Browsing by Author "Owora, Arthur"
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Item Asthma Among Children With Primary Ciliary Dyskinesia(American Medical Association, 2024-12-02) Zein, Joe; Owora, Arthur; Kim, Hyun Jo; Marozkina, Nadzeya; Gaston, Benjamin; Pediatrics, School of MedicineItem Development and Temporal Validation of an Electronic Medical Record-Based Insomnia Prediction Model Using Data from a Statewide Health Information Exchange(MDPI, 2023-05-05) Holler, Emma; Chekani, Farid; Ai, Jizhou; Meng, Weilin; Khandker, Rezaul Karim; Ben Miled, Zina; Owora, Arthur; Dexter, Paul; Campbell, Noll; Solid, Craig; Boustani, Malaz; Electrical and Computer Engineering, School of Engineering and TechnologyThis study aimed to develop and temporally validate an electronic medical record (EMR)-based insomnia prediction model. In this nested case-control study, we analyzed EMR data from 2011–2018 obtained from a statewide health information exchange. The study sample included 19,843 insomnia cases and 19,843 controls matched by age, sex, and race. Models using different ML techniques were trained to predict insomnia using demographics, diagnosis, and medication order data from two surveillance periods: −1 to −365 days and −180 to −365 days before the first documentation of insomnia. Separate models were also trained with patient data from three time periods (2011–2013, 2011–2015, and 2011–2017). After selecting the best model, predictive performance was evaluated on holdout patients as well as patients from subsequent years to assess the temporal validity of the models. An extreme gradient boosting (XGBoost) model outperformed all other classifiers. XGboost models trained on 2011–2017 data from −1 to −365 and −180 to −365 days before index had AUCs of 0.80 (SD 0.005) and 0.70 (SD 0.006), respectively, on the holdout set. On patients with data from subsequent years, a drop of at most 4% in AUC is observed for all models, even when there is a five-year difference between the collection period of the training and the temporal validation data. The proposed EMR-based prediction models can be used to identify insomnia up to six months before clinical detection. These models may provide an inexpensive, scalable, and longitudinally viable method to screen for individuals at high risk of insomnia.Item Digital detection of dementia (D3): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems(MDPI, 2022-10-11) Kleiman, Michael J.; Plewes, Abbi D.; Owora, Arthur; Grout, Randall W.; Dexter, Paul Richard; Fowler, Nicole R.; Galvin, James E.; Ben Miled, Zina; Boustani, Malaz; Medicine, School of MedicineBackground: Early detection of Alzheimer's disease and related dementias (ADRD) in a primary care setting is challenging due to time constraints and stigma. The implementation of scalable, sustainable, and patient-driven processes may improve early detection of ADRD; however, there are competing approaches; information may be obtained either directly from a patient (e.g., through a questionnaire) or passively using electronic health record (EHR) data. In this study, we aim to identify the benefit of a combined approach using a pragmatic cluster-randomized clinical trial. Methods: We have developed a Passive Digital Marker (PDM), based on machine learning algorithms applied to EHR data, and paired it with a patient-reported outcome (the Quick Dementia Rating Scale or QDRS) to rapidly share an identified risk of impairment to a patient's physician. Clinics in both south Florida and Indiana will be randomly assigned to one of three study arms: 1200 patients in each of the two populations will be administered either the PDM, the PDM with the QDRS, or neither, for a total of 7200 patients across all clinics and populations. Both incidence of ADRD diagnosis and acceptance into ADRD diagnostic work-up regimens is hypothesized to increase when patients are administered both the PDM and QDRS. Physicians performing the work-up regimens will be blind to the study arm of the patient. Discussion: This study aims to test the accuracy and effectiveness of the two scalable approaches (PDM and QDRS) for the early detection of ADRD among older adults attending primary care practices. The data obtained in this study may lead to national early detection and management program for ADRD as an efficient and beneficial method of reducing the current and future burden of ADRD, as well as improving the annual rate of newly documented ADRD in primary care practices.Item High flow nasal cannula use is associated with increased hospital length of stay for pediatric asthma(Wiley, 2023-11) Rogerson, Colin; Owora, Arthur; He, Tian; Carroll, Aaron; Schleyer, Titus; AbuSultaneh, Samer; Tu, Wanzhu; Mendonca, Eneida; Medicine, School of MedicineBackground High flow nasal cannula (HFNC) is a respiratory device increasingly used to treat asthma. Recent mechanistic studies have shown that nebulized medications may have reduced delivery with HFNC, which may impair asthma treatment. This study evaluated the association between HFNC use for pediatric asthma and hospital length of stay (LOS). Methods This was a retrospective matched cohort study. Cases included patients aged 2–18 years hospitalized between January 2010 and December 2021 with asthma and received HFNC treatment. Controls were selected using logistic regression propensity score matching based on demographics, vital signs, medications, imaging, and social and environmental determinants of health. The primary outcome was hospital LOS. Results A total of 23,659 encounters met eligibility criteria, and of these 1766 cases included HFNC treatment with a suitable matched control. Cases were well-matched in demographics, social and environmental determinants of health, and clinical characteristics including use of adjunctive asthma therapies. The median hospital LOS for study cases was significantly higher at 87 h (interquartile range [IQR]: 61–145) compared to 66 h (IQR: 43–105) in the matched controls (p < 0.01). There was no significant difference in the rate of intubation and mechanical ventilation (8.9% vs. 7.6%, p = .18); however, the use of NIV was significantly higher in the cases than the control group (21.3% vs. 6.7%, p < .01). Conclusion In this study of children hospitalized for asthma, HFNC use was associated with increased hospital LOS compared to matched controls. Further research using more granular data and additional relevant variables is needed to validate these findings.Item Prognostic models for predicting insomnia treatment outcomes: A systematic review(Elsevier, 2024) Holler, Emma; Du, Yu; Barboi, Cristina; Owora, Arthur; Anesthesia, School of MedicineObjective: To identify and critically evaluate models predicting insomnia treatment response in adult populations. Methods: Pubmed, EMBASE, and PsychInfo databases were searched from January 2000 to January 2023 to identify studies reporting the development or validation of multivariable models predicting insomnia treatment outcomes in adults. Data were extracted according to CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) guidelines and study quality was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). Results: Eleven studies describing 53 prediction models were included and appraised. Treatment response was most frequently assessed using wake after sleep onset (n = 10; 18.9%), insomnia severity index (n = 10; 18.9%), and sleep onset latency (n = 9, 17%). Dysfunctional Beliefs About Sleep (DBAS) score was the most common predictor in final models (n = 33). R2 values ranged from 0.06 to 0.80 for models predicting continuous response and area under the curve (AUC) ranged from 0.73 to 0.87 for classification models. Only two models were internally validated, and none were externally validated. All models were rated as having a high risk of bias according to PROBAST, which was largely driven by the analysis domain. Conclusion: Prediction models may be a useful tool to assist clinicians in selecting the optimal treatment strategy for patients with insomnia. However, no externally validated models currently exist. These results highlight an important gap in the literature and underscore the need for the development and validation of modern, methodologically rigorous models.Item Social risk factors for pediatric asthma exacerbations: A systematic review(medRxiv, 2023-09-20) Vinjimoor, Shriya; Vieira, Caroline; Rogerson, Colin; Owora, Arthur; Mendonca, Eneida A.; Pediatrics, School of MedicineObjective: This systematic review aims to identify social risk factors that influence pediatric asthma exacerbations. Methods: Cohort studies published between 2010 and 2020 were systematically searched on the OVID Medline, Embase, and PsycInfo databases. Using our established phased inclusion and exclusion criteria, studies that did not address a pediatric population, social risk factors, and asthma exacerbations were excluded. Out of a total of 707 initially retrieved articles, 3 prospective cohort and 6 retrospective cohort studies were included. Results: Upon analysis of our retrieved studies, two overarching domains of social determinants, as defined by Healthy People 2030, were identified as major risk factors for pediatric asthma exacerbations: Social/Community Context and Neighborhood/Built Environment. Social/Community factors including African American race and inadequate caregiver perceptions were associated with increased risk for asthma exacerbations. Patients in high-risk neighborhoods, defined by lower levels of education, housing, and employment, had higher rates of emergency department readmissions and extended duration of stay. Additionally, a synergistic interaction between the two domains was found such that patients with public or no health insurance and residence in high-risk neighborhoods were associated with excess hospital utilization attributable to pediatric asthma exacerbations. Conclusion: Social risk factors play a significant role in influencing the frequency and severity of pediatric asthma exacerbations.Item Technology caregiver intervention for Alzheimer’s disease (I-CARE): Feasibility and preliminary efficacy of Brain CareNotes(Wiley, 2023) Rodriguez, Miriam Jocelyn; Martinez Kercher, Vanessa; Jordan, Evan J.; Savoy, April; Hill, Jordan R.; Werner, Nicole; Owora, Arthur; Castelluccio, Pete; Boustani, Malaz A.; Holden, Richard J.; Medicine, School of MedicineBackground: The primary aim of the current pilot study was to examine enrollment rate, data completion, usability, acceptance and use of a mobile telehealth application, Brain CareNotes. A secondary aim was to estimate the application's effect in reducing caregiver burden and behavioral and psychological symptoms related to dementia (BPSD). Methods: Patient-caregiver dyads (n = 53) were recruited and randomized to intervention and control groups. Assessment of usability, acceptance, BPSD symptoms, and caregiver burden were collected at baseline, 3- and 6-month follow-up. Results: The enrollment rate was acceptable despite pandemic related challenges (53/60 target recruitment sample). Among randomized individuals, there was a retention rate of 85% and data completion was attained for 81.5% of those allocated to usual care and 88.5% of those allocated to Brain CareNotes. Mean caregiver-reported app usability at 6 months was 72.5 (IQR 70.0-90.0) on the System Usability Scale-considered "Good to Excellent"-and user acceptance was reasonable as indicated by 85%-90% of caregivers reporting they would intend to use the app to some degree in the next 6 months, if able. Regarding intervention effect, although differences in outcome measures between the groups were not statistically significant, compared to baseline, we found a reduction of caregiver burden (NPI-Caregiver Distress) of 1.0 at 3 months and 0.7 at 6 months for those in the intervention group. BPSD (NPI Total Score) was also reduced from baseline by 4.0 at 3 months and by 0.5 at 6 months. Conclusions: Brain CareNotes is a highly scalable, usable and acceptable mobile caregiver intervention. Future studies should focus on testing Brain CareNotes on a larger sample size to examine efficacy of reducing caregiver burden and BPSD.