The CIELab values and fastness properties of Osage orange dyed silk
\r\n\tIn this book, the different factors of liquefaction, the field methods and laboratory tests to identify a potentially liquefiable soil aim to be reviewed; in addition with history cases (ground behavior during the occurrence of an earthquake, state of stress, deformation, shear strength, flow, etc.).
\r\n\tA very important aspect of this topic is the presentation of the different constructive techniques used to ground improvement (vibrocompaction, dynamic compaction, jet grouting, chemical injection, replacement, etc.), placing special emphasis on those constructive methods used to solve problems on structures already located in areas of low relative density with liquefaction potential, where the installation of monitoring and control equipment is also required (tiltmeters, piezometers, topographic points, seismographs, pressure cells, etc.).
Dyeing was known as early as in the Indus Valley period (2600-1900 BC); this knowledge has been substantiated by findings of colored garments of cloth and traces of madder dye in the ruins of the Indus Valley Civilization at Mohenjodaro and Harappa. Natural dyes, dyestuff and dyeing are as old as textiles themselves. Man has always been interested in colors; the art of dyeing has a long past and many of the dyes go back into prehistory. It was practiced during the Bronze Age in Europe. The earliest written record of the use of natural dyes was found in China dated 2600 BC. [1-4] Primitive dyeing techniques included sticking plants to fabric or rubbing crushed pigments into cloth. The methods became more sophisticated with time and techniques using natural dyes from crushed fruits, berries and other plants, which were boiled into the fabric and which gave light and water fastness (resistance), were developed. After the accidental synthesis of mauveine by Perkin in Germany in 1856 and its subsequent commercialization, coal-tar dyes began to compete with natural dyes. With advances in chemical techniques, the manufacture of synthetic dyes became possible, leading to greater production efficiency in terms of quality, quantity and the potential to produce low-cost raw materials. As a result, natural dyes were progressively replaced by synthetic dyes, whereas over 80% of which is constituted of the aromatic azo type [5]. However, researches have shown that synthetic dyes are suspected to release harmful chemicals that are allergic, carcinogenic and detrimental to human health. In addition, textile industries produce huge amounts of polluted effluents that are normally discharged to surface water bodies and ground water aquifers. These wastes cause many damages to the ecological system of the receiving surface water, creating a lot of disturbance to the ground water resources [6-9]. Therefore, in 1996, ironically Germany became the first country to ban certain azo dyes [10].
The world-wide demand for fibers and safety dyes is increasing probably according to the greater awareness of the general consumers in the USA, Europe and Japan towards the highly pollutant procedures affecting fiber and textile coloration when using synthetic dyes which act as a sources of skin cancer, disorders and allergic contact dermatitis [11]. Therefore, interest in returning back to natural dyes as synthetic dyes substitute has increased considerably on account of their high compatibility with environment, relatively low toxicity and allergic effects, as well as availability of various natural coloring sources such as from plants, insects, minerals and fungi [12]
Natural dyes can be obtained from plants, animals and minerals, producing different colors like red, yellow, blue, black, brown and a combination of these. For technical application of natural dyes, a number of requirements have to be fulfilled;
Most problems are derived from technical demands, for example:
adaptation of traditional dyeing processes on modern equipment [13]
supply of dye-houses with the required amount of plant material [13]
standardization of extraction and dyeing of the plant material [14]
selection of plant material and processes that yield products with acceptable fastness properties [14]
From an industrial point of view it would be easier to resort to extracts despite there is at present no definite answer to this prospective solution. The simplest extract would be a watery one although not all the dye pigments are water-soluble. Use of organic solvents might give rise to extracts which are not completely water-soluble [15], provided that the solvent chosen guarantees a series of properties as follows:
Its extraction capacity is extremely high for practically all the natural pigments present in the raw materials of interest.
Its boiling temperature and latent heat of vaporization is quite low to allow its separation at low temperatures with minimum energy consumption;
Its reactivity with colors and pigments is insignificant to avoid any loss in the color quality [15].
Natural dyes are substantive and require a mordant to fix to the fabric, and prevent the color from either fading with exposure to light or washing out. These compounds aid a chemical reaction between the dye and the fiber, so that the dye is absorbed. Traditionally, mordants were found in nature. Wood ash or stale urine may have been used as an alkali mordant, and acids could be found in acidic fruits or rhubarb leaves.
There are three types of mordant: i) Metallic mordants; Metal salts of aluminium, chromium, iron, copper and tin, ii) Tannins; Myrobalan and sumach, iii) Oil mordants; mainly used in dyeing Turkey red color from madder by forming a complex with alum.
In order to obtain high color yield, different shades and good fastness properties, metallic salt mordants are normally employed [16]. The application of mordants for dye fixation was carried out by three methods; i- Pre-mordanting; dipping the fabric in the mordant solution before dyeing, ii- Simultaneous mordanting; addition of mordant in the dye bath during the dyeing, and iii- post mordanting; dipping the fabric in the mordant solution after dyeing [17].
Osage orange (Maclura pomifera) is a tree in the Moraceae family [18]. The common name is derived from its fruit, which resembles the shape of an orange, and from the fact that its hardwood was used by the Osage Indian tribe to make bows. It is native to Southern Oklahoma and Northern Texas, and is planted throughout the United States. Several compounds have been isolated and identified in various parts of this tree namely, isoflavonoids from the fruit, flavonols and xanthones from the heartwood and stem bark, and flavanones and xanthones from the root bark [19] It contains lectins, triterpenes, xanthones and flavone-type compounds such as Scandenone and auriculasin as shown in Figure 1 [20] Two predominant isoflavones, pomiferin and osajin, are derived from the simple isoflavone genistein by prenyl substitutions [21] as shown in Figure 2.
Chemical structures of scandenone (I) and auriculasin (II)
Chemical structures of Osajin (i) and pomiferin (ii)
The compounds, osajin, iso-osajin, pomiferin, and iso-pomifcrin have been characterized and their chemical structures determined. They are isoflavones with the following structures as shown in Figure 3. [19, 21].
Chemical Structure of iso-pomifcrin
As well as Osage orange was applied as an eco-friendly dye acting as one of the environmental problems solutions. New concepts in the cleaner production are being evaluated to solve the high water and energy consumption in textile industries.
The use of ultrasonic as a renewable source of energy in textile dyeing has been increased due to many advantages associated with it [22-25]. Ultrasonic energy represents a promising technique for assisting silk treatment, dyeing, and mordanting processes in comparison with the conventional heating technique. Sonic energy succeeded in accelerating the rate of, dyeing, and mordanting at lower temperatures rather than the conventional heating technique
Therefore, the present investigation was aimed at identifying the most appropriate leaching solvent for Osage orange pigments to produce an optimum concentrated extract used for dyeing protein fibers; silk and wool fabrics. This has been carried out ultrasonically in comparison with the classical thermal method, using water, in addition to the co-solvents of water-acetone, and water-ethanol mixtures at different concentrations, temperature and time intervals. The optimum condition of the efficiency of ultrasonic assisted dyeing and mordanting methods of Osage orange extraction on the quality of the dyed protein based materials were determined
Degummed and bleached plain Habotain silk fabric, and 100% mill scoured wool fabric purchased from Sherazad Com. New Zealand, were further washed with a solution containing 0.5 g/L of sodium carbonate and 2 g/L of non-ionic detergent (Nonidet ® P 40 Substitute purchased from Sigma- Aldrich NZ. Ltd.), keeping the material to liquor ratio at 1:50, for 30 min. at 40-45oC. The scoured materials were thoroughly washed and dried at ambient temperature. [23]
An analytical grade alum and the commercially cream of tartar were used as mordantas.
To select the best solvent for Osage orange, distilled water and other co-solvents, such as water-acetone, and water- ethanol mixtures all of analytical grade, were tested at concentrations of 10% v/v. 2 g of Osage orange powder (Hands Ashford NZ, LTD, ChristChurch, NZ) was suspended in 20 cm3 of solvent, and in thermostatic as well ultrasonic baths at 60 ◦C, for 120 min. Once water-acetone co-solvent, and ultrasonic assisted extraction were chosen as the preferable technique of extraction, 10 % w/v Osage orange powder, dissolved in (2.5- 25) % v/v acetone, at (25- 60) oC, for 30- 120 min, were carried out to determine the standardization method of extraction. [24]
1 % Osage orange extract was filtered and used as a dyeing bath. Silk and wool samples were added to the extract, and the dyeing parameters were studied ultrasonically keeping the material to liquor ratio at L: R of 1:30, for time intervals varied between 30- 120 min, at temperatures from 30-60oC. In terms of the pH used for dyeing; the pH values ranging from (3-11) were carried out to control the dye uptake.
On studying the mordanting methods, the optimum concentration of 8% w/v Osage orange extract was carried out. Stock solutions of 50 gm/ l alum and 25 g/l mixture of each of alum and cream of tartar were prepared. Two different methods of mordanting were used: (1) pre-mordanting method: the samples were first mordanted and then dyed without intermediate washing; and (2) post-mordanting method: the samples were first dyed and then mordanted. Ultrasonic assisted mordanting was carried out in comparison with the conventional heating method at 50-60oC, for 90 min, at pH 5. samples were rinsed, washed with 0.5 g/L sodium carbonate and 2 g/L of non-ionic detergent at 40-45oC for 30 min, keeping the material to liquor ratio at 1:50. Finally washed with water, and dried at ambient temperature. [23, 26, 27]
Dyestuff content of the dyed fabrics was determined according Kubelka–Munk equation [28] using Cary\n\t\t\t\t\t100 UV-Vis Spectrophotometer
R is the absolute reflectance of the sampled layer, K is the molar absorption coefficient and s is the scattering coefficient.
After which the samples were tested for color fastness to light and washing according to AATCC107-1997 [29] The CIE-Lab values of the dyeings were measured and the cylindrical co-ordinates of color were determined after exposure to arc lamp irradiation for 1, 2, 4, 6, 24, 48, and 72 hrs. The colors are given in an internationally commission (CIE L*a*b*) coordinates, L* corresponding to brightness, a* to red–green coordinate (positive sign = red, negative sign = green) and b* to yellow–blue coordinate (positive sign = yellow, negative sign = blue). [30-32].
Extraction was carried out by water, water-acetone, and water-ethanol mixtures at 25-60 ◦C for 30-120 min. The ultrasonic efficiency had been determined simultaneously with extraction parameters, and compared with the conventional heating method. The yield coefficients of co-solvents were definitively greater than water, with much higher values in case of ultrasonic. Water-acetone mixture was found to be the most selective co- solvent followed by water-ethanol. As shown in Figures [4, 5] water-acetone mixture released over 32% of the total dye absorbency, exhibiting 21% of the total color strength when dyeing the woolen sample. Water-ethanol extracted 27% dye and exhibited 19% color strength, while water extracted less than 21% dye, and exhibited 16% color strength. This was relative to [10, 6, and 4] % of absorbance and [18, 15, and 11] % color strength respectively with the co-ordinate solvents when using the conventional heating method.
Effect of solvents on the absorbency of Osage orange powder using the conventional [CH] and ultrasonic [US] assisted extraction
Figures 6, 7 and 8 showed the absorption, and color strength values of Osage orange powder extracted by acetone at different concentrations of (2.5 – 25) % v/v at temperatures from 25-60o C, for time intervals varied between 30-120 min.
The maximum values were achieved with 20 % v/v acetone at 60o C, for 60 min. The extraction parameters affected the color strength and are influenced by the properties of solvents such as, the dipole moment, dielectric constant, and refractive index values.
Effect of solvents on the color strength of Osage orange dyed wool using the conventional [CH] and ultrasonic [US] assisted extraction
Effect of acetone concentration on the absorbency and color strength of Osage orange dyed wool using ultrasonic assisted extraction
The solvent polarity can change the position of the absorption or emission band of molecules by solvating a solute molecule or any other molecular species introduced into the solvent matrix [33]. By the way, dye molecules are complex organic molecules which might carry charge centers and are thus prone to absorption changes in various media [33, 34]
Acetone acts as the non hydrogen-bond donating solvents (also called as non-HBD type of solvents), while water and ethanol are the hydrogen-bond donating solvents (also called as HBD type solvents) [33]. The absorbency values of Osage orange in these solvents are given in Figure 4, 5. It can noted from this figure that the absorption maximum of the extract is affected by the solvent type, thus the change in values can be noted as a probe for various types of interactions between the solute and the solvent.
Water and ethanol are considered as polar protic solvents, their polarity stems from the bond dipole of the O-H bond, whereas the large difference in the electro- negativities of the oxygen and hydrogen atom, combined with the small size of the hydrogen atom, warrant separating the Osage orange molecules that contain the OH groups from those polar compounds that do not. On the other hand acetone considered as dipolar aprotic solvent, containing a large multiple bond between carbon and either oxygen or nitrogen e.g. C-O double bond. [33-35].
Although water has the highest dielectric constant among ethanol and acetone solvents, its extraction demonstrated the lowest value of absorbency. This might due to the formation of strong hydrogen bond between the dyes extract and water molecules [33-35].
Effect of ultrasonic assisted extraction time on the absorbency of Osage orange at different temperatures
Effect of ultrasonic assisted extraction time on the color strength of Osage orange dyed wool at different temperatures
The dye absorbance is also influenced by the presence of co-solvents. Water-acetone mixture exhibited the highest value of absorbance, followed by the second water-ethanol mixture. In case water-acetone, the salvation of extract is non-HBD type of solvent mainly occurs through charge-dipole type of interaction, whereas in HBD type of solvent, the interaction also occurs by hydrogen bonding besides the usual ion-dipole interaction. In this situation, the methyl groups of acetone are responsible for the solvation of the dye extract. Thus, decreasing the amount of non-HBD acetone solvent “concentration” increasing the amount of HBD solvent (water) shall break these interactions with the dye molecule, thereby decreasing the value of absorbance. Water-ethanol mixtures belong to HBD type of solvents, whereas the dye cation is preferentially solvated by the alcoholic component in all mole fractions in aqueous mixtures with ethanol. It is well known that water makes strong hydrogen-bonded nets in the water-rich region, which are not easily disrupted by the co-solvent [33, 34]. This can explain the strong preferential salvation by the alcoholic component in this region since water preferentially interacts with itself rather than with the dye. In the alcohol-rich region, the alcohol molecules are freer to interact with the water and with the dye, since their nets formed by hydrogen bonds are weaker than in water. In this situation, the alcohol molecules can, to a greater or lesser extent, interact with water through hydrogen bonding [33-35]
Effect of Osage orange concentration (w/v)%, extracted ultrasonically in 20 % water/ acetone co-solvent at 60 oC for 90 min, on the color strength of silk and wool samples.
Wool fiber is considered as relatively easy fiber to dye, the ease with which the polymer system of wool will take in dye molecules is due to polarity of its polymer and its amorphous nature. The polarity will readily attract any polar Osage orange molecules and draw them into the polymer system. The studies of wool dyeing process have been in two distinct theories (The Gilbert- Rideal\'s and Donnan theories). The Gilbert and Rideal theory based on Langmuir\'s theories of surface adsorption [36], in which the activity coefficient of Osage orange extract ions adsorbed into the wool phase are reduced due to specific binding with sites on wool, which is the formation of ion pairs. This theory proposed that dyeing process is an anion exchange process, in which the Osage orange extract molecules displace smaller anions, depending on four steps: a) diffusion to fiber surface, b) transfer across that surface, c) diffusion within to appropriate "sites" and d) binding at those sites. On the other hand, according to the Donnan equilibrium theory, the Osage orange extract was considered to partition between the external solution and internal solution phase in the wool. The later phase is believed to contain a high concentration of fixed ionic groups, and hence solute molecules have reduced activity co-efficient in that phase due to coulombic interaction between the anionic groups (OH) in fact: O- of Osage orange extract and the protonated amino groups of wool. [36]
Higher color depth was expected from an increase in the extract concentration and the use of high concentrations of mordant [37]
To study the possibility of forming concentrated extracts, different amounts of Osage orange bark powder (1-10) g were extracted per the optimized 20 % water/ acetone co-solvent. The dyeing process was carried out on silk and wool samples at a liquor ratio of L: R 1:20, for 60 min. at 60 oC. It was noted that, the use of more concentrated extracts resulted in somewhat an increase in color depth; whereas the maximum color depth was achieved with 8% w/v powder on both fabrics as shown in Figure 9. The relative high K/S values for dyeings can be explained with the high amount of bark extracted for this series of dyeing experiments.
Dyeing temperature and time are important parameters influencing the quality of the dyed silk and wool samples. It is well known that dyeing at high temperature for a long time tends to decrease the fabric strength. [36-38] Therefore, it was proffered to dye the samples ultrasonically at temperatures ranging from 30 to 60 °C, relative to the dyeing time that was studied from 30 to 120 mins.
As shown in Figures 10 and 11, it is clear that the standard parameters of dyeing temperature and time were achieved after 90 mins, at 40 – 50 o C, and 50-60 o C in case of silk and wool respectively, where the color strength increases with the increase in dyeing temperature.
Generally, the increase in dye-uptake can be explained by the fiber swelling which enhanced the dye diffusion. [37] The effect of dyeing time was conducted to reveal the effect of power ultrasonic on the de-aggregation of dye molecules in the dye bath. It was denoted that the color strength obtained increased as the time increased. The decline in the dye-ability may be attributed to the hydrolytic decomposition of the extract molecules under the influence of sonic energy during prolonged dyeing. [38]
Effect of dye bath temperature at different time intervals on the color strength of silk samples dyed ultrasonically with 1% (w/v) Osage orange extract.
Effect of dye bath temperature at different time intervals on the color strength of wool samples dyed ultrasonically with 1% (w/v) Osage orange extract
As shown in Figures 12 and 13 the pH values of the dye bath, have a considerable effect on the dyeability of silk and wool fabrics with Osage orange extract under ultrasonic. As the pH increases the dyeability, decreases. The effect of dye bath pH can be attributed to the correlation between dye structure and the protein based materials.
Since the used dye is a water-soluble dye containing hydroxyl groups, it would interact ionic-ally with the protonated terminal amino groups of silk and wool fibers at acidic pH via ion exchange reaction.
The anion of the dye has a complex character, and when it is bound on fiber, further kinds of interactions take place together with ionic forces. This ionic attraction would increase the dye-ability of the fiber as clearly observed in Figures 12 and 13. At pH greater than 5, the ionic interaction between the hydroxyl anion of the dye and the protein fibers decreases due to the decreasing number of protonated terminal amino groups of silk and wool and thus lowering their dye-ability. It is to be mentioned that the lower dye-ability may be attributed to the enhanced desorption of the dye as its ionic bond is getting decreased [36].
Effect of the dye bath pH on the color strength and wave length of silk samples dyed ultrasonically with 1% Osage orange at 40-50oC for 90 min.
Effect of the dye bath pH on the color strength and wave length of woolen samples dyed ultrasonically with 1% Osage orange at 50-60 oC for 90 min.
As shown in Figures 14 and 15, ultrasonic [US] assisted mordanting method possesses a remarkable improvement in the color strength, in comparison with the classical thermal method [CH]. The obtained dyeings are governed by the descending dyeing sequence, and can be ranked as follows: pre-mordanting with a mixture of alum and cream of tartar followed by dyeing > post mordanting with alum > post mordanting with a mixture of alum and cream of tartar > premordanting with alum > unmordanted samples.
Silk and wool fabrics are highly receptive to mordants due to their amphoteric nature; they can absorb acids and bases with equal effectiveness. Mordants during natural dyeing, exhibits fast color due to their complex formation with the dye and fiber [27, 38]
Effect of mordant and mordanting method on the color strength of silk dyeings
It is clear that: (i) pre-mordanting with the nominated mordant brings about a significant enhancement in the K/S values of the obtained dyeings. (ii) the extent of improvement is governed by the physical and chemical states of the dye and degree of fixation, (iii) premordanting followed by dyeing gives dyeings with better fastness properties than those dyed without mordant and mordanting after dyeing (iv) the improvement in the dyeings color strength and fastness, reflects higher extent of dye adsorption, interaction and bridging with the pre-mordanted substrate via different conjugated bonds [27]
The low color strength in post-mordanting condition is due to the accumulation of the metal dye complex in form of clusters. [39]. The high aluminum content might provide useful eco-friendly chelating with Osage orange molecules presented in the extract that might resist their hydrolysis by water. [39]
The commercial cream of tartar (Potassiun Bitartrate) contains a small percentage of calcium tartrate is frequently employed as a mordant for wool. [40]. In this study it was recommended to apply cream of tartar with alum as preferable mordant to get good strong colors as discussed previously. It helps to soften fibers when alum is used, and can also help brighten the yellow color with good levelness. [40]
Effect of mordant and mordanting method on the color strength of wool dyeings
Sonic energy succeeded in accelerating the rate of mordanting at lower temperatures rather than the conventional heating technique. The exhibited improvement was generally attributed to the acoustic cavitation, which is the formation of gas-filled micro-bubbles or cavities in a liquid media, producing implosive collapse, which often forming fast-moving liquid jets, where large increases in temperature and pressure are generated. The micro-jets increase the diffusion of solute inside the intermediate spaces of silk and wool fabrics facilitate the de-aggregation of Osage orange molecules in the dye bath and thus increase the dye diffusion rate, and penetration through the fibers. [22-24, 41]
A variety of color hues were obtained with respect to the mordant. It was observed from the color fastness data that the extracted dye from Osage orange furnished different color hues with very good affinity for silk and wool fabrics in presence of alum and cream of tartar mordant as illustrated in Tables 1 and 2. The color intensity reached its highest value when the fabrics treated with a mixture of alum and cream of tartar. The brightness of the shades on the dyed samples might be due to the better absorption of Osage orange extract and the easy metal complex formation of mordant with the fibers. Data represented good to very good fastness to washing, because the mordant lead to the formation of a complex dye which aggregates the dye molecules into a large particles insoluble in water. Control samples exhibited poor fastness to washing due to the weak dye- fiber bond, and the ionization of the (OH) groups of the dye during washing under the alkaline condition. [42-45]
\n\t\t\t\tSampls\n\t\t\t | \n\t\t\t\n\t\t\t\tL*\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\ta*\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\tb*\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\tWash fastness\n\t\t\t | \n\t\t\t\n\t\t\t\tLight fastness\n\t\t\t | \n\t\t
Blank US | \n\t\t\t54.12 | \n\t\t\t4.71 | \n\t\t\t49.2 | \n\t\t\t3 | \n\t\t\t5 | \n\t\t
alum* CH | \n\t\t\t50.95 | \n\t\t\t3.57 | \n\t\t\t43.37 | \n\t\t\t3-4 | \n\t\t\t5-6 | \n\t\t
alum* US | \n\t\t\t55.05 | \n\t\t\t3.56 | \n\t\t\t51.67 | \n\t\t\t4 | \n\t\t\t6 | \n\t\t
Alum + cream of tartar * CH | \n\t\t\t50.57 | \n\t\t\t3.81 | \n\t\t\t37.31 | \n\t\t\t4 | \n\t\t\t6-7 | \n\t\t
Alum + cream of tartar * US | \n\t\t\t50.29 | \n\t\t\t6.43 | \n\t\t\t44.8 | \n\t\t\t4 | \n\t\t\t6-7 | \n\t\t
alum** CH | \n\t\t\t54.45 | \n\t\t\t4.83 | \n\t\t\t48.08 | \n\t\t\t3-4 | \n\t\t\t6 | \n\t\t
alum** US | \n\t\t\t54.99 | \n\t\t\t5.40 | \n\t\t\t52.12 | \n\t\t\t3-4 | \n\t\t\t6 | \n\t\t
Alum + cream of tartar ** CH | \n\t\t\t53.01 | \n\t\t\t3.96 | \n\t\t\t38.21 | \n\t\t\t3-4 | \n\t\t\t6 | \n\t\t
Alum + cream of tartar ** US | \n\t\t\t52.75 | \n\t\t\t5.52 | \n\t\t\t47.49 | \n\t\t\t3-4 | \n\t\t\t6 | \n\t\t
The CIELab values and fastness properties of Osage orange dyed silk
\n\t\t\t\tSample\n\t\t\t | \n\t\t\t\n\t\t\t\tL*\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\ta*\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\tb*\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\tWash fastness\n\t\t\t | \n\t\t\t\n\t\t\t\tLight fastness\n\t\t\t | \n\t\t
Blank US | \n\t\t\t68.97 | \n\t\t\t- 0.02 | \n\t\t\t55.19 | \n\t\t\t3 | \n\t\t\t5 | \n\t\t
alum* CH | \n\t\t\t65.07 | \n\t\t\t- 0.59 | \n\t\t\t60.08 | \n\t\t\t3-4 | \n\t\t\t5-6 | \n\t\t
alum* US | \n\t\t\t67.46 | \n\t\t\t- 2.93 | \n\t\t\t62.40 | \n\t\t\t4 | \n\t\t\t6 | \n\t\t
Alum + cream of tartar * CH | \n\t\t\t65.43 | \n\t\t\t- 0.14 | \n\t\t\t56.89 | \n\t\t\t4 | \n\t\t\t6-7 | \n\t\t
Alum + cream of tartar * US | \n\t\t\t64.11 | \n\t\t\t- 1.14 | \n\t\t\t52.60 | \n\t\t\t4 | \n\t\t\t6-7 | \n\t\t
alum** CH | \n\t\t\t64.61 | \n\t\t\t0.68 | \n\t\t\t62.40 | \n\t\t\t3-4 | \n\t\t\t6 | \n\t\t
alum** US | \n\t\t\t64.71 | \n\t\t\t1.56 | \n\t\t\t58.98 | \n\t\t\t3-4 | \n\t\t\t6 | \n\t\t
Alum + cream of tartar ** CH | \n\t\t\t64.54 | \n\t\t\t0.40 | \n\t\t\t60.72 | \n\t\t\t3-4 | \n\t\t\t6 | \n\t\t
Alum + cream of tartar ** US | \n\t\t\t65.03 | \n\t\t\t1.23 | \n\t\t\t55.29 | \n\t\t\t3-4 | \n\t\t\t6 | \n\t\t
The CIELab values and fastness properties of Osage orange dyed wool
In the light fastness test, mordanted colored samples exhibited better light fastness relative to the control ones. This may be due to: i) the aluminum metal in alum mordant protects, both by the steric and electronic effects of the weak point in the dye structure from attack by means of the reactive species during photochemical reaction, in addition ii) the aluminum metal in alum mordant promotes aggregation of the dye. By the way, the poor light fastness is due to the inherent susceptibility of the dye chromophore to the photochemical degradation. [46-51]
The colorimetric data indicated the depth and natural tone of the control and mordanted dyed samples. The L* values were found to be lower using alum and cream of tartar as mordant corresponding to deeper shades. The L* values were found to be higher in case of unmordanted dyed samples corresponding to lighter shades. Similarly, by using alum as mordant the L* values were also higher corresponding to lighter shades. The higher values of a* and b* indicated the brightness, representing the redness and yellowness hues respectively. As a result, alum and cream of tartar might be effectively used as mordant for Osage orange extract.
The light fading of the dyed samples was recorded in terms of the color difference (ΔE) as shown in Figures 16-20. It was denoted that sonic energy assisted alum and cream of tartar mordanting method, exhibited a lower degree of fading in comparison with the conventional heating and the application alum mordant in absence of cream of tartar, whereas the pre-mordanting method appears to be preferred having a great efficiency in lowering the degree of fading in comparison with the post mordanting
The dye physical state is generally important than the chemical structure in determining the color fastness on fibers. (31). It was recognized that the light fastness of many dyed systems has been found to increase with the increase dye concentration applied to the substrate [50].
The fiber swelling was increased with ultrasonic technique due to the sonic energy which i) improves the diffusion and penetration of the dye and mordant molecules inside the pores of the fabric, and ii) the fast breaking-down of the dye molecules, which became much more smaller in size and thus fully dispersed with much higher amount in the dyed samples relative to the samples subjected to the conventional heating method, resulted in, the lower degree of fading in case of ultrasonic assisted dyeing and mordantinting processes. [22-25, 41]
Mixture of alum and cream of tartar mordant renders the dye more bonded and more aggregated onto fibers, therefore the surface area of the dye accessible to light is reduced, and thereby the dye fades at lower degree with nearly constant rate of fading.
Aluminum ions apparently produce metal chelates with improved the overall fastness properties. This either could be evidence of the aggregation of dye molecules within the fiber or perhaps of the formation of dye-metal chelates that forms grater stability of the dye molecules when co-ordinated with the complex aluminum metal atom that might form quite large aggregates giving the highest light fast with the lowest fading degree. [23, 51], resulted in, the low and nearly constant fading rate in case of the mordanted samples.
Effect of Xenon arc lamp exposure time on the color retained of silk and wool blank dyed samples
Effect of Xenon arc lamp exposure time on the color retained of the premordanted dyed silk samples
Effect of Xenon arc lamp exposure time on the color retained of the post mordanted dyed silk samples
Effect of Xenon arc lamp exposure time on the color retained of the pre-mordanted dyed wool samples
Effect of Xenon arc lamp exposure time on the color retained of the post mordanted dyed wool samples
With the demand for more environmentally friendly methods and increasing productivity, the newer ultrasonic energy in assisting the extraction of Osage orange natural dye have been developed, over the conventional heating extraction methods, possessing a shorter extraction times and much higher dye absorbance and color strength at lower temperature. Water-acetone co-solvent, and ultrasonic have been found to be the suitable alternatives to the conventional water heating method. The maximum color yield of dye is dependent on solvent polarity. Solvation of dye molecules probably occurs via dipole-dipole interactions in non-hydrogen- bond donating solvents, whereas in hydrogen-bond donating solvents the phenomenon is more hydrogen bonding in nature. The dye uptake depends on (The Gilbert- Rideal\'s and Donnan theories) depending on the coulombic interaction between the anionic groups (OH) in fact: O- of Osage orange extract and the protonated amino groups of wool fibers.
This research demonstrated the standardization dyeing parameters of Osage orange natural yellow extract on protein based fabrics; silk and wool. The color strength increases with the increase in dyeing temperature and time due to the fiber swelling which enhanced the dye diffusion. The effect of dyeing time was conducted to reveal the effect of power ultrasonic on the de-aggregation of dye molecules in the dye bath. The decline in the dye-ability may be attributed to the hydrolytic decomposition of the extract molecules under the influence of sonic energy during prolonged dyeing. Osage orange is a water-soluble dye containing hydroxyl groups that interacts ionic-ally with the protonated terminal amino groups of silk and wool fibers at acidic pH 5 via ion exchange reaction. The lower dye-ability at pH greater than 5 may be attributed to the enhanced desorption of the dye as its ionic bond is getting decreased. Improvement in the dyeing color strength and fastness properties reflects the higher extent of dye adsorption, interaction and bridging with the pre-mordanted dyed samples via different conjugated bonds with the mixture of alum and cream of tartar mordant. The low color strength in post-mordanting method is due to the accumulation of the aluminum metal dye complex in form of clusters. Sonic energy succeeded in accelerating the rate of mordanting at lower temperatures rather than the conventional heating technique due to the acoustic cavitation which increases the diffusion of solute inside the intermediate spaces of silk and wool fabrics, facilitating the de-aggregation of Osage orange molecules in the dye bath and thus increases the dye diffusion rate, and its penetration through the fibers. A variety of hues were obtained with respect to the mordant. It was observed from the color fastness data that the extracted dye from Osage orange furnished different color hues with very good affinity for silk and wool fabrics in presence of a mixture of alum and cream of tartar mordant. Mordanted dyed samples exhibit better wash and light fastness, with a lowest degree of photo fading relative to the control ones.
Medical error is a leading cause of death and injury. Each year, between 210,000 and 440,000 patients who go to the hospital for care suffer from some types of preventable harm that contribute to their death [1]. High error rates with serious consequences are most likely to occur in the operating room [2]. A strong patient’s safety culture in the operating room is very important to improve quality and reduce risks of adverse event and medical errors. Thus, a flexible risk analysis technique becomes crucial.
\nA lot of methods and techniques, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used for safety risk analysis in the healthcare system. However, these methods have a limitation when dealing with rare event and complex systems. Khakzad indicated FTA unsuitable for complex problems with its limitation in explicitly representing dependencies of events, updating probabilities, and coping with uncertainties [3], while FMECA does not take into account multiple failure scenarios and causes. Bayesian Network (BN) is a powerful method for risk analysis. In contrast with other classical methods of dependability analysis, Bayesian networks provide a lot of benefits. Some of these benefits are the ability to model complex systems, to make predictions as well as diagnostics, to compute exactly the occurrence probability of an event, to update the calculations according to evidences, to represent multimodal variables, and to help modeling user-friendly by a graphical and compact approach [4].
\nIn this chapter, we propose two methods which can help to assess patient safety in different contexts using Bayesian network.
\nIn this part, we propose a method for the context of data availability. We will explain how we can use the classical Bayesian network for safety assessment in healthcare system.
\nIn the following, a methodology of risk analysis of the operating room using Bayesian networks is proposed. The methodology follows four steps (Figure 1) and it is part of continuous improvement process (CIP) [5].
\nMethodology of risk analysis for operating room using Bayesian network.
The first step involves determining the aim of the risk assessment process, the description of the problem, and the definition of the scope.
\nExample: risk of patient’s safety in the operating room.
\nThe second step is to identify potential risks that can affect the quality and the efficiency of the operating room process. In this step, we may encourage creativity and involvement of the operating room team.
\nThe third step is the risk modeling. It consists in the development of the Bayesian networks graph (definition and choice of the variables to represent the nodes, describe the states of each node, and build the structure of Bayesian networks in terms of links between the predefined nodes) and establishment of the quantitative relation between nodes through conditional probability. In this step, we can use the hospital data source and the expert’s judgment to feed the model.
\nThe last step is the analysis of the results: The model should give the best understanding of the risk problem. It is useful to discuss the goodness or appropriateness of the model. It is important to validate and calibrate the model using all available source of information (expert judgment, observation, statistical data…). We should then analyze and interpret the result of risk measures to support decision-making for safety improvement.
\nFinally, continuous improvement efforts must incorporate a risk assessment process to ensure the effectiveness and the quality of the process. The model must be updated with the new risks and factors.
\nThe operative processes include the preoperative, intraoperative, and postoperative stages of a surgery. We are going to study the operating room processes and in particular, the intraoperative stage. It starts when the patient enters the operating room and all members of the surgical team are expected to be in the operating room at this particular time. The process ends when the patient is able to leave the operating room. During this process, the patient is monitored, anesthetized, and prepped and the operation is performed. Because of the lack of availability of actual data risk, we will forward a risk analysis based on different sources accidents described in the international literature. We will limit our study to events that cause a significant deviation of the operating room process compared to normal process and which have serious consequences for the patient (re-intervention, hospitalization in intensive care, extension of the period of hospitalization, additional care, death…).
\nTo create and validate the structure of the network, we use Hugin software and more precisely Hugin Lite Evaluation.
\nFigure 2 illustrates the Bayesian network model of patient’s safety showing interrelationships of events that may lead to patient’s injury. The model has 13 nodes with one utility node. The nodes are assessed using a literature source. We present below the description of each nodes.
\nBayesian network for patient safety model for the operating room.
Surgery infection: the incidence of surgical site infections (SSI) depends upon the patient risk factors, surgical procedure, and practices observed by the operating team.
\nSurgical foreign body: leaving things inside the patient’s body, after surgery, is an uncommon but a dangerous error. Sponges and scissors used during surgery have been left inside patients’ bodies.
\nOperating on the wrong part of the body or wrong-site or wrong-patient or wrong-procedure surgeries: the frequency of surgery admissions experiencing a wrong site or wrong side or wrong patient or wrong procedure or wrong implant is 0.028 per 1000 admissions [6].
\nMedication error: wrong-dose, wrong-time, wrong-medication, or transcription errors. “A medication error is any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the health care professional, patient or consumer. Such events may be related to professional practice, health care products, procedures and systems including prescribing, order communication, product labelling, packaging and nomenclature; compounding; dispensing; distribution; administration; education; monitoring; use” [7]. In a review of medical records from hospitals in two American states, there was a significantly higher incidence of preventable drug-related adverse events in patients aged >64 than in patients aged 16–64 years (5% compared with 3%) [8]. Errors are also significantly more likely in children.
\nAnesthesia equipment failure: anesthesia equipment problems may contribute to morbidity and mortality. The frequency of anesthetic equipment problems is 0.05% during regional anesthesia, and 0.23% during general anesthesia [9].
\nOperation error: an error may occur in surgery due to different adverse events.
\nPatient injury: an error may or may not cause an adverse event. Adverse events are injuries that cause harm to the patient (death, life-threatening illness, disability at the time of discharge, prolongation of the hospital stay, etc.).
\nIn the following, some risk factors are given:
\nPatient risk: we consider two states for patient’s risk, high and normal. The risk in surgery can come from patients themselves.
\nAge: for the age factor, we assume that the patient may be child, elderly, or adult. The age can increase the patient’s risk, the risk of fall, and the risk of medication error. These risks are much higher for elderly and child than adult.
\nAnesthesia type: we consider two categories of anesthesia, regional and general. We assumed that “failure in anesthesia equipment” depends on anesthesia type as explained in [9].
\nThe conditional probabilities of states of different nodes and the marginal probabilities of some adverse events have been given as input data. Each risk of adverse events is considered with two states (true if the risk exists and false if not). The probabilities are given in Tables 1, 2, 3, 4, 5, 6, 7.
\nOperation error | \nTrue | \nFalse | \n||
---|---|---|---|---|
Patient risk | \nHigh | \nNormal | \nHigh | \nNormal | \n
No | \n0.01 | \n0.01 | \n0.99 | \n1 | \n
Small | \n0.18 | \n0.81 | \n0.009 | \n0 | \n
Severe | \n0.81 | \n0.18 | \n0.001 | \n0 | \n
Conditional probability for patient injury.
Age | \nAdult | \nElderly | \nChild | \n
---|---|---|---|
True | \n1.16E-5 | \n1.16E-4 | \n1.16E-4 | \n
False | \n0.9999884 | \n0.999884 | \n0.999884 | \n
Conditional probability for patient fall.
Age | \nAdult | \nElderly | \nChild | \n
---|---|---|---|
True | \n0.03 | \n0.05 | \n0.06 | \n
False | \n0.97 | \n0.95 | \n0.94 | \n
Conditional probability for medication error.
Physical state | \nWeak | \nNormal | \n||||
---|---|---|---|---|---|---|
Age | \nAdult | \nElderly | \nChild | \nAdult | \nElderly | \nChild | \n
High | \n0.6 | \n0.8 | \n0.9 | \n0 | \n0 | \n0 | \n
Normal | \n0.4 | \n0.2 | \n0.1 | \n1 | \n1 | \n1 | \n
Conditional probability for patient risk.
Anesthesia type | \nRegional | \nGeneral | \n
---|---|---|
True | \n5 × 10−5 | \n2.3 × 10−3 | \n
False | \n1–(5 × 10−5) | \n0.9977 | \n
Conditional probability for failure in anesthesia equipment.
Risk | \nProbabilities | \n
---|---|
Surgery infection | \n2.5 × 10−2 | \n
Wrong site | \n2.6 × 10−5 | \n
Foreign bodies | \n10−3 | \n
Probability of some adverse events.
Factor | \nState | \nOccurrence | \n
---|---|---|
Anesthesia type | \nRegional General | \n0.5 0.5 | \n
Physical state | \nWeak Normal | \n0.1 0.9 | \n
Age | \nAdult Elderly Child | \n0.5 0.2 0.3 | \n
Probability of some factors.
To aggregate the impact of injuries into a single risk measure, we use utility node “Patient Death.” So the task is to find the probability of patient’s death after a surgery by using only the correlations and the marginal frequencies.
\nAfter the structure of the Bayesian network is completed and probabilities are determined, the inference can be performed to estimate the probability of patient’s safety risk. We conduct the calculation using Hugin software. The dependency and the correlation among risks and factors are captured in nodes “Operation error” and “Patient injury.” Hence, the task is to find the probabilities of patient’s death after surgery by using only the correlations and the probabilities of adverse events and the frequency of influencing factors. The probability of the death of patient is 6.37 × 10−3. If the state of one or more variables is known, the model can be updated and the probability of patient injury and operation error will change. This should result in decision of not to operate the patient or postpone the surgery. For instance, the risk is much higher when the patient has a weak physical state; it is 0.02 instead of 5.03 × 10−3 for the risk of death if the patient has a normal physical state. Knowing the age of patient, we can estimate the risk of death; it is 4.98 × 10−3 for adult, 7.08 × 10−3 for elderly patient, and 8.2 × 10−3 for child (Table 8). It should be noted that the model and data used in this chapter have limitations. The model should be enhanced by taking into account different causes of adverse events. Data should be prevented from an adverse event database reporting system and from expert’s judgment.
\nRisk | \nProbabilities | \n
---|---|
Death of patient | \n6.37 × 10−3 | \n
Death of weak physical state patient | \n0.02 | \n
Death of normal physical state patient | \n5.03 × 10−3 | \n
Death of child patient | \n8.2 × 10−3 | \n
Death of adult patient | \n4.98 × 10−3 | \n
Death of elderly patient | \n7.08 × 10−3 | \n
Probability of the death of patient.
Several actions can be done to reduce risk and improve the safety of the patient in operating room. For instance, we can reduce the risk of retained foreign body during operation by using an appropriate sponge count and obtaining X-rays if needed to check for any retained foreign body. If we reduce this risk by 95%, the risk of the death of patient becomes 6.28 × 10−3. Furthermore, if we reduce the risk of surgery infection by 80%, the risk of the death of patient passes to 4.5 × 10−3 instead of 6.28 × 10−3. By acting only on “retained foreign body” and “surgery infection” adverse events, the risk can be reduced by 30%.
\nDue to the lack of data about adverse event and the fact that the adverse event reporting system does not exist, the input data of risk modeling will be provided by expert’s opinion. The quality of such data must be discussed. We must help experts to provide reliable quantitative data. This can be done with the fuzzy set theory. Including the expert’s judgment in the risk model is essential for providing a reliable risk picture supporting the decision-making. The second approach uses the FBN to analyze risk. Fuzzy Bayesian networks are a powerful approach for risk modeling and analysis. This is especially noticed when quantitative data are lacking and only qualitative or vague statements can be made as well when historical adverse events data are unavailable or insufficient to be used for safety assessment [10]. In this part, we present a real case of the children hospital in Rabat. To feed the model by the probabilities, we interviewed experts of the operating room. The calculation of probabilities is done out of Hugin software to conduct the fuzzy inference.
\nIn the following, a methodology of risk analysis of the operating room using FBN is proposed. The methodology follows five steps (Figure 3) and is part of the continuous improvement process (CIP). The first three steps are the same as the first proposed methodology explained above.
\nMethodology of risk analysis for operating room using fuzzy Bayesian network.
The fourth step is the fuzzy assessment of probability. We investigate the expert’s judgment to feed the model. Experts use a linguistic variable to describe the probabilities of occurrence of adverse events. We transform the linguistic expressions into fuzzy numbers. Since we have more than one expert, we must aggregate the different opinions. For that, we use the weight of the expert to take into account the reliability of the data.
\nThe last step is the analysis of the results: we should then analyze and interpret the results of risk measures to support decision-making for safety improvements.
\nFinally, the model must be implemented in Upgrading way as explained in the first method.
\nLet us consider the previous example that we modify according to expert’s opinion. Figure 4 illustrates the BN model of patient’s safety after modification. It shows interrelationships of events that may lead to patient’s injury. The model has eight nodes with one utility node added to estimate the risk of the patient’s death after surgery due to an error.
\nBayesian network for patient safety model for the operating room.
Surgeons and operating team of the children’s hospital IBN SINA of RABAT Morocco were asked to give judgments about the fuzzy probabilities regarding all the nodes. They use linguistic terms to describe the fuzzy probabilities and then refine them with membership functions. For example, “Very low” was assigned to node “PatientFall” and “Average” was assigned to technical defect and then were defined by the membership function (a, b, c). The other probabilities are given in Table 11 according to the answers given by experts. The likelihood of each criterion (Table 9) was represented by a range of five discrete values identified by the following linguistic terms: “extremely low” (L1), “very low” (L2), ”low” (L3), “average” (L4), and “high” (L5). The severity of each adverse event (Table 10) was represented by a range of five discrete values identified by the following linguistic terms: “negligible“ (S1), “minor“ (S2), “medium“ (S3), “major“ (S4), and “catastrophic“ (S5). These five values represent the states of the node “patient’s injury.”
\nSet | \nLinguistic variable | \nMeaning | \n
---|---|---|
L1 | \nExtremely low | \nNever seen | \n
L2 | \nVery low | \nOne time in my career | \n
L3 | \nLow | \nOccur in another hospital | \n
L4 | \nAverage | \nOccur in our hospital | \n
L5 | \nHigh | \nOccur in my domain | \n
Scale of the likelihood.
Set | \nLinguistic variable | \nMeaning | \n
---|---|---|
S1 | \nNegligible | \nConsequence minor without prejudice (simple delay) | \n
S2 | \nMinor | \nIncident with prejudice (disorganization) | \n
S3 | \nMedium | \nIncident with impact postponement, prolongation of hospitalization, not expected transfer in reanimation) | \n
S4 | \nMajor | \nSerious Consequence (re-intervention; permanent or partial disability) | \n
S5 | \nCatastrophic | \nVery serious Consequence (disability, death) | \n
Scale of the severity.
We interviewed three individuals from the operative team (surgeon, crew chief, and anesthesia nurse). They have a different point of view and confidence level toward their own subjective judgments due to the difference in background, working experience, and risk attitudes. Thus, a certain deviation exists in the data reliability among different interviewed individuals.
\nTable 11 represents the weight of each expert. Expert 1 has more experience and more precise answers about adverse events than the others, so he was given the higher weight 1/2, 1/3 was assigned to expert 2, and 1/6 to expert 3.
\nExpert | \nWeight | \n
---|---|
E1 | \nW1 = 1/2 | \n
E2 | \nW2 = 1/3 | \n
E3 | \nW3 = 1/6 | \n
Weight of expert’s opinion.
To deal with the deviation of experts answers, the aggregated fuzzy importance of each criterion, whose properties are used to produce a scalar measure of consensus degree, is computed by the weight of the criteria according to the judgment of the expert (Eq. (1)).
\nThe expert’s judgment about the likelihood and the severity of adverse events is given in Table 12. For instance, the probability (“high,” “L5”) and the severity (“catastrophic,” S5) have been assigned to the node “foreign body” by expert E1; expert E2 had a different judgment about the likelihood of the same event (L3, “Low”). As you can see, experts have different opinions; that is why we used the weight of each expert.
\nNodes | \nVariable | \nE1 | \nE2 | \nE3 | \n|||
---|---|---|---|---|---|---|---|
L | \nS | \nL | \nS | \nL | \nS | \n||
Lack of training | \nB1 | \nL4 | \nS3 | \nL3 | \nS3 | \nL4 | \nS4 | \n
Lack of materiel | \nB2 | \nL4 | \nS3 | \nL3 | \nS3 | \nL4 | \nS4 | \n
Technical defect | \nB3 | \nL4 | \nS3 | \nL3 | \nS2 | \nL4 | \nS4 | \n
Patient fall | \nB4 | \nL2 | \nS3 | \nL3 | \nS2 | \nL1 | \nS2 | \n
Medication error | \nB5 | \nL5 | \nS5 | \nL3 | \nS5 | \nL2 | \nS3 | \n
Surgery infection | \nB6 | \nL5 | \nS4 | \nL3 | \nS4 | \nL3 | \nS3 | \n
Foreign body | \nB7 | \nL5 | \nS5 | \nL3 | \nS5 | \nL2 | \nS4 | \n
Wrong site | \nB8 | \nL4 | \nS4 | \nL3 | \nS4 | \nL2 | \nS3 | \n
Expert’s judgment about the likelihood and the severity of adverse events.
Table 13 represents the fuzzification of the probabilities linguistic variable. For example, the triangular fuzzy number (0.00, 10−8, 2 × 10−8) is assigned to the linguistic variable (“Extremely low,” “L1”). The point (10−8, 1),with membership grade of 1, is the mean value; 0 and 2 × 10−8 are the left hand and right hand spreads of the triangular number, respectively (Table 13).
\nSet | \nLinguistic term | \nFunction | \n
---|---|---|
L1 | \nExtremely low | \nμ1(x) = (0.00, 10−8, 2 × 10−8) | \n
L2 | \nVery low | \nμ2(x) = (1.5 × 10−8, 10−7, 10−6) | \n
L3 | \nLow | \nμ3(x) = (0.9 × 10−6, 10−5, 2 × 10−5) | \n
L4 | \nAverage | \nμ4(x) = (1.5 × 10−5, 10−4, 2 × 10−4) | \n
L5 | \nVery high | \nμ5(x) = (1.5 × 10−4, 10−3, 2 × 10−3) | \n
Fuzzification of likelihood.
M2 represents the vector of probabilities of basic nodes obtained using Eq. (2) and the matrix of fuzzy probabilities estimated by experts and the weight of each expert are given in Table 5. This step aims to determine the fuzzy probabilities of basic events.
\nTable 14 describes the conditional probability of the node “Equipment Failure” represented by the variable N1, this variable has two states, namely true if the risk exists and false if not. If one of the three events B1, B2, and B3 occurs, the risk exists. 1f and 0f represent the crisp values 1 and 0 considered here as fuzzy number 1f (1,1,1) and 0f (0,0,0).
\nN1 | \nB4 | \nB5 | \nB6 | \nB7 | \nB8 | \nS1 | \nS2 | \nS3 | \nS4 | \nS5 | \n
---|---|---|---|---|---|---|---|---|---|---|
True | \nFalse | \nFalse | \nFalse | \nFalse | \nFalse | \n0f | \n0f | \n1f | \n0f | \n0f | \n
False | \nTrue | \nFalse | \nFalse | \nFalse | \nFalse | \n0f | \n1f | \n0f | \n0f | \n0f | \n
False | \nFalse | \nTrue | \nFalse | \nFalse | \nFalse | \n0f | \n0f | \n0f | \n0f | \n1f | \n
False | \nFalse | \nFalse | \nTrue | \nFalse | \nFalse | \n0f | \n0f | \n0f | \n1f | \n0f | \n
False | \nFalse | \nFalse | \nFalse | \nTrue | \nFalse | \n0f | \n0f | \n0f | \n0f | \n1f | \n
False | \nFalse | \nFalse | \nFalse | \nFalse | \nTrue | \n0f | \n0f | \n0f | \n1f | \n0f | \n
Conditional occurrence probability of “patient injury”.
Table 15 represents the conditional probability of the node “Patient injury,” the node has five states S1–S5 according to the severity of the harm caused to the patient. Here, the conditional probability is considered as crisp value according to the expert’s opinion. Based on the harm observed, experts gave a precise answer about severity.
\nB1 | \nB2 | \nB3 | \nN1 = True | \nN1 = False | \n
---|---|---|---|---|
True | \nTrue | \nTrue | \n1f | \n0f | \n
\n | \n | False | \n1f | \n0f | \n
\n | False | \nTrue | \n1f | \n0f | \n
\n | \n | False | \n1f | \n0f | \n
False | \nTrue | \nTrue | \n1f | \n0f | \n
\n | \n | False | \n1f | \n0f | \n
\n | False | \nTrue | \n1f | \n0f | \n
\n | \n | False | \n0f | \n1f | \n
Conditional occurrence probability of “equipment failure”.
After the structure of the BN is developed and probabilities are determined, the inference can be performed to estimate the probability of patient’s safety risk. The dependency and the correlation among risks and factors are captured in node “Patient injury.” Hence, the task is to find the probabilities of patient’s death after surgery by using the correlations and the fuzzy probabilities of adverse events. Using the fuzzy Bayesian rule, the probability that the injury severity will be catastrophic can be calculated as given in Eq. (3):
\nThe probability that the injury severity will be catastrophic (S5) is (1.5 × 10−4, 10−3, 2 × 10−3). Assuming that 80% of patients having a catastrophic injury die, the probability of the death of a patient after surgery due to an adverse event is (1.2 × 10−4, 0.8 × 10−3, 1.6 × 10−3). Using the center of the gravity method (Eq. (4)), we obtained COG = (8.4 × 10−4, 1/3). The probability of the death of a patient after surgery is the x-axis 8.4 × 10−4.
\nSeveral actions can be done to reduce risk and improve the safety of the patient in operating room. Using this model, if we reduce the risk of retained foreign body by 60%, the risk of the death of patient becomes 3.36 × 10−4.
\nIf the state of one or more variables is known, the model can be updated and the probability of patient injury will change.
\nOne of the main advantages of BN is their ability to help us to conduct inverse interference. For example, it is interesting to know, when a death is observed, what the posterior probability of a patient’s infection is. In addition, if the model contains more details witch integrate the main causes of adverse events, we can obtain more interesting results such as the probability of the death of the patient due to human error or lack of training or malfunction in the organization.
\nThe model presented must be updated when new information is available to better estimate the risk of patient safety in the operating room. The model should be enhanced by taking into account different causes of adverse events. The use of adverse event database reporting system may be very useful for getting statistics and determining the probabilities of occurrence of some adverse events. The model allows integrating a mixture source of information (probabilities from database and expert’s opinion).
\nSafety is very essential in the healthcare system. Therefore, we should use effective and flexible methods for risk analysis to improve safety. Bayesian Networks methods are used to model and analyze risk in the operating room. The second method uses, in addition to Bayesian Network, the fuzzy logic. It allows us to use the data provided by expert and deal with the vagueness and imprecision of information. Fuzzy Bayesian network seems more flexible and interpretable than conventional Bayesian network, especially in the context of lack of data concerning risk events. This approach supports human cognition using linguistic variables which is closer to reality.
\nThe application of the two approaches has been explained by the use of a simple model. The aim of this chapter is to propose flexible and effective methods in different context (data availability and lack of data) using Bayesian network.
\nHowever, when the size of the graph is important, the model becomes incomprehensive. We can resolve that by using object-oriented Bayesian network (OOBN). OOBN is a type of Bayesian network, comprising both instance node and usual node. An instance node is a subnetwork representing another Bayesian network. Using OOBNs, a large complex Bayesian network can be constructed as a hierarchy of sub-networks with desired levels of abstract and different levels of detail [11]. For instance, we can transform the node ‘surgery infection’ to a sub-network by analyzing and modeling the causes of this kind of injuries. Therefore, model construction is facilitated and communication between the model’s subnetworks is more effectively performed. OOBN has a better model readability which facilitates the extension and improvement of the model.
\nRemedy actions are always conducted by doctors and nurses upon hazardous occurrences. Timely rescue can largely reduce the practical risks of patient’s injury. By contrast, delayed remedies are of less use. It is therefore necessary to take into account the time. Consideration and incorporation of time-dependent in the risk assessment to represent equipment failure or human reliability are very important. This can be done through dynamic Bayesian network (DBN) models. DBN is an extension of Bayesian network; it is used to describe how variables influence each other over time based on the model derived from past data. A DBN can be thought as a Markov chain model with many states or a discrete time approximation of a differential equation with time steps. A dynamic Bayesian network methodology has been developed to model domino effects in [12]. Another application of DBN is presented in [13] to evaluate stochastic deterioration models.
\nThe Bayesian network presented is a model for assessing risk of patient’s safety in operating room. The model aims to capture and measure risks in the background knowledge (namely common causes and observed adverse events). Including the expert’s judgment in the risk model is essential for providing a reliable risk picture supporting the decision-making. The use of adverse event database reporting system may be very useful for getting statistics and determine the probabilities of occurrence of the adverse events.
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