Base excess (BE) and lactate concentration may predict mortality in critically ill patients. However, the predictive values of alactic BE (aBE; the sum of BE and lactate), or a combination of BE and lactate are unknown. The study aimed to investigate whether BE, lactate, and aBE measured on admission to ICU may predict the 28-day mortality for patients undergoing any form of shock. In 143 consecutive adults, arterial BE, lactate, and aBE were measured upon ICU admission. Receiver Operating Curve (ROC) characteristics and Cox proportional hazard regression models (adjusted to age, gender, forms of shock, and presence of severe renal failure) were then used to investigate any association between these parameters and 28-day mortality. aBE < −3.63 mmol/L was found to be associated with a hazard ratio of 3.19 (HR; 95% confidence interval (CI): 1.62–6.27) for mortality. Risk of death was higher for BE < −9.5 mmol/L (HR: 4.22; 95% CI: 2.21–8.05), particularly at lactate concentrations > 4.5 mmol/L (HR: 4.62; 95% CI: 2.56–8.33). A 15.71% mortality rate was found for the combined condition of BE > cut-off and lactate < cut-off. When BE was below but lactate above their respective cut-offs, the mortality rate increased to 78.91%. The Cox regression model demonstrated that the predictive values of BE and lactate were mutually independent and additive. The 28-day mortality in shock patients admitted to ICU can be predicted by aBE, but BE and lactate deliver greater prognostic value, particularly when combined. The clinical value of our findings deserves further prospective evaluation.

Patients with sepsis admitted to the intensive care unit often present with acid-base disorders. As the traditional interpretation might be clinically misleading, an alternative approach described by Stewart may allow one to quantify the individual components of acid-base abnormalities and provide an insight into their pathogenesis. The aim of our study was to compare the traditional and Stewart approaches in the analysis of acid-base disturbance. We analyzed arterial blood gases (ABG) taken from 43 ICU septic patients from admission to discharge categorising them according to SBE values. The traditional concept analysis was compared with the physicochemical approach using the Stewart equations. 990 ABGs were analysed. In the SBE < -2 mEq L⁻¹ group, hyperlactatemia was observed in 34.7% ABG, hypoalbuminemia in 100% and SIG acidosis in 42% ABG. Moreover, a Cl/Na ratio > 0.75 was present in 96.9% ABG. In the normal range SBE group, elevated lactates were present in 21.3% ABG, SIG acidosis in 14.9%, elevated Cl/Na ratio in 98.4% and hypoalbuminemia in all 324 ABG. In the metabolic alkalosis group (SBE > +2 mEq L⁻¹), hyperlactatemia was observed in 18.4% ABG, SIG acidosis in 5% ABG, Cl/Na ratio> 0.75 in 88.8%, while 99.1% samples revealed hypoalbuminemia. The use of the Stewart model may improve our understanding of the underlying pathophysiological mechanism and the true etiology of the derangements of acid-base disorders. Indeed, it proves that patients may suffer from mixed arterial blood gas disorders hidden under normal values of SBE and pH.

Intraoperative hypotension (IH) is a frequent phenomenon affecting a substantial number of patients undergoing general anesthesia. The occurrence of IH is related to significant perioperative complications, including kidney failure, myocardial injury, and even increased mortality. Despite advanced hemodynamic monitoring and protocols utilizing goal directed therapy, our management is still reactive; we intervene when the episode of hypotension has already occurred. This literature review evaluated the Hypotension Prediction Index (HPI), which is designed to predict and reduce the incidence of IH. The HPI algorithm is based on a machine learning algorithm that analyzes the arterial pressure waveform as an input and the occurrence of hypotension with MAP <65 mmHg for at least 1 min as an output. There are several studies, both retrospective and prospective, showing a significant reduction in IH episodes with the use of the HPI algorithm. However, the level of evidence on the use of HPI remains very low, and further studies are needed to show the benefits of this algorithm on perioperative outcomes.

Intraoperative hypotension (IH) is common in patients receiving general anesthesia and can lead to serious complications such as kidney failure, myocardial injury and increased mortality. The Hypotension Prediction Index (HPI) algorithm is a machine learning system that analyzes the arterial pressure waveform and alerts the clinician of an impending hypotension event. The purpose of the study was to compare the frequency of perioperative hypotension in patients undergoing major abdominal surgery with different types of hemodynamic monitoring. The study included 61 patients who were monitored with the arterial pressure-based cardiac output (APCO) technology (FloTrac group) and 62 patients with the Hypotension Prediction Index algorithm (HPI group). Our primary outcome was the time-weighted average (TWA) of hypotension below < 65 mmHg. The median TWA of hypotension in the FloTrac group was 0.31 mmHg versus 0.09 mmHg in the HPI group (p = 0.000009). In the FloTrac group, the average time of hypotension was 27.9 min vs. 8.1 min in the HPI group (p = 0.000023). By applying the HPI algorithm in addition to an arterial waveform analysis alone, we were able to significantly decrease the frequency and duration of perioperative hypotension events in patients who underwent major abdominal surgery.

This case report presents a 49 year-old female with type 1 diabetes admitted to the intensive care unit with acute respiratory failure and severe diabetic ketoacidosis with an initial measurement of blood glucose level of 1,200 mg L^-1, pH 6.78, serum HCO₃ ^- 3.2 mmoL L^-1 and BE –31.2 mmoL L^-1. Analysis of the blood gasometric parameters with the Stewart approach and the traditional Henderson-Hasselbalch concept enabled the discovery of metabolic acidosis caused by unidentified anions (mainly ketons). A treatment protocol with intensive fluid management with 0.9% NaCl, intensive intravenous insulin therapy, and potassium supplementation was administered. Analysis of the gasometric parameters after 12 hours of treatment according to the Stewart approach compared to the Henderson-Hasselbalch concept disclosed that metabolic acidosis caused by the unidentified anions has resolved almost completely and been replaced by metabolic hyperchloremic acidosis. The hyperchloremic acidosis was caused by the intensive fluid resuscitation with 0.9% NaCl, which contains a high chloride load, exceeding the chloride levels observed in human serum. Fluid management with balanced fluids other than saline was continued, together with intravenous insulin infusion, potassium supplementation, and 5% glucose administration. Analysis of this case study revealed the advantages of the Stewart approach to acid base abnormalities compared to the traditional Henderson-Hasselbalch concept. The Stewart approach allows the diagnosis of the exact causes of severe life-threatening metabolic acidosis and the appropriate modification of the therapeutic mangement of patients with diabetic ketoacidosis. This case report presents a 49 year-old female with type 1 diabetes admitted to the intensive care unit with acute respiratory failure and severe diabetic ketoacidosis with an initial measurement of blood glucose level of 1,200 mg L^-1, pH 6.78, serum HCO₃ ^- 3.2 mmoL L^-1 and BE –31.2 mmoL L^-1. Analysis of the blood gasometric parameters with the Stewart approach and the traditional Henderson-Hasselbalch concept enabled the discovery of metabolic acidosis caused by unidentified anions (mainly ketons). A treatment protocol with intensive fluid management with 0.9% NaCl, intensive intravenous insulin therapy, and potassium supplementation was administered. Analysis of the gasometric parameters after 12 hours of treatment according to the Stewart approach compared to the Henderson-Hasselbalch concept disclosed that metabolic acidosis caused by the unidentified anions has resolved almost completely and been replaced by metabolic hyperchloremic acidosis. The hyperchloremic acidosis was caused by the intensive fluid resuscitation with 0.9% NaCl, which contains a high chloride load, exceeding the chloride levels observed in human serum. Fluid management with balanced fluids other than saline was continued, together with intravenous insulin infusion, potassium supplementation, and 5% glucose administration. Analysis of this case study revealed the advantages of the Stewart approach to acid base abnormalities compared to the traditional Henderson-Hasselbalch concept. The Stewart approach allows the diagnosis of the exact causes of severe life-threatening metabolic acidosis and the appropriate modification of the therapeutic mangement of patients with diabetic ketoacidosis.

Interpreting acid base disturbances according to the physicochemical Stewart approach allows the cause of such abnormalities to be discovered. This method is based on three independent variables: SID (strong ion difference), mainly sodium and chloride; weak acids concentration — Atot, mainly albumins and phosphate; and carbon dioxide tension — pCO₂. These three independent variables are responsible for the change of water dissociation and for the change in H+ concentration and, consequently, the change in serum pH value. The SID value of the fluids administered to a patient is responsible for the change of serum SID value and therefore causes a change in the patient’s acid base status. During the infusion of a given fluid, the SID value of the serum becomes closer to the SID value of that fluid; on the other hand, the infusion causes a decrease in Atot concentration. In order to avoid acid base disturbances connected with fluid administration, the SID value of fluids being administered should be greater than 0 and lower then the serum SID. It has been suggested that fluids should be given of which the SID value is as close as possible to the actual serum HCO₃ concentration. Knowing the SID value of the fluid administered, and the serum HCO₃ concentration, one can expect a change of serum pH after a fluid infusion. Administering a fluid with a SID greater than the HCO₃ concentration causes a pH increase towards alkalosis. Likewise, administering a a fluid with a SID lower than the HCO₃ concentration causes a pH decrease towards acidosis. It seems that knowledge of the electrolyte concentration and the SID value of an administered fluid is an important factor regarding acid base disturbances. Interpreting acid base disturbances according to the physicochemical Stewart approach allows the cause of such abnormalities to be discovered. This method is based on three independent variables: SID (strong ion difference), mainly sodium and chloride; weak acids concentration — Atot, mainly albumins and phosphate; and carbon dioxide tension — pCO₂. These three independent variables are responsible for the change of water dissociation and for the change in H+ concentration and, consequently, the change in serum pH value. The SID value of the fluids administered to a patient is responsible for the change of serum SID value and therefore causes a change in the patient’s acid base status. During the infusion of a given fluid, the SID value of the serum becomes closer to the SID value of that fluid; on the other hand, the infusion causes a decrease in Atot concentration. In order to avoid acid base disturbances connected with fluid administration, the SID value of fluids being administered should be greater than 0 and lower then the serum SID. It has been suggested that fluids should be given of which the SID value is as close as possible to the actual serum HCO₃ concentration. Knowing the SID value of the fluid administered, and the serum HCO₃ concentration, one can expect a change of serum pH after a fluid infusion. Administering a fluid with a SID greater than the HCO₃ concentration causes a pH increase towards alkalosis. Likewise, administering a a fluid with a SID lower than the HCO₃ concentration causes a pH decrease towards acidosis. It seems that knowledge of the electrolyte concentration and the SID value of an administered fluid is an important factor regarding acid base disturbances.