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Open Access Publications from the University of California

Scholarly Works (10 results)

  • Article
  • Peer Reviewed
UC Irvine Previously Published Works (2021)

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.
    Cover page: Predictors of pediatric readmissions among patients with neurological conditions
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2020)

    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.
      Cover page: HealtheDataLab – a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2020)
      The COVID-19 pandemic has required greater minute-to-minute urgency of patient treatment in Intensive Care Units (ICUs), rendering the use of Randomized Controlled Trials (RCTs) too slow to be effective for treatment discovery. There is a need for agility in clinical research, and the use of data science to develop predictive models for patient treatment is a potential solution. However, rapidly developing predictive models in healthcare is challenging given the complexity of healthcare problems and the lack of regular interaction between data scientists and physicians. Data scientists can spend significant time working in isolation to build predictive models that may not be useful in clinical environments. We propose the use of an agile data science framework based on the Scrumban framework used in software development. Scrumban is an iterative framework, where in each iteration larger problems are broken down into simple do-able tasks for data scientists and physicians. The two sides collaborate closely in formulating clinical questions and developing and deploying predictive models into clinical settings. Physicians can provide feedback or new hypotheses given the performance of the model, and refinement of the model or clinical questions can take place in the next iteration. The rapid development of predictive models can now be achieved with increasing numbers of publicly available healthcare datasets and easily accessible cloud-based data science tools. What is truly needed are data scientist and physician partnerships ensuring close collaboration between the two sides in using these tools to develop clinically useful predictive models to meet the demands of the COVID-19 healthcare landscape.
        Cover page: Agile clinical research: A data science approach to scrumban in clinical medicine
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2021)

        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.
          Cover page: Multicenter study of risk factors of unplanned 30‐day readmissions in pediatric oncology
          • Article
          • Peer Reviewed
          Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health, Volume 22, Issue 5 (2021)

          Introduction: Children and adolescents are not impervious to the unprecedented epidemic of opioid misuse in the United States. In 2016 more than 88,000 adolescents between the ages of 12–17 reported misusing opioid medication, and evidence suggests that there has been a rise in opioid-related mortality for pediatric patients. A major source of prescribed opioids for the treatment of pain is the emergency department (ED). The current study sought to assess the complex relationship between opioid administration, pain severity, and parent satisfaction with children’s care in a pediatric ED.

          Methods: We examined data from a tertiary pediatric care facility. A health survey questionnaire was administered after ED discharge to capture the outcome of parental likelihood of providing a positive facility rating. We abstracted patient demographic, clinical, and top diagnostic information using electronic health records. Data were merged and multivariable models were constructed. 

          Results: We collected data from 15,895 pediatric patients between the ages of 0–17 years (mean = 6.69; standard deviation = 5.19) and their parents. Approximately 786 (4.94%) patients were administered an opioid; 8212 (51.70%) were administered a non-opioid analgesic; and 3966 (24.95%) expressed clinically significant pain (pain score >/= 4). Results of a multivariable regression analysis from these pediatric patients revealed a three-way interaction of age, pain severity, and opioid administration (odds ratio 1.022, 95% confidence interval, 1.006, 1.038, P = 0.007). Our findings suggest that opioid administration negatively impacted parent satisfaction of older adolescent patients in milder pain who were administered an opioid analgesic, but positively influenced the satisfaction scores of parents of younger children who were administered opioids. When pain levels were severe, the relationship between age and patient experience was not statistically significant.

          Conclusion: This investigation highlights the complexity of the relationship between opioid administration, pain severity, and satisfaction, and suggests that the impact of opioid administration on parent satisfaction is a function of the age of the child.

            Cover page: A Multivariable Model of Parent Satisfaction, Pain, and Opioid Administration in a Pediatric Emergency DepartmentCreative Commons 'BY' version 4.0 license
            • Article
            • Peer Reviewed
            UC Irvine Previously Published Works (2021)

            Background

            Cardiovascular and other circulatory system diseases have been implicated in the severity of COVID-19 in adults. This study provides a super learner ensemble of models for predicting COVID-19 severity among these patients.

            Method

            The COVID-19 Dataset of the Cerner Real-World Data was used for this study. Data on adult patients (18 years or older) with cardiovascular diseases between 2017 and 2019 were retrieved and a total of 13 of these conditions were identified. Among these patients, 33,042 admitted with positive diagnoses for COVID-19 between March 2020 and June 2020 (from 59 hospitals) were identified and selected for this study. A total of 14 statistical and machine learning models were developed and combined into a more powerful super learning model for predicting COVID-19 severity on admission to the hospital.

            Result

            LASSO regression, a full extreme gradient boosting model with tree depth of 2, and a full logistic regression model were the most predictive with cross-validated AUROCs of 0.7964, 0.7961, and 0.7958 respectively. The resulting super learner ensemble model had a cross validated AUROC of 0.8006 (range: 0.7814, 0.8163). The unbiased AUROC of the super learner model on an independent test set was 0.8057 (95% CI: 0.7954, 0.8159).

            Conclusion

            Highly predictive models can be built to predict COVID-19 severity of patients with cardiovascular and other circulatory conditions. Super learning ensembles will improve individual and classical ensemble models significantly.
              Cover page: A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2021)

              Background

              This study examined the association between race/ethnicity and health insurance payer type with pediatric opioid and non-opioid ordering in an inpatient hospital setting.

              Methods

              Cross-sectional inpatient encounter data from June 2013 to June 2018 was retrieved from a pediatric children's hospital in Southern California (N = 55,944), and statistical analyses were performed to determine associations with opioid ordering.

              Results

              There was a significant main effect of race/ethnicity on opioid and non-opioid orders. Physicians ordered significantly fewer opioid medications, but a greater number of non-opioid medications, for non-Hispanic African American children than non-Hispanic Asian, Hispanic/Latinx, and non-Hispanic White pediatric patients. There was also a main effect of health insurance payer type on non-opioid orders. Patients with government-sponsored plans (e.g., Medi-Cal, Medicare) received fewer non-opioid prescriptions compared with patients with both HMO and PPO coverage. Additionally, there was a significant race/ethnicity by insurance interaction on opioid orders. Non-Hispanic White patients with "other" insurance coverage received the greatest number of opioid orders. In non-Hispanic African American patients, children with PPO coverage received fewer opioids than those with government-sponsored and HMO insurance. For non-Hispanic Asian patients, children with PPO were prescribed more opioids than those with government-sponsored and HMO coverage.

              Conclusion

              Findings suggest that the relationship between race/ethnicity, insurance type, and physician decisions opioid prescribing is complex and multifaceted. Given that consistency in opioid prescribing should be seen regardless of patient background characteristics, future studies should continue to assess and monitor unequitable differences in care.
                Cover page: Race, Ethnicity, and Insurance: the Association with Opioid Use in a Pediatric Hospital SettingCreative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2021)
                This research examined whether pediatric inpatients without an anxiety/mood disorder are more likely to receive opioids in response to pain compared to patients diagnosed with a mental health condition. Research questions were tested using cross-sectional inpatient electronic medical record data. Propensity score matching was used to match patients with a disorder with patients without the disorder (anxiety analyses: N = 2892; mood analyses: N = 1042). Although patients with anxiety and mood disorders experienced greater pain, physicians were less likely to order opioids for these patients. Analyses also disclosed an interaction of anxiety with pain-the pain-opioid relation was stronger for patients without an anxiety disorder than for patients with an anxiety diagnosis. Instead, physicians were more likely to place non-opioid analgesic orders to manage the pain of patients with anxiety disorders. Findings imply that pain management decisions might be influenced by patient's mental health.
                  Cover page: Anxiety and Mood Disorders Impacting Physician Opioid Prescribing in the Pediatric Hospital Setting.Creative Commons 'BY' version 4.0 license
                  • Article
                  • Peer Reviewed
                  UC Irvine Previously Published Works (2022)

                  Importance

                  Identifying the associations between severe COVID-19 and individual cardiovascular conditions in pediatric patients may inform treatment.

                  Objective

                  To assess the association between previous or preexisting cardiovascular conditions and severity of COVID-19 in pediatric patients.

                  Design, setting, and participants

                  This retrospective cohort study used data from a large, multicenter, electronic health records database in the US. The cohort included patients aged 2 months to 17 years with a laboratory-confirmed diagnosis of COVID-19 or a diagnosis code indicating infection or exposure to SARS-CoV-2 at 85 health systems between March 1, 2020, and January 31, 2021.

                  Exposures

                  Diagnoses for 26 cardiovascular conditions between January 1, 2015, and December 31, 2019 (before infection with SARS-CoV-2).

                  Main outcomes and measures

                  The main outcome was severe COVID-19, defined as need for supplemental oxygen or in-hospital death. Mixed-effects, random intercept logistic regression modeling assessed the significance and magnitude of associations between 26 cardiovascular conditions and COVID-19 severity. Multiple comparison adjustment was performed using the Benjamini-Hochberg false discovery rate procedure.

                  Results

                  The study comprised 171 416 pediatric patients; the median age was 8 years (IQR, 2-14 years), and 50.28% were male. Of these patients, 17 065 (9.96%) had severe COVID-19. The random intercept model showed that the following cardiovascular conditions were associated with severe COVID-19: cardiac arrest (odds ratio [OR], 9.92; 95% CI, 6.93-14.20), cardiogenic shock (OR, 3.07; 95% CI, 1.90-4.96), heart surgery (OR, 3.04; 95% CI, 2.26-4.08), cardiopulmonary disease (OR, 1.91; 95% CI, 1.56-2.34), heart failure (OR, 1.82; 95% CI, 1.46-2.26), hypotension (OR, 1.57; 95% CI, 1.38-1.79), nontraumatic cerebral hemorrhage (OR, 1.54; 95% CI, 1.24-1.91), pericarditis (OR, 1.50; 95% CI, 1.17-1.94), simple biventricular defects (OR, 1.45; 95% CI, 1.29-1.62), venous embolism and thrombosis (OR, 1.39; 95% CI, 1.11-1.73), other hypertensive disorders (OR, 1.34; 95% CI, 1.09-1.63), complex biventricular defects (OR, 1.33; 95% CI, 1.14-1.54), and essential primary hypertension (OR, 1.22; 95% CI, 1.08-1.38). Furthermore, 194 of 258 patients (75.19%) with a history of cardiac arrest were younger than 12 years.

                  Conclusions and relevance

                  The findings suggest that some previous or preexisting cardiovascular conditions are associated with increased severity of COVID-19 among pediatric patients in the US and that morbidity may be increased among individuals children younger than 12 years with previous cardiac arrest.
                    Cover page: Association of Congenital and Acquired Cardiovascular Conditions With COVID-19 Severity Among Pediatric Patients in the US
                    • Article
                    • Peer Reviewed
                    UC Irvine Previously Published Works (2021)

                    Objective

                    The purpose of this report is to provide insight from pediatric stakeholders with a shared desire to facilitate a revision of the current United States regulatory pathways for the development of pediatric healthcare devices.

                    Methods

                    On August 5, 2020, a group of innovators, engineers, professors and clinicians met to discuss challenges and opportunities for the development of new medical devices for pediatric health and the importance of creating a regulatory environment that encourages and accelerates the research and development of such devices. On January 6, 2021, this group joined regulatory experts at a follow-up meeting.

                    Results

                    One of the primary issues identified was the need to present decision-makers with opportunities that change the return-on-investment balance between adult and pediatric devices to promote investment in pediatric devices.

                    Discussion/conclusion

                    Several proposed strategies were discussed, and these strategies can be divided into two broad categories: 1. Removal of real and perceived barriers to pediatric device innovation; 2. Increasing incentives for pediatric device innovation.
                      Cover page: Opportunities for Regulatory Changes to Promote Pediatric Device Innovation in the United States: Joint Recommendations From Pediatric Innovator Roundtables.
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