Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models

Syed, Shorabuddin; Baghal, Ahmad; Prior, Fred; Zozus, Meredith N.; Al-Shukri, Shaymaa; Syeda, Hafsa Bareen; Garza, Maryam Y.; Begum, Salma; Gates, Kim; Syed, Mahanazuddin; Sexton, Kevin W.

doi:10.4258/hir.2020.26.3.193

Cited by 9 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The selected features included Body mass index (BMI) recorded at the time of the procedure, demographics (age, gender, race, ethnicity, zip code), history of smoking or alcohol use, illicit substances use, visit type (inpatient, outpatient, emergency), comorbidities, current medications that can affect tolerance of moderate sedation, provider performing the procedure, and total procedure time. Co-morbid illness assessment was done by calculating the Elixhauser Comorbidity Index (ECI) score using the Van Walraven algorithm [ 11 , 12 ]. Patients were divided into 4 groups based on BMI: BMI<30, class I obesity (BMI 30–34.99), class II obesity (BMI 35–39.99) and class III obesity (BMI 40 and above).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures

Syed

Prior

et al. 2021

Studies in Health Technology and Informatics

Self Cite

View full text Add to dashboard Cite

Endoscopy procedures are often performed with either moderate or deep sedation. While deep sedation is costly, procedures with moderate sedation are not always well tolerated resulting in patient discomfort, and are often aborted. Due to lack of clear guidelines, the decision to utilize moderate sedation or anesthesia for a procedure is made by the providers, leading to high variability in clinical practice. The objective of this study was to build a Machine Learning (ML) model that predicts if a colonoscopy can be successfully completed with moderate sedation based on patients’ demographics, comorbidities, and prescribed medications. XGBoost model was trained and tested on 10,025 colonoscopies (70% – 30%) performed at University of Arkansas for Medical Sciences (UAMS). XGBoost achieved average area under receiver operating characteristic curve (AUC) of 0.762, F1-score to predict procedures that need moderate sedation was 0.85, and precision and recall were 0.81 and 0.89 respectively. The proposed model can be employed as a decision support tool for physicians to bolster their confidence while choosing between moderate sedation and anesthesia for a colonoscopy procedure.

show abstract

Section: Methodsmentioning

confidence: 99%

Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures

Syed

Prior

et al. 2021

Studies in Health Technology and Informatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Information on all primary and secondary end points was collected. Co-morbid illness assessment was done by calculating the Elixhauser Comorbidity Index (ECI) score using the Van Walraven algorithm 11 12 13 14 . Obstructive sleep apnea (OSA) was recorded as a separate comorbidity because it is not included in ECI and is an important factor to consider during sedation.…”

Section: Methodsmentioning

confidence: 99%

Patient characteristics and procedural outcomes of moderate sedation for endoscopic procedures in patients with obesity: A retrospective, propensity score-matched study

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background Endoscopic procedures are performed commonly with moderate sedation. Obesity can pose a challenge in its safe administration. This study was aimed at assessing outcomes of endoscopy procedures performed with moderate sedation in obese patients. Patients and methods This was a retrospective study of patients undergoing esophagogastroduodenoscopy (EGD) and/or colonoscopy with moderate sedation from July 17, 2017 to December 31, 2019. Demographics, comorbidities, outpatient medications and procedure-related outcomes (procedure time, recovery time, cardiopulmonary adverse events, 7-day post-procedure hospitalization, cecal intubation time, withdrawal time, tolerance of moderate sedation and sedation medications administered) were compared for patient with and without obesity after propensity score matching. Standard statistical methods were used for analysis. Results A total of 7601 procedures were performed with moderate sedation for 5746 patients. Propensity score matching identified 1360 and 1740 pairs of EGDs and colonoscopies with moderate sedation for patients with and without obesity. Recovery time was found to be shorter for obese patients undergoing EGD (OR: 0.989, 95 % CI: 0.981–.998; P = 0.01). Obese patients did not differ from non-obese patients in any other procedure-related outcomes for EGDs or colonoscopies. Conclusions Outcomes for endoscopy procedures performed with moderate sedation were noted to be similar between obese and non-obese patients. These findings suggest that moderate sedation can be used safely for endoscopic procedures in patients with obesity.

show abstract

“…19,24 The overall score ranges from − 19-89. 20,24 The individual numerical values associated with each speci c patient's set of conditions is calculated by totaling the sum of the weights. The four dichotomous variables were created by segmenting the VWECS into equal quartiles, representing elevated ranges of mortality risk.…”

Section: Statistical Analysis and Variablesmentioning

confidence: 99%

The Association of Implemented Care Team Configuration Models with Length of Stay and Charges in Hospitalized Patients with Congestive Heart Failure: A Retrospective Analysis of EHR Data

Williams,

Crump,

Parker

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: Clinicians are the conduits of high-quality care delivery. Clinicians have driven advancements in pharmacotherapeutics, devices, and related interventions and improved morbidity and mortality in patients with congestive heart failure over the past decade. Yet, the management of congestive heart failure has become extraordinarily complex and has fueled recommendations from the American Heart Association and the American College of Cardiology to optimize the composition of the care team to reduce the health, economic, and health system burden of high length of stay and hospital charges. Therefore, the purpose of this study was to identify the extent to which care team configuration models of non-specialty clinicians were associated with high length of stay and high charges during the hospitalizations of patients with congestive heart failure. Methods: This study performed a retrospective analysis of EHR-extracted data on 3,099 patients and their encounters from the Arkansas Clinical Data Repository. The data was analyzed using binomial logistic regression in which adjusted odds ratios reflected the association of specific care team configuration models (i.e., combination of clinical roles) with length of stay and hospital charges. Results: Care team configuration models that included physicians, residents, and advanced nurse practitioners were generally associated with greater odds of a patient having a high length of stay or a high charge hospital encounter. Registered nurses were the only specific care team role found within all care team configuration models and has consistently been associated with decreased length of stay and decreased charges. Additionally, the study validated the Van Walraven Elixhauser Comorbidity Score by finding that its quartiles were statistically significantly associated with length of stay and charges. Conclusions: Cardiologists, alone, cannot shoulder the burden of caring for hospitalized patients with congestive heart failure. Care team configuration data within electronic health record systems of hospitals could be an effective method of isolating and tracking high risk patients. Within multidisciplinary care teams, registered nurses may be particularly effective in advancing real-time risk stratification by applying the Van Walraven Elixhauser Comorbidity Score at the point-of-care.

show abstract

Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models

Cited by 9 publications

References 24 publications

Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures

Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures

Patient characteristics and procedural outcomes of moderate sedation for endoscopic procedures in patients with obesity: A retrospective, propensity score-matched study

The Association of Implemented Care Team Configuration Models with Length of Stay and Charges in Hospitalized Patients with Congestive Heart Failure: A Retrospective Analysis of EHR Data

Contact Info

Product

Resources

About