The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people.

BACKGROUND: Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation. METHODS: Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry. RESULTS: In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity. CONCLUSIONS: This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

External validation of yonsei nomogram predicting chronic kidney disease development after partial nephrectomy: An international, multicenter study.

OBJECTIVE: To externally validate Yonsei nomogram. METHODS: From 2000 through 2018, 3526 consecutive patients underwent on-clamp PN for cT1 renal masses at 23 centers were included. All patients had two kidneys, preoperative eGFR ≥60 ml/min/1.73 m2, and a minimum follow-up of 12 months. New-onset CKD was defined as upgrading from CKD stage I or II into CKD stage ≥III. We obtained the CKD-free progression probabilities at 1, 3, 5, and 10 years for all patients by applying the nomogram found at https://eservices.ksmc.med.sa/ckd/. Thereafter, external validation of Yonsei nomogram for estimating new-onset CKD stage ≥III was assessed by calibration and discrimination analysis. RESULTS AND LIMITATION: Median values of patients' age, tumor size, eGFR and follow-up period were 47 years (IQR: 47-62), 3.3 cm (IQR: 2.5-4.2), 90.5 ml/min/1.73 m2 (IQR: 82.8-98), and 47 months (IQR: 27-65), respectively. A total of 683 patients (19.4%) developed new-onset CKD. The 5-year CKD-free progression rate was 77.9%. Yonsei nomogram demonstrated an AUC of 0.69, 0.72, 0.77, and 0.78 for the prediction of CKD stage ≥III at 1, 3, 5, and 10 years, respectively. The calibration plots at 1, 3, 5, and 10 years showed that the model was well calibrated with calibration slope values of 0.77, 0.83, 0.76, and 0.75, respectively. Retrospective database collection is a limitation of our study. CONCLUSIONS: The largest external validation of Yonsei nomogram showed good calibration properties. The nomogram can provide an accurate estimate of the individual risk of CKD-free progression on long-term follow-up.

Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening.

Aims: Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection. Methods and results: We create a retrospective cohort of 21 183 patients at moderate- to high suspicion of PE and associate 23 793 CTPAs (10.0% PE-positive) with 320 746 ECGs and encounter-level clinical data (demographics, comorbidities, vital signs, and labs). We develop three machine learning models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating clinical data and an embedded representation of the ECG waveform. We find that a Fusion model [area under the receiver-operating characteristic curve (AUROC) 0.81 ± 0.01] outperforms both the ECG model (AUROC 0.59 ± 0.01) and EHR model (AUROC 0.65 ± 0.01). On a sample of 100 patients from the test set, the Fusion model also achieves greater specificity (0.18) and performance (AUROC 0.84 ± 0.01) than four commonly evaluated clinical scores: Wells' Criteria, Revised Geneva Score, Pulmonary Embolism Rule-Out Criteria, and 4-Level Pulmonary Embolism Clinical Probability Score (AUROC 0.50-0.58, specificity 0.00-0.05). The model is superior to these scores on feature sensitivity analyses (AUROC 0.66-0.84) and achieves comparable performance across sex (AUROC 0.81) and racial/ethnic (AUROC 0.77-0.84) subgroups. Conclusion: Synergistic deep learning of ECG waveforms with traditional clinical variables can increase the specificity of PE detection in patients at least at moderate suspicion for PE.

Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram.

OBJECTIVES: This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population. BACKGROUND: Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECGs) can assist diagnostic workflow. However, DL tools to estimate right ventricular (RV) function do not exist, whereas those to estimate left ventricular (LV) function are restricted to quantification of very low LV function only. METHODS: A multicenter study was conducted with data from 5 New York City hospitals: 4 for internal testing and 1 serving as external validation. We created novel DL models to classify left ventricular ejection fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation. RESULTS: We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used natural language processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, area under curve (AUC) at detection of LVEF ≤40%, 40% < LVEF ≤50%, and LVEF >50% was 0.94 (95% CI: 0.94-0.94), 0.82 (95% CI: 0.81-0.83), and 0.89 (95% CI: 0.89-0.89), respectively. For external validation, these results were 0.94 (95% CI: 0.94-0.95), 0.73 (95% CI: 0.72-0.74), and 0.87 (95% CI: 0.87-0.88). For regression, the mean absolute error was 5.84% (95% CI: 5.82%-5.85%) for internal testing and 6.14% (95% CI: 6.13%-6.16%) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (95% CI: 0.84-0.84) in both internal testing and external validation. CONCLUSIONS: DL on ECG data can be used to create inexpensive screening, diagnostic, and predictive tools for both LV and RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease.

Prognostic value of polygenic risk scores for adults with psychosis.

Polygenic risk scores (PRS) summarize genetic liability to a disease at the individual level, and the aim is to use them as biomarkers of disease and poor outcomes in real-world clinical practice. To date, few studies have assessed the prognostic value of PRS relative to standards of care. Schizophrenia (SCZ), the archetypal psychotic illness, is an ideal test case for this because the predictive power of the SCZ PRS exceeds that of most other common diseases. Here, we analyzed clinical and genetic data from two multi-ethnic cohorts totaling 8,541 adults with SCZ and related psychotic disorders, to assess whether the SCZ PRS improves the prediction of poor outcomes relative to clinical features captured in a standard psychiatric interview. For all outcomes investigated, the SCZ PRS did not improve the performance of predictive models, an observation that was generally robust to divergent case ascertainment strategies and the ancestral background of the study participants.

Pre-treatment clinical and gene expression patterns predict developmental change in early intervention in autism.

Early detection and intervention are believed to be key to facilitating better outcomes in children with autism, yet the impact of age at treatment start on the outcome is poorly understood. While clinical traits such as language ability have been shown to predict treatment outcome, whether or not and how information at the genomic level can predict treatment outcome is unknown. Leveraging a cohort of toddlers with autism who all received the same standardized intervention at a very young age and provided a blood sample, here we find that very early treatment engagement (i.e., <24 months) leads to greater gains while controlling for time in treatment. Pre-treatment clinical behavioral measures predict 21% of the variance in the rate of skill growth during early intervention. Pre-treatment blood leukocyte gene expression patterns also predict the rate of skill growth, accounting for 13% of the variance in treatment slopes. Results indicated that 295 genes can be prioritized as driving this effect. These treatment-relevant genes highly interact at the protein level, are enriched for differentially histone acetylated genes in autism postmortem cortical tissue, and are normatively highly expressed in a variety of subcortical and cortical areas important for social communication and language development. This work suggests that pre-treatment biological and clinical behavioral characteristics are important for predicting developmental change in the context of early intervention and that individualized pre-treatment biology related to histone acetylation may be key.

reval: A Python package to determine best clustering solutions with stability-based relative clustering validation.

Determining the best partition for a dataset can be a challenging task because of the lack of a priori information within an unsupervised learning framework and the absence of a unique clustering validation approach to evaluate clustering solutions. Here we present reval: a Python package that leverages stability-based relative clustering validation methods to select best clustering solutions as the ones that replicate, via supervised learning, on unseen subsets of data. The implementation of relative validation methods can contribute to the theory of clustering by fostering new approaches for the investigation of clustering results in different situations and for different data distributions. This work aims at contributing to this effort by implementing a package that works with multiple clustering and classification algorithms, hence allowing both the automation of the labeling process and the assessment of the stability of different clustering mechanisms.

Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry.

Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.

Feasibility of a telementoring approach as a practical training for transurethral enucleation of the benign prostatic hyperplasia using bipolar energy: a pilot study.

INTRODUCTION: Telementoring is one of the applications of telemedicine capable of bringing highly experienced surgeons to areas lacking expertise. In the current study, we aimed to assess a novel telementoring application during the learning curve of transurethral enucleation of the prostate using bipolar energy (TUEB). MATERIAL AND METHODS: A telementoring system was developed by our engineering department. This application was used to mentor ten prospective cases of TUEB performed by an expert endourologist (novice to the TUEB). A questionnaire was filled by the operating surgeon and the mentor to provide subjective evaluation of the telementoring system. Finally, the outcomes of these patients were compared to a control group consisting of ten consecutive patients performed by the mentor. RESULTS: Ten consecutive TUEB were performed using this telementoring application. Delayed and interrupted connection were experienced in two and one patients, respectively; however, their effect was minor, and they did not compromise the safety of the procedure. None of the patients required conversion to conventional transurethral resection of the prostate. Only one patient in our series experienced grade IIIb complication. CONCLUSION: The telementoring application for TUEB is promising. It is a simple and low-cost tool that could be a feasible option to ensure patients' safety during the initial phase of the learning curve without time and locations constraints for both the mentor and the trainee; However, it should be mentioned that telementoring cannot yet replace the traditional surgical training with the mentor and trainee being in the operative room. Further studies are required to confirm the current results.

Therapeutic Factors in a Psychiatric Group Therapy: a Preliminary Validation of Therapeutic Factors Inventory-8, Italian Version.

Several studies support group therapy effectiveness due to the activation in patients of unique psychological mechanisms defined as non-specific therapeutic factors (Therapeutic Factors-TFs), which shape the setting and, at the same time, enhance the specific group therapeutic factors. The objectives of this study were to preliminarly validate Therapeutic Factors Inventory-8 (TFI-8) Italian version and identify group therapeutic factors. In a psychiatric residential facility, a weekly psychotherapeutic group was evaluated during 1 year. One scale on group process (TFI-8, Ferrara-Group Experience Scale) and three clinical scales (Brief Symptom Inventory-53, Sheehan Disability Scale, WHO Quality of Life-Bref) were administered to participating patients. Internal consistency, Exploratory Factor Analysis (EFA), convergent validity of TFI-8 were assessed. Correlations between TFI-8 and other scale scores and selected variables were pwerformed. Our sample consisted of 64 participants. TFI-8 showed good internal consistency (Chronbach's alpha = 0.84), concurrent validity with Fe-GES (Rho = 0.42, p = 0.0008). EFA highlighted a single Factor, accounting for 92% of variance. TFI-8 was not significantly related to clinical scale scores. TFI-8 Italian version proved to be a valid and reliable tool which allowed us to identify one therapeutic factor indicating relational attraction in group therapy, composed of three dimensions: infusion of hope, cohesion and social learning.

Deep representation learning of electronic health records to unlock patient stratification at scale.

Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. Here we present an unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification at scale. We considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising a total of 57,464 clinical concepts. We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE) to transform patient trajectories into low-dimensional latent vectors. We evaluated these representations as broadly enabling patient stratification by applying hierarchical clustering to different multi-disease and disease-specific patient cohorts. ConvAE significantly outperformed several baselines in a clustering task to identify patients with different complex conditions, with 2.61 entropy and 0.31 purity average scores. When applied to stratify patients within a certain condition, ConvAE led to various clinically relevant subtypes for different disorders, including type 2 diabetes, Parkinson's disease, and Alzheimer's disease, largely related to comorbidities, disease progression, and symptom severity. With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights. This scalable framework can help better understand varying etiologies in heterogeneous sub-populations and unlock patterns for EHR-based research in the realm of personalized medicine.

"Motion or Emotion? Recognition of Emotional Bodily Expressions in Children With Autism Spectrum Disorder With and Without Intellectual Disability".

The recognition of emotional body movement (BM) is impaired in individuals with Autistic Spectrum Disorder ASD, yet it is not clear whether the difficulty is related to the encoding of body motion, emotions, or both. Besides, BM recognition has been traditionally studied using point-light displays stimuli (PLDs) and is still underexplored in individuals with ASD and intellectual disability (ID). In the present study, we investigated the recognition of happy, fearful, and neutral BM in children with ASD with and without ID. In a non-verbal recognition task, participants were asked to recognize pure-body-motion and visible-body-form stimuli (by means of point-light displays-PLDs and full-light displays-FLDs, respectively). We found that the children with ASD were less accurate than TD children in recognizing both the emotional and neutral BM, either when presented as FLDs or PLDs. These results suggest that the difficulty in understanding the observed BM may rely on atypical processing of BM information rather than emotion. Moreover, we found that the accuracy improved with age and IQ only in children with ASD without ID, suggesting that high level of cognitive resources can mediate the acquisition of compensatory mechanisms which develop with age.

Maternal and family predictors of infant psychological development in at-risk families: A multilevel longitudinal study.

The impact of different parenting-related variables on child psychological development is widely acknowledged. However, studies about the specific influence of maternal and family dimensions on child early developmental outcomes in at-risk dyads are still scarce. The aim of this longitudinal study was to investigate the short- and middle-term effects of prenatal and postnatal family and maternal features, and child attachment, on child psychological development at 3 and 24 months in at-risk families. Forty-two mothers with psychological, social and/or demographic risk conditions and their first-born infants were assessed longitudinally. Measurements of maternal personality, psychological and depressive symptoms, family socioeconomic status (SES), child-mother attachment, and infant general psychological development were collected at multiple time points, through validated questionnaires and/or mother-child observation. Maternal and family dimensions showed a significant effect on child psychological development over time. The expected detrimental role of reported maternal depressive symptoms was observed both at 3 and 24 months of child's age. Data also highlighted the negative contribution of low family SES and an unexpected positive influence of maternal personality trait of psychoticism on child psychological development at 24 months. We also found a positive association between attachment security and child developmental outcome. These findings might have relevant implications for the implementation of early prevention programs by differentiating the specific predictive role of maternal child and familial factors on child psychological development in at-risk families.