ABSTRACT
AIM: To describe a nursing staffing surge model in critical care units that can be used during a pandemic or crisis. This model may give useful guidance for hospitals or centers that must immediately react in response to the devastating challenges introduced by disease outbreaks. BACKGROUND: During the COVID-19 pandemic, many hospitals were challenged to maintain the quality of care and safe practice in critical care units while accommodating the daily rapidly increasing number of infected cases that needed critical care. The nursing staffing shortage in critical care units and its consequences were among the top issues to deal with. METHOD: This is a descriptive study about our experience in preparing for nursing staffing in critical care as a part of the COVID-19 surge plan. We have used evidence-based strategies to design our team-based model for staffing during the COVID-19 pandemic. RESULTS: The team-based model for staffing during the COVID-19 pandemic had shown success in dealing with the acute shortage of nursing staff in critical care units. We had implemented other additional supportive strategies to help enhance this staffing. CONCLUSION: With the support of available evidence-based resources and on-the-fly preparation and training, we were able to augment the tremendous increase in patient influx during the pandemic using the team-based model. IMPLICATIONS FOR NURSING MANAGEMENT: The team-based approach and other strategies included in this article can help support critical care units with staff during crises. However, we strongly recommend developing a nursing deployment policy that makes staff redeployment and re-allocation smoother, whenever needed.
Subject(s)
COVID-19 , Nursing Staff, Hospital , COVID-19/epidemiology , Critical Care , Humans , Pandemics , Personnel Staffing and Scheduling , WorkforceSubject(s)
COVID-19 , Pandemics , Critical Care , Humans , Intensive Care Units , Patient-Centered Care , SARS-CoV-2ABSTRACT
OBJECTIVE: To evaluate the hypothesis that the Modified Early Warning Score (MEWS) at the time of intensive care unit discharge is associated with readmission and to identify the MEWS that most reliably predicts intensive care unit readmission within 48 hours of discharge. METHODS: This was a retrospective observational study of the MEWSs of discharged patients from the intensive care unit. We compared the demographics, severity scores, critical illness characteristics, and MEWSs of readmitted and non-readmitted patients, identified factors associated with readmission in a logistic regression model, constructed a Receiver Operating Characteristic (ROC) curve of the MEWS in predicting the probability of readmission, and presented the optimum criterion with the highest sensitivity and specificity. RESULTS: The readmission rate was 2.6%, and the MEWS was a significant predictor of readmission, along with intensive care unit length of stay > 10 days and tracheostomy. The ROC curve of the MEWS in predicting the readmission probability had an AUC of 0.82, and a MEWS > 6 carried a sensitivity of 0.78 (95%CI 0.66 - 0.9) and specificity of 0.9 (95%CI 0.87 - 0.93). CONCLUSION: The MEWS is associated with intensive care unit readmission, and a score > 6 has excellent accuracy as a prognostic predictor.
Subject(s)
Critical Illness , Early Warning Score , Intensive Care Units/statistics & numerical data , Patient Readmission/statistics & numerical data , Adult , Female , Humans , Length of Stay , Male , Middle Aged , Patient Discharge , Prognosis , ROC Curve , Retrospective Studies , Sensitivity and Specificity , Severity of Illness Index , Tracheostomy/statistics & numerical dataABSTRACT
RESUMO Objetivo: Avaliar a hipótese de que o Modified Early Warning Score (MEWS) por ocasião da alta da unidade de terapia intensiva associa-se com readmissão, e identificar o nível desse escore que prediz com maior confiabilidade a readmissão à unidade de terapia intensiva dentro de 48 horas após a alta. Métodos: Este foi um estudo observacional retrospectivo a respeito do MEWS de pacientes que receberam alta da unidade de terapia intensiva. Comparamos dados demográficos, escores de severidade, características da doença crítica e MEWS de pacientes readmitidos e não readmitidos. Identificamos os fatores associados com a readmissão em um modelo de regressão logística. Construímos uma curva Característica de Operação do Receptor para o MEWS na predição da probabilidade de readmissão. Por fim, apresentamos o critério ideal com maior sensibilidade e especificidade. Resultados: A taxa de readmissões foi de 2,6%, e o MEWS foi preditor significante de readmissão, juntamente do tempo de permanência na unidade de terapia intensiva acima de 10 dias e traqueostomia. A curva Característica de Operação do Receptor relativa ao MEWS para predizer a probabilidade de readmissão teve área sob a curva de 0,82, e MEWS acima de 6 teve sensibilidade de 0,78 (IC95% 0,66 - 0,9) e especificidade de 0,9 (IC95% 0,87 - 0,93). Conclusão: O MEWS associa-se com readmissão à unidade de terapia intensiva, e o escore acima de 6 teve excelente precisão como preditor prognóstico.
ABSTRACT Objective: To evaluate the hypothesis that the Modified Early Warning Score (MEWS) at the time of intensive care unit discharge is associated with readmission and to identify the MEWS that most reliably predicts intensive care unit readmission within 48 hours of discharge. Methods: This was a retrospective observational study of the MEWSs of discharged patients from the intensive care unit. We compared the demographics, severity scores, critical illness characteristics, and MEWSs of readmitted and non-readmitted patients, identified factors associated with readmission in a logistic regression model, constructed a Receiver Operating Characteristic (ROC) curve of the MEWS in predicting the probability of readmission, and presented the optimum criterion with the highest sensitivity and specificity. Results: The readmission rate was 2.6%, and the MEWS was a significant predictor of readmission, along with intensive care unit length of stay > 10 days and tracheostomy. The ROC curve of the MEWS in predicting the readmission probability had an AUC of 0.82, and a MEWS > 6 carried a sensitivity of 0.78 (95%CI 0.66 - 0.9) and specificity of 0.9 (95%CI 0.87 - 0.93). Conclusion: The MEWS is associated with intensive care unit readmission, and a score > 6 has excellent accuracy as a prognostic predictor.
