Development and initial validation of the Bedside Paediatric Early Warning System score

Creation of a Pediatric Sedation Risk Assessment Scoring System: A Novel Method to Stratify Risk

This study aimed to create a pediatric sedation scoring system independent of the American Society of Anesthesiology Physical Status (ASA-PS) classification that is predictive of adverse events, facilitates objective stratification, and resource allocation. Multivariable regression and machine learning algorithm analysis of 134,973 sedation encounters logged in to the Pediatric Sedation Research Consortium (PSRC) database between July 2007 and June 2011. Patient and procedure variables were correlated with adverse events with resultant β -regression coefficients used to assign point values to each variable. Point values were then summed to create a risk assessment score. Validation of the model was performed with the 2011 to 2013 PSRC database followed by calculation of ROC curves and positive predictive values. Factors identified and resultant point values are as follows: 1 point: age ≤ 6 months, cardiac diagnosis, asthma, weight less than 5th percentile or greater than 95 th , and computed tomography (CT) scan; 2 points: magnetic resonance cholangiopancreatography (MRCP) and weight greater than 99th percentile; 4 points: magnetic resonance imaging (MRI); 5 points: trisomy 21 and esophagogastroduodenoscopy (EGD); 7 points: cough at the time of examination; and 18 points: bronchoscopy. Sum of patient and procedural values produced total risk assessment scores. Total risk assessment score of 5 had a sensitivity of 82.69% and a specificity of 26.22%, while risk assessment score of 11 had a sensitivity of 12.70% but a specificity of 95.29%. Inclusion of ASA-PS value did not improve model sensitivity or specificity and was thus excluded. Higher risk assessment scores predicted increased likelihood of adverse events during sedation. The score can be used to triage patients independent of ASA-PS with site-specific cut-off values used to determine appropriate sedation resource allocation.

Parents' Trigger Tool for Children with Medical Complexity - PAT-CMC: Development of a recognition tool for clinical deterioration at home

AIM

To develop a trigger tool for parents and lay caregivers of children with medical complexity (CMC) at home and to validate its content.

DESIGN

This was a multi-method study, using qualitative data, a Delphi method and a concept mapping approach.

METHODS

A three-round electronic Delphi was performed from December 2021 to April 2022 with a panel of 23 expert parents and 30 healthcare providers, supplemented by a preliminary qualitative exploration of children's signs of deterioration and three consensus meetings to develop the PArents' Trigger Tool for Children with Medical Complexity (PAT-CMC). Cognitive interviews with parents were performed to assess the comprehensiveness and comprehensibility of the tool. The COREQ checklist, the COSMIN guidelines and the CREDES guidelines guided the reporting respectively of the qualitative study, the development and content validity of the trigger tool and the Delphi study.

RESULTS

The PAT-CMC was developed and its content validated to recognize clinical deterioration at home. The tool consists of 7 main clusters of items: Breathing, Heart, Devices, Behaviour, Neuro-Muscular, Nutrition/Hydration and Other Concerns. A total of 23 triggers of deterioration were included and related to two recommendations for escalation of care, using a traffic light coding system.

CONCLUSION

Priority indicators of clinical deterioration of CMC were identified and integrated into a validated trigger tool designed for parents or other lay caregivers at home, to recognize signs of acute severe illness and initiate healthcare interventions.

IMPACT

The PAT-CMC was developed to guide families in recognizing signs of deterioration in CMC and has potential for initiating an early escalation of care. This tool may also be useful to support education provided by healthcare providers to families before hospital discharge.

PATIENT OR PUBLIC CONTRIBUTION

Parents of CMC were directly involved in the selection of relevant indicators of children's clinical deterioration and the development of the trigger tool. They were not involved in the design, conducting, reporting or dissemination plans of this research.

Predicting transfers to intensive care in children using CEWT and other early warning systems

Background and Objective

The Children's Early Warning Tool (CEWT), developed in Australia, is widely used in many countries to monitor the risk of deterioration in hospitalized children. Our objective was to compare CEWT prediction performance against a version of the Bedside Pediatric Early Warning Score (Bedside PEWS), Between the Flags (BTF), and the pediatric Calculated Assessment of Risk and Triage (pCART).

Methods

We conducted a retrospective observational study of all patient admissions to the Comer Children's Hospital at the University of Chicago between 2009-2019. We compared performance for predicting the primary outcome of a direct ward-to-intensive care unit (ICU) transfer within the next 12 h using the area under the receiver operating characteristic curve (AUC). Alert rates at various score thresholds were also compared.

Results

Of 50,815 ward admissions, 1,874 (3.7%) experienced the primary outcome. Among patients in Cohort 1 (years 2009-2017, on which the machine learning-based pCART was trained), CEWT performed slightly worse than Bedside PEWS but better than BTF (CEWT AUC 0.74 vs. Bedside PEWS 0.76, P < 0.001; vs. BTF 0.66, P < 0.001), while pCART performed best for patients in Cohort 2 (years 2018-2019, pCART AUC 0.84 vs. CEWT AUC 0.79, P < 0.001; vs. BTF AUC 0.67, P < 0.001; vs. Bedside PEWS 0.80, P < 0.001). Sensitivity, specificity, and positive predictive values varied across all four tools at the examined thresholds for alerts.

Conclusion

CEWT has good discrimination for predicting which patients will likely be transferred to the ICU, while pCART performed the best.

A Prognostic Model for Critically Ill Children in Locations With Emerging Critical Care Capacity

OBJECTIVES

To develop a clinical prediction model to risk stratify children admitted to PICUs in locations with limited resources, and compare performance of the model to nine existing pediatric severity scores.

DESIGN

Retrospective, single-center, cohort study.

SETTING

PICU of a pediatric hospital in Siem Reap, northern Cambodia.

PATIENTS

Children between 28 days and 16 years old admitted nonelectively to the PICU.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Clinical and laboratory data recorded at the time of PICU admission were collected. The primary outcome was death during PICU admission. One thousand five hundred fifty consecutive nonelective PICU admissions were included, of which 97 died (6.3%). Most existing severity scores achieved comparable discrimination (area under the receiver operating characteristic curves [AUCs], 0.71-0.76) but only three scores demonstrated moderate diagnostic utility for triaging admissions into high- and low-risk groups (positive likelihood ratios [PLRs], 2.65-2.97 and negative likelihood ratios [NLRs], 0.40-0.46). The newly derived model outperformed all existing severity scores (AUC, 0.84; 95% CI, 0.80-0.88; p < 0.001). Using one particular threshold, the model classified 13.0% of admissions as high risk, among which probability of mortality was almost ten-fold greater than admissions triaged as low-risk (PLR, 5.75; 95% CI, 4.57-7.23 and NLR, 0.47; 95% CI, 0.37-0.59). Decision curve analyses indicated that the model would be superior to all existing severity scores and could provide utility across the range of clinically plausible decision thresholds.

CONCLUSIONS

Existing pediatric severity scores have limited potential as risk stratification tools in resource-constrained PICUs. If validated, our prediction model would be a readily implementable mechanism to support triage of critically ill children at admission to PICU and could provide value across a variety of contexts where resource prioritization is important.

Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards

Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools are often complicated and time-consuming, rendering them impractical. To overcome this problem, we designed this study to create a deep learning prediction model that predicts critical events with simplified variables. This retrospective observational study included patients under the age of 18 who were admitted to the general ward of a tertiary children's hospital between 2020 and 2022. A critical event was defined as cardiopulmonary resuscitation, unplanned transfer to the intensive care unit, or mortality. The vital signs measured during hospitalization, their measurement intervals, sex, and age were used to train a critical event prediction model. Age-specific z-scores were used to normalize the variability of the normal range by age. The entire dataset was classified into a training dataset and a test dataset at an 8:2 ratio, and model learning and testing were performed on each dataset. The predictive performance of the developed model showed excellent results, with an area under the receiver operating characteristics curve of 0.986 and an area under the precision-recall curve of 0.896. We developed a deep learning model with outstanding predictive power using simplified variables to effectively predict critical events while reducing the workload of medical staff. Nevertheless, because this was a single-center trial, no external validation was carried out, prompting further investigation.

Impact of Rapid Response Teams on Pediatric Care: An Interrupted Time Series Analysis of Unplanned PICU Admissions and Cardiac Arrests

Pediatric rapid response teams (RRTs) are expected to significantly lower pediatric mortality in healthcare settings. This study evaluates RRTs' effectiveness in decreasing cardiac arrests and unexpected Pediatric Intensive Care Unit (PICU) admissions. A quasi-experimental study (2014-2017) at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, involved 3261 pediatric inpatients, split into pre-intervention (1604) and post-intervention (1657) groups. Baseline pediatric warning scores and monthly data on admissions, transfers, arrests, and mortality were analyzed pre- and post-intervention. Statistical methods including bootstrapping, segmented regression, and a Zero-Inflation Poisson model were employed to ensure a comprehensive evaluation of the intervention's impact. RRT was activated 471 times, primarily for respiratory distress (29.30%), sepsis (22.30%), clinical anxiety (13.80%), and hematological abnormalities (6.7%). Family concerns triggered 0.1% of activations. Post-RRT implementation, unplanned PICU admissions significantly reduced (RR = 0.552, 95% CI 0.485-0.628, p < 0.0001), and non-ICU cardiac arrests were eliminated (RR = 0). Patient care improvement was notable, with a -9.61 coefficient for PICU admissions (95% CI: -12.65 to -6.57, p < 0.001) and a -1.641 coefficient for non-ICU cardiac arrests (95% CI: -2.22 to -1.06, p < 0.001). Sensitivity analysis showed mixed results for PICU admissions, while zero-inflation Poisson analysis confirmed a reduction in non-ICU arrests. The deployment of pediatric RRTs is associated with fewer unexpected PICU admissions and non-ICU cardiopulmonary arrests, indicating improved PICU management. Further research using robust scientific methods is necessary to conclusively determine RRTs' clinical benefits.

Study protocol of a national multicentre prospective evaluation study assessing the validity and impact of the Dutch Paediatric Early Warning Score (PEWS) in the Netherlands

INTRODUCTION

Early recognition of clinical deterioration and timely intervention are important to improve morbidity and mortality in paediatric care. The Paediatric Early Warning Score (PEWS) is a scoring system aiming to identify hospitalised children at risk for deterioration. Currently, there is a large heterogeneity of PEWS systems in the Netherlands, with a considerable number remaining unvalidated or self-designed. Therefore, a consensus-based Dutch PEWS has been developed in a national study using the Core Outcome Measures in Effectiveness Trials initiative. The Dutch PEWS is a uniform system that integrates a core set of vital parameters together with pre-existing risk factors and uses risk stratification to proactively follow-up on patients at risk (so-called 'watcher patients'). This study aims to validate the Dutch PEWS and to determine its impact on improving patient safety in various hospital settings.

METHODS AND ANALYSIS

This national study will be a large multicentre evaluation study, in which the Dutch PEWS will be implemented and evaluated in 12 hospitals in the Netherlands. In this study, a mixed methods methodology will be used and evaluated on predefined outcome measures. To examine the validity of the Dutch PEWS, statistical analyses will be undertaken on quantitative data retrieved from electronic health records. Surveys among physicians and nurses; semistructured interviews with healthcare providers and parents; and daily evaluation forms are being conducted to determine the impact of the Dutch PEWS. The study is being conducted from December 2020 to June 2024.

Development and Temporal Validation of a Machine Learning Model to Predict Clinical Deterioration

OBJECTIVES

Early warning scores detecting clinical deterioration in pediatric inpatients have wide-ranging performance and use a limited number of clinical features. This study developed a machine learning model leveraging multiple static and dynamic clinical features from the electronic health record to predict the composite outcome of unplanned transfer to the ICU within 24 hours and inpatient mortality within 48 hours in hospitalized children.

METHODS

Using a retrospective development cohort of 17 630 encounters across 10 388 patients, 2 machine learning models (light gradient boosting machine [LGBM] and random forest) were trained on 542 features and compared with our institutional Pediatric Early Warning Score (I-PEWS).

RESULTS

The LGBM model significantly outperformed I-PEWS based on receiver operating characteristic curve (AUROC) for the composite outcome of ICU transfer or mortality for both internal validation and temporal validation cohorts (AUROC 0.785 95% confidence interval [0.780-0.791] vs 0.708 [0.701-0.715] for temporal validation) as well as lead-time before deterioration events (median 11 hours vs 3 hours; P = .004). However, LGBM performance as evaluated by precision recall curve was lesser in the temporal validation cohort with associated decreased positive predictive value (6% vs 29%) and increased number needed to evaluate (17 vs 3) compared with I-PEWS.

CONCLUSIONS

Our electronic health record based machine learning model demonstrated improved AUROC and lead-time in predicting clinical deterioration in pediatric inpatients 24 to 48 hours in advance compared with I-PEWS. Further work is needed to optimize model positive predictive value to allow for integration into clinical practice.

External validation of clinical severity scores to guide referral of paediatric acute respiratory infections in resource-limited primary care settings

Accurate and reliable guidelines for referral of children from resource-limited primary care settings are lacking. We identified three practicable paediatric severity scores (the Liverpool quick Sequential Organ Failure Assessment (LqSOFA), the quick Pediatric Logistic Organ Dysfunction-2, and the modified Systemic Inflammatory Response Syndrome) and externally validated their performance in young children presenting with acute respiratory infections (ARIs) to a primary care clinic located within a refugee camp on the Thailand-Myanmar border. This secondary analysis of data from a longitudinal birth cohort study consisted of 3010 ARI presentations in children aged ≤ 24 months. The primary outcome was receipt of supplemental oxygen. We externally validated the discrimination, calibration, and net-benefit of the scores, and quantified gains in performance that might be expected if they were deployed as simple clinical prediction models, and updated to include nutritional status and respiratory distress. 104/3,010 (3.5%) presentations met the primary outcome. The LqSOFA score demonstrated the best discrimination (AUC 0.84; 95% CI 0.79-0.89) and achieved a sensitivity and specificity > 0.80. Converting the scores into clinical prediction models improved performance, resulting in ~ 20% fewer unnecessary referrals and ~ 30-50% fewer children incorrectly managed in the community. The LqSOFA score is a promising triage tool for young children presenting with ARIs in resource-limited primary care settings. Where feasible, deploying the score as a simple clinical prediction model might enable more accurate and nuanced risk stratification, increasing applicability across a wider range of contexts.

Professional Success: Utilizing Simulation to Remediate and Retain Nursing Staff

Nursing education focuses on nursing theory and the ability to perform tasks. There is a lack of education related to prioritization of nursing tasks. Therefore, new nurses transitioning into their roles sometimes struggle and, as a result, leave their units or, often enough, our facility. We developed a Professional Success Program that includes cognitive prioritization exercises and simulation scenarios to assist these nurses. After utilizing the program, our facility has seen an increase in nurse retention.

Prediction of GutCheck NEC and Its Relation to Severity of Illness and Measures of Deterioration in Necrotizing Enterocolitis

BACKGROUND

Necrotizing enterocolitis (NEC) risk has been shown to arise from multiple sources and risk awareness may be supported using bedside tools.

PURPOSE

The purpose of this research was to examine the extent to which GutCheck NEC was associated with scores for clinical deterioration, severity of illness, and clinical outcome, and further to examine how scores might improve NEC prediction.

METHODS

A retrospective, correlational case-control study with infant data from 3 affiliated neonatal intensive care units was conducted.

RESULTS

Of 132 infants (44 cases, 88 controls), most were 28 weeks of gestation at birth and less (74%). Median age at NEC onset was 18 days (range: 6-34 days), with two-thirds diagnosed before 21 days. At 68 hours of life, higher GutCheck NEC scores were associated with NEC requiring surgery or resulting in death (relative risk ratio [RRR] = 1.06, P = .036), associations that persisted at 24 hours prior to diagnosis (RRR = 1.05, P = .046), and at the time of diagnosis (RRR = 1.05, P = .022) but showed no associations for medical NEC. GutCheck NEC scores were significantly correlated with pediatric early warning scores (PEWS) ( r > 0.30; P < .005) and SNAPPE-II scores ( r > 0.44, P < .0001). Increasing numbers of clinical signs and symptoms were positively associated with GutCheck NEC and PEWS at the time of diagnosis ( r = 0.19, P = .026; and r = 0.25, P = .005, respectively).

IMPLICATIONS FOR PRACTICE AND RESEARCH

GutCheck NEC provides structure to streamline assessment and communication about NEC risk. Yet, it is not intended to be diagnostic. Research is needed on how GutCheck NEC impacts timely recognition and treatment.

Outcome in Children Admitted to the First PICU in Malawi

OBJECTIVES

Dedicated PICUs are slowly starting to emerge in sub-Saharan Africa. Establishing these units can be challenging as there is little data from this region to inform which populations and approaches should be prioritized. This study describes the characteristics and outcome of patients admitted to the first PICU in Malawi, with the aim to identify factors associated with increased mortality.

DESIGN

Review of a prospectively constructed PICU database. Univariate analysis was used to assess associations between demographic, clinical and laboratory factors, and mortality. Univariate associations ( p < 0.1) for mortality were entered in two multivariable models.

SETTING

A recently opened PICU in a public tertiary government hospital in Blantyre, Malawi.

PATIENTS

Children admitted to PICU between August 1, 2017, and July 31, 2019.

INTERVENTIONS

None.

MEASUREMENT AND MAIN RESULTS

Of 531 included PICU admissions, 149 children died (28.1%). Mortality was higher in neonates (88/167; 52.7%) than older children (61/364; 16.8%; p ≤ 0.001). On univariate analysis, gastroschisis, trachea-esophageal fistula, and sepsis had higher PICU mortality, while Wilms tumor, other neoplasms, vocal cord papilloma, and foreign body aspiration had higher survival rates compared with other conditions. On multivariable analysis, neonatal age (adjusted odds ratio [AOR], 4.0; 95% CI, 2.0-8.3), decreased mental state (AOR, 5.8; 95 CI, 2.4-13.8), post-cardiac arrest (AOR, 2.0; 95% CI, 1.0-8.0), severe hypotension (AOR, 6.3; 95% CI, 2.0-19.1), lactate greater than 5 mmol/L (AOR, 4.2; 95% CI, 1.5-11.2), pH less than 7.2 (AOR, 3.1; 95% CI, 1.2-8.0), and platelets less than 150 × 10 9 /L (AOR, 2.4; 95% CI, 1.1-5.2) were associated with increased mortality.

CONCLUSIONS

In the first PICU in Malawi, mortality was relatively high, especially in neonates. Surgical neonates and septic patients were identified as highly vulnerable, which stresses the importance of improvement of PICU care bundles for these groups. Several clinical and laboratory variables were associated with mortality in older children. In neonates, severe hypotension was the only clinical variable associated with increased mortality besides blood gas parameters. This stresses the importance of basic laboratory tests, especially in neonates. These data contribute to evidence-based approaches establishing and improving future PICUs in sub-Saharan Africa.

Severity of illness and mortality among children admitted to a tertiary referral hospital in Botswana: A secondary data analysis of a prospective cohort study

Objectives

Data on triage practices of children admitted to Princess Marina Hospital in Gaborone, Botswana is limited. The inpatient triage, assessment, and treatment score was developed for low resource settings to predict mortality in children. We assess its performance among children admitted to Princess Marina Hospital and their demographic, clinical, and risk factors for death.

Methods

This was a secondary data analysis of a prospective cohort study comprising 299 children ages 1 month to 13 years admitted June to September 2018. Descriptive statistics, bivariate analysis, and multivariate logistic regression were used. Sensitivity and specificity data were generated for the inpatient triage, assessment, and treatment score.

Results

Thirteen children died (13/284, 4.6%). Comorbidity (adjusted odds ratio 4.0, p = 0.020) and high inpatient triage, assessment, and treatment score (adjusted odds ratio 5.0, p = 0.017) increased odds of death. The area under the receiver operating characteristic curve was 0.81. Using inpatient triage, assessment, and treatment cutoff of 4, the sensitivity, specificity, and likelihood ratio were 31%, 94%, and 5.0, respectively.

Conclusion

Implementing the inpatient triage, assessment, and treatment score in low resource settings may improve identification, treatment, and evaluation of the sickest children.

Effect of Diurnal Variation of Heart Rate and Respiratory Rate on Activation of Rapid Response System and Clinical Outcome in Hospitalized Children

Heart rate and respiratory rate display circadian variation. Pediatric single-parameter rapid response system is activated when heart rate or respiratory rate deviate from age-specific criteria, though activation criteria do not differentiate between daytime and nighttime, and unnecessary activation has been reported due to nighttime bradycardia. We evaluated the relationship between rapid response system activation and the patient’s clinical outcome by separately applying the criteria to daytime and nighttime in patients < 18. The observation period was divided into daytime and nighttime (8:00−20:00, and 20:00 to 8:00), according to which measured heart rate and respiratory rate were divided and rapid response system activation criteria were applied. We classified lower nighttime than daytime values into the ‘decreased group’, and the higher ones into the ‘increased group’, to analyze their effect on cardiopulmonary resuscitation occurrence or intensive care unit transfer. Nighttime heart rate and respiratory rate were lower than the daytime ones in both groups (both p values < 0.001), with no significant association with cardiopulmonary resuscitation occurrence or intensive care unit transfer in either group. Heart rate and respiratory rate tend to be lower at nighttime; however, their effect on the patient’s clinical outcome is not significant.

Validation of a modified bedside Pediatric Early Warning System score for detection of clinical deterioration in hospitalized pediatric oncology patients: A prospective cohort study

BACKGROUND

Hospitalized pediatric oncology patients are at risk of severe clinical deterioration. Yet Pediatric Early Warning System (PEWS) scores have not been prospectively validated in these patients. We aimed to determine the predictive performance of the modified BedsidePEWS score for unplanned pediatric intensive care unit (PICU) admission and cardiopulmonary resuscitation (CPR) in this patient population.

METHODS

We performed a prospective cohort study in an 80-bed pediatric oncology hospital in the Netherlands, where care has been nationally centralized. All hospitalized pediatric oncology patients aged 0-18 years were eligible for inclusion. A Cox proportional hazard model was estimated to study the association between BedsidePEWS score and unplanned PICU admissions or CPR. The predictive performance of the model was internally validated by bootstrapping.

RESULTS

A total of 1137 patients were included. During the study, 103 patients experienced 127 unplanned PICU admissions and three CPRs. The hazard ratio for unplanned PICU admission or CPR was 1.65 (95% confidence interval [CI]: 1.59-1.72) for each point increase in the modified BedsidePEWS score. The discriminative ability was moderate (D-index close to 0 and a C-index of 0.83 [95% CI: 0.79-0.90]). Positive and negative predictive values of modified BedsidePEWS score at the widely used cutoff of 8, at which escalation of care is required, were 1.4% and 99.9%, respectively.

CONCLUSION

The modified BedsidePEWS score is significantly associated with requirement of PICU transfer or CPR. In pediatric oncology patients, this PEWS score may aid in clinical decision-making for timing of PICU transfer.

Lack of Association Between a Quantified Lung Ultrasound Score and Illness Severity in Pediatric Emergency Department Patients With Acute Lower Respiratory Infections

OBJECTIVES

Lung ultrasound (LUS) may help determine illness severity in children with acute lower respiratory tract infections (LRTI) but limited pediatric studies exist. Our objective was to determine the association between LUS findings and illness severity in children with LRTI.

METHODS

We conducted a prospective study of patients <20 years with LRTI. Trained investigators performed standardized LUS examinations of 12 regions. Blinded sonologists reviewed examinations for individual pathologic features and also calculated a Quantified Lung Ultrasound Score (QLUS). We defined focal severity as QLUS of ≥2 in ≥1 region, and diffuse severity as QLUS of ≥1 in ≥3 regions. The primary outcome was the Respiratory component of the Pediatric Early Warning Score (RPEWS), a 14-item scale measuring respiratory illness severity. Secondary outcomes included hospital admission, length of stay, supplemental oxygen, and antibiotic use.

RESULTS

We enrolled 85 patients with LRTIs, 46 (54%) whom were hospitalized (5.4% intensive care). Median RPEWS was 1 (interquartile range 2). Neither individual features on ultrasound nor total QLUS were associated with RPEWS, hospitalization, length of stay, or oxygen use. Mean RPEWS was similar for participants regardless of focal (1.46 versus 1.26, P = .57) or diffuse (1.47 versus 1.21, P = .47) severity findings, but those with focal or diffuse severity, or isolated consolidation, had greater antibiotic administration (P < .001).

CONCLUSIONS

In children with LRTI, neither individual features nor QLUS were associated with illness severity. Antibiotics were more likely in patients with either focal or diffuse severity or presence of consolidation on ultrasound.

Admission Source Is Associated With the Risk of Rapid Response Team Activation in a Children's Hospital

OBJECTIVE

To evaluate source of admission to a children's hospital as a predictor of rapid response team (RRT) activation, both in the first 48 hours of admission and over the entire hospitalization.

METHODS

Retrospective cohort study of all patients admitted to the pediatric ward between March 1, 2013 and December 31, 2015. Source of admission was categorized as from the emergency department, transfer from another hospital facility, admission following a planned surgery, direct admission planned in advance, or unplanned direct admission. Information was collected including whether or not the patient had a RRT activation and survival to discharge. A Fisher's exact test was used to assess the association between source of admission and risk of rapid response.

RESULTS

Of 8083 admissions included in the study, 194 had at least one RRT event. The odds of having an RRT was significantly associated with source of admission (P < .001). Using admission from the emergency department as a reference group, planned elective admissions (odds ratio [OR] 0.27; P < .001) and admissions following planned surgery (OR 0.07; P < .001) were significantly associated with reduced odds of having at least one RRT activation during the admission. Planned elective admissions also demonstrated reduced odds of RRT in the first 48 hours of hospitalization (OR 0.14; P = .002). Source of admission was also associated with survival to discharge (P < .05).

CONCLUSION

Source of admission is associated with likelihood of RRT activation as well as with survival to discharge and should be considered by providers when assessing inpatient risk of decompensation.

Escalation of care in children at high risk of clinical deterioration in a tertiary care children's hospital using the Bedside Pediatric Early Warning System

Background

Escalation and de-escalation are a routine part of high-quality care that should be matched with clinical needs. The aim of this study was to describe escalation of care in relation to the occurrence and timing of Pediatric Intensive Care Unit (PICU) admission in a cohort of pediatric inpatients with acute worsening of their clinical condition.

Methods

A monocentric, observational cohort study was performed from January to December 2018. Eligible patients were children: 1) admitted to one of the inpatient wards other than ICU; 2) under the age of 18 years at the time of admission; 3) with two or more Bedside-Paediatric-Early-Warning-System (BedsidePEWS) scores ≥ 7 recorded at a distance of at least one hour and for a period of 4 h during admission. The main outcome -the 24-h disposition – was defined as admission to PICU within 24-h of enrolment or staying in the inpatient ward. Escalation of care was measured using an eight-point scale—the Escalation Index (EI), developed by the authors. The EI was calculated every 6 h, starting from the moment the patient was considered eligible. Analyses used multivariate quantile and logistic regression models.

Results

The 228 episodes included 574 EI calculated scores. The 24-h disposition was the ward in 129 (57%) and the PICU in 99 (43%) episodes. Patients who were admitted to PICU within 24-h had higher top EI scores [median (IQR) 6 (5–7) vs 4 (3–5), p < 0.001]; higher initial BedsidePEWS scores [median (IQR) 10(8–13) vs. 9 (8–11), p = 0.02], were less likely to have a chronic disease [n = 62 (63%) vs. n = 127 (98%), p < 0.0001], and were rated by physicians as being at a higher risk of having a cardiac arrest (p = 0.01) than patients remaining on the ward. The EI increased over 24 h before urgent admission to PICU or cardiac arrest by 0.53 every 6-h interval (CI 0.37–0.70, p < 0.001), while it decreased by 0.25 every 6-h interval (CI -0.36–0.15, p < 0.001) in patients who stayed on the wards.

Conclusion

Escalation of care was related to temporal changes in severity of illness, patient background and environmental factors. The EI index can improve responses to evolving critical illness.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03555-0.

Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU

OBJECTIVES

Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithms using electronic health records for identifying ICU transfer within 12 hours indicative of a child's condition.

DESIGN

Observational cohort study.

SETTING

Two urban, tertiary-care, academic hospitals (sites 1 and 2).

PATIENTS

Pediatric inpatients (age <18 yr).

INTERVENTIONS

None.

MEASUREMENT AND MAIN RESULTS

Our primary outcome was direct ward to ICU transfer. Using age, vital signs, and laboratory results, we derived logistic regression with regularization, restricted cubic spline regression, random forest, and gradient boosted machine learning models. Among 50,830 admissions at site 1 and 88,970 admissions at site 2, 1,993 (3.92%) and 2,317 (2.60%) experienced the primary outcome, respectively. Site 1 data were split longitudinally into derivation (2009-2017) and validation (2018-2019), whereas site 2 constituted the external test cohort. Across both sites, the gradient boosted machine was the most accurate model and outperformed a modified version of the Bedside Pediatric Early Warning Score that only used physiologic variables in terms of discrimination ( C -statistic site 1: 0.84 vs 0.71, p < 0.001; site 2: 0.80 vs 0.74, p < 0.001), sensitivity, specificity, and number needed to alert.

CONCLUSIONS

We developed and externally validated a novel machine learning model that identifies ICU transfers in hospitalized children more accurately than current tools. Our model enables early detection of children at risk for deterioration, thereby creating opportunities for intervention and improvement in outcomes.

Development and an initial validation of the Responses to Illness Severity Quantification (RISQ) score for severely malnourished children

Aim

To develop and perform an initial validation of a score to measure the severity of illness in hospitalised children with severe acute malnutrition (SAM).

Methods

A prospective study enrolled SAM children aged 6–59 months hospitalised in Borno State, Nigeria. Candidate items associated with inpatient mortality were combined and evaluated as candidate scores. Clinical and statistical methods were used to identify a preferred score.

Results

The 513 children enrolled had a mean age of 15.6 months of whom 48 (9%) died. Seven of the 10 evaluated items were significantly associated with mortality. Five different candidate scores were tested. The final score, Responses to Illness Severity Quantification (RISQ), included seven items: heart rate, respiratory rate, respiratory effort, oxygen saturation, oxygen delivery, temperature and level of consciousness. The mean RISQ score on admission was 2.6 in hospital survivors and 7.3 for children dying <48 h. RISQ scores <24 h before death had an area under the receiver operating characteristic curve (AUROC) of 0.93. The RISQ score performed similarly across differing clinical conditions with AUROCs 0.77–0.98 for all conditions except oedema.

Conclusion

The RISQ score can identify high‐risk malnourished children at and during hospital admission. Clinical application may help prioritise care and potentially improve survival.

Evaluation of a Pediatric Early Warning Score as a Predictor of Occult Invasive Bacterial Infection in the Pediatric Emergency Department

OBJECTIVES

The aims of the study were to evaluate the diagnostic performance of Pediatric Early Warning Score (PEWS) to predict occult invasive bacterial infection (IBI) in well-appearing pediatric emergency department (PED) patients without known risk factors for bacterial infection and to compare PEWS to heart rate (HR) and Emergency Severity Index (ESI).

METHODS

We performed a retrospective case-control analysis of febrile PED patients aged 60 days to 18 years over a 2-year period. Subjects were excluded if they were ill appearing, admitted to an intensive care unit, or had a known high-risk condition. Cases of occult IBI were included if they had a noncontaminant positive culture other than an isolated positive urine culture. Two febrile control subjects were identified for each case. Odds ratios and receiver operating characteristic curves were evaluated to determine performance characteristics of PEWS at triage and disposition, age-adjusted HR at triage and disposition, and ESI at triage.

RESULTS

Compared with 178 controls, 89 cases had higher disposition PEWS, higher disposition HR, lower ESI, and higher rate of hospital admission. Disposition PEWS ≥3 (odds ratio, 2.57; 95% confidence interval, 1.08-6.18), disposition HR > 99th percentile, and ESI demonstrated increased odds of occult IBI. Area under the receiver operating characteristic curve for disposition PEWS (0.56) was similar to triage PEWS (0.54), triage HR (0.54), disposition HR (0.58), and ESI (0.65).

CONCLUSIONS

Subjects with PEWS ≥3 at PED disposition have increased odds of occult IBI; however, PEWS has poor discriminative ability at all cutoffs. We cannot recommend PEWS used in isolation to predict occult IBI.

The predictive performance and impact of pediatric early warning systems in hospitalized pediatric oncology patients-A systematic review

Pediatric early warning systems (PEWS) arewidely used to identify clinically deteriorating patients. Hospitalized pediatric oncology patients are particularly prone to clinical deterioration. We assessed the PEWS performance to predict early clinical deterioration and the effect of PEWS implementation on patient outcomes in pediatric oncology patients. PubMED, EMBASE, and CINAHL databases were systematically searched from inception up to March 2020. Quality assessment was performed using the Prediction model study Risk-Of-Bias Assessment Tool (PROBAST) and the Cochrane Risk-of-Bias Tool. Nine studies were included. Due to heterogeneity of study designs, outcome measures, and diversity of PEWS, it was not possible to conduct a meta-analysis. Although the studies reported high sensitivity, specificity, and area under the receiver operating characteristics curve (AUROC) of PEWS detecting inpatient deterioration, overall risk of bias of the studies was high. This review highlights limited evidence on the predictive performance of PEWS for clinical deterioration and the effect of PEWS implementation.

Modification of a children's hospital pediatric early warning score (EWS): An evaluation of inter-rater reliability, nurses' critical thinking and perceptions of the tool

Pediatric early warning scores (EWS) have been utilized to assist the identification of children at risk for clinically decompensating, experiencing a cardiac or respiratory arrest, or requiring a transfer to a higher level of care. Although their use is widespread, little consistency exists between tools and research evaluating the effectiveness of these tools is lacking. This quasi-experimental project evaluated twenty-five medical-surgical staff nurses' use and perceptions as well as the inter-rater reliability of a newly modified pediatric EWS tool at a free standing, academic Midwestern pediatric hospital. The tool was modified utilizing existing literature and an interdisciplinary team's expertise. Five fictionalized patients, presented in case studies, were developed and nurses were asked to score these patients using the newly modified tool with rationale. Inter-rater reliability was assessed utilizing Fleiss' Kappa and qualitative questionnaire data was analyzed for emerging themes. Overall, Fleiss' Kappa showed that there was moderate agreement between the nurses' judgments and scoring, with scores primarily differing due to the difficulty level of each case study. Nurses' responses to a questionnaire indicated differing levels of comfort identifying and managing children that present with mid-range total scores as opposed to those who scored in the lower or higher ranges. This project's findings highlight nurses' concerns that an objective tool may not accurately describe a subjective assessment. The results of this project indicated that use of this tool, with some modifications to address nursing concerns, may help to identify clinically decompensating pediatric patients being treated on medical-surgical units.

Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach

Background

Timely decision-making regarding intensive care unit (ICU) admission for children with pneumonia is crucial for a better prognosis. Despite attempts to establish a guideline or triage system for evaluating ICU care needs, no clinically applicable paradigm is available.

Objective

The aim of this study was to develop machine learning (ML) algorithms to predict ICU care needs for pediatric pneumonia patients within 24 hours of admission, evaluate their performance, and identify clinical indices for making decisions for pediatric pneumonia patients.

Methods

Pneumonia patients admitted to National Taiwan University Hospital from January 2010 to December 2019 aged under 18 years were enrolled. Their underlying diseases, clinical manifestations, and laboratory data at admission were collected. The outcome of interest was ICU transfer within 24 hours of hospitalization. We compared clinically relevant features between early ICU transfer patients and patients without ICU care. ML algorithms were developed to predict ICU admission. The performance of the algorithms was evaluated using sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and average precision. The relative feature importance of the best-performing algorithm was compared with physician-rated feature importance for explainability.

Results

A total of 8464 pediatric hospitalizations due to pneumonia were recorded, and 1166 (1166/8464, 13.8%) hospitalized patients were transferred to the ICU within 24 hours. Early ICU transfer patients were younger (P<.001), had higher rates of underlying diseases (eg, cardiovascular, neuropsychological, and congenital anomaly/genetic disorders; P<.001), had abnormal laboratory data, had higher pulse rates (P<.001), had higher breath rates (P<.001), had lower oxygen saturation (P<.001), and had lower peak body temperature (P<.001) at admission than patients without ICU transfer. The random forest (RF) algorithm achieved the best performance (sensitivity 0.94, 95% CI 0.92-0.95; specificity 0.94, 95% CI 0.92-0.95; AUC 0.99, 95% CI 0.98-0.99; and average precision 0.93, 95% CI 0.90-0.96). The lowest systolic blood pressure and presence of cardiovascular and neuropsychological diseases ranked in the top 10 in both RF relative feature importance and clinician judgment.

Conclusions

The ML approach could provide a clinically applicable triage algorithm and identify important clinical indices, such as age, underlying diseases, abnormal vital signs, and laboratory data for evaluating the need for intensive care in children with pneumonia.

Characteristics and management of adolescents attending the ED with fever: a prospective multicentre study

OBJECTIVE

Most studies on febrile children have focused on infants and young children with serious bacterial infection (SBI). Although population studies have described an increased risk of sepsis in adolescents, little is known about febrile adolescents attending the emergency department (ED). We aimed to describe patient characteristics and management of febrile adolescents attending the ED.

DESIGN AND SETTING

The MOFICHE/PERFORM study (Management and Outcome of Febrile Children in Europe/Personalised Risk assessment in Febrile illness to Optimise Real-life Management across the European Union), a prospective multicentre study, took place at 12 European EDs. Descriptive and multivariable regression analyses were performed, comparing febrile adolescents (12-18 years) with younger children in terms of patient characteristics, markers of disease severity (vital signs, clinical alarming signs), management (diagnostic tests, therapy, admission) and diagnosis (focus, viral/bacterial infection).

RESULTS

37 420 encounters were included, of which 2577 (6.9%) were adolescents. Adolescents were more often triaged as highly urgent (38.9% vs 34.5%) and described as ill appearing (23.1% vs 15.6%) than younger children. Increased work of breathing and a non-blanching rash were present less often in adolescents, while neurological signs were present more often (1% vs 0%). C reactive protein tests were performed more frequently in adolescents and were more often abnormal (adjusted OR (aOR) 1.7, 95% CI 1.5 to 1.9). Adolescents were more often diagnosed with SBI (OR 1.8, 95% CI 1.6 to 2.0) and sepsis/meningitis (OR 2.3, 95% CI 1.1 to 5.0) and were more frequently admitted (aOR 1.3, 95% CI 1.2 to 1.4) and treated with intravenous antibiotics (aOR 1.7, 95% CI 1.5 to 2.0).

CONCLUSIONS

Although younger children presented to the ED more frequently, adolescents were more often diagnosed with SBI and sepsis/meningitis. Our data emphasise the importance of awareness of severe infections in adolescents.

Evaluation of Pediatric Early Warning System and Drooling Reluctance Oropharynx Others Leukocytosis scores as prognostic tools for pediatric caustic ingestion: a two-center, cross-sectional study

Caustic chemicals are widely distributed in our environment. Exposure to caustic agents is a lifelong problem associated with severe tissue and mucous membrane injuries. In pediatrics, corrosive exposure is the most common cause of nonpharmaceutical exposure presenting to poison control centers. Therefore, this study evaluated the role of the Pediatric Early Warning System (PEWS) and Drooling Reluctance Oropharynx Others Leukocytosis (DROOL) scores as early in-hospital outcome predictors following corrosive ingestion. The current study was a two-center, retrospective, cross-sectional study carried out among pediatric patients diagnosed with acute caustic ingestion during the past 4 years. Most exposure occurred accidentally among boys (59.4%) living in rural areas (51.9%) of preschool age (50% were 2-4 years old). Residence, body temperature, respiratory rate, vomiting, skin and mucosal burns, retrosternal pain, respiratory distress, Oxygen (O2) saturation, Glasgow Coma Scale score, HCO₃ level, total bilirubin level, anemia, leukocytosis, and presence of free peritoneal fluid were significant predictors of esophageal injuries (p < 0.05). DROOL and PEWS scoring were the most significant predictors of esophageal injuries with worthy predictive power, where odds ratio (95% confidence interval (CI)) was 1.76 (0.97-3.17) and 0.47 (0.21-0.99) for PEWS and DROOL, respectively. At a cutoff of < 6.5, the DROOL score could predict esophageal injuries excellently, with AUC = 0.931; sensitivity, 91.7%; specificity, 72.5%; and overall accuracy, 91.3%. At a cutoff of > 6.5, PEWS could significantly predict unfavorable outcomes, with AUC = 0.893; sensitivity, 94.4%; specificity, 71.9%; and overall accuracy, 89.3%. However, PEWS better predicted the need for admittance to the intensive care unit (ICU). Pediatric Early Warning System (PEWS) and Drooling Reluctance Oropharynx Others Leukocytosis (DROOL) are potentially useful accurate scorings that could predict the esophageal injuries and ICU admission following corrosive ingestion in pediatrics.

Mortality of Hemato-Oncologic Patients Admitted to a Pediatric Intensive Care Unit: A Single-Center Experience

INTRODUCTION

Mortality in children with hemato-oncologic disease admitted to a pediatric intensive care unit (PICU) is higher compared to the general population. The reasons for this fact remain unexplored. The aim of this study was to assess outcomes and trends in hemato-oncologic patients admitted to a PICU, with analytical emphasis on emergency admissions.

METHODS

Patients with a hemato-oncologic diagnosis admitted to a tertiary care university hospital PICU between 1 January 2009 and 31 December 2019 were retrospectively analyzed. Additionally, patient mortality 6 months after PICU admission and follow-up mortality until 31 December 2020 were recorded.

MEASUREMENTS AND MAIN RESULTS

We reviewed a total of 701 PICU admissions of 338 children with hemato-oncologic disease, of which 28.5% were emergency admissions with 200 admissions of 122 patients. Of these, 22 patients died, representing a patient mortality of 18.0% and an admission mortality of 11.0% in this group. Follow-up patient mortality was 25.4% in emergency-admitted children. Multivariable analysis revealed severe neutropenia at admission and invasive mechanical ventilation (IMV) as independent risk factors for PICU death (p = 0.029 and p = 0.002). The total number of PICU admissions of hemato-oncologic patients rose notably over time, from 44 in 2009 to 125 in 2019.

CONCLUSION

Although a high proportion of emergency PICU admissions of hemato-oncologic patients required intensive organ support, mortality seemed to be lower than previously reported. Moreover, in this study, total PICU admissions of the respective children rose notably over time.

The Association of the Nurse Work Environment and Patient Safety in Pediatric Acute Care

OBJECTIVES

Eighteen years ago, the Institute of Medicine estimated that medical errors in hospital were a major cause of mortality. Since that time, reducing patient harm and improving the culture of patient safety have been national health care priorities. The study objectives were to describe the current state of patient safety in pediatric acute care settings and to assess whether modifiable features of organizations are associated with better safety culture.

METHODS

An observational cross-sectional study used 2015-2016 survey data on 177 hospitals in four U.S. states, including pediatric care in general hospitals and freestanding children's hospitals. Pediatric registered nurses providing direct patient care assessed hospital safety and the clinical work environment. Safety was measured by items from the Agency for Healthcare Research and Quality's Culture of Patient Safety survey. Hospital clinical work environment was measured by the National Quality Forum-endorsed Practice Environment Scale.

RESULTS

A total of 1875 pediatric nurses provided an assessment of safety in their hospitals. Sixty percent of pediatric nurses gave their hospitals less than an excellent grade on patient safety; significant variation across hospitals was observed. In the average hospital, 46% of nurses report that mistakes are held against them and 28% do not feel safe questioning authority regarding unsafe practices. Hospitals with better clinical work environments received better patient safety grades.

CONCLUSIONS

The culture of patient safety varies across U.S. hospital pediatric settings. In better clinical work environments, nurses report more positive safety culture and higher safety grades.

Decision-making for children requiring interhospital transport: assessment of a novel triage tool

OBJECTIVE

The use of specialist retrieval teams to transport critically ill children is associated with reduced risk-adjusted mortality and morbidity; however, there is a paucity of data to guide decision-making related to retrieval team activation. We aimed to assess the accuracy of a novel triage tool designed to identify critically ill children at the time of referral for interhospital transport.

DESIGN

Prospective observational study.

SETTING

Regional paediatric retrieval and transport services.

PATIENTS

Data were collected for 1815 children referred consecutively for interhospital transport from 87 hospitals in Queensland and northern New South Wales.

INTERVENTION

Implementation of the Queensland Paediatric Transport Triage Tool.

MAIN OUTCOME MEASURES

Accuracy was assessed by calculating the sensitivity, specificity and negative predictive value for predicting transport by a retrieval team, or admission to intensive care following transport.

RESULTS

A total of 574 (32%) children were transported with a retrieval team. Prediction of retrieval (95% CIs): sensitivity 96.9% (95% CI 95.1% to 98.1%), specificity 91.4% (95% CI 89.7% to 92.9%), negative predictive value 98.4% (95% CI 97.5% to 99.1%). There were 412 (23%) children admitted to intensive care following transport. Prediction of intensive care admission: sensitivity 96.8% (95% CI 94.7% to 98.3%), specificity 81.2% (95% CI 79.0% to 83.2%), negative predictive value 98.9% (95% CI 98.1% to 99.4%).

CONCLUSIONS

The triage tool predicted the need for retrieval or intensive care admission with high sensitivity and specificity. The high negative predictive value indicates that, in our setting, children categorised as acutely ill rather than critically ill are generally suitable for interhospital transport without a retrieval team.

Implementation of a pediatric early warning score tool in a pediatric oncology Ward in Palestine

Background

Pediatric Early Warning Scores (PEWS) are nurse-administered clinical assessment tools utilizing vital signs and patient signs and symptoms to screen for patients at risk for clinical deterioration.^1–3 When utilizing a PEWS system, which consists of an escalation algorithm to alert physicians of high risk patients requiring a bedside evaluation and assessment, studies have demonstrated that PEWS systems can decrease pediatric intensive care (PICU) utilization, in-hospital cardiac arrests, and overall decreased mortality in high income settings. Yet, many hospital based settings in low and lower middle income countries (LMIC) lack systems in place for early identification of patients at risk for clinical deterioration.

Methods

A contextually adapted 16-h pediatric resuscitation program included training of a PEWS tool followed by implementation and integration of a PEWS system in a pediatric hematology/oncology ward in Beit Jala, Palestine. Four PDSA cycles were implemented post-implementation to improve uptake and scoring of PEWS which included PEWS tool integration into an existing electronic medical record (EMR), escalation algorithm and job aid implementation, data audits and ward feedback.

Results

Frequency of complete PEWS vital sign documentation reached a mean of 89.9%. The frequency and accuracy of PEWS scores steadily increased during the post-implementation period, consistently above 89% in both categories starting from data audit four and continuing thereafter. Accuracy of PEWS scoring was unable to be assessed during week 1 and 2 of data audits due to challenges with PEWS integration into the existing EMR (PDSA cycle 1) which were resolved by the 3rd week of data auditing (PDSA cycle 2).

Conclusions

Implementation of a PEWS scoring tool in an LMIC pediatric oncology inpatient unit is feasible and can improve frequency of vital sign collection and generate accurate PEWS scores.

Contribution to the literature

This study demonstrates how to effectively implement a PEWS scoring tool into an LMIC clinical setting.

This study demonstrates how to utilize a robust feedback mechanism to ensure a quality program uptake.

This study demonstrates an effective international partnership model that other institutions may utilize for implementation science.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07157-x.

Pediatric rapid response system innovations

Rapid Response Systems (RRSs) are an organizational approach to support the timely recognition and treatment of decompensating patients and are used in many pediatric hospitals. These systems are comprised of afferent and efferent Limbs, as well as oversight arms. When incorporated into an RRS, standardized care algorithms can be helpful in identifying deteriorating patients and improving behaviors of the multidisciplinary team. The aim of this paper is to provide an overview of pediatric RRS and provide an example in which standardized care algorithms developed for the efferent limb of a pediatric RRS were associated with improvement in early escalation of care.PLAIN LANGUAGE SUMMARYThe Rapid Response System (RRS) is used in hospitals to recognize and care for hospitalized patients that are decompensating outside of an Intensive Care Unit. RRSs are made up of two main response components. The afferent limb focuses on the recognition and calls for help; the efferent limb focuses on correcting the deteriorating patient's physiology. Much energy has been put into afferent limb development to identify worsening patients before they progress to full cardiac or respiratory arrest. Standardization of efferent limb care algorithms can assist in developing and maintaining a shared mental model of care to improve communication and function of the multidisciplinary team.

A NICE combination for predicting hospitalisation at the Emergency Department: a European multicentre observational study of febrile children

Background

Prolonged Emergency Department (ED) stay causes crowding and negatively impacts quality of care. We developed and validated a prediction model for early identification of febrile children with a high risk of hospitalisation in order to improve ED flow.

Methods

The MOFICHE study prospectively collected data on febrile children (0-18 years) presenting to 12 European EDs. A prediction models was constructed using multivariable logistic regression and included patient characteristics available at triage. We determined the discriminative values of the model by calculating the area under the receiver operating curve (AUC).

Findings

Of 38,424 paediatric encounters, 9,735 children were admitted to the ward and 157 to the PICU. The prediction model, combining patient characteristics and NICE alarming, yielded an AUC of 0.84 (95%CI 0.83-0.84).The model performed well for a rule-in threshold of 75% (specificity 99.0% (95%CI 98.9-99.1%, positive likelihood ratio 15.1 (95%CI 13.4-17.1), positive predictive value 0.84 (95%CI 0.82-0.86)) and a rule-out threshold of 7.5% (sensitivity 95.4% (95%CI 95.0-95.8), negative likelihood ratio 0.15 (95%CI 0.14-0.16), negative predictive value 0..95 (95%CI 0.95-9.96)). Validation in a separate dataset showed an excellent AUC of 0.91 (95%CI 0.90- 0.93). The model performed well for identifying children needing PICU admission (AUC 0.95, 95%CI 0.93-0.97). A digital calculator was developed to facilitate clinical use.

Interpretation

Patient characteristics and NICE alarming signs available at triage can be used to identify febrile children at high risk for hospitalisation and can be used to improve ED flow.

Funding

European Union, NIHR, NHS.

Adherence to the bedside paediatric early warning system (BedsidePEWS) in a pediatric tertiary care hospital

Background

The aim of this study is to describe the adherence to the Bedside Pediatric Early Warning System (BedsidePEWS) escalation protocol in children admitted to hospital wards in a large tertiary care children’s hospital in Italy.

Methods

This is a retrospective observational chart review. Data on the frequency and accuracy of BedsidePEWS score calculations, escalation of patient observations, monitoring and medical reviews were recorded.

Two research nurses performed weekly visits to the hospital wards to collect data on BedsidePEWS scores, medical reviews, type of monitoring and vital signs recorded. Data were described through means or medians according to the distribution. Inferences were calculated either with Chi-square, Student’s t test or Wilcoxon-Mann–Whitney test, as appropriate (P < 0.05 considered as significant).

Results

A total of 522 Vital Signs (VS) and score calculations [BedsidePEWS documentation events, (DE)] on 177 patient clinical records were observed from 13 hospital inpatient wards. Frequency of BedsidePEWS DE occurred < 3 times per day in 33 % of the observations. Adherence to the BedsidePEWS documentation frequency according to the hospital protocol was observed in 54 % of all patients; in children with chronic health conditions (CHC) it was significantly lower than children admitted for acute medical conditions (47 % vs. 69 %, P = 0.006). The BedsidePEWS score was correctly calculated and documented in 84 % of the BedsidePEWS DE. Patients in a 0–2 BedsidePEWS score range were all reviewed at least once a day by a physician. Only 50 % of the patients in the 5–6 score range were reviewed within 4 h and 42 % of the patients with a score ≥ 7 within 2 h.

Conclusions

Escalation of patient observations, monitoring and medical reviews matching the BedsidePEWS is still suboptimal. Children with CHC are at higher risk of lower compliance. Impact of adherence to predefined response algorithms on patient outcomes should be further explored.

How to implement a PEWS in a resource-limited setting: A quantitative analysis of the bedside-PEWS implementation in a hospital in northeast Brazil

OBJECTIVES

Quantitative analysis of the implementation of the bedside paediatric early warning system (B-PEWS) in a resource-limited setting. The B-PEWS serves to pre-emptively identify hospitalised children who are at risk for cardiopulmonary arrest and subsequently to provide critical care in time.

METHODS

We performed a retrospective review through the medical data records of patients after discharge from the paediatric ward of a philanthropic hospital in Brazil. Nurses' performance using the system was measured with various parameters.

RESULTS

A total of 499 patients were included, and a total of 8024 scores were checked. During the 21-week research period, the implementation rate increased significantly from 66.5% (SD 26.0) in Period 1 to 93.1% (SD 16.6) in Period 2. The number of scores that resulted in a correct total score went from 7.5% in Period 1 to 32.2% in Period 2, p < 0.001. There was an improvement in the correct choice of age group between the two periods (from 32.2% to 53.4%). There was no difference in the mean admission time of patients in the two periods: in the first period 4.8 days (SD 2.9) and in the second period 4.8 days (SD 4.1).

CONCLUSIONS

It is possible to implement a PEWS in resource-limited settings while achieving high implementation rates. However, this is a time- and energy-consuming process. Having an active and involved team that is responsible for implementation is key for a successful implementation. Factors that likely hindered implementation were a large change in workflow for the nursing staff, non-native speakers as main investigators.

Identifying the critically ill paediatric oncology patient: a study protocol for a prospective observational cohort study for validation of a modified Bedside Paediatric Early Warning System score in hospitalised paediatric oncology patients

INTRODUCTION

Hospitalised paediatric oncology patients are at risk to develop acute complications. Early identification of clinical deterioration enabling adequate escalation of care remains challenging. Various Paediatric Early Warning Systems (PEWSs) have been evaluated, also in paediatric oncology patients but mostly in retrospective or case-control study designs. This study protocol encompasses the first prospective cohort with the aim of evaluating the predictive performance of a modified Bedside PEWS score for non-elective paediatric intensive care unit (PICU) admission or cardiopulmonary resuscitation in hospitalised paediatric oncology patients.

METHODS AND ANALYSIS

A prospective cohort study will be conducted at the 80-bed Dutch paediatric oncology hospital, where all national paediatric oncology care has been centralised, directly connected to a shared 22-bed PICU. All patients between 1 February 2019 and 1 February 2021 admitted to the inpatient nursing wards, aged 0-18 years, with an International Classification of Diseases for Oncology (ICD-O) diagnosis of paediatric malignancy will be eligible. A Cox proportional hazard regression model will be used to estimate the association between the modified Bedside PEWS and time to non-elective PICU transfer or cardiopulmonary arrest. Predictive performance (discrimination and calibration) will be assessed internally using resampling validation. To account for multiple occurrences of the event of interest within each patient, the unit of study is a single uninterrupted ward admission (a clinical episode).

ETHICS AND DISSEMINATION

The study protocol has been approved by the institutional ethical review board of our hospital (MEC protocol number 16-572/C). We adapted our enrolment procedure to General Data Protection Regulation compliance. Results will be disseminated at scientific conferences, regional educational sessions and publication in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

Netherlands Trial Registry (NL8957).

Image-guided chest tube drainage in the management of chylothorax post cardiac surgery in children: a single-center case series

BACKGROUND

In children, chylothorax post cardiac surgery can be difficult to treat, may run a protracted course, and remains a source of morbidity and mortality.

OBJECTIVE

To analyze the experience with percutaneous image-guided chest-tube drainage in the management of post-cardiac-surgery chylothoraces in children.

MATERIALS AND METHODS

We conducted a single-center retrospective case series of 37 post-cardiac-surgery chylothoraces in 34 children (20 boys; 59%), requiring 48 drainage procedures with placement of 53 image-guided chest tubes over the time period 2004 to 2015. We analyzed clinical and procedural details, adverse events and outcomes. Median age was 0.6 years, median weight 7.2 kg.

RESULTS

Attempted treatments of chylothoraces prior to image-guided chest tubes included dietary restrictions (32/37, 86%), octreotide (12/37, 32%), steroids (7/37, 19%) and thoracic duct ligation (5/37, 14%). Image-guided chest tubes (n=43/53, 81%) were single unilateral in 29 children, bilateral in 4 (n=8/53, 15%), and there were two ipsilateral tubes in one (2/53, 4%). Effusions were isolated, walled-off, in 33/53 (62%). In 20/48 procedures (42%) effusions were septated/complex. The mean drainage through image-guided chest tubes was 17.3 mL/kg in the first 24 h, and 13.4 mL/kg/day from diagnosis to chest tube removal; total mean drainage from all chest tubes was 19.6 mL/kg/day. Nine major and 27 minor maintenance procedures were required during 1,207 tube-days (rate: 30 maintenance/1,000 tube-days). Median tube dwell time was 21 days (range 4-57 days). There were eight mild adverse events, three moderate adverse events and no severe adverse events related to image-guided chest tubes. Radiologic resolution was achieved in 26/37 (70%). Twenty-three children (68%) survived to discharge; 11 children (32%) died from underlying cardiac disease.

CONCLUSION

Management of chylothorax post-cardiac-surgery in children is multidisciplinary, requiring concomitant multipronged approaches, often through a protracted course. Multiple image-guided chest tube drainages can help achieve resolution with few complications. Interventional radiology involvement in tube care and maintenance is required. Overall, mortality remains high.

Elevated Heart Rate and Risk of Revisit With Admission in Pediatric Emergency Patients

OBJECTIVE

The aim of this study was to identify emergency department (ED) heart rate (HR) values that identify children at elevated risk of ED revisit with admission.

METHODS

We performed a retrospective cohort study of patients 0 to 18 years old discharged from a tertiary-care pediatric ED from January 2013 to December 2014. We created percentile curves for the last recorded HR for age using data from calendar year 2013 and used receiver operating characteristic (ROC) curves to characterize the performance of the percentiles for predicting ED revisit with admission within 72 hours. In a held-out validation data set (calendar year 2014 data), we evaluated test characteristics of last-recorded HR-for-age cut points identified as promising on the ROC curves, as well as those identifying the highest 5% and 1% of last recorded HRs for age.

RESULTS

We evaluated 183,433 eligible ED visits. Last recorded HR for age had poor discrimination for predicting revisit with admission (area under the curve, 0.61; 95% confidence interval, 0.58-0.63). No promising cut points were identified on the ROC curves. Cut points identifying the highest 5% and 1% of last recorded HRs for age showed low sensitivity (10.1% and 2.5%) with numbers needed to evaluate of 62 and 50, respectively, to potentially prevent 1 revisit with admission.

CONCLUSIONS

Last recorded ED HR discriminates poorly between children who are and are not at risk of revisit with admission in a pediatric ED. The use of single-parameter HR in isolation as an automated trigger for mandatory reevaluation prior to discharge may not improve revisit outcomes.

Study of the Relationship between ICU Patient Recovery and TCM Treatment in Acute Phase: A Retrospective Study Based on Python Data Mining Technology

Background

Data was mined with the help of an artificial intelligence system based on Python, data was collected, and a database was established using a Python crawler, and the relationship between the outcome of neurosurgery ICU patients and treatment using traditional Chinese medicine was ascertained through data management and statistical processing.

Method

The source data cases (n = 2237) were selected. By following the experimental design, data (n = 739) were obtained through artificial intelligence processing, including n = 480 in the group with traditional Chinese medicine treatment and n = 259 in the group without traditional Chinese medicine treatment. An evaluation was carried out using characteristics of patents' ICU stays and summated rating scales.

Results

There were statistical differences in 5 evaluation items (P < 0.05), and other comparison items also showed data with results favoring the outcomes in the intervention group using traditional Chinese medicine. Discussion. Traditional Chinese medicine as an alternative medical protocol effectively alleviates the stress and treatment fatigue brought about by modern medicine. Artificial intelligence data mining is a favorable medium to quantify this. Python will play a greater role in future clinical research because of its own characteristics.

Feasibility of Family-Assisted Severity of Illness Monitoring for Hospitalized Children in Low-Income Settings

OBJECTIVES

To determine the feasibility of having caregivers assist in recognition of clinical deterioration in children hospitalized with febrile illness in a resource-limited setting.

DESIGN

Single-center, prospective, interventional pilot study.

SETTING

General pediatric wards at Kenyatta National Hospital, Nairobi, Kenya's largest public tertiary-care hospital.

PATIENTS

Children hospitalized with acute febrile illness, accompanied by caregivers available at the bedside for 24 hours soon after hospital admission.

INTERVENTIONS

Caregivers were trained to recognize signs of critical illness using the Family-Assisted Severe Febrile Illness Therapy tool, which quantifies patients' work of breathing, mental status, and perfusion, producing color-coded flags to signal illness severity. Caregivers' Family-Assisted Severe Febrile Illness Therapy assessments were compared with healthcare professional assessments and to established Pediatric Early Warning Scores (PEWS). An initial study stage was followed by refinement of training and a larger second stage with intervention/control arms.

MEASUREMENTS AND MAIN RESULTS

A total of 107 patient/caregiver pairs were enrolled in the interventional arm; 106 caregivers underwent Family-Assisted Severe Febrile Illness Therapy training and were included in the analysis. Patient characteristics included median age 1.1 years (0.2-10 yr), 55 (52%) female, and diagnoses: pneumonia (64 [60%]), meningitis (38 [36%]), gastroenteritis (24 [23%]), and malaria (21 [20%]). Most caregivers had primary (34 [32%]) or secondary (53 [50%]) school education. Fourteen of 106 patients (13%) died during their stay, six within 2 days. Across all severity levels, caregiver Family-Assisted Severe Febrile Illness Therapy assessments matched professionals in 87% and 94% for stages 1 and 2, respectively. Caregiver Family-Assisted Severe Febrile Illness Therapy assessments had a moderate to strong correlation with coinciding Pediatric Early Warning Scores and were sensitive to life-threatening deterioration: for all six patients who died within 2 days of admission, caregiver assessment reached the highest alert level.

CONCLUSIONS

Caregiver involvement in recognition of critical illness in hospitalized children in low-resource settings may be feasible. This may facilitate earlier detection of clinical deterioration where staffing is severely limited by constrained resources. Further validation of the Family-Assisted Severe Febrile Illness Therapy tool is warranted, followed by its application in a larger multisite patient population to assess provider response and associated clinical outcomes.

The Association of Laboratory Test Abnormalities With Mortality Risk in Pediatric Intensive Care

OBJECTIVES

To determine the bivariable associations between abnormalities of 28 common laboratory tests and hospital mortality and determine how mortality risks changes when the ranges are evaluated in the context of commonly used laboratory test panels.

DESIGN

A 2009-2016 cohort from the Health Facts (Cerner Corporation, Kansas City, MO) database.

SETTING

Hospitals caring for children in ICUs.

PATIENTS

Children cared for in ICUs with laboratory data.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

There were 2,987,515 laboratory measurements in 71,563 children. The distribution of laboratory test values in 10 groups defined by population percentiles demonstrated the midrange of tests was within the normal range except for those measured predominantly when significant abnormalities are suspected. Logistic regression analysis at the patient level combined the population-based groups into ranges with nonoverlapping mortality odds ratios. The most deviant test ranges associated with increased mortality risk (mortality odds ratios > 5.0) included variables associated with acidosis, coagulation abnormalities and blood loss, immune function, liver function, nutritional status, and the basic metabolic profile. The test ranges most associated with survival included normal values for chloride, pH, and bicarbonate/total Co2. When the significant test ranges from bivariable analyses were combined in commonly used test panels, they generally remained significant but were reduced as risk was distributed among the tests.

CONCLUSIONS

The relative importance of laboratory test ranges vary widely, with some ranges strongly associated with mortality and others strongly associated with survival. When evaluated in the context of test panels rather than isolated tests, the mortality odds ratios for the test ranges decreased but generally remained significant as risk was distributed among the components of the test panels. These data are useful to develop critical values for children in ICUs, to identify risk factors previously underappreciated, for education and training, and for future risk score development.

Variation in hospital admission in febrile children evaluated at the Emergency Department (ED) in Europe: PERFORM, a multicentre prospective observational study

Objectives

Hospitalisation is frequently used as a marker of disease severity in observational Emergency Department (ED) studies. The comparison of ED admission rates is complex in potentially being influenced by the characteristics of the region, ED, physician and patient. We aimed to study variation in ED admission rates of febrile children, to assess whether variation could be explained by disease severity and to identify patient groups with large variation, in order to use this to reduce unnecessary health care utilization that is often due to practice variation.

Design

MOFICHE (Management and Outcome of Fever in children in Europe, part of the PERFORM study, www.perform2020.org), is a prospective cohort study using routinely collected data on febrile children regarding patient characteristics (age, referral, vital signs and clinical alarming signs), diagnostic tests, therapy, diagnosis and hospital admission.

Setting and participants

Data were collected on febrile children aged 0–18 years presenting to 12 European EDs (2017–2018).

Main outcome measures

We compared admission rates between EDs by using standardised admission rates after adjusting for patient characteristics and initiated tests at the ED, where standardised rates >1 demonstrate higher admission rates than expected and rates <1 indicate lower rates than expected based on the ED patient population.

Results

We included 38,120 children. Of those, 9.695 (25.4%) were admitted to a general ward (range EDs 5.1–54.5%). Adjusted standardised admission rates ranged between 0.6 and 1.5. The largest variation was seen in short admission rates (0.1–5.0), PICU admission rates (0.2–2.2), upper respiratory tract infections (0.4–1.7) and fever without focus (0.5–2.7). Variation was small in sepsis/meningitis (0.9–1.1).

Conclusions

Large variation exists in admission rates of febrile children evaluated at European EDs, however, this variation is largely reduced after correcting for patient characteristics and therefore overall admission rates seem to adequately reflect disease severity or a potential for a severe disease course. However, for certain patient groups variation remains high even after adjusting for patient characteristics.

Severity Trajectories of Pediatric Inpatients Using the Criticality Index

OBJECTIVES

To assess severity of illness trajectories described by the Criticality Index for survivors and deaths in five patient groups defined by the sequence of patient care in ICU and routine patient care locations.

DESIGN

The Criticality Index developed using a calibrated, deep neural network, measures severity of illness using physiology, therapies, and therapeutic intensity. Criticality Index values in sequential 6-hour time periods described severity trajectories.

SETTING

Hospitals with pediatric inpatient and ICU care.

PATIENTS

Pediatric patients never cared for in an ICU (n = 20,091), patients only cared for in the ICU (n = 2,096) and patients cared for in both ICU and non-ICU care locations (n = 17,023) from 2009 to 2016 Health Facts database (Cerner Corporation, Kansas City, MO).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Criticality Index values were consistent with clinical experience. The median (25-75th percentile) ICU Criticality Index values (0.878 [0.696-0.966]) were more than 80-fold higher than the non-ICU values (0.010 [0.002-0.099]). Non-ICU Criticality Index values for patients transferred to the ICU were 40-fold higher than those never transferred to the ICU (0.164 vs 0.004). The median for ICU deaths was higher than ICU survivors (0.983 vs 0.875) (p < 0.001). The severity trajectories for the five groups met expectations based on clinical experience. Survivors had increasing Criticality Index values in non-ICU locations prior to ICU admission, decreasing Criticality Index values in the ICU, and decreasing Criticality Index values until hospital discharge. Deaths had higher Criticality Index values than survivors, steeper increases prior to the ICU, and worsening values in the ICU. Deaths had a variable course, especially those who died in non-ICU care locations, consistent with deaths associated with both active therapies and withdrawals/limitations of care.

CONCLUSIONS

Severity trajectories measured by the Criticality Index showed strong validity, reflecting the expected clinical course for five diverse patient groups.

Predictors of disease severity in children presenting from the community with febrile illnesses: a systematic review of prognostic studies

INTRODUCTION

Early identification of children at risk of severe febrile illness can optimise referral, admission and treatment decisions, particularly in resource-limited settings. We aimed to identify prognostic clinical and laboratory factors that predict progression to severe disease in febrile children presenting from the community.

METHODS

We systematically reviewed publications retrieved from MEDLINE, Web of Science and Embase between 31 May 1999 and 30 April 2020, supplemented by hand search of reference lists and consultation with an expert Technical Advisory Panel. Studies evaluating prognostic factors or clinical prediction models in children presenting from the community with febrile illnesses were eligible. The primary outcome was any objective measure of disease severity ascertained within 30 days of enrolment. We calculated unadjusted likelihood ratios (LRs) for comparison of prognostic factors, and compared clinical prediction models using the area under the receiver operating characteristic curves (AUROCs). Risk of bias and applicability of studies were assessed using the Prediction Model Risk of Bias Assessment Tool and the Quality In Prognosis Studies tool.

RESULTS

Of 5949 articles identified, 18 studies evaluating 200 prognostic factors and 25 clinical prediction models in 24 530 children were included. Heterogeneity between studies precluded formal meta-analysis. Malnutrition (positive LR range 1.56-11.13), hypoxia (2.10-8.11), altered consciousness (1.24-14.02), and markers of acidosis (1.36-7.71) and poor peripheral perfusion (1.78-17.38) were the most common predictors of severe disease. Clinical prediction model performance varied widely (AUROC range 0.49-0.97). Concerns regarding applicability were identified and most studies were at high risk of bias.

CONCLUSIONS

Few studies address this important public health question. We identified prognostic factors from a wide range of geographic contexts that can help clinicians assess febrile children at risk of progressing to severe disease. Multicentre studies that include outpatients are required to explore generalisability and develop data-driven tools to support patient prioritisation and triage at the community level.

PROSPERO REGISTRATION NUMBER

CRD42019140542.

Factors associated with EMS transport decisions for pediatric patients after motor vehicle collisions

OBJECTIVE

Prehospital non-transport events occur when emergency medicine service (EMS) providers respond to a scene, but the patient is ultimately not transported to a hospital for evaluation. The objective of this study was to determine the rate of non-transport of pediatric patients who were involved in a motor vehicle collision (MVC) and the factors associated with non-transport decisions.

METHODS

We searched the National Emergency Medical Services Information System (NEMSIS) database using ICD-10 mechanism of injury codes to identify cases in which EMS responded to a pediatric occupant (age < 18 years) who had been involved in an MVC. We excluded interfacility transports, scene assists, deaths at the scene, and collisions that occurred outside the US. The outcome of interest was if pediatric patients were not transported to a hospital for evaluation. We performed univariate and multivariate analysis to identify which risk factors were associated with non-transport. We also analyzed regional variation and the reasons recorded for not transporting patients.

RESULTS

We identified 92,254 pediatric patients who were evaluated by EMS after an MVC, of which 31,404 (34.0%) were not transported to a hospital for evaluation. In our adjusted analysis, the factors associated with non-transport were age <1 year or >16 years, male sex, normal Glasgow Coma Scale (GCS = 15), level of training of EMS providers, response time later than 6 a.m., and region of the country. GCS was the most important factor, with only 3.0% (108/3,616) of patients not transported who had abnormal GCS (< 15). In cases of non-transport, 32.7% (10257) were due to patient or caregiver refusal, and 33.3% (10,442) were due to patients being discharged against medical advice. Only 11.5% (3,627) pediatric patients who were not transported were discharged based on an established protocol.

CONCLUSIONS

Pediatric patients were not transported after EMS responded to an MVC in approximately one-third of cases, and there was considerable variation in the rate of non-transports based on geographic region, provider level, and time of day. The majority of non-transports occurred because patients were discharged against medical advice or the patient/caregiver refused transport, which may indicate conflicting priorities between EMS providers and patients.

A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children

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.

Safety in Pediatric Hospice and Palliative Care: A Qualitative Study

Introduction

Patient safety is extensively studied in both adults and pediatric medicine; however, knowledge is limited regarding particular safety events in pediatric hospice and palliative care (HPC). Additionally, pediatric HPC lacks a unified definition of safe care. This qualitative study sought to explore caregiver views regarding safe care in pediatric HPC.

Methods

This is a secondary analysis of qualitative data from a multisite study utilizing semistructured interview data to evaluate parental perspectives of quality in pediatric home-based HPC programs across 3 different pediatric tertiary care hospitals. Eligible participants included parents and caregivers of children who were enrolled in a pediatric home-based hospice and palliative care program (HBHPC) from 2012 to 2016. The analysis was done using grounded theory methodology.

Results

Forty-three parents participated in 39 interviews across all 3 sites; 19 families were bereaved. Responses to the prompt regarding safe care produced 8 unique domains encompassing parental definitions of safe care in pediatric HPC.

Discussion

Parents of children in HPC programs describe "safe care" in novel ways, some of which echo Maslow's hierarchy of needs. The use of traditional hospital safety measures for patients receiving HPC could undermine the patient's goals or dignity, ultimately leading to harm to the patient.

Concluding summary

Patients' and families' unique goals and values must be considered when defining safety for children in this population. Future studies should continue to explore family perspectives of safety in the hospital and ambulatory settings and seek to identify measurable indicators in safety which are truly patient- and family-centered.

Generalizability of SuperAlarm via Cross-Institutional Performance Evaluation

Bedside patient monitors are ubiquitous tools in modern critical care units to provide timely patient status. However, current systems suffer from high volume of false alarms leading to alarm fatigue, one of top technical hazards in clinical settings. Many studies are racing to develop improved algorithms towards precision patient monitoring, while little has been done to investigate the aspect of algorithm generalizability across different health institutions. Our group has been developing an evolving framework termed SuperAlarm that extracts multivariate patterns in data streams (monitor alarms, electronic health records and physiologic waveforms) of modern health enterprise to predict patient deterioration and has demonstrated great potential in mitigating alarm fatigue. In this study, we further investigate the generalizability of SuperAlarm by designing a comprehensive approach to achieve performance comparison in predicting in-hospital code blue (CB) events across two health institutions. SuperAlarm model trained with alarm data in one institution is tested on both internal and external test sets. Results show comparable performance with sensitivity up to 80% within one-hour window of events and over 90% in reduction of false alarms in both institutions. Cross-institutional performance agreement can be further improved by predicting a more stringent CB subtype (cardiopulmonary arrest), with internal sensitivity lying within 95% confident interval of external one up to 8-hour before event onset. The cross-institutional performance comparison offers first-hand knowledge on both advantages and challenges in generalizing a prediction algorithm across different institutions, which hold key information to guide the design of model training and deployment strategy.