Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Niehaus IM

PROVIDER: S-EPMC8968996 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Niehaus Ines Marina IM Kansy Nina N Stock Stephanie S Dötsch Jörg J Müller Dirk D

BMJ open 20220330 3

<h4>Objectives</h4>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.<h4>Design</h4>Systematic review.<h4>Data source</h4>CINAHL, Embase and PubMed up to 7 October 2021.<h4>Eligibility criteria</h4>English or German language studies aiming to develop or validate a multivariable predictive model for 30-day paediatric UHRs ...[more]

PMID: 35354615

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Project description:BackgroundExisting readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians' impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available.ObjectiveThe objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors.MethodsWe extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors.ResultsWe developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported.ConclusionsThe performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

Project description:ImportanceAccurate identification of hospital readmission risk during a current hospitalization may enhance decision-making, facilitate targeted systems-level interventions, and avoid preventable readmissions.ObjectiveTo temporally and externally validate a suite of readmission risk prediction models across 48 children's hospitals to assess their generalizability and feasibility for future clinical implementation.Design, setting, and participantsThis prognostic study analyzed data from the Pediatric Health Information System (PHIS) database, which contains billing and resource use data from 48 US children's hospitals, including the derivation hospital (DH) and 47 hospitals participating in the PHIS database (hereafter other PHIS hospitals). Children aged 18 years or younger discharged from these hospitals between January 1, 2016, to December 31, 2019, were included. This cohort was divided as specified into the 3 prediction models at the DH: 6 months or older with no recent hospitalizations (new admission model [NAM]), 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), and 6 months or younger (young infant model [YIM]). Data were analyzed from August 9 to December 1, 2023.Main outcomes and measuresThe primary validation outcome was hospital-level discrimination measured with area under the receiver operating characteristic curve (AUROC). Predictors included demographic, clinical, and utilization variables. All-cause 30-day readmission was modeled for each hospital using logistic regression and parameter estimates from the DH. Calibration plots examined observed vs predicted outcome frequencies for each hospital.ResultsIn external validation, a total of 851 499 children were discharged from 48 hospitals (16 330 DH discharges and 835 169 other PHIS hospital discharges). The largest group of children was aged 5 to 14 years (281 193 [33.0%]). In temporal validation, the DH PHIS 2016-2018 cohort included 45 682 discharges. All-cause 30-day readmission rates were 7.2% for NAM, 35.5% for RAM, and 11.7% for YIM. The 2019 DH PHIS cohort included 16 330 discharges. All cause 30-day readmision rates were 7.2% for NAM, 35.1% for RAM, and 11.1% for YIM. Temporal validation demonstrated reduced discrimination across all 3 models (median AUROC, 0.65 [95% CI 0.62-0.67] for the NAM; 0.73 [95% CI 0.72-0.75) for RAM; 0.67 [95% CI 0.63-0.70) for the YIM compared with the original estimates (median AUROC 0.76 [95% CI 0.85-0.78] for the NAM; 0.84 [95% CI 0.83-0.84] for the RAM; 0.79 [95% CI 0.77-0.80] for the YIM). Overall readmission rates were 5.9% for NAM, 30.1% for RAM, and 7.6% for YIM. External validation yielded similiar findings as the temporal validation, although with demonstrable variation in performance across hospitals (median [range] AUROC, 0.64 [0.60-0.68] for the NAM; 0.73 [0.64-0.80] for the RAM; 0.65 [0.53-0.74] for the YIM). Most hospitals were poorly calibrated, with both significant overestimation and underestimation of observed risk. Of 47 other PHIS hospitals, only 3 for the RAM (6.4%) and 9 for both the NAM and YIM (19.1%) were adequately calibrated.Conclusions and relevanceThis prognostic study found that the readmission risk prediction models had reduced predictive accuracy across time and variability in hospital-level performance. These findings stress the importance of local validation prior to clinical implementation and suggest opportunities to improve generalizability, including multicenter derivation and expansion of candidate predictors.

Project description:BackgroundUnplanned hospital readmissions are a major source of morbidity among dialysis patients, in whom the risk of hospital readmission is exceptionally high. The contribution of dialysis facility staffing to hospital readmission has been largely overlooked.MethodsUsing annual data of dialysis patients from the United States Renal Data System from 2010 to 2013, we assessed dialysis facilities with a significantly worse (SW) and facilities with a nonsignificant (NS) standardized readmission ratio (SRR). SRR estimates were risk adjusted for patient factors, past year comorbidities, and index hospitalization characteristics. Facility staffing variables were compared between 2 exposure groups: facilities with SW and NS SRRs. Four measures of staffing, including patient-to-staffing ratio, were compared between SW and matched NS facilities.ResultsAbout 136,000-148,000 dialysis patients with 269,000-319,000 index hospital discharges were used to identify facilities with SW and facilities with NS SRR annually. Approximately 3-4% of facilities were identified as having SW SRR among > 5,000 facilities annually. The percent of nurses-to-total staff was significantly lower in 2010 for SW facilities than in matched NS facilities (42.5 vs. 45.6%, p = 0.012), but this disparity was attenuated by 2013 (44.8 vs. 44.7%, p = 0.949). There was a higher patient-to-nurse ratio for SW facilities than for NS facilities (mean 16.4 vs. 15.2, p = 0.038) in 2010 as well, and the disparity was reduced by 2013. The trends were similar for patient-to-total staff and patient-to-registered nurse, but not statistically significant.ConclusionsThis study found that dialysis facilities with SW 30-day readmission rates had lower proportions of nurses-to-total staff and higher patient-to-nurse ratios, but this disparity improved in recent years. Additional research is warranted focusing on how evidence-based staffing at dialysis facilities can contribute to reduction of hospital readmission, and this knowledge is needed to inform clinical practice guidelines and policy decisions regarding optimal dialysis patient staffing.

Project description:BackgroundEarly unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions.MethodsWe conducted a retrospective cohort study on 37,091 consecutive hospitalized adult patients with 55,933 discharges between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Patients who were aged < 20 years, were admitted for cancer-related treatment, participated in clinical trial, were discharged against medical advice, died during admission, or lived abroad were excluded. Predictors for analysis included 7 categories of variables extracted from hospital's medical record dataset. In total, four machine learning algorithms, namely logistic regression, random forest, extreme gradient boosting, and categorical boosting, were used to build classifiers for prediction. The performance of prediction models for 14-day unplanned readmission risk was evaluated using precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC).ResultsIn total, 24,722 patients were included for the analysis. The mean age of the cohort was 57.34 ± 18.13 years. The 14-day unplanned readmission rate was 1.22%. Among the 4 machine learning algorithms selected, Catboost had the best average performance in fivefold cross-validation (precision: 0.9377, recall: 0.5333, F1-score: 0.6780, AUROC: 0.9903, and AUPRC: 0.7515). After incorporating 21 most influential features in the Catboost model, its performance improved (precision: 0.9470, recall: 0.5600, F1-score: 0.7010, AUROC: 0.9909, and AUPRC: 0.7711).ConclusionsOur models reliably predicted 14-day unplanned readmissions and were explainable. They can be used to identify patients with a high risk of unplanned readmission based on influential features, particularly features related to diagnoses. The operation of the models with physiological indicators also corresponded to clinical experience and literature. Identifying patients at high risk with these models can enable early discharge planning and transitional care to prevent readmissions. Further studies should include additional features that may enable further sensitivity in identifying patients at a risk of early unplanned readmissions.

Project description:ObjectiveUnplanned hospital readmissions following surgical interventions are associated with adverse events and contribute to increasing health care costs. Despite numerous studies defining risk factors following lower extremity bypass surgery, evidence regarding readmission after endovascular interventions is limited. This study aimed to identify predictors of 30-day unplanned readmission following infrainguinal endovascular interventions.MethodsWe identified all patients undergoing an infrainguinal endovascular intervention in the targeted vascular module of the American College of Surgeons National Surgical Quality Improvement Program between 2012 and 2014. Perioperative outcomes were stratified by symptom status (chronic limb-threatening ischemia [CLI] vs claudication). Patients who died during index admission and those who remained in the hospital after 30 days were excluded. Indications for unplanned readmission related to the index procedure were evaluated. Multivariable logistic regression was used to identify preoperative and in-hospital (during index admission) risk factors of 30-day unplanned readmission.ResultsThere were 4449 patients who underwent infrainguinal endovascular intervention, of whom 2802 (63%) had CLI (66% tissue loss) and 1647 (37%) had claudication. The unplanned readmission rates for CLI and claudication patients were 16% (n = 447) and 6.5% (n = 107), respectively. Mortality after index admission was higher for readmitted patients compared with those not readmitted (CLI, 3.4% vs 0.7% [P < .001]; claudication, 2.8% vs 0.1% [P < .01]). Approximately 50% of all unplanned readmissions were related to the index procedure. Among CLI patients, the most common indication for readmission related to the index procedure was wound or infection related (42%), whereas patients with claudication were mainly readmitted for recurrent symptoms of peripheral vascular disease (28%). In patients with CLI, predictors of unplanned readmission included diabetes (odds ratio, 1.3; 95% confidence interval, 1.01-1.6), congestive heart failure (1.6; 1.1-2.5), renal insufficiency (1.7; 1.3-2.2), preoperative dialysis (1.4; 1.02-1.9), tibial angioplasty/stenting (1.3; 1.04-1.6), in-hospital bleeding (1.9; 1.04-3.5), in-hospital unplanned return to the operating room (1.9; 1.1-3.5), and discharge other than to home (1.5; 1.1-2.0). Risk factors for those with claudication were dependent functional status (3.5; 1.4-8.7), smoking (1.6; 1.02-2.5), diabetes (1.5; 1.01-2.3), preoperative dialysis (3.6; 1.6-8.3), procedure time exceeding 120 minutes (1.8; 1.1-2.7), in-hospital bleeding (2.9; 1.2-7.4), and in-hospital unplanned return to the operating room (3.4; 1.2-9.4).ConclusionsUnplanned readmission after endovascular treatment is relatively common, especially in patients with CLI, and is associated with substantially increased mortality. Awareness of these risk factors will help providers identify patients at high risk who may benefit from early surveillance, and prophylactic measures focused on decreasing postoperative complications may reduce the rate of readmission.

Project description: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.ConclusionThis 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.

Dataset Information

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

Objectives

Design

Data source

Eligibility criteria

Data extraction and synthesis

Results

Conclusion

Publications

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

Similar Datasets

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