Intra-operative Safety of an Autonomous Robotic System for Total Knee Replacement: A Review of 500 Cases in India

Background

Manual total knee arthroplasty (TKA) has been documented several times for their safety and complications data. In contrast, there is a limited evidence-based analysis for safety and complications of autonomous robotic-assisted (RA)-TKA. This study aimed to evaluate the safety features and intra-operative surgical complications associated with the use of Cuvis Joint™ autonomous robotic system for TKA.

Methods

The study included 500 consecutive patients who underwent TKA using the Cuvis Joint™ autonomous robotic system from November 2020 to November 2021. All surgeries were performed by a senior surgeon. Patients in whom the surgery was abandoned midway due to technical errors, were excluded from the analysis. In case of unilateral abandonment of the robotic arm during a bilateral RA-TKA, data of the side on which the surgery was completed with robotic assistance was recorded.

Results

There was no incidence of neurological injury, vascular injury, extensor mechanism disruption, or medial collateral ligament injury. There was one case of superficial abrasion of the patellar tendon; however, it did not require any intervention. There were no cases of midway abandonment due to threatened soft tissue injury. There was no intra-operative pin loosening or stress-related fractures at the pin sites. There was one case of Steinmann pin breakage and another case of drill bit breakage, which were removed without any damage to the bone.

Conclusion

The Cuvis Joint™ autonomous robotic system for TKA is safe with no significant intra-operative complications.

Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

Phenotypic presentation trends of transthyretin amyloid cardiomyopathy: are we getting better?

Introduction

Transthyretin amyloid cardiomyopathy (ATTR-CM) is being increasingly recognized due to recent advances in non-invasive imaging notably bone scintigraphy and newer effective therapies - particularly tafamidis, which was shown to improve survival and decrease heart failure hospitalizations in the ATTR-ACT trial. Earlier tafamidis use appeared to be more beneficial, as reflected by NYHA class I and II patients being associated with lower mortality and hospitalizations compared to NYHA class III. Increased awareness will likely lead to an increasing number of ATTR-CM patients being diagnosed with concerns regarding applicability of ATTR-ACT criteria on these patients.

Purpose

To investigate ATTR-CM phenotypic presentation trends based on initial clinical, biomarkers and transthoracic echocardiographic (TTE) data.

Methods

From 2005–2020, 116 ATTR-CM patients were seen at our amyloidosis center, who were stratified into 3 time periods based on the date of diagnosis: Early (21 patients, pre-2016), Mid (46 patients, 2016–2018), and Recent (49 patients, 2018–2020). ATTR-CM diagnosis was established based on the standard criteria of confirmed ATTR variant + typical TTE features; histological confirmation endomyocardial biopsy; or typical diffuse cardiac tracer uptake on bone scintigraphy while ruling out light chain amyloidosis. With less typical imaging, cardiac MRI typically served as a confirmatory test prior to pursuing histological confirmation. Demographics, cardiac biomarkers, diagnostic method used, and TTE variables that raise the suspicion of ATTR-CM were compared across time periods using one-way ANOVA test and Fischer's exact test.

Results

There was a significant change in the predominant method of diagnosis with the majority of patients in the Early time period diagnosed by endomyocardial biopsy, whereas in the Recent time period the majority of patients were diagnosed via pyrophosphate scintigraphy (Figure). Despite increasing number of patients being diagnosed, the clinical phenotype at diagnosis did not change significantly with similar proportion of patients NYHA class I/II, median daily diuretic dose, biomarkers, having at least one heart failure hospitalization prior to diagnosis, and similar TTE phenotype (Table). Only the age at diagnosis significantly increased across time periods. Women represented the minority of patients across all time periods.

Conclusion

Despite the increased awareness of ATTR-CM, there have been no major changes in the clinical, cardiac biomarker, or TTE phenotype on presentation in patients referred to our center for suspicion or a diagnosis of ATTR-CM. Our findings challenge the assumption that patients with ATTR-CM are being identified earlier with milder phenotypes. Women with ATTR-CM likely remain largely undiagnosed in our community. Continued education and knowledge dissemination are essential to identify ATTR-CM patients earlier to achieve better outcomes in this population.

Funding Acknowledgement

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Pranav Chandrashekar is supported by an educational grant from Pfizer, Inc paid to Oregon Health and Science University. Table 1

Management strategies for atrial fibrillation and flutter in patients with transthyretin cardiac amyloidosis

Background

Atrial fibrillation (AF) and flutter (AFL) are common in transthyretin cardiac amyloidosis (ATTR-CM). Ventricular rate control drugs in ATTR-CM are poorly tolerated but data addressing tolerability and efficacy of rhythm control strategies is limited.

Purpose

We report characteristics and outcomes of AF/AFL in a cohort with ATTR-CM.

Methods

A single center observational study of patients seen at our Amyloidosis Center with wild-type or hereditary ATTR-CM diagnosed between 2005–2019. Treatment was prescribed as per treating cardiologists.

Results

Eighty-four patients with ATTR-CM (average age 74±10 years, 94% male) had mean follow-up of 27.6±22.8 months. AF/AFL occurred in 61 patients (73%). Clinically significant rapid ventricular response (RVR) was common as well attempted rate control with AV node blockers (Table 1). However, discontinuation was frequent (80%), often for adverse effects of hypotension (33%), bradycardia (15%), or presyncope/syncope (10%). Rhythm control was initiated in 64%, most often with cardioversion (DCCV) or ablation (Table 2). Post-DCCV recurrence was common (91%) and time to recurrence did not differ with use of anti-arrhythmic drugs (AAD; 5.8 months (IQR 1.9–12.5) vs without AAD 6.2 months (IQR 1.9–12.5) p=0.83). TEE was performed for 33% of DCCV with thrombus seen in 11% of cases – all patients who were not anticoagulated at the time. TEE was otherwise deferred due to known AF/AFL duration <48 hours (13%) or adequate anticoagulation (54%). Ablation was performed in 23% of patients with AFL (all for typical AFL) with 2 patients (14%) having recurrence after mean of 60.9 months. Pulmonary vein isolation for AF was performed in 12% (86% for persistent AF) with 86% recurrence after median of 6.2 months (IQR 5.6–12.3). Most patients (62%) with rhythm control had subjective improvement (≥1 NYHA class or resolved palpitations). Among AAD, amiodarone was most well tolerated with only 8% of patient discontinuing due to side effects. DCCV and ablation resulted in no direct complications although one patient had a perforation of a previously unknown Zenker diverticulum during TEE pre-DCCV.

Conclusions

In our ATTR-CM cohort, AF/AFL was common. Rate control was poorly tolerated and often abandoned. While rhythm control of AF/AFL had a favorable safety profile and successful conversion to sinus rhythm led to symptomatic improvement in a majority of cases, durable success with rhythm control was limited, often requiring multiple therapies. DCCV is only modestly successful and not significant improved with AAD. Ablation was successful in cases of cavo-tricuspid isthmus dependent AFL but had limited success in AF.

Funding Acknowledgement

Type of funding sources: None.

TreeMap: a structured approach to fine mapping of eQTL variants

Motivation

Expression quantitative trait loci (eQTL) harbor genetic variants modulating gene transcription. Fine mapping of regulatory variants at these loci is a daunting task due to the juxtaposition of causal and linked variants at a locus as well as the likelihood of interactions among multiple variants. This problem is exacerbated in genes with multiple cis-acting eQTL, where superimposed effects of adjacent loci further distort the association signals.

Results

We developed a novel algorithm, TreeMap, that identifies putative causal variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. Guided by the hierarchical structure of linkage disequilibrium, TreeMap performs an organized search for individual and multiple causal variants. Via extensive simulations, we show that TreeMap detects co-regulating variants more accurately than current methods. Furthermore, its high computational efficiency enables genome-wide analysis of long-range eQTL. We applied TreeMap to GTEx data of brain hippocampus samples and transverse colon samples to search for eQTL in gene bodies and in 4 Mbps gene-flanking regions, discovering numerous distal eQTL. Furthermore, we found concordant distal eQTL that were present in both brain and colon samples, implying long-range regulation of gene expression.

Availability and implementation

TreeMap is available as an R package enabled for parallel processing at https://github.com/liliulab/treemap.

Supplementary information

Supplementary data are available at Bioinformatics online.

Fine-Needle Aspiration Cytology in Preoperative Diagnosis of Bone Lesions: A Three-Year Study in a Tertiary Care Hospital

AIM

The aim is to study the utility of fine-needle aspiration cytology (FNAC) in preoperative diagnosis of bone lesions in correlation with radiological and histopathological findings and to determine the spectrum and morphological features of various bone lesions on FNAC.

MATERIALS AND METHODS

A total of 275 cases of bone lesions were studied by FNAC over a period of 3 years. 196 procedures were performed by pathologists, and 107 procedures were guided. Cytology findings were correlated with that of histology on cellblocks or on subsequent surgical biopsies. Immunohistochemistry (IHC) was done wherever necessary.

RESULTS

Of the 275 cases, 49 lesions were inflammatory/infectious (granulomatous inflammation-19, nonspecific osteomyelitis-26, and fungal etiology-4), 16 were tumors of undefined neoplastic nature (aneurysmal bone cysts-12, and Langerhans cell histiocytosis-4), 99 lesions were benign (osteoblastoma-6, enchondroma-3, chondroblastoma-14, chondromyxoid fibroma-2, and Giant cell tumor-74), and 111 lesions were malignant (Osteosarcoma-36, chondrosarcoma-7, Ewing's sarcoma-28, lymphomas-4, plasma cell neoplasm-6, adamantinoma of long bone-1, and metastasis-29). Male to female ratio was 2:1, and the age range was between 4 and 84 years. Correlation with histology/cellblock was available in 149 tumors. Metastasis and round cell tumors such as Ewing's sarcoma and lymphoma were differentiated by IHC. The accuracy rate in cytological diagnosis of all bone lesions was 87.9% and for neoplasms was 93%. The discordance in the rest of the cases was due to inadequate cell material, and there were no false positives.

CONCLUSION

We conclude that FNAC is a simple and accurate preoperative diagnostic technique for assessment of bone tumors.

Somatic selection distinguishes oncogenes and tumor suppressor genes

Motivation

Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited.

Results

We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms.

Availability and implementation

An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust.

Supplementary information

Supplementary data are available at Bioinformatics online.

Biological relevance of computationally predicted pathogenicity of noncoding variants

Computational prediction of the phenotypic propensities of noncoding single nucleotide variants typically combines annotation of genomic, functional and evolutionary attributes into a single score. Here, we evaluate if the claimed excellent accuracies of these predictions translate into high rates of success in addressing questions important in biological research, such as fine mapping causal variants, distinguishing pathogenic allele(s) at a given position, and prioritizing variants for genetic risk assessment. A significant disconnect is found to exist between the statistical modelling and biological performance of predictive approaches. We discuss fundamental reasons underlying these deficiencies and suggest that future improvements of computational predictions need to address confounding of allelic, positional and regional effects as well as imbalance of the proportion of true positive variants in candidate lists.

Researchers can make use of a variety of computational tools to prioritize genetic variants and predict their pathogenicity. Here, the authors evaluate the performance of six of these tools in three typical biological tasks and find generally low concordance of predictions and experimental confirmation.

Current State of Electronic Consent Processes in Behavioral Health: Outcomes from an Observational Study

An integral element of value-based care is care team access to both physical and behavioral health data. Data release processes in both environments are governed by federal and state statutes. The requirements for obtaining consent are complex and often confusing. Little is known about the consent processes and practices in the behavioral health setting, specifically how patients and surrogates engage in the process and their interactions with electronic consent tools. This study analyzes the consent processes from the patient perspective at two community behavioral health clinics. Outcomes include description of the processes using electronic consent, workflows and consenter-provider interactions. Conclusions include need to streamline and standardize consent technologies and improve consenter engagement. This study supports the development of an electronic consent tool, My Data Choices (MDC), funded by the National Institute of Mental Health, that offers individuals with behavioral health conditions more control over their medical records.

Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis

Background

Pregnancy exposure registries are the primary sources of information about the safety of maternal usage of medications during pregnancy. Such registries enroll pregnant women in a voluntary fashion early on in pregnancy and follow them until the end of pregnancy or longer to systematically collect information regarding specific pregnancy outcomes. Although the model of pregnancy registries has distinct advantages over other study designs, they are faced with numerous challenges and limitations such as low enrollment rate, high cost, and selection bias.

Objective

The primary objectives of this study were to systematically assess whether social media (Twitter) can be used to discover cohorts of pregnant women and to develop and deploy a natural language processing and machine learning pipeline for the automatic collection of cohort information. In addition, we also attempted to ascertain, in a preliminary fashion, what types of longitudinal information may potentially be mined from the collected cohort information.

Methods

Our discovery of pregnant women relies on detecting pregnancy-indicating tweets (PITs), which are statements posted by pregnant women regarding their pregnancies. We used a set of 14 patterns to first detect potential PITs. We manually annotated a sample of 14,156 of the retrieved user posts to distinguish real PITs from false positives and trained a supervised classification system to detect real PITs. We optimized the classification system via cross validation, with features and settings targeted toward optimizing precision for the positive class. For users identified to be posting real PITs via automatic classification, our pipeline collected all their available past and future posts from which other information (eg, medication usage and fetal outcomes) may be mined.

Results

Our rule-based PIT detection approach retrieved over 200,000 posts over a period of 18 months. Manual annotation agreement for three annotators was very high at kappa (κ)=.79. On a blind test set, the implemented classifier obtained an overall F₁ score of 0.84 (0.88 for the pregnancy class and 0.68 for the nonpregnancy class). Precision for the pregnancy class was 0.93, and recall was 0.84. Feature analysis showed that the combination of dense and sparse vectors for classification achieved optimal performance. Employing the trained classifier resulted in the identification of 71,954 users from the collected posts. Over 250 million posts were retrieved for these users, which provided a multitude of longitudinal information about them.

Conclusions

Social media sources such as Twitter can be used to identify large cohorts of pregnant women and to gather longitudinal information via automated processing of their postings. Considering the many drawbacks and limitations of pregnancy registries, social media mining may provide beneficial complementary information. Although the cohort sizes identified over social media are large, future research will have to assess the completeness of the information available through them.

Evaluation of salivary glucose, amylase, and total protein in Type 2 diabetes mellitus patients

BACKGROUND

Diabetes mellitus is a complex multisystem metabolic disorder characterized by a deficit in the production of insulin. The oral complications of uncontrolled diabetes mellitus are devastating. Saliva is an organic fluid that can be collected noninvasively and by individuals with limited training. These reasons create an interest in evaluating the possibility of using saliva as a diagnostic tool.

AIMS AND OBJECTIVES

The aim of this study was to determine, if saliva can be used as a noninvasive tool to monitor glycemic control in Type 2 diabetes. Comparative assessment of salivary (glucose, amylase, total protein levels) in patients with Type 2 diabetes and controls.

MATERIALS AND METHODS

A total of 40 individuals, 20 with Type 2 diabetes and 20 controls of age group 40-60 years were selected for the study. Diabetic status was assessed by estimating random blood glucose levels. Unstimulated saliva was collected from each participant and investigated for glucose, amylase, and total protein levels. Salivary glucose estimation was performed using glucose-oxidase method, amylase by the direct substrate kinetic enzymatic method, and total protein by pyrogallol red dye end point method. All the parameters were subjected to statistical analysis using SPSS version 20.0.

RESULTS

Significantly higher salivary glucose, lower amylase, and total proteins were observed in patients with Type 2 diabetes than controls. There was no significant correlation between salivary and blood glucose levels.

CONCLUSION

These results suggest that diabetes influences the composition of saliva. Since a significant correlation was not observed between salivary and blood glucose levels, further research is needed to determine salivary glucose estimation as a diagnostic tool for diabetes mellitus.

A study on oral mucosal lesions in 3500 patients with dermatological diseases in South India

BACKGROUND

Oral mucosal lesions that are observed in the dermatological diseases are categorized under mucocutaneous conditions. The oral lesions in dermatological diseases may be the early aspects of the disease manifestation or the most significant clinical appearance or the only sign/and or symptom of such dermatological diseases and occasionally lesions occur simultaneously in the skin as well as mucous membrane.

AIM

This present study attempts to find out the prevalence of oral mucosal lesions in patients with dermatological diseases.

SUBJECTS AND METHODS

The study includes 3500 patients who attended out-patient Department of Dermatology. Patients with oral manifestation were subjected for clinical examination in the Department of Oral Pathology. Diagnostic procedures were performed to confirm the clinical oral diagnosis. The results of the study were analyzed by SPSS software version 19.0 (Armonk, NY) and presented as descriptive statistics. Correlation of oral manifestions with their respective dermatological disease was statistically analysed by Pearson's correlation test.(P < 0.05 were considered as statistically significant).

RESULTS

The prevalence rate of oral mucosal lesions in the present study was 1.8% (65/3500). The most frequent lesions observed were psoriasis 32.3% (21/65), lichen planus 18.4% (12/65), Stevens Johnson Syndrome 18.4% (12/65), pemphigus 10.7% (7/65), toxic epidermal necrolysis 4.6% (3/65), systemic lupus erythematosus 3% (2/65), discoid lupus erythematosus 1.5% (1/65), pemphigoid 1.5% (1/65). Gender distribution in the study population was statistically significant (P < 0.001). Employed and unemployed individuals in the study population were statistically significant (P < 0.001). Pearson's correlation analysis of oral manifestations with their respective dermatological disease showed r = 0.466 and signifies a positive correlation and is statistically significant at the 0.01 level (two-tailed).

CONCLUSION

The prevalence rate of oral mucosal lesions in patients with dermatological diseases was relatively low. However, predominant oral mucosal lesions observed in the study were autoimmune in origin with a high morbidity and mortality index. Hence, multidisciplinary approach will definitely help in the prognosis of patients.

Lesions of the Bones of the Hands and Feet: A Study of 50 Cases

Context.—The bones of the hands and feet constitute more than half of the bones in the human skeleton (106/ 206), but lesions occurring in them are infrequently reported. Although many of the lesions that occur in the rest of the skeletal bones can occur in bones of the hands or feet, their distribution and frequency differ.

Objective.—To study lesions involving bones of the hands or feet.

Design.—Retrospective study of all lesions involving bones of the hands or feet during the period from January 2000 to September 2006 from a university hospital in southern India. Clinical, radiologic, and pathologic features were reviewed.

Results.—The 50 lesions encountered included 23 (46.0%) infections/inflammatory lesions, 16 (32.0%) benign tumors, 6 (12.0%) malignant tumors, and 5 (10.0%) tumorlike lesions. Giant cell tumor was the most common benign tumor. Malignant tumors involved the nonphalangeal bones, with chondrosarcoma the most common.

Conclusions.—Lesions of the bones of the hands or feet are uncommon. Awareness and correlation of clinical, radiologic, and pathologic features help in making correct diagnoses.

Multifocal osseous involvement as the sole manifestation of Rosai-Dorfman disease

Rosai-Dorfman disease may involve extranodal sites exclusively. Osseous involvement as the sole manifestation of disease is uncommon. The present report describes a 60-year-old woman presenting with lytic lesions in the lower end of femur and mid-fibula on one side and unaccompanied by disease elsewhere.