Clinical use of lactate monitoring in critically ill patients

Increased blood lactate levels (hyperlactataemia) are common in critically ill patients. Although frequently used to diagnose inadequate tissue oxygenation, other processes not related to tissue oxygenation may increase lactate levels. Especially in critically ill patients, increased glycolysis may be an important cause of hyperlactataemia. Nevertheless, the presence of increased lactate levels has important implications for the morbidity and mortality of the hyperlactataemic patients. Although the term lactic acidosis is frequently used, a significant relationship between lactate and pH only exists at higher lactate levels. The term lactate associated acidosis is therefore more appropriate. Two recent studies have underscored the importance of monitoring lactate levels and adjust treatment to the change in lactate levels in early resuscitation. As lactate levels can be measured rapidly at the bedside from various sources, structured lactate measurements should be incorporated in resuscitation protocols

Quantitative assessment of required separator fluid volume in multi-infusion settings

Background: Administering a separator fluid between incompatible solutions can optimize the use of intravenous lumens. Factors affecting the required separator fluid volume to safely separate incompatible solutions are unknown. Methods: An intravenous tube (2-m, 2-mL, 6-French) containing methylene blue dye was flushed with separator fluid until a methylene blue concentration ⩽2% from initial was reached. Independent variables were administration rate, dye solvent (glucose 5% and NaCl 0.9%), and separator fluid. In the second part of the study, methylene blue, separator fluid, and eosin yellow were administered in various administration profiles using 2- and 4-mL (2 × 2 m, 4-mL, 6-French) intravenous tubes. Results: Neither administration rate nor solvent affected the separator fluid volume (p = 0.24 and p = 0.12, respectively). Glucose 5% as separator fluid required a marginally smaller mean ± SD separator fluid volume than NaCl 0.9% (3.64 ± 0.13 mL vs 3.82 ± 0.11 mL, p < 0.001). Using 2-mL tubing required less separator fluid volume than 4-mL tubing for methylene blue (3.89 ± 0.57 mL vs 4.91 ± 0.88 mL, p = 0.01) and eosin yellow (4.41 ± 0.56 mL vs 5.63 ± 0.15 mL, p < 0.001). Extended tubing required less separator fluid volume/mL of tubing than smaller tubing for both methylene blue (2 vs 4 mL, 1.54 ± 0.22 vs 1.10 ± 0.19, p < 0.001) and eosin yellow (2 vs 4 mL, 1.75 ± 0.22 vs 1.25 ± 0.03, p < 0.001). Conclusion: The separator fluid volume was neither affected by the administration rate nor by solvent. Glucose 5% required a marginally smaller separator fluid volume than NaCl 0.9%, however its clinical impact is debatable. A larger intravenous tubing volume requires a larger separator fluid volume. However, the ratio of separator fluid volume to the tubing’s volume decreases as the tubing volume increases

Time courses of urinary creatinine excretion, measured creatinine clearance and estimated glomerular filtration rate over 30 days of ICU admission

Purpose: Baseline urinary creatinine excretion (UCE) is associated with ICU outcome, but its time course is not known. Materials and methods: We determined changes in UCE, plasma creatinine, measured creatinine clearance (mCC) and estimated glomerular filtration (eGFR) in patients with an ICU-stay 30d without acute kidney injury stage 3. The Cockcroft-Gault, MDRD (modification of diet in renal disease) and CKD-EPI (chronic kidney disease epidemiology collaboration) equations were used. Results: In 248 patients with 5143 UCEs hospital mortality was 24%. Over 30d, UCE absolutely decreased in male survivors and non-survivors and female survivors and nonsurvivors by 0.19, 0.16, 0.10 and 0.05 mmol/d/d (all P < 0.001). Relative decreases in UCE were similar in all four groups: 1.3, 1.4, 1.2 and 0.9%/d respectively. Over 30d, mCC remained unchanged, but eGFR rose by 31% (CKD-EPI) and 73% (MDRD) and creatinine clearance estimated by Cockcroft-Gault by 59% (all P < 0.001). Conclusions: Over 1 month of ICU stay, UCE declined by 1%/d which may correspond to an equivalent decline in muscle mass. These rates of UCE decrease were similar in survivors, non-survivors, males and females underscoring the intransigent nature of this process. In contrast to measured creatinine clearance, estimates of eGFR progressively rose during ICU stay. (c) 2020 Published by Elsevier Inc

Machine learning in infection management using routine electronic health records:tools, techniques, and reporting of future technologies

Background: Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. Objectives: To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. Sources: A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014–2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. Content: Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. Implications: Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed

Towards more efficient use of intravenous lumens in multi-infusion settings:development and evaluation of a multiplex infusion scheduling algorithm

BACKGROUND: Multi-drug intravenous (IV) therapy is one of the most common medical procedures used in intensive care units (ICUs), operating rooms, oncology wards and many other hospital departments worldwide. As drugs or their solvents are frequently chemically incompatible, many solutions must be administered through separate lumens. When the number of available lumens is too low to facilitate the safe administration of these solutions, additional (peripheral) IV catheters are often required, causing physical discomfort and increasing the risk for catheter related complications. Our objective was to develop and evaluate an algorithm designed to reduce the number of intravenous lumens required in multi-infusion settings by multiplexing the administration of various parenteral drugs and solutions. METHODS: A multiplex algorithm was developed that schedules the alternating IV administration of multiple incompatible IV solutions through a single lumen, taking compatibility-related, pharmacokinetic and pharmacodynamic constraints of the relevant drugs into account. The conventional scheduling procedure executed by ICU nurses was used for comparison. The number of lumens required by the conventional procedure (LCONV) and multiplex algorithm (LMX) were compared. RESULTS: We used data from 175,993 ICU drug combinations, with 2251 unique combinations received by 2715 consecutive ICU patients. The mean ± SD number of simultaneous IV solutions was 2.8 ± 1.6. In 27% of all drug combinations, and 61% of the unique combinations the multiplex algorithm required fewer lumens (p  3 lm, versus 12% using the conventional procedure. CONCLUSION: The multiplex algorithm addresses a major issue that occurs in ICUs, operating rooms, oncology wards, and many other hospital departments where several incompatible drugs are infused through a restricted number of lumens. The multiplex algorithm allows for more efficient use of IV lumens compared to the conventional multi-infusion strategy

Psoriasis Is Common, Carries a Substantial Burden Even When Not Extensive, and Is Associated with Widespread Treatment Dissatisfaction

The impact of psoriasis on quality of life has been studied in select patient populations. Population-based data detailing the distribution of extent of disease, associated problems in everyday life, and treatment satisfaction for the US population have been lacking. Our population-based survey indicates that approximately 4.5 million adults have been diagnosed as having psoriasis. Most (59%) have little or no involvement, but 650,000 adults have at least three palms of body surface involved and more than 1,000,000 indicate substantial dissatisfaction with their treatment. Only 5% of patients (56,000) who report severe dissatisfaction with current therapy have extensive disease (10 palms). Many individuals with little psoriasis at the time of interview considered the disease to be a large problem in everyday life

Multi-infusion with integrated multiple pressure sensing allows earlier detection of line occlusions

Abstract Background Occlusions of intravenous (IV) tubing can prevent vital and time-critical medication or solutions from being delivered into the bloodstream of patients receiving IV therapy. At low flow rates (≤ 1 ml/h) the alarm delay (time to an alert to the user) can be up to 2 h using conventional pressure threshold algorithms. In order to reduce alarm delays we developed and evaluated the performance of two new real-time occlusion detection algorithms and one co-occlusion detector that determines the correlation in trends in pressure changes for multiple pumps. Methods Bench-tested experimental runs were recorded in triplicate at rates of 1, 2, 4, 8, 16, and 32 ml/h. Each run consisted of 10 min of non-occluded infusion followed by a period of occluded infusion of 10 min or until a conventional occlusion alarm at 400 mmHg occurred. The first algorithm based on binary logistic regression attempts to detect occlusions based on the pump’s administration rate Q(t) and pressure sensor readings P(t). The second algorithm continuously monitored whether the actual variation in the pressure exceeded a threshold of 2 standard deviations (SD) above the baseline pressure. When a pump detected an occlusion using the SD algorithm, a third algorithm correlated the pressures of multiple pumps to detect the presence of a shared occlusion. The algorithms were evaluated using 6 bench-tested baseline single-pump occlusion scenarios, 9 single-pump validation scenarios and 7 multi-pump co-occlusion scenarios (i.e. with flow rates of 1 + 1, 1 + 2, 1 + 4, 1 + 8, 1 + 16, and 1 + 32 ml/h respectively). Alarm delay was the primary performance measure. Results In the baseline single-pump occlusion scenarios, the overall mean ± SD alarm delay of the regression and SD algorithms were 1.8 ± 0.8 min and 0.4 ± 0.2 min, respectively. Compared to the delay of the conventional alarm this corresponds to a mean time reduction of 76% (P = 0.003) and 95% (P = 0.001), respectively. In the validation scenarios the overall mean ± SD alarm delay of the regression and SD algorithms were respectively 1.8 ± 1.6 min and 0.3 ± 0.2 min, corresponding to a mean time reduction of 77% and 95%. In the multi-pump scenarios a correlation > 0.8 between multiple pump pressures after initial occlusion detection by the SD algorithm had a mean ± SD alarm delay of 0.4 ± 0.2 min. In 2 out of the 9 validation scenarios an occlusion was not detected by the regression algorithm before a conventional occlusion alarm occurred. Otherwise no occlusions were missed. Conclusions In single pumps, both the regression and SD algorithm considerably reduced alarm delay compared to conventional pressure limit-based detection. The SD algorithm appeared to be more robust than the regression algorithm. For multiple pumps the correlation algorithm reliably detected co-occlusions. The latter may be used to localize the segment of tubing in which the occlusion occurs. Trial registration Not applicable

A fast and simple method for the simultaneous analysis of midazolam, 1-hydroxymidazolam, 4-hydroxymidazolam and 1-hydroxymidazolam glucuronide in human serum, plasma and urine

For the quantification of the sedative and anesthetic drug midazolam and its main (active) metabolites 1-hydroxymidazolam, 4-hydroxymidazolam and 1-hydroxymidazolam glucuronide in human serum, human EDTA plasma, human heparin plasma and human urine a single accurate method by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) has been developed. Protein precipitation as sample preparation, without the need of a time-consuming deglucuronidation step for the quantification of 1-hydroxymidazolam glucuronide, resulted in a simple and rapid assay suitable for clinical practice with a total runtime of only 1.1 min. The four components and the isotope-labeled internal standards were separated on a C18 column and detection was performed with a triple-stage quadrupole mass spectrometer operating in positive ionization mode. The method was validated based on the "Guidance for Industry Bioanalytical Method Validation" (Food and Drug Administration, FDA) and the "Guideline on bioanalytical method validation" of the European Medicines Agency (EMA). Linearity was proven over the ranges of 5-1500 μg/L for midazolam, 1-hydroxymidazolam and 4-hydroxymidazolam and 25-5000 μg/L for 1-hydroxymidazolam glucuronide, using a sample volume of 100 μL. Matrix comparison indicated that the assay is also applicable to other human matrices like EDTA and heparin plasma and urine. Stability experiments showed good results for the stability of midazolam, 1-hydroxymidazolam and 1-hydroxymidazolam glucuronide in serum, EDTA and heparin plasma and urine stored for 7 days under different conditions. At room temperature, 4-hydroxymidazo-lam is stable for 7 days in EDTA plasma, but stable for only 3 days in serum and heparin plasma and less than 24 h in urine. All four compounds were found to be stable in serum, EDTA plasma, heparin plasma and urine for 7 days after sample preparation and for 3 freeze-thaw cycles. The assay has been applied in therapeutic drug monitoring of midazolam for (pediatric) intensive care patients