[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-426562-105":3,"detail-sidebar-cat-0-en-105":85,"doc-detail-426562-en":134},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":84},105,"en","predicting-trajectories-of-hospitalizations-in-us-older-adults-with-newly-diagnosed-heart-failure","Predicting Trajectories of Hospitalizations in U.S. Older Adults with Newly Diagnosed Heart Failure","","Heart failure (HF) is a leading cause of hospitalizations among older adults, yet long-term hospitalization risk patterns and high-risk identification at diagnosis remain unclear. Using a national Medicare sample of beneficiaries newly diagnosed with HF between 2010 and 2015, patients were followed for up to 5 years to capture all-cause hospitalizations. Group-based trajectory modeling defined four hospitalization pathways, and LASSO regression identified diagnostic-time predictors. Calibration and discrimination were assessed using observed vs. predicted plots and c-statistics.",{"@graph":14,"@context":77},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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risk?",{"text":72,"@type":64},"LASSO regression identified patient characteristics at the time of diagnosis that were used to predict trajectory membership.",{"name":74,"@type":61,"acceptedAnswer":75},"What clinical implication follows from the trajectory results?",{"text":76,"@type":64},"Patients with consistently high hospitalization risk could be identified at diagnosis and should be targeted for interventions to improve heart failure management.","https://schema.org",{"og:url":32,"og:type":79,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":81,"canonical":32},"index,follow",{"doc_id":83,"site_id":7},426562,1790792337,{"code":4,"msg":86,"data":87},"success",[88,92,96,100,105,110,114,118,123,126,130],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Story & 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citation ID: igaf122.3644  \nPREDICTING TRAJECTORIES OF HOSPITALIZATIONS  \nIN U.S. OLDER ADULTS WITH NEWLY DIAGNOSED HEART FAILURE  \nHanzhang Xu1, Radha Dhingra2, Bradley Hammill2, Scott Lynch1, Jessica West2, Michael Green2, Lesley Curtis2, and Matthew Dupre2, 1. Duke University, Durham, North Carolina, United States, 2. Duke University School of Medicine, Durham, North Carolina, United States  \nHeart failure (HF) remains the leading cause of hospitalizations among older adults. Little is known about the longer-term risks of hospitalizations and whether high-risk patients can be identified at the time of HF diagnosis. We analyzed a national sample of Medicare beneficiaries with newly-diagnosed HF between 2010-2015. Patients were then followed for up to 5 years to identify all-cause hospitalizations after their initial diagnosis. Group-based trajectory models were used to identify patients with differing trajectories of hospitalizations and LASSO regression was used to identify patients’ characteristics at the time-of-diagnosis to predict their trajectory of hospitalizations. Model calibration was evaluated graphically (observed vs predicted) and discrimination was assessed using c-statistics. Of  \n84,597 beneficiaries (mean age: 77.4 [± 7.1] years and 58.3% male), we identified four distinct trajectories of hospitalizations over follow-up: Group 1 (n = 19,340; 22.9%) had “low risks”of hospitalization, Group 2 (n = 53,922; 57.9%) had elevated risks of admission shortly after diagnosis (“early risk”group), Group  \n3 (n = 5,035; 9.8%) had elevated risks at later stages of illness (“late risk” group), and Group 4 (n = 6,300; 9.4%) had consistently “high risks”of hospitalization. Models were well-calibrated predicting patients’ hospitalizations and discrimination (c-statistics) were 0.63 (Group 2), 0.67 (Group 3), and 0.77 (Group 4) when comparing to patients with low risks of hospitalization (Group 1) . Predicting hospitalizations among beneficiaries improved in those who were diagnosed with HF at younger ages (≤70 years). Patients with high risks of hospitalizations could be identified at the time-of-diagnosis and should be targeted for interventions to improve HF management.","cbCailj02lP4u77F","https://ap.wps.com/l/cbCailj02lP4u77F","pdf",959155,"English","# Abstract\n## Study design and data source\n## Methods for trajectory modeling and prediction\n## Results: four hospitalization trajectories\n## Model performance and clinical implications","[{\"question\":\"What was the main objective of the study?\",\"answer\":\"To predict longer-term hospitalization trajectories after newly diagnosed heart failure in U.S. older adults and determine whether high-risk patients could be identified at the time of diagnosis.\"},{\"question\":\"How were hospitalization trajectories identified?\",\"answer\":\"Group-based trajectory models were used to classify patients into distinct patterns of all-cause hospitalization risk over up to 5 years of follow-up.\"},{\"question\":\"What diagnostic-time findings helped predict hospitalization risk?\",\"answer\":\"LASSO regression identified patient characteristics at the time of diagnosis that were used to predict trajectory membership.\"},{\"question\":\"What clinical implication follows from the trajectory results?\",\"answer\":\"Patients with consistently high hospitalization risk could be identified at diagnosis and should be targeted for interventions to improve heart failure management.\"}]","Predicting Trajectories of Hospitalizations in U.S. Older Adults with Newly Diagnosed Heart Failure | PDF",1790640925]