Unbiased identification of patients with disorders of sex development

Discordance in chromosomal and self-reported sex in the UK Biobank: Implications for transgender- and intersex-inclusive data collection

There is growing need to distinguish between sex and gender. While sex is assigned at birth, gender is socially constructed and may not correspond to one's assigned sex. However, in most research studies, sex or gender is assessed in isolation or the terms are used interchangeably, which has implications for research accuracy and inclusivity. We used data from the UK Biobank to quantify the prevalence of disagreement between chromosomal and self-reported sex and identify potential reasons for discordance. Among approximately 200 individuals with sex discordance, 71% of discordances were potentially explained by the presence of intersex traits or transgender identity. The findings indicate that when describing sex- and/or gender-specific differences in health, researchers may be limited in their ability to draw conclusions regarding specific sex and/or gender health information.

Words matter: The language of difference in human genetics

Diversity, equity, and inclusion efforts in academia are leading publishers and journals to re-examine their use of terminology for commonly used scientific variables. This reassessment of language is particularly important for human genetics, which is focused on identifying and explaining differences between individuals and populations. Recent guidance on the use of terms and symbols in clinical practice, research, and publications is beginning to acknowledge the ways that language and concepts of difference can be not only inaccurate but also harmful. To stop perpetuating historical wrongs, those of us who conduct and publish genetic research and provide genetic health care must understand the context of the terms we use and why some usages should be discontinued. In this article, we summarize critiques of terminology describing disability, sex, gender, race, ethnicity, and ancestry in research publications, laboratory reports, diagnostic codes, and pedigrees. We also highlight recommendations for alternative language that aims to make genetics more inclusive, rigorous, and ethically sound. Even though norms of acceptable language use are ever changing, it is the responsibility of genetics professionals to uncover biases ingrained in professional practice and training and to continually reassess the words we use to describe human difference because they cause harm to patients.

Decisional Support Needed when Facing Tough Decisions: Survey of Parents with Children having Differences of Sex Development

Introduction

Parents of infants and young children newly diagnosed with differences of sex development (DSD) commonly face medical and psychosocial management decisions at a time when they are first learning about the condition and cannot consult their child for input. The aim of this study was to identify areas of greatest need for parental decisional support.

Methods

34 parents of children receiving care for DSD at one of three US children's hospitals participated in a survey to learn what clinical and psychosocial decisions needed to be made on behalf of their child. Parents were then asked to identify and focus on a "tough" decision and respond to questions assessing factors affecting decision-making, decision-making preferences, decisional conflict, and decision regret. Descriptive analyses were conducted.

Results

Decisions about surgery and aspects of sharing information about their child's condition with others were the two most frequently reported decisions overall, experienced by 97% and 88% of parents, as well as most frequently nominated as tough decisions. Many parents reported mild to moderate levels of decisional conflict (59%) and decision regret (74%). Almost all parents (94%) reported experiencing at least one factor as interfering with decision-making (e.g., "worried too much about choosing the 'wrong' option"). Parents universally reported a desire to be involved in decision-making - preferably making the final decision primarily on their own (79%), or together with their child's healthcare providers (21%). The majority of parents judged healthcare providers (82%) and patient/family organizations (58%) as trustworthy sources of information.

Discussion

Parents of children with DSD encounter medical, surgical, and psychosocial management decisions. Despite difficulties including emotional distress and informational concerns (including gaps and overload), parents express strong desires to play key roles in decision-making on behalf of their children. Healthcare providers can help identify family-specific needs through observation and inquiry in the clinical context. Together with families, providers should focus on specific clinical management decisions and support parental involvement in making decisions on behalf of young children with DSD.

A method to advance adolescent sexual health research: Automated algorithm finds sexual history documentation

Background:

We aimed to develop and validate a rule-based Natural Language Processing (NLP) algorithm to detect sexual history documentation and its five key components [partners, practices, past history of sexually transmitted infections (STIs), protection from STIs, and prevention of pregnancy] among adolescent encounters in the pediatric emergency and inpatient settings.

Methods

We iteratively designed a NLP algorithm using pediatric emergency department (ED) provider notes from adolescent ED visits with specific abdominal or genitourinary (GU) chief complaints. The algorithm is composed of regular expressions identifying commonly used phrases in sexual history documentation. We validated this algorithm with inpatient admission notes for adolescents. We calculated the sensitivity, specificity, negative predictive value, positive predictive value, and F1 score of the tool in each environment using manual chart review as the gold standard.

Results

In the ED test cohort with abdominal or GU complaints, 97/179 (54%) provider notes had a sexual history documented, and the NLP algorithm correctly classified each note. In the inpatient validation cohort, 97/321 (30%) admission notes included a sexual history, and the NLP algorithm had 100% sensitivity and 98.2% specificity. The algorithm demonstrated >97% sensitivity and specificity in both settings for detection of elements of a high quality sexual history including protection used and contraception. Type of sexual practice and STI testing offered were also detected with >97% sensitivity and specificity in the ED test cohort with slightly lower performance in the inpatient validation cohort.

Conclusion

This NLP algorithm automatically detects the presence of sexual history documentation and its key components in ED and inpatient settings.

Differences/Disorders of Sex Development: Medical Conditions at the Intersection of Sex and Gender

Defined as congenital conditions in which development of chromosomal, gonadal, or anatomic sex is atypical, differences or disorders of sex development (DSDs) comprise many discrete diagnoses ranging from those associated with few phenotypic differences between affected and unaffected individuals to those where questions arise regarding gender of rearing, gonadal tumor risk, genital surgery, and fertility. Controversies exist in numerous areas including how DSDs are conceptualized, how to refer to the set of conditions and those affected by them, and aspects of clinical management that extend from social media to legislative bodies, courts of law, medicine, clinical practice, and scholarly research in psychology and sociology. In addition to these aspects, this review covers biological and social influences on psychosocial development and adjustment, the psychosocial and psychosexual adaptation of people born with DSDs, and roles for clinical psychologists in the clinical management of DSDs. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Utility of the Current Procedural Terminology Codes for Prophylactic Stabilization for Defining Metastatic Femur Disease

Introduction:

Cohorts from the electronic health record are often defined by the Current Procedural Terminology (CPT) codes. The error prevalence of CPT codes for patients receiving surgical treatment of metastatic disease of the femur has not been investigated, and the predictive value of coding ontologies to identify patients with metastatic disease of the femur has not been adequately discussed.

Methods:

All surgical cases at a single academic tertiary institution from 2010 through 2015 involving prophylactic stabilization of the femur or fixation of a pathologic fracture of the femur were identified using the CPT and International Classification of Disease (ICD) codes. A detailed chart review was conducted to determine the procedure performed as documented in the surgical note and the patient diagnosis as documented in the pathology report, surgical note, and/or office visit notes.

Results:

We identified 7 CPT code errors of 171 prophylactic operations (4.1%) and one error of 71 pathologic fracture fixation s(1.4%). Of the 164 prophylactic operations that were coded correctly, 87 (53.0%) had metastatic disease. Of the 70 pathologic operations that were coded correctly, 41 (58%) had metastatic disease.

Discussion:

The error prevalence was low in both prophylactic stabilization and pathologic fixation groups (4.1% and 1%, respectively). The structured data (CPT and ICD-9 codes) had a positive predictive value for patients having metastatic disease of 53% for patients in the prophylactic stabilization group and 58% for patients in the pathologic fixation group. The CPT codes and ICD codes assessed in this analysis do provide a useful tool for defining a population in which a moderate proportion of individuals have metastatic disease in the femur at an academic medical center. However, verification is necessary.

A Review of Automatic Phenotyping Approaches using Electronic Health Records

Electronic Health Records (EHR) are a rich repository of valuable clinical information that exist in primary and secondary care databases. In order to utilize EHRs for medical observational research a range of algorithms for automatically identifying individuals with a specific phenotype have been developed. This review summarizes and offers a critical evaluation of the literature relating to studies conducted into the development of EHR phenotyping systems. This review describes phenotyping systems and techniques based on structured and unstructured EHR data. Articles published on PubMed and Google scholar between 2013 and 2017 have been reviewed, using search terms derived from Medical Subject Headings (MeSH). The popularity of using Natural Language Processing (NLP) techniques in extracting features from narrative text has increased. This increased attention is due to the availability of open source NLP algorithms, combined with accuracy improvement. In this review, Concept extraction is the most popular NLP technique since it has been used by more than 50% of the reviewed papers to extract features from EHR. High-throughput phenotyping systems using unsupervised machine learning techniques have gained more popularity due to their ability to efficiently and automatically extract a phenotype with minimal human effort.

Extracting information from the text of electronic medical records to improve case detection: a systematic review

BACKGROUND

Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality.

METHODS

A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed.

RESULTS

Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025).

CONCLUSIONS

Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall).