Disease Progression In Models Research Articles

AI in Neuro-Ophthalmology: Current Practice and Future Opportunities.

Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease. Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI. This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice. We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.

Ensemble of HMMs for Sequence Prediction on Multivariate Biomedical Data

Background: Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for the description and modeling of disease progression. Deciphering potential underlying unknowns from the distinct observation would substantially improve the understanding of pathological cascades. Hidden Markov Models (HMMs) have been successfully applied to the processing of possibly noisy continuous signals. We apply ensembles of HMMs to categorically distributed multivariate time series data, leaving space for expert domain knowledge in the prediction process. Methods: We use an ensemble of HMMs to predict the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. Results: We present a prediction pipeline that processes data paired with a configuration file, enabling us to train, validate and query an ensemble of HMMs. In particular, we provide a theoretical and practical framework for multivariate time-series inference based on HMMs that includes constructing multiple HMMs, each to predict a particular observable variable. Our analysis is conducted on pseudo-data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. Conclusions: We find that the model shows promising results for the data we tested. The strength of this approach is that HMMs are well understood, probabilistic and interpretable models, setting it apart from most Deep Learning approaches. We publish all code and evaluation pseudo-data in an open-source repository.

Prognostic factors and predictive model construction in patients with non-small cell lung cancer: a retrospective study

ObjectiveThe purpose of this study was to construct a nomogram model based on the general characteristics, histological features, pathological and immunohistochemical results, and inflammatory and nutritional indicators of patients so as to effectively predict the overall survival (OS) and progression-free survival (PFS) of patients with non-small cell lung cancer (NSCLC) after surgery.MethodsPatients with NSCLC who received surgical treatment in our hospital from January 2017 to June 2021 were selected as the study subjects. The predictors of OS and PFS were evaluated by univariate and multivariable Cox regression analysis using the Cox proportional risk model. Based on the results of multi-factor Cox proportional risk regression analysis, a nomogram model was established using the R survival package. The bootstrap method (repeated sampling for 1 000 times) was used to internally verify the nomogram model, and C-index was used to represent the prediction performance of the nomogram model. The calibration graph method was used to visually represent its prediction compliance, and decision curve analysis (DCA) was used to evaluate the application value of the model.ResultsUnivariate and multivariate analyses were used to identify independent prognostic factors and to construct a nomogram of postoperative survival and disease progression in operable NSCLC patients, with C-index values of 0.927 (907–0.947) and 0.944 (0.922–0.966), respectively. The results showed that the model had high predictive performance. Calibration curves for 1-year, 2-year, and 3-year OS and PFS show a high degree of agreement between the predicted probability and the actual observed probability. In addition, the results of the DCA curve show that the model has good clinical application value.ConclusionWe established a predictive model of survival prognosis and disease progression in patients with non-small cell lung cancer after surgery, which has good predictive performance and can guide clinicians to make the best clinical decision.

Predictive Partly Conditional Models for Longitudinal Ordinal Outcomes with Application to Alzheimer’s Disease Progression

AbstractAssessing time-dependent risk factors in relation to the risk of disease progression is challenging, yet important, especially for chronic diseases with slow progression. In this paper, the partly conditional model is extended for characterizing disease progression at time t with longitudinal ordinal outcomes in the presence of time-dependent covariates at time s$$(s < t)$$ ( s < t ) and time-dependent effects. Advantages of the method include direct modeling of disease progression, use of longitudinal risk factors, and flexibility in target period of progression $$u = t - s$$ u = t - s . A generalized estimating equation approach is adopted for parameter estimation, and a new regularity condition requiring a fixed prediction time window $$u_0$$ u 0 is established to consistently estimate the covariance of the estimated parameters. Extensive simulation studies are conducted to assess the properties of the proposed model alongside with an explicit demonstration of the implementation using existing statistical software. The proposed method is applied to a longitudinal Alzheimer’s disease dataset from the National Alzheimer’s Coordinating Center to assess effect of time-dependent cognitive complaint on Alzheimer’s Disease progression.

Epigenetic activation of SOX11 is associated with recurrence and progression of ductal carcinoma in situ to invasive breast cancer.

Risk of recurrence and progression of ductal carcinoma in situ (DCIS) to invasive cancer remains uncertain, emphasizing the need for developing predictive biomarkers of aggressive DCIS. Human cell lines and mouse models of disease progression were analyzed for candidate risk predictive biomarkers identified and validated in two independent DCIS cohorts. RNA profiling of normal mammary and DCIS tissues (n = 48) revealed that elevated SOX11 expression correlates with MKI67, EZH2, and DCIS recurrence score. The 21T human cell line model of DCIS progression to invasive cancer and two mouse models developing mammary intraepithelial neoplasia confirmed the findings. AKT activation correlated with chromatin accessibility and EZH2 enrichment upregulating SOX11 expression. AKT and HER2 inhibitors decreased SOX11 expression along with diminished mammosphere formation. SOX11 was upregulated in HER2+ and basal-like subtypes (P < 0.001). Longitudinal DCIS cohort (n = 194) revealed shorter recurrence-free survival in SOX11+ than SOX11- patients (P = 0.0056 in all DCIS; P < 0.0001 in HER2+ subtype) associated with increased risk of ipsilateral breast event/IBE (HR = 1.9, 95%CI = 1.2-2.9; P = 0.003). Epigenetic activation of SOX11 drives recurrence of DCIS and progression to invasive cancer, suggesting SOX11 as a predictive biomarker of IBE.

A Bayesian Joint Model of Longitudinal Kidney Disease Progression, Recurrent Cardiovascular Events, and Terminal Event in Patients with Chronic Kidney Disease

AbstractNearly 15% (37 million) of adults in the United States (US) have chronic kidney disease (CKD). The longitudinal decline of kidney function is intricately related to the development of cardiovascular disease (CVD) and eventual “terminal” event (kidney failure and mortality) in patients with CKD. Understanding the mechanism and risk factors underlying the three key outcome processes, (1) CKD progression, (2) CVD, and (3) subsequent terminal event in the CKD patient population remains incomplete. Thus, in this work, we develop a novel trivariate joint model to study the risk factors associated with the interdependent outcomes of kidney function (as measured by longitudinal estimated glomerular filtration rate), recurrent cardiovascular events, and the terminal event. Efficient estimation and inference is proposed within a Bayesian framework using Markov Chain Monte Carlo and Bayesian P-splines for hazard functions. The proposed Bayesian framework is directly generalizable beyond trivariate outcome processes to accommodate other potential modeling of complex multi-disease processes. The method is applied to study the aforementioned trivariate processes using data from the Chronic Renal Insufficiency Cohort Study, an ongoing prospective cohort study, established by the National Institute of Diabetes and Digestive and Kidney Diseases to address the rising epidemic of CKD in the US.

A unified framework for prediction of liver steatosis dynamics in response to different diet and drug interventions

Background & AimsNon-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder, characterized by the accumulation of excess fat in the liver, and is a driving factor for various severe liver diseases. These multi-factorial and multi-timescale changes are observed in different clinical studies, but these studies have not been integrated into a unified framework. In this study, we aim to present such a unified framework in the form of a dynamic mathematical model. MethodsFor model training and validation, we collected data for dietary or drug-induced interventions aimed at reducing or increasing liver fat. The model was formulated using ordinary differential equations (ODEs) and the mathematical analysis, model simulation, model formulation and the model parameter estimation were all performed in MATLAB ResultsOur mathematical model describes accumulation of fat in the liver and predicts changes in lipid fluxes induced by both dietary and drug interventions. The model is validated using data from a wide range of drug and dietary intervention studies and can predict both short-term (days) and long-term (weeks) changes in liver fat. Importantly, the model computes the contribution of each individual lipid flux to the total liver fat dynamics. Furthermore, the model can be combined with an established bodyweight model, to simulate even longer scenarios (years), also including the effects of insulin resistance and body weight. To help prepare for corresponding eHealth applications, we also present a way to visualize the simulated changes, using dynamically changing lipid droplets, seen in images of liver biopsies. ConclusionIn conclusion, we believe that the minimal model presented herein might be a useful tool for future applications, and to further integrate and understand data regarding changes in dietary and drug induced changes in ectopic TAG in the liver. With further development and validation, the minimal model could be used as a disease progression model for steatosis.

Hepatitis C screening in Lithuania: first-year results and scenarios for achieving WHO elimination targets.

The World Health Organization (WHO) has outlined a set of targets to achieve eliminating hepatitis C by 2030. In May 2022, Lithuanian health authorities initiated a hepatitis C virus (HCV) screening program to start working towards elimination. In the program, bonus was given to general practitioners (GPs) to promote and conduct anti-HCV tests for two situations: (1) one time testing for individuals born in 1945-1994 and (2) annual HCV testing for persons who inject drugs or are living with human immunodeficiency virus (HIV) regardless of age. This study aimed to model the current viral hepatitis C epidemiological status in Lithuania and to outline the requirements for WHO elimination targets using the first-year HCV screening results. Individuals were invited to participate in the anti-HCV screening by GPs during routine visits. Patients who tested positive were then referred to a gastroenterologist or infectious disease doctor for further confirmatory testing. If a patient received a positive RNA test and a fibrosis staging result of ≥ F2, the doctor prescribed direct-acting antivirals. Information on the patients screened, diagnosed, and treated was obtained from the National Health Insurance Fund. The Markov disease progression model, developed by the CDA Foundation, was used to evaluate the screening program results and HCV elimination progress in Lithuania. Between May 2022 and April 2023, 790,070 individuals underwent anti-HCV testing, with 11,943 individuals (1.5%) receiving positive results. Anti-HCV seroprevalence was found to be higher among males than females, 1.9% and 1.2%, respectively. Within the risk population tested, 2087 (31.1%) seropositive individuals were identified. When comparing the screening program results to WHO elimination targets through modelling, 2180 patients still need to be treated annually until 2030, along with expanding fibrosis restrictions. If an elimination approach was implemented, 1000 new infections would be prevented, while saving 150 lives and averting 90 decompensated cirrhosis cases and 110 hepatocellular carcinoma cases. During the first year of the Lithuanian screening program, GPs were able to screen 44% of the target population. However, the country will not meet elimination targets as it currently stands without increasing treatment levels and lifting fibrosis restrictions.

Five multivariate Duchenne muscular dystrophy progression models bridging six-minute walk distance and MRI relaxometry of leg muscles.

The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T2) of leg muscles in DMD clinical trials by developing multivariate disease progression models of Duchenne muscular dystrophy (DMD) using 6-min walk distance (6MWD) and MRI-T2. Clinical data were collected from the prospective and longitudinal ImagingNMD study. Disease progression models were developed by a nonlinear mixed-effect modeling approach. Univariate models of 6MWD and MRI-T2 of five muscles were developed separately. Age at assessment was the time metric. Multivariate models were developed by estimating the correlation of 6MWD and MRI-T2 modelvariables. Full model estimation approach for covariate analysis and five-fold cross validation were conducted. Simulations were performed to compare the models and predict the covariate effects on the trajectories of 6MWD and MRI-T2. Sigmoid Imax and Emax models best captured the profiles of 6MWD and MRI-T2 over age. Steroid use, baseline 6MWD, and baseline MRI-T2 were significant covariates. The median age at which 6MWD is half of its maximum decrease in the five models was similar, while the median age at which MRI-T2 is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T2 successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T2, supporting the use of MRI-T2. The developed models will guide drug developers in using the MRI-T2 to most efficient use in DMD clinical trials.

Longitudinal inference of multiscale markers in psychosis: from hippocampal centrality to functional outcome.

Multiscale neuroscience conceptualizes mental illness as arising from aberrant interactions across and within multiple biopsychosocial scales. We leverage this framework to propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impairments in episodic memory and social cognition, which lead to more severe negative symptoms and lower functional outcome. As psychosis represents a heterogeneous collection of biological and behavioral alterations that evolve over time, we further predict this disease progression for a subtype of the patient sample, with other patients showing normal-range performance on all variables. We sampled data from two cross-sectional datasets of first- and multi-episode psychosis, resulting in a sample of 163 patients and 119 non-clinical controls. To address our proposed disease progression model and evaluate potential heterogeneity, we applied a machine-learning algorithm, SuStaIn, to the patient data. SuStaIn uniquely integrates clustering and disease progression modeling and identified three patient subtypes. Subtype 0 showed normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity and episodic memory. Subtype 1 deteriorated from episodic memory to social cognition, negative symptoms, functional outcome to bilateral hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from bilateral hippocampal-cortical dysconnectivity to episodic memory and social cognition, functional outcome to negative symptoms. This first application of SuStaIn in a multiscale psychiatric model provides distinct disease trajectories of hippocampal-cortical connectivity, which might underlie the heterogeneous behavioral manifestations of psychosis.

Mouse models of CLL: In vivo modeling of disease initiation, progression, and transformation

Chronic lymphocytic leukemia (CLL) is a highly complex disease characterized by the proliferation of CD5+ B cells in lymphoid tissues. Current modern treatments have brought significant clinical benefits to CLL patients. However, there are still unmet needs. Patients relapse on Bruton's tyrosine kinase inhibitors and BCL2 inhibitors and often develop more aggressive diseases including Richter transformation (RT), an incurable complication of up to ∼10% patients. This evidence underscores the need for improved immunotherapies, combination treatment strategies, and predictive biomarkers. A mouse model that can recapitulate human CLL disease and certain components of the tumor immune microenvironment represents a promising preclinical tool for such purposes. In this review, we provide an overview of CRISPR-engineered and xenograft mouse models utilizing either cell lines, or primary CLL cells suitable for studies of key events driving the disease onset, progression and transformation of CLL. We also review how CRISPR/Cas9 established mouse models carrying loss-of-function lesions allow one to study key mutations driving disease progression. Finally, we discuss how next generation humanized mice might improve to generation of faithful xenograft mouse models of human CLL.

A Multiscale Closed-Loop Neurotoxicity Model of Alzheimer's Disease Progression Explains Functional Connectivity Alterations.

The accumulation of amyloid-β (Aβ) and hyperphosphorylated-tau (hp-tau) are two classical histopathological biomarkers in Alzheimer's disease (AD). However, their detailed interactions with the electrophysiological changes at the meso- and macroscale are not yet fully understood. We developed a mechanistic multiscale model of AD progression, linking proteinopathy to its effects on neural activity and vice-versa. We integrated a heterodimer model of prion-like protein propagation and a brain network model of Jansen-Rit neural masses derived from human neuroimaging data whose parameters varied due to neurotoxicity. Results showed that changes in inhibition guided the electrophysiological alterations found in AD, and these changes were mainly attributed to Aβ effects. Additionally, we found a causal disconnection between cellular hyperactivity and interregional hypersynchrony contrary to previous beliefs. Finally, we demonstrated that early Aβ and hp-tau depositions' location determine the spatiotemporal profile of the proteinopathy. The presented model combines the molecular effects of both Aβ and hp-tau together with a mechanistic protein propagation model and network effects within a closed-loop model. This holds the potential to enlighten the interplay between AD mechanisms on various scales, aiming to develop and test novel hypotheses on the contribution of different AD-related variables to the disease evolution.

280 Enhancing Nephrology Care Access: Development and Implementation of a Telenephrology Dashboard Through Human-Centered Design

OBJECTIVES/GOALS: Our objective is to develop a Telenephrology dashboard for the 150,000 Veterans that obtain care through the Iowa City Veterans Affairs Health Care System. Our goal is to create a comprehensive and user-friendly tool for monitoring kidney health and facilitating remote nephrology consultations. METHODS/STUDY POPULATION: We structured our intervention according to the five stages of human-centered design: (1) Empathize, (2) Define, (3) Ideate, (4) Prototype and (5) Test. During the empathy stage, the principal investigator spent 10 hours immersed in the clinical setting observing how nephrologists approach a remote nephrology consultation. These observations were augmented by unstructured interviews with clinicians and patients to better understand the process and dynamics. Following this, a rapid ideation workshop was convened to generate creative solutions that balance technical requirements with the needs of clinicians and patients. These led to rapid prototyping and testing to identify what elements of the prototypes worked and which needed improvement. RESULTS/ANTICIPATED RESULTS: Through the empathy and define stages, three needs were identified: (1) clarity in visualizing data, (2) accuracy of information, and (3) balancing standardization with individualization. During the rapid ideation workshop, the concept of a four-frame dashboard was settled upon. This led to the creation of five prototypes, which were tested. These were reconciled and modified to make a final product. This final product, the Telenephrology Dashboard, contains 5 elements that support nephrologists and supporting staff: (1) a graph of kidney function over time, (2) tables synthesizing lab data, (3) options to drill down events to specific times, (4) customization of views, and (5) integration of kidney disease progression models. DISCUSSION/SIGNIFICANCE: A Telenephrology dashboard was created to facilitate remote nephrology consultations through a Human-Centered Design process. Our next steps include determining if this dashboard may improve end-user satisfaction, referring clinician satisfaction, access to specialist care, and patient outcomes.

Estimating the Allocation of the Economic Value Generated by Utilization of All-Oral Direct-Acting Antivirals for Hepatitis C in the United States, 2015 to 2019

ObjectivesBetween 2013 to 2019, several all-oral direct-acting antivirals (DAAs) were launched with the potential to cure patients with hepatitis C virus (HCV). They generated economic value in terms of the health gains for patients and cost-savings for the US healthcare system. We estimated the share of this value allocated to 4 manufacturers vs society. MethodsFor 2015 to 2019, we estimated the incremental impact of DAAs on HCV health outcomes and costs. We used the Center for Disease Analysis Foundation Polaris Observatory database to estimate utilization. Per-patient projections of lifetime quality-adjusted life-years (QALYs) gained and medical costs avoided were based on a standard 9-state HCV disease-progression model for DAA treatment vs alternatives. Annual QALY gains were valued at $114 000 per QALY. Outcomes and costs were discounted at 3%. Estimated revenues were based on reported sales. ResultsAn estimated 1 080 000 patients received DAAs: 81.5% would not have received the pre-DAA standard of care. On average, these patients were projected to gain 4.4 QALYs and save $104 400 in lifetime healthcare costs, generating $531.8 billion in value. Those who would have received treatment gained 1.7 QALYs and saved $41 500 in lifetime costs, generating $47.4 billion in economic value. As treatment costs fell nearly 75%, the 4 manufacturers reported $37.4 billion from DAA sales—an allocation of 6.5% of the total value. ConclusionsThe significant majority (∼90%) of the economic value of curing HCV with DAAs were health benefits to patients and net cost-savings to society. DAA manufacturers received a minority share (6.5%) of the aggregate economic value generated.

A review of the use of machine learning in predictive analytics for patient health outcomes in pharmacy practice

Predictive analytics, empowered by machine learning, has emerged as a transformative force in healthcare, offering unparalleled opportunities for enhancing patient outcomes. The primary focus is on understanding the implications, applications, and challenges associated with the use of machine learning algorithms in predicting patient health outcomes. The paper begins by establishing the context with an overview of predictive analytics in healthcare and its evolution. Emphasis is placed on the critical role of patient health outcomes in pharmacy practice. The review explores the current landscape of predictive analytics in pharmacy practice, detailing traditional approaches, their limitations, and the advantages that machine learning brings to the forefront. An in-depth examination of applications follows, focusing on areas such as medication adherence prediction, disease progression modeling, and personalized medication regimens. Real-world case studies and success stories illustrate the practical impact of machine learning on patient outcomes. Addressing the importance of data sources, the paper discusses the diverse types of data employed in predictive analytics, ranging from electronic health records to patient-generated data and wearables. Ethical and privacy concerns are thoroughly explored, emphasizing the need for responsible data usage. The implications for pharmacists and healthcare providers are discussed, highlighting the evolving role of pharmacists in predictive analytics and the potential benefits and challenges for healthcare providers. The conclusion summarizes key findings and issues a call to action, encouraging further research and adoption of machine learning in pharmacy practice to harness its potential for improving patient outcomes.

A boosting-based prediction model for disease progression

With the intensification of the aging population in China, the number of patients with Parkinson's disease is showing a high growth trend. At present, the diagnosis of Parkinson's disease mainly relies on clinical assessment scales, but there are shortcomings such as poor accuracy, large scoring span and poor timeliness. Therefore, based on boosting, this paper uses a cross-validation algorithm to select the best model among 14 machine learning models and constructs the Multimode Parkinson model. We validate the performance of our model in predicting the extent of Parkinson's disease. Experimental results show that this method is superior to traditional algorithms and can be effectively applied to the treatment of Parkinson's disease.

Artificial intelligence and machine learning in axial spondyloarthritis.

To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring. Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results. Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.

Multi-scaled temporal modeling of cardiovascular disease progression: An illustration of proximal arteries in pulmonary hypertension

The progression of cardiovascular disease is intricately influenced by a complex interplay between physiological pathways, biochemical processes, and physical mechanisms. This study aimed to develop an in-silico physics-based approach to comprehensively model the multifaceted vascular pathophysiological adaptations. This approach focused on capturing the progression of proximal pulmonary arterial hypertension, which is significantly associated with the irreversible degradation of arterial walls and compensatory stress-induced growth and remodeling. This study incorporated critical characteristics related to the distinct time scales for the deformation, thus reflecting the impact of mean pressure on artery growth and tissue damage. The in-silico simulation of the progression of pulmonary hypertension was realized based on computational code combined with the finite element method (FEM) for the simulation of disease progression. The parametric studies further explored the consequences of these irreversible processes. This computational modeling approach may advance our understanding of pulmonary hypertension and its progression.

Validation of the Klinrisk chronic kidney disease progression model in the FIDELITY population.

Chronic kidney disease (CKD) affects >800 million individuals worldwide and is often underrecognized. Early detection, identification and treatment can delay disease progression. Klinrisk is a proprietary CKD progression risk prediction model based on common laboratory data to predict CKD progression. We aimed to externally validate the Klinrisk model for prediction of CKD progression in FIDELITY (a prespecified pooled analysis of two finerenone phase III trials in patients with CKD and type 2 diabetes). In addition, we sought to identify evidence of an interaction between treatment and risk. The validation cohort included all participants in FIDELITY up to 4years. The primary and secondary composite outcomes included a ≥40% decrease in estimated glomerular filtration rate (eGFR) or kidney failure, and a ≥57% decrease in eGFR or kidney failure. Prediction discrimination was calculated using area under the receiver operating characteristic curve (AUC). Calibration plots were calculated by decile comparing observed with predicted risk. At time horizons of 2 and 4years, 993 and 1795 patients experienced a primary outcome event, respectively. The model predicted the primary outcome accurately with an AUC of 0.81 for 2years and 0.86 for 4years. Calibration was appropriate at both 2 and 4years, with Brier scores of 0.067 and 0.115, respectively. No evidence of interaction between treatment and risk was identified for the primary composite outcome (P=.31). Our findings demonstrate the accuracy and utility of a laboratory-based prediction model for early identification of patients at the highest risk of CKD progression.

Diabetes mellitus does not alter mortality or hospitalisation risk in patients with newly diagnosed heart failure with preserved ejection fraction: Time to rethink pathophysiological models of disease progression.

Type 2 diabetes is a common and adverse prognostic co-morbidity for patients with heart failure with reduced ejection fraction (HFrEF). The effect of diabetes on long-term outcomes for heart failure with preserved ejection fraction (HFpEF) is less established. Prospective cohort study of patients referred to a regional HF clinic with newly diagnosed with HFrEF and HFpEF according to the 2016 European Society of Cardiology guidelines. The association between diabetes, all-cause mortality and hospitalisation was quantified using Kaplan-Meier or Cox regression analysis. Between 1st May 2012 and 1st May 2013, of 960 unselected consecutive patients referred with suspected HF, 464 and 314 patients met the criteria for HFpEF and HFrEF respectively. Within HFpEF and HFrEF groups, patients with diabetes were more frequently male and in both groups patients with diabetes were more likely to be treated with β-adrenoceptor antagonists and angiotensin converting enzyme inhibitors. After adjustment for age, sex, medical therapy and co-morbidities, diabetes was associated with increased mortality in individuals with HFrEF (HR 1.46 95% CI: 1.05-2.02; p = .023), but not in those with HFpEF (HR 1.26 95% CI 0.92-1.72; p = .146). In unselected patients with newly diagnosed HF, diabetes is not an adverse prognostic marker in patients with HFpEF, but is in HFrEF.