Acute tumor lysis syndrome (ATLS), a condition which results from a rapid destruction of tumor cells with massive release of cellular breakdown products, has been well described following the treatment of various malignancies. However, only a handful of cases of spontaneous ATLS have been reported in the literature. We describe the first reported case of spontaneous ATLS in acute myeloid leukemia (AML). A previously healthy 63 year old woman presented with a two month history of fatigue and a one week history of easy bruising. On admission she had oliguric acute renal failure, with marked elevation in serum uric acid and phosphate. A bone marrow biopsy showed AML M7 with fibrosis. The renal failure resolved with supportive care and institution of allopurinol therapy. Following this, AML induction chemotherapy resulted in complete remission. Her biochemical and clinical course were very similar to the classical ATLS seen in patients after chemotherapy. Therefore, this case represents a rare instance of acute renal failure from spontaneous ATLS, and in our opinion the first reported occurrence of spontaneous ATLS associated with AML.
Background: Statins are widely prescribed cholesterol-lowering medications in the US, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation. In this study, we aimed to develop and validate a pharmacological SAMS clinical phenotyping algorithm using electronic health records (EHRs) data from Minnesota Fairview. Methods: We retrieved structured and unstructured EHR data of statin users and manually ascertained a gold standard set of SAMS cases and controls using the SAMS-CI tool from clinical notes in 200 patients. We developed machine learning algorithms and rule-based algorithms that incorporated various criteria, including ICD codes, statin allergy, creatine kinase elevation, and keyword mentions in clinical notes. We applied the best performing algorithm to the statin cohort to identify SAMS. Results: We identified 16,889 patients who started statins in the Fairview EHR system from 2010-2020. The combined rule-based (CRB) algorithm, which utilized both clinical notes and structured data criteria, achieved similar performance compared to machine learning algorithms with a precision of 0.85, recall of 0.71, and F1 score of 0.77 against the gold standard set. Applying the CRB algorithm to the statin cohort, we identified the pharmacological SAMS prevalence to be 1.9% and selective risk factors which included female gender, coronary artery disease, hypothyroidism, use of immunosuppressants or fibrates. Conclusion: Our study developed and validated a simple pharmacological SAMS phenotyping algorithm that can be used to create SAMS case/control cohort for further analysis such as developing SAMS risk prediction model.
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