A case report of Paracetamol related pyroglutamic acidosis: mind the gap in a malnourished patient.

BACKGROUND: Pyroglutamic acidosis is a rare cause of high anion gap metabolic acidosis. Most cases of paracetamol related pyroglutamic acidosis are described in malnourished women and patients with kidney/liver failure, alcohol use or severe sepsis. In this report, we describe how pyroglutamic acidosis could be related to the use of chronic therapeutic paracetamol with only malnutrition as an associated risk factor. CASE PRESENTATION: We report a case of a 67-year-old male patient developing a pyroglutamic acidosis. The patient was initially admitted to hospital for infectious osteoarthritis and developed a metabolic acidosis during his hospital stay. Analgesics included daily therapeutic doses of paracetamol. What makes our case unusual is that our malnourished male patient did not have renal or hepatic failure. The diagnosis of paracetamol related pyroglutamic acidosis was made after ruling out the main causes of metabolic acidosis. It was further confirmed by urine organic acids measurement showing a markedly elevated level of pyroglutamic aciduria. Paracetamol was discontinued allowing a prompt correction of the anion gap. CONCLUSION: This case is a representative of pyroglutamic acidosis related to chronic therapeutic paracetamol with only malnutrition as an associated risk factor. Physicians should be aware of such unusual cause of metabolic acidosis, which may be more common than expected in hospitalized patients. A high clinical suspicion is needed when urine organic acids analysis is not available.

Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies.

BACKGROUND: There are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients' care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies. METHODS: Two datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology. RESULTS: A total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as "expert-level". Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances: to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases. CONCLUSION: Our study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.

Four- and sixteen-month clinical status of a cohort of patients following hospitalization for COVID-19.

BACKGROUND AND OBJECTIVES: Although many symptoms of post-COVID syndrome have been described, a comprehensive evaluation of their prevalence is lacking. We aimed to describe symptoms at 16 months from hospitalization for COVID-19. METHODS: A telephone assessment was performed one year later in a cohort of COVID-19 survivors hospitalized between March and May 2020 and already evaluated four months after discharge. Patients with relevant symptoms at 16 months, patients who presented symptoms at four months, and all intensive care unit patients were invited for assessment at an outpatient facility. At telephone consultation, respiratory, cognitive, and functional symptoms were assessed. Patients underwent pulmonary function tests, lung CT scans, and psychometric and cognitive tests at the outpatient facility. RESULTS: Among 478 patients evaluated four months after discharge, 317 (67 %) were assessed at telephone consultation and 124 at ambulatory assessment. At telephone assessment, ≥1 new symptom was reported by 216 patients (68 %), mainly fatigue (53 %), dyspnea (37 %), and memory difficulties (24 %). Seventy-nine patients (25 %) were asymptomatic at four months but declared ≥1 symptom one year later. In patients evaluated twice, the prevalence of cognitive impairment was 45 % at four months and 40 % at 16 months. Depression and post-traumatic symptoms prevalence remained stable, and the prevalence of anxiety significantly decreased. Dysfunctional breathing was detected in 32 % of patients. At 16 months after discharge, lung CT-scan exhibited abnormalities in 30/80 patients (38 %), compared to 52/85 patients (61 %) at four months. CONCLUSION: At 16 months after hospitalization for COVID-19, 68 % of patients declared symptoms, including patients whose symptoms appeared between 4 and 16 months. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04704388.