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Beltran TG, Lett E, Poteat T, Hincapie-Castillo JM. Computational phenotyping within electronic healthcare data to identify transgender people in the United States: A narrative review. Pharmacoepidemiol Drug Saf 2024; 33:e5732. [PMID: 38009550 DOI: 10.1002/pds.5732] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 11/07/2023] [Accepted: 11/10/2023] [Indexed: 11/29/2023]
Abstract
PURPOSE With the expansion of research utilizing electronic healthcare data to identify transgender (TG) population health trends, the validity of computational phenotype (CP) algorithms to identify TG patients is not well understood. We aim to identify the current state of the literature that has utilized CPs to identify TG people within electronic healthcare data and their validity, potential gaps, and a synthesis of future recommendations based on past studies. METHODS Authors searched the National Library of Medicine's PubMed, Scopus, and the American Psychological Association PsycInfo's databases to identify studies published in the United States that applied CPs to identify TG people within electronic healthcare data. RESULTS Twelve studies were able to validate or enhance the positive predictive value (PPV) of their CP through manual chart reviews (n = 5), hierarchy of code mechanisms (n = 4), key text-strings (n = 2), or self-surveys (n = 1). CPs with the highest PPV to identify TG patients within their study population contained diagnosis codes and other components such as key text-strings. However, if key text-strings were not available, researchers have been able to find most TG patients within their electronic healthcare databases through diagnosis codes alone. CONCLUSION CPs with the highest accuracy to identify TG patients contained diagnosis codes along with components such as procedural codes or key text-strings. CPs with high validity are essential to identifying TG patients when self-reported gender identity is not available. Still, self-reported gender identity information should be collected within electronic healthcare data as it is the gold standard method to better understand TG population health patterns.
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Affiliation(s)
- Theo G Beltran
- Department of Epidemiology, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA
- Center for Applied Transgender Studies, Chicago, Illinois, USA
| | - Elle Lett
- Center for Applied Transgender Studies, Chicago, Illinois, USA
- Center for Anti-Racism and Community Health, University of Washington School of Public Health, Seattle, Washington, USA
- Health Systems and Population Health, University of Washington School of Public Health, Seattle, Washington, USA
| | - Tonia Poteat
- Division of Healthcare in Adult Populations, Duke University School of Nursing, Durham, North Carolina, USA
| | - Juan M Hincapie-Castillo
- Department of Epidemiology, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA
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Castro Martínez JC, Santamaría-García H. Understanding mental health through computers: An introduction to computational psychiatry. Front Psychiatry 2023; 14:1092471. [PMID: 36824671 PMCID: PMC9941647 DOI: 10.3389/fpsyt.2023.1092471] [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] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Abstract
Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.
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Affiliation(s)
- Juan Camilo Castro Martínez
- Departamento de Psiquiatría y Salud Mental, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Hernando Santamaría-García
- Ph.D. Programa de Neurociencias, Departamento de Psiquiatría y Salud Mental, Pontificia Universidad Javeriana, Bogotá, Colombia
- Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
- Global Brain Health Institute, University of California, San Francisco – Trinity College Dublin, San Francisco, CA, United States
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Alcamo AM, Barren GJ, Becker AE, Hayes K, Fitzgerald JC, Balamuth F, Pennington JW, Curley MA, Tasker RC, Topjian AA, Weiss SL. Validation of a Computational Phenotype to Identify Acute Brain Dysfunction in Pediatric Sepsis. Pediatr Crit Care Med 2022; 23:1027-1036. [PMID: 36214585 PMCID: PMC9722537 DOI: 10.1097/pcc.0000000000003086] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVES To validate a computational phenotype that identifies acute brain dysfunction (ABD) based on clinician concern for neurologic or behavioral changes in pediatric sepsis. DESIGN Retrospective observational study. SETTING Single academic children's hospital. PATIENTS Four thousand two hundred eighty-nine index sepsis episodes. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS An existing computational phenotype of ABD was optimized to include routinely collected variables indicative of clinician concern for acute neurologic or behavioral change (completion of CT or MRI, electroencephalogram, or new antipsychotic administration). First, the computational phenotype was compared with an ABD reference standard established from chart review of 527 random sepsis episodes to determine criterion validity. Next, the computational phenotype was compared with a separate validation cohort of 3,762 index sepsis episodes to determine content and construct validity. Criterion validity for the final phenotype had sensitivity 83% (95% CI, 76-89%), specificity 93% (90-95%), positive predictive value 84% (77-89%), and negative predictive value 93% (90-96%). In the validation cohort, the computational phenotype identified ABD in 35% (95% CI 33-36%). Content validity was demonstrated as those with the ABD computational phenotype were more likely to have characteristics of neurologic dysfunction and severe illness than those without the ABD phenotype, including nonreactive pupils (15% vs 1%; p < 0.001), Glasgow Coma Scale less than 5 (44% vs 12%; p < 0.001), greater than or equal to two nonneurologic organ dysfunctions (50% vs 25%; p < 0.001), and need for intensive care (81% vs 65%; p < 0.001). Construct validity was demonstrated by higher odds for mortality (odds ratio [OR], 6.9; 95% CI, 5.3-9.1) and discharge to rehabilitation (OR, 11.4; 95% CI 7.4-17.5) in patients with, versus without, the ABD computational phenotype. CONCLUSIONS A computational phenotype of ABD indicative of clinician concern for new neurologic or behavioral change offers a valid retrospective measure to identify episodes of sepsis that involved ABD. This computational phenotype provides a feasible and efficient way to study risk factors for and outcomes from ABD using routinely collected clinical data.
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Affiliation(s)
- Alicia M. Alcamo
- Division of Critical Care Medicine, Department of Anesthesiology and Critical Care, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Anesthesiology and Critical Care, The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Pediatric Sepsis Program, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Gregory J. Barren
- Division of Emergency Medicine, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Andrew E. Becker
- Division of Critical Care Medicine, Department of Anesthesiology and Critical Care, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Katie Hayes
- Pediatric Sepsis Program, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Division of Emergency Medicine, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Julie C. Fitzgerald
- Division of Critical Care Medicine, Department of Anesthesiology and Critical Care, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Anesthesiology and Critical Care, The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Pediatric Sepsis Program, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Fran Balamuth
- Pediatric Sepsis Program, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Division of Emergency Medicine, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Jeffrey W. Pennington
- Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Martha A.Q. Curley
- Department of Anesthesiology and Critical Care, The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Family and Community Health, The University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
| | - Robert C. Tasker
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts, USA
- Selwyn College, Cambridge University, Cambridge, United Kingdom
| | - Alexis A. Topjian
- Division of Critical Care Medicine, Department of Anesthesiology and Critical Care, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Anesthesiology and Critical Care, The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Scott L. Weiss
- Division of Critical Care Medicine, Department of Anesthesiology and Critical Care, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Anesthesiology and Critical Care, The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Pediatric Sepsis Program, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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