObjectiveTo assess the impact of vital-sign monitoring on detection of deterioration and related clinical outcomes in hospitalised patients using WMS, in comparison with standard care.MethodsA systematic search was conducted in August 2020 using MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, CENTRAL, Health Technology Assessment databases and grey literature. Studies comparing the use of WMS against standard care for deterioration detection and related clinical outcomes in hospitalised patients were included. Deterioration related outcomes (primary) included unplanned intensive care admissions, rapid response team or cardiac arrest activation, total and major complications rate. Other clinical outcomes (secondary) included in-hospital mortality and hospital length of stay. Exploratory outcomes included alerting system parameters and clinical trial registry information.ResultsOf 8706 citations, 10 studies with different designs met the inclusion criteria, of which 7 were included in the meta-analyses. Overall study quality was moderate. The meta-analysis indicated that the WMS, when compared with standard care, was not associated with significant reductions in intensive care transfers (risk ratio, RR 0.87; 95% confidence interval, CI 0.66-1.15), rapid response or cardiac arrest team activation (RR 0.84; 95% CI 0.69-1.01), total (RR 0.77; 95% CI 0.44-1.32) and major (RR 0.55; 95% CI 0.24-1.30) complications prevalence. There was also no statistically significant association with reduced mortality (RR 0.48; 95% CI 0.18-1.29) and hospital length of stay (mean difference, MD - 0.09; 95% CI - 0.43 to 0.44).ConclusionThis systematic review indicates that there is no current evidence that implementation of WMS impacts early deterioration detection and associated clinical outcomes, as differing design/quality of available studies and diversity of outcome measures make it difficult to reach a definite conclusion. Our narrative findings suggested that alarms should be adjusted to minimise false alarms and promote rapid clinical action in response to deterioration. PROSPERO Registration number: CRD42020188633 .
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Project description:Background: In the acute resuscitation period after traumatic brain injury (TBI), one of the goals is to identify those at risk for secondary neurological decline (ND), represented by a constellation of clinical signs that can be identified as objective events related to secondary brain injury and independently impact outcome. We investigated whether continuous vital sign variability and waveform analysis of the electrocardiogram (ECG) or photoplethysmogram (PPG) within the first hour of resuscitation may enhance the ability to predict ND in the initial 48 hours after traumatic brain injury (TBI). Methods: Retrospective analysis of ND in TBI patients enrolled in the prospective Oximetry and Noninvasive Predictors Of Intervention Need after Trauma (ONPOINT) study. ND was defined as any of the following occurring in the first 48 h: new asymmetric pupillary dilatation (>2 mm), 2 point GCS decline, interval worsening of CT scan as assessed by the Marshall score, or intervention for cerebral edema. Beat-to-beat variation of ECG or PPG, as well as waveform features during the first 15 and 60 min after arrival in the TRU were analyzed to determine physiologic parameters associated with future ND. Physiologic and admission clinical variables were combined in multivariable logistic regression models predicting ND and inpatient mortality. Results: There were 33 (17%) patients with ND among 191 patients (mean age 43 years old, GCS 13, ISS 12, 69% men) who met study criteria. ND was associated with ICU admission (P < 0.001) and inpatient mortality (P < 0.001). Both ECG (AUROC: 0.84, 95% CI: 0.76,0.93) and PPG (AUROC: 0.87, 95% CI: 0.80, 0.93) analyses during the first 15 min of resuscitation demonstrated a greater ability to predict ND then clinical characteristics alone (AUROC: 0.69, 95% CI: 0.59, 0.8). Age (P = 0.02), Marshall score (P = 0.001), penetrating injury (P = 0.02), and predictive probability for ND by PPG analysis at 15 min (P = 0.03) were independently associated with inpatient mortality. Conclusions: Analysis of variability and ECG or PPG waveform in the first minutes of resuscitation may represent a non-invasive early marker of future ND.
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Project description:BackgroundTimely prediction of AKI in children can allow for targeted interventions, but the wealth of data in the electronic health record poses unique modeling challenges.MethodsWe retrospectively reviewed the electronic medical records of all children younger than 18 years old who had at least two creatinine values measured during a hospital admission from January 2014 through January 2018. We divided the study population into derivation, and internal and external validation cohorts, and used five feature selection techniques to select 10 of 720 potentially predictive variables from the electronic health records. Model performance was assessed by the area under the receiver operating characteristic curve in the validation cohorts. The primary outcome was development of AKI (per the Kidney Disease Improving Global Outcomes creatinine definition) within a moving 48-hour window. Secondary outcomes included severe AKI (stage 2 or 3), inpatient mortality, and length of stay.ResultsAmong 8473 encounters studied, AKI occurred in 516 (10.2%), 207 (9%), and 27 (2.5%) encounters in the derivation, and internal and external validation cohorts, respectively. The highest-performing model used a machine learning-based genetic algorithm, with an overall receiver operating characteristic curve in the internal validation cohort of 0.76 [95% confidence interval (CI), 0.72 to 0.79] for AKI, 0.79 (95% CI, 0.74 to 0.83) for severe AKI, and 0.81 (95% CI, 0.77 to 0.86) for neonatal AKI. To translate this prediction model into a clinical risk-stratification tool, we identified high- and low-risk threshold points.ConclusionsUsing various machine learning algorithms, we identified and validated a time-updated prediction model of ten readily available electronic health record variables to accurately predict imminent AKI in hospitalized children.
| S-EPMC7269342 | biostudies-literature
Project description:BackgroundDecisions regarding the evaluation of children with influenza infection rely on the likelihood of severe disease. The role of early vital signs as predictors of severe influenza infection in children is not well known. Our objectives were to determine the value of vital signs in predicting hospitalization/recurrent emergency department (ED) visits due to influenza infection in children.MethodsWe conducted a prospective study of children aged 6 months to 8 years of age with influenza like illness evaluated at an ED/UC from 2016-2018. All children underwent influenza testing by PCR. We collected heart rate, respiratory rate and temperature, and converted heart rate (HR) and respiratory rate (RR) to z-scores by age. HR z scores were further adjusted for temperature. Our primary outcome was hospitalization/recurrent ED visits within 72 hours. Vital sign predictors with p< 0.2 and other clinical covariates were entered into a multivariable logistic regression model to determine odds ratios (OR) and 95% CI; model performance was assessed using the Brier score and discriminative ability with the C statistic.ResultsAmong 1478 children, 411 (27.8%) were positive for influenza, of which 42 (10.2%) were hospitalized or had a recurrent ED visit. In multivariable analyses, adjusting for age, high-risk medical condition and school/daycare attendance, higher adjusted respiratory rate (OR 2.09, 95%CI 1.21-3.61, p = 0.0085) was a significant predictor of influenza hospitalization/recurrent ED visits.ConclusionsHigher respiratory rate adjusted for age was the most useful vital sign predictor of severity among young children with PCR-confirmed influenza.
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Project description:From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group.
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