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PLoS One ; 10(4): e0123803, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-25853822

RESUMO

BACKGROUND: Predictors of unscheduled return visits (URV), best time-frame to evaluate URV rate and clinical relationship between both visits have not yet been determined for the elderly following an ED visit. METHODS: We conducted a prospective-observational study including 11,521 patients aged ≥75-years and discharged from ED (5,368 patients (53.5%)) or hospitalized after ED visit (6,153 patients). Logistic Regression and time-to-failure analyses including Cox proportional model were performed. RESULTS: Mean time to URV was 17 days; 72-hour, 30-day and 90-day URV rates were 1.8%, 6.1% and 10% respectively. Multivariate analysis indicates that care-pathway and final disposition decisions were significantly associated with a 30-day URV. Thus, we evaluated predictors of 30-day URV rates among non-admitted and hospitalized patient groups. By using the Cox model we found that, for non-admitted patients, triage acuity and diagnostic category and, for hospitalized patients, that visit time (day, night) and diagnostic categories were significant predictors (p<0.001). For URV, we found that 25% were due to closely related-clinical conditions. Time lapses between both visits constituted the strongest predictor of closely related-clinical conditions. CONCLUSION: Our study shows that a decision of non-admission in emergency departments is linked with an accrued risk of URV, and that some diagnostic categories are also related for non-admitted and hospitalized subjects alike. Our study also demonstrates that the best time frame to evaluate the URV rate after an ED visit is 30 days, because this is the time period during which most URVs and cases with close clinical relationships between two visits are concentrated. Our results suggest that URV can be used as an indicator or quality.


Assuntos
Serviço Hospitalar de Emergência/estatística & dados numéricos , Admissão do Paciente/estatística & dados numéricos , Alta do Paciente/estatística & dados numéricos , Readmissão do Paciente/estatística & dados numéricos , Triagem/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Feminino , França , Humanos , Modelos Logísticos , Masculino , Modelos de Riscos Proporcionais , Estudos Prospectivos , Fatores de Tempo
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