Disease Prognosis Research Articles

Quality of life and survivorship in patients with low-grade ovarian cancer

ObjectiveHigh-grade (HGOC) and low-grade ovarian carcinoma (LGOC) are distinct malignancies with different biological features, treatment paradigms, and life expectancies. However, differences in quality of life (QOL), sleep, and depressive symptoms have not been examined by grade, and neither have inflammatory profiles associated with these symptoms. We aim to characterize QOL and biomarkers by OC grade. MethodsParticipants included patients with HGOC (N = 578) or LGOC (N = 85). Participants completed baseline assessments of psychosocial factors prior to primary surgery or neoadjuvant chemotherapy and contributed saliva for cortisol and blood for interleukin-6 (IL-6) quantification. Samples were collected intraoperatively to quantify tumor cortisol. General linear models were used to examine differences in biological and psychological variables by grade. ResultsAt baseline, patients with LGOC reported less depression (p = 0.018) and sleep disturbances (p = 0.014), but no significant difference in depressive mood (p = 0.11) or QOL (p = 0.51) compared to patients with HGOC, adjusting for age and disease stage. There were trends towards lower tumor cortisol levels (p = 0.078) in LGOC compared to HGOC. One-year post-diagnosis, we found a significant improvement in QOL and fatigue, and a decrease in vegetative depression and IL-6 levels irrespective of grade. ConclusionsWe present the first characterization of psychosocial experiences of patients with LGOC. Despite having a better disease prognosis, patients with LGOC were just as likely to have mood disturbances as those with HGOC. There was a trend towards differences in tumor cortisol by grade. Our findings highlight the need to address well-being in patients with both low- and high-grade ovarian malignancies.

Neuroprotection and mechanisms of ginsenosides in nervous system diseases: Progress and perspectives.

Ginsenosides are the primary component discernible from ginseng, including Rb1, Rb2, Rd, Rg1, Rg2, and compound K, and so forth. They have been shown to have multiple pharmacological activities. In recent years, more and more studies have been devoted to the neuroprotection of various ginsenosides against neurological diseases and their potential mechanisms. This paper comprehensively summarizes and reviews the neuroprotective effects of various ginsenosides on neurological diseases, especially acute and chronic neurodegenerative diseases, and their mechanisms, as well as their potential therapeutic applications to promote neuroprotection in disease prevention, treatment, and prognosis. Briefly, ginsenosides exert effective neuroprotective effects on neurological conditions, including stroke, Alzheimer's disease, Parkinson's disease, and brain/spinal cord injuries through a variety of molecular mechanisms, including anti-inflammatory, antioxidant, and anti-apoptotic. Among them, some signaling pathways play important roles in related processes, such as PI3K/Akt, TLR4/NF-κB, ROS/TXNIP/NLRP3, HO-1/Nrf2, Wnt/β-catenin, and Ca2+ pathway. In conclusion, the present study reviews the research progress on the neuroprotective effects of ginsenosides in the last decade, with the aim of furnishing essential theoretical underpinning and effective references for further research and exploration of the multiple medicinal values of Chinese herbal medicines and their small molecule compounds, including ginseng and panax ginseng. Because there is less evidence in the existing clinical studies, future research should be focused on clinical trials in order to truly reflect the clinical value of various ginsenosides for the benefit of patients.

Clinical care guidance in patients with diabetes and metabolic dysfunction-associated steatotic liver disease: A joint consensus.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease worldwide, affecting >30% of the global population. Metabolic dysregulation, particularly insulin resistance and its subsequent manifestation as type 2 diabetes mellitus, serves as the fundamental pathogenesis of metabolic liver disease. Clinical evidence of the recent nomenclature evolution is accumulating. The interaction and impacts are bidirectional between MASLD and diabetes in terms of disease course, risk, and prognosis. Therefore, there is an urgent need to highlight the multifaceted links between MASLD and diabetes for both hepatologists and diabetologists. The surveillance strategy, risk stratification of management, and current therapeutic achievements of metabolic liver disease remain the major pillars in a clinical care setting. Therefore, the Taiwan Association for the Study of the Liver (TASL), Taiwanese Association of Diabetes Educators, and Diabetes Association of the Republic of China (Taiwan) collaboratively completed the first guidance in patients with diabetes and MASLD, which provides practical recommendations for patient care.

Machine learning techniques for diagnosis of rare diseases from medical images

Introduction/Background: Rare diseases are life-threatening or chronically debilitating conditions that affect a small percentage of the population. Early and accurate diagnosis of rare diseases is challenging due to their complexity and limited understanding. Conventional diagnostic methods are often inadequate, time-consuming, and expensive. Machine learning (ML) has emerged as a promising technique for the analysis of medical images to support the diagnosis of various diseases including rare disorders. ML algorithms can learn complex patterns from large medical imaging datasets to aid clinicians in disease diagnosis, prognosis, and detection of complications. Materials and Methods: A structured search was conducted in electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar to identify peer-reviewed articles published between 2013 to 2023 related to ML-based diagnosis of rare diseases from medical images. A total of 187 articles were identified after removing duplicates. The titles and abstracts of retrieved articles were screened to determine their relevance based on the research objective. Finally, 51 full-text articles were selected for the final review. The key data extracted from selected studies included diseases, imaging modalities, ML algorithms, performance metrics, datasets, and limitations. Results: Most studies evaluated deep learning-based ML techniques, with convolutional neural networks (CNN) being the most applied algorithm. CNNs were mainly used to classify rare diseases based on specific imaging features in X-rays, CT, and MRI scans. Diseases frequently investigated included cardiovascular, neurological, and rare genetic disorders. Publicly available datasets such as Deep Lesion, MSD, and ChestX-ray14 were commonly utilized. Most studies reported high classification accuracy ranging from 80-95% on test datasets. However, limited generalizability due to small private datasets and lack of external validation were limitations. Discussion: The results demonstrate the potential of deep learning for the image-based diagnosis of rare diseases. Features learned from large imaging data sets enabled CNNs to distinguish subtle abnormalities. Hybrid models combining CNNs with other techniques like recurrent neural networks further improved performance. Overall, ML showed promise as a decision support tool. However, more multi-center validation studies are needed using larger and diverse datasets before clinical adoption. Standardization of performance metrics and reporting is also required to establish reliability and generalizability. Conclusion: This comprehensive review provided insights into ongoing research applying ML to medical images for rare disease diagnosis. While initial results are encouraging, further work is still necessary before ML can be reliably used in clinical decision making. Larger collaborative efforts and open-source datasets are needed to advance the field. Standardization of ML frameworks can also promote reproducible research and comparison of different methodologies.

Epidemiological, clinical, paraclinical, evolutionary, and therapeutic aspects of hypertensive patients infected by COVID-19: about 702 patients in our city

Abstract Introduction Hypertension (HT) is a major cardiovascular risk factor frequently observed in COVID-19 patients. This study aimed to determine the frequency of hypertensive patients infected with COVID-19 at a national hospital, describe their epidemiological, clinical, paraclinical, and therapeutic profile, their disease progression, and identify factors associated with death. Methodology A retrospective, descriptive, and analytical study was conducted over 24 months and 4 days, from 27 March 2020 to 30 March 2022, at national hospital. Included were hospitalized patients at a hospital with a confirmed COVID-19 diagnosis, known hypertensives, regardless of gender, and with a hospitalization record. COVID-19 diagnosis was based on a positive SARS-CoV-2 RT-PCR or a positive COVID Rapid Diagnostic Test. Data were collected using a survey form, with a p-value less than 0.05 considered statistically significant for bivariate analysis. Results The study enrolled 702 patients, a hospital frequency of 23.95%. The average age was 65.14 ± 12.24 years with a sex ratio of 0.81. Comorbidities were predominantly diabetes (40.5%) and dyslipidemia (4.8%). Common symptoms included cough (65%), dyspnea (42.3%), fever (47.4%), and asthenia (35.9%). Blood pressure was normal in 15.2%, high-normal in 16.8% of patients. HT was Grade I in 12.1%, Grade II in 10%, and Grade III in 6.8% of the population, with blood pressure unmeasurable in 0.3% of known hypertensive patients. SARSCOV2 infection was mild in 34.2% and moderate in 42.8% of cases. CT scans showed extensive (31.8%), severe (25.9%), moderate (25.6%), critical (8.5%), and minimal (7.9%) lung damage. Treatment mainly consisted of ACE inhibitors (29%), ACE inhibitors in combination with another antihypertensive (8.4%), and ARBs in combination with an antihypertensive (). The average hospital stay was 12.85 days with a favorable outcome in 81% and a mortality rate of 15.5%. Factors associated with death included not being vaccinated against COVID-19 (HR: 11.70; 95% CI: 1.324-103.519), critical clinical form (HR: 11.70; 95% CI: 1.324-103.519), pulmonary consolidation (HR: 8.87; 95% CI: 7.539-24.207), high creatinine (HR: 4.69; 95% CI: 1.360-16.181), critical CT thoracic level (HR: 2.42; 95% CI: 1.020-5.745), and not taking ARBs (HR: 6.02; 95% CI: 2.812-12.889). Conclusion HT can be associated with COVID-19, impacting the disease's prognosis with significant mortality. Preventing its incidence and morbidity/mortality is crucial.

Use of chatgpt-4 to explain kawasaki disease to parents and paediatric trainees

Abstract Background Paediatric trainees and parents of Kawasaki disease (KD) patients may face challenges when managing the disease due to insufficient accessibility to KD information. Chat Generative Pre-Trained Transformer (ChatGPT) based on large language models may help explain KD information to paediatric trainees and patient parents. Purpose This study aims to determine the usefulness of Chat Generative Pre- Trained Transformer -4 (ChatGPT-4) in explaining Kawasaki Disease (KD) and its management to parents and paediatric trainees managing KD patients. Methods We created 2 sets of clinical scenarios. In the first set, ChatGPT-4 was instructed to respond to 10 questions based on enquiries from parents of KD patients. Responses were scored in terms of "factual accuracy", "coherence", "comprehensiveness", and "humaneness" by 3 paediatric cardiologists using Likert scale of 0-10. Readability were calculated using Flesch reading-ease test. In the second set, ChatGPT-4 was instructed to respond to 8 KD-related questions based on enquiries from paediatric trainees. Responses are graded based on "relevance", "reliability" and "comprehensiveness" using Likert scale of 0-10 by 3 paediatric cardiologists independently. Reviewers would determine whether major advice from chatGPT-4 would be adopted in clinical judgement. Results For parent-targeted responses, ChatGPT-4 achieved the highest scores in ‘humaneness’ (median 9.00, IQR 8.00 to 9.00) and ‘coherence’ (median 8.00, IQR 7.00 to 8.00). Inaccurate information regarding disease prognosis, actions as well as prescription of medications and surgery is found in 80% of scenarios. Missing information regarding long-term coronary complications, antiplatelet management and cardiac assessments is found in all 10 scenarios. Mean readability of parent-targeted responses is 71.70 ± 6.26, a readability level easily understood by 12-year-olds. For paediatrician-targeted responses, ChatGPT-4 achieved the highest scores in ‘relevance’ (median 9.50, IQR 7.25 to 9.0). Inaccurate information regarding coronary interventions, patient education and immunization recommendations is spotted in 37.5% of the scenarios. Missing information regarding patient education and stress imaging is found in 25% of the scenarios. All reviewers would adopt ChatGPT-4’s advice in 87.5% of the scenarios. Conclusions ChatGPT-4 has significant limitations in accuracy and lacks salient information when providing KD recommendations for parents and paediatric trainees.Performance in parent-targeted questionsPerformance in trainee-targeted question

Triglyceride-glucose index predicts the prognosis of patients with ischemic cardiomyopathy

Abstract Objective A large number of studies have provided strong statistical evidence that the Triglyceride-glucose (TYG) index is associated with the development and prognosis of cardiovascular disease. However, little is known about the predictive value of TYG index for ischemic cardiomyopathy (ICM). This study aimed to investigate the predictive value of TYG index in patients with ICM. Methods Hospitalized patients ICM in our Hospital from April 2014 to December 2017 were included in this study. Inclusion criteria: previous definite diagnosis of coronary atherosclerotic heart disease (coronary angiography found one or more vessels stenosis > 50%) and echocardiography showed LVEF < 45 percent. In addition, the TYG index was calculated as follows :LN[fasting triglycerides (mg /dl)× fasting blood glucose (mg /dl)x1/2]. The endpoint of the study was MACE events, including all-cause death, myocardial infarction, ischemia-induced revascularization and heart failure (HF) rehospitalization. Results A total of 688 patients were included in this study and followed up for 1 year. In the multivariate Cox proportional hazards model, TYG index (adjusted HR 2.33 [95% CI: 1.30-4.20], P<0.01) were associated with an increased risk of MACE events, and the area under the ROC curve (AUC) was 0.604 (95%CI: 0.528-0.680, p <0.05). A total of 197 ICM patients with previously diagnosed diabetes were screened for analysis. We found that TYG index (adjusted HR 6.63 [95% CI: 2.15-20.47], P<0.01), and the AUC was 0.691 (95%CI: 0.562-0.820, p<0.05). Conclusion TYG index can predict the poor prognosis of ICM patients, and the predictive value of TyG index for ICM patients with diabetes is more significant.

Gender differences in coronary artery disease patterns

Abstract Background Gender differences in clinical presentation, natural history, and prognosis of coronary artery disease (CAD) have been reported. Fractional flow reserve (FFR) has also been shown to yield higher values in women than men. However, the gender differences in CAD patterns (focal vs diffuse) defined by coronary physiology remain to be studied. Methods This prospective study was performed at 23 sites and enrolled patients with at least one epicardial lesion with an FFR ≤ 0.80 intended to be treated by PCI. All patients underwent a standardized physiological protocol that mandated FFR pullbacks with pullback pressure gradient (PPG) calculation. The aim was to compare CAD patterns, defined by PPG, between genders. Results Overall, 1004 patients were included in the analysis, 236 females (250 vessels) and 768 males (794 vessels). Women were older (69.8 ± 10.3 vs. 67.0 ± 10.1, p <0.001), less frequently smokers (9.7% vs. 18.6%, p = 0.002), had less prior PCI in a non-target vessel (19.1% vs. 30.6%, p = 0.001) and had less prior MIs (14.4% vs. 21.5%, p = 0.021). On the baseline seven-item Seattle Angina Questionnaire (SAQ-7), females reported more physical limitation (70.9 ± 29.5 vs. 80.9 ± 25.1, p < 0.001), more frequent angina (76.7 ± 22.9 vs. 81.5 ± 20.3, p = 0.002) and a lower quality of life (51.8 ± 29.4 vs. 57.8 ± 29.1, p = 0.006) than men for the same degree of flow reduction (FFR 0.69 ± 0.12 vs. 0.67 ± 0.11, p=0.098). In women, the pattern of CAD demonstrated a more focal nature compared to men (PPG 0.65 ± 0.16 vs. 0.61 ± 0.16, p = 0.001). Also, women achieved higher post-PCI FFR (0.88 ± 0.07 vs. 0.87 ± 0.07, p = 0.02). The rate of periprocedural MI was not statistically different between genders (5.1% vs. 5.8%, p = 0.79) Conclusion Among patients with hemodynamically significant CAD, women report more symptoms and lower quality of life. Despite sharing a similar degree of flow impairment, women exhibited a more focal CAD phenotype than men, leading to superior physiological outcomes after PCI.Study flowchartPRO and Phisiology by gender

Clinical Deep Data Accumulation System (CLIDAS) reveals a "Lipid paradox" of hypertriglyceridemia in Japanese patients

Abstract Background The association between triglycerides (TG), HDL cholesterol (HDL-C) and the prognosis of cardiovascular disease has not been established. Purpose The aim of this study was to elucidate the relationships between the combination of TG and HDL-C levels and the risk of a major adverse cardiac and cerebral events (MACCE) and to verify the usefulness of TG and HDL-C as a risk stratification factor using the Clinical Deep Data Accumulation System (CLIDAS) database. Methods CLIDAS is a database that accumlates electronic medical records in seven tertiary hospitals in Japan including patient characteristics, medications, laboratory test, physiological test, cardiac catheterization, and PCI treatment. We analyzed 9,690 patients who underwent PCI between April 2014 and March 2020. These patients were stratified into the acute coronary syndrome (ACS) group (N=4,135; 43%) and the chronic coronary syndrome (CCS) group (N=5,555; 57%). Furthermore, patients were divided into four groups based on mean serum TG levels (175 mg/dL) and HDL-C levels (40 mg/dL) as cutoff values: high TG/low HDL-C, low TG/low HDL-C, high TG/high HDL-C, and low TG/high HDL-C, and compared for major adverse cardiac and cerebrovascular events (MACCE). The median follow-up duration was 2.5 years. Results The patients with high TG/low HDL-C showed the highest event rates in myocardial infarction among the ACS group. The hazard ratio for high TG/low HDL-C versus low TG/high HDL-C was 1.76 (95% CI, 1.08 to 2.87, P < 0.05) after adjustment for age, sex, eGFR, and mean LDL-C levels, while we observed no risk stratification by the combination of TG and HDL-C levels in the CCS group (Figure 1). In contrast, the patients with low TG levels showed the paradoxically higher event rates of MACCE in both the ACS and CCS groups, as well as cardiovascular death in the CCS group, compared to those with high TG levels (Figure 2). The hazard ratio for low TG/low HDL-C versus high TG/low HDL-C was 3.21 (95% CI, 1.44 to 7.13, P <0.005), and the hazard ratio for low TG/high HDL-C versus high TG/high HDL-C was 2.23 (95% CI, 1.03 to 4.84, P < 0.05) in the CCS group after adjustment for age, sex, eGFR, and mean LDL-C levels. Conclusion In the CLIDAS database, while high TG and low HDL-C levels stratified the risk of ACS, low TG levels showed worsening prognosis for CV death, suggesting a "Lipid paradox" in the real world.Figure 1Figure 2

Evidence for AI in healthcare

Abstract In healthcare, novel AI solutions are being created to address some of the biggest challenges in the prognosis, diagnosis, and treatment of disease, as well as clinician workflows and service improvement. The emergence of generative AI presents a compelling opportunity to revolutionize diagnostics, treatment planning, medical research, and patient engagement. The hypothesized uses of generative AI are broad, ranging from medical education, providing information to patients, generating synthetic patient data for the validation of AI tools, to the analysis of continuous data from wearables to detect early signs of disease. Adoption of digital solutions and AI in healthcare is slower than in other industries. The majority of clinicians don’t have direct experience with AI technologies, only a quarter have recommended a digital therapeutic, and less than a fifth have prescribed one. Safety, quality and confidence can be built through appropriate governance, testing, careful implementation and appropriate clinical use. There is a responsibility for AI developers, health systems and providers, as well as regulators, to create clear expectations for evidence and solutions. AI solutions require unique model evidence (evidence for the underlying algorithm) and solution evidence (evidence for the product in which the algorithm is embedded). Models will need validation first on internal and then on external datasets (internal and external validation). This will indicate how accurate and reliable the model is, but will be limited to the dataset that a client has access to. By examining ways to evaluate evidence for AI in healthcare, we can develop a better understanding of how best to use AI to its most appropriate advantages.

Sex-related outcomes in patients receiving shortened or longer dual antiplatelet treatment after percutaneous coronary intervention: a systematic review and meta-analysis

Abstract Background There are known gender disparities in the pathophysiology, clinical presentation, and prognosis of coronary artery disease between male and female patients. Although antiplatelet therapy cessation is more frequent among women, female gender is an independent factor for major bleeding after percutaneous coronary interventions. Considering that the ischemic and bleeding risk of women undergoing PCI differ from men, it follows that the safety and efficacy of shortened, one or 3 months, DAPT (S-DAPT) compared to longer DAPT (L-DAPT) after PCI might differ between the sexes. Purpose Our systematic review and meta-analysis aims to explore the safety and efficacy of S-DAPT versus L-DAPT in men and women having undergone PCI. Methods Three major databases (MEDLINE, Cochrane Central Register of Controlled Trials, and Scopus) were screened for eligible randomized – controlled trials, or subgroup or post – hoc analyses of them. The studies should compare S-DAPT with L-DAPT in male and female patients undergoing PCI, providing separate data. The endpoints were Net Adverse Clinical Events (NACE), Major Adverse Cardiac Events (MACE), all-cause mortality, myocardial infarction and major bleeding. All endpoints were used as per trial defined and should be evaluated in 12-months. The effect of different DAPT regimens was assessed by calculating the risk ratio (RR) with 95% confidence intervals (CIs), using a random-effects model (Mantel-Haenzel). Results A total of eight studies and 46,476 patients were included; women were underrepresented being only the 26% (N=12,063) of the total participants. MACE did not differ significantly in the two arms in the total sample (RR: 0.94, 95%CI: 0.86-1.04); however, they were significantly reduced in women treated with S-DAPT (RR: 0.82, 95%CI: 0.70-0.97) (p-interaction: 0.049). (Figure 1) S-DAPT was associated with a significant reduction in both NACE (RR: 0.91, 95% CI: 0.85-0.99) (Figure 2A) and major bleeding (RR: 0.73, 95% CI: 0.57-0.95) (Figure 2C) in the total sample, without significant differences between male and female patients (p-interaction: 0.14 and 0.053, respectively). All-cause mortality (RR: 0.86, 95%CI: 0.73-1.01) (Figure 2B) and myocardial infarction rates (RR: 1.03, 95%CI: 0.86-1.24) (Figure 2D) were similar in both S-DAPT and L-DAPT in the total sample, without any significant subgroup differences (p-interaction: 0.13 and 0.051, respectively). Conclusions Shortened duration of DAPT significantly decreased MACE in women, in contrast with male patients, showing that women may benefit from abbreviated duration of DAPT. Further studies specifically investigating the optimal duration of DAPT in women are warranted and a sex-based approach could be considered in the management of antiplatelet treatment after PCI.A. NACE B. Mortality C. Bleedings D. MI

Continuous monitoring of heart failure biomarker using a sensitive graphene-based electrical sensor

Abstract Heart Failure (HF) is a complex condition that imposes significant burdens on both patients and healthcare systems. The economic impact of HF therapy and management is substantial, underscoring the urgency for proactive measures to mitigate its development. Regular assessments play a pivotal role in identifying individuals at risk and initiating timely interventions to prevent or delay HF progression [1, 2]. NT-proBNP, an amine-terminated form of brain natriuretic peptide, emerges as a promising biomarker in the realm of chronic HF diagnosis, prognosis, and risk assessment [3]. Its utility lies in providing valuable insights into disease progression and prognosis. In recent years, biosensors based on Field Effect Transistors with Graphene (Gr) gate (GFET) have garnered attention for their rapid response, sensitivity, and on-chip integration [4]. Here, we present a sensitive miniaturized GFET sensor for continuous NT-proBNP measurement. The sensor is an array of GFET devices with sensing gates of a monolayer Gr decorated with aptamers as catch probes. Averaging over similar devices in an array approach ensures measurement consistency throughout the experiment. Moreover, the significant challenge posed by the Debye length in FET biosensors is effectively addressed through the functionalization with short folding aptamers [4]. The sensor is built based on a chip comprising an array of 28 individual micro GFETs (Fig. 1a). For the functionalization of Gr gates, we utilized 1-pyrene butyric acid N-hydroxysuccinimide ester as a linker between the aptamer probes and the Gr surface. Subsequently, the Gr surface was incubated with an amine-terminated aptamer solution (Fig. 1c). A schematic diagram of the sensory chip having a droplet of the sample is shown in Fig. 1b. To quantify NT-proBNP levels an 8µL droplet of the buffer solution containing a specific NT-proBNP concentration was deposited onto the sensor. The drain current (IDS) served as the sensor signal for each target concentration (Figs. 2a and 2b). The sensor signal decreased with increasing target concentration, while negligible response was observed when injecting blank buffers. This phenomenon can be attributed to the folding of aptamers upon binding to their target. Also, continuous measurement was conducted at a fixed gate voltage of 0.9V, recording the drain current while the target concentration gradually increased in the sample droplet. This process revealed a decrease in sensor current as we expected (Fig. 2c). The sensor exhibits remarkably low detection limits of 50 pg/mL and a linear range from 100 pg/mL to 10 ng/mL, which are highly conducive for early HF diagnosis and aligning well with reported NT-proBNP levels in patient biofluid [5]. The proposed sensor holds promise as a wearable device for continuous monitoring of high-risk patients. Additionally, this technology shows potential as a detection tool for acute heart failure in hospitalized patients.Sensor viewSensor responses

The association between endogenous erythropoietin levels and the risk of heart failure in a Chinese community-based population

Abstract Background Patients with heart failure (HF) often have elevated endogenous erythropoietin (EPO) levels, which are associated with disease severity and prognosis. Previous studies have indicated that in the special population elevated endogenous EPO levels are associated with HF and cardiovascular death. However, whether endogenous EPO levels are associated with the risk of HF in the general population remains to be elucidated. Purpose Our study aims to investigate the association between endogenous EPO levels and the risk of HF admission in the general population. Methods We conducted a prospective cohort study on subjects from a Beijing community in China who had no history of stroke or myocardial infarction. Endogenous EPO levels were measured at baseline for all participants. The endpoint of interest was admission with HF. Cox proportional hazard regression models were used to analyze the relationship between endogenous EPO levels and the occurrence of the endpoint. Results A total of 4870 participants were included. Among all participants, the mean (±SD) age was 56.72 ± 8.78 years, with 1802 (37.0%) being men. The median (interquartile range, IQR) EPO level was 13.19 (9.47-18.20) IU/L. During a mean 9.57-year follow-up, a total of 176 (3.6%) subjects experienced heart failure. Subjects were grouped in quintiles according to baseline endogenous EPO levels. Higher and lower endogenous EPO levels were both associated with increased risk of HF (Figure 1A). The Kaplan–Meier curves showed significant differences in cumulative hazards of heart failure among EPO quintiles (log-rank test: P＜0.05) (Figure 1B). After adjusting for covariates, including demographics, traditional risk factors for cardiovascular disease, renal function, and hemoglobin levels, and using the middle quintile (Q3) (11.72-14.94IU/L) of EPO levels as a reference, we found that the highest quintile (Q5) (>19.86 IU/L) was associated with a 159% increased risk of HF admission (HR 2.59, 95% CI 1.57-4.28, P = 0.0002). Similarly, the lowest quintile (Q1) (<8.66 IU/L) was associated with a 94% increased risk (HR 1.94, 95% CI 1.11-3.41, P = 0.0210) (Table 1). Conclusion There exists a "U-shaped" relationship between endogenous EPO levels and HF admission in the general population. Both higher and lower endogenous EPO levels were associated with an increased risk of HF admission in the community population. These findings differ from previous studies, so further research may be needed to explore the underlying pathophysiological mechanisms of endogenous EPO and HF.

The role of malnutrition and dependence on outside help in hospitalized elderly patients with decompensated heart failure

Abstract Introduction Despite recent achievement in the diagnosis and treatment of heart failure, the prognosis for disease remains unfavorable especially in elderly patients. It has been recently actively proposed to introduce a comprehensive geriatric assessment to determine the group of adverse outcome and frailty, however the assessment of geriatric syndromes in acute decompensated heart failure (ADHF) among hospitalized patients is poor. Aim To assess the prognostic role of malnutrition and dependence on outside help in hospitalized elderly patients with ADHF. Materials and methods The study included 60 patients over 75 years old who were hospitalized for ADHF. The median [IQR] age was 83.0 [77.0-87.0] years, 41.7% (n=25) were men. The preserved left ventricular ejection fraction (LVEF) prevailed – 51.2% (n=22), moderately reduced and low LVEF – 23.3% (n=10) and 25.6% (n=11), respectively. Total/severe dependence on outside help was determined with Barthel Index of ≤60 points, malnutrition – with a Mini Nutritional Assessment Scale of <17 points, sarcopenia with SARC-F ≥4 points. At discharge, all patients underwent bioimpedance analysis of body composition (BIA). The primary endpoint was hospital mortality. Results 48.3% (n=29) of hospitalized elderly patients with ADHF had total/severe dependence on outside help. This group of patients had a higher risk of malnutrition (OP 2.01, 95% CI 1.49-2.83, p=0.04) and sarcopenia (OP 8.25, 95% CI 1.55-44.02, p=0.01). Elderly patients with ADHF and malnutrition had a more severe functional class (NYHA III-IV) than patients without malnutrition (97.1% vs. 66.7%, respectively, p=0.02). There were no differences in ADHF phenotypes depending on the presence of malnutrition. The ability to self-serve according to Barthel Index has positively correlated with the BIA Index (r=0.483, p=0.04). The risk of sarcopenia on the SARC-F scale has negatively correlated with fat, lean and active cell mass according to BIA (r=-0.538, r=-0.599 and r=-0.532, respectively, p<0.05). Elderly patients with ADHF and a combination of total/severe dependence on outside help and malnutrition (27.9%, n=12) had higher inflammation level (neutrophil-lymphocytic index 5.6 [2.6-7.7] vs. 3.9 [2.0-6.0], p=0.03, respectively) and the need for longer intravenous diuretic therapy (OP 4.38, 95% CI 1.21-15.81, p=0.03) compared with patients without these disorders (72.1%, n=31), The primary endpoint was reached by 4 patients, in 100% of cases there was a combination of total/severe dependence on outside help and malnutrition. Conclusion The combination of total/severe dependence on outside help and malnutrition is an unfavorable prognostic marker of hospital mortality in hospitalized elderly patients with ADHF. A comprehensive geriatric assessment and determination of body composition can identify a risk group for an adverse outcome.

Utilizing unsupervised machine learning to uncover novel phenogroups within the broad QRS complex

Abstract Background Conduction disease can manifest as a broad QRS complex on the ECG. Broad QRS is conventionally categorised as left bundle branch block (LBBB) and right bundle branch block (RBBB) or non-specific intraventricular conduction delay (IVCD), based on QRS morphology. These subgroups were coined over a century ago and have been relevant for heart failure with reduced ejection fraction and cardiac resynchronization therapy (CRT). However, there may be more precise phenogroups underlying broad QRS complexes. Purpose Using unsupervised machine learning to define novel broad QRS phenogroups and characterise them using clinical variables and disease outcomes. Methods We extracted 51 relevant features using a Variational Autoencoder from 47,456 median-beat ECGs with broad QRS intervals (QRS ≥ 130ms) from a secondary care cohort. These were input into Discriminative Dimensionality Reduction via Learning a Tree (DDRTree), a clustering method that models the continuum of heterogeneity by projecting the underlying distribution as a tree structure in a low-dimensional space, while assigning distinct branch/cluster labels. Results Six distinct phenogroups were identified within broad QRS morphologies (Figure-1). Two branches were RBBB dominant (Branch 1: more males, high-comorbidity; Branch 6: low-comorbidity/healthy-RBBB). Two branches were LBBB dominant (Branch 4: mixed IVCD with CHB burden; Branch 5: more females, CRT responders). Two branches were IVCD/mixed-phenotype (Branch 2: IVCD-dominant, high risk of future cardiovascular events; Branch 3: average/core branch). Within LBBB, Branch 4 and 5 showed similar trends with improving echocardiography profiles and CRT response when adjusted for covariates, each compared against the core branch. Branch 5 was distinct due its high risk of future HF (HR 1.19 [1.07-1.33] p < 0.01), cardiovascular death (HR 1.35 [1.20-1.50] p < 0.0001), all-cause mortality (HR 1.08 [1.01-1.16] p < 0.05) and CHB (HR 1.35 [1.16-1.56] p < 0.0001) (Figure-2). Conversely within RBBB, Branch 1 and 6 showed stark differences across various traits. Branch 1 captured a high-risk profile associated to higher risk of cardiovascular death (HR 1.37 [1.24-1.52] p < 0.0001), all-cause mortality (HR 1.20 [1.12-1.29] p < 0.0001) and CHB (HR 1.15 [1.01-1.32] p < 0.05) (Figure-2), while Branch 6 retained a low-risk phenotype. The tree dimensions also revealed relevant trends in cardiac anatomy and valvular dysfunction; the top-left region in Figure-1 associated with higher left ventricular ejection fraction and lower left ventricular end-diastolic dimension, lower left ventricular end-systolic dimension and a greater CRT response. Conclusion Through unsupervised methods we have identified conduction disease phenogroups with additive value for disease prognosis and CRT response prediction beyond traditional LBBB and RBBB labels. These novel phenogroups may help guide precision care for patients with a broad QRS complex.Figure-1Figure-2

Overcoming non-usage of e-mental health: A qualitative study in a Dutch general practice

Abstract Background Worldwide percentages of people with mental health problems are substantial and increased during the COVID pandemic (to about 25 % in the general population). E-mental health has potential to help with waiting lists, rising health care costs and work pressure. To reach the desired effects, participants need to use the technology as intended. Non-usage though is a common problem. Research question is: what elements obstruct the intention to use e-mental Health interventions, plus what are barriers to bridging the intention-behaviour gap? Methods In a qualitative study, semi-structured interviews were conducted (interview guide). We recruited patients who were invited for an e-mental health intervention in a Dutch general practice but did not start the intervention. Data saturation occurred with 11 patients. Open, axial and selective coding was done on the transcribed interviews using the software ATLAS.ti. Results Five main themes were associated with non-use: 1) ‘Expectations about the eHealth in general’ were neutral to positive. 2) ‘thresholds to start e-mental health’ were: negative expectations about the content, lacking mental capacity due to mental problems, missing personal contact, the therapy with the mental health professional in general did not fit their needs or aversion towards more screen time. 3) ‘lack of planning’. 4) lacking encouragement from mental health professional 5) ‘Expectations from therapy in general’. (a.o. face-to-face contact and tailoring) Conclusions Embedding the e-mental health more into regular therapy has potential to overcome non-usage. The mental health care professional has social influence to increase usage, by a clear and convincing introduction plus joint review of the exercises during consultations. This study needs to be repeated in other clinical settings. Future research should also include age (computer use) and disease prognosis to define when e-mental health is a suitable solution for patients. Key messages • E-mental health interventions have potential to help with waiting lists, rising health care costs and workload, but non usage is a problem. • Embedding the e-mental health more into regular therapy has potential to overcome non-usage.

Trends and Disparities in Coronary Artery Disease and Obesity-Related Mortality in the United States From 1999-2022.

Almost half of the US adult population has obesity, which predisposes to atherosclerosis and can lead to poor prognosis in coronary artery disease (CAD). We aim to identify CAD and obesity-related mortality trends among adults in the United States stratified by age, sex, race and geographical location. The CDC-WONDER database was used to extract death certificate data for adults aged ≥ 25 years. Crude mortality rates (CMR) and age-adjusted mortality rates (AAMRs) per 100,000 persons were calculated, and temporal trends were described by calculating annual percent change (APC) and the average APC (AAPC) in the rates using Joinpoint regression analysis. From 1999 to 2022, a total of 273,761 CAD and obesity-related deaths were recorded in the United States. The AAMR increased consistently from 1999 to 2018 (APC: 4.3, 95% confidence interval (CI): 3.4-4.9) and surged thereafter till 2022 (APC: 11.4; 95% CI: 7.7-19.1). During the COVID-19 pandemic (2020-2022), AAMR almost doubled that of the rest of the study period. Additionally, the AAMR for males was nearly twice that of females. Non-Hispanic (NH) Blacks or African Americans displayed the highest AAMR, followed by NH Whites, Hispanic or Latino, and other NH populations. AAMRs showed minimal variation by census regions. Rural areas exhibited a higher AAMR (AAMR: 5.9, 95% CI: 5.8-5.9) than urban areas (AAMR: 4.4, 95% CI: 4.4-4.5). We observed increasing trends in CAD and obesity-related deaths throughout the study period reaching a peak during the COVID-19 pandemic.

Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease.

Diabetes is increasing in prevalence worldwide, with a 20% rise in prevalence predicted between 2021 and 2030, bringing an increased burden of complications, such as diabetic kidney disease (DKD). DKD is a leading cause of end-stage kidney disease, with significant impacts on patients, families and healthcare providers. DKD often goes undetected until later stages, due to asymptomatic disease, non-standard presentation or progression, and sub-optimal screening tools and/or provision. Deeper insights are needed to improve DKD diagnosis, facilitating the identification of higher-risk patients. Improved tools to stratify patients based on disease prognosis would facilitate the optimisation of resources and the individualisation of care. This review aimed to identify how multiomic approaches provide an opportunity to understand the complex underlying biology of DKD. This review explores how multiomic analyses of DKD are improving our understanding of DKD pathology, and aiding in the identification of novel biomarkers to detect disease earlier or predict trajectories. Effective multiomic data integration allows novel interactions to be uncovered and empathises the need for harmonised studies and the incorporation of additional data types, such as co-morbidity, environmental and demographic data to understand DKD complexity. This will facilitate a better understanding of kidney health inequalities, such as social-, ethnicity- and sex-related differences in DKD risk, onset and progression. Multiomics provides opportunities to uncover how lifetime exposures become molecularly embodied to impact kidney health. Such insights would advance DKD diagnosis and treatment, inform preventative strategies and reduce the global impact of this disease.

Association of hemoglobin, albumin, lymphocyte and platelet (HALP) score with testicular tumor aggressiveness and prognosis.

The hemoglobin, albumin, lymphocyte and platelet (HALP) score integrates readily available blood markers that reflect systemic inflammation, nutritional status, and immune response, all of which can influence cancer progression. This study investigated the association between the HALP score and clinicopathological characteristics in patients with testicular tumor. Data of patients who underwent radical orchiectomy for testicular tumors between January 2020 and January 2024 were reviewed. Preoperative serum tumor markers, hemogram parameters and albumin levels were recorded. Tumor stages were recorded from postoperative radiological imaging and serum tumor markers. The association between postoperative results and HALP score was analyzed. A total of 74 male patients were included in the study. The mean age of the patients was 30.27 ± 6.42 years. The mean HALP score in the patient group with metastasis and retroperitoneal lypmh node invasion (RPLNI) was statistically significantly lower than the patients without metastasis and RPLNI. HALP score decreased statistically significantly with increasing tumor T stage, N stage and M stage. In addition, the mean HALP score values of patients who received chemotherapy, developed progression and mortality were statistically significantly lower than those of patients who did not. Lower HALP scores are significantly associated with advanced disease and poorer prognosis in patients with testicular tumor. The HALP score, composed of routinely measured blood markers, may serve as a convenient and cost-effective prognostic tool to identify patients at higher risk and guide personalized management strategies.

The association of neutrophil-to-lymphocyte ratio with cardiovascular and all-cause mortality among the metabolic syndrome population

BackgroundThe neutrophil-to-lymphocyte ratio (NLR) has emerged as a novel inflammatory marker related to disease prognosis, this study aimed to evaluate the association between NLR and mortality in metabolic syndrome (MetS) patients.MethodsThis study used data from 13,156 participants with MetS, derived from the National Health and Nutrition Examination Survey from 1999 to 2020. The NLR was calculated, and its associations with cardiovascular disease (CVD) mortality and all-cause mortality were assessed by multivariate Cox regression, restricted cubic spline and Kaplan-Meier curves. The study performed subgroup analyses to validate the robustness of the findings in different populations. The predictive ability of NLR was evaluated using time-dependent receiver operating characteristic curve. The indirect impact of eGFR was explored by mediation analysis.ResultsAs NLR values increased, there was an obvious rise in the risk of mortality in MetS. The fully adjusted continuous model revealed a 16.0%, 14.4% elevated risk of CVD mortality (HR = 1.160; 95% CI: 1. 090-1.234, p < 0.0001) and all-cause mortality (HR = 1.144; 95% CI: 1. 086-1.206, p < 0.0001), respectively, with each one-unit increment in NLR. Comparing the highest to the lowest quartile of NLR, the top quartile exhibited a significantly increased risk of CVD mortality (HR = 2. 447; 95% CI: 1. 561-3. 836, p < 0.0001), and all-cause mortality (HR = 1. 53; 95% CI: 1. 188-1. 972, p = 0.001) among individuals with MetS. Subgroup analyses substantiated the stability of these associations in most populations. The curve under area for the 3, 5, and 10 years were 0.650, 0.716, and 0.645 for CVD mortality, and 0.746, 0.688, and 0.635 for all-cause mortality. Significantly, the eGFR acted as an intermediary in the relationship of NLR with CVD mortality and all-cause mortality, accounting for 9.85% and 9.86% of the effect, respectively.ConclusionThe NLR served as a significant indicator for assessing the risk of mortality in the MetS population. Consequently, we recommended the regular assessment of NLR in MetS populations as a potentially advantageous method for evaluating their risk of mortality.