Search Results (8)

Background/Objectives: Lactic acidosis is one of the most common causes of metabolic acidosis in hospitalized patients. It happens when lactic acid production exceeds lactic acid clearance. The elevation of lactate was commonly improved after the restoration of tissue perfusion. However, there are rare cases of severe lactate elevation (greater than 8 mmol/L) in the intraoperative period of thoracoscopic surgery. A poor prognosis with high morbidity and mortality characterizes these cases. Case Description: A 72-year-old man was admitted to the Soroka University Medical Center for thoracoscopic robotic left upper lobe lobectomy due to squamous cell carcinoma. At the end of surgery (overall, 8.5 h), the lactate level reached 10.2 mmol/L with the development of severe lactic metabolic acidosis. Thiamine was successfully given to patients to stimulate lactate clearance towards the cycle of tricarboxylic acids via pyruvate. Conclusions: Though the pathogenesis of this state in our case is not fully clear, it may have been induced by chemotherapy and during tumor manipulation by a surgeon. The successful recovery of blood lactic levels after thiamine treatment is suggestive of thiamine deficiency as a possible cause of lactic acidosis in our patient. Although we do not have data on the plasma thiamine level, we suggest that its determination in the perioperative period would be beneficial for excluding a probable thiamine deficiency in the case of severe lactic acidosis. Full article

Despite the high burden of fatigue in patients with advanced chronic kidney disease (CKD) stage 4–5 including dialysis, little is known about risk factors and treatments for fatigue. This paper provides an overview of measurement, associated risk factors and approaches to alleviate fatigue. Up to now, the 36-Item Short Form Health Survey (SF-36) is the most widely used tool to measure fatigue. Socio-demographic factors, including older age, female, white ethnicity, less education, unemployment and less social support, are risk factors for fatigue. More severe fatigue is related to anemia, protein energy wasting, inflammation, lactic acidosis, comorbidities (including diabetes, cardiovascular disease and chronic obstructive pulmonary disease), depression, anxiety, sleep disorders and uremic symptoms such as restless legs syndrome and pruritus. Dialysis treatment, dialysis-related low blood pressure and low dialysis adequacy, contribute to fatigue. Medication, such as β-blockers, antidepressant medication and medication for sleep disorders, are associated with fatigue, either as a result of the underlying condition or side effects. Currently, the principal treatments encompass pharmacological and nonpharmacological interventions. The utilization of erythropoiesis-stimulating agent (ESA) for anemia may alleviate fatigue. Physical exercise has been proved to be a promising therapeutic approach. Other potential treatments, such as L-carnitine, sodium bicarbonate, antidepressive medication (such as bupropion), psychological interventions and cold dialysis, require more research. To conclude, further research is needed to better understand the risk factors and underlying mechanism of fatigue, as well as to explore potential treatments for patients with advanced CKD. Full article

Background/Objectives: This study aimed to investigate trends in antidiabetic drug use and assess the risk of metformin-associated lactic acidosis (MALA) in patients with chronic kidney disease (CKD). Methods: A retrospective observational analysis based on the common data model was conducted using electronic medical records from 2010 to 2021. The patients included were aged ≥18, diagnosed with CKD and type 2 diabetes, and had received antidiabetic medications for ≥30 days. MALA was defined as pH ≤ 7.35 and arterial lactate ≥4 mmol/L. Results: A total of 8318 patients were included, with 6185 in CKD stages 1–2 and 2133 in stages 3a–5. Metformin monotherapy was the most prescribed regimen, except in stage 5 CKD. As CKD progressed, metformin use significantly declined; insulin and meglitinides were most frequently prescribed in end-stage renal disease. Over the study period, the use of SGLT2 inhibitors (13.3%) and DPP-4 inhibitors (24.5%) increased significantly, while sulfonylurea use decreased (p < 0.05). Metformin use remained stable in earlier CKD stages but significantly decreased in stage 3b or worse. The incidence rate (IR) of MALA was 1.22 per 1000 patient-years, with a significantly increased IR in stage 4 or worse CKD (p < 0.001). Conclusions: Metformin was the most prescribed antidiabetic drug in CKD patients in Korea with a low risk of MALA. Antidiabetic drug use patterns varied across CKD stages, with a notable decline in metformin use in advanced CKD and a rise in SGLT2 inhibitor prescriptions, underscoring the need for further optimized therapy. Full article

Lactic acidosis represents one of the most common conditions that can compromise the health of intensive care unit (ICU) patients, increasing the mortality of patients with high levels of Lactate who do not receive a proper treatment within the first 6 h of hospitalization. There are two enantiomers of lactic acid: L-lactic acid (when the concentration increases, it can lead to a state of severe acidemia risking cardiovascular collapse, causing an increase in mortality in ICU patients) and D lactic acid (produced in the human organism by microbiota and its production increases during some pathological status). Generally, increased levels of serum lactic acid could be due to numerous factors, including hypoxia (caused for example by septic/cardiogenic/hypovolemic or obstructive shock), specific pathologies (e.g., liver disease), use of some drugs (e.g., metformin), presence of toxins, and trauma. Since the underlying cause could be fatal for the ICU patient, it is important to understand the root of this clinical status with a view to correct it and prevent the risk of a poor clinical outcome. Prevention and early treatment are the keys to control the negative clinical consequences. The aim of this review is to revise the scientific literature for further confirmation about the importance of early identification of acidotic statuses and to underline how an early diagnosis can prevent the worst clinical outcome, especially for ICU patients who are more fragile compared to the general population. Full article

Background: Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. Methods: We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. Results: We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (n = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (n = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (n = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. Conclusions: Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes. Full article

Background: Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Results: Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). Conclusion: The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis. Full article

In Belgium, linezolid is indicated for pneumonia and skin and soft tissue infections, but is more broadly used, due to its oral bioavailability and activity against multiresistant organisms. This could increase the risk of adverse drug reactions (ADR), notably hematological disorders (anemia, thrombocytopenia), neuropathy, or lactic acidosis. We analyzed linezolid clinical use in relationship with occurrence of ADR in Belgian hospitals and highlighted risk factors associated with the development of thrombocytopenia. A retrospective analysis of electronic medical records and laboratory tests of adult patients treated with linezolid in four Belgian hospitals in 2016 allowed the collection of ADR for 248 linezolid treatments. Only 19.7% of indications were in-label. ADR included 43 thrombocytopenia, 17 anemia, 4 neuropathies, and 4 increases in lactatemia. In a multi-variate analysis, risk factors of thrombocytopenia were a treatment duration > 10 days, a glomerular filtration rate < 60 mL/min, and a Charlson index ≥ 4. Off-label use of linezolid is frequent in Belgium, and ADR more frequent than reported in the summary of product characteristics, but not statistically associated with any indication. This high prevalence of ADR could be related to a high proportion of patients presenting risk factors in our population, highlighting the importance of detecting them prospectively. Full article

Pregnancy is a challenge in the life of a woman with chronic kidney disease (CKD), but also represents an occasion for physicians to make or reconsider diagnosis of kidney disease. Counselling is particularly challenging in cases in which a genetic disease with a heterogeneous and unpredictable phenotype is discovered in pregnancy. The case reported regards a young woman with Stage-4 CKD, in which “Mitochondrial Encephalopathy, Lactic Acidosis, and Stroke-like episodes” (MELAS syndrome), was diagnosed during an unplanned pregnancy. A 31-year-old Caucasian woman, being followed for Stage-4 CKD, sought her nephrologist’s advice at the start of an unplanned pregnancy. Her most recent data included serum creatinine 2–2.2 mg/dL, Blood urea nitrogen (BUN) 50 mg/dL, creatinine clearance 20–25 mL/min, proteinuria at about 2 g/day, and mild hypertension which was well controlled by angiotensin-converting enzyme inhibitors (ACEi); her body mass index (BMI) was 21 kg/m² (height 152 cm, weight 47.5 kg). Her medical history was characterized by non-insulin-dependent diabetes mellitus (at the age of 25), Hashimoto’s thyroiditis, and focal segmental glomerulosclerosis. The patient’s mother was diabetic and had mild CKD. Mild hearing impairment and cardiac hypertrophy were also detected, thus leading to suspect a mitochondrial disease (i.e., MELAS syndrome), subsequently confirmed by genetic analysis. The presence of advanced CKD, hypertension, and proteinuria is associated with a high, but difficult to quantify, risk of preterm delivery and progression of kidney damage in the mother; MELAS syndrome is per se associated with an increased risk of preeclampsia. Preterm delivery, associated with neurological impairment and low nephron number can worsen the prognosis of MELAS in an unpredictable way. This case underlines the importance of pregnancy as an occasion to detect CKD and reconsider diagnosis. It also suggests that mitochondrial disorders should be considered in the differential diagnosis of kidney impairment in patients who display an array of other signs and symptoms, mainly type-2 diabetes, kidney disease, and vascular problems, and highlights the difficulties encountered in counselling and the need for further studies on CKD in pregnancy. Full article