Clinical trial emulation can identify new opportunities to enhance the regulation of drug safety in pregnancy.

From the perspective of most regulatory agencies, it is usually unethical to perform interventional clinical trials on pregnant people. While this policy recognizes the vulnerability of an expectant mother and unborn child, it has created a public health emergency for millions of pregnant patients through a dearth of robust safety data for many common drugs. To address this problem, we harnessed an enterprise collection of 2.8M electronic health records (EHRs) originally collected from routine primary care, leveraging the data linkage between mothers and their babies to create a surrogate for randomized, controlled drug trials in this population. To demonstrate the feasibility of our clinical trial emulation platform to stimulate new hypotheses for post-market drug surveillance, we identified 1,054 drugs historically prescribed to pregnant patients and developed a medication history-wide association study and follow-up evidence synthesis platform (leveraging expert clinician review and real-world data analysis) to test the effects of maternal exposure to these drugs on the incidence of neurodevelopmental defects in their children. Our results replicate known teratogenic risks and existing knowledge on drug structure-related teratogenic risks. Herein, we highlight 5 common drug classes that we believe warrant further assessment of their safety in pregnancy. We also discuss our efforts to develop a discovery-to-regulatory framework that could allow for pragmatic translation of our results to enhanced regulatory policy. Collectively, our work presents a simple approach to evaluating the utility of EHRs in guiding new regulatory review programs focused on improving the delicate equipoise of accuracy and ethics inherent to assessing drug safety in an extremely vulnerable patient population.

A new tool to measure acuity in the community: a case study

The provision of acute healthcare within patients own home (i.e. hospital in the home) is an important method of providing individualised patient-centred care that reduces the need for acute hospital admissions and enables early hospital discharge for appropriate patient groups. The Hospital in the Home (HitH) model of care ensures that this approach maximises patient safety and limits potential risk for patients. As HitH services have seen record numbers of patient referrals in the past 2 years, there is now a greater need to measure and understand the acuity and dependency levels of the caseload. Through an expert clinician development process at one NHS trust, aspects of procedural complexity, interdisciplinary working, risk stratification and comorbidities were used to quantify acuity and dependency. This paper uses a case study approach to present a new method of measuring this important concept.

Characterising Residual Limb Morphology and Prosthetic Socket Design Based on Expert Clinician Practice

Functional, comfortable prosthetic limbs depend on personalised sockets, currently designed using an iterative, expert-led process, which can be expensive and inconvenient. Computer-aided design and manufacturing (CAD/CAM) offers enhanced repeatability, but far more use could be made from clinicians’ extensive digital design records. Knowledge-based socket design using smart templates could collate successful design features and tailor them to a new patient. Based on 67 residual limb scans and corresponding sockets, this paper develops a method of objectively analysing personalised design approaches by expert prosthetists, using machine learning: principal component analysis (PCA) to extract key categories in anatomic and surgical variation, and k-means clustering to identify local ‘rectification’ design features. Rectification patterns representing Total Surface Bearing and Patella Tendon Bearing design philosophies are identified automatically by PCA, which reveals trends in socket design choice for different limb shapes that match clinical guidelines. Expert design practice is quantified by measuring the size of local rectifications identified by k-means clustering. Implementing smart templates based on these trends requires clinical assessment by prosthetists and does not substitute training. This study provides methods for population-based socket design analysis, and example data, which will support developments in CAD/CAM clinical practice and accuracy of biomechanics research.

Characterising Residual Limb Morphology and Prosthetic Socket Design based on Expert Clinician Practice

Functional, comfortable prosthetic limbs depend on personalised sockets, currently designed using an iterative, expert-led process, which can be expensive and inconvenient. Computer aided design and manufacturing (CAD/CAM) offers enhanced repeatability, but far more use could be made from clinicians’ extensive digital design records. Knowledge-based socket design using smart templates could collate successful design features and tailor them to a new patient. Based on 67 residual limb scans and corresponding sockets, this paper develops a method of objectively analysing personalised design approaches by expert prosthetists, using machine learning: principal component analysis (PCA) to extract key categories in anatomic and surgical variation, and K-Means Clustering to identify local ‘rectification’ design features. Rectification patterns representing Total Surface Bearing and Patella Tendon Bearing design philosophies are identified automatically by PCA, which reveals trends in socket design choice for different limb shapes that match clinical guidelines. Expert design practice is quantified by measuring the size of local rectifications identified by k-means clustering. Implementing smart templates based on these trends requires clinical assessment by prosthetists and does not substitute training. This study provides methods for population-based socket design analysis, and example data, which will support developments in CAD/CAM clinical practice and accuracy of biomechanics research.

Development and validation of the Sorting non-trauMatIc adoLescent knEe pain (SMILE) tool – a development and initial validation study

Background Despite the commonality of adolescent knee pain, there are no tools to support medical doctors to correctly diagnose knee pain. This study aimed to develop and evaluate a support tool for diagnosing the most common types of non-traumatic adolescent knee pain. Method A systematic search on Medline identified the literature on clinical tests and diagnoses of adolescent knee pain. The search was supplemented by textbooks and transformed into a diagnostic flowchart based on onset, symptoms, and pain localisation. This tool was revised based on feedback from general practitioners and experts in sports medicine. The tool was evaluated on two separate days with blinded assessors. Overall, 27 participants (aged 10–17 years) with non-traumatic knee pain were included. All participants were diagnosed by medical doctors or medical students, without and with the use of the tool. Diagnoses were compared to a gold standard (expert clinician). An interview to inform optimisations of the tool was performed with the assessors. Percentage agreement with the gold standard, and Kappa statistic for interrater reliability were calculated. Results The final tool improved diagnostic agreement with the gold standard from 22.7% (95% CI 10.3–35.1) to 77.3% (95% CI 64.9–89.7). Inter-rater reliability increased from poor agreement k = − 0.04 (95% CI, − 0.12-0.04) to moderate agreement k = 0.56 (95% CI, 0.40–0.72). Conclusion This simple diagnostic tool is quick to use and may assist doctors in diagnosing non-traumatic knee pain in adolescents.

Inherited Neuromuscular Disorders: Which Role for Serum Biomarkers?

Inherited neuromuscular disorders (INMD) are a heterogeneous group of rare diseases that involve muscles, motor neurons, peripheral nerves or the neuromuscular junction. Several different lab abnormalities have been linked to INMD: sometimes they are typical of the disorder, but they usually appear to be less specific. Sometimes serum biomarkers can point out abnormalities in presymtomatic or otherwise asymptomatic patients (e.g., carriers). More often a biomarker of INMD is evaluated by multiple clinicians other than expert in NMD before the diagnosis, because of the multisystemic involvement in INMD. The authors performed a literature search on biomarkers in inherited neuromuscular disorders to provide a practical approach to the diagnosis and the correct management of INMD. A considerable number of biomarkers have been reported that support the diagnosis of INMD, but the role of an expert clinician is crucial. Hence, the complete knowledge of such abnormalities can accelerate the diagnostic workup supporting the referral to specialists in neuromuscular disorders.

50 Studies Every Urologist Should Know

This book describes the most influential studies that have shaped today’s clinical practice of urology. Its scope spans the spectrum of clinical urology, including genitourinary oncology (prostate, renal, bladder, and testis cancer), stone disease, benign prostatic hyperplasia, erectile dysfunction, and female urology. Most included studies are landmark randomized controlled trials related to questions of therapy, but the selection also includes prospective observational cohort studies as well as some case series that have resulted in paradigm-shifting changes in patient management. Each individual study is succinctly presented using a standardized format that focuses on the most important aspects relating to its design and main findings. This is followed by a summary of relevant, closely related studies as well as a brief critique of each study’s limitations. The evidence-based format of this book is further underscored by the frequent reference to relevant clinical practice guidelines. Each chapter closes with a clinical scenario that presents a management question for which an expert clinician proposes an answer based on the current best evidence.

Development and validation of the Sorting non-trauMatIc adoLescent knEe pain (SMILE) tool – a development and initial validation study

Background: Despite the commonality of adolescent knee pain, there are no tools to support medical doctors to correctly diagnose knee pain. This study aimed to develop and evaluate a support tool for diagnosing the most common types of non-traumatic adolescent knee pain.Method: A systematic search on Medline identified the literature on clinical tests and diagnoses of adolescent knee pain. The search was supplemented by textbooks and transformed into a diagnostic flowchart based on onset, symptoms, and pain localisation. This tool was revised based on feedback from general practitioners and experts in sports medicine. The tool was evaluated on two separate days with blinded assessors. Overall, 27 participants (aged 10-17 years) with non-traumatic knee pain were included. All participants were diagnosed by medical doctors or medical students, without and with the use of the tool. Diagnoses were compared to a gold standard (expert clinician). An interview to inform optimisations of the tool was performed with the assessors. Percentage agreement with the gold standard, and Kappa statistic for interrater reliability were calculated.Results: The final tool improved diagnostic agreement with the gold standard from 22.7% (95% CI 10.3-35.1) to 77.3% (95% CI 64.9-89.7). Inter-rater reliability increased from poor agreement k=-0.04 (95% CI, -0.12-0.04) to moderate agreement k=0.56 (95% CI, 0.40-0.72).Conclusion: This simple diagnostic tool is quick to use and may assist doctors in diagnosing non-traumatic knee pain in adolescents.

Priority concerns for people with intellectual and developmental disabilities during the COVID-19 pandemic

Background The approach taken to support individuals during the coronavirus disease 2019 (COVID-19) pandemic needs to take into account the requirements of people with intellectual disabilities and/or autism, who represent a major vulnerable group, with higher rates of co-occurring health conditions and a greater risk of dying prematurely. To date, little evidence on COVID-related concerns have been produced and no report has provided structured feedback from the point of view of people with intellectual disabilities and/or autism or of their family/carers. Aims To provide systemised evidence-based information of the priority concerns for people with intellectual disabilities and/or autism regarding the COVID-19 pandemic. Method Senior representatives of major UK-based professional and service-user representative organisations with a stake in the care of people with intellectual disabilities and/or autism were contacted to provide a list of concerns across three domains: ‘mental health and challenging behaviour’, ‘physical health and epilepsy’ and ‘social circumstances and support’. The feedback was developed into statements on frequently reported priorities. These statements were then rated independently by expert clinicians. A video-conference meeting to reconcile outliers and to generate a consensus statement list was held. Results Thirty-two organisations were contacted, of which 26 (81%) replied. From the respondent's data, 30 draft consensus statements were generated. Following expert clinician review, there was initially strong consensus for seven statements (23%), increasing to 27 statements (90%) following video conferencing. Conclusions These recommendations highlight the expectations of people with intellectual disabilities and/or autism in the current pandemic. This could support policymakers and professionals’ deliver and evidence person-centred care.

“‘You lose your hair, what’s the big deal?’ I was so embarrassed, I was so self-conscious, I was so depressed:” a qualitative interview study to understand the psychosocial burden of alopecia areata

Background Alopecia areata (AA) is characterized by hair loss that can affect the scalp and body. This study describes the psychosocial burden of AA. Methods Participants diagnosed with AA who had experienced ≥50% scalp hair loss according to the Severity of Alopecia Tool (SALT) were identified by clinicians. A semi-structured interview guide, developed with expert clinician input, included open-ended questions to explore patients’ experiences of living with AA. Data were thematically analyzed to identify concepts and relationships. Results Participants (n = 45, 58% female, mean age 33.3 years [range 15–72], mean SALT 67.2 [range 0–100]) described the AA diagnosis as “devastating”. Both males and females reported emotional and psychological impacts of AA including feeling sad/depressed (n = 21), embarrassed/ashamed (n = 10) and angry/frustrated (n = 3). Patients felt helpless (n = 5) due to the unpredictability of disease recurrence, and anxious (n = 19) about judgement from others. Many patients avoided social situations (n = 18), which impaired relationships and increased isolation. Coping strategies included concealment of hair loss through wigs or make-up, although fear of the displacement of these coverings also caused anxiety and the avoidance of activities that could result in scalp exposure (n = 22). Some patients became more accepting of AA over time, which lessened the emotional impact, though efficacious treatment was still desired. A conceptual framework was developed, and a conceptual model was created to depict the relationship between the physical signs/symptoms and the associated psychosocial effects of AA. Conclusion AA impairs patients’ emotional and psychological wellbeing, relationships and lifestyles. Greater disease awareness and effective treatments are needed.