Infecciones respiratorias superiores virales en una central de emergencias: estudio de cohorte retrospectiva / Upper respiratory tract infection on an Emergency Department: retrospective cohort

Estimar la frecuencia de cuadro de vías aéreas superiores (CVAS) como motivo de consulta no programada, describir el proceso de atención y explorar la variación tras la implementación de una nueva estrategia de gestión para la atención, así como el efecto en los indicadores de calidad y seguridad de atención. Cohorte retrospectiva que incluyó consultas por CVAS entre 01/01/2015 y 31/12/2016 de Demanda Espontánea (consultas de baja complejidad de la Central de Emergencia de Adultos), en el Hospital Italiano de Buenos Aires. La prevalencia global del período 2015-2016 resultó 12,01% (21.581/179.597). La intervención múltiple, resultó efectiva en términos de disminución de estudios complementarios (19% antes y 17% después con p=0,001), disminución de laboratorios (9% antes y 8% después con p=0,009), y reducción del tiempo de atención (media de 51 minutos antes y 42 minutos después, con p=0,001). No hubo diferencias significativas en la incidencia acumulada de reconsultas a los 7 días (12,72% antes y 13,11% después con p=0,400) ni en la tasa de internaciones a los 7 días (0,42% antes y 0,38% después con p=0,651) desde la consulta índice (primer consulta en guardia). En un sistema sobresaturado, se requiere fortalecer los sistemas de atención primaria que conforman la puerta de entrada de la salud para garantizar la correcta utilización de los recursos disponibles, la solicitud de estudios apropiados y la indicación correcta de antibióticos. Muchas lecciones aprendidas facilitaron la organización y la reestructuración necesarias durante la pandemia COVID-19 (AU)

To estimate upper respiratory tract infection (URTI) frequency as a reason for unscheduled consultation, to describe the care process and to explore the variation after the implementation of a new management strategy for care, as well as the effect on quality and security indicators of care. Retrospective cohort which included consecutive consultations by URTI between 01/01/2015 and 12/31/2016 for ambulatory clinic (low complexity consultations at the Emergency Department), at the Hospital Italiano de Buenos Aires. The prevalence for the period 2015-2016 was 12.01% (21,581/179,597). The multiple intervention was effective in terms of reduction of complementary studies (19% before and 17% after; p =0.001), reduction of laboratories (9% before and 8% after; p=0.009), and reduction of attention time (mean of 51 minutes before and 42 minutes after; p=0.001). During the follow up, there were no significant differences in the cumulative incidence of reconsultations at 7 days (12.72% before and 13.11% after; p=0.400) or in the rate of hospitalizations at 7 days (0.42% before and 0.38% after; p=0.651) from the index consultation. In an overcrowded system, it is necessary to strengthen the primary care systems that make up the gateway to health to guarantee the correct use of available resources, the request for appropriate studies and the correct indication of antibiotics. Many lessons learned facilitated the organization and restructuring of the Emergency Department needed during the COVID-19 pandemic (AU)

A rule-based electronic phenotyping algorithm for detecting clinically relevant cardiovascular disease cases.

BACKGROUND: The implementation of electronic medical records (EMR) is becoming increasingly common. Error and data loss reduction, patient-care efficiency increase, decision-making assistance and facilitation of event surveillance, are some of the many processes that EMRs help improve. In addition, they show a lot of promise in terms of data collection to facilitate observational epidemiological studies and their use for this purpose has increased significantly over the recent years. Even though the quantity and availability of the data are clearly improved thanks to EMRs, still, the problem of the quality of the data remains. This is especially important when attempting to determine if an event has actually occurred or not. We sought to assess the sensitivity, specificity, and agreement level of a codes-based algorithm for the detection of clinically relevant cardiovascular (CaVD) and cerebrovascular (CeVD) disease cases, using data from EMRs. METHODS: Three family physicians from the research group selected clinically relevant CaVD and CeVD terms from the international classification of primary care, Second Edition (ICPC-2), the ICD 10 version 2015 and SNOMED-CT 2015 Edition. These terms included both signs, symptoms, diagnoses and procedures associated with CaVD and CeVD. Terms not related to symptoms, signs, diagnoses or procedures of CaVD or CeVD and also those describing incidental findings without clinical relevance were excluded. The algorithm yielded a positive result if the patient had at least one of the selected terms in their medical records, as long as it was not recorded as an error. Else, if no terms were found, the patient was classified as negative. This algorithm was applied to a randomly selected sample of the active patients within the hospital's HMO by 1/1/2005 that were 40-79 years old, had at least one year of seniority in the HMO and at least one clinical encounter. Thus, patients were classified into four groups: (1) Negative patients (2) Patients with CaVD but without CeVD; (3) Patients with CeVD but without disease CaVD; (4) Patients with both diseases. To facilitate the validation process, a stratified sample was taken so that each of the groups represented approximately 25% of the sample. Manual chart review was used as the gold standard for assessing the algorithm's performance. One-third of the patients were assigned randomly to each reviewer (Cohen's kappa 0.91). Both coded and un-coded (free text) sections of the EMR were reviewed. This was done from the first present clinical note in the patients chart to the last one registered prior to 1/1/2005. RESULTS: The performance of the algorithm was compared against manual chart review. It yielded high sensitivity (0.99, 95% CI 0.938-0.9971) and acceptable specificity (0.86, 95% CI 0.818-0.895) for detecting cases of CaVD and CeVD combined. A qualitative analysis of the false positives and false negatives was performed. CONCLUSIONS: We developed a simple algorithm, using only standardized and non-standardized coded terms within an EMR that can properly detect clinically relevant events and symptoms of CaVD and CeVD. We believe that combining it with an analysis of the free text using an NLP approach would yield even better results.