Characteristics and predictors of 7- and 30-day hospital readmissions to pediatric neurology

Drug-related risk of hospital readmission in children with chronic diseases, a systematic review

BACKGROUND

Drug-related problems (DRPs) are one of the leading causes of hospital readmissions. Children with chronic diseases are more likely to experience DRPs than adults. The burden and characteristics of drug-related readmissions at and after hospital discharge in children remain unclear.

OBJECTIVE

We aimed to summarize the impact of DRPs at and after hospital discharge on the risk of readmissions in children with chronic diseases.

METHODS

We conducted a systematic review searching PubMed from inception until January 2022. Study selection criteria were studies assessing the impact of different factors at discharge and after discharge on the risk of hospital readmissions in children with chronic diseases, reporting an assessment of DRPs. DRP could be the only risk factor assessed or one among others. Included studies were assessed with the Risk of Bias in Non-Randomized Studies - of Exposure (ROBINS-E) tool. We summarized the qualitative impact of the reported DRPs on hospital readmission as conclusive (significant association) or inconclusive.

RESULTS

Of the 4734 studies initially identified, 13 met inclusion criteria. Eleven studies were retrospective, using electronic health records. The studies assessed the impact of DRPs at or after discharge according to the type of medication (in 6 studies), number of medication (in 5 studies) and medication nonadherence (in 2 studies). From the 44 reported associations between DRPs and the risk of readmission 26 (59% [95% CI, 43%-73%]) were conclusive, of which 81% increased the risk and 19% decreased the risk, and 17 (39% [95% CI, 24%-55%]) were inconclusive.

CONCLUSION

The impact of DRPs on hospital readmissions in children with chronic diseases displayed conflicting results, estimated associations having potentially a serious risk of bias. We need more evidence with a lower risk of bias.

Performance Characteristics of a Machine-Learning Tool to Predict 7-Day Hospital Readmissions

OBJECTIVES

To develop an institutional machine-learning (ML) tool that utilizes demographic, socioeconomic, and medical information to stratify risk for 7-day readmission after hospital discharge; assess the validity and reliability of the tool; and demonstrate its discriminatory capacity to predict readmissions.

PATIENTS AND METHODS

We performed a combined single-center, cross-sectional, and prospective study of pediatric hospitalists assessing the face and content validity of the developed readmission ML tool. The cross-sectional analyses used data from questionnaire Likert scale responses regarding face and content validity. Prospectively, we compared the discriminatory capacity of provider readmission risk versus the ML tool to predict 7-day readmissions assessed via area under the receiver operating characteristic curve analyses.

RESULTS

Overall, 80% (15 of 20) of hospitalists reported being somewhat to very confident with their ability to accurately predict readmission risk; 53% reported that an ML tool would influence clinical decision-making (face validity). The ML tool variable exhibiting the highest content validity was history of previous 7-day readmission. Prospective provider assessment of risk of 413 discharges showed minimal agreement with the ML tool (κ = 0.104 [95% confidence interval 0.028-0.179]). Both provider gestalt and ML calculations poorly predicted 7-day readmissions (area under the receiver operating characteristic curve: 0.67 vs 0.52; P = .11).

CONCLUSIONS

An ML tool for predicting 7-day hospital readmissions after discharge from the general pediatric ward had limited face and content validity among pediatric hospitalists. Both provider and ML-based determinations of readmission risk were of limited discriminatory value. Before incorporating similar tools into real-time discharge planning, model calibration efforts are needed.

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.

Length of Stay in Pediatric Neurology Hospital Admissions

OBJECTIVE

To describe inpatient length of stay patterns, identify key drivers related to prolonged length of stay, and evaluate the relationship between length of stay and readmission in pediatric neurology.

METHODS

This was a retrospective review of patients <19 years old admitted with a principal neurologic diagnosis to our hospital between January 2017 and July 2019. Scheduled admissions and hospital admissions lasting >30 days were excluded from analysis. Length of stay was obtained in addition to demographic characteristics, principal discharge diagnosis, multispecialty care, use of multiple antiseizure medications, inpatient hospital costs (ie, claims paid), and pediatric intensive care unit (ICU) admission for unplanned admissions and 7- and 30-day readmissions.

RESULTS

There were a total of 1579 unplanned admissions. The most common reasons for admission were seizure (n = 942), headache (n = 161), other neurologic diagnosis (n = 121), and psychiatric disorders/functional neurologic disorder (n = 60). Children admitted to the hospital for a neurologic condition have an average length of stay of 2.8±5.0 days for unplanned admissions, 4.5±7.4 days for 7-day readmissions, and 5.2±7.5 days for 30-day readmissions. Average inpatient hospital costs were $44 075±56 976 for unplanned admissions, $60 361±71 427 for 7-day readmissions, and $55 434±56 442 for 30-day readmissions. Prolonged length of stay and increased hospital costs were associated with pediatric ICU admission, multispecialty care, 7- and 30-day readmission, multiple antiseizure medications, and psychiatric disorders / functional neurologic disorders.

CONCLUSIONS

Pediatric ICU admission, multispecialty care, readmission, multiple antiseizure medications, and psychiatric disorder / functional neurologic disorder prolong length of stay and increase hospital costs.

Targeted Clinical Interventions for Reducing Pediatric Readmissions

BACKGROUND

In this interventional study, we addressed the selection and application of clinical interventions on pediatric patients identified as at risk by a predictive model for readmissions.

METHODS

A predictive model for readmissions was implemented, and a team of providers expanded corresponding clinical interventions for at-risk patients at a freestanding children's hospital. Interventions encompassed social determinants of health, outpatient care, medication reconciliation, inpatient and discharge planning, and postdischarge calls and/or follow-up. Statistical process control charts were used to compare readmission rates for the 3-year period preceding adoption of the model and clinical interventions with those for the 2-year period after adoption of the model and clinical interventions. Potential financial savings were estimated by using national estimates of the cost of pediatric inpatient readmissions.

RESULTS

The 30-day all-cause readmission rates during the periods before and after predictive modeling (and corresponding 95% confidence intervals [CI]) were 12.5% (95% CI: 12.2%-12.8%) and 11.1% (95% CI: 10.8%-11.5%), respectively. More modest but similar improvements were observed for 7-day readmissions. Statistical process control charts indicated nonrandom reductions in readmissions after predictive model adoption. The national estimate of the cost of pediatric readmissions indicates an associated health care savings due to reduced 30-day readmission during the 2-year predictive modeling period at $2 673 264 (95% CI: $2 612 431-$2 735 364).

CONCLUSIONS

A combination of predictive modeling and targeted clinical interventions to improve the management of pediatric patients at high risk for readmission was successful in reducing the rate of readmission and reducing overall health care costs. The continued prioritization of patients with potentially modifiable outcomes is key to improving patient outcomes.

Predictors of pediatric readmissions among patients with neurological conditions

Background

Unplanned readmission is one of many measures of the quality of care of pediatric patients with neurological conditions. In this multicenter study, we searched for novel risk factors of readmission of patients with neurological conditions.

Methods

We retrieved hospitalization data of patients less than 18 years with one or more neurological conditions. This resulted in a total of 105,834 encounters from 18 hospitals. We included data on patient demographics, prior healthcare resource utilization, neurological conditions, number of other conditions/diagnoses, number of medications, and number of surgical procedures performed. We developed a random intercept logistic regression model using stepwise minimization of Akaike Information Criteria for variable selection.

Results

The most important neurological conditions associated with unplanned pediatric readmissions include hydrocephalus, inflammatory diseases of the central nervous system, sleep disorders, disease of myoneural junction and muscle, other central nervous system disorder, other spinal cord conditions (such as vascular myelopathies, and cord compression), and nerve, nerve root and plexus disorders. Current and prior healthcare resource utilization variables, number of medications, other diagnoses, and certain inpatient surgical procedures were associated with changes in odds of readmission. The area under the receiver operator characteristic curve (AUROC) on the independent test set is 0.733 (0.722, 0.743).

Conclusions

Pediatric patients with certain neurological conditions are more likely to be readmitted than others. However, current and prior healthcare resource utilization remain some of the strongest indicators of readmission within this population as in the general pediatric population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-020-02028-0.

Serious Diagnoses for Headaches After ED Discharge

BACKGROUND

Headache is a common complaint among children presenting to the emergency department (ED) and can be due to serious neurologic and nonneurologic diagnoses (SNNDs). We sought to characterize the children discharged from the ED with headache found to have SNNDs at revisits.

METHODS

We performed a multicenter retrospective cohort study using data from 45 pediatric hospitals from October 1, 2015, to March 31, 2019. We included pediatric patients (≤18 years) discharged from the ED with a principal diagnosis of headache, excluding patients with concurrent or previous SNNDs or neurosurgeries. We identified rates and types of SNNDs diagnosed within 30 days of initial visit and compared these rates with those of control groups defined as patients with discharge diagnoses of cough, chest pain, abdominal pain, and soft tissue complaints.

RESULTS

Of 121 621 included patients (57% female, median age 12.4 years, interquartile range: 8.8-15.4), 608 (0.5%, 95% confidence interval: 0.5%-0.5%) were diagnosed with SNNDs within 30 days. Most were diagnosed at the first revisit (80.8%); 37.5% were diagnosed within 7 days. The most common SNNDs were benign intracranial hypertension, cerebral edema and compression, and seizures. A greater proportion of patients with SNNDs underwent neuroimaging, blood, and cerebrospinal fluid testing compared with those without SNNDs (P < .001 for each). The proportion of SNNDs among patients diagnosed with headache (0.5%) was higher than for control cohorts (0.0%-0.1%) (P < .001 for each).

CONCLUSIONS

A total 0.5% of pediatric patients discharged from the ED with headache were diagnosed with an SNND within 30 days. Further efforts to identify at-risk patients remain a challenge.

Quality assessment of weekend discharge: a systematic review and meta-analysis

PURPOSE

Hospital bed utility and length of stay affect the healthcare budget and quality of patient care. Prior studies already show admission and operation on weekends have higher mortality rates compared with weekdays, which has been identified as the 'weekend effect.' However, discharges on weekends are also linked with quality of care, and have been evaluated in the recent decade with different dimensions. This meta-analysis aims to discuss weekend discharges associated with 30-day readmission, 30-day mortality, 30-day emergency department visits and 14-day follow-up visits compared with weekday discharges.

DATA SOURCES

PubMed, EMBASE, Cochrane Library and ClinicalTrials.gov were searched from January 2000 to November 2019.

STUDY SELECTION

Preferred reporting items for systematic reviews and meta-analyses guidelines were followed. Only studies published in English were reviewed. The random-effects model was applied to assess the effects of heterogeneity among the selected studies.

DATA EXTRACTION

Year of publication, country, sample size, number of weekday/weekend discharges, 30-day readmission, 30-day mortality, 30-day ED visits and 14-day appointment follow-up rate.

RESULTS OF DATA SYNTHESIS

There are 20 studies from seven countries, including 13 articles from America, in the present meta-analysis. There was no significant difference in odds ratio (OR) in 30-day readmission, 30-day mortality, 30-day ED visit, and 14-day follow-up between weekday and weekend. However, the OR for 30-day readmission was significantly higher among patients in the USA, including studies with high heterogeneity.

CONCLUSION

In the USA, the 30-day readmission rate was higher in patients who had been discharged on the weekend compared with the weekday. However, interpretation should be cautious because of data limitation and high heterogeneity. Further intervention should be conducted to eliminate any healthcare inequality within the healthcare system and to improve the quality of patient care.

Quality improvement strategies improve pediatric neurology inpatient discharges before noon

Background

Early hospital discharge is an important quality improvement (QI) measure that has not been well studied in pediatric neurology. The objective of our study was to implement strategies to improve hospital discharge times for patients admitted to the pediatric neurology service.

Methods

This was a pilot QI study of hospital discharge before noon (DBN) in pediatric neurology patients admitted to a tertiary care children's hospital. The study duration was 6 months (12/2017-05/2018)-first 3 months preintervention and next 3 months postintervention. Strategies focusing on preidentifying MRI candidates and those needing home care services, identifying pharmacy preference, reviewing overnight video EEGs first thing in the morning, and implementing morning huddles, etc., were implemented. Demographic and clinical data were collected, including age, sex, race, and reasons for delay in discharge. Chi-square, t test, and survival analysis (log-rank test) were performed to determine differences between baseline and post-QI implementation.

Results

One hundred ninety-one patients were included in the study. There were 76 participants before the implementation of the study and 115 participants during the study. DBN percentage increased in the intervention period, from a baseline of 40.7% to 60.8%. Survival analysis showed that the discharge time after QI implementation improved significantly (p = 0.043).

Conclusions

Our study successfully identified the factors associated with late discharge and developed effective strategies to improve DBN in an inpatient pediatric neurology setting.