Papers by FREDERICA MONTANARI LOURENCATO
Research Square (Research Square), Jun 7, 2022
Objectives: To describe the process of implementing a palliative care team (PCT) in a Brazilian p... more Objectives: To describe the process of implementing a palliative care team (PCT) in a Brazilian public tertiary university hospital and compare this intervention as an active in-hospital search (Strategy I) with the Emergency Department (Strategy II). Methods: We described the development of a complex Palliative Care Team (PCT). We evaluated the following primary outcomes-hospital discharge, death (in-hospital and follow-up mortality) or transferand performance outcomes-Perception Index (difference in days between hospitalization and the evaluation by the PTC), follow-up index (difference in days between the PTC evaluation and the primary outcome), and the in-hospital stay. Results: We included 1203 patients-Strategy I (587; 48.8%) and Strategy II (616; 51.2%). In both Strategies, male and elderly patients were prevalent. Most came from internal medicine I (39.3%) and II (57.9%)-p<0.01. General clinical conditions (40%) and Oncology-I (27.7%) and II (32.4%)-represented the majority of the population. Over 70% of all patients had PPS 10 and ECOG 4 above 85%. There was a reduction of patients identi ed in ICU from I (20.9%) to II (9.2%)-p <0.01, reduction in the ward from I (60.8%) to II (42.5%)-p <0.01-and a signi cant increase from I (18.2%) to II (48.2%) in the emergency department-p <0.01. Regarding in-hospital mortality, 50% of patients remained alive within 35 days of hospitalization (Strategy I), while for Strategy II, 50% were alive within 20 days of hospitalization (p<0 .01). As for post-discharge mortality, in Strategy II, 50% of patients died ten days after hospital discharge, while in Strategy I, this number was 40 days (p<0.01). In the Cox multivariate regression model, adjusting for possible confounding factors, Strategy II increased 30% the chance of death. The perception index decreased from 10.9 days to 9.1 days, there was no change in follow-up (12 days), and the duration of inhospital stay dropped from 24.3 to 20.7 days-p <0.01. The primary demand was the de nition of prognosis (56.7%). Conclusion: The present work showed that early intervention by an elaborate and complex PCT in the ED was associated with a faster perception of the need for palliative care and in uenced a reduction in the length of hospital stay in a very dependent and compromised old population.
LOURENÇATO, F. M. Implementation of a palliative care service in the Emergency Hospital Service o... more LOURENÇATO, F. M. Implementation of a palliative care service in the Emergency Hospital Service of a public university hospital. 2020. 1159f.
International Journal of Emergency Medicine, Sep 16, 2022
Objectives: To describe the process of implementing a palliative care team (PCT) in a Brazilian p... more Objectives: To describe the process of implementing a palliative care team (PCT) in a Brazilian public tertiary university hospital and compare this intervention as an active in-hospital search (strategy I) with the Emergency Department (strategy II). Methods: We described the development of a complex Palliative Care Team (PCT). We evaluated the following primary outcomes: hospital discharge, death (in-hospital and follow-up mortality) or transfer, and performance outcomes-Perception Index (difference in days between hospitalization and the evaluation by the PTC), follow-up index (difference in days between the PTC evaluation and the primary outcome), and the in-hospital stay. Results: We included 1203 patients-strategy I (587; 48.8%) and strategy II (616; 51.2%). In both strategies, male and elderly patients were prevalent. Most came from internal medicine I (39.3%) and II (57.9%), p < 0.01. General clinical conditions (40%) and Oncology I (27.7%) and II (32.4%) represented the majority of the population. Over 70% of all patients had PPS 10 and ECOG 4 above 85%. There was a reduction of patients identified in ICU from I (20.9%) to II (9.2%), p < 0.01, reduction in the ward from I (60.8%) to II (42.5%), p < 0.01 and a significant increase from I (18.2%) to II (48.2%) in the emergency department, p < 0.01. Regarding in-hospital mortality, 50% of patients remained alive within 35 days of hospitalization (strategy I), while for strategy II, 50% were alive within 20 days of hospitalization (p < 0.01). As for post-discharge mortality, in strategy II, 50% of patients died 10 days after hospital discharge, while in strategy I, this number was 40 days (p < 0.01). In the Cox multivariate regression model, adjusting for possible confounding factors, strategy II increased 30% the chance of death. The perception index decreased from 10.9 days to 9.1 days, there was no change in follow-up (12 days), and the duration of in-hospital stay dropped from 24.3 to 20.7 days, p < 0.01. The primary demand was the definition of prognosis (56.7%). Conclusion: The present work showed that early intervention by an elaborate and complex PCT in the ED was associated with a faster perception of the need for palliative care and influenced a reduction in the length of hospital stay in a very dependent and compromised old population.
Revista de Saúde Pública, 2015
OBJECTIVE To assess the impact of implementing long-stay beds for patients of low complexity and ... more OBJECTIVE To assess the impact of implementing long-stay beds for patients of low complexity and high dependency in small hospitals on the performance of an emergency referral tertiary hospital. METHODS For this longitudinal study, we identified hospitals in three municipalities of a regional department of health covered by tertiary care that supplied 10 long-stay beds each. Patients were transferred to hospitals in those municipalities based on a specific protocol. The outcome of transferred patients was obtained by daily monitoring. Confounding factors were adjusted by Cox logistic and semiparametric regression. RESULTS Between September 1, 2013 and September 30, 2014, 97 patients were transferred, 72.1% male, with a mean age of 60.5 years (SD = 1.9), for which 108 transfers were performed. Of these patients, 41.7% died, 33.3% were discharged, 15.7% returned to tertiary care, and only 9.3% tertiary remained hospitalized until the end of the analysis period. We estimated the Charls...
OBJECTIVE: To assess the impact of implementing long-stay beds for patients of low complexity and... more OBJECTIVE: To assess the impact of implementing long-stay beds for patients of low complexity and high dependency in small hospitals on the performance of an emergency referral tertiary hospital.
METHODS: For this longitudinal study, we identified hospitals in three municipalities of a regional department of health covered by tertiary care that supplied 10 long-stay beds each. Patients were transferred to hospitals in those municipalities based on a specific protocol. The outcome of transferred patients was obtained by daily monitoring. Confounding factors were
adjusted by Cox logistic and semiparametric regression.
RESULTS: Between September 1, 2013 and September 30, 2014, 97 patients were transferred, 72.1% male, with a mean age of 60.5 years (SD = 1.9), for which 108 transfers were performed. Of these patients, 41.7% died, 33.3% were discharged, 15.7% returned to tertiary care, and only 9.3% tertiary remained hospitalized until the end of the analysis period. We estimated the Charlson comorbidity index – 0 (n = 28 [25.9%]), 1 (n = 31 [56.5%]) and ≥ 2 (n = 19 [17.5%]) – the only variable that increased the chance of death or return to the tertiary hospital (Odds Ratio = 2.4; 95%CI 1.3;4.4). The length of stay in long-stay beds was 4,253 patient days, which would represent 607 patients at the tertiary hospital, considering the average hospital stay of seven days. The tertiary hospital increased the number of patients treated in 50.0% for Intensive Care, 66.0% for Neurology and 9.3% in total. Patients stayed in long-stay beds mainly in the first 30 (50.0%) and 60 (75.0%) days.
CONCLUSIONS: Implementing long-stay beds increased the number of patients treated in tertiary care, both in general and in system bottleneck areas such as Neurology and Intensive Care. The Charlson index of comorbidity is associated with the chance of patient death or return to tertiary care, even when adjusted for possible confounding factors.
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Papers by FREDERICA MONTANARI LOURENCATO
METHODS: For this longitudinal study, we identified hospitals in three municipalities of a regional department of health covered by tertiary care that supplied 10 long-stay beds each. Patients were transferred to hospitals in those municipalities based on a specific protocol. The outcome of transferred patients was obtained by daily monitoring. Confounding factors were
adjusted by Cox logistic and semiparametric regression.
RESULTS: Between September 1, 2013 and September 30, 2014, 97 patients were transferred, 72.1% male, with a mean age of 60.5 years (SD = 1.9), for which 108 transfers were performed. Of these patients, 41.7% died, 33.3% were discharged, 15.7% returned to tertiary care, and only 9.3% tertiary remained hospitalized until the end of the analysis period. We estimated the Charlson comorbidity index – 0 (n = 28 [25.9%]), 1 (n = 31 [56.5%]) and ≥ 2 (n = 19 [17.5%]) – the only variable that increased the chance of death or return to the tertiary hospital (Odds Ratio = 2.4; 95%CI 1.3;4.4). The length of stay in long-stay beds was 4,253 patient days, which would represent 607 patients at the tertiary hospital, considering the average hospital stay of seven days. The tertiary hospital increased the number of patients treated in 50.0% for Intensive Care, 66.0% for Neurology and 9.3% in total. Patients stayed in long-stay beds mainly in the first 30 (50.0%) and 60 (75.0%) days.
CONCLUSIONS: Implementing long-stay beds increased the number of patients treated in tertiary care, both in general and in system bottleneck areas such as Neurology and Intensive Care. The Charlson index of comorbidity is associated with the chance of patient death or return to tertiary care, even when adjusted for possible confounding factors.
METHODS: For this longitudinal study, we identified hospitals in three municipalities of a regional department of health covered by tertiary care that supplied 10 long-stay beds each. Patients were transferred to hospitals in those municipalities based on a specific protocol. The outcome of transferred patients was obtained by daily monitoring. Confounding factors were
adjusted by Cox logistic and semiparametric regression.
RESULTS: Between September 1, 2013 and September 30, 2014, 97 patients were transferred, 72.1% male, with a mean age of 60.5 years (SD = 1.9), for which 108 transfers were performed. Of these patients, 41.7% died, 33.3% were discharged, 15.7% returned to tertiary care, and only 9.3% tertiary remained hospitalized until the end of the analysis period. We estimated the Charlson comorbidity index – 0 (n = 28 [25.9%]), 1 (n = 31 [56.5%]) and ≥ 2 (n = 19 [17.5%]) – the only variable that increased the chance of death or return to the tertiary hospital (Odds Ratio = 2.4; 95%CI 1.3;4.4). The length of stay in long-stay beds was 4,253 patient days, which would represent 607 patients at the tertiary hospital, considering the average hospital stay of seven days. The tertiary hospital increased the number of patients treated in 50.0% for Intensive Care, 66.0% for Neurology and 9.3% in total. Patients stayed in long-stay beds mainly in the first 30 (50.0%) and 60 (75.0%) days.
CONCLUSIONS: Implementing long-stay beds increased the number of patients treated in tertiary care, both in general and in system bottleneck areas such as Neurology and Intensive Care. The Charlson index of comorbidity is associated with the chance of patient death or return to tertiary care, even when adjusted for possible confounding factors.