The model is structured in 2 parts: A decision tree is used to calculate the proportion of the population that fall into one of a number of cohorts according to their underlying condition and test result. The decision tree calculates the proportion of patients who will receive a false negative (FN), false positive (FP), true negative (TN), or true positive (TP) NT-proBNP test result according to the sensitivity, specificity and prevalence data. Patients with a positive test result (levels above the chosen threshold) are then referred for echocardiography and specialist clinical assessment to determine if they have heart failure or not. A Markov model then evaluates patients health and cost outcomes according to their cohort once the initial NT-proBNP test result is determined accounting for waiting times for diagnostic tests. For more detailed explanation of the model structure, please refer to the technical report in Appendix O. A number of assumptions were made when developing the model. The key assumptions are outlined below but are also discussed in more detail in Appendix O: Echocardiography plus specialist clinical assessment is 100% accurate. False negative patients are subsequently correctly diagnosed through 1 of 2 possible channels: A patient is hospitalised due their undiagnosed heart failure and are diagnosed during admission A patient re-presents to their GP within 6 months where the NT-proBNP test is repeated. The committee considered that after this we could assume that their NT- proBNP levels would be over 400pg/ml and therefore the patient would be referred for an echocardiogram and specialist clinical assessment and be correctly diagnosed. Chronic Heart Failure Diagnosing heart failure There is no mortality or morbidity benefit of treatment for HF-PEF patients. 