Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...
We conducted a prospective evaluation of drug-induced severe hyponatremia (adverse drug reaction (ADR)) through the Prospective Pharmacovigilance Program from Laboratory Signals at Hospital over a period of 10 years. Cases of serum sodium (Na(s)) < 116 mM were recorded from July 2007 to June 2017 (first period). Also cases of Na(s) 116-122 mM were recorded from July 2012 to June 2017 (second period). Drugs were the primary cause of severe hyponatremia. The incidence rate of Na(s) < 116 mM by drugs was increased threefold over the decade. Compared with other causes of hyponatremia, patients with adverse drug reaction-serum sodium (ADR-Na(s)) in the first period were older (79 years (interquartile range (IQR) 68.6-89 vs. 65 years (IQR 48-81); P < 0.001) and were more often women (70.8% vs. 48.9% men, P < 0.001); in the second period were also older (79 years (IQR 65.3-89) vs. 63 years (IQR 46-80.6); P < 0.001) and were more often women (70% vs. 53%, P = 0.002), and ADR-Na(s) occurred more often in summer. The most frequent therapeutic groups of culprit drugs were the cardiovascular system and nervous system. The 65.3% in the first period and 71.2% in the second period of the ADR-Na(s) cases responded to hydration and had been diagnosed with hypovolemic hyponatremia.
Objective The implementation of pharmacogenetics (PGx) in clinical practice is an essential tool for personalized medicine. However, clinical laboratories must validate their procedures before being used to perform PGx studies in patients, in order to confirm that they are adequate for the intended purposes. MethodsWe designed a validation process for our in-house pharmacogenetic PCR-based method assay. ResultsThe concordance to reference, repeatability and reproducibility was 100%. Sensitivity and specificity were 100% for the detection of variant diplotypes in CYP2C9, CYP3A5, TPMT, DPYD and UGT1A1 genes. The sensitivity was lower in the detection of CYP2C19 variants due to a limitation in the design that prevents the detection of CYP2C19 *2/*10 diplotype. ConclusionsThe success of implementing clinical pharmacogenetic testing into routine clinical practice is dependent on the precision of genotyping. Limitations must be bearing in mind to guarantee the quality of PGx assays in clinical laboratory practice. We provided objective evidence that the necessary requirements in our laboratory-development assay were fulfilled.
Many factors have been described to contribute to voriconazole (VCZ) interpatient variability in plasma concentrations, especially CYP2C19 genetic variability. In 2014, Hicks et al. presented data describing the correlation between VCZ plasma concentrations and CYP2C19 diplotypes in immunocompromised pediatric patients and utilized pharmacokinetic modeling to extrapolate a more suitable VCZ dose for each CYP2C19 diplotype. In 2017, in our hospital, a clinical protocol was developed for individualization of VCZ in immunocompromised patients based on preemptive genotyping of CYP2C19 and dosing proposed by Hicks et al., Clinical Pharmacogenetics Implementation Consortium (CPIC) clinical guidelines, and routine therapeutic drug monitoring (TDM). We made a retrospective review of a cohort of 28 immunocompromised pediatric patients receiving VCZ according to our protocol. CYP2C19 gene molecular analysis was preemptively performed using PharmArray®. Plasma trough concentrations were measured by immunoassay analysis until target concentrations (1–5.5 μg/ml) were reached. Sixteen patients (57.14%) achieved VCZ trough target concentrations in the first measure after the initial dose based on PGx. This figure is similar to estimations made by Hicks et al. in their simulation (60%). Subdividing by phenotype, our genotyping and TDM-combined strategy allow us to achieve target concentrations during treatment/prophylaxis in 90% of the CYP2C19 Normal Metabolizers (NM)/Intermediate Metabolizers (IM) and 100% of the Rapid Metabolizers (RM) and Ultrarapid Metabolizers (UM) of our cohort. We recommended modifications of the initial dose in 29% (n = 8) of the patients. In RM ≥12 years old, an increase of the initial dose resulted in 50% of these patients achieving target concentrations in the first measure after initial dose adjustment based only on PGx information. Our experience highlights the need to improve VCZ dose predictions in children and the potential of preemptive genotyping and TDM to this aim. We are conducting a multicenter, randomized clinical trial in patients with risk of aspergillosis in order to evaluate the effectiveness and efficiency of VCZ individualization: VORIGENIPHARM (EudraCT: 2019-000376-41).
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