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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.,

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. The inclusion of polygenic risk scores does not improve the performance of standard-of-care predictive models of disease outcomes in patients with psychosis., Author(s): Isotta Landi [sup.1] [sup.2] [sup.3] [sup.4] , Deepak A. Kaji [sup.1] [sup.2] [sup.3] , Liam Cotter [sup.1] [sup.2] [sup.3] [sup.4] , Tielman Van Vleck [sup.3] [sup.4] [sup.5] [sup.6] , [...]

Objectives Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented in narrative clinical notes rather than as structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of extraction of such information. Materials and Methods Psychiatric encounter notes from Mount Sinai Health System (MSHS, n  = 300) and Weill Cornell Medicine (WCM, n  = 225) were annotated to create a gold-standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (eg, social network , instrumental support , and loneliness). Results For extracting SS/SI, the RBS obtained higher macroaveraged F1-scores than the LLM at both MSHS (0.89 versus 0.65) and WCM (0.85 versus 0.82). For extracting the subcategories, the RBS also outperformed the LLM at both MSHS (0.90 versus 0.62) and WCM (0.82 versus 0.81). Discussion and Conclusion Unexpectedly, the RBS outperformed the LLMs across all metrics. An intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS was designed and refined to follow the same specific rules as the gold-standard annotations. Conversely, the LLM was more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages, although additional replication studies are warranted. [ABSTRACT FROM AUTHOR]

Background: With their unmatched ability to interpret and engage with human language and context, large language models (LLMs) hint at the potential to bridge AI and human cognitive processes. This review explores the current application of LLMs, such as ChatGPT, in the field of psychiatry. Methods: We followed PRISMA guidelines and searched through PubMed, Embase, Web of Science, and Scopus, up until March 2024. Results: From 771 retrieved articles, we included 16 that directly examine LLMs' use in psychiatry. LLMs, particularly ChatGPT and GPT-4, showed diverse applications in clinical reasoning, social media, and education within psychiatry. They can assist in diagnosing mental health issues, managing depression, evaluating suicide risk, and supporting education in the field. However, our review also points out their limitations, such as difficulties with complex cases and potential underestimation of suicide risks. Conclusion: Early research in psychiatry reveals LLMs' versatile applications, from diagnostic support to educational roles. Given the rapid pace of advancement, future investigations are poised to explore the extent to which these models might redefine traditional roles in mental health care. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

BACKGROUND: Synchrony is an essential component of interactive exchanges. In mother-infant interaction, synchrony underlies reciprocity and emotive regulation. A severe lack of synchrony is indeed a core issue within the communication and interaction deficit that characterizes autism spectrum disorders (ASD) in accordance with the DSM-5 classification. Based on emerging evidence that music therapy can improve the communication and regulation ability in children with ASD, we aim to verify quantitatively whether: 1) children with ASD improve synchrony with their therapist during music therapy sessions, and 2) this ability persists in different structured contexts. PARTICIPANTS AND PROCEDURE: Twenty-five children, aged from 4 to 6 years (M = 57.80, SD = 16.70), with an autistic disorder diagnosis based on DSM IV-TR and the Autism Diagnostic Observation Schedule (ADOS), participated in the study. An observational tool for coding behaviors and emotive states of synchrony (Child Behavioral and Emotional status Code [CBEC] and Adult Behavioral and Emotional status Code [ABEC]) was applied in video recorded sessions of improvisational music therapy (IMT) for the subject-therapist pair. For each subject, we considered the 20 central minutes of the first, tenth and twentieth session of IMT. To verify the persistence of effect in a different context with a different adult, we administered and coded the interactive ADOS section (anticipation of a routine with objects) applied after session 20 of therapy. RESULTS: During the IMT cycle, the amount of synchronic activity increases, with a significant difference from Session 1 to Session 20 in behavioral synchrony and emotional attunement. Also, the increase of synchrony is confirmed at the end of the therapy cycle as measured by an interactive ADOS section. CONCLUSIONS: Synchrony is an effective indicator of efficacy for music therapy in children with ASD, in particular to evaluate the expansion of positive emotive exchanges. [ABSTRACT FROM AUTHOR]

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

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.