Preditores e motivos da permanência a maior em internações clínicas de um hospital universitário

• Ano de defesa: 2025
• Autor(a) principal: Rinaldi, Juliana Ranalli
• Orientador(a): Mascarenhas, Silvia Helena Zem
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Enfermagem - PPGEnf
• Palavras-chave em Português: Tempo de internação; Alta hospitalar; Atenção à saúde; Controle de infecção
• Palavras-chave em Inglês: Length of stay; Hospital discharge; Delivery of health care; Infection control
• Área do conhecimento CNPq: CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO::ADMINISTRACAO PUBLICA
• Link de acesso: https://hdl.handle.net/20.500.14289/22605

This study identified risk predictors for prolonged hospital stay and the reasons that delayed timely discharge in clinical admissions at a public university hospital. This is a quantitative, analytical, and descriptive study based on the analysis of 250 medical records, considering clinical, sociodemographic, and functional variables. Validated tools were used, such as the Blaylock Risk Assessment Screening Score (BRASS), the Fugulin scale, and the Charlson Comorbidity Index (CCI). Cases of prolonged stay, as defined by the Management System of the Table of Procedures, Medications and OPM of the Brazilian Unified Health System (SIGTAP/SUS), had the reasons for delayed discharge classified based on the Day of Care Survey (clinical reasons) and the National Delayed Discharge Criteria of the Scottish National Health Service (social reasons). The list of patients with Healthcare-Associated Infections (HAIs) was provided by the Hospital Infection Control Service (SCIRAS). It was found that 39.6% of patients had a prolonged hospital stay, accounting for 30% of hospital bed-days. The Fugulin scale showed the best predictive performance (AUC = 0.639), measuring functional complexity; BRASS showed high sensitivity (88.1%) for early screening (AUC = 0.593), and CCI had the lowest isolated predictive power (AUC = 0.566). The combined use of the three tools increased predictive accuracy for prolonged hospital stay. Multivariate analysis revealed that Intensive Care Unit (ICU) admission increased the chance of prolonged stay by 2.85 times; HAI presence increased risk by 7.1 times; greater functional dependence increased risk by 14% per additional point on the Fugulin scale; and genitourinary diagnoses increased risk sixfold. The investigation of the reasons for prolonged hospital stay revealed a predominance of clinical factors (74%), particularly the need for parenteral medication and continuous monitoring. Social reasons included home preparation and caregiver, with 20 cases. The results reinforce that early discharge planning is essential to reduce hospital length of stay, minimize adverse events, lower hospital costs, and promote efficient bed utilization.