Literature DB >> 32045308

Differences in Outcomes Associated With Individual Radiologists for Emergency Department Patients With Headache Imaged With CT: A Retrospective Cohort Study of 25,596 Patients.

Matthew S Davenport1,2,3, Shokoufeh Khalatbari4, Nahid Keshavarzi4, Michael Connolly1, Keith E Kocher5, Suzanne T Chong1, Ashok Srinivasan1.   

Abstract

OBJECTIVE. The purpose of this study was to determine whether diagnostic radiologists impart variation into resource use and patient outcomes in emergency department (ED) patients undergoing CT for headache. MATERIALS AND METHODS. This was a single-institution retrospective quality assurance cohort study of 25,596 unique adult ED patients undergoing head CT for headache from January 2012 to October 2017. CT examinations were interpreted by 55 attending radiologists (25 neuroradiologists, 30 radiologists of other specialties) who each interpreted a mean of 1469.8 ± 787.9 CT examinations. Risk adjustment for variables thought to influence outcome included baseline risk (demographics, Elixhauser comorbidity score), clinical factors (vital signs, ED triage and pain scores, laboratory data, hydrocephalus, prior intracranial hemorrhage, neurosurgical consultation within last 12 months), and system factors (time of CT, physician experience, neuroradiology training). Multivariable models were built to analyze the effect of individual radiologists on subsequent outcomes. Any p value less than 0.007 was considered significant after Bonferroni correction. RESULTS. The study found 57.5% (14,718/25,596) of CT interpretations were performed by neuroradiologists, and most patients (98.1% [25,119/25,596]) had no neurosurgical history. After risk adjustment, individual radiologists were not an independent predictor of hospital admission (p = 0.49), 30-day readmission (p = 0.30), 30-day mortality (p = 0.14), or neurosurgical intervention (p = 0.04) but did predict MRI use (p < 0.001; odds ratio [OR] range among radiologists, 0.009-38.2), neurology consultation (p < 0.001; OR range, 0.4-3.2), and neurosurgical consultation (p < 0.001; OR range, 0.1-9.9). CONCLUSION. Radiologists with different skills, experience, and practice patterns appear interchangeable for major clinical outcomes when interpreting CT for headache in the ED, but their differences predict differential use of downstream health care resources. Resource use measures are potential quality indicators in this cohort.

Entities:  

Keywords:  headache; outcome; quality; resource; utilization

Mesh:

Year:  2020        PMID: 32045308      PMCID: PMC8029644          DOI: 10.2214/AJR.19.22189

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  15 in total

1.  A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data.

Authors:  Carl van Walraven; Peter C Austin; Alison Jennings; Hude Quan; Alan J Forster
Journal:  Med Care       Date:  2009-06       Impact factor: 2.983

2.  Comorbidity measures for use with administrative data.

Authors:  A Elixhauser; C Steiner; D R Harris; R M Coffey
Journal:  Med Care       Date:  1998-01       Impact factor: 2.983

3.  Measuring Diagnostic Radiologists: What Measurements Should We Use?

Authors:  Matthew S Davenport; David B Larson
Journal:  J Am Coll Radiol       Date:  2019-02-02       Impact factor: 5.532

4.  Developing Quality Measures for Diagnostic Radiologists: Part 2.

Authors:  Jason N Itri; Kesav Raghavan; Samir B Patel; Jennifer C Broder; Samantha Tierney; Diedra Gray; Judy Burleson; Scott MacDonald; David J Seidenwurm
Journal:  J Am Coll Radiol       Date:  2018-08-28       Impact factor: 5.532

5.  Headaches and neuroimaging: high utilization and costs despite guidelines.

Authors:  Brian C Callaghan; Kevin A Kerber; Robert J Pace; Lesli E Skolarus; James F Burke
Journal:  JAMA Intern Med       Date:  2014-05       Impact factor: 21.873

6.  Peer Feedback, Learning, and Improvement: Answering the Call of the Institute of Medicine Report on Diagnostic Error.

Authors:  David B Larson; Lane F Donnelly; Daniel J Podberesky; Arnold C Merrow; Richard E Sharpe; Jonathan B Kruskal
Journal:  Radiology       Date:  2016-09-27       Impact factor: 11.105

7.  Novel Quality Indicators for Radiologists Interpreting Abdominopelvic CT Images: Risk-Adjusted Outcomes Among Emergency Department Patients With Right Lower Quadrant Pain.

Authors:  Matthew S Davenport; Shokoufeh Khalatbari; James H Ellis; Richard H Cohan; Suzanne T Chong; Keith E Kocher
Journal:  AJR Am J Roentgenol       Date:  2018-04-18       Impact factor: 3.959

8.  Imaging results in a consecutive series of 530 new patients in the Birmingham Headache Service.

Authors:  C E Clarke; J Edwards; D J Nicholl; A Sivaguru
Journal:  J Neurol       Date:  2010-03-03       Impact factor: 4.849

9.  Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Authors:  Hude Quan; Vijaya Sundararajan; Patricia Halfon; Andrew Fong; Bernard Burnand; Jean-Christophe Luthi; L Duncan Saunders; Cynthia A Beck; Thomas E Feasby; William A Ghali
Journal:  Med Care       Date:  2005-11       Impact factor: 2.983

10.  A new Elixhauser-based comorbidity summary measure to predict in-hospital mortality.

Authors:  Nicolas R Thompson; Youran Fan; Jarrod E Dalton; Lara Jehi; Benjamin P Rosenbaum; Sumeet Vadera; Sandra D Griffith
Journal:  Med Care       Date:  2015-04       Impact factor: 2.983

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Review 2.  Is There a Doctors' Effect on Patients' Physical Health, Beyond the Intervention and All Known Factors? A Systematic Review.

Authors:  Christoph Schnelle; Justin Clark; Rachel Mascord; Mark A Jones
Journal:  Ther Clin Risk Manag       Date:  2022-07-21       Impact factor: 2.755

3.  The Doctors' Effect on Patients' Physical Health Outcomes Beyond the Intervention: A Methodological Review.

Authors:  Christoph Schnelle; Mark A Jones
Journal:  Clin Epidemiol       Date:  2022-07-18       Impact factor: 5.814

4.  Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection.

Authors:  Jihang Sun; Haoyan Li; Bei Wang; Jianying Li; Michelle Li; Zuofu Zhou; Yun Peng
Journal:  BMC Med Imaging       Date:  2021-07-08       Impact factor: 1.930

  4 in total

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