scholarly journals Effect of the Independent Acid Base Variables on Anion Gap Variation in Cardiac Surgical Patients: A Stewart-Figge Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Michalis Agrafiotis ◽  
Ilias Keklikoglou ◽  
Sofia Papoti ◽  
George Diminikos ◽  
Konstantinos Diplaris ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Retrospective Study ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Anion Gap ◽  
Surgical Patients ◽  
Strong Ion Difference ◽  
Strong Ion Gap ◽  
Acid Base

Purpose. To determine the effect of each of independent acid base variables on the anion gap (AG) value in cardiac surgical patients.Methods. This retrospective study involved 128 cardiac surgical patients admitted for postoperative care. The variation of AG (AGvar) between the day of admission and the first postoperative day was correlated via a multiple linear regression model with the respective variations of the independent acid base variables, that is, apparent strong ion difference (SIDa), strong ion gap (SIG), carbon dioxide (PCO2), and albumin and phosphate concentrations.Results. The variations of all the above variables contributed significantly to the prediction ofAGvar(adjustedR2=0.9999,F=201890.24, andP<0.001). According to the standardized coefficients (β),  SIGvar(β= 0.948,P<0.001),[Albumin]var(β= 0.260,P<0.001), and[Phosphate]var(β= 0.191,P<0.001) were the major determinants ofAGvarwith lesser contributions fromSIDa, var(β= 0.071,P<0.001) andPCO2, var(β= −0.067,P<0.001).Conclusions. All the independent acid base variables contribute to the prediction of the AG value. However, albumin and phosphate and SIG variations seem to be the most important predictors, while AG appears to be rather stable with changes in PCO2andSIDa.

scholarly journals Base-excess chloride; the best approach to evaluate the effect of chloride on the acid-base status: A retrospective study

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250274
Author(s):  
Bulent Gucyetmez ◽  
Filiz Tuzuner ◽  
Hakan Korkut Atalan ◽  
Uğur Sezerman ◽  
Kaan Gucyetmez ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Base Excess ◽  
Tertiary Care ◽  
Strong Ion Difference ◽  
Acid Base ◽  
Entire Cohort ◽  
Acid Base Status ◽  
Evaluation Approaches

To practically determine the effect of chloride (Cl) on the acid-base status, four approaches are currently used: accepted ranges of serum Cl values; Cl corrections; the serum Cl/Na ratio; and the serum Na-Cl difference. However, these approaches are governed by different concepts. Our aim is to investigate which approach to the evaluation of the effect of Cl is the best. In this retrospective cohort study, 2529 critically ill patients who were admitted to the tertiary care unit between 2011 and 2018 were retrospectively evaluated. The effects of Cl on the acid-base status according to each evaluative approach were validated by the standard base excess (SBE) and apparent strong ion difference (SIDa). To clearly demonstrate only the effects of Cl on the acid-base status, a subgroup that included patients with normal lactate, albumin and SIG values was created. To compare approaches, kappa and a linear regression model for all patients and Bland-Altman test for a subgroup were used. In both the entire cohort and the subgroup, correlations among BECl, SIDa and SBE were stronger than those for other approaches (r = 0.94 r = 0.98 and r = 0.96 respectively). Only BECl had acceptable limits of agreement with SBE in the subgroup (bias: 0.5 mmol L-1) In the linear regression model, only BECl in all the Cl evaluation approaches was significantly related to the SBE. For the evaluation of the effect of chloride on the acid-base status, BECl is a better approach than accepted ranges of serum Cl values, Cl corrections and the Cl/Na ratio.

scholarly journals Stability of the Strong Ion Gap versus the Anion Gap over Extremes of PCO2 and pH

2007 ◽  
Vol 35 (3) ◽  
pp. 370-373 ◽  
Author(s):  
T. J. Morgan ◽  
D. M. Cowley ◽  
S. L. Weier ◽  
B. Venkatesh
Keyword(s):  
Negative Charge ◽  
Respiratory Acidosis ◽  
Anion Gap ◽  
Normal Blood ◽  
Strong Ion Difference ◽  
Blood Gas ◽  
Strong Ion Gap ◽  
Acid Base ◽  
Buffer Base

The strong ion gap (SIG) is under evaluation as a scanning tool for unmeasured ions. SIG is calculated by subtracting [buffer base], which is ([A-]+[HCO3-]), from the apparent strong ion difference, which is ([Na+] + [K+]+[Ca++]+[Mg++]-[Cl-]-[L-lactate]). A- is the negative charge on albumin and phosphate. We compared the pH stability of the SIG with that of the anion gap (AG). In normal and hypoalbuminaemic hyperlactaemic blood, PCO2 was reduced stepwise in vitro from >200 mmHg to < 20 mmHg, with serial blood gas and electrolyte analyses, and [albumin] and [phosphate] measurement on completion. Respective [haemoglobin], [albumin], [phosphate] and [lactate] in normal blood were 156 (0.9) g/l, 44 (2) g/l, 1.14 (0.06) mmol/l and 1.7 (0.8) mEq/l, and in hypoalbuminaemic blood 116 (0.9) g/l, 24 (2) g/l, 0.78 (0.06) mmol/l and 8.5 (0.5) mEq/l. pH increased from <6.85 to >7.55, causing significant falls in [Na+] and elevations in [Cl-]. Initial and final SIG values did not differ, showing no correlation with pH. Mean SIG was 0.5±1.5 mEq/l. AG values were directly correlated with pH (normal: R2=0.51, hypoalbuminaemic: R2=0.65). Final AG values significantly exceeded initial values (normal blood: 15.9 (1.7) mEq/l versus 8.9 (1.8) mEq/l, P<0.01; hypoalbuminaemic blood: 16.5 (0.8) mEq/l versus 11.8 (2.0) mEq/l, P<0.05). We conclude that, unlike the AG, the SIG is not affected by severe respiratory acidosis and alkalosis, enhancing its utility in acid-base disturbances.

scholarly journals Base-excess chloride; the best approach to evaluating the effect of chloride on the acid- base status: a retrospective study

2020 ◽  
Author(s):  
Bulent Gucyetmez ◽  
Filiz Tuzuner ◽  
Hakan Korkut Atalan ◽  
Ugur Sezerman ◽  
Kaan Gucyetmez ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Base Excess ◽  
Strong Ion Difference ◽  
Acid Base ◽  
Standard Base Excess ◽  
Serum Chloride ◽  
Acid Base Status ◽  
Standard Base

Abstract Background: To determine the effect of chloride on the acid-base status, four approaches are currently used: 1) accepted ranges of serum chloride values; 2) chloride corrections, such as chloride deficiency/excess and chloride modification; 3) the Cl/Na ratio; and 4) the sodium- chloride difference, such as base-excess chloride. However, these approaches are governed by different concepts, and they can evaluate the effects of chloride on the acid-base status differently. Our aim is to investigate which approach to the evaluation of the effect of chloride is the best.Methods: In this retrospective cohort study, 2529 critically ill patients who were admitted to the tertiary care unit were evaluated between 2011 and 2018. Patient characteristics and blood gas parameters at the ICU admission and outcomes were recorded. The effects of chloride on the acid-base status according to each evaluative approach were validated by the standard base excess and apparent strong ion difference. To compare approaches, kappa and Bland-Altman tests and a linear regression model were used. Results: In the linear regression model for all patients, only base-excess chloride in all the chloride evaluation approaches was significantly related to the standard base excess. In the subgroup, the correlation and limits of agreement between base-excess chloride and the standard base excess were the strongest (r2=0.92 p<0.001 bias: 0.5mmol/L). Conclusions: For the evaluation of the effect of chloride on the acid-base status, base-excess chloride is a better approach than accepted ranges of serum chloride values, chloride corrections and the Cl/Na ratio.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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