Sheldon Magder

Heart-lung interactions occur primarily because of two components of lung inflation, changes in pleural pressure and changes in transpulmonary pressure. Of these, changes in pleural pressure dominate during spontaneous breathing. Because the heart is surrounded by pleural pressure, during inspiration the environment of the heart falls relative to the rest of the body. This alters inflow into the right heart and outflow from the left heart. Alterations in transpulmonary pressure can alter the outflow from the right heart and the inflow to the left heart. These interactions are modified by the cardiac and respiratory frequency, ventricular function and magnitude of the respiratory efforts.

The incidence of delayed graft function in patients undergoing kidney transplantation remains significant. Optimal fluid therapy has been shown to decrease delayed graft function after renal transplantation. Traditionally, the perioperative volume infusion regimen in this patient population has been guided by central venous pressure as an estimation of the patient’s volume status and mean arterial pressure, but this is based on sparse evidence from mostly retrospective observational studies. Excessive volume infusion to the point of no further fluid responsiveness can damage the endothelial glycocalyx and is no longer considered to be the best approach. However, achievement of adequate flow to maintain sufficient tissue perfusion without maximization of cardiac filling remains a challenge. Novel minimally invasive technologies seem to reliably assess volume responsiveness, heart function and perfusion adequacy. Prospective comparative clinical studies are required to better understand the use of dynamic analyses of flow parameters for adequate fluid management in kidney transplant recipients. We review perioperative fluid assessment techniques and discuss conventional and novel monitoring strategies in the kidney transplant recipient.

Purpose: To obtain a point prevalence estimate of alterations in central venous pressure (CVP) produced by active expiration in a consecutive series of intensive care patients. Methods: We evaluated CVP tracings taken by the nurses at their morning shift change in a consecutive series of 60 cardiac surgery and 59 noncardiac surgery patients. We also assessed change in values due to the change in transducer level. Three physicians and a nurse instructor independently reviewed the tracings and determined whether there was evidence of forced expiration and whether it was type A, defined by decreasing CVP during expiration, or type B, defined by increasing CVP during expiration. Results: Agreement for CVP value was 96% between a physician and a bedside nurse. Twenty-nine percent of participants had active expiration, evenly distributed between A and B types. Active expiration was not related to the type of surgery, use of bronchodilators, and the presence of chronic obstructive lung disease or abdominal distention. Active expiration was more common in nonventilated patients and patients not receiving vasopressor drugs, suggesting they were more awake. Conclusion: Active expiration is common in critically ill patients. Failure to recognize it can result in important errors in the estimation of CVP and other hemodynamic measurements.

Volume infusions are one of the commonest clinical interventions in critically ill patients yet the relationship of volume to cardiac output is not well understood. Blood volume has a stressed and unstressed component but only the stressed component determines flow. It is usually about 30 % of total volume. Stressed volume is relatively constant under steady state conditions. It creates an elastic recoil pressure that is an important factor in the generation of blood flow. The heart creates circulatory flow by lowering the right atrial pressure and allowing the recoil pressure in veins and venules to drain blood back to the heart. The heart then puts the volume back into the systemic circulation so that stroke return equals stroke volume. The heart cannot pump out more volume than comes back. Changes in cardiac output without changes in stressed volume occur because of changes in arterial and venous resistances which redistribute blood volume and change pressure gradients throughout the vasculature. Stressed volume also can be increased by decreasing vascular capacitance, which means recruiting unstressed volume into stressed volume. This is the equivalent of an auto-transfusion. It is worth noting that during exercise in normal young males, cardiac output can increase five-fold with only small changes in stressed blood volume. The mechanical characteristics of the cardiac chambers and the circulation thus ultimately determine the relationship between volume and cardiac output and are the subject of this review.

This cohort study compared the prognostic significance of strong ion gap (SIG) with other acid-base markers in the critically ill. The relationships between SIG, lactate, anion gap (AG), anion gap albumin-corrected (AG-corrected), base excess or strong ion difference-effective (SIDe), all obtained within the first hour of intensive care unit (ICU) admission, and the hospital mortality of 6878 patients were analysed. The prognostic significance of each acid-base marker, both alone and in combination with the Admission Mortality Prediction Model (MPM0 III) predicted mortality, were assessed by the area under the receiver operating characteristic curve (AUROC). Of the 6878 patients included in the study, 924 patients (13.4 %) died after ICU admission. Except for plasma chloride concentrations, all acid-base markers were significantly different between the survivors and non-survivors. SIG (with lactate: AUROC 0.631, confidence interval [CI] 0.611–0.652; without lactate: AUROC 0.521, 95 % CI 0.500–0.542) only had a modest ability to predict hospital mortality, and this was no better than using lactate concentration alone (AUROC 0.701, 95 % 0.682–0.721). Adding AG-corrected or SIG to a combination of lactate and MPM0 III predicted risks also did not substantially improve the latter’s ability to differentiate between survivors and non-survivors. Arterial lactate concentrations explained about 11 % of the variability in the observed mortality, and it was more important than SIG (0.6 %) and SIDe (0.9 %) in predicting hospital mortality after adjusting for MPM0 III predicted risks. Lactate remained as the strongest predictor for mortality in a sensitivity multivariate analysis, allowing for non-linearity of all acid-base markers. The prognostic significance of SIG was modest and inferior to arterial lactate concentration for the critically ill. Lactate concentration should always be considered regardless whether physiological, base excess or physical-chemical approach is used to interpret acid-base disturbances in critically ill patients.