Intraoperative Fluid Management Based on Arterial Pulse Pressure Variation During High-Risk Surgery
Hypovolaemia and tissue hypoperfusion can pass undetected during and after major surgery. The resulting systemic inflammatory response and organ dysfunction, often not clinically apparent for several days, may lead to increased morbidity and mortality and prolonged hospital stay.
In this regard, intraoperative optimization of circulatory status by volume loading has been shown to improve the outcome of patients undergoing high-risk surgery.
Indeed, several reports (1-7) have shown that monitoring and maximizing stroke volume by volume loading (until stroke volume reaches a plateau, actually the plateau of the Frank-Starling curve) during high-risk surgery decreases the incidence of post-operative complications and the length of hospital stay.
Unfortunately, this strategy has required so far the measurement of stroke volume by a cardiac output monitor, as well as a specific training period for the operators (8), and hence is not applicable in many institutions as well as in many countries.
The arterial pulse pressure variation (∆PP) induced by mechanical ventilation is known to be a very accurate predictor of fluid responsiveness, i.e. of the position on the preload/stroke volume relationship (Frank-Starling curve) (9).
By increasing cardiac preload, volume loading induces a rightward shift on the preload/stroke volume relationship and hence a decrease in ∆PP. Patients who have reached the plateau of the Frank-Starling relationship can be identified as patients in whom ∆PP is low (9).
Therefore, the clinical and intraoperative goal of “maximizing stroke volume by volume loading” can be achieved simply by minimizing ∆PP.
We designed the present study to investigate whether monitoring and minimizing ∆PP by volume loading during high-risk surgery may improve post-operative outcome and decrease the duration of post-operative hospital stay.
Procedure: fluid management based on arterial pulse pressure variation
|Study Design:||Allocation: Randomized
Endpoint Classification: Safety/Efficacy Study
Intervention Model: Parallel Assignment
Masking: Single Blind
Primary Purpose: Supportive Care
|Official Title:||Intraoperative Fluid Management Based on Arterial Pulse Pressure Variation During High-Risk Surgery|
- The primary outcome measure is the duration of postoperative hospital stay. [ Time Frame: at days 1,2, and 5, as well as at ICU discharge, and hospital discharge. ]
- Number of post-operative complications, duration of mechanical ventilation, duration of ICU stay [ Time Frame: Post-opertaive days 1,2,5; ICU discharge; hospital discharge ]
|Study Start Date:||September 2005|
|Study Completion Date:||January 2006|
Principal investigator Dr. Marcel Rezende Lopes, Department of Anesthesia and Critical Care, Santa Casa de Misericórdia de Passos, Passos MG, Brazil, firstname.lastname@example.org
Study design Single centre randomized controlled trial
Investigation centre Department of Anesthesia and Critical Care, Santa Casa de Misericórdia de Passos, Passos MG, Brazil
- medico-surgical pre-operative decision of post-operative ICU admission (because of co-morbidities or/and the surgical procedure)
- age > 18 yr
- elective surgery
- no informed consent
- cardiac arrhythmias
- body mass index > 40
- patients undergoing surgery with an open thorax
- patients undergoing neurosurgery
- enrolment in any other protocol
Ethics committee The study has been approved by the ethical committee of Santa Casa de Misericórdia de Passos (Passos, MG, Brazil).
Informed consent Written informed consent will be required for each patient.
Start date Sept 22, 2005
Finishing date January 23, 2006
Randomisation Randomisation will be done pre-operatively using sealed envelopes. Patients will be assigned to a Control group (group C) or to an Intervention group (group I)
Intraoperative monitoring In group I, a specific multiparameter bedside monitor (DX 2020, Dixtal, Sao Paulo, SP, Brazil) will be utilized to record continuously and simultaneously the ECG, the pulse oximetry signal, the arterial pressure curve and the capnographic signal.
A specific software is uploaded into this monitor, allowing the recognition of respiratory cycles (from the analysis of the capnographic signal) and the automatic calculation and display of ∆PP.
Intervention During the surgical procedure, patients will be managed according to standard of care at Santa Casa de Misericórdia de Passos.
Group C will receive per-operative fluid at the discretion of the anesthetist. Group I will receive additional hydroxyethylstarch 6% (HES) boluses in order to minimize and maintain ∆PP ≤ 10%. During the postoperative period, both groups will be managed by intensivists (in the ICU) and clinicians (in the wards) not involved in the intraoperative management nor in data collection. These individuals will not be informed of patient allocation.
Data collection Over the study period all data will be collected prospectively. Patients will be followed up until hospital discharge.
Data collection prior to surgery Parameters to be recorded before the surgical procedure are presented in table 1.
The body mass index is calculated according to the standard formula (BMI = weight/height2).
Serum creatinine, prothrombin time, hemoglobin, and platelets will be obtained from routine pre-operative biological tests.
Data collection during surgery Parameters to be recorded during the surgical procedure are presented in table 2.
Data collection after surgery Data collection prior to and during surgery will be done by one individual. Data collection after surgery will be done by another individual not aware of patient allocation.
Information and parameters to be collected after surgery are presented in tables 3 and 4.
During the 24h following ICU admission, blood lactate will be measured every 6h and the mean lactate value will be calculated over the first 24h ICU period.
Postoperative infectious, respiratory, cardiovascular, abdominal, hematologic, and renal complications (table 4) will be recorded according to criteria previously used by other investigators (10-11).
Duration of mechanical ventilation, of ICU stay, and of hospital stay, as well as hospital mortality will be recorded too.
Data will be collected systematically at days 1,2, and 5, as well as at ICU discharge, and hospital discharge.
Statistical analysis Data will be analysed comparing patients in group C with those in group I on an intention-to-treat basis.
The primary outcome measure is the duration of postoperative hospital stay. On the basis of our own hospital registry, the mean duration of postoperative hospital stay in group C is a priori estimated at 16 ± 8 days (mean ± SD).
According to previous publications (1,2), we postulated that the mean duration of postoperative hospital stay in group I could be 35% lower.
A sample size of 33 patients in each group was calculated for a 0.05 difference (two sided) with a power of 80% (12).
Secondary outcome measures are the number of post-operative complications per patient, as well as the duration of mechanical ventilation and ICU stay.
Interim analyses and stopping rules An intermediate analysis after the enrolment of the first 33 patients is planed, in order to readjust the population sample size if necessary, or to stop the trial in case a significant reduction in length of hospital stay (primary endpoint) is observed.
Role of funding source Dixtal (Sao Paulo, SP, Brazil) will provide and upload the automatic calculation software in a bedside monitor owned by Santa Casa de Misericórdia de Passos (Passos, MG, Brazil).
Dixtal had no role in the study design, and will have no role in data collection, data analysis, data interpretation, or writing of the report. The corresponding author (Dr F. Michard) will have full access to all data in the study and will have final responsibility for the decision to submit for publication.
Table 1: Patients characteristics before surgery:
- Sex M/F, Age (yr), Weight (kg), Height (cm), BMI (kg/m2), ASA physical status
- Chronic disease: Renal failure requiring dialysis, Renal failure without dialysis, Cirrhosis, Chronic obstructive pulmonary disease, Hypertension, Peripheral vascular disease, Coronary artery disease, Other cardiopathy, Diabetes mellitus, Cerebrovascular disease
- Pre-operative biological tests: Serum creatinine (micromol/l), Prothrombin time (%), Hemoglobin (g/dl), Platelets (/microl)
Table 2: Patients characteristics during surgery:
- Type of surgery: Upper gastro-intestinal, Hepato-biliary, Lower gastro-intestinal, Urology, Other
- Respiratory settings: Tidal volume (mL), Respiratory frequency (/min)
- Physiologic status/START of surgery: Heart rate (/min), Mean arterial pressure (mmHg), SpO2(%), ∆PP (%), Hemoglobin (g/dl)
- Physiologic status/END of surgery: Heart rate (/min), Mean arterial pressure (mmHg), SpO2 (%), ∆PP (%), Hemoglobin (g/dl), Fluid balance, Volume of crystalloid infused (ml), Volume of colloid infused (ml), Volume of red cells infused (ml), Volume of fresh frozen plasma infused (ml), Total volume infused (ml), Total volume infused (ml/kg/h), Duration of surgery (hours)
Table 3: Patients characteristics after surgery:
- ICU admission : Mean arterial pressure (mmHg), Heart rate (bpm), SpO2 (%), Lactate (mmol/l)
- 24 hr after ICU admission: Mean arterial pressure (mmHg), Heart rate (/min), SpO2 (%), Vasoactive support (if yes please indicate name and dosage), Lactate (mmol/l), Mean lactate over 24h (mmol/l)
Table 4: Post-operative complications
- Infection: Pneumonia, Abdominal, Urinary tract, Central venous catheter, Wound
- Respiratory: Pleural effusion, Pneumothorax, Pulmonary embolism, Respiratory support > 24 h, Acute lung injury
- Cardiovascular: Arrhythmia, Hypotension, Acute pulmonary edema, Acute myocardial infarction, Cardiac arrest, Stroke
- Abdominal: Clostridium difficile diarrhoea, Acute bowel obstruction, Upper gastro-intestinal bleed, Anastomotic leak
- Coagulopathy: Platelet count < 100000/microl, Prothrombin time < 50%
- Renal: Urine output < 500 ml/day, Serum creatinine > 170 micromol/l, Dialysis for acute renal failure
- Total number of complications
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Please refer to this study by its ClinicalTrials.gov identifier: NCT00479011
|Santa Casa de Misericordia de Passos|
|Passos, Minas Gerais, Brazil, 37900 000|
|Principal Investigator:||Marcel Lopes, Rezende||Santa Casa de Misericordia de Passos|