Acute kidney injury associated to sulfamethoxazole urine crystal: The importance of clinical suspicion.

Management of acute kidney injury (AKI) associated with drug-induced crystal nephropathy can be difficult, and timely diagnosis is critical to resolve this condition. We present the case of a 55-year-old woman with history of systemic lupus erythematosus (SLE), who, after treatment with trimethoprim/sulfamethoxazole (TMP/SMX) for suspected Pneumocystis jirovecii pneumonia, developed severe AKI. Automated urinary sediment initially reported hematuria, leukocyturia and "uric acid crystals". She did not have allergic symptoms, clinical manifestations of active SLE nor hyperuricemia. AKI persisted despite volume expansion with crystalloids. Due to SMX exposure, it was suspected that "uric acid crystals" could be in reality "SMX crystals", and were a possible cause of crystal nephropathy. TMP/SMX was withheld and urinary alkalization was performed, with subsequent resolution of AKI. SMX urine crystals were posteriorly confirmed by Fourier transform infrared spectroscopy.

Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets.

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil's case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms.

Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios.

BACKGROUND: COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. PURPOSE: This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. METHODS: Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. Different scarcity scenarios were simulated, including symptomatic essential workforce management and absence of diagnostic tests. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. RESULTS: Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity, yielding a 100% sensitivity and 22.6% specificity with a prior of 0.9999; 76.7% for both sensitivity and specificity with a prior of 0.2933; and 0% sensitivity and 100% specificity with a prior of 0.001. Regarding background scarcity context, resources allocation can be significantly improved when model-based patient selection is observed, compared to random choice. CONCLUSIONS: Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.