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The purpose of this study is to determine whether a decision support system can improve the adherence to thresholds for low blood pressure by anesthesia providers, which in turn prevents their patients from having organ injury.
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Blood pressure management is an important part of anesthesia. Many factors contribute to a change in blood pressure during a surgical procedure, such as blood loss, manipulation by surgeons, and there are several mechanisms through which anesthesia itself changes blood pressure. Although a high blood pressure also occurs during anesthesia, most of these factors lower a patient's blood pressure. When a patient's blood pressure becomes too low, the internal organs become at risk of receiving not enough blood (low perfusion or hypoperfusion). This low perfusion state can result in organ damage (ischemia) because of an insufficient supply of oxygen and glucose. Hence the important task of anesthesia providers to maintain the blood pressure of patients, using a wide range of drugs and other interventions.
A big challenge in blood pressure management is to know when a low blood pressure indeed results in low perfusion of organs. There is a large variation between patients in how susceptible they are to low blood pressure, as well as a difference between the organs in how easily they are damaged because of low perfusion. Elder patients, or patients with preexisting hypertension, heart problems or other cardiovascular diseases are more prone to a low blood pressure and are more likely to develop organ ischemia when there is a low blood pressure. The kidneys, the heart and the brain are the organs that are most at risk of organ damage. As one cannot measure the perfusion states of individual organs in individual patients, it is very difficult to know 'how low to go' with a patient's blood pressure.
Recent studies have used large datasets of patients to demonstrate that there is statistical association between low blood pressure during surgery and various types of organ injury. As patients are already treated for low blood pressure by anesthesia providers, this suggests that patients have low organ perfusion states despite the current treatment standards. A patient's blood pressure is not simply a dial that can be adjusted to a specific level. Finding the right level of interventions can be difficult in some patients. Consequently, lower blood pressures are common in anesthesia, even with the current standards of blood pressure management.
In this proposed study the investigators will implement two forms of decision support to assist anesthesia providers in blood pressure management. The decision support aims to educate anesthesia providers about the risks of low blood pressures in direct relation to the patients that they treat. One form of decision support will provide automated notifications through pagers and through the anesthesia information management system. These automated notifications pop up when the patient's blood pressure drops below a level that is associated with a risk of organ injury, and thus alerts the anesthesia provider of the blood pressure and its associated risk. The second form of decision support will send a postoperative email the day after the procedure when the patient has had a low blood pressure for particular duration. This email then provides feedback to the anesthesia provider by informing them of the increased risks of organ injury that are associated with that low blood pressure.
The study will look at both a change in patient outcome and a change in blood pressure management and will be performed at the Vanderbilt University Medical Center (VUMC). The change in patient outcome will primarily be studied through the occurrence of acute kidney injury in the first days following the procedure at the VUMC. The change in blood pressure management (provider behavior) will be studied by observing the depth and duration of low pressures during anesthesia, and the number of interventions that have been used to treat the blood pressure. Patient outcome will be studied by comparison of a baseline phase - before the decision support is implemented and uses historic data- and the intervention phase - the period during which the intervention is active. Only routinely collected clinical data will be used for these analyses: no additional data collection is required.
As it is impossible to know which form of decision support will be the most effective, the first three months of the intervention period will be a 'nested cluster-randomized trial'. The anesthesia providers (not the patients) will be randomized to either the automated notifications or the feedback emails. After three months all anesthesia providers will receive both forms of decision support for the remainder of the intervention period. The reason why anesthesia providers are randomized only during the first three months is that cross-over or contamination between the two groups is expected. This contamination could make it impossible to study the effect of the decision support on patient outcome, as there will be no longer any difference between the study groups.
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22,435 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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