Analysis Of Longitudinal Data Research Articles

Appendicular lean mass index and motor function in ambulatory patients with Duchenne muscular dystrophy.

Appendicular lean mass index (ALMI) has been linked to motor function in patients with Duchenne muscular dystrophy (DMD). However, quantification of the relationship between ALMI and disease-specific clinical outcome assessment trajectories is needed. The purpose of this study was to determine associations between dual-energy x-ray absorptiometry (DXA) derived estimates of ALMI and motor function in ambulatory patients with DMD. A retrospective analysis of longitudinal clinical visit data from 137 glucocorticoid-treated patients with DMD collected via structured motor assessment protocol evaluated associations between ALMI and motor function indexed by the North Star Ambulatory Assessment (NSAA) and 10 Meter Walk/run Test (10MWT). Body composition was assessed using DXA. ALMI was calculated by dividing arm and leg lean mass by height in m2; fat mass index (FMI) was calculated by dividing whole body fat mass by height in m2. Linear mixed-effects models were used to estimate associations between ALMI and motor function, controlling for age and FMI. The full prediction model (age, age,2 ALMI, and FMI) explained 57% of the variance in NSAA scores and 63% of the variance in 10MWT speed. A 1 kg/m2 higher ALMI value predicted a 5.4-point higher NSAA score (p < .001) and 0.45 m/s faster 10MWT speed (p < .001). A 1 kg/m2 higher FMI value predicted a 1.5-point lower NSAA score (p < .001) and 0.14 meters/second slower 10MWT speed (p < .001). DXA-derived estimates of ALMI and FMI are associated with motor function in DMD and may explain variation in DMD disease progression.

A longitudinal qualitative study on physician experience in managing multimorbidity across the COVID-19 pandemic in Odisha, India

While many studies have documented adverse impact of multiple chronic conditions or multimorbidity on COVID-19 outcomes in patients, there is scarcity of report on how physicians managed these patients. We investigated the experiences and challenges of clinicians in managing patients with multimorbidity throughout the COVID-19 pandemic in Odisha state, India. To understand the factors influencing illness management and the adaptive responses of physicians alongside the evolving pandemic, we followed a longitudinal qualitative study design. Twenty-three physicians comprising general practitioners, specialists, and intensivists, were telephonically interviewed in-depth. Saldana’s longitudinal qualitative data analysis method was employed for data analysis. COVID-19 pandemic initially diverted the attention of health systems, resulting in reduced care. With time, the physicians overcame fear, anxiety, and feelings of vulnerability to COVID-19 and started prioritising patients with multimorbidity for treatment and vaccination. All physicians recommended teleconsultation and digital health records to benefit chronic illness care during future public health crises. The findings underscore the transformative potential of physician resilience and adaptation during the COVID-19 pandemic, emphasizing the importance of prioritizing patients with multimorbidity, incorporating teleconsultation, and implementing digital health records in healthcare systems to enhance chronic illness care and preparedness for future public health crises.

Efficacy and safety of a mineral and vitamin treatment on symptoms of antenatal depression: 12-week fully blinded randomised placebo-controlled trial (NUTRIMUM).

Broad-spectrum micronutrients (minerals and vitamins) have shown benefit for treatment of depressive symptoms. To determine whether additional micronutrients reduce symptoms of antenatal depression. Eighty-eight medication-free pregnant women at 12-24 weeks gestation, who scored ≥13 on the Edinburgh Postnatal Depression Scale (EPDS), were randomised 1:1 to micronutrients or active placebo (containing iodine and riboflavin), for 12 weeks. Micronutrient doses were generally between recommended dietary allowance and tolerable upper level. Primary outcomes (EPDS and Clinical Global Impression - Improvement Scale (CGI-I)) were analysed with constrained longitudinal data analysis. Seventeen (19%) women dropped out, with no group differences, and four (4.5%) gave birth before trial completion. Both groups improved on the EPDS, with no group differences (P = 0.1018); 77.3% taking micronutrients and 72.7% taking placebos were considered recovered. However, the micronutrient group demonstrated significantly greater improvement, based on CGI-I clinician ratings, over time (P = 0.0196). The micronutrient group had significantly greater improvement on sleep and global assessment of functioning, and were more likely to identify themselves as 'much' to 'very much' improved (68.8%) compared with placebo (38.5%) (odds ratio 3.52, P = 0.011; number needed to treat: 3). There were no significant group differences on treatment-emergent adverse events, including suicidal ideation. Homocysteine decreased significantly more in the micronutrient group. Presence of personality difficulties, history of psychiatric medication use and higher social support tended to increase micronutrient response compared with placebo. This study highlights the benefits of active monitoring on antenatal depression, with added efficacy for overall functioning when taking micronutrients, with no evidence of harm. Trial replication with larger samples and clinically diagnosed depression are needed.

Nursing researchers'concern about research activities during the COVID-19 pandemic: A secondary analysis of longitudinal survey data in Japan.

The coronavirus disease(COVID-19) pandemic has negatively affected research activities across various fields. This study aimed to determine nursing researchers'concerns about research activities during the COVID-19 pandemic in Japan and subsequent changes brought on by it. For this study, we conducted descriptive statistics and text mining analyses using data from two surveys conducted by the Japan Academy of Nursing Science (JANS) in the early days of the pandemic (first survey: mid-2020) and after 2 years (second survey: early 2022). Concerns about research activities were observed in 89% and 80% of the nursing researchers in the first and second surveys, respectively. Furthermore, concerns about "Difficulty in collecting research data" and "Content and quality of your research" were stronger in the second survey. Text mining analyses revealed that in the first survey, they were concerned about environmental changes and restrictions when proceeding with research during the COVID-19 pandemic, which was unfamiliar at the time. In the second survey, after overcoming environmental changes in the early stages of the pandemic, nursing researchers'concerns shifted to anxiety about the future, such as concerns about degree acquisition, employment and career advancement, and research results. The current study highlights various concerns among nursing researchers regarding research activities that have evolved over time during the pandemic. Academic societies must flexibly construct support measures for nursing researchers when a new infectious disease occurs. Such measures should be sensitive to the prevailing social circumstances and the evolving needs of researchers.

Clinical nSights: A software platform to accelerate real world oncology analyses.

e23316 Background: Real world data analyses are critical to assess drug safety and effectiveness, including in patient populations underrepresented in clinical trials. Significant barriers to performing such analyses remain, including: (1) incomplete access to electronic health record (EHR) data due to patient privacy concerns, (2) lack of data harmonization within and across EHRs, (3) requirements for time- and labor-intensive data curation efforts, and (4) necessity of coding or data science expertise given the absence of user-friendly platforms. We present Clinical nSights, a software platform that addresses these challenges to enable researchers and clinicians to perform rapid, robust, and scalable real world oncology analyses. Methods: With input EHR data from multiple health systems, we developed intuitive user interfaces and a scalable researcher friendly programming environment for real world clinical analytics. We used (1) nCognito, a previously described HIPAA-compliant de-identification algorithm, to de-identify EHRs across data modalities; (2) the combination of a de novo knowledge graph, vector and distribution similarity metrics, and manual review to harmonize data types including but not limited to laboratory tests, disease codes, and drugs; and (3) a series of Bidirectional Encoder Representations from Transformers (BERT) models to perform augmented curation of variables of interest (e.g., cancer staging, tumor biomarkers) from unstructured clinical records. We developed a user interface for users to create and analyze defined patient cohorts. Results: Clinical nSights facilitates intuitive cohort creation via flexible specification of inclusion and exclusion criteria, including options to perform temporal linking between criteria and propensity score matching. The ability to specify criteria documented in de-identified clinical notes expands the repertoire of available features for cohort creation beyond those captured in structured EHR fields, including but not limited to cancer stage and tumor biomarker status (e.g., HER2 status, microsatellite instability). Data harmonization enables scalable longitudinal analyses of laboratory test data across cancer types, including changes in measurements in the years leading up to and following initial cancer diagnosis. Augmented curation improves the coverage of phenotypes including immune-related toxicities of checkpoint inhibitors and symptoms experienced by patients prior to a cancer diagnosis. Using the no-code interface, users can easily quantify and visualize outcomes such as overall survival and hospitalization rates. Conclusions: Clinical nSights addresses important challenges in the collection, curation, and analysis of real world oncology data. By enabling a broad range of users to rapidly generate and test hypotheses, this platform has the potential to significantly improve care delivery and patient outcomes.

Health-related quality of life (HRQoL) outcomes from the randomized, double-blind phase 3 KEYNOTE-671 study of perioperative pembrolizumab for early-stage non-small-cell lung cancer (NSCLC).

8012 Background: Neoadjuvant pembrolizumab + cisplatin-based chemotherapy (neoadj pembro + chemo), resection, and adjuvant (adj) pembro (pembro arm; n=397) significantly improved EFS, OS, pCR, and mPR and had an expected safety profile versus neoadj placebo (pbo) + chemo, resection, and adj pbo (pbo arm; n=400) in patients (pts) with resectable stage II, IIIA, or IIIB (N2) NSCLC. We present prespecified pt-reported outcome (PRO) endpoints from KEYNOTE-671. Methods: Pts completed EORTC QLQ-C30 and QLQ-LC13 questionnaires at baseline (BL), the last scheduled presurgery visit, adj cycles 1-4, 7, 10, and 13, and each post treatment visit. A constrained longitudinal data analysis model was used to estimate least squares mean (LSM) score changes from BL to neoadj wk 11 and adj wk 10 (latest time of ≥60% completion and ≥80% compliance) in QLQ-C30 global health status (GHS)/QoL, physical functioning (PF), role functioning (RF), and dyspnea and QLC-LC13 cough and chest pain in all treated pts who completed ≥1 PRO assessment. Data are from interim analysis 2 (10 Jul 2023 cutoff). Results: Across arms, questionnaire completion was ≥87% at neoadj wk 11 and ≥62% at adj wk 10; compliance was ≥87% and ≥92%, respectively. There were no differences in LSM change from BL in the neoadj or adj phase for any PRO score (Table). Conclusions: Adding perioperative pembro maintained HRQoL in both the neoadj and adj settings versus neoadj chemo and surgery alone in pts with resectable early-stage NSCLC. Together with the significant efficacy improvements and absence of new safety signals, HRQoL data support the perioperative pembro regimen as a new standard of care. Clinical trial information: NCT03425643 . [Table: see text]

At-home, telehealth-supported ketamine treatment for depression: Findings from longitudinal, machine learning and symptom network analysis of real-world data

BackgroundImproving safe and effective access to ketamine therapy is of high priority given the growing burden of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal. MethodsIn this longitudinal study, moderately-to-severely depressed patients received four doses of ketamine at home over four weeks within a supportive digital health context. Treatment was structured to resemble methods of therapeutic psychedelic trials. Patients receiving a second course of treatment were also examined. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression. We conducted preregistered machine learning and symptom network analyses to investigate outcomes (osf.io/v2rpx). ResultsA sample of 11,441 patients was analyzed, demonstrating a modal antidepressant response from both non-severe (n = 6384, 55.8 %) and severe (n = 2070, 18.1 %) baseline depression levels. Adverse events were detected in 3.0–4.8 % of participants and predominantly neurologic or psychiatric in nature. A second course of treatment helped extend improvements in patients who responded favorably to initial treatment. Improvement was most strongly predicted by lower depression scores and age at baseline. Symptoms of Depressed mood and Anhedonia sustained depression despite ongoing treatment. LimitationsThis study was limited by the absence of comparison or control groups and lack of a fixed-dose procedure for ketamine administration. ConclusionsAt-home, telehealth-supported ketamine administration was largely safe, well-tolerated, and associated with improvement in patients with depression. Strategies for combining psychedelic-oriented therapies with rigorous telehealth models, as explored here, may uniquely address barriers to mental health treatment.

Persisting exercise ventilatory inefficiency in subjects recovering from COVID-19. Longitudinal data analysis 34 months post-discharge

BackgroundSARS-CoV-2 infection has raised concerns about long-term health repercussions. Exercise ventilatory inefficiency (EVin) has emerged as a notable long-term sequela, potentially impacting respiratory and cardiovascular health. This study aims to assess the long-term presence of EVin after 34 months and its association with cardiorespiratory health in post-COVID patients.MethodsIn a longitudinal study on 32 selected post-COVID subjects, we performed two cardiopulmonary exercise tests (CPETs) at 6 months (T0) and 34 months (T1) after hospital discharge. The study sought to explore the long-term persistence of EVin and its correlation with respiratory and cardiovascular responses during exercise. Measurements included also V̇O2peak, end-tidal pressure of CO2 (PETCO2) levels, oxygen uptake efficiency slope (OUES) and other cardiorespiratory parameters, with statistical significance set at p < 0.05. The presence of EVin at both T0 and T1 defines a persisting EVin (pEVin).ResultsOut of the cohort, five subjects (16%) have pEVin at 34 months. Subjects with pEVin, compared to those with ventilatory efficiency (Evef) have lower values of PETCO2 throughout exercise, showing hyperventilation. Evef subjects demonstrated selective improvements in DLCO and oxygen pulse, suggesting a recovery in cardiorespiratory function over time. In contrast, those with pEvin did not exhibit these improvements. Notably, significant correlations were found between hyperventilation (measured by PETCO2), oxygen pulse and OUES, indicating the potential prognostic value of OUES and Evin in post-COVID follow-ups.ConclusionsThe study highlights the clinical importance of long-term follow-up for post-COVID patients, as a significant group exhibit persistent EVin, which correlates with altered and potentially unfavorable cardiovascular responses to exercise. These findings advocate for the continued investigation into the long-term health impacts of COVID-19, especially regarding persistent ventilatory inefficiencies and their implications on patient health outcomes.

Evaluating a nurse-led narrative interview intervention with cancer patients with a first diagnosis: A feasibility study

Narrative Medicine and Nursing are clinical-supporting intervention methodologies that aim to enhance professionals’ communication skills and place patients at the heart of their therapeutic path. A narrative interview (NI) is a communication tool between practitioner and patient. The role of NI is debated extensively in the literature, but no studies focus on its use by nurses responsible for first-diagnosed cancer patients.This study aimed to evaluate the feasibility and utility of NI, carried out by Nurses, in managing people recently diagnosed with cancer.A pilot mixed-methods study with before-and-after-intervention evaluation, qualitative longitudinal data analysis, and concurrent data triangulation was conducted. The Mini-Mental Adjustment to Cancer assessed disease adaptation, while the Psychological Distress Inventory investigated psychological distress. The qualitative analysis of the narrative interviews assessed the usefulness of this intervention.13 out of 14 eligible patients took voluntary part in the study. Of those, 9 (69 %) completed T1 and T2 questionnaires and NI. The following five themes emerged from thematic analysis: reaction to the disease, feelings related to the new condition of life, changes, importance of relationships and perception of care. Patients reported being highly stressed and recognized the importance of a supporting social network for better coping with the condition.The adoption of an NI approach at the time of cancer diagnosis is feasible. Due to the limited sample size, it is unclear if NI may positively impact psychological distress in this patient population. Further studies would benefit from the integration of an additional investigation tool aiming to clarify whether NI promotes disease awareness in cancer patients. Furthermore, the recruitment of a larger sample is equally recommended.

Prevalence, incidence, mortality, and healthcare resource use for generalised pustular psoriasis, palmoplantar pustulosis, and plaque psoriasis in England: a population-based cohort study.

Generalised pustular psoriasis (GPP) and palmoplantar pustulosis (PPP) are chronic, inflammatory skin conditions. Accumulating evidence shows that GPP and PPP have different characteristics compared with plaque psoriasis and are distinct clinical entities. To assess the epidemiology, comorbidities, mortality and healthcare use for patients with GPP and PPP compared with those with plaque psoriasis in England. A cohort study involving analyses of longitudinal electronic health record data in the Clinical Practice Research Datalink Aurum database and linked hospital and mortality data between 2008 and 2019. The primary study outcome was the incidence and prevalence rates for GPP, PPP and plaque psoriasis in England. Secondary outcomes included survival rates and healthcare resource use (HCRU) by disease type. We identified 373 patients with GPP, 1,828 with PPP and 224,223 with plaque psoriasis. The mean age was 55.9 years (standard deviation [SD]: 18.6) for patients with GPP, 51.5 years (SD: 16.4) for those with PPP, and 48.5 years (SD: 19.1) for those with plaque psoriasis; 62.5% and 65.9% of patients with GPP and PPP, respectively, were women, compared with 49.4% of those with plaque psoriasis. About half of the patients were overweight or obese at baseline (GPP, 48.6%; PPP, 56.0%; and plaque psoriasis, 45.9%). The incidence rates for GPP, PPP and plaque psoriasis were 0.25 (95% CI: 0.21-0.28), 2.01 (95% CI: 1.92-2.11) and 103.2 (95% CI: 102.5-103.9) per 100,000 person-years, respectively. From 2008 to 2019, the prevalence rates per 100,000 persons ranged from 1.61 to 3.0 for GPP, 1.1 to 18.7 for PPP and 1771.0 to 1903.8 for plaque psoriasis. Survival rates were lower for patients with GPP, particularly those who were over 55 years old and those with a history of ≥1 comorbidity in each cohort. HCRU was lower in the plaque psoriasis cohort and highest in the GPP cohort, particularly among those who had ≥1 GPP flare. Our results provide further evidence that GPP is a distinct disease with different epidemiology, lower survival and higher HCRU than plaque psoriasis in England.

Recommendations on Methods for Assessing Multimorbidity Changes Over Time: Aligning the Method to the Purpose.

The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity. We identify and discuss methods aligned to specific research objectives with the goals of (i) establishing a common language for assessing longitudinal changes in multimorbidity, (ii) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and (iii) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes. We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with 4 general typologies for research objectives in the longitudinal study of multimorbidity: (i) examine individual change in multimorbidity, (ii) identify subgroups that follow similar trajectories of multimorbidity progression, (iii) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and (iv) examine the coprogression of multimorbidity with key health domains. This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

MD3F: Multivariate Distance Drift Diffusion Framework for High-Dimensional Datasets.

High-dimensional biomedical datasets have become easier to collect in the last two decades with the advent of multi-omic and single-cell experiments. These can generate over 1000 measurements per sample or per cell. More recently, focus has been drawn toward the need for longitudinal datasets, with the appreciation that important dynamic changes occur along transitions between health and disease. Analysis of longitudinal omics data comes with many challenges, including type I error inflation and corresponding loss in power when thousands of hypothesis tests are needed. Multivariate analysis can yield approaches with higher statistical power; however, multivariate methods for longitudinal data are currently limited. We propose a multivariate distance-based drift-diffusion framework (MD3F) to tackle the need for a multivariate approach to longitudinal, high-throughput datasets. We show that MD3F can result in surprisingly simple yet valid and powerful hypothesis testing and estimation approaches using generalized linear models. Through simulation and application studies, we show that MD3F is robust and can offer a broadly applicable method for assessing multivariate dynamics in omics data.

The well-being equation: How inner fulfilment drives the impact of older persons on the Ghanaian society.

Understanding and managing the complex processes of ageing is a critical function of gerontological nursing, especially when it comes to older people's well-being and their contributions to society. Globally, older persons contribute in many ways to families and communities. However, the relationship between older person's overall well-being and their propensity to contribute to society remains an important gap in research. The study examined the association between well-being and the impact of older persons on Ghanaian society. A secondary analysis of longitudinal survey data of the 2014/15 Study on Global Ageing and Adult Health (SAGE Wave 2) conducted by the World Health Organization was used. The multilevel logistic regression technique was used to examine four dimensions of well-being and their associations with high social contribution among older persons. The output was reported as odds ratios (OR). The results show that older persons who had high physical and psychological well-being were more likely to contribute to society (OR = 1.25, 95% CI = 0.93, 1.68), (OR = 1.75, 95% CI = 1.32, 2.33). However, those with high levels of emotional and spiritual well-being were less likely to make social contributions (OR = 0.66, 95% CI = 0.49, 0.88), (OR = 0.88, 95% CI = 0.66, 1.18). This study shows a positive association between well-being and older persons' societal impact. Good mental and physical health encourage societal involvement among older persons, while high emotional and spiritual well-being may lead to less societal contribution. These findings are important for nursing policies promoting social contribution and well-being among older persons 60 years and over in Ghana.

Interventional analytics in skilled nursing facilities associated with reduced readmissions.

To assess differences in longitudinal profiles for 30-day risk-adjusted readmission rates in skilled nursing facilities (SNFs) associated with Penn Medicine's Lancaster General Hospital (LGH) that implemented an interventional analytics (IA) platform vs other LGH facilities lacking IA vs other SNFs in Pennsylvania vs facilities in all other states. Retrospective longitudinal analysis of CMS readmissions data from 2017 through 2022, and cross-sectional analysis using CMS quality metrics data. CMS SNF quality performance data were aggregated and compared with risk-adjusted readmissions by facility and time period. Each SNF was assigned to a cohort based on location, referral relationship with LGH, and whether it had implemented IA. Multivariable mixed effects modeling was used to compare readmissions by cohort, whereas quality measures from the fourth quarter of 2022 were compared descriptively. LGH profiles differed significantly from both state and national profiles, with LGH facilities leveraging IA demonstrating an even greater divergence. In the most recent 12 months ending in the fourth quarter of 2022, LGH SNFs with IA had estimated readmission rates that were 15.24, 12.30, and 13.06 percentage points lower than the LGH SNFs without IA, Pennsylvania, and national cohorts, respectively (all pairwise P < .0001). SNFs with IA also demonstrated superior CMS claims-based quality metric outcomes for the 12 months ending in the fourth quarter of 2022. SNFs implementing the studied IA platform demonstrated statistically and clinically significant superior risk-adjusted readmission rate profiles compared with peers nationally, statewide, and within the same SNF referral network (P < .0001). A more detailed study on the use of IA in this setting is warranted.

Disease Course of Clinically Isolated Optic Neuritis.

Optic neuritis is the most common optic neuropathy in young adults and a frequent manifestation of multiple sclerosis. Its clinical course is pertinent to the design of visual pathway neuroprotection trials. This is a secondary analysis of longitudinal data from the TONE trial, which included 103 patients from 12 German academic tertiary centers with acute unilateral optic neuritis as a clinically isolated syndrome and baseline high-contrast visual acuity <0.5 decimal. Patients were randomized to 1,000 mg methylprednisolone i.v./d plus either erythropoietin (33,000 IU/d) or placebo (saline solution) for 3 days. They were followed up at standardized intervals with a battery of tests including high-contrast visual acuity, low-contrast letter acuity, contrast sensitivity, visual fields, visual evoked potentials, and retinal optical coherence tomography. At 6 months, participants answered a standardized questionnaire on vision-related quality of life (NEI-VFQ 25). We describe the disease course with mixed-effects piecewise linear models and calculate structure-function correlations using Pearson r. Because erythropoietin had no effect on the visual system, we use pooled (treatment-agnostic) data. Patients experienced initial rapid and then decelerating improvements of visual function with thinning of inner and thickening of outer retinal layers. At 6 months, visual parameters were positively correlated with inner and negatively correlated with outer retinal thickness changes. Peripapillary retinal nerve fiber layer thinning predominantly occurred in sectors without previous swelling. At 6 months, macular ganglion cell and inner plexiform layer thinning was weakly correlated with the P100 peak time (r = -0.11) and moderately correlated with the amplitude of visual evoked potentials (r = 0.35). Only functional outcomes were at least moderately correlated with vision-related quality of life. The longitudinal data from this large study cohort may serve as a reference for the clinical course of acute optic neuritis. The pattern of correlation between visual evoked potentials and inner retinal thinning may argue that the latter is mostly due to ganglion cell loss, rather than dysfunction. Visual pathway neuroprotection trials with functional outcomes are needed to confirm that candidate drugs will benefit patients' vision-related quality of life. ClinicalTrials.gov, NCT01962571.

Factors Associated With Change in S3-NIV Score Over Time in People With Chronic Respiratory Failure Treated With Long-Term Home Noninvasive Ventilation

IntroductionMonitoring changes in symptoms over time during long-term nocturnal home non-invasive ventilation (NIV) using patient-reported outcome measures is crucial. This study aimed to identify factors associated with changes in the S3-NIV total score, its two domains (“respiratory symptoms” and “sleep and NIV-related side effects”) and individual item responses. MethodsWe conducted a retrospective, longitudinal data analysis of a cohort of adults with chronic respiratory failure treated with NIV. Data were obtained from a French homecare provider. Multivariate linear and multinomial ordinal mixed effect models were used to identify factors associated with changes in S3-NIV scores over time. ResultsMedian follow-up was 2 years for 2135 participants. Each participant completed a median of five S3-NIV questionnaires; totaling 11,359 analyzed questionnaires. Type of respiratory condition, sex, age and time since NIV initiation were associated with change in S3-NIV score over time. NIV adherence was not associated with total S3-NIV score but high adherence was associated with more severe respiratory symptoms and an improvement in sleep and NIV-related side effects during the follow-up. Intensity of pressure support was associated with a lower total S3-NIV score and more side effects. Face masks and supplemental oxygen were associated with a lower S3-NIV total score. ConclusionChanges in S3-NIV scores over time are associated with the individual's characteristics and NIV settings. Analysis of the two domains and individual items of the S3-NIV could increase understanding of the difficulties experienced by people on NIV.

Predicting the Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using an Explainable AI Approach

Mild Cognitive Impairment (MCI) is a cognitive state frequently observed in older adults, characterized by significant alterations in memory, thinking, and reasoning abilities that extend beyond typical cognitive decline. It is worth noting that around 10–15% of individuals with MCI are projected to develop Alzheimer’s disease, effectively positioning MCI as an early stage of Alzheimer’s. In this study, a novel approach is presented involving the utilization of eXtreme Gradient Boosting to predict the onset of Alzheimer’s disease during the MCI stage. The methodology entails utilizing data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Through the analysis of longitudinal data, spanning from the baseline visit to the 12-month follow-up, a predictive model was constructed. The proposed model calculates, over a 36-month period, the likelihood of progression from MCI to Alzheimer’s disease, achieving an accuracy rate of 85%. To further enhance the precision of the model, this study implements feature selection using the Recursive Feature Elimination technique. Additionally, the Shapley method is employed to provide insights into the model’s decision-making process, thereby augmenting the transparency and interpretability of the predictions.

Differential sex-related effect of left ventricular ejection fraction trajectory on the risk of mortality and heart failure readmission following hospitalization for acute heart failure: A longitudinal study.

There is limited information on the sex-specific longitudinal changes of left ventricular ejection fraction (LVEF) after an acute heart failure (AHF) hospitalization. We aimed to investigate whether LVEF trajectories over time and their impact on mortality and AHF readmission rates differ between men and women. We conducted a retrospective sex-specific analysis of longitudinal LVEF measurements (n = 9581) in 3383 patients with an index hospitalization for AHF in a single tertiary-level hospital. Statistical techniques suited for longitudinal data analysis were used. The mean age of the sample was 73.8 ± 11.2 years, and 47.9% were women. The mean LVEF was 49.4 ± 15.3%. At a median follow-up of 2.58 years (interquartile range 0.77-5.62), we registered 2197 deaths (64.9%) and 2597 AHF readmissions in 1302 (38.5%) patients. The longitudinal analysis showed that women had consistently higher LVEF values throughout the follow-up with both trajectories characterized by an early peak-approximately at 1 year-followed by decreasing values in men but a plateau in women. Multivariate between-sex comparisons across LVEF categories revealed that women had lower rates of AHF readmissions when LVEF ≤40%. On the contrary, women displayed an excess risk of AHF readmissions when LVEF >60%. A trend in the same direction was found for cardiovascular and all-cause mortality. Sex was a significant factor in determining the follow-up trajectory of LVEF and predicting differences in outcomes after an AHF admission. The findings suggest that women have a higher risk of AHF readmissions at higher LVEF values, while men have a higher risk at lower LVEF values. For all-cause and cardiovascular mortality, the same direction of the association was inferred but they were not significant.

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas.

There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.