Systolic Dysfunction Predicts 30 Day Heart Failure Specific Readmission in US Academic Medical Centers and All-Cause Readmission in Academic Safety Net Hospitals

2011 ◽  
Vol 17 (8) ◽  
pp. S70
Author(s):  
Edward F. Gibbons ◽  
Ellen Robinson ◽  
Paula Minton-Foltz ◽  
J. Richard Goss
Keyword(s):  
Heart Failure ◽  
Systolic Dysfunction ◽  
Safety Net ◽  
Academic Medical Centers ◽  
Medical Centers ◽  
Academic Medical

scholarly journals Identifying Heart Failure from Electronic Health Records: A Systematic Evidence Review

2021 ◽  
Author(s):  
Rebecca T. Levinson ◽  
Jennifer R. Malinowski ◽  
Suzette J. Bielinski ◽  
Luke V. Rasmussen ◽  
Quinn S. Wells ◽  
...  
Keyword(s):  
Heart Failure ◽  
Electronic Health Records ◽  
Academic Medical Centers ◽  
Significant Heterogeneity ◽  
Health Records ◽  
Algorithm Development ◽  
Medical Centers ◽  
Academic Medical ◽  
Evidence Review ◽  
Electronic Health

ABSTRACTBackgroundHeart failure (HF) is a complex syndrome associated with significant morbidity and healthcare costs. Electronic health records (EHRs) are widely used to identify patients with HF and other phenotypes. Despite widespread use of EHRs for phenotype algorithm development, it is unclear if the characteristics of identified populations mirror those of clinically observed patients and reflect the known spectrum of HF phenotypes.MethodsWe performed a subanalysis within a larger systematic evidence review to assess the different methods used for HF algorithm development and their application to research and clinical care. We queried PubMed for articles published up to November 2020. Out of 318 studies screened, 25 articles were included for primary analysis and 15 studies using only International Classification of Diseases (ICD) codes were evaluated for secondary analysis. Results are reported descriptively.ResultsHF algorithms were most often developed at academic medical centers and the V.A. One health system was responsible for 8 of 10 HF algorithm studies. HF and congestive HF were the most frequent phenotypes observed and less frequently, specific HF subtypes and acute HF. Diagnoses were the most common data type used to identify HF patients and echocardiography was the second most frequent. The majority of studies used rule-based methods to develop their algorithm. Few studies used regression or machine learning methods to identify HF patients. Validation of algorithms varied considerably: only 52.9% of HF and 44.4% of HF subtype algorithms were validated, but 75% of acute HF algorithms were. Demographics of any study population were reported in 68% of algorithm studies and 53% of ICD-only studies. Fewer than half reported demographics of their HF algorithm-identified population. Of those reporting, most identified majority male (>50%) populations, including both algorithms for HF with preserved ejection fraction.ConclusionThere is significant heterogeneity in phenotyping methodologies used to develop HF algorithms using EHRs. Validation of algorithms is inconsistent but largely relies on manual review of patient records. The concentration of algorithm development at one or two sites may reduce potential generalizability of these algorithms to identify HF patients at non-academic medical centers and in populations from underrepresented regions. Differences between the reported demographics of algorithm-identified HF populations those expected based on HF epidemiology suggest that current algorithms do not reflect the full spectrum of HF patient populations.

Quality of care for patients hospitalized with heart failure at academic medical centers

1999 ◽  
Vol 137 (6) ◽  
pp. 1028-1034 ◽  
Author(s):  
Anju Nohria ◽  
Ya-Ting Chen ◽  
David J. Morton ◽  
Robin Walsh ◽  
Peter H. Vlasses ◽  
...  
Keyword(s):  
Heart Failure ◽  
Quality Of Care ◽  
Academic Medical Centers ◽  
Medical Centers ◽  
Academic Medical

Unplanned Reoperation and Implant Revision After Total Wrist Arthroplasty

Hand ◽  
2020 ◽  
pp. 155894471989881 ◽  
Author(s):  
Taylor M. Pong ◽  
Wouter F. van Leeuwen ◽  
Kamil Oflazoglu ◽  
Philip E. Blazar ◽  
Neal Chen
Keyword(s):  
Risk Factors ◽  
Implant Removal ◽  
Academic Medical Centers ◽  
Implant Design ◽  
Factors Associated ◽  
Medical Centers ◽  
Total Wrist Arthroplasty ◽  
Academic Medical ◽  
Related Risk ◽  
Wrist Arthroplasty

Background: Total wrist arthroplasty (TWA) is a treatment option for many debilitating wrist conditions. With recent improvements in implant design, indications for TWA have broadened. However, despite these improvements, there are still complications associated with TWA, such as unplanned reoperation and eventual implant removal. The goal of this study was to identify risk factors for an unplanned reoperation or implant revision after a TWA at 2 academic medical centers between 2002 and 2015. Methods: In this retrospective study, 24 consecutive TWAs were identified using CPT codes. Medical records were manually reviewed to identify demographic, patient- or disease-related, and surgery-related risk factors for reoperation and implant removal after a primary TWA. Results: Forty-six percent of wrists (11 of 24 TWAs performed) had a reoperation after a median of 3.4 years, while 29% (7 of 24) underwent implant revision after a median of 5 years. Two patients had wrist surgery prior to their TWA, both eventually had their implant removed ( P = .08). There were no risk factors associated with reoperation or implant removal. Conclusion: Unplanned reoperation and implant removal after a primary TWA are common. Approximately 1 in 3 wrists are likely to undergo revision surgery. We found no factors associated with reoperation or implant removal; however, prior wrist surgery showed a trend toward risk of implant removal after TWA.

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