Disease Trajectory In Patients Research Articles

Disease progression in chronic heart failure is linear: Insights from multistate modelling.

Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF. Consecutive patients (n = 4918) were enrolled (median age 75 [67-81] years, 61.3% men, 44% with HF and reduced ejection fraction). We generated a model by observing events during the first 2 years of follow-up. The model yielded surprisingly accurate predictions of how a population with HF will behave during subsequent years. As examples, the predicted transition probability from hospitalization to death was 0.11; the observed probabilities were 0.13, 0.14, and 0.16 at 3, 4, and 5 years, respectively. Similarly, the predicted transition intensity for rehospitalization was 0.35; the observed probabilities were 0.38, 0.34, and 0.35 at 3, 4, and 5 years, respectively. A multivariable model including covariates thought to influence outcome did not improve accuracy. Predicted average life expectancy was approximately 10 years for the unadjusted model and 13 years for the multivariable model, consistent with the observed mortality of 41% at 5 years. A multistate Markov chain model for patients with chronic HF suggests that the proportion of patients transitioning each year from a given state to another remains constant. This finding suggests that the course of HF at a population level is more linear than is commonly supposed and predictable based on current patient status.

New Disease Trajectories of Heart Failure: Challenges in Determining the Ideal Timing of Palliative Care Implementation.

Background: The disease trajectory of heart failure (HF), along with other organ failures, is still being elucidated. The trajectory is represented as a descending saw-tooth curve, indicating the frequent exacerbations and hospitalizations and slow progression to death. However, the clinical pattern of HF is no longer unique because of the definition of three distinct phenotypes, according to different values of ejection fraction (EF): HF with reduced EF (HFrEF), mildly reduced EF (HFmEF), and preserved EF (HFpEF). Patients with HFrEF have access to pharmacological and nonpharmacological treatments that have been shown to reduce mortality, unlike the other two classes for which no effective therapies are present. Therefore, their disease trajectories are markedly different. Methods: In this study, multiple new disease trajectories of HFrEF are being proposed, ranging from a complete and persistent recovery to rapid clinical deterioration and premature death. These new trajectories pose challenges to early implementation of palliative care (PC), as indicated in the guidelines. Results: From these considerations, we discuss how the improved prognosis of HFrEF because of effective treatment could paradoxically delay the initiation of early PC, especially with the insufficient palliative knowledge and training of cardiologists, who usually believe that PC is required only at the end of life. Conclusions: The novel therapeutic approaches for HF discussed in this study highlight the clinical specificity and peculiar needs of patients with HF. The changing model of disease trajectories of patients with HF will provide new opportunities for the early implementation of PC.

18F-fluorodeoxyglucose positron emission tomography-computed tomography in the localization of the lesions in the osteogenic region of breast cancer bone metastases after therapy.

Accurate efficacy evaluation of bone metastases (BMs) from breast cancer (BC) is an intractable issue in clinical practice, for which solutions are urgently needed. This study aimed to investigate the utility of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) in the response evaluation of bone metastasis of BC. In total, 22 patients diagnosed with BC and BM were enrolled. These patients underwent repeated 18F-FDG PET/CT evaluations. The patients and each BM site were divided into two groups based on their response to treatment: progressive disease (PD) and nonprogressive disease (non-PD). We analyzed and compared the changes in PET and CT images, as well as the serum concentration of carcinoembryonic antigen (CEA), carbohydrate antigen 153 (CA153), alkaline phosphatase (ALP), and calcium (Ca) over the same time frame. The immunohistochemistry (IHC) of primary lesions between groups and between the primary focus and BM with high 18F-FDG uptake were compared and analyzed. Maximum standard uptake value (SUVmax) after therapy [area under the curve (AUC): 0.932] and Δ-value of SUVmax (AUC: 0.811) on 18F-FDG PET imaging proved significantly valuable for the efficacy of therapy outcomes related to BM lesions (P<0.05). In terms of overall evaluation of BM, age and human epidermal growth factor receptor 2 (HER2) expression were significantly lower in the PD group than in the non-PD group (P<0.05). There were marked differences in CEA after therapy, the changes of CEA, and CA153 (∆-value) between the groups (P<0.05). The SUVmax and Ca concentration after therapy and ∆-value of SUVmax, along with the levels of CA153, CEA, and ALP, were valuable indicators for evaluating the efficacy of individual BMs (P<0.05). IHC of BM in the PD group showed differences compared to primary lesions, with antigen Ki-67 being downregulated in metastatic lesions and HER2 being downregulated in a portion of BMs (2 of 6). Meanwhile, the expression of estrogen receptor (ER) and progesterone receptor (PR) remained relatively unchanged. 18F-FDG PET/CT confers precise assessment of the posttreatment efficacy pertaining to BM in BC. This modality facilitates the identification of poor effect lesions following extant therapies and localization for pathological assessment and may substantially contribute to evaluating therapeutic efficacy, refining treatment strategies, and predicting the disease trajectory of patients with BC and BM.

Participation in Advance Care Planning Among Medically At-Risk Rural Veterans: Protocol for a Personalized Engagement Model.

Many of the challenges in advanced care planning (ACP) conversations are linked to the waxing and waning progress of serious illnesses. Conversations with patients about future medical care decisions by a surrogate decision maker have historically been left until late in the patient's disease trajectory. These conversations often happen at a time when the patient is already very ill. The challenge in effective early ACP and serious illness conversations is to create a situation where patients appreciate the link between current and future medical care. Setting the stage to make these conversations more accessible includes using telehealth to have conversations at the patient's place of choice. The personalization used includes addressing the current medical and social needs of the patient and ensuring that expressed needs are addressed as much as possible. Engaging patients in these conversations allows the documentation of patient preferences in the electronic health record (EHR), providing guidelines for future medical care. The objective of our telehealth serious illness care conversations program was to successfully recruit patients who lacked up-to-date documentation of ACP in their EHR. Once these patients were identified, we engaged in meaningful, structured conversations to address the veterans' current needs and concerns. We developed a recruitment protocol that increased the uptake of rural veterans' participation in serious illness care conversations and subsequent EHR documentation. The recruitment protocol outlined herein used administrative data to determine those patients who have not completed or updated formal ACP documentation in the EHR and who are at above-average risk for death in the next 3-5 years. The key features of the telehealth serious illness care conversations recruitment protocol involve tailoring the recruitment approach to address current patient concerns while emphasizing future medical decision-making. As of September 2022, 196 veterans had completed this intervention. The recruitment method ensures that the timing of the intervention is patient driven, allowing for veterans to engage in ACP at a time and place convenient for them and their identified support persons. The recruitment protocol has been successful in actively involving patients in ACP conversations, leading to an uptick in completed formal documentation of ACP preferences within the EHR for this specific population. This documentation is then available to the medical team to guide future medical care. RR1-10.2196/55080.

A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial.

Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant. The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients' adherence behavior. This prospective, randomized controlled study is nested in a large-scale international project named "Enhancing therapy adherence among metastatic breast cancer patients" (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients' engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning-based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907). The recruitment process started in May 2023 and is expected to conclude on December 2023. The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer. ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181. DERR1-10.2196/48852.

Effect of dapagliflozin on outpatient worsening heart failure in patients with mildly reduced or preserved ejection fraction: the DELIVER trial

Abstract Background Hospitalization is recognized as a sentinel event in the disease trajectory of patients with heart failure (HF), but not all patients experiencing decompensation are hospitalized. Outpatient intensification of diuretics is common in response to worsening symptoms suggestive of HF, yet its prognostic and clinical relevance is less certain. Purpose To evaluate the prognostic importance of outpatient oral diuretic intensification and assess the treatment effect of dapagliflozin across the spectrum of worsening HF events. Methods DELIVER was a global, randomized clinical trial of dapagliflozin vs. placebo in 6,263 participants with HF with mildly reduced or preserved ejection fraction. In this prespecified landmark analysis, we assessed the association between various non-fatal worsening HF events (those requiring hospitalization, urgent outpatient visits requiring intravenous HF therapies, and outpatient oral diuretic intensification) and rates of subsequent mortality. Diuretic intensification was defined as new initiation (among those not on diuretics at baseline) or dose uptitration sustained for at least 30 days (among those on diuretics at baseline). We also examined the treatment effect of dapagliflozin on an expanded composite endpoint of CV death, HF hospitalization, urgent HF visit, or outpatient oral diuretic intensification. Results In DELIVER, 4,532 (72%) patients experienced no worsening HF event, while 789 (13%) had outpatient oral diuretic intensification, 86 (1%) required an urgent HF visit, 585 (9%) had a HF hospitalization, and 271 (4%) died of CV causes as a first manifestation of worsening HF. Patients with a first presentation manifesting as outpatient oral diuretic intensification experienced rates of subsequent mortality that were higher (10 per 100py) than those without a worsening HF event (4 per 100py) but similar to rates of subsequent death following urgent HF visit (10 per 100py). Patients with a HF hospitalization as a first presentation of worsening HF had the highest rates of subsequent death (35 per 100py); Figure 1. The addition of outpatient diuretic intensification to the adjudicated DELIVER primary endpoint (CV death, HF hospitalization, or urgent HF visit) increased the overall number of patients experiencing an event from 1,122 to 1,731. Dapagliflozin reduced the need for outpatient diuretic intensification alone (HR 0.72; 95% CI: 0.64-0.82; P&amp;lt;0.001) and when analyzed as a part of an expanded composite endpoint of worsening HF (HR 0.76; 95% CI: 0.69-0.84; P&amp;lt;0.001); Figure 2. Conclusions Even in patients with HF with mildly reduced or preserved ejection fraction, hospitalization as a first presentation of worsening HF is associated with significant subsequent mortality. Moreover, oral diuretic intensification in ambulatory care was frequent, adversely prognostic, and significantly reduced by dapagliflozin.Figure 1Figure 2

Outpatient Worsening Among Patients With Mildly Reduced and Preserved Ejection Fraction Heart Failure in the DELIVER Trial.

Hospitalization is recognized as a sentinel event in the disease trajectory of patients with heart failure (HF), but not all patients experiencing clinical decompensation are ultimately hospitalized. Outpatient intensification of diuretics is common in response to symptoms of worsening HF, yet its prognostic and clinical relevance, specifically for patients with HF with mildly reduced or preserved ejection fraction, is uncertain. In this prespecified analysis of the DELIVER trial (Dapagliflozin Evaluation to Improve the Lives of Patients With Preserved Ejection Fraction Heart Failure), we assessed the association between various nonfatal worsening HF events (those requiring hospitalization, urgent outpatient visits requiring intravenous HF therapies, and outpatient oral diuretic intensification) and rates of subsequent mortality. We further examined the treatment effect of dapagliflozin on an expanded composite end point of cardiovascular death, HF hospitalization, urgent HF visit, or outpatient oral diuretic intensification. In DELIVER, 4532 (72%) patients experienced no worsening HF event, whereas 789 (13%) had outpatient oral diuretic intensification, 86 (1%) required an urgent HF visit, 585 (9%) had an HF hospitalization, and 271 (4%) died of cardiovascular causes as a first presentation. Patients with a first presentation manifesting as outpatient oral diuretic intensification experienced rates of subsequent mortality that were higher (10 [8-12] per 100 patient-years) than those without a worsening HF event (4 [3-4] per 100 patient-years) but similar to rates of subsequent death after an urgent HF visit (10 [6-18] per 100 patient-years). Patients with an HF hospitalization as a first presentation of worsening HF had the highest rates of subsequent death (35 [31-40] per 100 patient-years). The addition of outpatient diuretic intensification to the adjudicated DELIVER primary end point (cardiovascular death, HF hospitalization, or urgent HF visit) increased the overall number of patients experiencing an event from 1122 to 1731 (a 54% increase). Dapagliflozin reduced the need for outpatient diuretic intensification alone (hazard ratio, 0.72 [95% CI, 0.64-0.82]) and when analyzed as a part of an expanded composite end point of worsening HF or cardiovascular death (hazard ratio, 0.76 [95% CI, 0.69-0.84]). In patients with HF with mildly reduced or preserved ejection fraction, worsening HF requiring oral diuretic intensification in ambulatory care was frequent, adversely prognostic, and significantly reduced by dapagliflozin. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03619213.

Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients.

Our study investigates whether graph-based fusion of imaging data with non-imaging electronic health records (EHR) data can improve the prediction of the disease trajectories for patients with coronavirus disease 2019 (COVID-19) beyond the prediction performance of only imaging or non-imaging EHR data. We present a fusion framework for fine-grained clinical outcome prediction [discharge, intensive care unit (ICU) admission, or death] that fuses imaging and non-imaging information using a similarity-based graph structure. Node features are represented by image embedding, and edges are encoded with clinical or demographic similarity. Experiments on data collected from the Emory Healthcare Network indicate that our fusion modeling scheme performs consistently better than predictive models developed using only imaging or non-imaging features, with area under the receiver operating characteristics curve of 0.76, 0.90, and 0.75 for discharge from hospital, mortality, and ICU admission, respectively. External validation was performed on data collected from the Mayo Clinic. Our scheme highlights known biases in the model prediction, such as bias against patients with alcohol abuse history and bias based on insurance status. Our study signifies the importance of the fusion of multiple data modalities for the accurate prediction of clinical trajectories. The proposed graph structure can model relationships between patients based on non-imaging EHR data, and graph convolutional networks can fuse this relationship information with imaging data to effectively predict future disease trajectory more effectively than models employing only imaging or non-imaging data. Our graph-based fusion modeling frameworks can be easily extended to other prediction tasks to efficiently combine imaging data with non-imaging clinical data.

CauRuler: Causal irredundant association rule miner for complex patient trajectory modelling

Background and Objectives.Discovering causal associations between variables is one of the main goals of clinical trials, with the ultimate aim of identifying the causes of specific health status. Prior knowledge of causal paths could help ensure patients do not develop the resultant conditions. In recent years, thanks to the enormous amount of health data stored with the support of digital tools, attempts have been made to employ Machine Learning to infer causality. Those methodologies suffer from some deficiencies in controlling cofounders when analysing causality, as well as providing causal rules general enough to be useful in healthcare practice. Conversely, this work presents and evaluates CauRuler, a new approach to deal with causality from association rules. The proposed approach uses a pruning strategy to reduce the association rule set, which does not compromise the causality learning capability of the algorithm. This behaviour makes the algorithm suitable for exploiting large health databases with thousands of patients and medical instances. CauRuler can control a larger number of confounders than other proposals, bringing robustness to causal analysis and avoiding the identification of spurious associations. Additionally, the method generalizes causality using anti-monotone properties to obtain complex and general causal paths. The method can target correct causal associations in complex medical databases with retrospective data. Method:CauRuler extends association rule mining with an irredundancy property so that the set of rules learnt is reduced in size and generalized. General association rules, conformed by fewer items, enable controlling more confounding variables to verify, with more statistical evidence on available data, if they represent causal paths in patient disease trajectories. Results:CauRuler has been tested on a complex real medical database (3,5 M visits to the primary care services between 2019 and 2020, and controlling over 15.000 different variables including diagnoses and demographic and other clinical patient data). The reduction of the rule set achieved by the pruning strategy goes from 7.732 to 2.240 rules, from which 46 have been found to have causality relationships in the patient trajectories, and generalized to 14 rules tested as true causal relationships thanks to the confounding analysis. These rules have been validated by clinicians with the support of a graphical map. The obtained causal paths control in average of 906 confounder variables, retrieving robust results. Conclusions:Causal relationships enable predicting causal paths between health conditions according to patient trajectories. Knowing these causal paths is crucial for understanding and preventing the appearance or worsening of diseases in patients. CauRuler, with high demanding thresholds, has proven its efficiency and effectiveness in targeting previously known causal associations between diagnoses, reaching consensus in the medical community. Softening these thresholds should help target interesting general causal paths.

Patient Disease Trajectories in Rheumatoid Arthritis Patients Treated with Baricitinib 4-mg in FourPhase 3 Clinical Studies.

We sought to identify and compare treatment response groups based on individual patient responses (rather than group mean response) over time on the Clinical Disease Activity Index (CDAI) for rheumatoid arthritis (RA), in patients treated with baricitinib 4-mg in 4phase 3studies. Trajectory subgroups were identified within each study using growth mixture modeling. Following grouping, baseline characteristics and disease measures were summarized and compared. In each study, three response trajectories were identified. In the three studies of patients naïve to biological disease-modifying anti-rheumatic drugs (bDMARDs) patients had, on average, high disease activity, as measured by CDAI. In these studies, a group of rapid responders (65-71% of patients) had the lowest baseline CDAI scores and achieved mean CDAI ≤ 10 by week 16. Gradual responders (10-17%) had higher baseline CDAI, but generally achieved low disease activity (CDAI ≤ 10) by week 24. A group of partial responders (18-22%) had higher baseline CDAI and did not achieve mean CDAI ≤ 10. In bDMARD-experienced patients, the subgroups were rapid responders, who achieved mean CDAI ≤ 10 (42% of patients); partial responders, with mean CDAI decrease of ~ 15 points from baseline (42% of patients); and limited responders (15% of patients). Changes in modified total sharp score (mTSS; assessed only in biologic-naïve patients) were below the smallest detectable difference at 24/52weeks for > 90% of patients in each group, excepting partial responders in RA-BEGIN (≥ 75% no detectable change). In patients receiving baricitinib 4-mg, lower baseline CDAI was generally associated with rapid response, while higher baseline CDAI scores were generally seen for patients who either reached treatment targets more gradually, or who had a partial or limited response. Maintenance of response was observed with continued baricitinib treatment in all response groups and generally included maintenance of mTSS.

Using similar patients to predict complication in patients with diabetes, hypertension, and lipid disorder: a domain knowledge-infused convolutional neural network approach.

This study aims to develop a convolutional neural network-based learning framework called domain knowledge-infused convolutional neural network (DK-CNN) for retrieving clinically similar patient and to personalize the prediction of macrovascular complication using the retrieved patients. We use the electronic health records of 169434 patients with diabetes, hypertension, and/or lipid disorder. Patients are partitioned into 7 subcohorts based on their comorbidities. DK-CNN integrates both domain knowledge and disease trajectory of patients over multiple visits to retrieve similar patients. We use normalized discounted cumulative gain (nDCG) and macrovascular complication prediction performance to evaluate the effectiveness of DK-CNN compared to state-of-the-art models. Ablation studies are conducted to compare DK-CNN with reduced models that do not use domain knowledge as well as models that do not consider short-term, medium-term, and long-term trajectory over multiple visits. Key findings from this study are: (1) DK-CNN is able to retrieve clinically similar patients and achieves the highest nDCG values in all 7 subcohorts; (2) DK-CNN outperforms other state-of-the-art approaches in terms of complication prediction performance in all 7 subcohorts; and (3) the ablation studies show that the full model achieves the highest nDCG compared with other 2 reduced models. DK-CNN is a deep learning-based approach which incorporates domain knowledge and patient trajectory data to retrieve clinically similar patients. It can be used to assist physicians who may refer to the outcomes and past treatments of similar patients as a guide for choosing an effective treatment for patients.

Data-driven identification of heart failure disease states and progression pathways using electronic health records

Heart failure (HF) is a leading cause of morbidity, healthcare costs, and mortality. Guideline based segmentation of HF into distinct subtypes is coarse and unlikely to reflect the heterogeneity of etiologies and disease trajectories of patients. While analyses of electronic health records show promise in expanding our understanding of complex syndromes like HF in an evidence-driven way, limitations in data quality have presented challenges for large-scale EHR-based insight generation and decision-making. We present a hypothesis-free approach to generating real-world characteristics and progression patterns of HF. Patient disease state snapshots are extracted from the complaints mentioned in unstructured clinical notes. Typical disease states are generated by clustering and characterized in terms of their distinguishing features, temporal relationships, and risk of important clinical events. Our analysis generates a comprehensive “disease phenome” of real-world patients computed from large, noisy, secondary-use EHR datasets created in a routine clinical setting.

Rehospitalization of Patients with Advanced Heart Failure Receiving Continuous, Palliative Dobutamine or Milrinone

This study aims to determine the incidence of all-cause hospitalization in patients with advanced heart failure (AHF) receiving ambulatory continuous, intravenous dobutamine versus milrinone for palliative intent. Despite medical optimization, patients with AHF develop refractory symptoms, resulting in frequent hospitalizations. Previous trials precede modern care standards. Data regarding inotrope choice in palliation are limited. This retrospective analysis included 222 patients with AHF and reduced left ventricular ejection fraction discharged on palliative dobutamine (n=135) or milrinone (n=87). The primary outcome was incidence of all-cause rehospitalization compared by treatment type. Demographics between groups were similar. In the milrinone arm, more patients were discharged on β blockers (62% vs 22%; p <0.001); fewer patients were discharged to hospice (6% vs 30%). More patients in the milrinone arm than in the dobutamine arm were rehospitalized within 180 days (80% vs 59%; p=0.002); when patients discharged to hospice were excluded, this difference was no longer significant (83% vs 74%; p=0.14). Overall mortality was lower in the milrinone arm (63% vs 80%; p=0.006); survival was longer (median: 228 vs 52 days; p <0.001). Patients receiving milrinone spent more days alive and out of the hospital at 90 days after discharge (70 vs 37 days; p <0.001). In conclusion, in patients with AHF receiving palliative inotropes, there was no difference in rehospitalization when excluding patients discharged to hospice. Milrinone use was associated with decreased mortality and longer survival. Agent selection must closely align with the patient's disease trajectory.

Can COPD Exacerbations Be Picked Up Early via a Weekly Medication Question Through a Smartphone Application?

BackgroundExacerbations affect the disease trajectory of patients with COPD and result in an acute drop of functional status and physical activity. Timely detection of exacerbations by non-medical healthcare professionals is needed to counteract this decline. The use of digital health applications in patient interaction allows embedded detection of exacerbations. However, it is unknown if this is an effective way to pick up exacerbations.MethodWe investigated the detection of exacerbations in patients with COPD enrolled in a physical activity coaching program, by prompting a weekly question on changes in medication via the smartphone application. Data on response rate and occurrence of exacerbations were collected.ResultsResponse rate to the medication question, evaluated in 42 patients, was 72% (n = 497). A change in medication was reported through the smartphone application in 38 (7.6%) of the answered questions. The response rate was significantly lower at 6 months follow-up compared to the first month (p =0.03). When evaluating the occurrence of adverse events in a subset of patients who completed 6 months of follow-up (n = 27), 18 exacerbations were registered in eight patients, of which 10 of these exacerbations (56%) were picked up by the medication question in the coaching application.ConclusionElectronic interaction through a weekly medication question, embedded in a smartphone application, is feasible to support the detection of the occurrence of COPD exacerbations and can be used complementary to regular forms of detecting exacerbations. Compliance and smartphone literacy should be optimized when further using this method to report on COPD exacerbations.

Optimal Timing of Allogeneic Stem Cell Transplantation for Primary Myelofibrosis

Primary myelofibrosis (PMF) is a chronic myeloproliferative neoplasm characterized by cytopenias, splenomegaly, and risk of leukemic transformation. In light of newer therapies, such as ruxolitinib, that are not curative but improve quality of life, the timing of transplantation needs more in-depth analysis to determine which patients would benefit from an early versus a delayed transplantation strategy. Because prospective clinical trials are impractical for diseases with only one curative option, such as PMF, we developed a Markov cohort model to simulate the long-term disease trajectory in patients with PMF and predict the optimal timing of transplantation stratified by Dynamic International Prognostic Scoring System (DIPSS) risk. In this decision model, a hypothetical cohort of patients begins in the alive with PMF state and can transition monthly to other health states. Transition probabilities were acquired from published literature. We performed probabilistic analyses by jointly varying all model parameters over 1000 simulations. Irrespective of DIPSS risk, all patients with PMF benefited from transplantation with respect to life expectancy gained. Life expectancy gains from transplantation peaked at 9.7 months (95% confidence interval [CI], 9.5 to 9.9 months) from diagnosis in patients with high-risk disease and at 16.6 months (95% CI, 16.4 to 16.8 months) from diagnosis in patients with intermediate-2 disease. Patients with intermediate-1 risk had a delayed peak in net gain in life expectancy at 20.5 months (95% CI, 20.2 to 20.7 months). Patients with low-risk disease had a greater net gain in life expectancy the longer that transplantation was delayed; this trend plateaued at 29 to 45 months. Our modeling suggests that preparation for transplantation is indicated upfront for patients diagnosed with intermediate-2 risk and high-risk PMF, whereas this can be delayed for low-risk or intermediate-1 risk disease.

Disease evolution and risk-based disease trajectories in congestive heart failure patients

Congestive Heart Failure (CHF) is among the most prevalent chronic diseases worldwide, and is commonly associated with comorbidities and complex health conditions. Consequently, CHF patients are typically hospitalized frequently, and are at a high risk of premature death. Early detection of an envisaged patient disease trajectory is crucial for precision medicine. However, despite the abundance of patient-level data, cardiologists currently struggle to identify disease trajectories and track the evolution patterns of the disease over time, especially in small groups of patients with specific disease subtypes. The present study proposed a five-step method that allows clustering CHF patients, detecting cluster similarity, and identifying disease trajectories, and promises to overcome the existing difficulties. This work is based on a rich dataset of patients’ records spanning ten years of hospital visits. The dataset contains all the health information documented in the hospital during each visit, including diagnoses, lab results, clinical data, and demographics. It utilizes an innovative Cluster Evolution Analysis (CEA) method to analyze the complex CHF population where each subject is potentially associated with numerous variables. We have defined sub-groups for mortality risk levels, which we used to characterize patients’ disease evolution by refined data clustering in three points in time over ten years, and generating patients’ migration patterns across periods. The results elicited 18, 23, and 25 clusters respective to the first, second, and third visits, uncovering clinically interesting small sub-groups of patients. In the following post-processing stage, we identified meaningful patterns. The analysis yielded fine-grained patient clusters divided into several finite risk levels, including several small-sized groups of high-risk patients. Significantly, the analysis also yielded longitudinal patterns where patients' risk levels changed over time. Four types of disease trajectories were identified: decline, preserved state, improvement, and mixed-progress. This stage is a unique contribution of the work.The resulting fine partitioning and longitudinal insights promise to significantly assist cardiologists in tailoring personalized interventions to improve care quality. Cardiologists could utilize these results to glean previously undetected relationships between symptoms and disease evolution that would allow a more informed clinical decision-making and effective interventions.

Young‐onset dementia in memory clinics in the Netherlands: PRECODE‐GP

AbstractBackgroundThe disease trajectory of patients with young onset dementia differs from older patients, as do healthcare requirements. To improve specialized care, more insight in young onset dementia is crucial. The aim of PRECODE‐GP is therefore 1) to conduct an (annual) inventory of the diagnostic workup in all memory clinics in the Netherlands, and 2) to set up a prospective nationwide database including newly diagnosed younger patients with dementia.MethodsPRECODE‐GP is part of the PRECODE project, a collaboration between four Dutch Alzheimer centers, Knowledgecenter Dementia at Young Age, Nivel and Alzheimer Nederland. Governance is embedded in the Dutch Memory Clinic Network. Memory clinics are asked annually to fill out a survey on their current diagnostic workup. Eligible patients (&lt;70 years; any type of dementia) are identified by local specialists and recruited via an informed consent procedure providing consent for future linkage to other datasets. Demographics, test results and (etiological) diagnosis are collected in an online database.ResultsPRECODE‐GP was initiated in July 2019 and has been approved in 36 memory clinics, approval procedure is ongoing in additional 10 clinics. Based on the annual survey (filled out by 21 clinics), standard imaging of the brain occurs in almost half of memory clinics and neuropsychological examination in one third. CSF is performed when deemed necessary by the clinician. A median 250(141‐396) new patients visited the memory clinics yearly, of whom 111(74‐180)(48%) were diagnosed with dementia and 12(4‐36)(6%) were &lt;70 years. To date, 201 young onset dementia patients (age 62±8, 51% female, MMSE 22±5) are included in our prospective database, diagnosed with 140(69%) Alzheimer’s dementia (AD), 29(14%) frontotemporal dementia (FTD), 14(7%) dementia with Lewy Bodies (DLB) and 18(9%) other dementia types. Patients with FTD were youngest (61±6 years), subjects with DLB were most often male (79%), in AD MMSE was lowest (21±5).ConclusionWe took the first step to include patients with young onset dementia via all Dutch memory clinics. As a next step, we plan to link data to existing registries, to gain valuable knowledge on disease trajectories. All needed to improve quality of life for patients with young onset dementia.

Abstract 11187: Perceptions of Patient Heart Failure Severity and Caregiver Inflammatory Markers: Interactions with Race and Education

Background: Heart failure (HF) caregivers (CGs) may experience stress due to unpredictable patient disease trajectory. Interleukin-6 (IL-6) and high-sensitivity C-reactive protein (hs-CRP) are reported to be elevated in dementia CGs, but less is known about HF CGs response to stress or relationships with social determinants of health such as education and race. Objective: To determine if social factors (race, education) modify the relationship between patient disease severity trajectory (NYHA class) and CG stress as measured by inflammatory biomarkers of IL-6 and hs-CRP among HF CGs. Methods: A secondary analysis was conducted using baseline data from Caregiver Opportunities for Optimizing Lifestyle (COOL) trial. Education was dichotomized into technical/high school or less versus college or post-graduate degree. Race was dichotomized as African American (AA) or White/Asian due to the low number of Asian CGs. Two-way analysis of variance tests were conducted for IL-6 and hs-CRP to test interaction effects between CG perceived NYHA class and education or race. Results: Participants (n=127) were predominantly female (n=117, 92.1%), AA (n=74, 58.3 %), and the majority (70.1%) reported a college or post-graduate education. Perceived NYHA class were reported as: I (n=32, 25.6%), II (n=31, 24.8%), III (n= 47, 37.6%), and IV (n=15, 12.0%). For hs-CRP, there was a significant interaction between race and NYHA class (p=.013). Higher hs-CRP levels were noted in class II, compared to class I or class III for AA CGs (p=.004). White/Asian CGs did not have significant hs-CRP differences between NYHA classes (p=.634). While similar trends were observed for IL-6 between race and NYHA class, it did not reach statistical significance. In the model for education and perceived disease severity, NYHA class was significant (p=.038) for hs-CRP. In the post hoc test for NYHA class, hs-CRP was highest among NYHA class II (p&lt;.02) for lower education levels. For IL-6, NYHA class was significant (p=.047); but the interaction effect with education was not significant (p=.687). Conclusions: Our findings suggest that CGs of AA race and/or lower education may experience higher stress earlier in the HF disease trajectory and may therefore benefit from targeted CG support interventions.

Decision Analysis of Allogeneic Stem Cell Transplantation for Primary Myelofibrosis

Introduction: Primary myelofibrosis (PMF) is a chronic myeloproliferative neoplasm characterized by cytopenias, splenomegaly and a risk of leukemic transformation. In light of newer therapies such as ruxolitinib that are not curative but can improve quality of life, the timing of transplant needs more in-depth analysis to determine which patients would benefit from an early versus delayed transplant strategy.Methods: We developed a Markov cohort model to simulate the long-term disease trajectory in patients with PMF and predict the optimal timing of transplant stratified by a Dynamic International Prognostic Scoring System (DIPSS) risk. Our model consisted of five health states including alive with PMF, alive after leukemic transformation, alive after transplant, alive after relapse and death. Transition probabilities between health states were acquired from published literature on the natural history of the disease and outcomes following transplantation. The model was run over a patient's lifetime until all patients transitioned to the death state. We used a cycle length of one-month to represent the natural progression of PMF. The structure of the Markov model is delineated in Figure 1. In this decision model, a hypothetical cohort of patients begins in the Alive-PMF state and can transition after each monthly cycle to other health states. Patients could remain in an alive state for any number of cycles without transitioning to another health state, indicated by the arrow wheels. We performed probabilistic analyses by jointly varying all model parameters over 1000 simulations and calculated 95% confidence intervals (CI) for the model outcome.Results: Regardless of DIPSS risk, all patients with PMF benefited from a transplant with respect to life expectancy gained (Figure 2). Life expectancy gains from a transplant among patients with high-risk disease peak at 9.7 months (95% CI: 9.5-9.9) from diagnosis, while patients with intermediate-2 disease have a peak gain in life expectancy at 16.6 months (95% CI: 16.4-16.8). Intermediate-1 DIPSS risk patients have a more delayed time frame where the net gain in life expectancy from transplant begins to slow at 20.5 months (95% CI: 20.2-20.7). Patients with low risk DIPSS had greater net gain in life expectancy the longer transplant was delayed; this trend plateaued at 29 to 45 months, when thereafter net gain in life expectancy begins to be lost (Figure 1).Conclusion: Our modeling suggests that transplant processes including donor selection and pre-transplant work-up are indicated upfront for patients diagnosed with intermediate-2 and high risk PMF, while this can be delayed for patients with low or intermediate-1 risk disease. This model should provide clinicians with guidance on when to refer eligible patients with PMF for transplantation. [Display omitted] DisclosuresKekre: Novartis: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Celgene: Consultancy, Honoraria.

Insights into the challenges faced by chronically critically ill patients, their families and healthcare providers: An interpretive description

ObjectiveTo describe and understand the challenges of patients with chronic critical illness (CCI), their families and healthcare providers during the disease trajectory. Methodology/DesignThis qualitative study follows Thorne’s methodology of interpretive description and was guided by the ethnographic method of observation and participation. Five cases, each comprising one patient with chronic critical illness their family members and several healthcare providers, were followed from the intensive care unit of a university hospital in Switzerland across different settings. In total, five patients with chronic critical illness, 12 family members and 92 healthcare providers (nurses, nursing students, care assistants, physiotherapists and occupational therapists and physicians) were observed. FindingsRegardless of the medical diagnoses and disease trajectories of the patients with chronic critical illness, all cases faced three main challenges: 1. Dealing with the unpredictability of the disease trajectory beyond the underlying disease. 2. Coping with the complexity of care. 3. Perceiving communication challenges despite all involved parties’ dependency on it. ConclusionUnpredictability is not only a unique characteristic of the prolonged disease trajectory of patients with chronic critical illness, but also one of the main challenges of the participants. Therefore, the way unpredictability is handled is dependent on changes in the complexity of care and communication, highlighting the need for participation, information, empathy, clarity and honesty among all participants.