Individual Patient Research Articles

Predicting immune risk in treatment-naïve HIV patients using a machine learning algorithm: a decision tree algorithm based on micronutrients and inversion of the CD4/CD8 ratio

IntroductionMicronutrients have significant functional implications for the human immune response, and the quality of food is a major factor affecting the severity and mortality caused by HIV in individuals undergoing antiretroviral therapy. A decrease in CD4 lymphocyte count and an increase in CD8 lymphocyte count are the hallmarks of HIV infection, which causes the CD4/CD8 ratio to invert from a normal value of >1.6 to <1.0. In this study, we tried to analyze whether the nutritional status of HIV-positive patients has an impact on the CD4/CD8 ratio inversion by utilizing a machine learning (ML) algorithm.MethodsIn this study, 55 confirmed HIV-positive patients who had not started their anti-retroviral therapy were included after obtaining their informed, written consent. Moreover, 55 age-and sex-matched relatives and caregivers of the patients who tested negative in the screening were enrolled as controls. All individual patient data points were analyzed for model development with an 80–20 train–test split. Four trace elements, zinc (Zn), phosphate (P), magnesium (Mg), and calcium (Ca), were utilized by implementing a random forest classifier. The target of the study was the inverted CD4/CD8 ratio.ResultsThe data of 110 participants were included in the analysis. The algorithm thus generated had a sensitivity of 80% and a specificity of 83%, with a likelihood ratio (LR+) of 4.8 and LR-of 0.24. The utilization of the ML algorithm adds to the limited evidence that currently exists regarding the role of micronutrients, especially trace elements, in the causation of immune risk. Our inherent strength lies in the fact that this study is one of the first studies to utilize an ML-based decision tree algorithm to classify immune risk in HIV patients.ConclusionOur study uniquely corroborated the nutritional data to the immune risk in treatment-naïve HIV patients through the utilization of a decision tree ML algorithm. This could subsequently be an important classification and prognostic tool in the hands of clinicians.

Vascular Perspectives of the Midfacial Superficial Musculoaponeurotic System.

Objectives: Presently, data on the vascularization of the superficial musculoaponeurotic system of the face (SMAS) are lacking. Thus, the present study aimed to provide new conclusive data about the topography, density, and relationship of the SMAS blood vessels with other components, namely, the fibrous connective tissue and muscles. Methods: The study included a control lot of 42 cases from the archive of the radiology department. In this group, nuclear magnetic resonance angiography (MRA) was performed in order to identify the main sources of vascular supply. In the second group, tissue samples were collected from the midfacial region of 45 patients from the Oro-Maxillo-Facial and Plastic and Reconstructive Surgery clinics of 'St. Spiridon' County Clinical Emergency Hospital, Iasi. These patients received surgery for excision of tumoral formations that did not involve SMAS components. These samples underwent micro-CT analysis, hematoxylin and eosin (HE) staining, as well as immunohistochemical (IHC) staining for collagen type III, muscle tissue, and the vascular endothelium. Results: We discovered the particular way in which the SMAS components interrelate with vascularization and the regional differences between them. We have discovered a new vascular network specific to the SMAS, highlighted by both the micro-CT technique and microscopy on slides with special IHC staining. Significant differences were observed in the topographic arrangement, density, and relationships of the microscopic vasculature across midfacial regions. IHC staining provided morphological and functional information about the structure and vascularization of SMAS. Conclusions: The MRA technique could not detect the structural blood vessels of the SMAS and other methods for their in vivo visualization must be sought. The blood vessels of the SMAS mainly follow the topography of the muscle fibers. From the SMAS layer where they are found, the distribution branches reach the stroma of the region and the hypoderm. Our data can contribute to the development of surgical techniques tailored to each individual patient, as well as the enhancement of methods for stimulating cutaneous angiogenesis, improving scarring in this region, and advancing biotissue engineering techniques.

TRAITER: Transformer-guided diagnosis and prognosis of heart failure using cell nuclear morphology and DNA damage marker.

Heart failure (HF), a major cause of morbidity and mortality, necessitates precise diagnostic and prognostic methods. This study presents a novel deep learning approach, Transformer-based Analysis of Images of Tissue for Effective Remedy (TRAITER), for HF diagnosis and prognosis. Employing image segmentation techniques and a Vision Transformer, TRAITER predicts HF likelihood from cardiac tissue cell nuclear morphology images and the potential for left ventricular reverse remodeling (LVRR) from dual-stained images with cell nuclei and DNA damage markers. In HF prediction using 31,158 images from 9 patients, TRAITER achieved 83.1% accuracy. For LVRR prediction with 231,840 images from 46 patients, TRAITER attained 84.2% accuracy for individual images and 92.9% for individual patients. TRAITER outperformed other neural network models in terms of receiver operating characteristics, and precision-recall curves. Our method promises to advance personalized HF medicine decision-making. The source code and data are available at the following link: Https://github.com/HamanoLaboratory/predict-of-HF-and-LVRR. Supplementary data are available at Bioinformatics online.

Vagus nerve stimulation for epilepsy: A narrative review of factors predictive of response.

Vagus nerve stimulation (VNS) is an established therapy for drug-resistant epilepsy. However, there is a lack of reliable predictors of VNS response in clinical use. The identification of factors predictive of VNS response is important for patient selection and stratification as well as tailored stimulation programming. We conducted a narrative review of the existing literature on prognostic markers for VNS response using clinical, demographic, biochemical, and modality-specific information such as from electroencephalography (EEG), magnetoencephalography, and magnetic resonance imaging (MRI). No individual marker demonstrated sufficient predictive power for individual patients, although several have been suggested, with some promising initial findings. Combining markers from underresearched modalities such as T1-weighted MRI morphometrics and EEG may provide better strategies for treatment optimization.

Myeloid Sarcoma: Novel Advances Regarding Molecular Pathogenesis, Presentation and Therapeutic Options

Myeloid sarcoma (MS), an extramedullary form of acute myeloid leukemia (AML) is a rare tumor mass of myeloid blasts. It can disseminate to any one or multiple anatomical sites, with (synchronous MS) or without (isolated MS) bone marrow (BM) involvement. The aim of this review is to describe the most recent advances in MS regarding diagnosis, molecular background, various clinical manifestations from several organs, and treatment approaches. Due to the lack of prospective, randomized clinical trials, therapeutic decisions are a challenge for the clinician. In the era of novel targeted AML treatments, a critical analysis of how to decide the best option for individual patients, also covering the possible central nervous system (CNS) prophylaxis is provided. For the majority of the patients, AML induction chemotherapy, followed by hematopoietic stem cell transplantation (HSCT) is generally recommended. This paper discusses the role of radiotherapy, the treatment of refractory and relapsed disease, along with the therapeutic approach of difficult-to-treat patients, due to specific problems related to different anatomical sites of MS.

Reinforcement Learning to Optimize Ventilator Settings for Patients on Invasive Mechanical Ventilation: Retrospective Study.

One of the significant changes in intensive care medicine over the past 2 decades is the acknowledgment that improper mechanical ventilation settings substantially contribute to pulmonary injury in critically ill patients. Artificial intelligence (AI) solutions can optimize mechanical ventilation settings in intensive care units (ICUs) and improve patient outcomes. Specifically, machine learning algorithms can be trained on large datasets of patient information and mechanical ventilation settings. These algorithms can then predict patient responses to different ventilation strategies and suggest personalized ventilation settings for individual patients. In this study, we aimed to design and evaluate an AI solution that could tailor an optimal ventilator strategy for each critically ill patient who requires mechanical ventilation. We proposed a reinforcement learning-based AI solution using observational data from multiple ICUs in the United States. The primary outcome was hospital mortality. Secondary outcomes were the proportion of optimal oxygen saturation and the proportion of optimal mean arterial blood pressure. We trained our AI agent to recommend low, medium, and high levels of 3 ventilator settings-positive end-expiratory pressure, fraction of inspired oxygen, and ideal body weight-adjusted tidal volume-according to patients' health conditions. We defined a policy as rules guiding ventilator setting changes given specific clinical scenarios. Off-policy evaluation metrics were applied to evaluate the AI policy. We studied 21,595 and 5105 patients' ICU stays from the e-Intensive Care Unit Collaborative Research (eICU) and Medical Information Mart for Intensive Care IV (MIMIC-IV) databases, respectively. Using the learned AI policy, we estimated the hospital mortality rate (eICU 12.1%, SD 3.1%; MIMIC-IV 29.1%, SD 0.9%), the proportion of optimal oxygen saturation (eICU 58.7%, SD 4.7%; MIMIC-IV 49%, SD 1%), and the proportion of optimal mean arterial blood pressure (eICU 31.1%, SD 4.5%; MIMIC-IV 41.2%, SD 1%). Based on multiple quantitative and qualitative evaluation metrics, our proposed AI solution outperformed observed clinical practice. Our study found that customizing ventilation settings for individual patients led to lower estimated hospital mortality rates compared to actual rates. This highlights the potential effectiveness of using reinforcement learning methodology to develop AI models that analyze complex clinical data for optimizing treatment parameters. Additionally, our findings suggest the integration of this model into a clinical decision support system for refining ventilation settings, supporting the need for prospective validation trials.

Artificial Intelligence and Advanced Technology in Glaucoma: A Review.

Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions. This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions. A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included. The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care. Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.

Challenging antibiotic stewardship: antibiotic use in the management of acute pancreatitis without waiting for a CT scan or CRP marker

Madam, Acute pancreatitis is an acute inflammation of the pancreas presenting as severe abdominal pain and elevated pancreatic enzymes. The global burden of Acute pancreatitis has increased by 3.07% per year, for the last 56 years.1 Gallstones, chronic alcohol, and hypertriglyceridemia are the main risk factors involved in the pathogenesis of Acute pancreatitis worldwide.2 However, in Pakistan gall stones impacting the common bile duct and hypertriglyceridemia are the main culprits. There has been an extensive debate regarding the use of prophylactic antibiotics and the outcome of acute pancreatitis. The cumulative evidence suggests that the prompt use of antibiotics, before obtaining serum markers, is not associated with a significant decrease in secondary pancreatic infection.3 Multiple studies have shown that the use of antibiotics, deviating from the recent guidelines, boosts antibiotic resistance and the risk of invasive fungal infections. A 2020 study conducted by Soulountsi has also rendered this practice unjustified.4 In studies supporting the use of prophylactic antibiotics in improving outcomes, the quality of evidence is low suggesting that the approach should be more tailored to individual patient conditions while adhering to the guidelines. Despite the sterile nature of the early stages of acute pancreatitis, some clinicians continue to opt for prophylactic use, however, it is strictly against the international consensus. Unfortunately, in Pakistan, there is a common practice of using antibiotics in acute pancreatitis within 72hrs, without waiting for the CT scan results and inflammatory markers screening. Antimicrobial resistance is an anticipated outcome of such practice. The prevalence of antimicrobial resistance to commonly prescribed antibiotics is already very high.5 Not weighing the risk-benefit ratio will not only contribute to increasing resistance but also predispose to avoidable and potentially fatal fungal infections. The appropriate approach to management of Acute pancreatitis should be based on a thorough knowledge of recent guidelines, carrying out proper investigations, and making an individualized plan for the patient based on clinical findings. This will not only minimize the burden of complications but also provide more room for improvements in the management of acute pancreatitis.

Innovation in cancer pharmacotherapy through integrative consideration of germline and tumor genomes.

Precision cancer medicine is widely established, and numerous molecularly targeted drugs for various tumor entities are approved or in development. Personalized pharmacotherapy in oncology has so far been based primarily on tumor characteristics, e.g., somatic mutations. However, the response to drug treatment also depends on pharmacological processes summarized under the term ADME (absorption, distribution, metabolism, and excretion). Variations in ADME genes have been the subject of intensive research for more than five decades, considering individual patients' genetic makeup, referred to as pharmacogenomics (PGx). The combined impact of a patient's tumor and germline genome is only partially understood and often not adequately considered in cancer therapy. This may be attributed, in part, to the lack of methods for combined analysis of both data layers. Optimized personalized cancer therapies should, therefore, aim to integrate molecular information about the tumor and the germline, taking into account existing PGx guidelines for drug therapy. Moreover, such strategies should provide the opportunity to consider genetic variants of previously unknown functional significance. Bioinformatic analysis methods and corresponding algorithms for data interpretation need to be developed to consider PGx data in interdisciplinary molecular tumor boards, where cancer patients are discussed to provide evidence-based recommendations for clinical management based on individual tumor profiles. Significance Statement The era of personalized oncology has seen the emergence of drugs tailored to genetic variants associated with cancer biology. However, full potential of targeted therapy remains untapped due to the predominant focus on acquired tumor-specific alterations. Optimized cancer care must integrate tumor and patient genomes, guided by pharmacogenomic principles. An essential prerequisite for realizing truly personalized drug treatment of cancer patients is the development of bioinformatic tools for comprehensive analysis of all data layers generated in modern precision oncology programs.

Deep Learning-Based Integration of Morphology and Immunophenotype Predicts Molecular Genetic Subtypes of Multiple Myeloma

Abstract Multiple myeloma, the second most common hematologic malignancy, is characterized by recurrent molecular genetic variants which portend important prognostic and therapeutic implications. These variants, which include chromosomal aneuploidies, translocations, and other structural changes, are currently identified by performing karyotyping and fluorescence in situ hybridization analysis on bone marrow biopsy samples. Comprehensive variant testing for full characterization of multiple myeloma cases can take days to weeks, is costly, and is not available to all patients. Past studies have explored to what extent cellular morphologic information from bone marrow aspirates or immunophenotypic information from flow cytometry can predict these genetic variants via the use of machine learning algorithms. However, the integration of multiple hematopathology data modalities using machine learning towards improving the diagnosis and characterization of hematologic neoplasms has yet to be explored. To this end, we developed a deep learning-based algorithm which, for individual patients, integrates tens-of-thousands of cell images from Wright-stained bone marrow aspirate smears with hundreds-of-thousands of events from myeloma flow cytometry panels to predict the presence or absence of particular molecular genetic variants among 85 cases of multiple myeloma. Attention-based multi-instance learning models were developed utilizing convolutional neural networks to analyze cell image data and multi-layer perceptrons to analyze flow cytometry data. Models trained solely on cell image data showed variable performance in predicting genetic subtypes of multiple myeloma cases: t(11;14), AUROC = 0.795; 1q+, AUROC = 0.743; del(17p), AUROC = 0.520. Similar models trained solely on flow cytometry data showed fair performance for all subtypes: t(11;14), AUROC = 0.761; 1q+, AUROC = 0.761; del(17p), AUROC = 0.787. However, models which combine features among high-attention events from both cell image and flow cytometry data yield significantly improved performance: t(11;14), AUROC = 0.892; 1q+, AUROC = 0.789; del(17p), AUROC = 0.876. Similarly, models trained to predict established prognostic classifications of multiple myeloma cases performed best when cell image and flow cytometry data is integrated: cell images alone, AUROC = 0.721; flow cytometry alone, AUROC = 0.827; both data combined, AUROC = 0.839. Additional improvements in prognostic classification were obtained when utilizing a Kronecker product to more completely utilize pairwise interactions between cell image and flow cytometry features: AUROC = 0.864. This study is, to our knowledge, the first to utilize deep learning to integrate multiple hematopathology data modalities towards automated diagnosis or subclassification of hematologic neoplasms in individual patients. The results of this study demonstrate the feasibility of accurately predicting molecular genetic subtypes of multiple myeloma cases solely from cell morphology and flow cytometric information. With further refinement, these promising models could be applied in low resource settings as well as research settings to better understand the biological relationship between plasma cell genetics with morphology and immunophenotype.

Pharmacokinetics and Safety of a Novel Extended-Release Microsphere Formulation of Risperidone in Patients with Schizophrenia or Schizoaffective Disorder.

Risperidone extended-release injectable suspension (R-ERIS; marketed as RYKINDO) is a novel immediate-release version of risperidone formulated as extended-release microspheres for biweekly intramuscular injection to treat schizophrenia in adults. The pharmacokinetics (PK) and safety of R-ERIS were evaluated in a multicenter, randomized, open-label, multiple-dose study in patients with stable schizophrenia or schizoaffective disorder. Eligible patients (N = 108) 18 to 65 years old were randomized (1:1) to receive IM injections of R-ERIS 25 mg or the comparator, a biweekly risperidone long-acting injectable (BW-RLAI; marketed as RISPERDAL CONSTA) 25 mg for a total of 5 injections. The primary objective was to evaluate the relative bioavailability of active moiety (risperidone plus 9-hydroxyrisperidone) at steady state. Blood samples were analyzed for risperidone and 9-hydroxyrisperidone using a validated, specific, and sensitive liquid chromatography with tandem mass spectrometry method. Plasma concentration-time data were analyzed using non-compartmental methods. Pharmacokinetic parameters were calculated based on individual patient PK profiles. Safety was assessed using standard measures. At steady state, mean plasma concentrations of the active moiety were similar for R-ERIS and BW-RLAI. R-ERIS rapidly released risperidone after the injection without apparent lag time. Plasma active moiety levels reached steady state after the second injection of R-ERIS. The elimination of the drug was completed approximately 2weeks earlier for R-ERIS as compared to that for BW-RLAI. R-ERIS was safe and well tolerated. Overall, R-ERIS exhibited a faster onset and offset than BW-RLAI and statistical analysis of exposure parameters demonstrated bioequivalence at steady state.

Diagnosis of acute lymphoblastic leukaemia: an overview of the current genomic classification, diagnostic approaches, and future directions.

B-acute lymphoblastic leukaemia (B-ALL) is a haematological disease resulting from haematopoietic system dysfunction, leading to the unchecked growth of immature B lymphoblasts. The disease's complexity is underscored by the spectrum of genetic aberrations that underlie B-ALL entities, necessitating advanced genetic analyses for precise classification and risk determination. Prior to the adoption of next-generation sequencing into standard diagnostic practices, up to 30% of B-ALL cases were not assigned to specific entities due to the limitations of traditional diagnostic methods. The advent of comprehensive genomic analysis, especially whole-genome transcriptome sequencing, has significantly enhanced our understanding of B-ALL's molecular heterogeneity, paving the way for the exploration of novel, tailored treatment strategies. Furthermore, recent technological innovations, such as optical genome mapping, methylation profiling, and single-cell sequencing, have propelled forward the fields of cancer research and B-ALL management. These innovations introduce novel diagnostic approaches and prognostic markers, facilitating a deeper, more nuanced understanding of individual patient disease profiles. This review focuses on the latest diagnostic standards and assays for B-ALL, the importance of new technologies and biomarkers in enhancing diagnostic accuracy, and the expected role of innovative advancements in the future diagnosis and treatment of B-ALL.

Effectiveness and Safety of Dupuytren Contracture Treatments: A Systematic Review and Meta-Analysis Using the GRADE Approach.

There is currently no consensus on the optimal treatment for Dupuytren contracture. Prior meta-analyses have been limited by suboptimal data synthesis methodologies. We conducted an updated evidence review comparing the effectiveness and safety of percutaneous needle fasciotomy (PNF), collagenase clostridium histolyticum (CCH), and limited fasciectomy (LF) using the GRADE approach. CENTRAL, MEDLINE, and Embase were searched for randomized controlled trials comparing outcomes following PNF, CCH, and LF for Dupuytren contracture treatment. Outcomes of interest included residual contracture, recurrence rate, hand function, pain, global satisfaction, and adverse events. Time points included 3-months, 1-year, and 2-3 years. Seventeen publications (1,010 patients) were included. High to moderate certainty evidence showed no clinically important difference in long-term contracture reduction (PNF vs. LF (mean difference (MD): 7.6°; 95% CI: 1.8°-13.4°), CCH vs. LF (MD: 4.8°; 95% CI: -1.3°-10.9°)). Moderate certainty evidence indicated that LF provides the lowest risk of long-term recurrence (PNF vs. LF (relative risk (RR): 12.3; 95% CI: 1.6-92.4), CCH vs. LF (RR: 9.5; 95% CI 1.2-73.4)), LF has a higher risk of serious adverse events than PNF (RR: 0.5; 95% CI 0.3-0.9), and CCH has a higher risk of overall adverse events than PNF (RR: 4.8; 95% CI 2.9-7.0). CCH, PNF, and LF are equally effective in long-term contracture reduction. However, LF yields more durable results at a higher risk of rare but serious adverse events. Current evidence suggests the use of PNF over CCH. However, ultimate treatment decisions should be tailored to individual patient preferences.

Primary Care Informatics: Vitalizing the Bedrock of Health Care.

Primary care informatics (PCI) professionals address workflow and technology solutions in a wide spectrum of health, ranging from optimizing the experience of the individual patient in the clinic room to supporting the health of populations and augmenting the work of frontline primary care clinical teams. PCI overlaps uniquely with 2 disciplines with an impact on societal health-primary care and health informatics. Primary care is a gateway to health care access and aims to synthesize and coordinate numerous, complex elements of patients' health and medical care in a holistic manner. However, over the past 25 years, primary care has become a specialty in crisis: in a post-COVID-19 world, workforce shortages, clinician burnout, and continuing challenges in health care access all contribute to difficulties in sustaining primary care. Informatics professionals are poised to change this trajectory. In this viewpoint, we aim to inform readers of the discipline of PCI and its importance in the design, support, and maintenance of essential primary care services. Although this work focuses on primary care in the United States, which includes general internal medicine, family medicine, and pediatrics (and depending on definition, includes specialties such as obstetrics and gynecology), many of the principles outlined can also be applied to comparable health care services and settings in other countries. We highlight (1) common global challenges in primary care, (2) recent trends in the evolution of PCI (personalized medicine, population health, social drivers of health, and team-based care), and (3) opportunities to move forward PCI with current and emerging technologies using the 4Cs of primary care framework. In summary, PCI offers important contributions to health care and the informatics field, and there are many opportunities for informatics professionals to enhance the primary care experience for patients, families, and their care teams.

Patient-centered care model based on self-efficacy to improve self-care and quality of life of people with type 2 diabetes mellitus: A PLS-SEM approach.

The current model for managing type 2 diabetes mellitus (T2DM) is healthcare provider-centered rather than patient-centered. This approach may overlook individual patients' unique needs, potentially impacting the effectiveness of T2DM management goals. This study aimed to develop a patient-centered care model based on self-efficacy to enhance self-care and improve the quality of life for individuals with T2DM. The study employed a cross-sectional design with a sample size of 250 respondents recruited through multistage random sampling, Jember regency, East Java, Indonesia. The study variables included exogenous factors (people with T2DM, situational treatment, family, and healthcare services) and endogenous factors (self-efficacy, self-care, and quality of life). Data were collected from August to December 2022 using a questionnaire and analyzed descriptively and inferentially using SEM-PLS. The developed model was a good fit with strong predictive relevance (SRMR = 0.065; Q2 = 0.049). All exogenous factors-people with T2DM, situational treatment, family, and healthcare services-significantly affected self-efficacy (42.2%, 37%, 8.1%, and 17.3%; p <0.001). Self-efficacy had a 61.6% effect on self-care, and self-care had a 27.1% effect on quality of life (p <0.001). Only situational treatment factors had a direct effect on self-care (21.7%; p <0.001). All exogenous factors also influenced the quality of life through self-efficacy and self-care (7%, 6.2%, 1.3%, and 2.9%; p <0.05). A patient-centered care model based on self-efficacy for managing T2DM can improve self-care behaviors and quality of life for individuals with T2DM. This model can be utilized by nurses in healthcare services to enhance the management of T2DM.

Comparative efficacy of chemo-immunotherapy combination regimens in the frontline setting for NSCLC based on reconstructed patient data

Immune checkpoint inhibitors (ICIs) have revolutionised the treatment of metastatic NSCLC and have become standard first-line therapy both as monotherapy, for patients with PD-L1 expression ≥50%, and in combination with chemotherapy (CT), regardless of PD-L1 expression. This study used an artificial intelligence technique, the IPDfromKM method, to reconstruct individual patient data from Kaplan-Meier curves of phase III randomised clinical trial results to provide a comparative overview of different first-line chemo-immunotherapy options. Overall survival (OS) was estimated using hazard ratios and restricted mean survival time (RMST). Ten clinical trials were included in the analysis. In the squamous population, combinations of cemiplimab + CT (HR = 0.56), pembrolizumab + CT (HR = 0.67), and nivolumab + ipilimumab + CT (HR = 0.71) significantly improved OS compared with CT alone, with no difference between treatments. At longer follow-up, nivolumab + ipilimumab + CT showed longer RMST compared to pembrolizumab + CT in the PD-L1 < 1% subgroup (24.9 months vs. 22.8 months). In non-squamous NSCLC, the survival benefit of ICIs + CT was much more homogeneous, with similar results across the different options. Overall, pembrolizumab + CT showed the best results both in terms of HR (0.68, 95%CI 0.60-0.77) and RMST at long follow-up (30.4 months in the PDL-1 ≥ 1% subgroup and 24 months in the PDL-1 < 1% population). In conclusion, there are some differences between frontline options for treating metastatic NSCLC based on tumour histology and PD-L1 expression. However, further head-to-head trials and longer follow-up are needed to clarify the clinical impact of these differences.

Sex Differences in Frequency, Severity, and Distribution of Cerebral Microbleeds

Cerebral small vessel disease (SVD) is associated with various cerebrovascular outcomes, but data on sex differences in SVD are scarce. To investigate whether the frequency, severity, and distribution of cerebral microbleeds (CMB), other SVD markers on magnetic resonance imaging (MRI), and outcomes differ by sex. This cohort study used pooled individual patient data from the Microbleeds International Collaborative Network, including patients from 38 prospective cohort studies in 18 countries between 2000 and 2018, with clinical follow-up of at least 3 months (up to 5 years). Participants included patients with acute ischemic stroke or transient ischemic attack with available brain MRI. Data were analyzed from April to December 2023. Outcomes of interest were presence of CMB, lacunes, and severe white matter hyperintensities determined on MRI. Additionally, mortality, recurrent ischemic stroke, and intracranial hemorrhage during follow-up were assessed. Multivariable random-effects logistic regression models, Cox regression, and competing risk regression models were used to investigate sex differences in individual SVD markers, risk of recurrent cerebrovascular events, and death. A total of 20 314 patients (mean [SD] age, 70.1 [12.7] years; 11 721 [57.7%] male) were included, of whom 5649 (27.8%) had CMB. CMB were more frequent in male patients, and this was consistent throughout different age groups, locations, and in multivariable models (female vs male adjusted odds ratio [aOR], 0.86; 95% CI, 0.80-0.92; P < .001). Female patients had fewer lacunes (aOR, 0.82; 95% CI, 0.74-0.90; P < .001) but a higher prevalence of severe white matter hyperintensities (aOR, 1.10; 95% CI, 1.01-1.20; P = .04) compared with male patients. A total of 2419 patients (11.9%) died during a median (IQR) follow-up of 1.4 (0.7-2.5) years. CMB presence was associated with a higher risk of mortality in female patients (hazard ratio, 1.15; 95% CI, 1.02-1.31), but not male patients (hazard ratio, 0.95; 95% CI, 0.84-1.07) (P for interaction = .01). A total of 1113 patients (5.5%) had recurrent ischemic stroke, and 189 patients (0.9%) had recurrent intracranial hemorrhage, with no sex differences. This cohort study using pooled individual patient data found varying frequencies of individual SVD markers between female and male patients, indicating potential pathophysiological differences in manifestation and severity of SVD. Further research addressing differences in pathomechanisms and outcomes of SVD between female and male patients is required.

Genetic Biomarkers in Heart Failure: From Gene Panels to Polygenic Risk Scores.

This review aims to provide a comprehensive overview of the current understanding of genetic markers associated with heart failure (HF) and its underlying causative diseases, such as cardiomyopathies. It highlights the relevance of genetic biomarkers in diagnosing HF, predicting prognosis, potentially identifying its preclinical stages and identifying targets to enable the implementation of individualized medicine approaches. The prevalence of HF is increasing due to an aging population but with greater access to disease-modifying therapies. Advanced diagnostic tools such as cardiac magnetic resonance, nuclear imaging, and AI-enabled diagnostic testing are now being utilized to further characterize HF patients. Additionally, the importance of genetic testing in HF diagnosis and management is increasingly being recognized. Genetic biomarkers, including single nucleotide polymorphisms (SNPs) and rare genetic variants, are emerging as crucial tools for diagnosing HF substrates, determining prognosis and increasingly directing therapy. These genetic insights are key to optimizing HF management and delivering personalized treatment tailored to individual patients. HF is a complex syndrome affecting millions globally, characterized by high mortality and significant economic burden. Understanding the underlying etiologies of HF is essential for improving management and clinical outcomes. Recent advances highlight the use of multimodal assessments, including AI-enabled diagnostics and genetic testing, to better characterize and manage HF. Genetic biomarkers are particularly promising in identifying preclinical HF stages and providing personalized treatment options. The genetic contribution to HF is heterogeneous, with both monogenic and polygenic bases playing a role. These developments underscore the shift towards personalized medicine in HF management.

Best practice guide for patellofemoral pain based on synthesis of a systematic review, the patient voice and expert clinical reasoning

ObjectiveDefine a best practice guide for managing people with patellofemoral pain (PFP).MethodsA mixed-methods convergent segregated synthesis of meta-analysed data with a thematic analysis of semistructured interviews and focus groups. Agreement...

Nutritional Modulation of the Gut-Brain Axis: A Comprehensive Review of Dietary Interventions in Depression and Anxiety Management.

Mental health is an increasing topic of focus since more than 500 million people in the world suffer from depression and anxiety. In this multifactorial disorder, parameters such as inflammation, the state of the microbiota and, therefore, the patient's nutrition are receiving more attention. In addition, food products are the source of many essential ingredients involved in the regulation of mental processes, including amino acids, neurotransmitters, vitamins, and others. For this reason, this narrative review was carried out with the aim of analyzing the role of nutrition in depression and anxiety disorders. To reach the review aim, a critical review was conducted utilizing both primary sources, such as scientific publications and secondary sources, such as bibliographic indexes, web pages, and databases. The search was conducted in PsychINFO, MedLine (Pubmed), Cochrane (Wiley), Embase, and CinAhl. The results show a direct relationship between what we eat and the state of our nervous system. The gut-brain axis is a complex system in which the intestinal microbiota communicates directly with our nervous system and provides it with neurotransmitters for its proper functioning. An imbalance in our microbiota due to poor nutrition will cause an inflammatory response that, if sustained over time and together with other factors, can lead to disorders such as anxiety and depression. Changes in the functions of the microbiota-gut-brain axis have been linked to several mental disorders. It is believed that the modulation of the microbiome composition may be an effective strategy for a new treatment of these disorders. Modifications in nutritional behaviors and the use of ergogenic components are presented as important non-pharmacological interventions in anxiety and depression prevention and treatment. It is desirable that the choice of nutritional and probiotic treatment in individual patients be based on the results of appropriate biochemical and microbiological tests.