Assessing the risk of early unplanned rehospitalisation in preterm babies: EPIPAGE 2 study

Reed, Robert A.; Morgan, Andreï S.; Zeitlin, Jennifer; Jarreau, Pierre‐Henri; Torchin, Héloïse; Pierrat, Véronique; Ancel, Pierre–Yves; Khoshnood, Babak

doi:10.1186/s12887-019-1827-6

Cited by 8 publications

(5 citation statements)

References 55 publications

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“…These findings are higher overall than reported in previous studies, although the trends by GA are consistent. The rates of rehospitalization found by Reed et al 49 50 Our study found that in the first 2 years of life, total healthcare charges for EP infants were significantly higher than for infants born at more than 28 wGA; total all-cause healthcare charges in USD, inflated to 2018 values, were 5.4 times higher in the EP cohort ($843 499) than in the M-LP cohort ($158 430), and 2.3 times higher than in the VP cohort ($379 581). Excluding birth hospitalization, charges associated with the treatment of respiratory comorbidities accounted for 52.7% of the all-cause costs for the EP cohort, vs 43.5% for the M-LP cohort.…”

Section: Discussionmentioning

confidence: 90%

Burden of Comorbidities and Healthcare Resource Utilization Among Medicaid-Enrolled Extremely Premature Infants

Mowitz

Gao

Sipsma

et al. 2022

Journal of Health Economics and Outcomes Research

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Background: The effect of gestational age (GA) on comorbidity prevalence, healthcare resource utilization (HCRU), and all-cause costs is significant for extremely premature (EP) infants in the United States. Objectives: To characterize real-world patient characteristics, prevalence of comorbidities, rates of HCRU, and direct healthcare charges and societal costs among premature infants in US Medicaid programs, with respect to GA and the presence of respiratory comorbidities. Methods: Using International Classification of Diseases, Ninth/Tenth Revision, Clinical Modification codes, diagnosis and medical claims data from 6 state Medicaid databases (1997-2018) of infants born at less than 37 weeks of GA (wGA) were collected retrospectively. Data from the index date (birth) up to 2 years corrected age or death, stratified by GA (EP, ≤28 wGA; very premature [VP], >28 to <32 wGA; and moderate to late premature [M-LP], ≥32 to <37 wGA), were compared using unadjusted and adjusted generalized linear models. Results: Among 25 573 premature infants (46.1% female; 4462 [17.4%] EP; 2904 [11.4%] VP; 18 207 [71.2%] M-LP), comorbidity prevalence, HCRU, and all-cause costs increased with decreasing GA and were highest for EP. Total healthcare charges, excluding index hospitalization and all-cause societal costs (US dollars), were 2 to 3 times higher for EP than for M-LP (EP $74 436 vs M-LP $27 541 and EP $28 504 vs M-LP $15 892, respectively). Conclusions: Complications of preterm birth, including prevalence of comorbidities, HCRU, and costs, increased with decreasing GA and were highest among EP infants during the first 2 years in this US analysis.

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Section: Discussionmentioning

confidence: 90%

Burden of Comorbidities and Healthcare Resource Utilization Among Medicaid-Enrolled Extremely Premature Infants

Mowitz

Gao

Sipsma

et al. 2022

Journal of Health Economics and Outcomes Research

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“…Constructing a parsimonious model with the potential for clinical use was a key priority. This model was a variant of a model we had previously published ( 29 ), with continuous predictors replacing their categorical versions. The 10 predictors included in this model were: sex (binary), gestational age in days (continuous), small for gestational age (SGA) status (binary; weight below the 10th percentile for gestational age), receipt of nitric oxide (binary), receipt of surfactant (binary), bronchopulmonary dysplasia (BPD) ( 30 ) [categorical; none, mild (≥28 days oxygen and breathing room air to week 36), moderate (≥28 days oxygen and mechanical ventilation or continuous airway pressure/FiO 2 >21% at week 36) or severe (≥28 days oxygen and mechanical ventilation or continuous airway pressure/FiO 2 >30% at week 36)], early onset neonatal infection (binary; either no infection or likely infection with antibiotics started at <72 h after birth for ≥5 days or infection confirmed via positive blood or cerebrospinal fluid culture prior to 72 h of life), post-menstrual age at discharge (PMA) in days (continuous), discharge weight in grams (continuous) and breastfeeding status at discharge (categorical; baby in receipt of either no breast milk, mixed feeding or exclusive breastfeeding at discharge).…”

Section: Methodsmentioning

confidence: 99%

“…Ten imputations and 50 iterations were used for imputation. Further details can be found in our earlier publication using the same dataset ( 29 ).…”

Section: Methodsmentioning

confidence: 99%

Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

et al. 2021

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Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

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“…Two of the 28 included studies reported risk predictive models of performance. All five of the identified predictive models in the studies demonstrated sub-optimal performance (C-statistic of 0.62, 0.69, and 0.62, respectively) [ 23 , 24 ].…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

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

et al. 2023

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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.

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Assessing the risk of early unplanned rehospitalisation in preterm babies: EPIPAGE 2 study

Cited by 8 publications

References 55 publications

Burden of Comorbidities and Healthcare Resource Utilization Among Medicaid-Enrolled Extremely Premature Infants

Burden of Comorbidities and Healthcare Resource Utilization Among Medicaid-Enrolled Extremely Premature Infants

Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

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

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