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The definition of sepsis has evolved significantly over the past three decades. Today, sepsis is defined as a dysregulated host immune response to microbial invasion leading to end organ dysfunction. Septic shock is characterized by hypotension requiring vasopressors after adequate fluid resuscitation with elevated lactate. Early recognition and intervention remain hallmarks for sepsis management. We addressed the current literature and assimilated thought regarding optimum initial resuscitation of the patient with sepsis. A nuanced understanding of the physiology of lactate is provided in our review. Physiologic and practical knowledge of steroid and vasopressor therapy for sepsis is crucial and addressed. As blood purification may interest the nephrologist treating sepsis, we have also added a brief discussion of its status.

We develop a global collocation based meshfree method for simulation of solute transport in the saturated aquifer formations. We prove that the projected model is free from the limitations of mesh or grid which are used in finite element method (FEM) and finite difference method (FDM). We apply this model on different one and two-dimensional synthetic aquifer problems with advection-dispersion phenomenon. The results of these problems demonstrate that the proposed model is free from the limitations of unstable convergence and has a fixed range (i.e., 2-3) of shape parameter hence it requires less number of simulations to calibrate the model. More importantly, the proposed model is simple to code and shows a higher degree of unanimity with the analytical solution, making it a viable alternative to grid-based traditional methods.

Hollow buoyant microspheres of Zidovudine with Eudragit RS100 were successfully prepared by the oil in water emulsion solvent diffusion technique with better percentage yield. The problem associated with less Entrapment efficiency of drug in microspheres was overcome by modification of aqueous phase. The concentration of polymer also affected Buoyancy of microspheres in same proportionality. All the formulation exhibited excellent to good flow properties when evaluated for micromeritic properties. The drug release pattern was greatly affected by the concentration of polymer. The release was slower in the high polymer concentration formulation as compare to low polymer concentration formulations. The drug release of various formulations of microsphere was through Zero order model and drug release mechanism was through Non-Fickian case-II transport mechanism.

Critical Care Echocardiography refers to the goal-directed use of use of transthoracic or transesophageal echocardiography and represents one of the most common applications of Critical Care Ultrasound. Critical Care Echocardiography can be performed at the point-of-care, is easily repeated following changes in clinical status, and does not expose the patient to ionizing radiation. Nephrologists who participate in the care of patients in the intensive care unit will regularly encounter Critical Care Echocardiography as part of the decision-making and bedside management of ICU patients. The four primary indications for CCE are the characterization of shock, evaluation of preload tolerance, volume responsiveness, and the serial hemodynamic assessment to evaluate response to therapeutic interventions. This manuscript provides an overview of the anatomical structures that are routinely assessed in basic Critical Care Echocardiography, describe how these findings are incorporated into the clinical assessment of critically ill patients, and an introduction to some common applications of advanced Critical Care Echocardiography.

Intravenous contrast media (CM) is often used in clinical practice to enhance CT scan imaging. For many years, contrast-induced nephropathy (CIN) was thought to be a common occurrence and to result in dire consequences. When treating patients with abnormal renal function, it is not unusual that clinicians postpone, cancel, or replace contrast-enhanced imaging with other, perhaps less informative tests. New studies however have challenged this paradigm and the true risk attributable to intravenous CM for the occurrence of CIN has become debatable. In this article, we review the latest relevant medical literature and aim to provide an evidence-based answer to questions surrounding the risk, outcomes, and potential mitigation strategies of CIN after intravenous CM administration.

In the inverse groundwater modelling problems, the objective functions generally used contain several local minima which render the conventional gradient-based optimization unsuitable for such problems. The recently used individual population-based evolutionary methods such as differential evolution (DE) algorithm and particle swarm optimization (PSO) are often observed to get stuck into sub-optimal solution. In this study to address this issue, a hybrid- metaheuristic Differential Evolution- Particle Swarm Optimization (DE-PSO) is proposed to obtain aquifer parameters. PSO introduces a perturbation in each generation to increase the diversity in the population of DE to improve its fitness value. The developed hybrid DE-PSO optimization is coupled with finite element method (FEM) based simulator to get a simulation-optimization (SO) model. Initially, the proposed SO model is tested on a synthetic irregular domain problem to estimate aquifer transmissivity values which are compared with available zonation pattern values. Later, the SO model is applied to the Mahi Right Bank Canal (MRBC) heterogeneous unconfined aquifer system and the optimally obtained results are compared with the DE, PSO and genetic algorithm (GA) methods respectively. The performance of the hybrid DE-PSO model is also tested using various fit- independent statistics for the reliability and accuracy. The results of this study show that the hybrid-metaheuristic based DE-PSO optimization algorithm is an efficient and robust tool for inverse groundwater problem of estimating the aquifer parameters.

Purpose. PaO2 to FiO2 ratio (P/F) is used to assess the degree of hypoxemia adjusted for oxygen requirements. The Berlin definition of Acute Respiratory Distress Syndrome (ARDS) includes P/F as a diagnostic criterion. P/F is invasive and cost-prohibitive for resource-limited settings. SaO2/FiO2 (S/F) ratio has the advantages of being easy to calculate, noninvasive, continuous, cost-effective, and reliable, as well as lower infection exposure potential for staff, and avoids iatrogenic anemia. Previous work suggests that the SaO2/FiO2 ratio (S/F) correlates with P/F and can be used as a surrogate in ARDS. Quantitative correlation between S/F and P/F has been verified, but the data for the relative predictive ability for ICU mortality remains in question. We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. Using a machine-learning approach, we hope to demonstrate the relative mortality predictive capacities of S/F and P/F. Methods. We extracted data from the eICU Collaborative Research Database. The features age, gender, SaO2, PaO2, FIO2, admission diagnosis, Apache IV, mechanical ventilation (MV), and ICU mortality were extracted. Mortality was the dependent variable for our prediction models. Exploratory data analysis was performed in Python. Missing data was imputed with Sklearn Iterative Imputer. Random assignment of all the encounters, 80% to the training (n = 26690) and 20% to testing (n = 6741), was stratified by positive and negative classes to ensure a balanced distribution. We scaled the data using the Sklearn Standard Scaler. Categorical values were encoded using Target Encoding. We used a gradient boosting decision tree algorithm variant called XGBoost as our model. Model hyperparameters were tuned using the Sklearn RandomizedSearchCV with tenfold cross-validation. We used AUC as our metric for model performance. Feature importance was assessed using SHAP, ELI5 (permutation importance), and a built-in XGBoost feature importance method. We constructed partial dependence plots to illustrate the relationship between mortality probability and S/F values. Results. The XGBoost hyperparameter optimized model had an AUC score of .85 on the test set. The hyperparameters selected to train the final models were as follows: colsample_bytree of 0.8, gamma of 1, max_depth of 3, subsample of 1, min_child_weight of 10, and scale_pos_weight of 3. The SHAP, ELI5, and XGBoost feature importance analysis demonstrates that the S/F ratio ranks as the strongest predictor for mortality amongst the physiologic variables. The partial dependence plots illustrate that mortality rises significantly above S/F values of 200. Conclusion. S/F was a stronger predictor of mortality than P/F based upon feature importance evaluation of our data. Our study is hypothesis-generating and a prospective evaluation is warranted. Take-Home Points. S/F ratio is a noninvasive continuous method of measuring hypoxemia as compared to P/F ratio. Our study shows that the S/F ratio is a better predictor of mortality than the more widely used P/F ratio to monitor and manage hypoxemia.

Most of the groundwater simulation models are based on certain element (finite element method) or grid (finite difference method) formulation which is used for obtaining the transient head variation in the aquifer system. Since groundwater management scenarios often demand to call these simulation models several hundred times to deliver the acceptable decision variables, it is essential that an efficient and accurate system simulation technique should be adopted. In this paper, a recently developed global collocation approach based meshfree (Mfree) model is used as a groundwater state estimation tool. The developed model is first tested on a two-dimensional confined aquifer synthetic flow problem where the results are compared with the available analytical and numerical solutions. Different time steps and varied pumping schedules were also assessed for a performance check. Later the tested model is applied to a synthetic aquifer flow problem having a close analogy to real field variability. The results obtained through the projected Mfree model are the testimony of accuracy and successful application of the developed model.

Influence functions are analytical tools from robust statistics that can help interpret the decisions of black-box machine learning models. Influence functions can be used to attribute changes in the loss function due to small perturbations in the input features. The current work on using influence functions is limited to the features available before the last layer of deep neural networks (DNNs). We extend the influence function approximation to DNNs by computing gradients in an end-to-end manner and relate changes in the loss function to individual input features using an efficient algorithm. We propose an accurate mortality prediction neural network and show the effectiveness of extended influence functions on the eICU dataset. The features chosen by proposed extended influence functions were more like those selected by human experts than those chosen by other traditional methods.

Introduction Acute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. Although early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI as defined by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 up to 48 hours in advance of onset using convolutional neural networks (CNNs) and patient electronic health record (EHR) data. Methods A CNN prediction system was developed to use EHR data gathered during patients’ stays to predict AKI up to 48 hours before onset. A total of 12,347 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. An XGBoost AKI prediction model and the sequential organ failure assessment (SOFA) scoring system were used as comparators. The outcome was AKI onset. The model was trained on routinely collected patient EHR data. Measurements included area under the receiver operating characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advance prediction of AKI onset. Results On a hold-out test set, the algorithm attained an AUROC of 0.86 and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window before onset. Conclusion A CNN machine learning-based AKI prediction model outperforms XGBoost and the SOFA scoring system, revealing superior performance in predicting AKI 48 hours before onset, without reliance on serum creatinine (SCr) measurements., Graphical abstract

Rationale and objectivesAcute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. While early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI KDIGO Stage 2 or 3 up to 72 hours in advance of onset using convolutional recurrent neural nets (CNN) and patient Electronic Health Record (EHR) data.MethodsA CNN prediction system was developed to continuously and automatically monitor for incipient AKI. 7122 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database.New Predictors and Established PredictorsNew predictor - CNN machine learning-based AKI prediction model. Established predictors - XGBoost AKI prediction model and the Sequential Organ Failure Assessment (SOFA) scoring system.OutcomesAKI onset.Analytical ApproachThe model was trained on routinely-collected patient EHR data. Measurements included Area Under the Receiver Operating Characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for 72 hour advance prediction of AKI onset.ResultsOn a hold-out test set, the algorithm attained an AUROC of 0.85 and PPV of 0.25, relative to a cohort AKI prevalence of 5.21%, for long-horizon AKI prediction at a 72-hour window prior to onset.ConclusionsA CNN machine learning-based AKI prediction model outperforms XGBoost and the SOFA scoring system, demonstrating superior performance in predicting acute kidney injury 72 hours prior to onset, without reliance on changes in serum creatinine.

The intensive care unit (ICU) is an incredibly complex environment, and ICU rounds are mentally taxing. The cognitive biases that tend to arise in mentally taxing environments such as the ICU pose a risk to patients. This review discusses 10 common cognitive biases and logical fallacies using examples in Nephrology Critical Care. Our objective is to promote metacognition (i.e., an awareness of one’s cognition) among physicians. A state of metacognition is not a panacea, but aspiring for metacognition allows the critical care physician to improve chances for optimal patient outcomes.

Skin morphology and composition play complementary roles in protecting plantar skin from stress- and deformation-induced injury., Plantar skin on the soles of the feet has a distinct morphology and composition that is thought to enhance its tolerance to mechanical loads, although the individual contributions of morphology and composition have never been quantified. Here, we combine multiscale mechanical testing and computational models of load bearing to quantify the mechanical environment of both plantar and nonplantar skin under load. We find that morphology and composition play distinct and complementary roles in plantar skin’s load tolerance. More specifically, the thick stratum corneum provides protection from stress-based injuries such as skin tears and blisters, while epidermal and dermal compositions provide protection from deformation-based injuries such as pressure ulcers. This work provides insights into the roles of skin morphology and composition more generally and will inform the design of engineered skin substitutes as well as the etiology of skin injury.

Pressure-regulated volume control (PRVC) is a mode of ventilation in which the ventilator attempts to achieve set tidal volume at lowest possible airway pressure. This mode of ventilation is being commonly used as the initial mode of ventilation in many intensive care units. We describe two cases where this adaptive mode of ventilation became maladaptive leading to patient-ventilator dyssynchrony.

Background Hyponatremia in the setting of cirrhosis is a common electrolyte disorder with few therapeutic options. The free water retention is due to non-osmotic vasopressin secretion resulting from the cirrhosis-associated splanchnic vasodilatation. Therefore, vasoconstrictive therapy may correct this electrolyte abnormality. The aim of this study was to assess the efficacy of midodrine and octreotide as a therapeutic approach to increasing urinary electrolyte free water clearance (EFWC) in the correction of cirrhosis-associated hyponatremia. Methods This observational study consisted of 10 patients with cirrhosis-associated hyponatremia. Hypovolemia was ruled out as the cause of the hyponatremia with a 48-hour albumin challenge (25 gm IV q6 hrs). Patients whose hyponatremia failed to improve with albumin challenge were started on midodrine and octreotide at 10 mg po tid and 100 mcg sq tid respectively, with rapid up-titration as tolerated to respective maximal doses of 15 mg tid and 200 mcg tid within the first 24 hours. We assessed urinary electrolyte-free water clearance (EFWC) and serum sodium concentration before and 72 hours after treatment. Results Pre-treatment serum sodium levels ranged from 119 mmol/L to 133 mmol/L. The mean pre-treatment serum sodium concentration ± SEM was 124 mmol/L ± 1.6 vs. 130 mmol/L ± 1.5 post treatment (p = 0.00001). The mean pre-treatment urinary EFWC ± SEM was 0.33 L ± 0.07 vs 0.82 L ± 0.11 post treatment (p = 0.0003). Conclusion Our data shows a statistically significant increase in serum sodium concentration and urinary EFWC with the use of midodrine and octreotide in the treatment of cirrhosis-associated hyponatremia.

Meshfree methods are often tested in literature for the rectangular domain with uniform aquifer properties (e.g. transmissivity or hydraulic conductivity) and constant groundwater head boundary conditions. In this paper, a multiquadric meshfree (Mfree) groundwater model is developed for a real field irregular domain aquifer system which is capable of incorporating variability like heterogeneity, temporally and spatially varied groundwater head and flow boundary conditions. The developed model is free from the inadequacy of meshing which eventually saves the time on computing resources by calculating the groundwater head-derivatives at different nodes sprinkled over the entire flow domain. Initially, two synthetic problems with pre- existing analytical and grid based results are tested against the presented model. Sensitivity analysis of different parameters like time step size, nodal density and support size of basis function are also investigated. Subsequently Mfree solutions are obtained for a large Mahi Right Bank Canal (MRBC) unconfined aquifer located in Gujarat India. The Mfree solution performance on two synthetic and one real field large aquifer problem revealed that the projected method is advantageous over other grid based simulator in terms of computational time consideration. Such a simulation is preferable in groundwater management models where flow models are coupled with the optimization problem.

Background: Social and lifestyle changes are responsible for the increasing prevalence of coronary heart disease risk factors. Objective of the study was to correlate blood pressure and five food group as a coronary heart disease risk factor in Indian population.Methods: 31 male executives over the age of 30 years working in industry were included in the study. Systolic and diastolic blood pressures were measured with the help of a standard mercury sphygmomanometer.Results: Statistical analysis was carried out and Pearson Correlation was applied. The study showed significant correlation between systolic blood pressure and food group I and food group IV consumption.Conclusions: The present study highlights observation that the blood pressure as a risk factor for coronary heart disease is silently affecting the middle aged Indian male population due to unhealthy life style.

The safety and efficacy of gallbladder extracorporeal shock-wave lithotripsy combined with 600 mg/day ursodiol were examined in 85 patients with radiolucent gallstones, 15 with lightly calcified gallstones, and 12 with radiolucent stones pretreated for greater than or equal to 2 months with 600 mg/day ursodiol. Results were compared with those of a well-matched lithotripsy-eligible group of 32 subjects treated with ursodiol alone (no lithotripsy). Pretreatment with ursodiol significantly improved while gallstone calcification interfered with fragmentation. Small gallstone size and number also aided fragmentation. Biliary lithotripsy plus ursodiol increased efficacy twofold compared with ursodiol therapy alone (47% vs. 22% of subjects gallstone free; P less than 0.02). Gallstones did not disappear in any subject with calcified gallstones (P less than 0.001) vs. lithotripsy). Product-limit analysis showed that the efficacy for gallstone dissolution increases in the following order: ursodiol alone, lithotripsy-ursodiol, lithotripsy-ursodiol pretreated with ursodiol (P less than 0.02, pairwise). Similar mean gallstone-dissolution rate constants (stone size divided by time to disappear) of stone fragments and whole gallstones during ursodiol therapy suggest that most fragments disappear by dissolution not expulsion. This finding explains why fragmentation appears to be the key predictor of disappearance and even partial fragmentation accelerates gallstone clearance.