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

Hospital readmission following acute illness among children 2-23 months old in sub-Saharan Africa and South Asia: a secondary analysis of CHAIN cohort

Background

Children in low and middle-income countries remain vulnerable following hospital-discharge. We estimated the incidence and correlates of hospital readmission among young children admitted to nine hospitals in sub-Saharan Africa and South Asia.

Methods

This was a secondary analysis of the CHAIN Network prospective cohort enrolled between 20th November 2016 and 31st January 2019. Children aged 2-23 months were eligible for enrolment, if admitted for an acute illness to one of the study hospitals. Exclusions were requiring immediate resuscitation, inability to tolerate oral feeds in their normal state of health, had suspected terminal illness, suspected chromosomal abnormality, trauma, admission for surgery, or their parent/caregiver was unwilling to participate and attend follow-up visits. Data from children discharged alive from the index admission were analysed for hospital readmission within 180-days from discharge. We examined ratios of readmission to post-discharge mortality rates. Using models with death as the competing event, we evaluated demographic, nutritional, clinical, and socioeconomic associations with readmission.

Findings

Of 2874 children (1239 (43%) girls, median (IQR) age 10.8 (6.8-15.6) months), 655 readmission episodes occurred among 506 (18%) children (198 (39%) girls): 391 (14%) with one, and 115 (4%) with multiple readmissions, with a rate of: 41.0 (95% CI 38.0-44.3) readmissions/1000 child-months. Median time to readmission was 42 (IQR 15-93) days. 460/655 (70%) and 195/655 (30%) readmissions occurred at index study hospital and non-study hospitals respectively. One-third (N = 213/655, 33%) of readmissions occurred within 30 days of index discharge. Sites with fewest readmissions had the highest post-discharge mortality. Most readmissions to study hospitals (371/450, 81%) were for the same illness as the index admission. Age, prior hospitalisation, chronic conditions, illness severity, and maternal mental health score, but not sex, nutritional status, or physical access to healthcare, were associated with readmission.

Interpretation

Readmissions may be appropriate and necessary to reduce post-discharge mortality in high mortality settings. Social and financial support, training on recognition of serious illness for caregivers, and improving discharge procedures, continuity of care and facilitation of readmission need to be tested in intervention studies. We propose the ratio of readmission to post-discharge mortality rates as a marker of overall post-discharge access and care.

Funding

The Bill & Melinda Gates Foundation (OPP1131320).

Birth Weight and Gestational Age as Modifiers of Rehospitalization after Neonatal Intensive Care Unit Admission

OBJECTIVE

 This study aimed to assess interaction effects between gestational age and birth weight on 30-day unplanned hospital readmission following discharge from the neonatal intensive care unit (NICU).

STUDY DESIGN

 This is a retrospective study that uses the study site's Children's Hospitals Neonatal Database and electronic health records. Population included patients discharged from a NICU between January 2017 and March 2020. Variables encompassing demographics, gestational age, birth weight, medications, maternal data, and surgical procedures were controlled for. A statistical interaction between gestational age and birth weight was tested for statistical significance.

RESULTS

 A total of 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge. Statistical interaction between birth weight and gestational age was statistically significant, indicating that the odds of readmission among low birthweight premature patients increase with increasing gestational age, whereas decrease with increasing gestational age among their normal or high birth weight peers.

CONCLUSION

 The effect of gestational age on odds of hospital readmission is dependent on birth weight.

KEY POINTS

· Population included patients discharged from a NICU between January 2017 and March 2020.. · A total of 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge.. · The effect of gestational age on odds of hospital readmission is dependent on birth weight..

Machine learning for hospital readmission prediction in pediatric population

BACKGROUND AND OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Factors associated with hospital admission and 30-day readmission for children less than 18 years of age in 2018 in France: a one-year nationwide observational study

BACKGROUND

Nationwide data for children for short-stay hospitalisation (SSH) and associated factors are scarce. This retrospective study of children in France < 18 years of age followed after their birth or birthday in 2018 focused on at least one annual SSH, stay < 1 night or ≥ 1 night, or 30-day readmission ≥ 1 night.

METHODS

Children were selected from the national health data system (SNDS), which includes data on long-term chronic disease (LTD) status with full reimbursement and complementary universal coverage based on low household income (CMUC). Uni and multivariate quasi-Poisson regression were applied for each outcome.

RESULTS

Among 13.211 million children (94.4% population, 51.2% boys), CMUC was identified for 17.5% and at least one LTD for 4% (0-<1 year: 1.5%; 14-<18 year: 5.2%). The most frequent LTDs were pervasive developmental diseases (0.53%), asthma (0.24%), epilepsy (0.17%), and type 1 diabetes (0.15%). At least one SSH was found for 8.8%: SSH < 1 night (4.9%), SSH ≥ 1 night (4.5%), readmission (0.4%). Children with at least one SSH were younger (median 6 vs. 9 years) and more often had CMUC (21%), a LTD (12%), an emergency department (ED) visit (56%), or various primary healthcare visits than all children. Those with a SSH ≥1 night vs. < 1 night were older (median: 9 vs. 4 years). They had the same frequency of LTD (13.4%) but more often an ED visit (78% vs. 42%). Children with readmissions were younger (median 3 years). They had the highest levels of CMUC (29.3%), LTD (34%), EDs in their municipality (35% vs. 29% for the whole population) and ED visits (87%). In adjusted analysis, each outcome was significantly less frequent among girls than boys and more frequent for children with CMUC. LTDs with the largest association with SSH < 1 night were cystic fibrosis, sickle cell diseases (SCD), diabetes type 1, those with SSH ≥1 night type 1 diabetes epilepsy and SCD, and those for readmissions lymphoid leukaemia, malignant neoplasm of the brain, and SCD. Among all SSH admissions of children < 10 years, 25.8% were potentially preventable.

CONCLUSION

Higher SSH and readmission rates were found for children with certain LTD living in low-income households, suggesting the need or increase of specific policy actions and research.

The impact of unplanned 30-day readmission as a quality indicator in pediatric surgery

Introduction

Hospital readmission is one of the indicators used to assess quality of service provided in healthcare. Based on accumulated knowledge, risk management teams assess data related to readmissions to find curative solutions for underlying factors. The current article's aim is investigating readmission routes within the workplace in paediatric surgery service during the first 30 days post discharge from Mater Dei Hospital (MDH).

Materials and method

A retrospective study of children's hospital readmissions between October 2017 and November 2019 was performed, strictly before COVID-19 pandemic. Demographics and clinical records including age, gender, pre-existing comorbidities, diagnosis during primary admission and readmission, procedure carried out, ASA grade, length of stay, and outcomes were collected. All children re-admitted under a single paediatric surgical department within 30 days from initial admission to tertiary referral hospital were included. Patients undergoing emergency visitation without subsequent admissions were excluded. Readmissions were classified into cohorts: elective and emergency, depending on the nature of primary admission. Contributing factors and outcomes were compared.

Results

935 surgical admissions (221 elective and 714 emergencies) were registered at MDH over the given period, with an average hospital stay of 3.62 days. Total readmission rate was 1.7% (n = 16). 25% (n = 4) of readmissions were post elective, 75% (n = 12) post emergency admission, with an average stay of 4.37 days and no mortalities. 43.7% (n = 7) were re-admissions post-surgical intervention. Further surgical interventions were necessary in 25% (n = 4) of readmitted patients, the remainder (n = 12) treated conservatively.

Conclusion

Published reports concerning paediatric surgical readmission rates are limited, challenging healthcare systems. Most readmissions area voidable; therefore, healthcare workers must provide adequate strategies tailored to their resources, efficient multidisciplinary approaches with improved communication to decrease morbidity and prevent readmissions.

Risk factors associated with 30-day unplanned hospital readmission for patients with mental illness

Unplanned hospital readmission rate is up to 43% in mental health settings, which is higher than in general health settings. Unplanned readmissions delay the recovery of patients with mental illness and add financial burden on families and healthcare services. There have been efforts to reduce readmissions with a particular interest in identifying patients at higher readmission risk after index admission; however, the results have been inconsistent. This systematic review synthesized risk factors associated with 30-day unplanned hospital readmissions for patients with mental illness. Eleven electronic databases were searched from 2010 to 30 September 2021 using key terms of 'mental illness', 'readmission' and 'risk factors'. Sixteen studies met the selection criteria for this review. Data were synthesized using content analysis and presented in narrative and tabular form because the extracted risk factors could not be pooled statistically due to methodological heterogeneity of the included studies. Consistently cited readmission predictors were patients with lower educational background, unemployment, previous mental illness hospital admission and more than 7 days of the index hospitalization. Results revealed the complexity of identifying unplanned hospital readmission predictors for people with mental illness. Policymakers need to specify the expected standards that written discharge summary must reach general practitioners concurrently at discharge. Hospital clinicians should ensure that discharge summary summaries are distributed to general practitioners for effective ongoing patient care and management. Having an advanced mental health nurse for patients during their transition period needs to be explored to understand how this role could ensure referrals to the general practitioner are eventuated.

Anticholinergic Medications and Parent-Reported Anticholinergic Symptoms in Neurologically Impaired Children

CONTEXT

Children with severe neurological impairment and polypharmacy are exposed to anticholinergic (AC) medications that may have anticholinergic side effects, but this is understudied. Anticholinergic Cognitive Burden (ACB) scores measure total anticholinergic burden for a medication regimen, and scores ≥3 have been associated with increased morbidity and mortality in adults.

OBJECTIVE

We assessed the relationship between ACB scores and parent-reported anticholinergic symptoms in children.

METHODS

Cross-sectional study of patients one to 18 years-old with ICD-defined severe neurological impairment and polypharmacy. At routine clinical visits, total ACB scores were computed for all medications. Parent-reported AC symptoms (constipation, drowsiness, difficulty concentrating, dry mouth, or urinary problems) were assessed. Multivariable logistic regression was used to test the association between total ACB scores ≥3 for scheduled medications and the presence of AC symptoms, adjusted for age and recent acute healthcare utilization.

RESULTS

Among 123 unique patients, 87% were prescribed AC medications. Common AC medication classes included: systemic antihistamines (64%), anxiolytics (53%), antidepressants (30%), H2 blockers (22%), and muscle relaxants (20%). Total ACB scores ≥3 were observed in 44% for scheduled medications and in 63% of patients for scheduled plus PRN medications. Total ACB scores ≥3 were significantly associated with an increased odds of ≥1 anticholinergic symptoms for scheduled medications (OR: 3.1; 95% CI: 1.4, 6.7) and for scheduled plus PRN medications (OR: 2.9; 95% CI: 1.3, 6.4).

CONCLUSION

If replicated in larger populations, the association between elevated total ACB scores and anticholinergic side effects in children should prompt clinicians to consider deprescribing potentially unneeded anticholinergic medications.

Risk factors associated with 31-day unplanned hospital readmission in newborns: a systematic review

The purpose of this study is to synthesize evidence on risk factors associated with newborn 31-day unplanned hospital readmissions (UHRs). A systematic review was conducted searching CINAHL, EMBASE (Ovid), and MEDLINE from January 1st 2000 to 30th June 2021. Studies examining unplanned readmissions of newborns within 31 days of discharge following the initial hospitalization at the time of their birth were included. Characteristics of the included studies examined variables and statistically significant risk factors were extracted from the inclusion studies. Extracted risk factors could not be pooled statistically due to the heterogeneity of the included studies. Data were synthesized using content analysis and presented in narrative and tabular form. Twenty-eight studies met the eligibility criteria, and 17 significant risk factors were extracted from the included studies. The most frequently cited risk factors associated with newborn readmissions were gestational age, postnatal length of stay, neonatal comorbidity, and feeding methods. The most frequently cited maternal-related risk factors which contributed to newborn readmissions were parity, race/ethnicity, and complications in pregnancy and/or perinatal period.

CONCLUSION

This systematic review identified a complex and diverse range of risk factors associated with 31-day UHR in newborn. Six of the 17 extracted risk factors were consistently cited by studies. Four factors were maternal (primiparous, mother being Asian, vaginal delivery, maternal complications), and two factors were neonatal (male infant and neonatal comorbidities). Implementation of evidence-based clinical practice guidelines for inpatient care and individualized hospital-to-home transition plans, including transition checklists and discharge readiness assessments, are recommended to reduce newborn UHRs.

WHAT IS KNOWN

• Attempts have been made to identify risk factors associated with newborn UHRs; however, the results are inconsistent.

WHAT IS NEW

• Six consistently cited risk factors related to newborn 31-day UHRs. Four maternal factors (primiparous, mother being Asian, vaginal delivery, maternal complications) and 2 neonatal factors (male infant and neonatal comorbidities). • The importance of discharge readiness assessment, including newborn clinical fitness for discharge and parental readiness for discharge. Future research is warranted to establish standardised maternal and newborn-related variables which healthcare providers can utilize to identify newborns at greater risk of UHRs and enable comparison of research findings.

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

OBJECTIVES

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

DESIGN

Systematic review.

DATA SOURCE

CINAHL, Embase and PubMed up to 7 October 2021.

ELIGIBILITY CRITERIA

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

DATA EXTRACTION AND SYNTHESIS

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

RESULTS

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

CONCLUSION

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

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

OBJECTIVE

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSION

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

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

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

Nurse-Caregiver Communication of Hospital-To-Home Transition Information at a Tertiary Pediatric Hospital in Western Australia: A Multi-Stage Qualitative Descriptive Study

PURPOSE

To observe and describe nurse-caregiver communication of hospital-to-home transition information at the time of discharge at a tertiary children's hospital of Western Australia.

DESIGN AND METHODS

A multi-stage qualitative descriptive design involved 31 direct clinical observations of hospital-to-home transition experiences, and semi-structured interviews with 20 caregivers and 12 nurses post-discharge. Eleven caregivers were re-interviewed 2-4 weeks post-discharge. Transcripts of audio recordings and field notes were analyzed using content analysis. Medical records were examined to determine patients' usage of hospital services within 30 days of discharge.

RESULTS

Four themes emerged from the content analysis: structure of hospital-to-home transition information; transition information delivery; readiness for discharge; and recovery experience post-hospital discharge. Examination of medical records found seven patients presented to the Emergency Department within 2-19 days post-discharge, of which three were readmitted. Primary caregivers of three readmitted patients all had limited English proficiency.

CONCLUSION

The study affirmed the complexity of transitioning pediatric patients from hospital to home. Inconsistent content and delivery of information impacted caregivers' perception of readiness for discharge and the recovery experience.

PRACTICE IMPLICATIONS

Nurses need to assess readiness for discharge to identify individual needs using a validated tool. Inclusion of education on hospital-to-home transition information and discharge planning/process is required in the orientation program for junior and casual staff to ensure consistency of information delivery. Interpreter services should be arranged for caregivers with limited language proficiency throughout the hospital stay especially when transition information is being provided. Nurses should apply teach-back techniques to improve caregivers' comprehension of information.

Complexity of Medication Regimens for Children With Neurological Impairment

IMPORTANCE

Parents of children with severe neurological impairment (SNI) manage complex medication regimens (CMRs) at home, and clinicians can help support parents and simplify CMRs.

OBJECTIVE

To measure the complexity and potentially modifiable aspects of CMRs using the Medication Regimen Complexity Index (MRCI) and to examine the association between MRCI scores and subsequent acute visits.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study was conducted between April 1, 2019, and December 31, 2020, at a single-center, large, hospital-based, complex care clinic. Participants were children with SNI aged 1 to 18 years and 5 or more prescribed medications.

EXPOSURE

Home medication regimen complexity was assessed using MRCI scores. The total MRCI score is composed of 3 subscores (dosage form, dose frequency, and specialized instructions).

MAIN OUTCOMES AND MEASURES

Patient-level counts of subscore characteristics and additional safety variables (total doses per day, high-alert medications, and potential drug-drug interactions) were analyzed by MRCI score groups (low, medium, and high score tertiles). Associations between MRCI score groups and acute visits were tested using Poisson regression, adjusted for age, complex chronic conditions, and recent health care use.

RESULTS

Of 123 patients, 73 (59.3%) were male with a median (interquartile range [IQR]) age of 9 (5-13) years. The median (IQR) MRCI scores were 46 (35-61 [range, 8-139]) overall, 29 (24-35) for the low MRCI group, 46 (42-50) for the medium MRCI group, and 69 (61-78) for the high MRCI group. The median (IQR) counts for the subscores were 6 (4-7) dosage forms per patient, 7 (5-9) dose frequencies per patient, and 5 (4-8) instructions per patient, with counts increasing significantly across higher MRCI groups. Similar trends occurred for total daily doses (median [IQR], 31 [20-45] doses), high-alert medications (median [IQR], 3 [1-5] medications), and potential drug-drug interactions (median [IQR], 3 [0-6] interactions). Incidence rate ratios of 30-day acute visits were 1.26 times greater (95% CI, 0.57-2.78) in the medium MRCI group vs the low MRCI group and 2.42 times greater (95% CI, 1.10-5.35) in the high MRCI group vs the low MRCI group.

CONCLUSIONS AND RELEVANCE

Higher MRCI scores were associated with multiple dose frequencies, complicated by different dosage forms and instructions, and associated with subsequent acute visits. These findings suggest that clinical interventions to manage CMRs could target various aspects of these regimens, such as the simplification of dosing schedules.

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

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

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.

Epidemiology of readmissions in early infancy following nonelective cesarean delivery

OBJECTIVE

Determine incidence and risk factors for readmissions in early infancy.

STUDY DESIGN

Secondary analysis of data from the Cesarean Section Optimal Antibiotic Prophylaxis trial. All unplanned revisits (unplanned clinic, ER visits, and hospital readmissions) and hospital readmissions (initial discharge to 3-month follow-up) were analyzed.

RESULTS

295 (15.9%) of 1850 infants had revisits with risk factors being ethnicity (adjusted odds ratio (aOR): 0.6 for Hispanic), maternal postpartum antibiotics (1.89), azithromycin treatment (1.22), small for gestational age (1.68), apnea (3.82), and hospital stay after birth >90th percentile (0.49). 71 (3.8%) of 1850 infants were readmitted with risk factors being antenatal steroids (aOR 2.49), elective repeat C/section (0.72), postpartum maternal antibiotics (2.22), O₂ requirement after delivery room (2.82), and suspected/proven neonatal sepsis (0.55).

CONCLUSION(S)

Multiple risk factors were identified, suggesting potential impact on the neonatal microbiome (maternal postpartum antibiotics) or issues related to access/cost of care (Hispanic ethnicity associated with fewer revisits).

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSIONS

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