Recognition, diagnostic practices, and cancer outcomes among patients with unintentional weight loss (UWL) in primary care.

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.

Survey of the Faculty Status of Optometrists at Academic Medical Centers in the United States.

SIGNIFICANCE: This is the first study of the prevalence and faculty status of optometrists practicing at academic medical centers in the United States. PURPOSE: This study aimed to quantify the number of optometrists at academic medical centers, along with faculty rank and post-doctoral training programs. METHODS: The official Web sites of academic medical centers and schools of medicine in the United States were examined during the 2021 to 2022 academic year to identify departments of ophthalmology and collect faculty profiles of employed optometrists. Institutional data were cross-referenced and analyzed by geographic distribution. Data were gathered from the Association of Schools and Colleges of Optometry and Accreditation Council on Optometry Education to identify post-graduate training programs in optometry. RESULTS: A total of 192 academic medical centers were identified, of which 121 (63.02%) had a residency or fellowship program in ophthalmology and/or optometry. One hundred twenty-five (65.10%) of these institutions had at least one staff optometrist. Seven hundred eighteen optometrists were found at these institutions, representing 1.83% of the 39,205 practicing optometrists estimated in the United States. Of the 718 optometrists, 369 (51.39%) held an academic appointment at a medical school. The most common academic rank was assistant professor (184 [25.63%]), followed by instructor (138 [19.22%]), associate professor (34 [4.74%]), and full professor (13 [1.81%]). The distribution of academic rank was similar across all regions but varied between institutions as to whether all, some, or none of the optometric faculty were appointed by a medical school. Of 296 optometry residency programs in the United States, 21 (7.09%) were at an academic medical center. Of 15 optometric fellowship programs in the United States, 3 (20%) were at an academic medical center. Of the 192 institutions in this study, 22 (11.46%) had a post-doctoral optometric training program. CONCLUSIONS: This study shows the distribution of academic rank and post-doctoral training programs for optometrists at academic medical centers.

Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study.

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.

An informatics infrastructure to catalyze cancer control research and practice.

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.

Facilitating Cancer Epidemiologic Efforts in Cleveland via Creation of Longitudinal De-Duplicated Patient Data Sets.

BACKGROUND: Cleveland, Ohio, is home to three major hospital systems serving approximately 80% of the Northeast Ohio population. The Cleveland Clinic, University Hospitals Health System, and MetroHealth are direct competitors for primary and specialty care, and patient overlap between these systems is high. Fragmentation of health data that exist in silos at these health systems produces an overestimation of disease burden due to double and sometimes triple counting of patients. As a result, longitudinal population-based studies across the Cleveland patient population are impeded unless accurate and actionable clinically derived health data sets can be created. METHODS: The Cleveland Institute for Computational Biology has developed the De-Duplicate and De-Identify Research Engine (DeDeRE) that, without any exchange of personal health identifiers (PHI) between health systems, will effectively de-duplicate the patients between one or more health entities. RESULTS: The immediate utility of this software for cancer epidemiology is the increased accuracy in measuring cancer burden and the potential to perform longitudinal studies with de-duplicated, de-identified data sets. CONCLUSIONS: The DeDeRE software developed and tested here accomplishes its goals without exposing PHIs using a state-of-the-art, trusted privacy preservation network enabled by a hash-based matching algorithm. IMPACT: This paper will guide the reader through the functions currently developed in DeDeRE and how a healthcare organization (HCO) employing the release version of this technology can begin sharing data with one or more additional HCOs in a collaborative and noncompetitive manner to create a regional population health resource for cancer researchers.See all articles in this CEBP Focus section, "Modernizing Population Science."

Cardiovascular conditions in persons with multiple sclerosis, neuromyelitis optica and transverse myelitis.

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.

Acute and Fatal Isoniazid-Induced Hepatotoxicity: A Case Report and Review of the Literature.

This paper describes a case of an acute and fatal isoniazid-induced hepatotoxicity and provides a review of the literature. A 65-year-old female diagnosed with latent Mycobacterium tuberculosis infection was receiving oral isoniazid 300 mg daily. She was admitted to the hospital for epigastric and right sided flank pain of one-week duration. Laboratory results and imaging confirmed hepatitis. After ruling out all other possible causes, she was diagnosed with isoniazid-induced acute hepatitis (probable association by the Naranjo scale). After discharge, the patient was readmitted and suffered from severe coagulopathy, metabolic acidosis, acute kidney injury, hepatic encephalopathy, and cardiorespiratory arrest necessitating two rounds of cardiopulmonary resuscitation. Despite maximal hemodynamic support, the patient did not survive. A review of the literature, from several European countries and the United States of America, revealed a low incidence of mortality due to isoniazid-induced hepatotoxicity when used as a single agent for latent Mycobacterium tuberculosis infection. As for the management, the first step consists of withdrawing isoniazid and rechallenge is usually discouraged. Few treatment modalities have been proposed; however there is no robust evidence to support any of them. Routine monitoring for hepatotoxicity in patients receiving isoniazid is warranted to prevent morbidity and mortality.