BACKGROUND: In patients with persistent atrial fibrillation (AF), extensive ablation for substrate modification, such as linear ablation or complex fractionated atrial electrogram ablation in addition to pulmonary vein isolation (PVI) remains controversial. Previous studies investigating extensive ablation have demonstrated its varying efficacy, suggesting the possible heterogeneity of its efficacy. Aging is a major risk factor for AF and is associated with atrial remodeling. We aimed to compare the efficacy and safety of the extensive ablation strategy compared with PVI alone strategy between young and elderly patients.
METHODS: This study is a post-hoc analysis of the multicenter, randomized controlled, noninferiority trial investigating the efficacy and safety of PVI-only (PVI-alone arm) compared with extensive ablation (PVI-plus arm) in patients with persistent AF (EARNEST-PVI trial). We divided the overall population into 2 groups based on age and assessed treatment effects.
RESULTS: In the young group (age < 65 years, N = 206), there was no significant difference in the recurrence rate between the PVI-alone group and PVI-plus group [hazard ratio (HR): 1.00, 95%CI: 0.57-1.73, p = 0.987], whereas the recurrence rate was significantly lower in the PVI-plus group compared to the PVI-alone group in the elderly group (age ≥ 65 years, N = 291) (HR: 0.47, 95%CI: 0.29-0.76, p = 0.0021) (p for interaction = 0.0446). There were no fatal procedural complications.
CONCLUSIONS: In patients with persistent AF, the extensive ablation strategy was more effective than the PVI-alone strategy in elderly patients, while the effectiveness of both approaches was comparable in young patients.
BACKGROUND: URL: https://clinicaltrials.gov; Unique identifier: NCT03514693. URL: https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000022454 Unique ID issued by UMIN: UMIN000019449.

BACKGROUND: In Huntington\'s disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process.
OBJECTIVE: To improve stratification of Huntington\'s disease individuals for clinical trials.
METHODS: We used data from 451 gene positive individuals with Huntington\'s disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement.
RESULTS: The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %).
CONCLUSIONS: This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.

OBJECTIVE: Patients with persistent atrial fibrillation (AF) experience 50% recurrence despite pulmonary vein isolation (PVI), and no consensus is established for secondary treatments. The aim of our i-STRATIFICATION study is to provide evidence for stratifying patients with AF recurrence after PVI to optimal pharmacological and ablation therapies, through in silico trials.
RESULTS: A cohort of 800 virtual patients, with variability in atrial anatomy, electrophysiology, and tissue structure (low-voltage areas, LVAs), was developed and validated against clinical data from ionic currents to electrocardiogram. Virtual patients presenting AF post-PVI underwent 12 secondary treatments. Sustained AF developed in 522 virtual patients after PVI. Second ablation procedures involving left atrial ablation alone showed 55% efficacy, only succeeding in the small right atria (<60 mL). When additional cavo-tricuspid isthmus ablation was considered, Marshall-PLAN sufficed (66% efficacy) for the small left atria (<90 mL). For the bigger left atria, a more aggressive ablation approach was required, such as anterior mitral line (75% efficacy) or posterior wall isolation plus mitral isthmus ablation (77% efficacy). Virtual patients with LVAs greatly benefited from LVA ablation in the left and right atria (100% efficacy). Conversely, in the absence of LVAs, synergistic ablation and pharmacotherapy could terminate AF. In the absence of ablation, the patient\'s ionic current substrate modulated the response to antiarrhythmic drugs, being the inward currents critical for optimal stratification to amiodarone or vernakalant.
CONCLUSIONS: In silico trials identify optimal strategies for AF treatment based on virtual patient characteristics, evidencing the power of human modelling and simulation as a clinical assisting tool.

BACKGROUND: Pain management is a major unmet need in people with rheumatoid arthritis (RA). Although many patients are treated with disease modifying anti-rheumatic drugs (DMARDS), including biologic therapies, many people with RA continue to experience significant pain. We aimed to determine whether performing a comprehensive pain evaluation is feasible in people with active RA receiving conventional DMARDs and biologic therapies.
METHODS: The BIORA-PAIN feasibility study was an open-label, randomised trial, which recruited participants suitable for treatment with biologic therapy. The primary feasibility outcomes were recruitment, randomisation and retention of eligible participants. All participants underwent pain assessment for nociceptive, neuropathic and nociplastic pain during the 12-month study period, with quarterly assessments for VAS (Visual Analogue Scale) pain, painDETECT and QST (quantitative sensory testing). This trial was registered in clinicaltrials.gov NCT04255134.
RESULTS: During the study period, 93 participants were screened of whom 25 were eligible: 13 were randomised to adalimumab and 12 to abatacept. Participant recruitment was lower than expected due to the COVID-19 pandemic. Pain assessments were practical in the clinical trial setting. An improvement was observed for VAS pain from baseline over 12 months, with a mean (SEM) of 3.7 (0.82) in the abatacept group and 2.3 (1.1) in the adalimumab group. There was a reduction in painDETECT and improvement in QST measures in both treatment groups during the study. Participant feedback included that some of the questionnaire-based pain assessments were lengthy and overlapped in their content. Adverse events were similar in both groups. There was one death due to COVID-19.
CONCLUSIONS: This first-ever feasibility study of a randomised controlled trial assessing distinct modalities of pain in RA met its progression criteria. This study demonstrates that it is feasible to recruit and assess participants with active RA for specific modalities of pain, including nociceptive, neuropathic and nociplastic elements. Our data suggests that it is possible to stratify people for RA based on pain features. The differences in pain outcomes between abatacept and adalimumab treated groups warrant further investigation.
BACKGROUND: NCT04255134, Registered on Feb 5, 2020.

Environmental-origin microbiota significantly influences Red Heart Qu (RH_Qu) stratification, but their microbial migration and metabolic mechanisms remain unclear. Using high-throughput sequencing and metabolomics, we divided the stratification of RH_Qu into three temperature-based stages. Phase I features rising temperatures, causing microbial proliferation and a two-layer division. Phase II, characterized by peak temperatures, sees the establishment of thermotolerant species like Bacillus, Thermoactinomyces, Rhodococcus, and Thermoascus, forming four distinct layers and markedly altering metabolite profiles. The Huo Quan (HQ), developing from the Pi Zhang (PZ), is driven by the tyrosine-melanin pathway and increased MRPs (Maillard reaction products). The Hong Xin evolves from the Rang, associated with the phenylalanine-coumarin pathway and QCs (Quinone Compounds) production. Phase III involves the stabilization of the microbial and metabolic profile as temperatures decline. These findings enhance our understanding of RH_Qu stratification and offer guidance for quality control in its fermentation process.

The red Noctiluca scintillans (RNS) blooms often break out near Pingtan Island, in the northern Taiwan Strait from April to June. It is essential to gain insights into their formation mechanism to predict and provide early warnings for these blooms. Previous studies and observations showed that RNS blooms are the most likely to occur when winds are weak and shifting in direction. To explore this phenomenon further, we employed a high-resolution coastal model to investigate the hydrodynamics influencing RNS blooms around Pingtan Island from April to June 2022. The model results revealed that seawater exhibited weak circulation but strong stratification during RNS blooms. Residence time were examined through numerical experiments by releasing passive neutrally buoyant particles in three bays of Pingtan Island. The results showed a significantly longer residence time during RNS blooms, indicating reduced flushing capabilities within the bays, which could give RNS a stable environment to multiply and aggregate. This hydrodynamic condition provided a favorable basis for RNS blooms breakout near Pingtan Island. The shifts and weakening of the prevailing northeast wind contributed substantially to weakening the flow field around Pingtan Island and played a crucial role in creating the hydrodynamics conducive to RNS blooms. Our study offers fresh insights into the mechanisms underpinning RNS blooms formation near Pingtan Island, providing a vital framework for forecasting RNS blooms in this region.

Patients with multimorbidity have increased and more complex healthcare needs, posing their management a challenge for healthcare systems. This study aimed to describe their primary healthcare utilization and associated factors. A population-based cross-sectional study was conducted in a Spanish basic healthcare area including all patients with chronic conditions, differentiating between having multimorbidity or not. Sociodemographic, functional, clinical and service utilization variables were analyzed, stratifying the multimorbid population by the Adjusted Morbidity Groups (AMG) risk level, sex and age. A total of 6036 patients had multimorbidity, 64.2% being low risk, 28.5% medium risk and 7.3% high risk. Their mean age was 64.1 years and 63.5% were women, having on average 3.5 chronic diseases, and 25.3% were polymedicated. Their mean primary care contacts/year was 14.9 (7.8 with family doctors and 4.4 with nurses). Factors associated with primary care utilization were age (B-coefficient [BC] = 1.15;95% Confidence Interval [CI] = 0.30-2.01), female sex (BC = 1.04; CI = 0.30-1.78), having a caregiver (BC = 8.70; CI = 6.72-10.69), complexity (B-coefficient = 0.46; CI = 0.38-0.55), high-risk (B-coefficient = 2.29; CI = 1.26-3.32), numerous chronic diseases (B-coefficient = 1.20; CI = 0.37-2.04) and polypharmacy (B-coefficient = 5.05; CI = 4.00-6.10). This study provides valuable data on the application of AMG in multimorbid patients, revealing their healthcare utilization and the need for a patient-centered approach by primary care professionals. These results could guide in improving coordination among professionals, optimizing multimorbidity management and reducing costs derived from their extensive healthcare utilization.

BACKGROUND: Osteosarcoma stratification relies on clinical parameters and histological response. We developed a new personalized stratification using less invasive circulating tumor DNA (ctDNA) quantification.
METHODS: Plasma from patients homogeneously treated in the prospective protocol OS2006, at diagnosis, before surgery and end of treatment, were sequenced using low-passage whole-genome sequencing (lpWGS) for copy number alteration detection. We developed a prediction tool including ctDNA quantification and known clinical parameters to estimate patients\' individual risk of event.
RESULTS: ctDNA quantification at diagnosis (diagCPA) was evaluated for 183 patients of the protocol OS2006. diagCPA as a continuous variable was a major prognostic factor, independent of other clinical parameters, including metastatic status [diagCPA hazard ratio (HR) = 3.5, P = 0.002 and 3.51, P = 0.012, for progression-free survival (PFS) and overall survival (OS)]. At the time of surgery and until the end of treatment, diagCPA was also a major prognostic factor independent of histological response (diagCPA HR = 9.2, P < 0.001 and 11.6, P < 0.001, for PFS and OS). Therefore, the addition of diagCPA to metastatic status at diagnosis or poor histological response after surgery improved the prognostic stratification of patients with osteosarcoma. We developed the prediction tool PRONOS to generate individual risk estimations, showing great performance ctDNA quantification at the time of surgery and the end of treatment still required improvement to overcome the low sensitivity of lpWGS and to enable the follow-up of disease progression.
CONCLUSIONS: The addition of ctDNA quantification to known risk factors improves the estimation of prognosis calculated by our prediction tool PRONOS. To confirm its value, an external validation in the Sarcoma 13 trial is underway.

BACKGROUND: Patients with polypharmacy suffer from complex medical conditions involving a large healthcare burden. This study aimed to describe the characteristics and utilization of primary care (PC) and hospital care (HC) and factors associated in chronic patients with polypharmacy, stratifying by adjusted morbidity groups (AMG) risk level, sex and age, and comparing with non-polypharmacy.
METHODS: Cross-sectional study conducted in a Spanish basic healthcare area. Studied patients were those over 18 years with chronic diseases identified by the AMG tool from Madrid electronic clinical record, which was the data source. Sociodemographic, sociofunctional, clinical and healthcare utilization variables were described and compared by risk level, sex, age and having or not polypharmacy. Factors associated with healthcare utilization in polypharmacy patients were determined by a negative binomial regression model.
RESULTS: In the area studied, 61.3% patients had chronic diseases, of which 16.9% had polypharmacy vs. 83.1% without polypharmacy. Patients with polypharmacy (vs. non-polypharmacy) mean age was 82.7 (vs. 52.7), 68.9% (vs. 60.7%) were women, and 22.0% (vs. 1.2%) high risk. Their average number of chronic diseases was 4.8 (vs. 2.2), and 95.6% (vs. 56.9%) had multimorbidity. Their mean number of annual healthcare contacts was 30.3 (vs. 10.5), 25.9 (vs. 8.8) with PC and 4.4 (vs. 1.7) with HC. Factors associated with a greater PC utilization in patients with polypharmacy were elevated complexity, high risk level and dysrhythmia. Variables associated with a higher HC utilization were also increased complexity and high risk, in addition to male sex, being in palliative care, having a primary caregiver, suffering from neoplasia (specifically lymphoma or leukaemia) and arthritis, whereas older age and immobilization were negatively associated.
CONCLUSIONS: Polypharmacy population compared to non-polypharmacy was characterized by a more advanced age, predominance of women, high-risk, complexity, numerous comorbidities, dependency and remarkable healthcare utilization. These findings could help healthcare policy makers to optimize the distribution of resources and professionals within PC and HC systems, aiming for the improvement of polypharmacy management and rational use of medicines while reducing costs attributed to healthcare utilization by these patients.

Landslides are among the most destructive geological disasters that seriously damage human life and infrastructures. Landslides mainly occur in mountainous regions around the world. One of the key processes to reduce these damages is to uncover landslide-exposed areas through different data-driven methods such as Geographical Information System (GIS) and multi-criteria decision-making (MCDM). In the literature, there are many studies developed with these fundamental tools. In this study, unlike the literature, a new landslide susceptibility assessment model is proposed by integrating GIS with the stratified best-worst method (S-BWM). This model has four main dimensions and 16 sub-dimensions under topography, environment-land, location, and hydrological factors, weighted with the S-BWM. A network was created considering the different states that may arise in the importance weights of these dimensions in the future. The transition probabilities of these states were predicted and injected into the classical BWM. Then, maps were created for these dimensions and classifications for each sub-dimension according to the map characteristics. Finally, the most susceptive landslide locations were determined with GIS-based calculations. To demonstrate the model\'s applicability, a case study was conducted for the Erzurum region, one of Turkey\'s landslide-prone regions. In addition, besides the landslide map, an analysis and discussion about the spatial distribution of susceptibility classes was presented, contributing to the study\'s robustness. In the results of landslide susceptibility analysis, landslides are higher in the range of about 1600-2500 m. Approximately 42% (35.59 sq. km) of the study area has high landslide susceptibility, while 58% (64.41 sq. km) has medium and low landslide susceptibility.