Post-Hospital Syndrome and Hyponatremia

Introduction: Post-hospital syndrome (PHS) is defined as a period of vulnerability during the first 30 days after a patient is discharged from hospital, in which multiple factors come into play. Hyponatremia is the most frequent hydroelectrolytic disorder in hospitalized patients and may be related to the appearance of PHS. Objective: The objective is to estimate the prevalence of PHS that is assessed as the rate of readmissions in the first 30 days after discharge, in patients with hyponatremia. Material and Methods: It is a descriptive observational study of patients with hyponatremia who were discharged from 1 September 2010 to 2 February 2020 at the Internal Medicine Service of the Hospital University of San Juan (Alicante, Spain). Results: Of the 25 included patients, 5 (20%) were readmitted within a month of discharge, after a mean of 11.4 days (standard deviation [SD] 5.1). The overall mortality of the study was 20% (n = 5), with one case of death in the first 30 days post-hospitalization (4%). In 12 patients (48%) the origin of the hyponatremia was undetermined. The most frequently recorded etiology for the condition was pharmacological (n = 7, 28%), and there was pronounced variability in its clinical and laboratory study. The most widely used corrective measure was drug withdrawal, in 16 patients (64%). Water intake restriction was the most common treatment after discharge (5 patients, 20%), followed by urea (2 patients, 8%), while tolvaptan was not used. Conclusion: Hyponatremia may be the cause of PHS, which could increase the rate of early readmission. Hyponatremia is an underdiagnosed and undertreated entity, so it is necessary to apply an appropriate system to optimize its management and, in future studies, to assess its impact on PHS

Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development and validation of the PRIORITY model

Objectives: We aimed to develop and validate a prediction model, based on clinical history and examination findings on initial diagnosis of coronavirus disease 2019 (COVID-19), to identify patients at risk of critical outcomes. Methods: We used data from the SEMI-COVID-19 Registry, a cohort of consecutive patients hospitalized for COVID-19 from 132 centres in Spain (23rd March to 21st May 2020). For the development cohort, tertiary referral hospitals were selected, while the validation cohort included smaller hospitals. The primary outcome was a composite of in-hospital death, mechanical ventilation, or admission to intensive care unit. Clinical signs and symptoms, demographics, and medical history ascertained at presentation were screened using least absolute shrinkage and selection operator, and logistic regression was used to construct the predictive model. Results: There were 10 433 patients, 7850 in the development cohort (primary outcome 25.1%, 1967/7850) and 2583 in the validation cohort (outcome 27.0%, 698/2583). The PRIORITY model included: age, dependency, cardiovascular disease, chronic kidney disease, dyspnoea, tachypnoea, confusion, systolic blood pressure, and SpO2 ≤93% or oxygen requirement. The model showed high discrimination for critical illness in both the development (C-statistic 0.823; 95% confidence interval (CI) 0.813, 0.834) and validation (C-statistic 0.794; 95%CI 0.775, 0.813) cohorts. A freely available web-based calculator was developed based on this model (https://www.evidencio.com/models/show/2344). Conclusions: The PRIORITY model, based on easily obtained clinical information, had good discrimination and generalizability for identifying COVID-19 patients at risk of critical outcomes