Prediction of 7-Day Readmission Risk for Pediatric Trauma Patients

Delaplain, Patrick T.; Guner, Yigit S.; Feaster, William; Wallace, Elizabeth; Gibbs, David; Gholizadeh, Masoumeh; Reyna, Troy M.; Ehwerhemuepha, Louis

doi:10.1016/j.jss.2020.03.068

Cited by 10 publications

(13 citation statements)

References 22 publications

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“…Predictive modeling for pediatric oncology hospital readmissions has largely been left unaddressed in medical and data science literature. These patients are, however, universally seen as patients at the highest risk for readmission (as captured in existing models for the general pediatric population) 6,11,12 . The challenge with general pediatric readmissions models is that the models often suffer from poor specificity in stratifying risk in oncology.…”

Section: Discussionmentioning

confidence: 99%

“…Planned encounters were identified using the ICD-10-CM diagnosis codes, Z00-Z13, which indicate visits for examinations and screenings as well as chemotherapy, immunotherapy, and radiation therapy; and encounters for specific health care such as surgeries (Z40-Z53). 12 The 30-day readmission status was calculated and all planned readmissions, defined by the conditions previously indicated, were excluded.…”

Section: Methodsmentioning

confidence: 99%

“…Inclusion criteria for hospitals in this study were that they (a) contributed data to all the database tables required for this study and (b) had data from at least 500 pediatric admissions for neoplastic conditions within the time period of the study (from 2000 to 2017). Planned encounters were identified using the ICD‐10‐CM diagnosis codes, Z00‐Z13, which indicate visits for examinations and screenings as well as chemotherapy, immunotherapy, and radiation therapy; and encounters for specific health care such as surgeries (Z40‐Z53) 12 . The 30‐day readmission status was calculated and all planned readmissions, defined by the conditions previously indicated, were excluded.…”

Section: Methodsmentioning

confidence: 99%

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Multicenter study of risk factors of unplanned 30‐day readmissions in pediatric oncology

et al. 2021

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Background Pediatric oncology patients have high rates of hospital readmission but there is a dearth of research into risk factors for unplanned 30‐day readmissions among this high‐risk population. Aim In this study, we built a statistical model to provide insight into risk factors of unplanned readmissions in this pediatric oncology. Methods We retrieved 32 667 encounters from 10 418 pediatric patients with a neoplastic condition from 16 hospitals in the Cerner Health Facts Database and built a mixed‐effects model with patients nested within hospitals for inference on 75% of the data and reserved the remaining as an independent test dataset. Results The mixed‐effects model indicated that patients with acute lymphoid leukemia (in relapse), neuroblastoma, rhabdomyosarcoma, or bone/cartilage cancer have increased odds of readmission. The number of cancer medications taken by the patient and the administration of chemotherapy were associated with increased odds of readmission for all cancer types. Wilms Tumor had a significant interaction with administration of chemotherapy, indicating that the risk due to chemotherapy is exacerbated in patients with Wilms Tumor. A second two‐way interaction between recent history of chemotherapy treatment and infections was associated with increased odds of readmission. The area under the receiver operator characteristic curve (and corresponding 95% confidence interval) of the mixed‐effects model was 0.714 (0.702, 0.725) on the independent test dataset. Conclusion Readmission risk in oncology is modified by the specific type of cancer, current and past administration of chemotherapy, and increased health care utilization. Oncology‐specific models can provide decision support where model built on other or mixed population has failed.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Multicenter study of risk factors of unplanned 30‐day readmissions in pediatric oncology

et al. 2021

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“…These criteria ensure that we excluded potential noise data and that there is enough data to estimate model performance by hospital. We included multiple index visits and readmissions for individual patients and each visit that was itself a readmission was treated as an index visit for estimating subsequent readmission [22,23].…”

Section: Datamentioning

confidence: 99%

“…The platform is compliant with Health Insurance Portability and Accountability Act of 1996 (HIPAA) [20] with end-to-end encryption. Herein, the platform is utilized to provide methodological improvement to a previous study on unplanned readmissions at a single pediatric institution [21], and for developing a multi-center model for predicting general all-cause 30-day readmissions [21][22][23][24][25] among pediatric-age patients (patients less than 18 years) using the Cerner(R) Health Facts Deidentified Database which has recently been updated and renamed as the Cerner Real World Data. Previous research in the application of machine learning in medicine has predominantly been in adult medicine.…”

Section: Introductionmentioning

confidence: 99%

HealtheDataLab – a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions

Ehwerhemuepha

Gasperino

Bischoff

et al. 2020

BMC Med Inform Decis Mak

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Background: There is a shortage of medical informatics and data science platforms using cloud computing on electronic medical record (EMR) data, and with computing capacity for analyzing big data. We implemented, described, and applied a cloud computing solution utilizing the fast health interoperability resources (FHIR) standardization and state-of-the-art parallel distributed computing platform for advanced analytics.Methods: We utilized the architecture of the modern predictive analytics platform called Cerner® HealtheDataLab and described the suite of cloud computing services and Apache Projects that it relies on. We validated the platform by replicating and improving on a previous single pediatric institution study/model on readmission and developing a multi-center model of all-cause readmission for pediatric-age patients using the Cerner® Health Facts Deidentified Database (now updated and referred to as the Cerner Real World Data). We retrieved a subset of 1.4 million pediatric encounters consisting of 48 hospitals' data on pediatric encounters in the database based on a priori inclusion criteria. We built and analyzed corresponding random forest and multilayer perceptron (MLP) neural network models using HealtheDataLab. Results: Using the HealtheDataLab platform, we developed a random forest model and multi-layer perceptron model with AUC of 0.8446 (0.8444, 0.8447) and 0.8451 (0.8449, 0.8453) respectively. We showed the distribution in model performance across hospitals and identified a set of novel variables under previous resource utilization and generic medications that may be used to improve existing readmission models. Conclusion:Our results suggest that high performance, elastic cloud computing infrastructures such as the platform presented here can be used for the development of highly predictive models on EMR data in a secure and robust environment. This in turn can lead to new clinical insights/discoveries.

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Race, Ethnicity, and Insurance: the Association with Opioid Use in a Pediatric Hospital Setting

Ehwerhemuepha

Donaldson

Kain

et al. 2020

J. Racial and Ethnic Health Disparities

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Prediction of 7-Day Readmission Risk for Pediatric Trauma Patients

Cited by 10 publications

References 22 publications

Multicenter study of risk factors of unplanned 30‐day readmissions in pediatric oncology

Multicenter study of risk factors of unplanned 30‐day readmissions in pediatric oncology

HealtheDataLab – a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions

Race, Ethnicity, and Insurance: the Association with Opioid Use in a Pediatric Hospital Setting

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