How well can hospital readmission be predicted in a cohort of hospitalized children? A retrospective, multicenter study

A multicenter randomized trial to compare automatic versus as-needed follow-up for children hospitalized with common infections: The FAAN-C trial protocol

INTRODUCTION

Physicians commonly recommend automatic primary care follow-up visits to children being discharged from the hospital. While automatic follow-up provides an opportunity to address postdischarge needs, the alternative is as-needed follow-up. With this strategy, families monitor their child's symptoms and decide if they need a follow-up visit in the days after discharge. In addition to being family centered, as-needed follow-up has the potential to reduce time and financial burdens on both families and the healthcare system. As-needed follow-up has been shown to be safe and effective for children hospitalized with bronchiolitis, but the extent to which hospitalized children with other common conditions might benefit from as-needed follow-up is unclear.

METHODS

The Follow-up Automatically versus As-Needed Comparison (FAAN-C, or "fancy") trial is a multicenter randomized controlled trial. Children who are hospitalized for pneumonia, urinary tract infection, skin and soft tissue infection, or acute gastroenteritis are eligible to participate. Participants are randomized to an as-needed versus automatic posthospitalization follow-up recommendation. The sample size estimate is 2674 participants and the primary outcome is all-cause hospital readmission within 14 days of discharge. Secondary outcomes are medical interventions and child health-related quality of life. Analyses will be conducted in an intention-to-treat manner, testing noninferiority of as-needed follow-up compared with automatic follow-up.

DISCUSSION

FAAN-C will elucidate the relative benefits of an as-needed versus automatic follow-up recommendation, informing one of the most common decisions faced by families of hospitalized children and their medical providers. Findings from FAAN-C will also have implications for national quality metrics and guidelines.

Medical Complexity, Language Use, and Outcomes in the Pediatric ICU

OBJECTIVES

To determine whether use of a language other than English (LOE) would be associated with medical complexity, and whether medical complexity and LOE together would be associated with worse clinical outcomes.

METHODS

The primary outcome of this single-site retrospective cohort study of PICU encounters from September 1, 2017, through August 31, 2022 was an association between LOE and medical complexity. Univariable and multivariable analyses were performed between demographic factors and medical complexity, both for unique patients and for all encounters. We investigated outcomes of initial illness severity (using Pediatric Logistic Organ Dysfunction-2), length of stay (LOS), days without mechanical ventilation or organ dysfunction using a mixed effects regression model, controlling for age, sex, race and ethnicity, and insurance status.

RESULTS

There were 6802 patients and 10 011 encounters. In multivariable analysis for all encounters, Spanish use (adjusted odds ratio [aOR], 1.29; 95% confidence interval [CI], 1.11-1.49) and language other than English or Spanish (LOES) (aOR, 1.36; 95% CI, 1.02-1.80) were associated with medical complexity. Among unique patients, there remained an association between use of Spanish and medical complexity in multivariable analysis (aOR, 1.26; 95% CI, 1.05-1.52) but not between LOES and medical complexity (aOR, 1.30; 95% CI, 0.92-1.83). Children with medical complexity (CMC) who used an LOES had fewer organ dysfunction-free days (P = .003), PICU LOS was 1.53 times longer (P = .01), and hospital LOS was 1.45 times longer (P = .01) compared with CMC who used English.

CONCLUSIONS

Use of an LOE was independently associated with medical complexity. CMC who used an LOES had a longer LOS.

Discharge Time of Day and 30-day Hospital Reutilization at an Academic Children's Hospital

BACKGROUND

Pediatric hospital discharge is a complex process. Although morning discharges are operationally preferred, little is known about the association between discharge time of day and discharge outcomes. We assessed whether children discharged from the hospital in the evening have a higher 30-day hospital reutilization rate than those discharged in the morning or afternoon.

METHODS

We conducted a retrospective cohort study on discharges from a children's hospital between July 2016 and December 2019. The cohort was divided into morning, afternoon, and evening discharges. Multivariable modified least-squares regression was used to compare 30-day all-cause hospital reutilization rates between morning, afternoon, and evening discharges while adjusting for demographic and clinical characteristics.

RESULTS

Among 24 994 hospital discharges, 6103 (24.4%) were in the morning, 13 786 (55.2%) were in the afternoon, and 5105 (20.4%) were in the evening. The unadjusted 30-day hospital reutilization rates were 14.1% in children discharged in the morning, 18.2% in children discharged in the afternoon, and 19.3% in children discharged in the evening. The adjusted 30-day hospital reutilization rate was lowest in the morning (6.1%, 95% confidence interval [CI] 4.1%-8.2%), followed by afternoon (9.0%, 95% CI 7.0%-11.0%) and evening discharges (10.1%, 95% CI 8.0%-12.3%). Morning discharge had a significantly lower adjusted 30-day all-cause hospital reutilization rate compared with evening discharge (P < .001), whereas afternoon and evening discharges were not significantly different (P = .06).

CONCLUSIONS

The adjusted 30-day all-cause hospital reutilization rate was higher for evening discharges compared with morning discharges, whereas the rate was not significantly different between afternoon and evening discharges.

National Characteristics of Emergency Care for Children with Neurologic Complex Chronic Conditions

Introduction

Most pediatric emergency care occurs in general emergency departments (GED), where less pediatric experience and lower pediatric emergency readiness may compromise care. Medically vulnerable pediatric patients, such as those with chronic, severe, neurologic conditions, are likely to be disproportionately affected by suboptimal care in GEDs; however, little is known about characteristics of their care in either the general or pediatric emergency setting. In this study our objective was to compare the frequency, characteristics, and outcomes of ED visits made by children with chronic neurologic diseases between general and pediatric EDs (PED).

Methods

We conducted a retrospective analysis of the 2011-2014 Nationwide Emergency Department Sample (NEDS) for ED visits made by patients 0-21 years with neurologic complex chronic conditions (neuro CCC). We compared patient, hospital, and ED visits characteristics between GEDs and PEDs using descriptive statistics. We assessed outcomes of admission, transfer, critical procedure performance, and mortality using multivariable logistic regression.

Results

There were 387,813 neuro CCC ED visits (0.3% of 0-21-year-old ED visits) in our sample. Care occurred predominantly in GEDs, and visits were associated with a high severity of illness (30.1% highest severity classification score). Compared to GED visits, PED neuro CCC visits were comprised of individuals who were younger, more likely to have comorbid conditions (32.9% vs 21%, P < 0.001), and technology assistance (65.4% vs. 45.9%) but underwent fewer procedures and had lower ED charges ($2,200 vs $1,520, P < 0.001). Visits to PEDs had lower adjusted odds of critical procedures (adjusted odds ratio [aOR] 0.74, 95% confidence interval [CI] 0.62-0.87), transfers (aOR 0.14, 95% CI 0.04-0.56), and mortality (aOR 0.38, 95% CI 0.19-0.75) compared to GEDs.

Conclusion

Care for children with neuro CCCs in a pediatric ED is associated with less resource utilization and lower rates of transfer and mortality. Identifying features of PED care for neuro CCCs could lead to lower costs and mortality for this population.

Machine learning for hospital readmission prediction in pediatric population

BACKGROUND AND OBJECTIVE

Pediatric readmissions are a burden on patients, families, and the healthcare system. In order to identify patients at higher readmission risk, more accurate techniques, as machine learning (ML), could be a good strategy to expand the knowledge in this area. The aim of this study was to develop predictive models capable of identifying children and adolescents at high risk of potentially avoidable 30-day readmission using ML.

METHODS

Retrospective cohort study was carried out with 9,080 patients under 18 years old admitted to a tertiary university hospital. Demographic, clinical, and biochemical data were collected from electronic databases. We randomly divided the dataset into training (75 %) and testing (25 %), applied downsampling, repeated cross-validation with five folds and ten repetitions, and the hyperparameter was optimized of each technique using a grid search via racing with ANOVA models. We applied six ML classification algorithms to build the predictive models, including classification and regression tree (CART), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), decision tree and logistic regression (LR). The area under the receiver operating curve (AUC), sensitivity, specificity, Youden's J-index and accuracy were used to evaluate the performance of each model.

RESULTS

The avoidable 30-day hospital readmissions rate was 9.5 %. Some algorithms presented similar AUC, both in the dataset training and in the dataset testing, such as XGBoost, RF, GBM and CART. Considering the Youden's J-index, the algorithm that presented the best index was XGBoost with bagging imputation, with AUC of 0.814 (J-index of 0.484). Cancer diagnosis, age, red blood cells, leukocytes, red cell distribution width and sodium levels, elective admission, and multimorbidity were the most important characteristics to classify between readmission and non-readmission groups.

CONCLUSION

Machine learning approaches, especially XGBoost, can predict potentially avoidable 30-day pediatric hospital readmission into tertiary assistance. If implemented in the computer hospital system, our model can help in the early and more accurate identification of patients at readmission risk, targeting health strategic interventions.

Annual Variation in 30-Day Risk-Adjusted Readmission Rates in U.S. Children's Hospitals

OBJECTIVE

Reducing pediatric readmissions has become a national priority; however, the use of readmission rates as a quality metric remains controversial. The goal of this study was to examine short-term stability and long-term changes in hospital readmission rates.

METHODS

Data from the Pediatric Health Information System were used to compare annual 30-day risk-adjusted readmission rates (RARRs) in 47 US children's hospitals from 2016 to 2017 (short-term) and 2016 to 2019 (long-term). Pearson correlation coefficients and weighted Cohen's Kappa statistics were used to measure correlation and agreement across years for hospital-level RARRs and performance quartiles.

RESULTS

Median (IQR) 30-day RARRs remained stable from 7.7% (7.0-8.3) in 2016 to 7.6% (7.0-8.1) in 2019. Individual hospital RARRs in 2016 were strongly correlated with the same hospital's 2017 rate (R2 = 0.89 [95% confidence interval (CI) 0.80-0.94]) and moderately correlated with those in 2019 (R2 = 0.49 [95%CI 0.23-0.68]). Short-term RARRs (2016 vs 2017) were more highly correlated for medical conditions than surgical conditions, but correlations between long-term medical and surgical RARRs (2016 vs 2019) were similar. Agreement between RARRs was higher when comparing short-term changes (0.73 [95%CI 0.59-0.86]) than long-term changes (0.45 [95%CI 0.27-0.63]). From 2016 to 2019, RARRs increased by ≥1% in 7 (15%) hospitals and decreased by ≥1% in 6 (13%) hospitals. Only 7 (15%) hospitals experienced reductions in RARRs over the short and long-term.

CONCLUSIONS

Hospital-level performance on RARRs remained stable with high agreement over the short-term suggesting stability of readmission measures. There was little evidence of sustained improvement in hospital-level performance over multiple years.

Predictive Modeling to Identify Children With Complex Health Needs At Risk for Hospitalization

BACKGROUND

Identifying children at high risk with complex health needs (CCHN) who have intersecting medical and social needs is challenging. This study's objectives were to (1) develop and evaluate an electronic health record (EHR)-based clinical predictive model ("model") for identifying high-risk CCHN and (2) compare the model's performance as a clinical decision support (CDS) to other CDS tools available for identifying high-risk CCHN.

METHODS

This retrospective cohort study included children aged 0 to 20 years with established care within a single health system. The model development/validation cohort included 33 months (January 1, 2016-September 30, 2018) and the testing cohort included 18 months (October 1, 2018-March 31, 2020) of EHR data. Machine learning methods generated a model that predicted probability (0%-100%) for hospitalization within 6 months. Model performance measures included sensitivity, positive predictive value, area under receiver-operator curve, and area under precision-recall curve. Three CDS rules for identifying high-risk CCHN were compared: (1) hospitalization probability ≥10% (model-predicted); (2) complex chronic disease classification (using Pediatric Medical Complexity Algorithm [PMCA]); and (3) previous high hospital utilization.

RESULTS

Model development and testing cohorts included 116 799 and 27 087 patients, respectively. The model demonstrated area under receiver-operator curve = 0.79 and area under precision-recall curve = 0.13. PMCA had the highest sensitivity (52.4%) and classified the most children as high risk (17.3%). Positive predictive value of the model-based CDS rule (19%) was higher than CDS based on the PMCA (1.9%) and previous hospital utilization (15%).

CONCLUSIONS

A novel EHR-based predictive model was developed and validated as a population-level CDS tool for identifying CCHN at high risk for future hospitalization.

Validation of the HOSPITAL score as predictor of 30-day potentially avoidable readmissions in pediatric hospitalized population: retrospective cohort study

Potentially avoidable pediatric readmissions are a burden to patients and their families. Identifying patients with higher risk of readmission could help minimize hospital costs and facilitate the targeting of care interventions. HOSPITAL score is a tool developed and widely used to predict adult patient's readmissions; however its predictive capacity for pediatric readmissions has not yet been evaluated. The aim of the study was to validate the HOSPITAL score application to predict 30-day potentially avoidable readmissions in a pediatric hospitalized population. This is a retrospective cohort study with patients under 18 years old admitted to a tertiary university hospital (n = 6,344). The HOSPITAL score was estimated for each admission. Subsequently, we classified the patients as low (0-4), intermediate (5-6), and high (7-12) risk groups. In order to estimate the discrimination power, the sensitivity, specificity, and accuracy were determined by the receiver operating characteristics (ROC) and the calibration by the Hosmer-Lemeshow goodness-of-fit. The 30-day hospital readmission was 11.70% (745). The accuracy was 0.80 (CI 95%, 0.77, 0.83), with a sensitivity of 70.96% and specificity of 78.29%, and a good calibration (p = 0.34).    Conclusion: HOSPITAL score showed a good discrimination and can be used to predict 30-day potentially avoidable readmission in a large pediatric population with different medical diagnoses. Our study validates and expands the usefulness of the HOSPITAL score as a tool to predict avoidable hospital readmissions for pediatric population. What is Known: •   Pediatric readmissions burden patients, the family network, and the health system. In addition, it influences negatively child development. •   The HOSPITAL score is one of the tools developed and widely used to identify patients at high risk of hospital readmission, but its predictive capacity for pediatric readmissions has not been yet assessed. What is New: • The HOSPITAL score showed good ability to identify a risk of 30-day potentially avoidable readmission in a pediatric population in different clinical contexts and diagnoses. • Our study expands the usefulness of the HOSPITAL score as a tool for predicting hospital readmissions for children and adolescents.

Exploring gaps and opportunities in primary care following an asthma hospital admission: a multisite mixed-methods study of three data sources

OBJECTIVE

Explore gaps and opportunities in primary care for children following a hospital admission for asthma.

DESIGN

Exploratory mixed-methods, using linked hospital and primary care administration data.

SETTING

Eligible children, aged 3-18 years, admitted to one of three hospitals in Victoria, Australia between 2017 and 2018 with a clinical diagnosis of asthma.

RESULTS

767 caregivers of eligible children participated, 39 caregivers completed a semistructured interview and 277 general practitioners (GPs) caring for 360 children completed a survey. Over 90% (n=706) of caregivers reported their child had a regular GP. However, few (14.1%, n=108) attended a GP in the 24 hours prior to index admission or in the 7 days after (35.8%, n=275). Children readmitted for asthma (34.2%, n=263), compared with those not readmitted (65.8%, n=504), were less likely to have visited a GP in the non-acute phase of their asthma in the 12 months after index admission (22.1% vs 42.1%, respectively), and their GP was more likely to report not knowing the child had an asthma admission (52.8% vs 39.2%, respectively). Fewer GPs reported being extremely confident managing children with poorly controlled asthma (11.9%, n=43) or post-discharge (16.7%, n=60), compared with children with well-controlled asthma (36.4%, n=131), with no difference by child readmission status.

CONCLUSIONS

Given the exploratory design and descriptive approach, it is unknown if the differences by child readmission status have any causal relationship with readmission. Nonetheless, improving preventative patterns of primary care visits, timely communication between hospitals and primary care providers, and guideline concordant care by GPs are needed.

Complication risks and costs associated with Ponte osteotomies in surgical treatment of adolescent idiopathic scoliosis: insights from a national database

PURPOSE

Risks of Ponte osteotomies (POs) used for posterior spinal fusion (PSF) for Adolescent Idiopathic Scoliosis (AIS) are challenging to assess because of the rarity of complications. Using a national administrative claims database, we evaluated trends, costs and complications associated with PO used in PSF for AIS patients.

METHODS

Using ICD-9/CPT codes, we identified patients (ages 10-18) with AIS who underwent PSF (± PO) between 2007 and 2015 in the IBM® MarketScan® Commercial Databases. Costs and trends of POs were evaluated. Odds of neurological complications and readmissions within 90 days and reoperations within 90 days and 2 years were assessed.

RESULTS

We identified 8881 AIS patients who had undergone PSF, of which 8193 had 90-day follow-up and 4248 had 2-year follow-up. Overall, 28.8% had PO. Annual rate of POs increased from 17.3 to 35.2% from 2007 to 2015 (p < 0.001). Risk-adjusted multivariable logistic regression demonstrated no relationship between POs and neurologic complications (p = 0.543). POs were associated with higher odds for readmission (1.52 [1.21-1.91]; p < 0.001) and reoperation (2.03 [1.13-3.59]; p = 0.015) within 90 days, but there were no differences in the odds of reoperation within 2 years (p = 0.836). Median hospital costs were $15,854 (17.4%) higher for patients with POs (p < 0.001) and multivariable modeling demonstrated POs to be an independent predictor of increased costs (p < 0.001).

CONCLUSION

Annual rate of POs increased steadily from 2007 to 2015. POs were not associated with increased odds of neurological complications but had higher costs and higher rates of readmissions and reoperations within 90 days. By 2 years, differences in reoperation rate were not significant.

LEVEL OF EVIDENCE

III.

Development and Validation of an Integrated Suite of Prediction Models for All-Cause 30-Day Readmissions of Children and Adolescents Aged 0 to 18 Years

IMPORTANCE

Readmission is often considered a hospital quality measure, yet no validated risk prediction models exist for children.

OBJECTIVE

To develop and validate a tool identifying patients before hospital discharge who are at risk for subsequent readmission, applicable to all ages.

DESIGN, SETTING, AND PARTICIPANTS

This population-based prognostic analysis used electronic health record-derived data from a freestanding children's hospital from January 1, 2016, to December 31, 2019. All-cause 30-day readmission was modeled using 3 years of discharge data. Data were analyzed from June 1 to November 30, 2021.

MAIN OUTCOMES AND MEASURES

Three models were derived as a complementary suite to include (1) children 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), (2) children 6 months or older with no prior hospitalizations in the last 6 months (new admission model [NAM]), and (3) children younger than 6 months (young infant model [YIM]). Generalized mixed linear models were used for all analyses. Models were validated using an additional year of discharges.

RESULTS

The derivation set contained 29 988 patients with 48 019 hospitalizations; 50.1% of these admissions were for children younger than 5 years and 54.7% were boys. In the derivation set, 4878 of 13 490 admissions (36.2%) in the RAM cohort, 2044 of 27 531 (7.4%) in the NAM cohort, and 855 of 6998 (12.2%) in the YIM cohort were followed within 30 days by a readmission. In the RAM cohort, prior utilization, current or prior procedures indicative of severity of illness (transfusion, ventilation, or central venous catheter), commercial insurance, and prolonged length of stay (LOS) were associated with readmission. In the NAM cohort, procedures, prolonged LOS, and emergency department visit in the past 6 months were associated with readmission. In the YIM cohort, LOS, prior visits, and critical procedures were associated with readmission. The area under the receiver operating characteristics curve was 83.1 (95% CI, 82.4-83.8) for the RAM cohort, 76.1 (95% CI, 75.0-77.2) for the NAM cohort, and 80.3 (95% CI, 78.8-81.9) for the YIM cohort.

CONCLUSIONS AND RELEVANCE

In this prognostic study, the suite of 3 prediction models had acceptable to excellent discrimination for children. These models may allow future improvements in tailored discharge preparedness to prevent high-risk readmissions.

Predictive Modeling of Hospital Readmission: Challenges and Solutions

Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, e.g. 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.

Factors Related to Pediatric Readmissions of Four Major Diagnostic Categories in Hawai`i

Readmissions are a key quality measure for health care decision making and understanding variables associated with readmissions has become a crucial research area. This study identified patient-level factors that might be associated with pediatric readmissions using a database that included inpatient data from 2008 to 2017 from Hawai`i. Four major diagnostic categories with the most pediatric readmissions in the state were identified: respiratory, digestive, mental, and nervous system diseases and disorders. The associations between readmission and patient-level variables, such as age, sex, race/ethnicity, insurance status, and Charlson Comorbidity Index (CCI), were determined for each diagnosis and for overall readmissions. CCI and insurance were the strongest predictors when all diagnoses were combined. However, for some diagnoses, there was weak or no association between CCI, insurance, and readmission. This suggests that diagnosis-specific analysis of predictors of readmission may be more useful than looking at predictors of readmission for all diagnoses combined. While this study focused on patient variables, future studies should also incorporate how hospital variables may also be related to diagnosis.

Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review

OBJECTIVES

To summarise multivariable predictive models for 30-day unplanned hospital readmissions (UHRs) in paediatrics, describe their performance and completeness in reporting, and determine their potential for application in practice.

DESIGN

Systematic review.

DATA SOURCE

CINAHL, Embase and PubMed up to 7 October 2021.

ELIGIBILITY CRITERIA

English or German language studies aiming to develop or validate a multivariable predictive model for 30-day paediatric UHRs related to all-cause, surgical conditions or general medical conditions were included.

DATA EXTRACTION AND SYNTHESIS

Study characteristics, risk factors significant for predicting readmissions and information about performance measures (eg, c-statistic) were extracted. Reporting quality was addressed by the 'Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis' (TRIPOD) adherence form. The study quality was assessed by applying six domains of potential biases. Due to expected heterogeneity among the studies, the data were qualitatively synthesised.

RESULTS

Based on 28 studies, 37 predictive models were identified, which could potentially be used for determining individual 30-day UHR risk in paediatrics. The number of study participants ranged from 190 children to 1.4 million encounters. The two most common significant risk factors were comorbidity and (postoperative) length of stay. 23 models showed a c-statistic above 0.7 and are primarily applicable at discharge. The median TRIPOD adherence of the models was 59% (P₂₅-P₇₅, 55%-69%), ranging from a minimum of 33% to a maximum of 81%. Overall, the quality of many studies was moderate to low in all six domains.

CONCLUSION

Predictive models may be useful in identifying paediatric patients at increased risk of readmission. To support the application of predictive models, more attention should be placed on completeness in reporting, particularly for those items that may be relevant for implementation in practice.

Family-Rated Pediatric Health Status Is Associated With Unplanned Health Services Use

OBJECTIVE

Self-rated health is a common self-reported health measure associated with morbidity, mortality, and health care use. The objective was to investigate the association of family-rated health status (FRH) in pediatric care with administrative indicators, patient and respondent features, and unplanned health services use.

PATIENTS AND METHODS

Data were taken from Child-Hospital Consumer Assessment of Healthcare Providers and Systems surveys collected between 2015 and 2019 in Alberta, Canada and linked with administrative health records. Three analyses were performed: correlation to assess association between administrative indicators of health status and FRH, logistic regression to assess respondent and patient characteristics associated with FRH, and automated logistic regression to assess the association between FRH and unplanned health services use within 90 days of discharge.

RESULTS

A total of 6236 linked surveys were analyzed. FRH had small but significant associations with administrative indicators. Models of FRH had better fit with patient and respondent features. Respondent relationship to child, child age, previous hospitalizations, and number of comorbidities were significantly associated with ratings of FRH. Automated models of unplanned services use included FRH as a feature, and poor ratings of health were associated with increased odds of emergency department visits (adjusted odds ratio: 2.15, 95% confidence interval: 1.62-2.85) and readmission (adjusted odds ratio: 2.48, 95% confidence interval: 1.62-2.85).

CONCLUSION

FRH is a simple, single-item global rating of health for pediatric populations that provides accessible and useful information about pediatric health care needs. The results of this article serve as a reminder that family members are valuable sources of information that can improve care and potentially prevent unplanned health services use.

Association of Race/Ethnicity and Social Determinants with Rehospitalization for Mental Health Conditions at Acute Care Children's Hospitals

OBJECTIVE

To evaluate associations of race/ethnicity and social determinants with 90-day rehospitalization for mental health conditions to acute care nonpsychiatric children's hospitals.

STUDY DESIGN

We conducted a retrospective cohort analysis of mental health hospitalizations for children aged 5-18 years from 2016 to 2018 at 32 freestanding US children's hospitals using the Children's Hospital Association's Pediatric Health Information System database to assess the association of race/ethnicity and social determinants (insurance payer, neighborhood median household income, and rurality of patient home location) with 90-day rehospitalization. Risk factors for rehospitalization were modeled using mixed-effects multivariable logistic regression.

RESULTS

Among 23 556 index hospitalizations, there were 1382 mental health rehospitalizations (5.9%) within 90 days. Non-Hispanic Black children were 26% more likely to be rehospitalized than non-Hispanic White children (aOR 1.26, 95% CI 1.08-1.48). Those with government insurance were 18% more likely to be rehospitalized than those with private insurance (aOR 1.18, 95% CI 1.04-1.34). In contrast, those living in a suburban location were 22% less likely to be rehospitalized than those living in an urban location (suburban: aOR 0.78, 95% CI 0.63-0.97).

CONCLUSIONS

Non-Hispanic Black children and those with public insurance were at greatest risk for 90-day rehospitalization, and risk was lower in those residing in suburban locations. Future work should focus on upstream interventions that will best attenuate social disparities to promote equity in pediatric mental healthcare.

Evaluation of advanced practice provider related return visits to a pediatric emergency department and their outcomes

BACKGROUND

While multiple studies have evaluated physician-related return visits (RVs) to a pediatric emergency department (PED) limited data exists for Advanced Practice Provider (APP)-related RVs, hence our study aimed to evaluate APP-related RVs and their outcomes in a PED.

METHODS

We conducted a retrospective review of 72-h RVs where clinical care was independently provided by an APP during the index visit from January 2018 to December 2019. We extracted patient demographics, index and return visits' characteristics and outcomes. Reasons for RVs were categorized as progression of illness, medication-related, callbacks and others. Index visits were assessed for any diagnostic errors; impact of which to the patient was classified as none, minor or major.

RESULTS

Our APP-related RV rate was 2.1% (653/30,328). 462 eligible RVs were included in the final analysis. Majority of RVs were for medical reasons (n = 442, 95.7%); lower acuity (Emergency Severity Index ≥3, n = 426, 92.2%); due to persistence/progression of illness (n = 403; 87.2%) with viral illness being the common diagnosis (n = 159; 34.4%). 12 (2.6%) RVs were secondary to callbacks (8 radiology callbacks; 4 false positive blood cultures). Diagnostic errors were noted in 14 (3%) encounters of which 3 resulted in a major impact; radiological (7 fractures) and ophthalmological (2 corneal abrasions and 2 foreign bodies) misses constituted the majority of these.

CONCLUSIONS

APP-related RVs for low acuity medical patients remain low and are associated with good outcomes. Diagnostic errors account for a minority of these RVs. Focused interventions targeting provider errors can further decrease these RVs.

A Description of Children Dependent on Long Term Ventilation via Tracheostomy and Their Hospital Resource Use

PURPOSE

To describe the proportion of children with an index hospitalization in 2014 who had established long-term invasive ventilator dependence (LTVD), and determine regional variation in hospital length of stay, charges, and readmissions.

DESIGN AND METHODS

Multicenter, longitudinal, retrospective cohort study using a recently established algorithm to identify children with LTVD from the Pediatric Health Information System database with an index hospitalization at least once during 2014, excluding normal newborn care or chemotherapy, and the subset with established LTVD. Hospitals were grouped by geographic regions. Analysis included descriptive statistics and multi-variable mixed modeling for length of stay, charges, and readmissions.

RESULTS

Of the 615,883 unique children discharged from 45 children's hospitals in 2014, 2235 (0.4%) had established LTVD. Of these, 342 (15%) were hospitalized in the Northeast, 677 (30%) Midwest, 733 (32%) South and 481 (22%) West. Most had at least two complex chronic conditions (97%) and used a medical device for at least two body systems (71%). No statistically significant regional variation was found for length of stay, charges, or readmissions after adjustment for child demographics, admission type, disposition, primary diagnosis, ICU stay, and number of chronic conditions.

CONCLUSIONS

This study characterized the population of children with LTVD hospitalized in 2014. No regional variation was found for length of stay, charges, or readmissions.

PRACTICE IMPLICATIONS

Children with established LTVD make up a small subset of all children admitted to children's hospitals however, they require substantial, costly, multifaceted care as most have additional complex chronic conditions and require multiple medical devices.

Identifying Children at Readmission Risk: At-Admission versus Traditional At-Discharge Readmission Prediction Model

The timing of 30-day pediatric readmissions is skewed with approximately 40% of the incidents occurring within the first week of hospital discharges. The skewed readmission time distribution coupled with delay in health information exchange among healthcare providers might offer a limited time to devise a comprehensive intervention plan. However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges. In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process. We also compared proposed models with the standard at-discharge readmission prediction model. Using the Hospital Cost and Utilization Project database, this prognostic study included pediatric hospital discharges in Florida from January 2016 through September 2017. Four machine learning algorithms—logistic regression with backward stepwise selection, decision tree, Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting—were developed for at-admission and at-discharge models using a recursive feature elimination technique with a repeated cross-validation process. The performance of the at-admission and at-discharge model was measured by the area under the curve. The performance of the at-admission model was comparable with the at-discharge model for all four algorithms. SVM with Polynomial Kernel algorithms outperformed all other algorithms for at-admission and at-discharge models. Important features associated with increased readmission risk varied widely across the type of prediction model and were mostly related to patients’ demographics, social determinates, clinical factors, and hospital characteristics. Proposed at-admission readmission risk decision support model could help hospitals and providers with additional time for intervention planning, particularly for those targeting social determinants of children’s overall health.

Early Use of Bronchodilators and Outcomes in Bronchiolitis

BACKGROUND AND OBJECTIVES

There are no effective interventions to prevent hospital admissions in infants with bronchiolitis. The American Academy of Pediatrics recommends against routine bronchodilator use for bronchiolitis. The objective of this study was to characterize trends in and outcomes associated with the use of bronchodilators for bronchiolitis.

METHODS

This is a multicenter retrospective study of infants <12 months of age with bronchiolitis from 49 children's hospitals from 2010 to 2018. The primary outcomes were rates of hospital admissions, ICU admissions, emergency department (ED) return visits after initial ED discharge, noninvasive ventilation, and invasive ventilation. Multivariable logistic regression was used to evaluate the rates of outcomes among hospitals with high and low early use of bronchodilators (on day of presentation).

RESULTS

A total of 446 696 ED visits of infants with bronchiolitis were included. Bronchodilator use, hospital admissions, and ED return visits decreased between 2010 and 2018 (all P < .001). ICU admissions and invasive and noninvasive ventilation increased over the study period (all P < .001). Hospital-level early bronchodilator use (hospitals with high versus low use) was not associated with differences in patient-level hospital admissions, ICU admissions, ED return visits, noninvasive ventilation, or invasive ventilation (all P > .05).

CONCLUSIONS

In a large study of infants at children's hospitals, bronchodilator therapy decreased significantly from 2010 to 2018. Hospital-level early bronchodilator use was not associated with a reduction in any outcomes. This study supports the current American Academy of Pediatrics recommendation to limit routine use of bronchodilators in infants with bronchiolitis.

Noninvasive Ventilation and Outcomes in Bronchiolitis

OBJECTIVES

Evaluation of potential benefits of noninvasive ventilation for bronchiolitis has been precluded in part by the absence of large, adequately powered studies. The objectives of this study were to characterize temporal trends in and associations between the use of noninvasive ventilation in bronchiolitis and two clinical outcomes, invasive ventilation, and cardiac arrest.

DESIGN

Multicenter retrospective cross-sectional study.

SETTING

Forty-nine U.S. children's hospitals participating in the Pediatric Health Information System database.

PATIENTS

Infants under 12 months old who were admitted from the emergency department with bronchiolitis between January 1, 2010, and December 31, 2018.

MEASUREMENTS AND MAIN RESULTS

Primary outcomes were rates of noninvasive ventilation, invasive ventilation, and cardiac arrest. Trends over time were assessed with univariate logistic regression. In the main analysis, hospital-level multivariable logistic regression evaluated rates of outcomes including invasive ventilation and cardiac arrest among hospitals with high and low utilization of noninvasive ventilation. The study included 147,288 hospitalizations of infants with bronchiolitis. Across the entire study population, noninvasive and invasive ventilation increased between 2010 and 2018 (2.9-8.7%, 2.1-4.0%, respectively; p < 0·001). After adjustment for markers of severity of illness, hospital-level noninvasive ventilation (high vs low utilization) was not associated with differences in invasive ventilation (5.0%, 1.8%, respectively, adjusted odds ratio, 1.8; 95% CI, 0·7-4·6) but was associated with increased cardiac arrest (0.36%, 0.02%, respectively, adjusted odds ratio, 25.4; 95% CI, 4.9-131.0).

CONCLUSIONS

In a large cohort of infants at children's hospitals, noninvasive and invasive ventilation increased significantly from 2010 to 2018. Hospital-level noninvasive ventilation utilization was not associated with a reduction in invasive ventilation but was associated with higher rates of cardiac arrest even after controlling for severity. Noninvasive ventilation in bronchiolitis may incur an unintended higher risk of cardiac arrest, and this requires further investigation.

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation

Objectives To assess whether adding clinical information and written discharge documentation variables improves prediction of paediatric 30-day same-hospital unplanned readmission compared with predictions based on administrative information alone. Methods A retrospective matched case-control study audited the medical records of patients discharged from a tertiary paediatric hospital in Western Australia (WA) between January 2010 and December 2014. A random selection of 470 patients with unplanned readmissions (out of 3330) were matched to 470 patients without readmissions based on age, sex, and principal diagnosis at the index admission. Prediction utility of three groups of variables (administrative, administrative and clinical, and administrative, clinical and written discharge documentation) were assessed using standard logistic regression and machine learning. Results Inclusion of written discharge documentation variables significantly improved prediction of readmission compared with models that used only administrative and/or clinical variables in standard logistic regression analysis (χ2 17=29.4, P=0.03). Highest prediction accuracy was obtained using a gradient boosted tree model (C-statistic=0.654), followed closely by random forest and elastic net modelling approaches. Variables highlighted as important for prediction included patients' social history (legal custody or patient was under the care of the Department for Child Protection), languages spoken other than English, completeness of nursing admission and discharge planning documentation, and timing of issuing discharge summary. Conclusions The variables of significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary add value to prediction models. What is known about the topic? Despite written discharge documentation playing a critical role in the continuity of care for paediatric patients, limited research has examined its association with, and ability to predict, unplanned hospital readmissions. Machine learning approaches have been applied to various health conditions and demonstrated improved predictive accuracy. However, few published studies have used machine learning to predict paediatric readmissions. What does this paper add? This paper presents the findings of the first known study in Australia to assess and report that written discharge documentation and clinical information improves unplanned rehospitalisation prediction accuracy in a paediatric cohort compared with administrative data alone. It is also the first known published study to use machine learning for the prediction of paediatric same-hospital unplanned readmission in Australia. The results show improved predictive performance of the machine learning approach compared with standard logistic regression. What are the implications for practitioners? The identified social and written discharge documentation predictors could be translated into clinical practice through improved discharge planning and processes, to prevent paediatric 30-day all-cause same-hospital unplanned readmission. The predictors identified in this study include significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary.

Trends in Length of Stay and Readmissions in Children's Hospitals

BACKGROUND AND OBJECTIVES

Patient complexity at US children's hospitals is increasing. Hospitals experience concurrent pressure to reduce length of stay (LOS) and readmissions, yet little is known about how these common measures of resource use and quality have changed over time. Our aim was to examine temporal trends in medical complexity, hospital LOS, and readmissions across a sample of US children's hospitals.

METHODS

Retrospective cohort study of hospitalized patients from 42 children's hospitals in the Pediatric Health Information System from 2013 to 2017. After excluding deaths, healthy newborns, obstetric care, and low volume service lines, we analyzed trends in medical complexity, LOS, and 14-day all-cause readmissions using generalized linear mixed effects models, adjusting for changes in patient factors and case-mix.

RESULTS

Between 2013 and 2017, a total of 3 355 815 discharges were included. Over time, the mean case-mix index and the proportion of hospitalized patients with complex chronic conditions or receiving intensive care increased (P < .001 for all). In adjusted analyses, mean LOS declined 3% (61.1 hours versus 59.3 hours from 2013 to 2017, P < .001), whereas 14-day readmissions were unchanged (7.0% vs 6.9%; P = .03). Reductions in adjusted LOS were noted in both medical and surgical service lines (3.6% and 2.0% decline, respectively; P < .001).

CONCLUSIONS

Across US children's hospitals, adjusted LOS declined whereas readmissions remained stable, suggesting that children's hospitals are providing more efficient care for an increasingly complex patient population.

Predictors of hospital readmission in infants less than 3 months old

AIM

To examine rates and predictors of 7-day readmission in infants hospitalised before 3 months of age with infectious and non-infectious conditions.

METHODS

Retrospective population-based data-linkage study of 121 854 infants from a 5-year metropolitan birth cohort (2008-2012). Cox proportional hazard models were used to examine associations between infant and maternal factors with 7-day readmission.

RESULTS

A total of 11 669 (9.6%) infants were hospitalised at least once by 3 months of age (median 23 days old, 56% male) with 12 602 total index hospitalisations. Infection-related conditions accounted for 29.4% (n = 3705). Readmission within 7 days occurred after 4.8% of all index hospitalisations and 5.4% of infection-related hospitalisations. Age ≤21 days was the strongest readmission risk factor (hazard ratio 7.7 (95% confidence interval 4.7-12.7) compared to infants 61-90 days old). Other risk factors included shorter index hospitalisations, younger maternal age and multi-gravidity.

CONCLUSION

Hospitalisations and readmissions occur for many young infants. Risk factors for readmission should inform risk-based management guidelines.

Resources and Costs Associated With Repeated Admissions to PICUs

Supplemental Digital Content is available in the text.

To determine the costs and hospital resource use from all PICU patients readmitted with a PICU stay within 12 months of hospital index discharge.

Timing of Co-occurring Chronic Conditions in Children With Neurologic Impairment

BACKGROUND

Children with neurologic impairment (NI) are at risk for developing co-occurring chronic conditions, increasing their medical complexity and morbidity. We assessed the prevalence and timing of onset for those conditions in children with NI.

METHODS

This longitudinal analysis included 6229 children born in 2009 and continuously enrolled in Medicaid through 2015 with a diagnosis of NI by age 3 in the IBM Watson Medicaid MarketScan Database. NI was defined with an existing diagnostic code set encompassing neurologic, genetic, and metabolic conditions that result in substantial functional impairments requiring subspecialty medical care. The prevalence and timing of co-occurring chronic conditions was assessed with the Agency for Healthcare Research and Quality Chronic Condition Indicator system. Mean cumulative function was used to measure age trends in multimorbidity.

RESULTS

The most common type of NI was static (56.3%), with cerebral palsy (10.0%) being the most common NI diagnosis. Respiratory (86.5%) and digestive (49.4%) organ systems were most frequently affected by co-occurring chronic conditions. By ages 2, 4, and 6 years, the mean (95% confidence interval [CI]) numbers of co-occurring chronic conditions were 3.7 (95% CI 3.7-3.8), 4.6 (95% CI 4.5-4.7), and 5.1 (95% CI 5.1-5.2). An increasing percentage of children had ≥9 co-occurring chronic conditions as they aged: 5.3% by 2 years, 10.0% by 4 years, and 12.8% by 6 years.

CONCLUSIONS

Children with NI enrolled in Medicaid have substantial multimorbidity that develops early in life. Increased attention to the timing and types of multimorbidity in children with NI may help optimize their preventive care and case management health services.

Validity of the LACE index for identifying frequent early readmissions after hospital discharge in children

The LACE index scoring tool has been designed to predict hospital readmissions in adults. We aimed to evaluate the ability of the LACE index to identify children at risk of frequent readmissions. We analysed data from alive-discharge episodes (1 April 2017 to 31 March 2019) for 6546 males and 5875 females from birth to 18 years. The LACE index predicted frequent all-cause readmissions within 28 days of hospital discharge with high accuracy: the area under the curve = 86.9% (95% confidence interval = 84.3-89.5%, p < 0.001). Two-graph receiver operating characteristic curve analysis revealed the LACE index cutoff to be 4.3, where sensitivity equals specificity, to predict frequent readmissions. Compared with those with a LACE index score = 0-4 (event rates, 0.3%), those with a score > 4 (event rates, 3.7%) were at increased risk of frequent readmissions: age- and sex-adjusted odds ratio = 12.4 (95% confidence interval = 8.0-19.2, p < 0.001) and death within 30 days of discharge: OR = 5.0 (95% CI = 1.5-16.7). The ORs for frequent readmissions were between 6 and 14 for children of different age categories (neonate, infant, young child and adolescent), except for patients in the child category (6-12 years) where odds ratio was 2.8.Conclusion: The LACE index can be used in healthcare services to identify children at risk of frequent readmissions. Focus should be directed at individuals with a LACE index score above 4 to help reduce risk of readmissions. What is Known: • The LACE index scoring tool has been widely used to predict hospital readmissions in adults. What is New: • Compared with children with a LACE index score of 0-4 (event rates, 0.3%), those with a score > 4 are at increased risk of frequent readmissions by 14-fold. • The cutoff of a LACE index of 4 may be a useful level to identify children at increased risk of frequent readmissions.

Pediatric asthma hospitalization: individual and environmental characteristics of high utilizers in South Texas

OBJECTIVE

Few studies have examined factors affecting the high frequency of hospitalization for pediatric asthma. This study identifies individual and environmental characteristics of children with asthma from a low-income community with a high number of hospitalizations.

METHODS

The study population included 902 children admitted at least once to a children's hospital in South Texas because of asthma from 2010 to 2016. The population was divided into three groups by utilization frequency (high: ≥4 times, medium: 2-3 times, or low: 1 time). Individual-level factors at index admission and environmental factors were included for the analysis. Unadjusted and adjusted multivariate ordered logistic regression models were applied to identify significant characteristics of high hospital utilizers.

RESULTS

The high utilization group comprised 2.4% of total patients and accounted for substantial hospital resource utilization: 10.8% of all admissions and 13.5% of days stayed in the hospital. Patients in the high utilization group showed longer length of stay (LOS) and shorter time between admissions on average than the other two groups. The multivariate ordered logistic regression models revealed that age of 5-11 years (OR = 0.57, 95%CI = 0.35-0.93), longer LOS (2 days: OR = 1.80, 95%CI = 1.15-2.84; ≥3 days: OR = 3.38, 95%CI = 2.10-5.46), warm season at index admission (OR = 1.49, 95%CI = 1.01-2.20), and higher average ozone level in children's residential neighborhoods (OR = 1.78, 95%CI = 1.01-3.14) were significantly associated with a higher number of asthma hospitalizations.

CONCLUSIONS

The findings suggest the importance of monitoring high hospital utilizers and establishing strategies for such patients based on their characteristics to reduce repeated hospitalizations and to increase optimal use of hospital resources.

A 5-year retrospective cohort study of unplanned readmissions in an Australian tertiary paediatric hospital

Objective The aim of this study was to examine the characteristics and prevalence of all-cause unplanned hospital readmissions at a tertiary paediatric hospital in Western Australia from 2010 to 2014. Methods A retrospective cohort descriptive study was conducted. Unplanned hospital readmission was identified using both 28- and 30-day measurements from discharge date of an index hospital admission to the subsequent related unplanned admission date. This allowed international comparison. Results In all, 73132 patients with 134314 discharges were identified. During the 5-year period, 4070 discharges (3.03%) and 3330 patients (4.55%) were identified as 30-day unplanned hospital readmissions. There were minimal differences in the rate of readmissions on Days 28, 29 and 30 (0.2%). More than 50% of readmissions were identified as a 5-day readmission. Nearly all readmissions for croup and epiglottitis occurred by Day 5; those for acute bronchiolitis and obstructive sleep apnoea requiring tonsillectomy and/or adenoidectomy occurred by Day 15 and those for acute appendicitis and abdominal and pelvic pain occurred by Day 30. Conclusion This study highlights the variability in the distribution of time intervals from discharge to readmission among diagnoses, suggesting the commonly used 28- or 30-day readmission measurement requires review. It is crucial to establish an appropriate measurement for specific paediatric conditions related to readmissions for the accurate determination of the prevalence and actual costs associated with readmissions. What is known about this topic? Unplanned hospital readmissions result in inefficient use of health resources. Australia has used 28 days to measure unplanned readmissions. However, the 30-day measurement is commonly used in the literature. Only five Australian studies were identified with a focus on readmissions associated with specific paediatric health conditions. What does this paper add? This is the first known study examining paediatric all-cause unplanned same-hospital readmissions in Western Australia. The study used both 28- and 30-day measures from discharge to unplanned readmission to allow international comparison. More than half the unplanned hospital readmissions occurred between Day 0 and Day 5 following discharge from the index admission. Time intervals from discharge date to readmission date varied for diagnosis-specific readmissions of paediatric patients. What are the implications for practitioners? Targeting the top principal index admission diagnoses identified for paediatric readmissions is critical for improvement in the continuity of discharge care delivery, health resource utilisation and associated costs. Because 52% of unplanned readmissions occurred in the first 5 days, urgent investigation and implementation of prevention strategies are required, especially when the readmission occurs on the date of discharge.

The Role of Caregiver-Reported Risks in Predicting Adverse Pediatric Outcomes

OBJECTIVE

Certain social risk factors (e.g., housing instability, food insecurity) have been shown to directly and indirectly influence pediatric health outcomes; however, there is limited understanding of which social factors are most salient for children admitted to the hospital. This study examines how caregiver-reported social and medical characteristics of children experiencing an inpatient admission are associated with the presence of future health complications.

METHODS

Caregivers of children experiencing an inpatient admission (N = 249) completed a predischarge questionnaire designed to capture medical and social risk factors across systems (e.g., patient, caregiver, family, community, healthcare environment). Electronic health record (EHR) data were reviewed for child demographic data, chronic disease status, and subsequent emergency department visits or readmissions (i.e., acute events) 90 days postindex hospitalization. Associations between risk factors and event presence were estimated using odds ratios (ORs) and confidence intervals (CI), both unadjusted and adjusted OR (aOR) for chronic disease and age.

RESULTS

Thirty-three percent (N = 82) of children experienced at least one event. After accounting for child age and chronic disease status, caregiver perceptions of child's health being generally "poor" or "not good" prior to discharge (aOR = 4.7, 95% CI = 2.3, 9.7), having high care coordination needs (aOR = 3.2, 95% CI = 1.6, 6.1), and experiencing difficulty accessing care coordination (aOR = 2.5, 95% CI = 1.4, 4.7) were significantly associated with return events.

CONCLUSIONS

Caregiver report of risks may provide valuable information above and beyond EHR records to both determine risk of future health problems and inform intervention development.

Leveraging the Incidence, Burden, and Fiscal Implications of Unplanned Hospital Revisits for the Prioritization of Prevention Efforts in Pediatric Surgery

OBJECTIVE

To characterize procedure-level burden of revisit-associated resource utilization in pediatric surgery with the goal of establishing a prioritization framework for prevention efforts.

SUMMARY OF BACKGROUND DATA

Unplanned hospital revisits are costly to the health care system and associated with lost productivity on behalf of patients and their families. Limited objective data exist to guide the prioritization of prevention efforts within pediatric surgery.

METHODS

Using the Pediatric Health Information System (PHIS) database, 30-day unplanned revisits for the 30 most commonly performed pediatric surgical procedures were reviewed from 47 children's hospitals between January 1, 2012 and March 31, 2015. The relative contribution of each procedure to the cumulative burden of revisit-associated length of stay and cost from all procedures was calculated as an estimate of public health relevance if prevention efforts were successfully applied (higher relative contribution = greater potential public health relevance).

RESULTS

159,675 index encounters were analyzed with an aggregate 30-day revisit rate of 10.8%. Four procedures contributed more than half of the revisit-associated length of stay burden from all procedures, with the highest relative contributions attributable to complicated appendicitis (18.4%), gastrostomy (13.4%), uncomplicated appendicitis (13.0%), and fundoplication (9.4%). Four procedures contributed more than half of the revisit-associated cost burden from all procedures, with the highest relative contributions attributable to complicated appendicitis (18.8%), gastrostomy (14.6%), fundoplication (10.4%), and uncomplicated appendicitis (10.2%).

CONCLUSIONS AND RELEVANCE

A small number of procedures account for a disproportionate burden of revisit-associated resource utilization in pediatric surgery. Gastrostomy, fundoplication, and appendectomy should be considered high-priority targets for prevention efforts within pediatric surgery.

Parent Perspectives During Hospital Readmissions for Children With Medical Complexity: A Qualitative Study

OBJECTIVES

Children with medical complexity (CMC) have high readmission rates, but relatively little is known from the parent perspective regarding care experiences surrounding and factors contributing to readmissions. We aimed to elicit parent perspectives on circumstances surrounding 30-day readmissions for CMC.

METHODS

We conducted 20 semistructured interviews with parents of CMC experiencing an unplanned 30-day readmission at 1 academic medical center between December 2016 and January 2018, asking about topics such as previous discharge experiences, medical services and resources, and home environment and social support. Interviews were recorded, professionally transcribed, and analyzed thematically by using a modified grounded theory approach.

RESULTS

Children ranged in age from 0 to 15 years, with neurologic complex chronic conditions being predominant (35%). Although the majority of parents did not identify any factors that they perceived to have contributed to readmission, themes emerged regarding challenges associated with chronicity of care and transitions of care that might influence readmissions, including frequency of hospital use, symptom confusion, lack of inpatient continuity, resources needed but not received, and difficulty filling prescriptions.

CONCLUSIONS

Parents identified multiple challenges associated with chronicity of medical management and transitions of care for CMC. Future interventions aiming to improve continuity and communication between admissions, ensure that home services are provided when applicable and prescriptions are filled, and provide comprehensive support for families in both the short- and long-term may help improve patient and family experiences while potentially decreasing readmissions.

Development and Validation of a Web-Based Pediatric Readmission Risk Assessment Tool

OBJECTIVES

Accurately predicting and reducing risk of unplanned readmissions (URs) in pediatric care remains difficult. We sought to develop a set of accurate algorithms to predict URs within 3, 7, and 30 days of discharge from inpatient admission that can be used before the patient is discharged from a current hospital stay.

METHODS

We used the Children's Hospital Association Pediatric Health Information System to identify a large retrospective cohort of 1 111 323 children with 1 321 376 admissions admitted to inpatient care at least once between January 1, 2016, and December 31, 2017. We used gradient boosting trees (XGBoost) to accommodate complex interactions between these predictors.

RESULTS

In the full cohort, 1.6% of patients had at least 1 UR in 3 days, 2.4% had at least 1 UR in 7 days, and 4.4% had at least 1 UR within 30 days. Prediction model discrimination was strongest for URs within 30 days (area under the curve [AUC] = 0.811; 95% confidence interval [CI]: 0.808-0.814) and was nearly identical for UR risk prediction within 3 days (AUC = 0.771; 95% CI: 0.765-0.777) and 7 days (AUC = 0.778; 95% CI: 0.773-0.782), respectively. Using these prediction models, we developed a publicly available pediatric readmission risk scores prediction tool that can be used before or during discharge planning.

CONCLUSIONS

Risk of pediatric UR can be predicted with information known before the patient's discharge and that is easily extracted in many electronic medical record systems. This information can be used to predict risk of readmission to support hospital-discharge-planning resources.

Risk factors associated with 30-day all-cause unplanned hospital readmissions at a tertiary children's hospital in Western Australia

AIM

To identify risk factors associated with 30-day all-cause unplanned hospital readmission at a tertiary children's hospital in Western Australia.

METHODS

An administrative paediatric inpatient dataset was analysed retrospectively. Patients of all ages discharged between 1 January 2010 and 31 December 2014 were included. Demographic and clinical information at the index admission was examined using multivariate logistic regression analysis.

RESULTS

A total of 3330 patients (4.55%) experienced at least one unplanned readmission after discharge. Readmission was more likely to occur in patients who were either older than 16 years (odds ratio (OR) = 1.46; 95% confidence interval (CI) 1.07-1.98), utilising private insurance as an inpatient (OR = 1.16; 95% CI 1.00-1.34), with greater socio-economic advantage (OR = 1.20; 95% CI 1.02-1.41), admitted on Friday (OR = 1.21; 95% CI 1.05-1.39), discharged on Friday/Saturday/Sunday (OR = 1.26, 95% CI 1.10-1.44; OR = 1.34, 95% CI 1.15-1.57; OR = 1.24, 95% CI 1.05-1.47, respectively), with four or more diagnoses at the index admission (OR = 2.41; 95% CI 2.08-2.80) or hospitalised for 15 days or longer (OR = 2.39; 95% CI 1.88-2.98). Area under receiver operating characteristic curve of the predictive model is 0.645.

CONCLUSIONS

A moderate discriminative ability predictive model for 30-day all-cause same hospital readmission was developed. A structured discharge plan is suggested to be commenced from admission to ensure continuity of care for patients identified as being at higher risk of readmission. A recommendation is made that a designated staff member be assigned to co-ordinate the plan, including assessment of patients' and primary carers' readiness for discharge. Further research is required to establish comprehensive paediatric readmission rates by accessing linkage data to capture different hospital readmissions.

Which children account for repeated admissions within 1 year in a Brazilian pediatric intensive care unit?

OBJECTIVE

While studies have focused on early readmissions or readmissions during the same hospitalization in a pediatric intensive care unit, little is known about the children with recurrent admissions. We sought to assess the characteristics of patients readmitted within 1 year in a Brazilian pediatric intensive care unit.

METHODS

This was a retrospective study carried out in a tertiary pediatric intensive care unit. The outcome was the maximum number of readmissions experienced by each child within any 365-day interval during a 5-year follow-up period.

RESULTS

Of the 758 total eligible admissions, 75 patients (9.8%) were readmissions. Those patients accounted for 33% of all pediatric intensive care unit bed care days. Median time to readmission was 73 days for all readmissions. Logistic regression showed that complex chronic conditions (odds ratio 1.07), severe to moderate cognitive disability (odds ratio 1.08), and use of technology assistance (odds ratio 1.17) were associated with readmissions. Multiple admissions had a significantly prolonged duration of mechanical ventilation (8 vs. 6 days), longer length of pediatric intensive care unit (7 vs 4 days) and hospital stays (20 vs 9 days), and higher mortality rate (21.3% vs 5.1%) compared with index admissions.

CONCLUSION

The rate of pediatric intensive care unit readmissions within 1 year was low; however, it was associated with a relevant number of bed care days and worse outcomes. A 30-day index of readmission may be inadequate to mirror the burden of pediatric intensive care unit readmissions. Patients with complex chronic conditions, poor functional status or technology assistance are at higher risk for readmissions. Future studies should address the impact of qualitative interventions on healthcare and recurrent admissions.

[Detection of risk factors for preventable paediatric hospital readmissions]

INTRODUCTION AND OBJECTIVES

Readmission rate is an indicator of the quality of hospital care. The aim of the study is to identify potential preventable factors for paediatric readmission.

MATERIAL AND METHODS

A descriptive, analytical, longitudinal, and single centre study was carried out in the Paediatric Hospitalisation ward of a tertiary hospital during the period from June 2012 to November 2015. Readmission was defined as the one that occurs in the first 30 days of previous admission, as very early readmission if it occurs in the first 48hours, early readmission in the 2-7 days, and late readmission if occurs after 7 days. Preventable readmission is defined as one that takes place in the first 15 days and for the same reason as the first admission. Epidemiological and clinical variables were analysed. A univariate and multivariate study was carried out.

RESULTS

In the study period, 5,459 patients were admitted to the paediatric hospital, of which 226 of them were readmissions (rate of readmission: 4.1%). When the hospital occupation rate was greater than 70%, the overall percentage of readmissions was significantly higher (8.5% vs 2.5%; P<.001). In the multivariate analysis, it was found that having a chronic disease and the number of visits to emergency care units before admission, are predictive factors of preventable readmission.

CONCLUSIONS

The rate of readmissions is greater in the periods of higher care pressure. The readmission of patients with chronic condition is preventable, and therefore strategies must be designed to try to avoid them.

Epidemiology of Children With Multiple Complex Chronic Conditions in a Mixed Urban-Rural US Community

OBJECTIVES

Children with multiple complex chronic conditions (MCCs) represent a small fraction of our communities but a disproportionate amount of health care cost and mortality. Because the temporal trends of children with MCCs within a geographically well-defined US pediatric population has not been previously assessed, health care planning and policy for this vulnerable population is limited.

METHODS

In this population-based, repeated cross-sectional study, we identified and enrolled all eligible children residing in Olmsted County, Minnesota, through the Rochester Epidemiology Project, a medical record linkage system of Olmsted County residents. The pediatric complex chronic conditions classification system version 2 was used to identify children with MCCs. Five-year period prevalence and incidence rates were calculated during the study period (1999-2014) and characterized by age, sex, ethnicity, and socioeconomic status (SES) by using the housing-based index of socioeconomic status, a validated individual housing-based SES index. Age-, sex-, and ethnicity-adjusted prevalence and incidence rates were calculated, adjusting to the 2010 US total pediatric population.

RESULTS

Five-year prevalence and incidence rates of children with MCCs in Olmsted County increased from 1200 to 1938 per 100 000 persons and from 256 to 335 per 100 000 person-years, respectively, during the study period. MCCs tend to be slightly more prevalent among children with a lower SES and with a racial minority background.

CONCLUSIONS

Both 5-year prevalence and incidence rates of children with MCCs have significantly increased over time, and health disparities are present among these children. The clinical and financial outcomes of children with MCCs need to be assessed for formulating suitable health care planning given limited resources.

Developing Prediction Models for 30-Day Unplanned Readmission Among Children With Medical Complexity

OBJECTIVES

To target interventions to prevent readmission, we sought to develop clinical prediction models for 30-day readmission among children with complex chronic conditions (CCCs).

METHODS

After extracting sociodemographic and clinical characteristics from electronic health records for children with CCCs admitted to an academic medical center, we constructed a multivariable logistic regression model to predict readmission from characteristics obtainable at admission and then a second model adding hospitalization and discharge variables to the first model. We assessed model performance using c-statistic and calibration curves and internal validation using bootstrapping. We then created readmission risk scoring systems from final model β-coefficients.

RESULTS

Of the 2296 index admissions involving children with CCCs, 188 (8.2%) had unplanned 30-day readmissions. The model with admission characteristics included previous admissions, previous emergency department visits, number of CCC categories, and medical versus surgical admission (c-statistic 0.65). The model with hospitalization and discharge factors added discharge disposition, length of stay, and weekday discharge to the admission variables (c-statistic 0.67). Bootstrap samples had similar c-statistics, and slopes did not suggest significant overfitting for either model. Readmission risk was 3.6% to 4.9% in the lowest risk quartile versus 15.9% to 17.6% in the highest risk quartile (or 3.6-4.5 times higher) for both models.

CONCLUSIONS

Clinical variables related to the degree of medical complexity and illness severity can stratify children with CCCs into groups with clinically meaningful differences in the risk of readmission. Future research will explore whether these models can be used to target interventions and resources aimed at decreasing readmissions.

An Innovative Model to Predict Pediatric Emergency Department Return Visits

OBJECTIVES

Return visit (RV) to the emergency department (ED) is considered a benchmarking clinical indicator for health care quality. The purpose of this study was to develop a predictive model for early readmission risk in pediatric EDs comparing the performances of 2 learning machine algorithms.

METHODS

A retrospective study based on all children younger than 15 years spontaneously returning within 120 hours after discharge was conducted in an Italian university children's hospital between October 2012 and April 2013. Two predictive models, artificial neural network (ANN) and classification tree (CT), were used. Accuracy, specificity, and sensitivity were assessed.

RESULTS

A total of 28,341 patient records were evaluated. Among them, 626 patients returned to the ED within 120 hours after their initial visit. Comparing ANN and CT, our analysis has shown that CT is the best model to predict RVs. The CT model showed an overall accuracy of 81%, slightly lower than the one achieved by the ANN (91.3%), but CT outperformed ANN with regard to sensitivity (79.8% vs 6.9%, respectively). The specificity was similar for the 2 models (CT, 97% vs ANN, 98.3%). In addition, the time of arrival and discharge along with the priority code assigned in triage, age, and diagnosis play a pivotal role to identify patients at high risk of RVs.

CONCLUSIONS

These models provide a promising predictive tool for supporting the ED staff in preventing unnecessary RVs.

Factors Associated with Readmission of Pediatric Patients in a Developing Nation

OBJECTIVES

To determine the incidence of readmission in pediatric patients in a tertiary care hospital in a developing nation and to ascertain factors precipitating readmissions.

METHODS

A prospective study was conducted from February 2016 through January 2017 at a tertiary care hospital. Children between 1 mo to 15 y of age were included if they were readmitted within 60 d of discharge. The risk factors for readmission were determined on the basis of medical record review and a structured questionnaire and the ascribed cause of readmission was grouped into three categories: Patient specific factors, Hospital specific factors and Unrelated/ New illness.

RESULTS

The readmission rate was 3%, out of which 80.66% were found to be causally related to the index admission. Several sociodemographic characteristics i.e. lack of health information like television, lower socioeconomic status, absence of adequate breastfeeding, lower age, migrants were found to be significantly associated with readmission along with other patient specific factors like presence of cardiac disease, presence of comorbid conditions like anemia, malnutrition, and global developmental delay. The most important cause for readmission was determined as patient specific (48.66%) followed by hospital specific (38%) and unknown/unrelated factors (13.33%).

CONCLUSIONS

The progression of the primary illness and social determinants of pediatric readmissions are important contributing risk factors for readmission in developing countries in pediatric patients. Multicentric studies are needed from this region of the world to include different hospital readmissions rate and to address the issue of potential preventability of pediatric readmissions.

Temporal phenotyping of medically complex children via PARAFAC2 tensor factorization

OBJECTIVE

Our aim is to extract clinically-meaningful phenotypes from longitudinal electronic health records (EHRs) of medically-complex children. This is a fragile set of patients consuming a disproportionate amount of pediatric care resources but who often end up with sub-optimal clinical outcome. The rise in available electronic health records (EHRs) provide a rich data source that can be used to disentangle their complex clinical conditions into concise, clinically-meaningful groups of characteristics. We aim at identifying those phenotypes and their temporal evolution in a scalable, computational manner, which avoids the time-consuming manual chart review.

MATERIALS AND METHODS

We analyze longitudinal EHRs from Children's Healthcare of Atlanta including 1045 medically complex patients with a total of 59,948 encounters over 2 years. We apply a tensor factorization method called PARAFAC2 to extract: (a) clinically-meaningful groups of features (b) concise patient representations indicating the presence of a phenotype for each patient, and (c) temporal signatures indicating the evolution of those phenotypes over time for each patient.

RESULTS

We identified four medically complex phenotypes, namely gastrointestinal disorders, oncological conditions, blood-related disorders, and neurological system disorders, which have distinct clinical characterizations among patients. We demonstrate the utility of patient representations produced by PARAFAC2, towards identifying groups of patients with significant survival variations. Finally, we showcase representative examples of the temporal phenotypic trends extracted for different patients.

DISCUSSION

Unsupervised temporal phenotyping is an important task since it minimizes the burden on behalf of clinical experts, by relegating their involvement in the output phenotypes' validation. PARAFAC2 enjoys several compelling properties towards temporal computational phenotyping: (a) it is able to handle high-dimensional data and variable numbers of encounters across patients, (b) it has an intuitive interpretation and (c) it is free from ad-hoc parameter choices. Computational phenotypes, such as the ones computed by our approach, have multiple applications; we highlight three of them which are particularly useful for medically complex children: (1) integration into clinical decision support systems, (2) interpretable mortality prediction and 3) clinical trial recruitment.

CONCLUSION

PARAFAC2 can be applied to unsupervised temporal phenotyping tasks where precise definitions of different phenotypes are absent, and lengths of patient records are varying.

Risk factors associated with paediatric unplanned hospital readmissions: a systematic review

OBJECTIVE

To synthesise evidence on risk factors associated with paediatric unplanned hospital readmissions (UHRs).

DESIGN

Systematic review.

DATA SOURCE

CINAHL, EMBASE (Ovid) and MEDLINE from 2000 to 2017.

ELIGIBILITY CRITERIA

Studies published in English with full-text access and focused on paediatric All-cause, Surgical procedure and General medical condition related UHRs were included.

DATA EXTRACTION AND SYNTHESIS

Characteristics of the included studies, examined variables and the statistically significant risk factors were extracted. Two reviewers independently assessed study quality based on six domains of potential bias. Pooling of extracted risk factors was not permitted due to heterogeneity of the included studies. Data were synthesised using content analysis and presented in narrative form.

RESULTS

Thirty-six significant risk factors were extracted from the 44 included studies and presented under three health condition groupings. For All-cause UHRs, ethnicity, comorbidity and type of health insurance were the most frequently cited factors. For Surgical procedure related UHRs, specific surgical procedures, comorbidity, length of stay (LOS), age, the American Society of Anaesthesiologists class, postoperative complications, duration of procedure, type of health insurance and illness severity were cited more frequently. The four most cited risk factors associated with General medical condition related UHRs were comorbidity, age, health service usage prior to the index admission and LOS.

CONCLUSIONS

This systematic review acknowledges the complexity of readmission risk prediction in paediatric populations. This review identified four risk factors across all three health condition groupings, namely comorbidity; public health insurance; longer LOS and patients<12 months or between 13-18 years. The identification of risk factors, however, depended on the variables examined by each of the included studies. Consideration should be taken into account when generalising reported risk factors to other institutions. This review highlights the need to develop a standardised set of measures to capture key hospital discharge variables that predict unplanned readmission among paediatric patients.

Encounters From Device Complications Among Children With Medical Complexity

OBJECTIVES

Children with medical complexity (CMC) are commonly assisted by medical devices to support essential body functions, although complications may lead to preventable emergency department (ED) and hospital use. Our objective was to identify predictors of device-complicated ED visits and hospitalizations.

METHODS

This single-center retrospective cohort study included patients referred to a Pediatric Complex Care Program between April 1, 2014, and April 30, 2016, assisted by at least 1 medical device. Hospitalizations and ED visits in the year before enrollment were rated for likelihood for being due to device complications. Interrater reliability among 3 independent reviewers was assessed. Bivariate followed by multivariate logistic regression clustered by patient helped us identify associations between demographic, clinical, and device characteristics associated with device-complicated ED or hospital encounters.

RESULTS

Interrater reliability was high (κ = 0.92). Among 98 CMC, device-complicated encounters represented 17% of 258 hospitalizations and 31% of 228 ED visits. Complications of 3 devices (central venous catheters, enteral tubes, and tracheostomy tubes) accounted for 13% of overall hospitalizations and 28% of overall ED visits. Central venous catheter presence (adjusted odds ratio [aOR] 3.2 [95% confidence interval (CI) 1.1-9.5]) was associated with device-complicated ED visits. Gastrojejunostomy/jejunostomy tube presence (aOR 3.3 [95% CI 1.5-7.5]) or tracheostomies with (aOR 8.1 [95% CI 2.3-28.5]) or without (aOR 4.5 [95% CI 1.7-7.5]) ventilator use was associated with device-complicated hospitalizations. Clinical variables were poor predictors of device-complicated encounters.

CONCLUSIONS

Device-complicated ED visits and hospitalizations comprised a substantial proportion of total hospital and ED use. Developing interventions to prevent device complications may be a promising strategy to reduce overall CMC use.

A Novel Model for Enhanced Prediction and Understanding of Unplanned 30-Day Pediatric Readmission

OBJECTIVES

To develop a model to assist clinicians in reducing 30-day unplanned pediatric readmissions and to enhance understanding of risk factors leading to such readmissions.

METHODS

Data consisting of 38 143 inpatient clinical encounters at a tertiary pediatric hospital were retrieved, and 50% were used for training on a multivariate logistic regression model. The pediatric Rothman Index (pRI) was 1 of the novel candidate predictors considered. Multivariate model selection was conducted by minimization of Akaike Information Criteria. The area under the receiver operator characteristic curve (AUC) and values for sensitivity, specificity, positive predictive value, relative risk, and accuracy were computed on the remaining 50% of the data.

RESULTS

The multivariate logistic regression model of readmission consists of 7 disease diagnosis groups, 4 measures of hospital resource use, 3 measures of disease severity and/or medical complexities, and 2 variables derived from the pRI. Four of the predictors are novel, including history of previous 30-day readmissions within last 6 months (P < .001), planned admissions (P < .001), the discharge pRI score (P < .001), and indicator of whether the maximum pRI occurred during the last 24 hours of hospitalization (P = .005). An AUC of 0.79 (0.77-0.80) was obtained on the independent test data set.

CONCLUSIONS

Our model provides significant performance improvements in the prediction of unplanned 30-day pediatric readmissions with AUC higher than the LACE readmission model and other general unplanned 30-day pediatric readmission models. The model is expected to provide an opportunity to capture 39% of readmissions (at a selected operating point) and may therefore assist clinicians in reducing avoidable readmissions.

Complex Care Hospital Use and Postdischarge Coaching: A Randomized Controlled Trial

OBJECTIVES

We sought to examine the effect of a caregiver coaching intervention, Plans for Action and Care Transitions (PACT), on hospital use among children with medical complexity (CMC) within a complex care medical home at an urban tertiary medical center.

METHODS

PACT was an 18-month caregiver coaching intervention designed to influence key drivers of hospitalizations: (1) recognizing critical symptoms and conducting crisis plans and (2) supporting comprehensive hospital transitions. Usual care was within a complex care medical home. Primary outcomes included hospitalizations and 30-day readmissions. Secondary outcomes included total charges and mortality. Intervention effects were examined with bivariate and multivariate analyses.

RESULTS

From December 2014 to September 2016, 147 English- and Spanish-speaking CMC <18 years old and their caregivers were randomly assigned to PACT (n = 77) or usual care (n = 70). Most patients were Hispanic, Spanish-speaking, and publicly insured. Although in unadjusted intent-to-treat analyses, only charges were significantly reduced, both hospitalizations and charges were lower in adjusted analyses. Hospitalization rates (per 100 child-years) were 81 for PACT vs 101 for usual care (adjusted incident rate ratio: 0.61 [95% confidence interval 0.38-0.97]). Adjusted mean charges per patient were $14 206 lower in PACT. There were 0 deaths in PACT vs 4 in usual care (log-rank P = .04).

CONCLUSIONS

Among CMC within a complex care program, a health coaching intervention designed to identify, prevent, and manage patient-specific crises and postdischarge transitions appears to lower hospitalizations and charges. Future research should confirm findings in broader populations and care models.

Well-Child Visits of Medicaid-Insured Children with Medical Complexity

OBJECTIVE

Well-child visits (WCVs) help optimize children's health. We measured annual WCVs for children with medical complexity (CMC) and correlated WCVs with hospitalizations.

STUDY DESIGN

This was a retrospective analysis of 93 121 CMC aged 1-18 years continuously enrolled in 10 state Medicaid programs in the Truven MarketScan Database between 2010 and 2014. CMC had a complex chronic condition or 3 or more chronic conditions of any complexity identified from International Classification of Diseases, Ninth Revision codes, and the use of 1 or more chronic medications. We measured the number of years with 1 or more WCVs. The χ² test and logistic regression were used to assess the relationships of WCV-years with the children's characteristics and hospitalization.

RESULTS

Over 5 years, 13.4% of CMC had 0 WCVs; 17.3% had WCVs in 1 year, 40.8% had WCVs in 2-3 years, and 28.5% had WCVs in 4-5 years. Fewer children received WCVs in 4-5 years when enrolled in Medicaid fee-for-service compared with managed care (20.9% vs 31.5%; P < .001) and when enrolled due to a disability compared with another reason (18.2% vs 32.2%; P < .001). The percentage of CMC hospitalized decreased as the number of years receiving WCV increased (21.5% at 0 years vs 16.9% at 5 years; P < .001). The adjusted odds of hospitalization were higher in CMC with WCVs in 0-4 years compared with CMC with WCVs in all 5 years (OR range across years, 1.1 [95% CI, 1.0-1.2] to 1.3 [95% CI, 1.3-1.4]).

CONCLUSIONS

Most Medicaid-insured CMC do not receive annual WCVs consistently over time. Children with fewer annual WCVs have a higher likelihood of hospitalization. Further investigation is needed to improve the use of WCVs in CMC.