- Browse by Subject
Browsing by Subject "Misdiagnosis"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Impact of Delayed Diagnosis and Misdiagnosis for Patients with Transthyretin Amyloid Cardiomyopathy (ATTR-CM): A Targeted Literature Review(Springer, 2021-06) Rozenbaum, Mark H.; Large, Samuel; Bhambri, Rahul; Stewart, Michelle; Whelan, Jo; van Doornewaard, Alexander; Dasgupta, Noel; Masri, Ahmad; Nativi-Nicolau, Jose; Medicine, School of MedicineIntroduction: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressive, fatal and under-recognized disease. This targeted literature review assessed the extent and consequences of diagnostic delay and misdiagnosis in ATTR-CM. Methods: The Embase database was searched together with proceedings of eight cardiology conferences to identify publications or abstracts on ATTR-CM. Outcomes of interest were time from symptom onset to diagnosis, rates of delayed diagnosis and misdiagnosis, and costs, healthcare resource use or clinical outcomes whilst undiagnosed/misdiagnosed. Results: Twenty-three articles were included. Weighted means of reported mean and median diagnostic delays were 6.1 and 3.4 years for wild-type (ATTRwt-CM) and 5.7 and 2.6 years for hereditary (ATTRv-CM). Misdiagnosis occurred in 34-57% of patients when reported. Evaluation and misdiagnosis by multiple healthcare providers before receiving an ATTR-CM diagnosis was common, and there was evidence that patients undergo unnecessary or inappropriate evaluations or treatments while misdiagnosed. Diagnostic "red flags" were reported to be underused. Data on the consequences of delay for patients and health systems were sparse, but given the progressive nature of ATTR-CM, delay is likely to have adverse consequences. Conclusion: ATTR-CM patients commonly experience diagnostic delay and misdiagnosis. Efforts are required to provide timely diagnosis so that patients can benefit from earlier access to new disease-modifying therapies.Item Predicting misdiagnosed adult-onset type 1 diabetes using machine learning(Elsevier, 2022) Cheheltani, Rabee; King, Nicholas; Lee, Suyin; North, Benjamin; Kovarik, Danny; Evans-Molina, Carmella; Leavitt, Nadejda; Dutta, Sanjoy; Pediatrics, School of MedicineAims: It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes. Methods: In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Electronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical history. Results: The model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to <1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in AEMR. Conclusions: This algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset.