Motor unit number index of the upper trapezius (MUNIX-Trapezius) is a candidate biomarker for bulbar lower motor neuron function; however, reliability data is incomplete. To assess MUNIX-Trapezius ...reliability in controls, we conducted a systematic review, a cross-sectional study (n = 20), and a meta-analysis. We demonstrated a high inter- and intra-rater intraclass correlation (0.86 and 0.94, respectively), indicating that MUNIX-Trapezius is reliable with between-study variability moderated by age and MUNIX technique. With further validation, this measure can serve as a disease monitoring and response biomarker of bulbar function in the therapeutic development for amyotrophic lateral sclerosis.
Amyotrophic lateral sclerosis is a progressive neurodegenerative disorder leading to muscle weakness and respiratory failure. Arimoclomol, a heat-shock protein-70 (HSP70) co-inducer, is ...neuroprotective in animal models of amyotrophic lateral sclerosis, with multiple mechanisms of action, including clearance of protein aggregates, a pathological hallmark of sporadic and familial amyotrophic lateral sclerosis. We aimed to evaluate the safety and efficacy of arimoclomol in patients with amyotrophic lateral sclerosis.
ORARIALS-01 was a multinational, randomised, double-blind, placebo-controlled, parallel-group trial done at 29 centres in 12 countries in Europe and North America. Patients were eligible if they were aged 18 years or older and met El Escorial criteria for clinically possible, probable, probable laboratory-supported, definite, or familial amyotrophic lateral sclerosis; had an ALS Functional Rating Scale-Revised score of 35 or more; and had slow vital capacity at 70% or more of the value predicted on the basis of the participant's age, height, and sex. Patients were randomly assigned (2:1) in blocks of 6, stratified by use of a stable dose of riluzole or no riluzole use, to receive oral arimoclomol citrate 1200 mg/day (400 mg three times per day) or placebo. The Randomisation sequence was computer generated centrally. Investigators, study personnel, and study participants were masked to treatment allocation. The primary outcome was the Combined Assessment of Function and Survival (CAFS) rank score over 76 weeks of treatment. The primary outcome and safety were analysed in the modified intention-to-treat population. This trial is registered with ClinicalTrials.gov, NCT03491462, and is completed.
Between July 31, 2018, and July 17, 2019, 287 patients were screened, 245 of whom were enrolled in the trial and randomly assigned. The modified intention-to-treat population comprised 239 patients (160 in the arimoclomol group and 79 in the placebo group): 151 (63%) were male and 88 (37%) were female; mean age was 57·6 years (SD 10·9). CAFS score over 76 weeks did not differ between groups (mean 0·51 SD 0·29 in the arimoclomol group vs 0·49 0·28 in the placebo group; p=0·62). Cliff's delta comparing the two groups was 0·039 (95% CI –0·116 to 0·194). Proportions of participants who died were similar between the treatment groups: 29 (18%) of 160 patients in the arimoclomol group and 18 (23%) of 79 patients in the placebo group. Most deaths were due to disease progression. The most common adverse events were gastrointestinal. Adverse events were more often deemed treatment-related in the arimoclomol group (104 65%) than in the placebo group (41 52%) and more often led to treatment discontinuation in the arimoclomol group (26 16%) than in the placebo group (four 5%).
Arimoclomol did not improve efficacy outcomes compared with placebo. Although available biomarker data are insufficient to preclude future strategies that target the HSP response, safety data suggest that a higher dose of arimoclomol would not have been tolerated.
Orphazyme.
Deep brain stimulation (DBS) has been investigated for neuropsychiatric disorders. In this phase 1 trial, we treated four posttraumatic stress disorder (PTSD) patients with DBS delivered to the ...subgenual cingulum and the uncinate fasciculus. In addition to validated clinical scales, patients underwent neuroimaging studies and psychophysiological assessments of fear conditioning, extinction, and recall. We show that the procedure is safe and potentially effective (55% reduction in Clinical Administered PTSD Scale scores). Posttreatment imaging data revealed metabolic activity changes in PTSD neurocircuits. During psychophysiological assessments, patients with PTSD had higher skin conductance responses when tested for recall compared to healthy controls. After DBS, this objectively measured variable was significantly reduced. Last, we found that a ratio between recall of extinguished and nonextinguished conditioned responses had a strong correlation with clinical outcome. As this variable was recorded at baseline, it may comprise a potential biomarker of treatment response.
Abstract Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for the accurate prognosis of other ...outcomes, especially when impacted by co-morbidities such as HIV infection. Brain magnetic resonance imaging (MRI) characterises the extent and severity of disease and may enable more accurate prediction of complications and poor outcomes. We analysed clinical and brain MRI data from a prospective longitudinal study of 216 adults with TBM; 73 (34%) were HIV-positive, a factor highly correlated with mortality. We implemented an end-to-end framework to model clinical and imaging features to predict disease progression. Our model used state-of-the-art machine learning models for automatic imaging feature encoding, and time-series models for forecasting, to predict TBM progression. The proposed approach is designed to be robust to missing data via a novel tailored model optimisation framework. Our model achieved a 60% balanced accuracy in predicting the prognosis of TBM patients over the six different classes. HIV status did not alter the performance of the models. Furthermore, our approach identified brain morphological lesions caused by TBM in both HIV and non-HIV-infected, associating lesions to the disease staging with an overall accuracy of 96%. These results suggest that the lesions caused by TBM are analogous in both populations, regardless of the severity of the disease. Lastly, our models correctly identified changes in disease symptomatology and severity in 80% of the cases. Our approach is the first attempt at predicting the prognosis of TBM by combining imaging and clinical data, via a machine learning model. The approach has the potential to accurately predict disease progression and enable timely clinical intervention.
Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited ...training available. Despite recent advances in the use of Artificial Intelligence (AI) to automate many ultrasound imaging analysis tasks, no AI-enabled LUS solutions have been proven to be clinically useful in ICUs, and specifically in LMICs. Therefore, we developed an AI solution that assists LUS practitioners and assessed its usefulness in a low resource ICU.
This was a three-phase prospective study. In the first phase, the performance of four different clinical user groups in interpreting LUS clips was assessed. In the second phase, the performance of 57 non-expert clinicians with and without the aid of a bespoke AI tool for LUS interpretation was assessed in retrospective offline clips. In the third phase, we conducted a prospective study in the ICU where 14 clinicians were asked to carry out LUS examinations in 7 patients with and without our AI tool and we interviewed the clinicians regarding the usability of the AI tool.
The average accuracy of beginners' LUS interpretation was 68.7% 95% CI 66.8-70.7% compared to 72.2% 95% CI 70.0-75.6% in intermediate, and 73.4% 95% CI 62.2-87.8% in advanced users. Experts had an average accuracy of 95.0% 95% CI 88.2-100.0%, which was significantly better than beginners, intermediate and advanced users (p < 0.001). When supported by our AI tool for interpreting retrospectively acquired clips, the non-expert clinicians improved their performance from an average of 68.9% 95% CI 65.6-73.9% to 82.9% 95% CI 79.1-86.7%, (p < 0.001). In prospective real-time testing, non-expert clinicians improved their baseline performance from 68.1% 95% CI 57.9-78.2% to 93.4% 95% CI 89.0-97.8%, (p < 0.001) when using our AI tool. The time-to-interpret clips improved from a median of 12.1 s (IQR 8.5-20.6) to 5.0 s (IQR 3.5-8.8), (p < 0.001) and clinicians' median confidence level improved from 3 out of 4 to 4 out of 4 when using our AI tool.
AI-assisted LUS can help non-expert clinicians in an LMIC ICU improve their performance in interpreting LUS features more accurately, more quickly and more confidently.
Abstract Muscle ultrasound has been shown to be a valid and safe imaging modality to assess muscle wasting in critically ill patients in the intensive care unit (ICU). This typically involves manual ...delineation to measure the rectus femoris cross-sectional area (RFCSA), which is a subjective, time-consuming, and laborious task that requires significant expertise. We aimed to develop and evaluate an AI tool that performs automated recognition and measurement of RFCSA to support non-expert operators in measurement of the RFCSA using muscle ultrasound. Twenty patients were recruited between Feb 2023 and July 2023 and were randomized sequentially to operators using AI (n = 10) or non-AI (n = 10). Muscle loss during ICU stay was similar for both methods: 26 ± 15% for AI and 23 ± 11% for the non-AI, respectively ( p = 0.13). In total 59 ultrasound examinations were carried out (30 without AI and 29 with AI). When assisted by our AI tool, the operators showed less variability between measurements with higher intraclass correlation coefficients (ICCs 0.999 95% CI 0.998–0.999 vs. 0.982 95% CI 0.962–0.993) and lower Bland Altman limits of agreement (± 1.9% vs. ± 6.6%) compared to not using the AI tool. The time spent on scans reduced significantly from a median of 19.6 min (IQR 16.9–21.7) to 9.4 min (IQR 7.2–11.7) compared to when using the AI tool ( p < 0.001). AI-assisted muscle ultrasound removes the need for manual tracing, increases reproducibility and saves time. This system may aid monitoring muscle size in ICU patients assisting rehabilitation programmes.
