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Rao G, Ufholz K, Saroufim P, Menegay H, Beno M. Recognition, diagnostic practices, and cancer outcomes among patients with unintentional weight loss (UWL) in primary care. Diagnosis (Berl) 2023; 10:267-274. [PMID: 37080911 DOI: 10.1515/dx-2023-0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/07/2023] [Indexed: 04/22/2023]
Abstract
OBJECTIVES Identify the incidence, rate of physician recognition, diagnostic practices and cancer outcomes for unintentional weight loss (UWL). METHODS We completed a secondary analysis of structured and unstructured EHR data collected from adult patients between January 1, 2020 and December 31, 2021. We used four common definitions to define UWL, excluding patients with known causes of weight loss, intentional weight loss, and pregnancy. Unstructured physicians' notes were used to identify both intentional weight loss (e.g. dieting) as well as physician recognition of UWL. Cancer outcomes were identified within 12 months of UWL using diagnostic codes. Physician actions (lab tests, etc.) in response to UWL were identified through manual chart review. RESULTS Among 29,494 established primary care patients with a minimum of two weight measurements in 2020 and in 2021, we identified 290 patients who met one or more criteria for UWL (1 %). UWL was recognized by physicians in only 60 (21 %). UWL was more common and more likely to be recognized among older patients. Diagnostic practices were quite variable. A complete blood count, complete metabolic profile, and thyroid stimulating hormone level were the three most common tests ordered in response to UWL. Five patients were diagnosed with cancer within 12 months of UWL (3 in whom UWL was recognized; two in whom it was not.). CONCLUSIONS Unintentional weight loss is poorly recognized across a diverse range of patients. A lack of research-informed guidance may explain both low rates of recognition and variability in diagnostic practices.
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Affiliation(s)
- Goutham Rao
- Department of Family Medicine and Community Health, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kelsey Ufholz
- Department of Family Medicine and Community Health, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Paola Saroufim
- Department of Family Medicine and Community Health, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Harry Menegay
- University Hospitals of Cleveland, Cleveland, OH, USA
| | - Mark Beno
- Population & Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
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Ostropolets A, Albogami Y, Conover M, Banda JM, Baumgartner WA, Blacketer C, Desai P, DuVall SL, Fortin S, Gilbert JP, Golozar A, Ide J, Kanter AS, Kern DM, Kim C, Lai LYH, Li C, Liu F, Lynch KE, Minty E, Neves MI, Ng DQ, Obene T, Pera V, Pratt N, Rao G, Rappoport N, Reinecke I, Saroufim P, Shoaibi A, Simon K, Suchard MA, Swerdel JN, Voss EA, Weaver J, Zhang L, Hripcsak G, Ryan PB. Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study. J Am Med Inform Assoc 2023; 30:859-868. [PMID: 36826399 PMCID: PMC10114120 DOI: 10.1093/jamia/ocad009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/04/2023] [Accepted: 01/23/2023] [Indexed: 02/25/2023] Open
Abstract
OBJECTIVE Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations' variability on patient characteristics. MATERIALS AND METHODS Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams' cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. RESULTS On average, the teams' interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts' size varied from one-third of the master cohort size to 10 times the cohort size (2159-63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3-16.2%). The teams' cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. CONCLUSIONS Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.
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Affiliation(s)
- Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
| | - Yasser Albogami
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mitchell Conover
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - William A Baumgartner
- Division of General Internal Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Priyamvada Desai
- Research IT, Technology and Digital Solutions, Stanford Medicine, Stanford, California, USA
| | - Scott L DuVall
- VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Stephen Fortin
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - James P Gilbert
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | | | - Joshua Ide
- Johnson & Johnson, Titusville, New Jersey, USA
| | - Andrew S Kanter
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
| | - David M Kern
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Chungsoo Kim
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea
| | - Lana Y H Lai
- Department of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, UK
| | - Chenyu Li
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Feifan Liu
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Kristine E Lynch
- VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Evan Minty
- O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada
| | | | - Ding Quan Ng
- Department of Pharmaceutical Sciences, School of Pharmacy & Pharmaceutical Sciences, University of California, Irvine, California, USA
| | - Tontel Obene
- Mississippi Urban Research Center, Jackson State University, Jackson, Mississippi, USA
| | - Victor Pera
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, University of South Australia, Adelaide, Australia
| | - Gowtham Rao
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Nadav Rappoport
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Israel
| | - Ines Reinecke
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Paola Saroufim
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Azza Shoaibi
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Katherine Simon
- VA Tennessee Valley Health Care System, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Marc A Suchard
- Department of Biostatistics, University of California, Los Angeles, California, USA
- Department of Human Genetics, University of California, Los Angeles, California, USA
| | - Joel N Swerdel
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Erica A Voss
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - James Weaver
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
| | - Linying Zhang
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
- Medical Informatics Services, New York-Presbyterian Hospital, New York, New York, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
- Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey, USA
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Issa JA, Saroufim P, Saade E, Donskey C. 411. Characterization of Antibiotic Prophylaxis Prior to Trans Rectal Ultrasound Guided Prostate Needle Biopsy (TRUS PNB): A 5-Year Nationwide Study among Patients in the United States Veterans Health Administration. Open Forum Infect Dis 2022. [PMCID: PMC9751923 DOI: 10.1093/ofid/ofac492.488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background Prophylactic antibiotics are used prior to TRUS PNB to reduce risk for infectious complications such as UTI, prostatitis, epididymitis, orchitis, bacteremia and sepsis. Fluoroquinolones have been commonly used for prophylaxis, however, the incidence of post TRUS PNB infections caused by fluoroquinolone resistant Escherichia coli has increased. The purpose of this study was to characterize the prophylaxis agents used nationwide in the Veterans Health Administration (VHA) prior to TRUS PNB. Methods We conducted a review of records of all patients undergoing TRUS PNB in the VHA database from January 1st, 2013 to December 31st, 2018. We collected data about outpatient oral prophylaxis antibiotics, inpatient injectable antibiotics and microbiology results from rectal swabs and urine cultures performed within 90 days prior to the procedure. Results Of 153,055 patients undergoing prostate biopsy between January 1st, 2013 and December 31st, 2018, 27.6% (n=42,319) had urine cultures and 3.9% (n=5,294) had rectal swab cultures done within the 90-day window prior to the procedure. Among these who had urine and rectal swabs culture, 3.0% (n=1,292) and 20.1% (n=1,062) were positive for E. coli, respectively; 40.2% of E. coli isolates recovered from urine and 89.6% recovered from rectal swabs were resistant to fluoroquinolones. Table 1 shows the frequencies and percentages of oral prophylactic agents administered within the 90 days prior to the procedure and injectable antibiotics administered on the day of the procedure. Frequency and percentage of oral prophylactic agents administered within the 90 days prior to the procedure and injectable antibiotics administered on the day of the procedure
![]() Frequency and percentage of oral prophylactic agents administered within the 90 days prior to the procedure and injectable antibiotics administered on the day of the procedure Conclusion Despite the high rate of recovery of fluoroquinolone-resistant E. coli in pre-procedure urine and rectal swabs, oral fluoroquinolones remained the most frequently used prophylactic agents prior to TRUS PNB. This inadequate prophylactic coverage may increase the risk of infectious complications following prostate biopsy. Disclosures Elie Saade, MD, MPH, FIDSA, Janssen: Advisor/Consultant.
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Affiliation(s)
| | | | - Elie Saade
- Case Western Reserve University, Cleveland, Ohio
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Rose J, Dong W, Kim U, Hnath J, Statler A, Saroufim P, Song S, Ascha M, Menegay H, Tian Y, Beno M, Koroukian SM. An informatics infrastructure to catalyze cancer control research and practice. Cancer Causes Control 2022; 33:899-911. [PMID: 35380304 PMCID: PMC10865999 DOI: 10.1007/s10552-022-01571-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 03/07/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE A disconnect often exists between those with the expertise to manage and analyze complex, multi-source data sets, and the clinical, social services, advocacy, and public health professionals who can pose the most relevant questions and best apply the answers. We describe development and implementation of a cancer informatics infrastructure aimed at broadening the usability of community cancer data to inform cancer control research and practice; and we share lessons learned. METHODS We built a multi-level database known as The Ohio Cancer Assessment and Surveillance Engine (OH-CASE) to link data from Ohio's cancer registry with community data from the U.S. Census and other sources. Space-and place-based characteristics were assigned to individuals according to residential address. Stakeholder input informed development of an interface for generating queries based on geographic, demographic, and disease inputs and for outputting results aggregated at the state, county, municipality, or zip code levels. RESULTS OH-CASE contains data on 791,786 cancer cases diagnosed from 1/1/2006 to 12/31/2018 across 88 Ohio counties containing 1215 municipalities and 1197 zip codes. Stakeholder feedback from cancer center community outreach teams, advocacy organizations, public health, and researchers suggests a broad range of uses of such multi-level data resources accessible via a user interface. CONCLUSION OH-CASE represents a prototype of a transportable model for curating and synthesizing data to understand cancer burden across communities. Beyond supporting collaborative research, this infrastructure can serve the clinical, social services, public health, and advocacy communities by enabling targeting of outreach, funding, and interventions to narrow cancer disparities.
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Affiliation(s)
- Johnie Rose
- Case Western Reserve University Center for Community Health Integration, 11000 Cedar Ave., Ste. 402, Cleveland, OH, 44106-7136, USA.
- Case Comprehensive Cancer Center, Cleveland, OH, USA.
| | - Weichuan Dong
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Uriel Kim
- Case Western Reserve University Center for Community Health Integration, 11000 Cedar Ave., Ste. 402, Cleveland, OH, 44106-7136, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Joseph Hnath
- Case Western Reserve University Center for Community Health Integration, 11000 Cedar Ave., Ste. 402, Cleveland, OH, 44106-7136, USA
| | - Abby Statler
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Taussig Cancer Institute, The Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Paola Saroufim
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Sunah Song
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Mustafa Ascha
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Harry Menegay
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Ye Tian
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Mark Beno
- Cleveland Institute for Computational Biology, Case Western Reserve University/University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Siran M Koroukian
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
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Saroufim P, Zweig SA, Conway DS, Briggs FBS. Cardiovascular conditions in persons with multiple sclerosis, neuromyelitis optica and transverse myelitis. Mult Scler Relat Disord 2018; 25:21-25. [PMID: 30014877 DOI: 10.1016/j.msard.2018.07.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 07/02/2018] [Accepted: 07/06/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND Cardiovascular conditions are associated with poorer outcomes in multiple sclerosis (MS). Whether the burden of cardiovascular conditions differs between those with demyelinating disease and unaffected controls is not clear. The objective of this study is to investigate the burden and age of onset of cardiovascular conditions in a US population with MS, neuromyelitis optica spectrum disorder (NMOSD), or transverse myelitis (TM) to unaffected controls adjusting for likely confounders. METHODS Using a case-control study design, we compared the burden of self-reported diabetes mellitus type 2, heart disease, hyperlipidemia, and hypertension in cases with MS (N = 1,548), NMOSD (N = 306), and TM (N = 145) to controls (N = 677), adjusting for demographics, smoking history, obesity, family history of individual cardiovascular conditions, and presence of other cardiovascular conditions. The age of onset for individual cardiovascular conditions were also compared between cases and controls. RESULTS MS cases were 48% more likely to have ever had hypertension than controls (p = 0.01). The prevalence of other cardiovascular conditions did not differ across cases and controls. There were also no differences in the age of cardiovascular disease onset between cases and controls. CONCLUSION Cardiovascular conditions are as common in those with demyelinating diseases compared to unaffected individuals, with hypertension being more common among those with MS.
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Affiliation(s)
- Paola Saroufim
- Neuroimmunological Disorders Gene-Environment Epidemiology Laboratory, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States; Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States
| | - Sophia A Zweig
- Neuroimmunological Disorders Gene-Environment Epidemiology Laboratory, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Devon S Conway
- The Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Farren B S Briggs
- Neuroimmunological Disorders Gene-Environment Epidemiology Laboratory, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States.
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