Interpreting and using the arterial blood gas analysis

Lian, Jin Xiong

doi:10.1097/01.ccn.0000372212.89520.18

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…On these cases, the dominant features that are adjusted to account for missing Lactate are Arterial PaCO 2 (available on 38.3 % of examples when Lactate is missing) and AST (available on 68.1 % of examples when Lactate is missing). Lactate and PaCO 2 are both related to anaerobic respiration, while Lactate levels and liver function, as indexed by AST, are related to metabolic acidosis (Lian 2010). Another example, Bicarbonate (HCO3) is missing on 25,414 (51.6 %) of examples.…”

Section: Discussion and Interpretation Of Resultsmentioning

confidence: 99%

A dynamic ensemble approach to robust classification in the presence of missing data

et al. 2015

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Many real-world datasets suffer from missing or incomplete data. In the healthcare setting, for example, certain patient measurement parameters, such as vitals and/or lab values, may be missing due to insufficient monitoring. When present, however, these features could be highly discriminative in predicting aspects of patient state. Therefore, it is desirable to incorporate these sparsely measured features into a predictive model. Training predictive algorithms on such datasets is complicated by the missing data. Overcoming this problem is usually achieved by first estimating values for the missing data, which is referred to as data imputation. Without strong prior knowledge about the relationship between features though, it is common to fill in missing values with their respective population mean or median. The accuracy of this approach is limited, however, and may simply inject noise into the data. We propose a two-stage machine learning algorithm that learns a dynamic classifier ensemble from an incomplete dataset without data imputation. The algorithm is very simple to implement and applicable across a wide range of problems. Our method first employs a variant of AdaBoost to learn a set of low-dimensional classifiers, each of which abstains from predicting if its dependent feature(s) are missing. Our novel contribution is the secondary dynamic ensemble learning stage in which the low-dimensional classifiers are combined using a dynamic weighting that depends on the pattern of measured features in the present input data. This allows the model to be resilient to missing data by adjusting the strength of certain classifiers to account for missing features. We apply our algorithm to early detection of hemodynamic instability in ICU patients. Providing an effective risk score of hemodynamic instability has the potential to give the clinician sufficient time to intervene, thereby reducing the chance of organ damage due to insufficient blood perfusion. We compare the results of our algorithm to other common missing data approaches, including mean imputation and multiple imputation methods, and discuss the advantages of the approach given the constraints of the application domain (e.g., high specificity to combat hospital alarm fatigue).

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Section: Discussion and Interpretation Of Resultsmentioning

confidence: 99%

A dynamic ensemble approach to robust classification in the presence of missing data

et al. 2015

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“…Many articles have been written on the fundamental concepts and interpretation of ABG [27][28][29][30][31][32][33][34][35][36][37][38] .…”

Section: Interpreting Abg Resultsmentioning

confidence: 99%

Arterial Blood Gases

Yap

2011

Proceedings of Singapore Healthcare

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Arterial blood gases (ABG) results reflect underlying pathology and interpretation of the results are often compounded by ongoing disease processes and clinical interventions. While ABG specimens should be analysed immediately for optimal results the Clinical and Laboratory Standards Institute (CLSI) has recommended a window of 30 minutes at room temperature from blood collection to ABG analysis. A fresh and simple approach to interpreting ABG is provided.

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“…An arterial blood gas (ABG) test is one of the most frequently performed tests in intensive care units (ICU). It is the most accurate method of assessing the oxygenation level by determining the level of oxygen pressure in arterial blood (PaO 2 ) [6,7]. The ABG test is used to detect hypoxia (0-80 mm Hg), normoxia (80-100 mm Hg), and hyperoxia (> 100 mm Hg), but it can also be used to measure parameters such as the level of carbon dioxide pressure in arterial blood (PaCO 2 ), pH, concentration of bicarbonate in arterial blood (HCO 3 -), and excess or deficit of base in arterial blood (BE), thus allowing the assessment of ventilation and the body's acid-base balance [8].…”

Section: Arterial Blood Gas Testmentioning

confidence: 99%

Usefulness of oxygen reserve index (ORi) in clinical practice

Barud

2023

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Oxygen is the most common and widely used drug. Oxygen therapy is used not only among mechanically ventilated patients in intensive care units, but also in the perioperative period and in patients requiring oxygen supplementation in other hospital wards. The main methods of monitoring the blood oxygenation level include arterial blood gases and pulse oximetry. A new parameter that allows the monitoring of patients' oxygenation status is the oxygen reserve index (ORi). The ORi provides easy, non-invasive, bedside monitoring of oxygen reserve capacity. The oxygen reserve index reflects the partial pressure of oxygen in arterial blood (PaO 2 ) in the range 100-200 mm Hg. It therefore allows the detection of mild hyperoxia, enabling the safe use of lower concentrations of oxygen in the breathing mixture. It is also a useful tool in predicting impending hypoxia. This paper summarizes the usefulness of oxygen reserve index monitoring in various clinical situations in everyday anaesthesiology practice. StreszczenieTlen jest najczęściej i najpowszechniej używanym lekiem. Tlenoterapia stosowana jest wśród wentylowanych mechanicznie pacjentów oddziałów intensywnej terapii, w okresie okołooperacyjnym, a także u pacjentów wymagających suplementacji tlenem, przebywających na innych oddziałach szpitalnych. Do głównych metod monitorowania poziomu utlenowania krwi w organizmie należą oznaczanie gazometrii krwi tętniczej oraz pulsoksymetria. Nowym parametrem pozwalającym na monitorowanie gospodarki tlenowej jest indeks rezerwy tlenowej (ORi). ORi umożliwia łatwe, nieinwazyjne, przyłóżkowe monitorowanie poziomu rezerwy tlenowej organizmu. Indeks rezerwy tlenowej odzwierciedla wartości ciśnienia parcjalnego tlenu we krwi tętniczej (PaO 2 ) w zakresie 100-200 mm Hg. Pozwala on na wykrywanie łagodnej hiperoksji, co umożliwia bezpieczne stosowanie niższych stężeń tlenu w mieszaninie oddechowej. Jest również przydatnym narzędziem w przewidywaniu zagrażającej hipoksji. W artykule omówiono przydatność monitorowania indeksu rezerwy tlenowej w różnych sytuacjach klinicznych z codziennej praktyki anestezjologicznej.

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Interpreting and using the arterial blood gas analysis

Cited by 7 publications

References 28 publications

A dynamic ensemble approach to robust classification in the presence of missing data

A dynamic ensemble approach to robust classification in the presence of missing data

Arterial Blood Gases

Usefulness of oxygen reserve index (ORi) in clinical practice

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