Clinical Impact of Prospective Whole Genome Sequencing in Sarcoma Patients

With more than 70 different histological sarcoma subtypes, accurate classification can be challenging. Although characteristic genetic events can largely facilitate pathological assessment, large-scale molecular profiling generally is not part of regular diagnostic workflows for sarcoma patients. We hypothesized that whole genome sequencing (WGS) optimizes clinical care of sarcoma patients by detection of diagnostic and actionable genomic characteristics, and of underlying hereditary conditions. WGS of tumor and germline DNA was incorporated in the diagnostic work-up of 83 patients with a (presumed) sarcomas in a tertiary referral center. Clinical follow-up data were collected prospectively to assess impact of WGS on clinical decision making. In 12/83 patients (14%), the genomic profile led to revision of cancer diagnosis, with change of treatment plan in eight. All twelve patients had undergone multiple tissue retrieval procedures and immunohistopathological assessments by regional and expert pathologists prior to WGS analysis. Actionable biomarkers with therapeutic potential were identified for 30/83 patients. Pathogenic germline variants were present in seven patients. In conclusion, unbiased genomic characterization with WGS identifies genomic biomarkers with direct clinical implications for sarcoma patients. Given the diagnostic complexity and high unmet need for new treatment opportunities in sarcoma patients, WGS can be an important extension of the diagnostic arsenal of pathologists.

Managing pregnancy in a spinal muscular atrophy type III patient in Indonesia: a case report

Background Spinal muscular atrophy is a genetic disorder characterized by degeneration of lower motor neurons, leading to progressive muscular atrophy and even paralysis. Spinal muscular atrophy usually associated with a defect of the survival motor neuron 1 (SMN-1) gene. Classification of spinal muscular atrophy is based on the age of onset and maximum motor function milestone achieved. Although spinal muscular atrophy can be screened for in newborns, and even confirmed earlier genetically, this remains difficult in Third World countries such as Indonesia. Case presentation A 28-year-old Asian woman in the first trimester of her second pregnancy, was referred to the neurology department from the obstetric department. Her milestone history showed she was developmentally delayed and the ability to walk independently was reached at 26 months old. At 8 years old, she started to stumble and lose balance while walking. At this age, spinal muscular atrophy was suspected because of her clinical presentations, without any molecular genetic testing. She was married at the age of 25 years and was soon pregnant with her first child. At the gestational age of 32 weeks, her first pregnancy was ended by an emergency caesarean section because of premature rupture of the membranes. In this second pregnancy, she was referred early to the general hospital from the district hospital to receive multidisciplinary care. She and her first daughter underwent genetic testing for spinal muscular atrophy, which has been readily available in our institution since 2018, to confirm the diagnosis and prepare for genetic counseling. Conclusions Managing pregnancy in a patient with spinal muscular atrophy should be performed collaboratively. In this case, genetic testing of spinal muscular atrophy and the collaborative management of this patient allowed the clinical decision making and genetic counseling throughout her pregnancy and delivery.

Promoting Equity When Using the SNOT-22 Score: A Scoping Review and Literature Review

It is established that non-white people experience worse health outcomes than white people within the same population. Equity addresses differences between patient subgroups, allowing needs-based distribution of resources. The use of quality-of-life (QoL) tools to assist clinical decision making such as the SNOT-22 for chronic rhinosinusitis promotes equality, not equity, as quality-of-life (QoL) tools provide the same criteria of symptom scoring across diverse populations. We considered the effects of ethnicity and race on SNOT-22 scores and whether these scores should be adjusted to improve equity. PubMed and MEDLINE provided papers for a scoping review. A combination of the following search terms was used: patient-reported outcome measures (PROM) (OR) quality of life; (AND) race (OR) ethnicity (OR) disparities; (AND) otolaryngology (OR) SNOT-22 (OR) sinusitis. The first study identified no evidence of ethnic variability in SNOT-22 scores. However, the study did not represent the local population, including 86% white people. Other studies identified baseline SNOT-22 disparities with respect to population demographics, gender, and age. Ethnic differences appear to exist in acute sinusitis symptomatology. In other fields both within and outside of otorhinolaryngology, ethnic differences exist with regard to QoL tools. This scoping review identified a paucity of data in rhinology. However, evidence implies some form of correction to QoL scores could help promote equity for non-white patients.

Patients’ and physicians’ experiences with remote consultations in primary care, during the covid-19 pandemic: a multi-method rapid review of the literature.

BackgroundDuring the COVID-19 pandemic, many countries implemented remote consultations in primary care to protect patients and staff from infection.AimThe aim of this review was to synthesise the literature exploring patients’ and physicians’ experiences with remote consultations in primary care, during the pandemic, with the further aim of informing their future delivery.Design & settingRapid literature review.MethodWe searched PubMed and PsychInfo for studies that explored patients’ and physicians’ experiences with remote consultations in primary care. To determine the eligibility of studies, we reviewed their titles and abstracts, prior to the full paper. We then extracted qualitative and quantitative data from those that were eligible, and synthesised the data using thematic and descriptive synthesis.ResultsA total of twenty-four studies were eligible for inclusion in the review. Most were performed in the United States of America (n=7, 29%) or Europe (n=7, 29%). Patient and physician experiences were categorised into perceived ‘advantages’ and ‘issues’. Key advantages experienced by patients and physicians included: ‘Reduced risk of COVID-19’ and ‘Increased convenience’, while key issues included: ‘a lack of confidence in / access to required technology’ and a ‘loss of non-verbal communication’, which exacerbated clinical decision making.ConclusionThis review identified a number of advantages and issues experienced by patients and physicians using remote consultations in primary care. The results suggest that, while remote consultations are more convenient, and protect patients and staff against COVID-19, they result in the loss of valuable non-verbal communication, and are not accessible to all.

Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses

Background: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. Method: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. Results: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. Conclusion: The proposed model is efficient and can minimize the revision complexities of implants.

Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications

Culture-independent metagenomic detection of microbial species has the potential to provide rapid and precise real-time diagnostic results. However, it is potentially limited by sequencing and classification errors. We use simulated and real-world data to benchmark rates of species misclassification using 100 reference genomes for each of ten common bloodstream pathogens and six frequent blood culture contaminants (n=1600). Simulating both with and without sequencing error for both the Illumina and Oxford Nanopore platforms, we evaluated commonly used classification tools including Kraken2, Bracken, and Centrifuge, utilising mini (8GB) and standard (30-50GB) databases. Bracken with the standard database performed best, the median percentage of reads across both sequencing platforms identified correctly to the species level was 98.46% (IQR 93.0:99.3) [range 57.1:100]. For Kraken2 with a mini database, a commonly used combination, median species-level identification was 79.3% (IQR 39.1:88.8) [range 11.2:100]. Classification performance varied by species, with E. coli being more challenging to classify correctly (59.4% to 96.4% reads with correct species, varying by tool used). By filtering out shorter Nanopore reads (<3500bp) we found performance similar or superior to Illumina sequencing, despite higher sequencing error rates. Misclassification was more common when the misclassified species had a higher average nucleotide identity to the true species. Our findings highlight taxonomic misclassification of sequencing data occurs and varies by sequencing and analysis workflow. This “bioinformatic contamination” should be accounted for in metagenomic pipelines to ensure accurate results that can support clinical decision making.

Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example

Background There are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment is an important part of clinical decision making. Taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example, this study adopted the missing data processing evaluation criteria more suitable for clinical decision making, aiming at systematically exploring the performance and applicability of single machine learning algorithms and ensemble learning (EL) under different data missing scenarios, as well as whether they had more advantages than traditional methods, so as to provide basis and reference for the selection of suitable missing data processing method in practical clinical decision making. Methods The whole process consisted of four main steps: (1) Based on the original complete data set, missing data was generated by simulation under different missing scenarios (missing mechanisms, missing proportions and ratios of missing proportions of each group). (2) Machine learning and traditional methods (eight methods in total) were applied to impute missing values. (3) The performances of imputation techniques were evaluated and compared by estimating the sensitivity, AUC and Kappa values of prediction models. (4) Statistical tests were used to evaluate whether the observed performance differences were statistically significant. Results The performances of missing data processing methods were different to a certain extent in different missing scenarios. On the whole, machine learning had better imputation performance than traditional methods, especially in scenarios with high missing proportions. Compared with single machine learning algorithms, the performance of EL was more prominent, followed by neural networks. Meanwhile, EL was most suitable for missing imputation under MAR (the ratio of missing proportion 2:1) mechanism, and its average sensitivity, AUC and Kappa values reached 0.908, 0.924 and 0.596 respectively. Conclusions In clinical decision making, the characteristics of missing data should be actively explored before formulating missing data processing strategies. The outstanding imputation performance of machine learning methods, especially EL, shed light on the development of missing data processing technology, and provided methodological support for clinical decision making in presence of incomplete data.

A clinical prediction tool for extended-spectrum β-lactamase-producing Enterobacteriaceae urinary tract infection

Background Prevalence of extended-spectrum beta-lactamase-producing-Enterobacteriaceae (ESBL-E) has risen in patients with urinary tract infections. The objective of this study was to determine explore the risk factors of ESBL-E infection in hospitalized patients and establish a predictive model. Methods This retrospective study included all patients with an Enterobacteriaceae-positive urine sample at the first affiliated hospital of Jinan university from January 2018 to December 2019. Antimicrobial susceptibility patterns of ESBL-E were analyzed, and multivariate analysis of related factors was performed. From these, a nomogram was established to predict the possibility of ESBL-E infection. Simultaneously, susceptibility testing of a broad array of carbapenem antibiotics was performed on ESBL-E cultures to explore possible alternative treatment options. Results Of the total 874 patients with urinary tract infections (UTIs), 272 (31.1%) were ESBL-E positive. In the predictive analysis, five variables were identified as independent risk factors for ESBL-E infection: male gender (OR = 1.607, 95% CI 1.066–2.416), older age (OR = 4.100, 95% CI 1.678–12.343), a hospital stay in preceding 3 months (OR = 1.872, 95% CI 1.141–3.067), invasive urological procedure (OR = 1.810, 95% CI 1.197–2.729), and antibiotic use within the previous 3 months (OR = 1.833, 95% CI 1.055–3.188). In multivariate analysis, the data set was divided into a training set of 611 patients and a validation set of 263 patients The model developed to predict ESBL-E infection was effective, with the AuROC of 0.650 (95% CI 0.577–0.725). Among the antibiotics tested, several showed very high effectiveness against ESBL-E: amikacin (85.7%), carbapenems (83.8%), tigecycline (97.1%) and polymyxin (98.2%). Conclusions The nomogram is useful for estimating a UTI patient’s likelihood of infection with ESBL-E. It could improve clinical decision making and enable more efficient empirical treatment. Empirical treatment may be informed by the results of the antibiotic susceptibility testing.

A review of COVID-19 therapeutics in pregnancy and lactation

Pregnant people have an elevated risk of severe COVID-19-related complications compared to their non-pregnant counterparts, underscoring the need for safe and effective therapies. In this review, we summarize published data on COVID-19 therapeutics in pregnancy and lactation to help inform clinical decision-making about their use in this population. Although no serious safety signals have been raised for many agents, data clearly have serious limitations and there are many important knowledge gaps about the safety and efficacy of key therapeutics used for COVID-19. Moving forward, diligent follow-up and documentation of outcomes in pregnant people treated with these agents will be essential to advance our understanding. Greater regulatory push and incentives are needed to ensure studies to obtain pregnancy data are expedited.

Eliminating Explicit and Implicit Biases in Health Care: Evidence and Research Needs

Health care providers hold negative explicit and implicit biases against marginalized groups of people such as racial and ethnic minoritized populations. These biases permeate the health care system and affect patients via patient–clinician communication, clinical decision making, and institutionalized practices. Addressing bias remains a fundamental professional responsibility of those accountable for the health and wellness of our populations. Current interventions include instruction on the existence and harmful role of bias in perpetuating health disparities, as well as skills training for the management of bias. These interventions can raise awareness of provider bias and engage health care providers in establishing egalitarian goals for care delivery, but these changes are not sustained, and the interventions have not demonstrated change in behavior in the clinical or learning environment. Unfortunately, the efficacy of these interventions may be hampered by health care providers’ work and learning environments, which are rife with discriminatory practices that sustain the very biases US health care professions are seeking to diminish. We offer a conceptual model demonstrating that provider-level implicit bias interventions should be accompanied by interventions that systemically change structures inside and outside the health care system if the country is to succeed in influencing biases and reducing health inequities. Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.