A Validated Method for Identifying Unplanned Pediatric Readmission

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.

Garnering effective telehealth to help optimize multidisciplinary team engagement (GET2HOME) for children with medical complexity: Protocol for a pragmatic randomized control trial

BACKGROUND

Children and young adults with medical complexity (CMC) experience high rates of healthcare reutilization following hospital discharge. Prior studies have identified common hospital-to-home transition failures that may increase the risk for reutilization, including medication, technology and equipment issues, financial concerns, and confusion about which providers can help with posthospitalization needs. Few interventions have been developed and evaluated for CMC during this transition period.

OBJECTIVE

We will compare the effectiveness of the garnering effective telehealth 2 help optimize multidisciplinary team engagement (GET2HOME) transition bundle intervention to the standard hospital-based care coordination discharge process by assessing healthcare reutilization and patient- and family-centered outcomes.

DESIGNS, SETTINGS, AND PARTICIPANTS

We will conduct a pragmatic 2-arm randomized controlled trial (RCT) comparing the GET2HOME bundle intervention to the standard hospital-based care discharge process on CMC hospitalized and discharged from hospital medicine at two sites of our pediatric medical center between November 2022 and February 2025. CMC of any age will be identified as having complex chronic disease using the Pediatric Medical Complexity Algorithm tool. We will exclude CMC who live independently, live in skilled nursing facilities, are in custody of the county, or are hospitalized for suicidal ideation or end-of-life care.

INTERVENTION

We will randomize participants to the bundle intervention or standard hospital-based care coordination discharge process. The bundle intervention includes (1) predischarge telehealth huddle with inpatient providers, outpatient providers, patients, and their families; (2) care management discharge task tracker; and (3) postdischarge telehealth huddle with similar participants within 7 days of discharge. As part of the pragmatic design, families will choose if they want to complete the postdischarge huddle. The standard hospital-based discharge process includes a pharmacist, social worker, and care management support when consulted by the inpatient team but does not include huddles between providers and families.

MAIN OUTCOME AND MEASURES

Primary outcome will be 30-day urgent healthcare reutilization (unplanned readmission, emergency department, and urgent care visits). Secondary outcomes include 7-day urgent healthcare reutilization, patient- and family-reported transition quality, quality of life, and time to return to baseline using electronic health record and surveys at 7, 30, 60, and 90 days following discharge. We will also evaluate heterogeneity of treatment effect for the intervention across levels of financial strain and for CMC with high-intensity neurologic impairment. The primary analysis will follow the intention-to-treat principle with logistic regression used to study reutilization outcomes and generalized linear mixed modeling to study repeated measures of patient- and family-reported outcomes over time.

RESULTS

This pragmatic RCT is designed to evaluate the effectiveness of enhanced discharge transition support, including telehealth huddles and a care management discharge tool, for CMC and their families. Enrollment began in November 2022 and is projected to complete in February 2025. Primary analysis completion is anticipated in July 2025 with reporting of results following.

Inpatient Screening of Parental Adversity and Strengths

BACKGROUND AND OBJECTIVES

Social adversities, including health-harming social risks and adverse childhood experiences, contribute to poor outcomes after hospital discharge. Screening for social adversities is increasingly pursued in outpatient settings. Identifying and addressing such adversities has been linked to improved child outcomes. Screening for social adversities and strengths in the inpatient setting may contribute to better transitions from hospital to home. Our goal was twofold: 1. to use qualitative methods to understand parent perspectives around screening tools for potential use in inpatient settings; and 2. to develop a family-friendly inpatient screening tool for social adversity.

METHODS

We used in-depth, cognitive qualitative interviews with parents to elicit their views on existing screening tools covering social adversities and strengths. We partnered with a local nonprofit to recruit parents who recently had a child hospitalized or visited the emergency department. There were 2 phases of the study. In the first phase, we used qualitative methods to develop a screening prototype. In the second phase, we obtained feedback on the prototype.

RESULTS

We interviewed 18 parents who identified 3 major themes around screening: 1. factors that promote parents to respond openly and honestly during screening; 2. feedback about screening tools and the prototype; and 3. screening should include resources.

CONCLUSIONS

Social adversity routinely affects children; hospitalization is an important time to screen families for adversity and potential coexisting strengths. Using qualitative parent feedback, we developed the family friendly Collaborate to Optimize Parent Experience screening tool.

Using Geomarkers and Sociodemographics to Inform Assessment of Caregiver Adversity and Resilience

BACKGROUND AND OBJECTIVES

A high level of caregiver adverse childhood experiences (ACEs) and/or low resilience is associated with poor outcomes for both caregivers and their children after hospital discharge. It is unknown if sociodemographic or area-based measures (ie, "geomarkers") can inform the assessment of caregiver ACEs or resilience. Our objective was to determine if caregiver ACEs or resilience can be identified by using any combinations of sociodemographic measures, geomarkers, and/or caregiver-reported household characteristics.

METHODS

Eligible participants for this cohort study were English-speaking caregivers of children hospitalized on a hospital medicine team. Caregivers completed the ACE questionnaire, Brief Resilience Scale, and strain surveys. Exposures included sociodemographic characteristics available in the electronic health record (EHR), geomarkers tied to a patient's geocoded home address, and household characteristics that are not present in the EHR (eg, income). Primary outcomes were a high caregiver ACE score (≥4) and/or a low BRS Score (<3).

RESULTS

Of the 1272 included caregivers, 543 reported high ACE or low resilience, and 63 reported both. We developed the following regression models: sociodemographic variables in EHR (Model 1), EHR sociodemographics and geomarkers (Model 2), and EHR sociodemographics, geomarkers, and additional survey-reported household characteristics (Model 3). The ability of models to identify the presence of caregiver adversity was poor (all areas under receiver operating characteristics curves were <0.65).

CONCLUSIONS

Models using EHR data, geomarkers, and household-level characteristics to identify caregiver adversity had limited utility. Directly asking questions to caregivers or integrating risk and strength assessments during pediatric hospitalization may be a better approach to identifying caregiver adversity.

Dexamethasone Versus Prednisone in Children Hospitalized With Asthma Exacerbation

OBJECTIVES

Dexamethasone is increasingly used for the management of children hospitalized with asthma in place of prednisone, yet data regarding the effectiveness of dexamethasone in children with asthma exacerbation severe enough to require hospitalization are limited. Our objective is to compare the effectiveness of dexamethasone versus prednisone in children hospitalized with an asthma exacerbation on 30-day reutilization.

METHODS

We conducted a retrospective cohort study at an urban, quaternary children's hospital of children aged 4 to 17 years, hospitalized from January 1, 2014 to December 31, 2017, with a primary discharge diagnosis of asthma. A covariate-balanced propensity score was derived to account for physician discretion in steroid selection. A generalized linear model, including inverse probability treatment weighting, was used to detect differences in 30-day return utilization (unplanned readmission or emergency department visit) between children whose first dose of corticosteroid was dexamethasone versus prednisone.

RESULTS

Inclusion criteria were met by 1161 patients, of which 510 (44%) first received dexamethasone versus 651 (56%) who first received prednisone. The total cohort had a mean age of 8.5 years (SD 3.4). The covariate-balanced cohort had no significant differences in demographic characteristics or illness severity between groups. The dexamethasone group had a return utilization of 3.9% (20 of 510) versus 2.2% (14 of 651) for children treated with prednisone. The propensity score-adjusted analysis revealed the steroid treatment was not found to significantly affect the 30-day reutilization (adjusted odds ratio [aOR] 1.61; 95%CI 0.80-3.31).

CONCLUSIONS

The initial steroid choice (dexamethasone versus prednisone) was not associated with 30-day reutilization after hospitalization for an asthma exacerbation.

Readmission after hospitalization with Staphylococcus aureus bacteremia in children

BACKGROUND

Readmission rate is an important quality measure and can inform patient care. However, readmission of S. aureus bacteremia in children requires further research.

METHODS

We performed a population-based longitudinal observational study using the State Inpatient Database from New York, Florida, and Washington, 2009-2015. Children aged 18 years or younger hospitalized with S. aureus bacteremia were included. The outcome of non-elective readmission was assessed by developing Cox proportional hazards regression models.

RESULTS

Of 1240 children with S. aureus bacteremia, 223 (18.0%) and 351 (28.3%) had non-elective readmission within 30 days and 90 days after discharge, respectively. On multivariable analysis, catheter related infection (hazard ratio, HR: 1.79, 95% confidence interval, CI: 1.31-2.45) was associated with 30-day readmission. The median cost of the original hospitalization for S. aureus bacteremia was $29 914 (interquartile range, IQR: $13 276-$71 284), and that of 30 day readmission was $10 956 (IQR: $5765-$24 753).

CONCLUSIONS

S. aureus bacteremia is associated with a high rate of readmission in children, particularly in those with catheter related infection. Hospitalization with S. aureus bacteremia and readmission are costly. Future research should seek interventions to improve outcomes of S. aureus bacteremia in children, and the results of this study can serve as a benchmark.

Pediatric malnutrition screening and assessment tools: Analyzing the gaps

Pediatric healthcare professionals know that children who are malnourished have worse clinical outcomes than do their well-nourished peers. The body of evidence in the literature on the topic of pediatric malnutrition (PMN) is growing and supports this conclusion. The goal of this article is to present the current state of practice related to PMN screening, the use of malnutrition risk screening (MNRS) tools, malnutrition assessment, and the use of malnutrition assessment tools. The purpose is also to discuss best practice for MNRS and assessment and to consider the gaps in practice where additional work is needed.

Prevalence of Social Risks on Inpatient Screening and Their Impact on Pediatric Care Use

OBJECTIVES

Screening for social determinants of health in the inpatient setting is uncommon. However, social risk factors documented in billing and electronic medical record data are associated with increased pediatric care use. We sought to describe (1) the epidemiology of social risks and referral acceptance and (2) association between social risks identified through routine inpatient screening and care use.

METHODS

Parents of children ages 0 to 18 admitted to a general pediatric floor at an academic children's hospital completed a psychosocial screening survey from October 2017 to June 2019. The survey covered the following domains: finances, housing, food security, medications, and benefits. Patient characteristics and care use outcomes were abstracted from the electronic medical record and compared by using Pearson's χ² or the Wilcoxon rank test and logistic regression analyses.

RESULTS

Of 374 screened families, 141 (38%) had a positive screen result, of whom 78 (55%) reported >1 need and 64 (45%) accepted a community resource. In bivariate analyses, patients with a positive screen result had higher 30-day readmission (10% vs 5%; P = .05), lower median household income ($62 321 vs $71 460; P < .01), lower parental education (P < .01), public insurance (57% vs 43%; P < .01), lived in a 1-parent household (30 vs 12%; P < .01), and had a complex chronic condition (35% vs 23%; P = .01) compared with those with a negative screen result. There was no difference in care reuse by screening status in adjusted analyses.

CONCLUSIONS

Social risks are common in the pediatric inpatient setting. Children with medical complexity offer a good target for initial screening efforts.

Short-term Focused Feedback: A Model to Enhance Patient Engagement in Research and Intervention Delivery

BACKGROUND

Our grant from the Patient-Centered Outcomes Research Institute (PCORI) focused on the use of nurse home visits postdischarge for primarily pediatric hospital medicine patients. While our team recognized the importance of engaging parents and other stakeholders in our study, our project was one of the first funded to address transitions of care issues in patients without chronic illness; little evidence existed about how to engage acute stakeholders longitudinally.

OBJECTIVE

This manuscript describes how we used both a short-term focused feedback model and longitudinal engagement methods to solicit input from parents, home care nurses, and other stakeholders throughout our 3-year study.

RESULTS

Short-term focused feedback allowed the study team to collect feedback from hundreds of stakeholders. Initially, we conducted focus groups with parents with children recently discharged from the hospital. We used this feedback to modify our nurse home visit intervention, then used quality improvement methods with continued short-term focus feedback from families and nurses delivering the visits to adjust the visit processes and content. We also used their feedback to modify the outcome collection. Finally, during the randomized controlled trial, we added a parent to the study team to provide longitudinal input, as well as continued to solicit short-term focused feedback to increase recruitment and retention rates.

CONCLUSION

Research studies can benefit from soliciting short-term focused feedback from many stakeholders; having this variety of perspectives allows for many voices to be heard, without placing an undue burden on a few stakeholders.

Differential Impact of Home Nurse Contact After Discharge by Financial Strain, Primary Care Access, and Medical Complexity

BACKGROUND

The Hospital to Home Outcomes (H2O) trials examined the effectiveness of postdischarge nurse support on reuse after pediatric discharge. Unexpectedly, children randomly assigned to a nurse visit had higher rates of reuse than those in the control group. Participants in randomized control trials are heterogeneous. Thus, it is possible that the effect of the intervention differed across subgroups (ie, heterogeneity of treatment effect [HTE]). We sought to determine if different subgroups responded differently to the interventions.

METHODS

The H2O trial is a randomized controlled trial comparing standard hospital discharge processes with a nurse home visit within 96 hours of discharge. The second trial, H2O II, was similar, except the tested intervention was a postdischarge nurse phone call. For the purposes of the HTE analyses, we examined our primary trial outcome measure: a composite of unplanned 30-day acute health care reuse (unplanned readmission or emergency department or urgent care visit). We identified subgroups of interest before the trials related to (1) financial strain, (2) primary care access, (3) insurance, and (4) medical complexity. We used logistic regression modeling with an interaction term between subgroup and treatment group (intervention or control).

RESULTS

For the phone call trial (H2O II), financial strain significantly modified the effect of the intervention such that the subgroup of children with high financial strain who received the intervention experienced more reuse than their control counterparts.

CONCLUSIONS

In HTE analyses of 2 randomized controlled trials, only financial strain significantly modified the nurse phone call. A family's financial resources may affect the utility of postdischarge support.

Readmissions Following Hospitalization for Infection in Children With or Without Medical Complexity

OBJECTIVE

To describe the prevalence and characteristics of infection-related readmissions in children and to identify opportunities for readmission reduction and estimate associated cost savings.

STUDY DESIGN

Retrospective analysis of 380,067 nationally representative index hospitalizations for children using the 2014 Nationwide Readmissions Database. We compared 30-day, all-cause unplanned readmissions and costs across 22 infection categories. We used the Inpatient Essentials database to measure hospital-level readmission rates and to establish readmission benchmarks for individual infections. We then estimated the number of readmissions avoided and costs saved if hospitals achieved the 10th percentile of hospitals' readmission rates (ie, readmission benchmark). All analyses were stratified by the presence/absence of a complex chronic condition (CCC).

RESULTS

The overall 30-day readmission rate was 4.9%. Readmission rates varied substantially across infections and by presence/absence of a CCC (CCC: range, 0%-21.6%; no CCC: range, 1.5%-8.6%). Approximately 42.6% of readmissions (n = 3,576) for children with a CCC and 54.7% of readmissions (n = 5,507) for children without a CCC could have been potentially avoided if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated savings of $70.8 million and $44.5 million, respectively. Bronchiolitis, pneumonia, and upper respiratory tract infections were among infections with the greatest number of potentially avoidable readmissions and cost savings for children with and without a CCC.

CONCLUSION

Readmissions following hospitalizations for infection in children vary significantly by infection type. To improve hospital resource use for infections, future preventative measures may prioritize children with complex chronic conditions and those with specific diagnoses (eg, respiratory illnesses).

Derivation and validation of a prediction model for neonate unplanned rehospitalization in a tertiary center in China

BACKGROUND

To construct and externally validate a prediction model for neonate unplanned rehospitalization within 31 days of discharge.

METHODS

A retrospective study was performed in the Department of Neonatology of the Children's Hospital of Fudan University. A binominal regression method was applied to construct and validate the prediction model. Analysis was performed on a total of 11,116 neonates with an index admission between 11/1/2016 and 12/31/2018. Neonates admitted from 11/1/2016 to 1/31/2018 were used for the selection of prognostic variables and construction of the model. Model validation was then performed with neonates admitted from 2/1/2018 to 12/31/2018.

RESULTS

The rehospitalization rate for neonates was 3.27% (373/11,116). A total of 512 neonates were enrolled for the construction of the prediction model. Gestational age (GA), NICU length of stay (LOS), nonmedical order discharge and younger maternal age were strongly correlated with rehospitalization. By incorporating these 4 strong risk factors, we constructed a model to predict neonate unplanned rehospitalization within 31 days of discharge. The formula was turned into a nomogram for use in clinical practice. The nomogram has a total score of 180, with a predicted risk from 0 to 100%. Neonates are at high risk for rehospitalization if they have a total score greater than 39 points, according to the cutoff point established by the Youden index. The model was shown to have good discriminatory ability, with area under the receiver operating characteristic curves of 0.68 and 0.65 in the model construction and validation datasets, respectively. A total of 39 points is the cutoff for follow-up.

CONCLUSIONS

The model is able to predict neonate unplanned rehospitalization well. A total score greater than 39 indicates that follow-up is necessary.

Performance of Pediatric Readmission Measures

BACKGROUND

Readmission rates are frequently used as a hospital quality metric; yet multiple measures exist to evaluate pediatric readmission rates. We sought to assess how four different measures of pediatric readmission compare with assessment of both preventable and unplanned readmission.

METHODS

Clinicians on hospital medicine, cardiology, neonatology, and neurology teams reviewed medical records for 30-day readmissions using an abstraction tool with high interrater reliability for preventability assessment. Readmissions between July 2014 and June 2016 were classified separately as preventable or not preventable and planned or unplanned. We compared the classifications to four existing readmission metrics: all-cause readmission, unplanned readmission/time flag classification, the pediatric all-condition readmission, and potentially preventable readmission. We calculated sensitivity and specificity for all readmission metrics.

RESULTS

Among 30-day readmissions considered, 1,643 were eligible for medical record review; 1,125 reviews were completed by the clinical teams (68%). On medical record review, the majority of readmissions were determined not preventable (85%). Only 15% were classified as unplanned and preventable. None of the four readmission measures had appropriate sensitivity or specificity for identifying preventable readmission. The unplanned readmission/time flag classification had the highest sensitivity (95%) and specificity (90%) in identifying unplanned readmissions.

CONCLUSION

None of the existing pediatric readmission measures can reliably determine preventability. The unplanned readmission/time flag measure performed best in identifying unplanned readmissions.

Effect of Parental Adverse Childhood Experiences and Resilience on a Child's Healthcare Reutilization

BACKGROUND

Adverse childhood experiences (ACEs) are associated with poor health outcomes in adults. Resilience may mitigate this effect. There is limited evidence regarding how parents' ACEs and resilience may be associated with their children's health outcomes.

OBJECTIVE

To determine the association of parental ACEs and resilience with their child's risk of unanticipated healthcare reutilization.

DESIGN, SETTING, AND PARTICIPANTS

We conducted a prospective cohort study (August 2015 to October 2016) at a tertiary, freestanding pediatric medical center in Cincinnati, Ohio. Eligible participants were English-speaking parents of children hospitalized on a Hospital Medicine or Complex Services team. A total of 1,320 parents of hospitalized children completed both the ACE questionnaire and the Brief Resilience Scale Survey.

EXPOSURE

Number of ACEs and Brief Resilience Scale Score among parents.

MAIN OUTCOMES

Unanticipated reutilization by children, defined as returning to the emergency room, urgent care, or being readmitted to the hospital within 30 days of hospital discharge.

RESULTS

In adjusted analyses, children of parents with 4 or more ACEs had 1.69-times higher odds (95% CI, 1.11-2.60) of unanticipated reutilization after an index hospitalization, compared with children of parents with no ACEs. Resilience was not significantly associated with reutilization.

CONCLUSION

Parental history of ACEs is strongly associated with higher odds of their child having unanticipated healthcare reutilization after a hospital discharge, highlighting an intergenerational effect. Screening may be an important tool for outcome prediction and intervention guidance following pediatric hospitalization.

Comparing Software Determination of Readmission Preventability With Chart Review, Provider, and Family Assessments

OBJECTIVES

To explore the concordance between software, chart reviewer, provider, and parent perspectives when assessing whether readmissions are preventable or clinically related to the initial admission.

METHODS

Providers and parents of patients readmitted within 3 days to a tertiary children's hospital were enrolled in this single-site observational study. 3M Potentially Preventable Readmissions Grouping Software, chart reviewers, discharge and readmission providers, and parents assessed if readmissions were clinically related to the index admission or potentially preventable. Agreement between perspectives was measured by using Cohen's κ values.

RESULTS

The software found 67 of 118 (57%) clinically related readmissions; the identical 67 of 118 cases (57%) were found to be potentially preventable. Chart reviewers found 107 of 125 (86%) clinically related and 60 of 125 (47%) preventable readmissions compared to 68 of 92 (74%) and 27 of 92 (28%) for discharge physicians and 69 of 93 (74%) and 33 of 93 (34%) for readmitting physicians. Parents reported 9 of 36 (25%) preventable readmissions. Cohen κ values revealed no to minimal agreement on clinical relatedness of readmissions between software and chart reviewer, discharge provider, and readmission provider (0.12-0.20), whereas chart reviewers and providers had weak to moderate agreement with each other (0.43-0.75). There was no to minimal agreement on preventability between software and the other perspectives (-0.04 to 0.21), whereas chart reviewers and providers had minimal to weak agreement (0.27-0.56).

CONCLUSIONS

Measurement of preventable readmissions remains problematic, and using financial penalties for readmissions on the basis of software determinations may be unwise given low levels of agreement. Chart review supplemented by information from providers and families offers a more inclusive way to identify potentially preventable readmissions.

How Measurement Variability Affects Reporting of a Single Readmission Metric

Readmissions are an important quality measure for public reporting, payment, and collaborative research. Lack of measure standardization may lead to inconsistent reporting of outcomes across study sites. In this study, we examined the impact of measurement variability on reporting of a single readmission metric, 30-day all-condition readmission rates (ARRs). We conducted a secondary database analysis of 2006-2008 Medicaid Analytic eXtract data merged from four states of children younger than 21 years. We calculated 30-day ARRs for this cohort using three previously described models varying in their inclusions and exclusions of index hospitalizations and readmissions. The 30-day ARR was highest for the model allowing each readmission to serve as an index admission for subsequent readmissions (ARR: 7%); intermediate for the model allowing one index admission and more than one readmissions in each 30-day period (ARR: 6.2%); and lowest for the model allowing only one readmission in each 30-day period (ARR: 5.6%). Similar variation was seen when stratifying patients by individual diagnostic groups. In conclusion, measurement variability impacts reported outcomes of a single readmission metric. To improve the value of readmission as a quality metric, stakeholders engaged in multisite quality improvement or research should ensure that definitions are standardized across sites.

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.

Discharge Medical Complexity, Change in Medical Complexity and Pediatric 30-day Readmission

BACKGROUND

While medical complexity is associated with pediatric readmission risk, less is known about how increases in medical complexity during hospitalization affect readmission risk.

METHODS

We conducted a five-year retrospective, case-control study of pediatric hospitalizations at a tertiary care children's hospital. Cases with a 30-day unplanned readmission were matched to controls based on admission seasonality and distance from the hospital. Complexity variables included the number of medications prescribed at discharge, medical technology, and the need for home healthcare services. Change in medical complexity variables included new complex chronic conditions and new medical technology. We estimated odds of 30-day unplanned readmission using adjusted conditional logistic regression.

RESULTS

Of 41,422 eligible index hospitalizations, we included 595 case and 595 control hospitalizations. Complexity: Polypharmacy after discharge was common. In adjusted analyses, being discharged with ≥2 medications was associated with higher odds of readmission compared with being discharged without medication; children with ≥5 discharge medications had a greater than four-fold higher odds of readmission. Children assisted by technology had higher odds of readmission compared with children without technology assistance. Change in complexity: New diagnosis of a complex chronic condition (Adjusted Odds Ratio (AOR) = 1.75; 1.11-2.75) and new technology (AOR = 1.84; 1.09-3.10) were associated with higher risk of readmission when adjusting for patient characteristics. However, these associations were not statistically significant when adjusting for length of stay.

CONCLUSION

Polypharmacy and use of technology at discharge pose a substantial readmission risk for children. However, added technology and new complex chronic conditions do not increase risk when accounting for length of stay.

Connecting Hospital to Home: Characteristics of and Rehospitalization Rates in Hospitalized Children With Private-Duty Nursing

OBJECTIVES

Children with medical complexity are frequently hospitalized and have extensive health care needs. Private-duty nursing (PDN) is a service on which some children with medical complexity rely to live at home, but little is known about patients discharged with PDN. Our objective for this study was to describe the characteristics and longitudinal outcomes of patients with PDN who are hospitalized.

METHODS

This study was a 1-year retrospective study of patients receiving PDN who were hospitalized at a quaternary freestanding children's hospital; there was an additional 2-year follow-up. Patient characteristics, rehospitalization rates, length of stay, mortality, and hospital charges were identified. Descriptive statistics were performed to characterize trends, and a time-to-event analysis was used to characterize unplanned rehospitalization.

RESULTS

Among 8187 unique patients who were hospitalized in the initial study year (June 1, 2014 to -May 31, 2015), 188 patients (2%) used PDN. Of patients using PDN, 94% used gastrointestinal devices. The median index length of stay was 4 days (interquartile range 2-6). Two-year mortality was 12%. Cumulative all-cause rehospitalization rates were 18% by 30 days, 62% by 365 days, and 87% within 2 years; the median rehospitalization frequency was 3 per patient. The most common reasons for unplanned rehospitalization were infection (41%) and device complication (10%). During the study period, 11% of both rehospitalizations and total hospital days were attributed to patients with PDN. Unplanned rehospitalizations of patients with PDN accounted for $117 million in hospital charges.

CONCLUSIONS

One in 50 patients hospitalized at a single center were discharged with PDN, which accounted for a disproportionate level of hospital use. Future research should be used to address whether the access and quality of PDN may impact rehospitalization.

[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.

Machine Learning Readmission Risk Modeling: A Pediatric Case Study

BACKGROUND

Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.

OBJECTIVE

To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.

MATERIALS

An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child's treatment administrative cost.

METHODS

Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size.

RESULTS

Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms.

CONCLUSIONS

We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

Pediatric Readmissions Within 3 Days of Discharge: Preventability, Contributing Factors, and Necessity

OBJECTIVES

Among pediatric 30-day readmissions, 20% to 30% are preventable, and ∼25% are within 3 days of discharge. We investigated the preventability, contributing factors, and necessity of 3-day pediatric readmissions.

METHODS

We enrolled patients who were readmitted within 3 days at a freestanding tertiary children's hospital in this single-site observational study from July 2016 to February 2017. We performed chart reviews and interviews with discharge and readmission providers and families. Preventability was defined by the chart reviewer's determination. Contributing factors for readmission, demographics, and clinical characteristics were analyzed for association with preventability and necessity. We analyzed qualitative data using content analysis.

RESULTS

Of the 125 readmission cases included, 60 (48%) were preventable per chart reviewer compared with 27 of 92 (29%) per discharge providers, 33 of 93 (35%) per readmission providers, and 9 of 36 (25%) per families. Preventability was associated with the following contributing factors: problems with clinical decision-making in 54 of 125 (43%) readmissions (P < .001), issues with the discharge process in 25 of 125 (20%) readmissions (P = .01), clinically related admission and readmission (P = .004), and weekday of initial discharge (P = .02). Seventeen percent were unnecessary per readmission provider. Clinically unnecessary readmissions were associated with Hispanic ethnicity (P = .02), outside-hospital transfer (P = .05), and problems with clinical decision-making (P = .01). Qualitative data highlighted disagreement on readiness for discharge and the necessity of readmission among various providers and family.

CONCLUSIONS

More than one-half of 3-day readmissions were considered either preventable or unnecessary. Clinical decision-making, discharge processes, and improving consensus among families and providers may be valuable areas for future efforts to reduce readmission.

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.

Effects of a 1-Time Nurse-Led Telephone Call After Pediatric Discharge: The H2O II Randomized Clinical Trial

Importance

Families often struggle after discharge of a child from the hospital. Postdischarge challenges can lead to increased use of urgent health care services.

Objective

To determine whether a single nurse-led telephone call after pediatric discharge decreased the 30-day reutilization rate for urgent care services and enhanced overall transition success.

Design, Setting, and Participants

This Hospital-to-Home Outcomes (H2O) randomized clinical trial included 966 children and adolescents younger than 18 years (hereinafter referred to as children) admitted to general medicine services at a free-standing tertiary care children's hospital from May 11 through October 31, 2016. Data were analyzed as intention to treat and per protocol.

Interventions

A postdischarge telephone call within 4 days of discharge compared with standard discharge.

Main Outcomes and Measures

The primary outcome was the 30-day reutilization rate for urgent health care services (ie, unplanned readmission, emergency department visit, or urgent care visit). Secondary outcomes included additional utilization measures, as well as parent coping, return to normalcy, and understanding of clinical warning signs measured at 14 days.

Results

A total of 966 children were enrolled and randomized (52.3% boys; median age [interquartile range], 2.4 years [0.5-7.8 years]). Of 483 children randomized to the intervention, the nurse telephone call was completed for 442 (91.5%). Children in the intervention and control arms had similar reutilization rates for 30-day urgent health care services (intervention group, 77 [15.9%]; control group, 63 [13.1%]; P = .21). Parents of children in the intervention group recalled more clinical warning signs at 14 days (mean, 1.8 [95% CI, 1.7-2.0] in the intervention group; 1.5 [95% CI, 1.4-1.6] in the control group; ratio of intervention to control, 1.2 [95% CI, 1.1-1.3]).

Conclusions and Relevance

Although postdischarge nurse contact did not decrease the reutilization rate of postdischarge urgent health care services, this method shows promise to bolster postdischarge education.

Trial Registration

ClinicalTrials.gov Identifier: NCT02081846.

Postdischarge Nurse Home Visits and Reuse: The Hospital to Home Outcomes (H2O) Trial

BACKGROUND

Hospital discharge is stressful for children and families. Poor transitional care is linked to unplanned health care reuse. We evaluated the effects of a pediatric transition intervention, specifically a single nurse home visit, on postdischarge outcomes in a randomized controlled trial.

METHODS

We randomly assigned 1500 children hospitalized on hospital medicine, neurology services, or neurosurgery services to receive either a single postdischarge nurse-led home visit or no visit. We excluded children discharged with skilled home nursing services. Primary outcomes included 30-day unplanned, urgent health care reuse (composite measure of unplanned readmission, emergency department, or urgent care visit). Secondary outcomes, measured at 14 days, included postdischarge parental coping, number of days until parent-reported return to normal routine, and number of "red flags" or clinical warning signs a parent or caregiver could recall.

RESULTS

The 30-day reuse rate was 17.8% in the intervention group and 14.0% in the control group. In the intention-to-treat analysis, children randomly assigned to the intervention group had higher odds of 30-day health care use (odds ratio: 1.33; 95% confidence interval: 1.003-1.76). In the per protocol analysis, there were no differences in 30-day health care use (odds ratio: 1.14; confidence interval: 0.84-1.55). Postdischarge coping scores and number of days until returning to a normal routine were similar between groups. Parents in the intervention group recalled more red flags at 14 days (mean: 1.9 vs 1.6; P < .01).

CONCLUSIONS

Children randomly assigned to the intervention had higher rates of 30-day postdischarge unplanned health care reuse. Parents in the intervention group recalled more clinical warning signs 2 weeks after discharge.

Parental Adverse Childhood Experiences and Resilience on Coping After Discharge

BACKGROUND AND OBJECTIVES

Adults with a history of adverse childhood experiences (ACEs) (eg, abuse) have suboptimal health outcomes. Resilience may blunt this effect. The effect of parental ACEs (and resilience) on coping with challenges involving their children (eg, hospitalization) is unclear. We sought to quantify ACE and resilience scores for parents of hospitalized children and evaluate their associations to parental coping after discharge.

METHODS

We conducted a prospective cohort study at a children's hospital (August 2015-May 2016). Eligible participants were English-speaking parents of children hospitalized on the Hospital Medicine or Complex Services team. The ACE questionnaire measured the responding parent's past adversity (ACE range: 0-10; ≥4 ACEs = high adversity). The Brief Resilience Scale (BRS) was used to measure their resilience (range: 1-5; higher is better). The primary outcome was measured by using the Post-Discharge Coping Difficulty Scale via a phone call 14 days post-discharge (range: 0-100; higher is worse). Associations were assessed by using multivariable linear regression, adjusting for parent- and patient-level covariates.

RESULTS

A total of 671 (81% of eligible parents) responded. Respondents were primarily women (90%), employed (66%), and had at least a high school degree (65%); 60% of children were white, 54% were publicly insured. Sixty-four percent of parents reported ≥1 ACE; 19% had ≥4 ACEs. The mean Brief Resilience Scale score for parents was 3.95. In adjusted analyses, higher ACEs and lower resilience were significantly associated with more difficulty coping after discharge.

CONCLUSIONS

More parental adversity and less resilience are associated with parental coping difficulties after discharge, representing potentially important levers for transition-focused interventions.

US pediatric trauma patient unplanned 30-day readmissions

PURPOSE

We sought to determine readmission rates and risk factors for acutely injured pediatric trauma patients.

METHODS

We produced 30-day unplanned readmission rates for pediatric trauma patients using the 2013 National Readmission Database (NRD).

RESULTS

In US pediatric trauma patients, 1.7% had unplanned readmissions within 30days. The readmission rate for patients with index operating room procedures was no higher at 1.8%. Higher readmission rates were seen in patients with injury severity scores (ISS)=16-24 (3.4%) and ISS ≥25 (4.9%). Higher rates were also seen in patients with LOS beyond a week, severe abdominal and pelvic region injuries (3.0%), crushing (2.8%) and firearm injuries (4.5%), and in patients with fluid and electrolyte disorders (3.9%). The most common readmission principal diagnoses were injury, musculoskeletal/integumentary diagnoses and infection. Nearly 39% of readmitted patients required readmission operative procedures. Most common were operations on the musculoskeletal system (23.9% of all readmitted patients), the integumentary system (8.6%), the nervous system (6.6%), and digestive system (2.5%).

CONCLUSIONS

Overall, the readmission rate for pediatric trauma patients was low. Measures of injury severity, specifically length of stay, were most useful in identifying those who would benefit from targeted care coordination resources.

LEVEL OF EVIDENCE

This is a Level III retrospective comparative study.

Repeated Critical Illness and Unplanned Readmissions Within 1 Year to PICUs

OBJECTIVES

To determine the occurrence rate of unplanned readmissions to PICUs within 1 year and examine risk factors associated with repeated readmission.

DESIGN

Retrospective cohort analysis.

SETTING

Seventy-six North American PICUs that participated in the Virtual Pediatric Systems, LLC (VPS, LLC, Los Angeles, CA).

PATIENTS

Ninety-three thousand three hundred seventy-nine PICU patients discharged between 2009 and 2010.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Index admissions and unplanned readmissions were characterized and their outcomes compared. Time-to-event analyses were performed to examine factors associated with readmission within 1 year. Eleven percent (10,233) of patients had 15,625 unplanned readmissions within 1 year to the same PICU; 3.4% had two or more readmissions. Readmissions had significantly higher PICU mortality and longer PICU length of stay, compared with index admissions (4.0% vs 2.5% and 2.5 vs 1.6 d; all p < 0.001). Median time to readmission was 30 days for all readmissions, 3.5 days for readmissions during the same hospitalization, and 66 days for different hospitalizations. Having more complex chronic conditions was associated with earlier readmission (adjusted hazard ratio, 2.9 for one complex chronic condition; hazard ratio, 4.8 for two complex chronic conditions; hazard ratio, 9.6 for three or more complex chronic conditions; all p < 0.001 compared no complex chronic condition). Most specific complex chronic condition conferred a greater risk of readmission, and some had considerably higher risk than others.

CONCLUSIONS

Unplanned readmissions occurred in a sizable minority of PICU patients. Patients with complex chronic conditions and particular conditions were at much higher risk for readmission.