IU Indianapolis ScholarWorks :: Browsing by Author "Li, Xiaochun"

Browsing by Author "Li, Xiaochun"

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Advancing diabetes management in adolescents: Comparative effectiveness of mobile self‐monitoring blood glucose technology and family‐centered goal setting
(Wiley, 2018-06) Hannon, Tamara S.; Yazel-Smith, Lisa G.; Hatton, Amy S.; Stanton, Jennifer L.; Moser, Elizabeth A. S.; Li, Xiaochun; Carroll, Aaron E.; Pediatrics, School of Medicine
Background As adolescents gain autonomy, it remains important for parents to be involved with diabetes management to avoid deterioration in glycemic control. Technologies for self‐monitoring of blood glucose (SMBG) allow for remote monitoring in real‐time by parents. This research compared 3 strategies for improving SMBG and diabetes self‐care in the short‐term. These strategies were: (1) health information technology (HIT)‐enhanced blood glucose meter that shared blood glucose data among patients, their parent, and care providers, and allowed for text messaging; (2) family‐centered goal setting; and (3) a combination of (1) and (2). Methods One hundred twenty‐eight participants enrolled; 97 adolescent‐parent pairs attended clinic at 3‐month intervals during the 6‐month intervention. Differences between treatment groups were evaluated using analysis of variance (ANOVAs) for continuous variables and χ2 tests for frequencies. Within patient changes were evaluated using paired t tests. Results Participants in the HIT‐enhanced SMBG group had no change in mean glycosylated hemoglobin (HbA1c). Participants assigned to family‐centered goal setting had a non‐significant decrease in HbA1c of −0.3% (P = .26) from baseline to 6 months. Participants in the combined approach had a significant decrease in HbA1c of −0.6% (P = .02) from baseline to 3 months, but the decrease of −0.4% at 6 months was non‐significant (P = .51). The change in HbA1c from baseline to 3 months was greater for the combined approach than for the HIT‐enhanced SMBG (P = .05) or family‐centered goal setting (P = .01). Conclusions Our data suggest that utilizing the family‐centered goal setting strategy when implementing HIT‐enhanced diabetes technology deserves further study.
Age of Transfused Red Blood Cells and Health Outcomes in Two Surgical Cohorts
(Elsevier, 2019-03) Khan, Sikandar H.; Devnani, Rohit; LaPradd, Michelle; Landrigan, Matt; Gray, Alan; Kelley, Andrea; Eckert, George J.; Li, Xiaochun; Khan, Babar A.; Medicine, School of Medicine
Rationale: Red blood cells (RBC) undergo morphologic and biochemical changes during storage which may lead to adverse health risks upon transfusion. In prior studies, the effect of RBC age on health outcomes has been conflicting. We designed the study to assess the effects of RBC units' storage duration on health outcomes specifically for hospitalized patients undergoing hip fracture surgery or coronary artery bypass grafting (CABG) surgery. Methods: Using International Classification of Diseases (ICD) 9 codes, hip fracture surgery and CABG surgery patients, who received RBC transfusions between 2008 and 2013, were retrospectively identified from the electronic medical records system. Hip fracture surgery and CABG cohorts were sub-divided into 3 blood age groups based upon RBC unit age at the time of transfusion: young blood (RBC units stored less than or equal to 14 days), old blood (RBC units were stored for greater than or equal to 28 days), or mixed blood for the remaining patients. Outcome variables were 30-day, 90-day, and inpatient mortality as well as hospital length of stay. Results: A total of 3,182 patients were identified: 1,121 with hip fractures and 2,061 with CABG. Transfusion of old blood was associated with higher inpatient mortality in the hip fracture surgery cohort (OR 166.8, 95% CI 1.067-26064.7, p = 0.04) and a higher 30-day mortality in the CABG cohort (OR 4.55, 95% CI 1.01-20.49, p = 0.03). Conclusions: Transfusing RBC units stored for greater than or equal to 28 days may be associated with a higher mortality for patients undergoing hip fracture or CABG.
Building Prediction Models for Dementia: The Need to Account for Interval Censoring and the Competing Risk of Death
(2019-08) Marchetti, Arika L.; Bakoyannis, Giorgos; Li, Xiaochun; Gao, Sujuan; Yiannoutsos, Constantin
Context. Prediction models for dementia are crucial for informing clinical decision making in older adults. Previous models have used genotype and age to obtain risk scores to determine risk of Alzheimer’s Disease, one of the most common forms of dementia (Desikan et al., 2017). However, previous prediction models do not account for the fact that the time to dementia onset is unknown, lying between the last negative and the first positive dementia diagnosis time (interval censoring). Instead, these models use time to diagnosis, which is greater than or equal to the true dementia onset time. Furthermore, these models do not account for the competing risk of death which is quite frequent among elder adults. Objectives. To develop a prediction model for dementia that accounts for interval censoring and the competing risk of death. To compare the predictions from this model with the predictions from a naïve analysis that ignores interval censoring and the competing risk of death. Methods. We apply the semiparametric sieve maximum likelihood (SML) approach to simultaneously model the cumulative incidence function (CIF) of dementia and death while accounting for interval censoring (Bakoyannis, Yu, & Yiannoutsos, 2017). The SML is implemented using the R package intccr. The CIF curves of dementia are compared for the SML and the naïve approach using a dataset from the Indianapolis Ibadan Dementia Project. Results. The CIF from the SML and the naïve approach illustrated that for healthier individuals at baseline, the naïve approach underestimated the incidence of dementia compared to the SML, as a result of interval censoring. Individuals with a poorer health condition at baseline have a CIF that appears to be overestimated in the naïve approach. This is due to older individuals with poor health conditions having an elevated risk of death. Conclusions. The SML method that accounts for the competing risk of death along with interval censoring should be used for fitting prediction/prognostic models of dementia to inform clinical decision making in older adults. Without controlling for the competing risk of death and interval censoring, the current models can provide invalid predictions of the CIF of dementia.
A Comparison of Error Rates Between Intravenous Push Methods: A Prospective, Multisite, Observational Study
(Lippincott, Williams & Wilkins, 2018-03) Hertig, John B.; Degnan, Daniel D.; Scott, Catherine R.; Lenz, Janelle R.; Li, Xiaochun; Anderson, Chelsea M.; Biostatistics, School of Public Health
Objectives Current literature estimates the error rate associated with the preparation and administration of all intravenous (IV) medications to be 9.4% to 97.7% worldwide. This study aims to compare the number of observed medication preparation and administration errors between the only commercially available ready-to-administer product (Simplist) and IV push traditional practice, including a cartridge-based syringe system (Carpuject) and vials and syringes. Methods A prospective, multisite, observational study was conducted in 3 health systems in various states within the United States between December 2015 and March 2016 to observe IV push medication preparation and administration. Researchers observed a ready-to-administer product and IV push traditional practice using a validated observational method and a modified data collection sheet. All observations were reconciled to the original medication order to determine if any errors occurred. Results Researchers collected 329 observations (ready to administer = 102; traditional practice = 227) and observed 260 errors (ready to administer = 25; traditional practice = 235). The overall observed error rate for ready-to-administer products was 2.5%, and the observed error rate for IV push traditional practice was 10.4%. Conclusions The ready-to-administer group demonstrated a statistically significant lower observed error rate, suggesting that use of this product is associated with fewer observed preparation and administration errors in the clinical setting. Future studies should be completed to determine the potential for patient harm associated with these errors and improve clinical practice because it relates to the safe administration of IV push medications.
Design and rationale of a randomized trial: Using short stay units instead of routine admission to improve patient centered health outcomes for acute heart failure patients (SSU-AHF)
(Elsevier, 2018-09) Fish-Trotter, Hannah; Collins, Sean; Danagoulian, Shooshan; Hunter, Benton; Li, Xiaochun; Levy, Phillip D.; Messina, Frank; Pressler, Susan; Pang, Peter S.; School of Nursing
Nearly 85% of acute heart failure (AHF) patients who present to the emergency department (ED) with acute heart failure are hospitalized. Once hospitalized, within 30 days post-discharge, 27% of patients are re-hospitalized or die. Attempts to improve outcomes with novel therapies have all failed. The evidence for existing AHF therapies are poor: No currently used AHF treatment is known to improve long-term outcomes. ED treatment is largely the same today as 40 years ago. Admitting patients who could have avoided hospitalization may contribute to adverse outcomes. Hospitalization is not benign; patients enter a vulnerable phase post-discharge, at increased risk for morbidity and mortality. When hospitalization is able to be shortened or avoid completely, certain risks can be mitigated, including risk of medication errors, in-hospital falls, delirium, nosocomial infections, and other iatrogenic complications. Additionally, patients would prefer to be home, not hospitalized. Furthermore, hospitalization and re-hospitalization for AHF predominantly affects patients of lower socioeconomic status (SES). Avoiding hospitalization in patients who do not require admission may improve outcomes and quality of life, while reducing costs. Short stay unit (SSU: <24 h, also referred to as an ‘observation unit’) management of AHF may be effective for lower risk patients. However, to date there have only been small studies or retrospective analyses on the SSU management for AHF patients. In addition, SSU management has been considered ‘cheating’ for hospitals trying to avoid 30-day readmission penalties, as SSUs or observation units do not count as an admission. However, more recent analyses demonstrate differential use of observation status has not led to decreases in re-admission, suggesting this concern may be misplaced. Thus, we propose a robust clinical effectiveness trial to demonstrate the effectiveness of this patient-centered strategy.
Design and rationale of the B-lines lung ultrasound guided emergency department management of acute heart failure (BLUSHED-AHF) pilot trial
(Elsevier, 2018) Russell, Frances M.; Ehrman, Robert R.; Ferre, Robinson; Gargani, Luna; Noble, Vicki; Rupp, Jordan; Collins, Sean P.; Hunter, Benton; Lane, Kathleen A.; Levy, Phillip; Li, Xiaochun; O'Connor, Christopher; Pang, Peter S.; Emergency Medicine, School of Medicine
Background Medical treatment for acute heart failure (AHF) has not changed substantially over the last four decades. Emergency department (ED)-based evidence for treatment is limited. Outcomes remain poor, with a 25% mortality or re-admission rate within 30 days post discharge. Targeting pulmonary congestion, which can be objectively assessed using lung ultrasound (LUS), may be associated with improved outcomes. Methods BLUSHED-AHF is a multicenter, randomized, pilot trial designed to test whether a strategy of care that utilizes a LUS-driven treatment protocol outperforms usual care for reducing pulmonary congestion in the ED. We will randomize 130 ED patients with AHF across five sites to, a) a structured treatment strategy guided by LUS vs. b) a structured treatment strategy guided by usual care. LUS-guided care will continue until there are ≤15 B-lines on LUS or 6h post enrollment. The primary outcome is the proportion of patients with B-lines ≤ 15 at the conclusion of 6 h of management. Patients will continue to undergo serial LUS exams during hospitalization, to better understand the time course of pulmonary congestion. Follow up will occur through 90 days, exploring days-alive-and-out-of-hospital between the two arms. The study is registered on ClinicalTrials.gov (NCT03136198). Conclusion If successful, this pilot study will inform future, larger trial design on LUS driven therapy aimed at guiding treatment and improving outcomes in patients with AHF.
Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data
(Springer, 2019-05-10) Fadel, William F.; Urbanek, Jacek K.; Albertson, Steven R.; Li, Xiaochun; Chomistek, Andrea K.; Harezlak, Jaroslaw; Biostatistics, School of Public Health
Wearable accelerometers provide an objective measure of human physical activity. They record high frequency unlabeled three-dimensional time series data. We extract meaningful features from the raw accelerometry data and based on them develop and evaluate a classification method for the detection of walking and its sub-classes, i.e. level walking, descending stairs and ascending stairs. Our methodology is tested on a sample of 32 middle-aged subjects for whom we extracted features based on the Fourier and wavelet transforms. We build subject-specific and group-level classification models utilizing a tree-based methodology. We evaluate the effects of sensor location and tuning parameters on the classification accuracy of the tree models. In the group-level classification setting, we propose a robust feature inter-subject normalization and evaluate its performance compared to unnormalized data. The overall classification accuracy for the three activities at the subject-specific level was on average 87.6%, with the ankle-worn accelerometers showing the best performance with an average accuracy 90.5%. At the group-level, the average overall classification accuracy for the three activities using the normalized features was 80.2% compared to 72.3% for the unnormalized features. In summary, a framework is provided for better use and feature extraction from raw accelerometry data to differentiate among different walking modalities as well as considerations for study design.
Doubly Robust Estimation of Causal Effect: Upping the Odds of Getting the Right Answers
(AHA, 2020) Li, Xiaochun; Shen, Changyu; Biostatistics, School of Public Health
Propensity score–based methods or multiple regressions of the outcome are often used for confounding adjustment in analysis of observational studies. In either approach, a model is needed: A model describing the relationship between the treatment assignment and covariates in the propensity score–based method or a model for the outcome and covariates in the multiple regressions. The 2 models are usually unknown to the investigators and must be estimated. The correct model specification, therefore, is essential for the validity of the final causal estimate. We describe in this article a doubly robust estimator which combines both models propitiously to offer analysts 2 chances for obtaining a valid causal estimate and demonstrate its use through a data set from the Lindner Center Study.
Estimation of treatment effect in a subpopulation: An empirical Bayes approach
(Taylor & Francis, 2016) Shen, Changyu; Li, Xiaochun; Jong, Jaesik; Department of Biostatistics, Richard M. Fairbanks School of Public Health
It is well recognized that the benefit of a medical intervention may not be distributed evenly in the target population due to patient heterogeneity, and conclusions based on conventional randomized clinical trials may not apply to every person. Given the increasing cost of randomized trials and difficulties in recruiting patients, there is a strong need to develop analytical approaches to estimate treatment effect in subpopulations. In particular, due to limited sample size for subpopulations and the need for multiple comparisons, standard analysis tends to yield wide confidence intervals of the treatment effect that are often noninformative. We propose an empirical Bayes approach to combine both information embedded in a target subpopulation and information from other subjects to construct confidence intervals of the treatment effect. The method is appealing in its simplicity and tangibility in characterizing the uncertainty about the true treatment effect. Simulation studies and a real data analysis are presented.
Extending Achilles Heel Data Quality Tool with New Rules Informed by Multi-Site Data Quality Comparison
(IOS, 2019) Huser, Vojtech; Li, Xiaochun; Zhang, Zuoyi; Jung, Sungjae; Woong Park, Rae; Banda, Juan; Razzaghi, Hanieh; Londhe, Ajit; Natarajan, Karthik; Biostatistics, School of Public Health
Large healthcare datasets of Electronic Health Record data became indispensable in clinical research. Data quality in such datasets recently became a focus of many distributed research networks. Despite the fact that data quality is specific to a given research question, many existing data quality platform prove that general data quality assessment on dataset level (given a spectrum of research questions) is possible and highly requested by researchers. We present comparison of 12 datasets and extension of Achilles Heel data quality software tool with new rules and data characterization measures.

Browsing by Author "Li, Xiaochun"

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