National estimates of 30-day readmissions among children hospitalized for asthma in the United States

Hospital service use for young people with chronic health conditions: A population-based matched retrospective cohort study

AIM

This study aims to identify the hospitalised morbidity associated with three common chronic health conditions among young people using a population-based matched cohort.

METHODS

A population-level matched case-comparison retrospective cohort study of young people aged ≤18 years hospitalised with asthma, type 1 diabetes (T1D) or epilepsy during 2005-2018 in New South Wales, Australia using linked birth, health and mortality records. The comparison cohort was matched on age, sex and residential postcode. Adjusted rate ratios (ARR) were calculated by sex and age group.

RESULTS

There were 65 055 young people hospitalised with asthma, 6648 with epilepsy, and 2209 with T1D. Young people with epilepsy (ARR 10.95; 95% confidence interval (CI) 9.98-12.02), T1D (ARR 8.64; 95% CI 7.72-9.67) or asthma (ARR 4.39; 95% CI 4.26-4.53) all had a higher risk of hospitalisation than matched peers. Admission risk was highest for males (ARR 11.00; 95% CI 9.64-12.56) and females with epilepsy (ARR 10.83; 95% CI 9.54-12.29) compared to peers. The highest admission risk by age group was for young people aged 10-14 years (ARR 5.50; 95% CI 4.77-6.34) living with asthma, children aged ≤4 years (ARR 12.68; 95% CI 11.35-14.17) for those living with epilepsy, and children aged 5-9 years (ARR 9.12; 95% CI 7.69-10.81) for those living with T1D compared to peers.

CONCLUSIONS

The results will guide health service planning and highlight opportunities for better management of chronic health conditions, such as further care integration between acute, primary and community health services for young people.

Health Effects of the Asthma Care Program under the Universal Coverage Scheme in Children and Young Adults in Thailand

This study aimed to evaluate the effect of the asthma care program available under the Universal Coverage Scheme (UCS) in Thailand on hospital admissions per 100,000 population, its regional and seasonal variation, readmission within 28 days, and the asthma-specific fatality rate of patients aged 0–29 years in 2009–2016 compared with those in 2007–2008. A retrospective study was conducted using data sources from the UCS register and in-patient databases from the National Health Security Office (NHSO), Thailand. Hospital admissions per 100,000 population was the highest among those aged 0–4 years, but the trends decreased from 470.8 to 288.1 per 100,000 population in 2010–2014. The hospital admission rates were high in Southern Thailand and common in rainy seasons. The readmission rates within 28 days slightly decreased in all age groups in 2016 compared to those in 2007. The case fatality rate of patients aged 20–29 years decreased from 0.40% in 2007 to 0.34% in 2016. The readmission rate within 28 days and case fatality rate were the highest in patients aged 20–29 years. In conclusion, the asthma hospital admission, readmission, and case fatality rates declined over time along with the investment in the asthma care program under the UCS in Thailand. The highest hospital admission rates in patients aged 0–4 years and the readmission and case fatality rates in patients aged 20–29 years should be given more attention. Recordings of individual service utilization data in asthma patients, including quality of care provided, should be monitored to improve the asthma care system.

Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission

OBJECTIVE

To identify and contrast risk factors for six-month pediatric asthma readmissions using traditional models (Cox proportional-hazards and logistic regression) and artificial neural-network modeling.

METHODS

This retrospective cohort study of the 2013 Nationwide Readmissions Database included children 5 to 18 years old with a primary diagnosis of asthma. The primary outcome was time to asthma readmission in the Cox model, and readmission within 180 days in logistic regression. A basic neural network construction with 2 hidden layers and multiple replications considered all dataset variables and potential variable interactions to predict 180-day readmissions. Logistic regression and neural-network models were compared on area-under-the receiver-operating curve.

RESULTS

Of 18,489 pediatric asthma hospitalizations, 1858 were readmitted within 180 days. In Cox and logistic models, longer index length of stay, public insurance, and nonwinter index admission seasons were associated with readmission risk, whereas micropolitan county was protective. In neural-network modeling, 9 factors were significantly associated with readmissions. Four overlapped with the Cox model (nonwinter-month admission, long length of stay, public insurance, and micropolitan hospitals), whereas 5 were unique (age, hospital bed number, teaching-hospital status, weekend index admission, and complex chronic conditions). The area under the curve was 0.592 for logistic regression and 0.637 for the neural network.

CONCLUSIONS

Different methods can produce different readmission models. Relying on traditional modeling alone overlooks key readmission risk factors and complex factor interactions identified by neural networks.

Thirty-day unplanned readmission in hospitalised asthma patients in the USA

INTRODUCTION

Hospital quality improvement and hospital performance are commonly evaluated using parameters such as average length of stay (LOS), patient safety measures and rates of hospital readmission. Thirty-day readmission (30-DR) rates are widely used as a quality indicator and a quantifiable metric for hospitals since patients are often readmitted for the exacerbation of conditions from index admission. The quality of patient education and postdischarge care can influence readmission rates. We report the 30-DR rates of patients with asthma using a national dataset for the year 2013.

OBJECTIVES

The aim of our study was to assess the 30- day readmission (30-DR) rate as well as, the causes and predictors of readmissions.

STUDY DESIGNS/METHODS

Using the Nationwide Readmission Database (NRD) (2013), we identified primary discharge diagnoses of asthma by using International Classification of Diseases, Ninth Revision, Clinical Modification code '493'. Categorical and continuous variables were assessed by a χ² test and a Student's t-test, respectively. The independent predictors of unplanned 30-DR were detected by multivariate analysis. We used sampling weights, which are provided in the NRD, to generate the national estimates.

RESULTS

There were 130 490 (weighted N=311 173) inpatient asthma admissions during 2013. The overall 30-DR for asthma was 11.9%. The associated factors for 30-DR were age 45-84 years (40.32% vs 29.05%; p<0.001), enrolment in Medicare (49.33% vs 30.61% p<0.001), extended LOS (mean, 4.40±0.06 vs 3.25±0.04 days; p<0.001), higher mean cost (US$8593.91 vs US$6741.31; p<0.001) and higher disposition against medical advice (DAMA) (4.14% vs 1.51%; p<0.001). The factors that increased the chance of 30-DR were advanced age (≥45-64 vs ≤17 years; OR 4.61, 95% CI 4.04 to 5.27, p<0.0001), male sex (OR 1.19, 95% CI 1.13 to 1.26, p<0.0001), a higher Charlson Comorbidity Index (CCI) (OR 1.16, 95% CI 1.14 to 1.18, p<0.0001), DAMA (OR 2.32, 95% CI 2.08 to 2.59, p<0.0001), non-compliance with medication (OR 1.34, 95% CI 1.24 to 1.46, p<0.0001), post-traumatic stress disorder (OR 1.48, 95% CI 1.22 to 1.79, p<0.0001), alcohol use (OR 1.45, 95% CI 1.27 to 1.65, p<0.0001), gastro-oesophageal reflux disease (OR 1.20, 95% CI 1.14 to 1.27, p<0.0001), obstructive sleep apnoea (OR 1.11, 95% CI 1.03 to 1.18, p<0.0042) and hypertension (OR 1.11, 95% CI 1.06 to 1.17, p<0.0001).

CONCLUSIONS

We found that the overall 30-DR rate for asthma was 11.9% all-cause readmission. Major causes of 30-DR were asthma exacerbation (36.74%), chronic obstructive pulmonary disease (11.47%), respiratory failure (6.46%), non-specific pneumonia (6.19%), septicaemia (3.61%) and congestive heart failure (3.32%). One-fourth of the revisits occurred in the first week, while half of the revisits took place in the first 2 weeks. Education regarding illness and the importance of medicine compliance could play a significant role in preventing asthma-related readmission.

Risk Factors for Pediatric Asthma Readmissions: A Systematic Review

OBJECTIVE

To systematically review the literature on pediatric asthma readmission risk factors.

STUDY DESIGN

We searched PubMed/MEDLINE, CINAHL, Scopus, PsycINFO, and Cochrane Central Register of Controlled Trials for published articles (through November 2019) on pediatric asthma readmission risk factors. Two authors independently screened titles and abstracts and consensus was reached on disagreements. Full-text articles were reviewed and inclusion criteria applied. For articles meeting inclusion criteria, authors abstracted data on study design, patient characteristics, and outcomes, and 4 authors assessed bias risk.

RESULTS

Of 5749 abstracts, 74 met inclusion criteria. Study designs, patient populations, and outcome measures were highly heterogeneous. Risk factors consistently associated with early readmissions (≤30 days) included prolonged length of stay (OR range, 1.1-1.6) and chronic comorbidities (1.7-3.2). Risk factors associated with late readmissions (>30 days) included female sex (1.1-1.6), chronic comorbidities (1.5-2), summer discharge (1.5-1.8), and prolonged length of stay (1.04-1.7). Across both readmission intervals, prior asthma admission was the most consistent readmission predictor (1.3-5.4).

CONCLUSIONS

Pediatric asthma readmission risk factors depend on the readmission interval chosen. Prior hospitalization, length of stay, sex, and chronic comorbidities were consistently associated with both early and late readmissions.

TRIAL REGISTRATION

CRD42018107601.

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.

Unplanned Readmissions of Children With Epilepsy in the United States

BACKGROUND

The burden and characteristics of unplanned readmission after epilepsy-related discharge in children in the United States is not known.

METHODS

We undertook a retrospective cohort study of children aged one to 17 years discharged after a nonelective hospitalization for epilepsy, sampled from the Healthcare Cost and Utilization Project's 2013 and 2014 Nationwide Readmissions Database. Descriptive statistics and logistic regression models were used to examine the characteristics of initial hospitalization and risk factors for readmission.

RESULTS

A total of 42,873 admissions for unique patients were identified, with 4470 (10.4%) leading to readmission within 30 days. The most common readmission diagnosis was epilepsy (24.9%). Neurodevelopmental diagnoses including cerebral palsy, intellectual disability, and developmental delay were associated with increased odds of readmission. Longer hospitalization, gastrostomy, and tracheostomy were also associated with readmission, but continuous electroencephalography use was not. Children insured by Medicare had a readmission rate of 34.4%, whereas there were no associations of readmission with other sociodemographic characteristics such as neighborhood, income, and sex.

CONCLUSIONS

Seizures are among the most frequent reasons for hospitalization in children. Establishing a benchmark readmission rate for pediatric epilepsy of 10.4% may be useful to health systems designing quality improvement efforts. Clinical factors were more strongly associated with readmission than demographic characteristics. Interventions to reduce pediatric epilepsy readmissions may have the highest yield when targeting children with neurodevelopmental comorbidities.

Risk factors for readmission of children hospitalized with acute asthma attacks in South China

OBJECTIVE

Asthma seriously endangers the health of children. Re-hospitalization for childhood asthma consumes a large amount of medical and health resources. The present study aimed to assess the rates and risk factors for hospital readmission of children with acute asthma attacks in south China.

METHODS

We retrospectively enrolled 1702 acute asthma patients aged < 18 years from 1/1/2007 to 12/31/2015. Patients who were readmitted within one year after index hospital discharge were divided into a readmission group. Index hospitalization data were acquired from clinical records. Chi-square tests and multivariate logistic regression analyses were used to evaluate the risk factors.

RESULTS

There were 90 (5.29%) readmitted patients from 1702 acute asthma patients. Considering the large sample differences between readmitted and single-admitted patients, 123 single-admitted patients (1.5-fold as many as the readmitted group) were randomly selected. Further univariate Chi-square tests and multivariate logistic regression analyses showed that the predictors of readmission included eczema history (odds ratio (OR) 3.122, p = 0.003) and mycoplasma pneumoniae (MP) antibody immunoglobulin M(IgM) (OR 2.386, p = 0.037).

CONCLUSIONS

Among the children admitted to a hospital in south China with acute asthma attacks, 5.29% were readmitted within the following year. Patients with history of eczema and positive MP antibody IgM had significantly increased the chances of one-year readmission, underlining the importance of targeted long-term postdischarge follow-up of these children.

Influence of a Comorbid Diagnosis of Seizure on 30-Day Readmission Rates Following Hospitalization for an Index Stroke

OBJECTIVE

To examine the association of a comorbid seizure diagnosis with early hospital readmission rates following an index hospitalization for stroke in the United States.

METHODS

Retrospective analysis of the 2014 National Readmission Database. The study population included adult patients (age >18 years old) with stroke, identified using the International Classification of Disease Ninth Revision, Clinical Modification (ICD-9-CM) codes 433.X1, 434.X1, and 436 for ischemic stroke as well as 430, 431, 432.0, 432.1, and 432.9 for hemorrhagic stroke. A subgroup of patients with a secondary discharge diagnosis of seizures was identified using the ICD-9-CM codes 780.39 and 345.X. We computed all-cause 30-day readmission rates for all strokes and by stroke type (ischemic versus hemorrhagic). Finally, we used a multivariable logistic regression model to examine the independent association between seizure and readmission by stroke type.

RESULTS

Of 271,148 stroke patients, 6.3% (16,970) had a secondary discharge diagnosis of seizures including 5.0% (11,562) of patients with ischemic stroke and 13.4% (5,409) with hemorrhagic stroke. Overall readmission rate for stroke patients was 11.9% (hemorrhagic stroke: 14.2% versus ischemic strokes: 11.6%). Thirty-day readmission rate was higher in patients with seizures for all strokes (15.6% versus 11.7%, P value <.001), ischemic strokes (15.0% versus11.4%, P value <.001), and hemorrhagic strokes (16.7% versus 13.8%, P value <.001). After adjusting for several patient-specific and healthcare system-specific confounders, hospitalized stroke patients with comorbid seizure diagnosis were more likely than those without seizures to be readmitted within 30 days (OR: 1.20, 95% CI: 1.14-1.25).

CONCLUSION

The presence of a comorbid diagnosis of seizure disorder in a hospitalized stroke patient significantly raises the occurrence of early hospital readmission in the United States.

Does participation in the community outreach for asthma care and healthy lifestyles (COACH) program alter subsequent use of hospital services for children discharged with asthma?

Introduction: Transition from hospital to home is a challenging time for children with asthma and their caregivers because of the high risk for reutilization of acute hospital services. Detecting effective quality improvement initiatives to reduce utilization of urgent services in children discharged with asthma is an important clinical and public health question. This study was designed to identify the role of a multimodal, nurse-driven, inpatient initiated Community Outreach for Asthma Care and Healthy lifestyles (COACH) program on subsequent use of hospital services for pediatric patients with asthma.Methods: We utilized comparative effectiveness design to identify the difference in recurrent emergency department (ED) visits and/or admissions within 12-months after discharge between patients with asthma who engaged in the COACH program (Intervention group) and those who did not (Comparison group). We used administrative databases of hospitals included in the Meridian Health system to identify the number of and time to asthma-related readmissions and ED re-attendances.Results: We found no difference in the rate or number of recurrent hospital-based services used within 12 months, but found a reduction in ED re-visitation and/or readmission within 30 days for COACH program participants prior to and after adjustment for age, race/ethnicity, insurance status, and clinical presentation (Odd Ratio 0.44, 95% Confidence Interval 0.20, 0.93).Conclusion: Participation in the COACH program decreases the likelihood for subsequent use of hospital services within a month of discharge for children with asthma. Enhanced post-discharge interactions with families may reduce long-term reuse of hospital-based services for COACH program participants.

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.

Association between blood eosinophil count and risk of readmission for patients with asthma: Historical cohort study

Background

Recent studies have demonstrated an association between high blood eosinophil counts and greater risk of asthma exacerbations. We sought to determine whether patients hospitalized for an asthma exacerbation were at greater risk of readmission if they had a high blood eosinophil count documented before the first hospitalization.

Methods

This historical cohort study drew on 2 years of medical record data (Clinical Practice Research Datalink with Hospital Episode Statistics linkage) of patients (aged ≥5 years) admitted to hospital in England for asthma, with recorded blood eosinophil count within 1 baseline year before admission. We analyzed the association between high blood eosinophil count (≥0.35x10⁹ cells/L) and readmission risk during 1 year of follow-up after hospital discharge, with adjustment for predefined, relevant confounders using forward selection.

Results

We identified 2,613 eligible patients with asthma-related admission, of median age 51 years (interquartile range, 36–69) and 76% women (1,997/2,613). Overall, 835/2,613 (32.0%) had a preadmission high blood eosinophil count. During the follow-up year, 130/2,613 patients (5.0%) were readmitted for asthma, including 55/835 (6.6%) with vs. 75/1,778 (4.2%) without high blood eosinophil count at baseline (adjusted hazard ratio [HR] 1.49; 95% CI 1.04–2.13, p = 0.029). The association was strongest in never-smokers (n = 1,296; HR 2.16, 95% CI 1.27–3.68, p = 0.005) and absent in current smokers (n = 547; HR 1.00, 95% CI 0.49–2.04, p = 0.997).

Conclusions

A high blood eosinophil count in the year before an asthma-related hospitalization is associated with increased risk of readmission within the following year. These findings suggest that patients with asthma and preadmission high blood eosinophil count require careful follow-up, with treatment optimization, after discharge.