Critical Path Institute

To examine risk factors associated with fall events among hospitalized individuals with cancer. 188 individuals with cancer from the inpatient oncology unit of a large, urban, public hospital in New York satisfied requirements for inclusion. A retrospective case-control analysis of electronic health records was conducted from January to December 2022. Descriptive statistics were used to investigate factors associated with fall events and falls with injuries. Bivariate analyses showed that being male, having a preference for the English language, having a solid tumor diagnosis, having metastatic cancer, receiving chemotherapy, using psychotropic medications, and having any isolation precautions were associated with falls among individuals with cancer. High Morse Fall Scale scores, English-language preference, and the combination of receiving chemotherapy and any isolation precautions were found to be highly predictive of a fall event during hospitalization among individuals with cancer. Incorporating oncology-specific patient risk factors into screening or assessment tools and individualized interventions enhances safety.

BackgroundAlzheimer’s disease (AD), particularly its autosomal dominant form (ADAD), presents unique challenges for drug development. Clinical trials involving cognitively healthy individuals with ADAD can provide a useful model for sporadic Alzheimer's disease. They can aid in studying the early stages of the disease, which is challenging in sporadic cases due to heterogeneity. The CPAD consortium integrates clinical trial and observational data to develop tools to accelerate drug development. A key goal is to create a comprehensive drug‐disease‐trial model for ADAD, using data from multiple sources to address critical questions on tau and amyloid pathologies, biomarker‐cognition relationships, and disease progression.MethodsThe Alzheimer’s Prevention Initiative ADAD Colombia Trial1 is a placebo‐controlled clinical trial of crenezumab in 252 cognitively unimpaired Presenilin 1 (PSEN1) E280A kindred members. Initial research questions guiding this study include: (1) evaluating regional tau accumulation patterns in ADAD compared to sporadic AD to determine whether the same brain regions show the fastest tau accumulation; and (2) estimating the association between changes in regional tau and amyloid PET to assess whether local amyloid‐tau relationships within regions are stronger than across‐brain correlations. We use a linear mixed effect model to evaluate longitudinal outcomes with a model specification including fixed effects for mutation status, time, interaction terms, and random effects structured to account for individual heterogeneity. Model comparison techniques are used to select the most parsimonious random effects structure, focused on regional tau accumulation patterns and amyloid‐tau relationship dynamics across brain regions.ResultsLinear mixed effect models revealed distinct tau accumulation patterns in carriers compared to non‐carriers. Regional tau protein accumulation differed across brain regions, with specific variations in baseline amyloid‐related dynamics and demographic effects. The analysis distinguished cognitive progression characteristics between ADAD and sporadic AD, highlighting longitudinal neurodegenerative mechanisms.ConclusionWe characterize distinct tau accumulation patterns and amyloid‐tau interactions, particularly in ADAD mutation carriers, offering valuable insights into early disease progression. We tested our model assumptions and methods, further strengthening the framework for advancing therapeutic research and understanding the neurodegenerative mechanisms underlying both ADAD and sporadic AD.

There are several major challenges limiting our ability to test analgesic efficacy for treatment of neonatal pain, and progress in analgesic drug studies in neonates has stalled. One significant issue is the reliance of clinical pain assessments on traditional behavioural and vital signs-based measures and the exclusion of novel brain-based biomarkers. In this review protocol, we outline our strategy to assess the reliability, validity, and interpretability of an electroencephalography (EEG)-based response biomarker for assessment of acute somatic nociceptive pain in neonates. To standardise EEG analysis and generate the outcome of interest, we will perform an individual participant data (IPD) meta-analysis using data from neonates aged 34-44-week postmenstrual age that have had EEG recorded during acute somatic nociceptive skin-breaking procedures. Relevant data from both published and grey literature will be identified by searching six databases (MEDLINE, Embase, CINAHL, Web of Science, Scopus, Google Scholar), two clinical trial registry platforms (ClinicalTrials.gov, WHO ICTRP), and by consulting expert opinion. We will assess availability bias, data accuracy, and data quality by cross-referencing provided data with data descriptions in the literature, identifying duplicates and nonsensical values, and extracting quality control metrics. Data will be synthesised via a two-stage IPD meta-analysis using a random effects modelling approach grouped by site. Reliability (inter- and intra-rater) outcomes will be measured as Gwet's AC1 coefficient. Validity (known-groups and known-stimuli) outcomes will be measured as EEG response magnitude differences between clinically meaningfully different stimuli. Interpretability will be addressed by providing normative values, in both original and standardised units. The purpose of this study is to establish the reliability, validity, and interpretability of a specific EEG-based response biomarker for assessing acute somatic nociceptive pain in neonates. It will provide an overview of available data and how EEG is being used globally to assess acute neonatal pain. If sufficient IPD are made available and the outcome is reliable, valid, and interpretable, this work will support the use of EEG-based outcome measures as primary endpoints in clinical trials assessing analgesic efficacy in neonates. The protocol was registered with PROSPERO on 14 July 2023: CRD42023444809.

Evidence-based tobacco use treatment (TUT) improves clinical outcomes, yet few clinicians initiate TUT for hospitalized patients who smoke. Clinical decision support (CDS) tools embedded in electronic health records (EHRs) offer opportunities to guide clinicians toward desired behaviors. CDS alerts informed by behavioral economics (BE-CDS) may increase TUT by presenting preselected orders and requiring justification to opt out. We conducted a pilot study to evaluate the impact of a BE-CDS alert on TUT ordering for inpatients who smoke. In a two-arm randomized trial at a large US academic health system, 50 clinicians were randomized to receive either a BE-CDS alert or a standard reminder encouraging inpatient pharmacotherapy and outpatient follow-up. Alerts were triggered upon chart entry for eligible patients. The primary outcome was the rate of any TUT-related order (medication, counseling, or referral). Clinician feedback on alert appropriateness and workflow impact was also collected. From June 2022 to December 2023, 635 inpatients met the inclusion criteria. There were no significant differences in rates of any TUT order (25.2% vs. 28.5%, p = .27), inpatient medication (17.2% vs. 18.4%, p = .62), or discharge medication (7.1% vs. 6.6%, p = .90). Referral to outpatient follow-up was higher in the BE-CDS group (8.4% vs. 3.3%; OR = 2.54, p = .01). Number of alerts delivered, and White patient race, were associated with increased TUT rates. Clinicians preferred the BE-CDS format but cited alert workflow placement as more influential than alert design. BE-CDS improved referral rates but did not significantly impact overall TUT. Further research should explore deeper drivers of inpatient TUT decision-making. ClinicalTrials.gov. NCT04738643. Registered February 4, 2021. https://clinicaltrials.gov/study/NCT04738643. Compared to the standard electronic practice reminders, behavioral economics-informed clinical decision support alerts increased referrals to outpatient tobacco use treatment services for inpatients who smoke, but did not increase tobacco treatment intervention rates during the inpatient stay. Several variables appear to influence rates of tobacco use treatment, including the number of alert interactions and patient race. This pilot study suggests that the barriers to inpatient tobacco interventions may be complex and therefore insensitive to simple behavioral economic nudges.

A comprehensive, patient-centered conceptual model of early Parkinson’s is lacking and is greatly needed. A systematic review and meta-synthesis of qualitative and quantitative research was conducted by a multi-stakeholder taskforce using JBI Mixed Methods Review criteria and GRADE-CERQual standards for assessment of evidence. Over 340 symptoms and impacts were identified across ten symptom domains (Movement, Cognitive, Psychiatric, Sleep, Sensory, Speech, Digestive, Urinary, Sexual, Autonomic) and two impact domains (Physical and Psychosocial functioning). A wide range of motor and non-motor symptoms were present in early disease, with strongest support for tremor, dexterity, gait, stiffness, slow movements, cognitive, mood, and sleep alterations, urinary dysfunction, constipation, pain, and fatigue. These affected mobility, self-concept, coping, effort of living, interactions and important activities, with evidence of many understudied concepts. This model offers the most comprehensive catalogue of symptoms and impacts in Parkinson’s to date and will support clinical practice and endpoint selection for therapeutic trials.

Data-driven approaches to clinical research are necessary for understanding and effectively treating infectious diseases. However, challenges such as issues with data validity, lack of collaboration, and difficult-to-treat infectious diseases (e.g., those that are rare or newly emerging) hinder research. Prioritizing innovative methods to facilitate the continued use of data generated during routine clinical care for research, but in an organized, accelerated, and shared manner, is crucial. This study investigates the potential of CURE ID, an open-source platform to accelerate drug-repurposing research for difficult-to-treat diseases, with COVID-19 as a use case. Data from eight US health systems were analyzed using least absolute shrinkage and selection operator (LASSO) regression to identify key predictors of 28-day all-cause mortality in COVID-19 patients, including demographics, comorbidities, treatments, and laboratory measurements captured during the first two days of hospitalization. Key findings indicate that age, laboratory measures, severity of illness indicators, oxygen support administration, and comorbidities significantly influenced all-cause 28-day mortality, aligning with previous studies. This work underscores the value of collaborative repositories like CURE ID in providing robust datasets for prognostic research and the importance of factor selection in identifying key variables, helping to streamline future research and drug-repurposing efforts.

BackgroundExtracellular matrix components, including stromal collagen, may prevent tumoral infiltration by T cells, resulting in poor response to checkpoint inhibitors (CPI) in non-small cell lung cancer (NSCLC). To assess the impact of collagen on CPI outcomes, we developed an approach to extract and compute collagen fiber-based features from hematoxylin and eosin (H&E)-stained slides, which we associate with CPI outcomes and lymphocyte infiltration in a NSCLC cohort.MethodsPathExploreTM models1 were deployed on WSIs from 95 PD-(L)1 inhibitor-treated NSCLC patients enrolled in the Bergonié Institut Profiling precision medicine study (NCT02534649; Institut Bergonié, Bordeaux, France; IRB approval number 2015-A01649-40.). Lymphocyte densities were extracted in cancer and stroma. Quantitative Multimodal Anisotropy Imaging (QMAI), a polarization-based modality to image tissue components with birefringence (e.g., collagen fibers), was performed with a custom Olympus BX63 microscope. Brightfield WSI were used to match WSI at pixel-level accuracy (figure 1A-C). Polarization WSI were used for fiber extraction and computation of fiber-related features (figure 1D). Individual fiber features were compared to model-predicted lymphocyte density, PD-L1 tumor proportion score (TPS), and progression-free (PFS) and overall survival (OS). FDR correction was performed with Benjamini-Hochberg.ResultsQMAI-derived median collagen fiber width was inversely correlated with lymphocyte density both in cancer (ρ=-0.38; p<0.001) and in stroma (ρ=-0.54; p<0.001). Median fiber width was significantly associated with decreased PFS (HR=1.42, q=0.034) and OS (HR=1.47, q=0.026). Median fiber width was significantly increased in samples with TPS <1% (p=0.004). Low median fiber width was associated with better PFS (HR=1.27, p=0.040) and OS (HR=1.32, p=0.030) independent of PD-L1 status. CPI-treated patients with high PD-L1 (>1%) and low fiber width had significantly better PFS (HR=0.56, p=0.034) and OS (HR=0.41, p=5.6e-3) than all other patients.ConclusionsUsing QMAI, an imaging modality that extracts features of individual collagen fibers from H&E-stained slides, increased collagen width is associated with decreased lymphocyte infiltration, lower PD-L1 levels, and decreased survival in CPI-treated NSCLC. These results support the hypothesis that aligned collagen promotes immune exclusion, show potential value of collagen as a predictive biomarker for CPI response, and support the development collagen alignment altering therapies.1For research use only. Not for use in diagnostic procedures.Ethics ApprovalInstitut Bergonié, Bordeaux, France; IRB approval number 2015-A01649-40 Written informed consent was obtained from all study subjects prior to taking part in the study.ConsentWritten informed consent was obtained from the patient for publication of this abstract and any accompanying images. A copy of the written consent is available for review by the editor of this journal. 1378 Figure 1QMAI allows the extraction of individual collagen fibers from H&E-stained histology slides. A) QMAI microscopy workflow. B) Representative H&E-stained NSCLC specimen from which individual collagen fibers (C; colored lines) were extracted. Scale bar 0.1mm

Chromoblastomycosis, an implantation mycosis, is a neglected tropical disease that causes decreased quality of life, stigma, and disability. The global burden of disease is unknown and data on disease epidemiology and outcomes are severely limited by a lack of access to needed diagnostic tools and therapeutics. The World Health Organization outlined targets for chromoblastomycosis in the Road Map for Neglected Tropical Diseases 2021-2030, but little progress has been made in initiating and implementing an effective control program globally. This lack of guiding policy and progress led to the recent formation of a Global Chromoblastomycosis Working Group which has developed a global chromoblastomycosis strategy. We describe this strategy, which outlines specific steps needed to improve technical progress, strategy and service delivery, and enablers. Clinicians, researchers, public and government officials, patients, and policy makers can align their time, expertise, and resources to improve the lives of communities affected by chromoblastomycosis through this strategy.