You searched for:"Pedro Martins Pereira Kurtz"
We found (3) results for your search.Abstract
Critical Care Science. 07-03-2024;36:e20240118en
DOI 10.62675/2965-2774.20240118-en
Abstract
Revista Brasileira de Terapia Intensiva. 02-25-2010;21(4):349-352
DOI 10.1590/S0103-507X2009000400003
OBJECTIVES: Arterial pulse pressure respiratory variation is a good predictor of fluid response in ventilated patients. Recently, it was shown that respiratory variation in arterial pulse pressure correlates with variation in pulse oximetry plethysmographic waveform amplitude. We wanted to evaluate the correlation between respiratory variation in arterial pulse pressure and respiratory variation in pulse oximetry plethysmographic waveform amplitude, and to determine whether this correlation was influenced by norepinephrine administration. METHODS: Prospective study of sixty patients with normal sinus rhythm on mechanical ventilation, profoundly sedated and with stable hemodynamics. Oxygenation index and invasive arterial pressure were monitored. Respiratory variation in arterial pulse pressure and respiratory variation in pulse oximetry plethysmographic waveform amplitude were recorded simultaneously in a beat-to-beat evaluation, and were compared using the Pearson coefficient of agreement and linear regression. RESULTS: Thirty patients (50%) required norepinephrine. There was a significant correlation (K = 0.66; p < 0.001) between respiratory variation in arterial pulse pressure and respiratory variation in pulse oximetry plethysmographic waveform amplitude. Area under the ROC curve was 0.88 (range, 0.79 - 0.97), with a best cutoff value of 14% to predict a respiratory variation in arterial pulse pressure of 13. The use of norepinephrine did not influence the correlation (K = 0.63, p = 0.001, respectively). CONCLUSIONS: Respiratory variation in arterial pulse pressure above 13% can be accurately predicted by a respiratory variation in pulse oximetry plethysmographic waveform amplitude of 14%. The use of norepinephrine does not alter this relationship.
Abstract
Revista Brasileira de Terapia Intensiva. 02-25-2010;21(4):353-358
DOI 10.1590/S0103-507X2009000400004
OBJECTIVE: To predict readmission in intensive care unit analyzing the first 24 hours data after intensive care unit admission. METHODS: The first intensive care unit admission of patients was analyzed from January to May 2009 in a mixed unit. Readmission to the unit was considered those during the same hospital stay or within 3 months after intensive care unit discharge. Deaths during the first admission were excluded. Demographic data, use of mechanical ventilation, and report of stay longer than 3 days were submitted to uni and multivariate analysis for readmission. RESULTS: Five hundred seventy-seven patients were included (33 excluded deaths). The readmission group had 59 patients, while 518 patients were not readmitted. The lead time between the index admission and readmission was 9 (3-28) days (18 were readmitted in less than 3 days), and 10 died. Patients readmitted at least once to the intensive care unit had the differences below in comparison to the control group: older age: 75 (67-81) versus 67 (56-78) years, P<0.01; admission for respiratory insufficiency or sepsis: 33 versus 13%, P<0.01; medical admission: 49 versus 32%, P<0.05; higher SAPS II score: 27 (21-35) versus 23 (18-29) points, P<0.01; Charlson index: 2 (1-2) versus 1 (0-2) points, P<0.01; first ICU stay longer than 3 days: 35 versus 23%, P<0.01. After logistic regression, higher age, Charlson index and admission for respiratory and sepsis were independently associated to readmissions in intensive care unit. CONCLUSION: Age, comorbidities and respiratory- and/or sepsis-related admission are associated with increased readmission risk in the studied sample.