101 research outputs found
The prevalence of pulmonary aspergillosis in coronavirus disease 19 (COVID-19) patients in Shebin El-Kom teaching hospital in Egypt
No full textBackground
A high rate of invasive fungal infection has been demonstrated among critically COVID-19 ill patients admitted to the ICU, with high odds of mortality. Simple and rapid risk stratification methods are mandatory to recognize severe patients.
Objectives
The aims was to study the prevalence of invasive fungal infection in Corona virus disease 19 (COVID-19) patients, the effect of some inflammatory markers that lead to the development and progression of invasive fungal infection and to assess the value of PCR in early and rapid detection of invasive fungal infection in immune compromised patients with COVID-19.
Methods
This study was conducted at the period from October 2020 to October 2021 on two groups classified as following: Group I: included 120 immuno-compromised inpatients (2-80 years), (68 males and 52 females) from ICUs. Group II: included 40 outpatient's COVID-19 (4 – 56 years). All basic laboratory biomarkers at time of admission were recorded.
Results
Of this study showeda highly significant increase in neutrophil/ lymph, IL6,CRP, D-dimer and malondialdhyde (MDA) in COVID-19 patients in ICU compared with outpatient one with P value < 0.001). No significant difference between them in LDH, ferritin and procalcitonine. The most common isolated organisms (167 isolates) from group I (230 samples from 120 patients) were bacterial spp. (111/167, 66.5%)followed by Candida spp. (30, 17.9%), Aspergillus spp. (11, 6.6%) while mucormycosis was 5 isolates (3%) and associated bacterial infection represented 5.9%of all. Out of 120 patients suspected of complaining of BSI 17 (14.1%) of them proved to be fungemia. The most common isolated yeast was Candida spp. (11/120, 9.1%) followed by Aspergillus spp. (6/120, 5%). While out of 20 patients (group I) suspected of complaining of eye infections, mucormycosis was represented by 5/20 (25%). Fungaemia was detected by PCR and blood culture in 50 high risk ICU patients was 22/50 (44%) and 17/50 (34%) respectively. PCR is more sensitive than blood culture, as blood culture failed to detect 5 cases of fungemia with a significant difference (P-value <0.05).
Conclusion
Increase in neutrophil/lymph, IL6,CRP, D-dimer and MDA in COVID-19 ICU patients compared with outpatients may be significant biomarkers used to detect severity of disease in ICU patients and monitor treatment. Also decrease in immunity as results of corticostorides admission, lead to presence of fungaemia in some patients in ICU
Multicomponent image segmentation: A comparative analysis between a hybrid genetic algorithm and self-organizing maps
No full textImage segmentation is an essential process in image analysis. Several methods have been developed to segment multicomponent images and the success of these methods depends on the characteristics of the acquired image and the percentage of imperfections in the process of its acquisition. Many of the segmentation methods are parametric, which means that many parameters need to be computed or provided before the segmentation process, and any method that works on one type of multicomponent image cannot necessarily work on another. In addition, many segmentation methods are supervised, where a priori knowledge is needed, such as the number of classes. To overcome these obstacles, a self-organizing map (SOM), which is an unsupervised nonparametric method, was used to segment four different types of multicomponent images (Landsat, SPOT, IKONOS and CASI), and the results compared to those of a new nonparametric unsupervised genetic algorithm (GA) for image segmentation. To improve the performance of the GA, a hill-climbing process and another random heuristic module were added to escape the local-minima trap and to improve the speed of the GA; the new algorithm is called the hybrid genetic algorithm (HGA). Verification of the results was performed using two different techniques: field verification and the functional model. These verification techniques show that the HGA is more accurate in multicomponent image segmentation than the SOM.ARIA E, 1973, P 20 INT SOC PHOT RE, P117; BAKER EB, 1987, P 2 INT C GEN ALG L, P14; BHANU B, 1995, IEEE T SYST MAN CYB, V25, P1543, DOI 10.1109-21.478442; BRICE CR, 1970, ARTIF INTELL, V1, P205, DOI 10.1016-0004-3702(70)90008-1; CHANG YL, 1994, IEEE T IMAGE PROCESS, V3, P868; Chun DN, 1996, PATTERN RECOGN, V29, P1195, DOI 10.1016-0031-3203(95)00148-4; Cohen J, 1960, EDUC PSYCHOL MEAS, V20, P46; COLLET C, 1995, GRESTI STUDY RES GRO, V2, P569; CONGALTON RG, 1991, REMOTE SENS ENVIRON, V37, P35, DOI 10.1016-0034-4257(91)90048-B; Cormen T., 2001, INTRO ALGORITHMS; DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1; Haupt R L, 2004, PRACTICAL GENETIC AL; Holland J. H., 1975, ADAPTATION NATURAL A; Jiang T., 2001, ELECT NOTES THEORETI, V46, P1; KHUNKAY S, 1997, P 1997 INT C INF COM, V2, P713; Kim EY, 2000, IEEE SIGNAL PROC LET, V7, P301, DOI 10.1109-97.873564; KIM HJ, 1998, ELECTRON LETT, V34, P1394; Kohavi R., 1998, APPL MACHINE LEARNIN, V30, P271; Kohonen T., 2001, SPRINGER SERIES INFO, V30; Levine M. D., 1985, VISION MAN MACHINE; Lo Bosco G, 2001, 11TH INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING, PROCEEDINGS, P262; Ng SC, 1996, IEEE SIGNAL PROC MAG, V13, P38, DOI 10.1109-79.543974; OHLANDER R, 1978, COMPUT VISION GRAPH, V8, P313, DOI 10.1016-0146-664X(78)90060-6; OJOLA T, 1998, PATTERN RECOGN, V19, P1213; PARZEN E, 1962, ANN MATH STAT, V33, P1065, DOI 10.1214-aoms-1177704472; Pham DL, 2000, ANNU REV BIOMED ENG, V2, P315, DOI 10.1146-annurev.bioeng.2.1.315; Pratt WK, 1991, DIGITAL IMAGE PROCES; Schalkoff R.J, 1992, PATTERN RECOGNITION; Shapiro L., 2001, COMPUTER VISION; Xu BG, 2002, AATCC REV, V2, P42; Yao KC, 2000, PATTERN RECOGN, V33, P1575, DOI 10.1016-S0031-3203(99)00135-1; YIN HJ, 1995, NEURAL COMPUT, V7, P1178, DOI 10.1162-neco.1995.7.6.1178; Yoshimura M, 1999, PATTERN RECOGN, V32, P2041, DOI 10.1016-S0031-3203(99)00004-7; ZHANG P, 2003, P IEEE C EV COMP CEC, P634; Zouagui T, 2004, PATTERN RECOGN, V37, P1785, DOI 10.1016-j.patcog.2003.12.01462
Evaluation the role of conventional and Xpert MTB/RIF assays as point-of-care tests of Mycobacterium tuberculosis infections, especially during the COVID-19 pandemic in Menoufia, Egypt
No full textBackground
Tuberculosis (TB) is a destructive pulmonary disease, which was the most fatal infectious disease in the world for many years before the COVID-19 outbreak. During pandemic, COVID-19 was the main concern in every clinic and there were overlapping respiratory diseases resulting in delaying of the diagnosis and treatment of TB. Xpert MTB/RIF assay and Ziehl–Neelsen (ZN) stain are the most commonly used point-of-care test (POCT) assays for TB that were endorsed by WHO allowing a quick treatment turnaround time of a few minutes or hours, hence avoiding patient loss to follow-up. The aim of the study was to evaluate the role of Xpert MTB/RIF as a POCT for early, rapid, and accurate diagnosis of pulmonary and extrapulmonary TB during the COVID-19 pandemic and its role for exclusion of non-mycobacterial TB infections and to evaluate the proportion of patients with active pulmonary TB among COVID-19 patients and to study the difference of some inflammatory markers between patients with COVID-19, patients with pulmonary TB, and patients infected by both TB and COVID-19.
Patients and methods
This study was conducted from February 2018 to December 2021 (including the peak period of COVID-19 on 835 suspected TB patients (629 + 206 suspected COVID-19 patients six of them were proved pulmonary TB). Patients were from Shebin El-Kom Teaching Hospital and Chest hospital, Menoufiya). All 835 (pulmonary and extrapulmonary samples) patients were tested by gene Xpert MTB/RIF including 441 of them tested by ZN only. For detection of sensitivity, specificity positive predictive value (PPV), negative predictive value (NPV), and accuracy we selected 103 samples who were tested by the three methods (gene Xpert MTB/RIF, ZN staining, and culture on LJ media). For studying the difference of some inflammatory markers between patients with COVID-19, patients with pulmonary TB, and patients infected by both TB and COVID-19, 206 patients who were suspected of comorbid TB and COVID-19 during the pandemic were divided into three groups: group I positive for TB and COVID-19 (N = 6), group II positive COVID-19 only (N = 100), and group III were positive pulmonary TB only (N = 50) (NB: 50 patients were excluded due to incomplete data). Blood samples were taken for complete blood count, erythrocyte sedimentation rate, malondialdehyde, interleukin-6, C-reactive protein, D-dimer, ferritin, lactate dehydrogenase, calprotectin, and procalcitonin. Nasal swabs were needed for confirmation of COVID-19 by PCR.
Results
Compared with culture as a gold standard, sensitivity, specificity, PPV, and NPV for ZN smear were 77.1, 100, 100, and 53.8%, respectively. As regards the results of XPERT MTB/RIF assay, from the 103 samples examined, 89 (86%) were positive and 14 (14%) were negative. Eight false-positive results were recorded, compared with culture. The sensitivity was 98.8%, specificity was 61.9%, PPV was 91%, and NPV was 92.8%. There was a significant increase within groups in MDA, procalcitonin, ESR, and calprotectin with P value of 0.22, 0.015, 0.000, and 0.009, respectively.
Conclusion
Xpert MTB/RIF as POCT for TB diagnosis is more sensitive and specific than traditional methods of diagnosis using ZN to overcome the challenges with weak testing infrastructure especially during the COVID-19 pandemic. Serum calprotectin was significantly increased in the COVID-19 group compared with the TB group C-reactive protein, which was significantly increased in the TB group compared with COVID-19 group
Ordinal optimization for dynamic network reconfiguration
No full textMotivated by the challenge of efficiently reconfiguring distribution networks for power loss reduction, this study presents an approach for finding a minimum loss radial configuration for a power network using ordinal optimization. Ordinal optimization relies on order comparison and goal softening to make the problem solution easier and the computation more efficient. The successful application of ordinal optimization to such a complex optimization problem required the investigation of several algorithmic parameters. The solution algorithm was implemented in a software package, where an acceptable solution is considered good enough if it is in the top mpercent of the solutions with a probability P. Testing it on 33- and 136-bus systems, minimal power loss results were obtained on the 33-bus system that are in the top 0.03percent of the search space. Comparing the experimental results with other recently published methods showed the effectiveness of ordinal optimization for minimum loss calculations and motivated further studies in smart-grid-like scenarios, where the results obtained for different load levels were in the top 0.13percent of the search space. © 2011 Copyright Taylor and Francis Group, LLC.Abdelaziz A. Y., 2009, IEEE POW EN SOC M CA; Abdelaziz AY, 2010, ELECTR POW SYST RES, V80, P943, DOI 10.1016-j.epsr.2010.01.001; Baran M. E., 1989, IEEE T POWER DELIVER, V4, P101; Braverman M., 2007, 22 ANN IEEE C COMP C, P225; BUNCH JB, 1982, IEEE T POWER AP SYST, V101, P284, DOI 10.1109-TPAS.1982.317104; Carreno EM, 2008, IEEE T POWER SYST, V23, P1542, DOI 10.1109-TPWRS.2008.2002178; CASTRO CA, 1990, ELECTR POW SYST RES, V19, P137, DOI 10.1016-0378-7796(90)90064-A; CHIANG HD, 1990, IEEE T POWER DELIVER, V5, P1568, DOI 10.1109-61.58002; CIVANLAR S, 1988, IEEE T POWER DELIVER, V3, P1217, DOI 10.1109-61.193906; Debs A. S., 1987, MODERN POWER SYSTEM, P180; de Oliveira LW, 2010, INT J ELEC POWER, V32, P840, DOI 10.1016-j.ijepes.2010.01.030; Dogrusoz U., 1994, INT C COMP INF APR, V6, P46; Fusheng Li, 2009, P 6 ANN IEEE COMM SO, P1, DOI 10.1109-ICUT.2009.5405702; GOSWAMI SK, 1992, IEEE T POWER DELIVER, V7, P1484, DOI 10.1109-61.141868; Ho Y. C., 1992, DISCRETE EVENT DYN S, V2, P61, DOI 10.1007-BF01797280; Ho Y. C., 1994, P 33 IEEE C DEC CONT, V2, P1470; Ho Y.C., 2007, ORDINAL OPTIMIZATION, P7; Kachem M. A., 2000, ELECT POWER ENERGY S, V22, P269; Kashem MA, 1999, IEE P-GENER TRANSM D, V146, P563, DOI 10.1049-ip-gtd:19990694; Lau TWE, 1997, J OPTIMIZ THEORY APP, V93, P455, DOI 10.1023-A:1022614327007; Mantovani JRS, 2000, SBA CONTROLE AUTOMAC, V11, P150; MAYEDA W, 1965, IEEE T CIRCUITS SYST, VCT12, P181; Merlin A, 1975, P 5 POW SYST COMP C, P1; Morton AB, 2000, IEEE T POWER DELIVER, V15, P996, DOI 10.1109-61.871365; NARA K, 1992, IEEE T POWER SYST, V7, P1044, DOI 10.1109-59.207317; Ravibabu P, 2008, IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering. SIBIRCON 2008, DOI 10.1109-SIBIRCON.2008.4602603; SHERMAN J, 1950, ANN MATH STAT, V21, P124, DOI 10.1214-aoms-1177729893; SHIRMOHAMMADI D, 1989, IEEE T POWER DELIVER, V4, P1492, DOI 10.1109-61.25637; Sivanagaraju S, 2006, ELECTR POW COMPO SYS, V34, P249, DOI 10.1080-15325000500240854; Sivanagaraju S, 2008, ELECTR POW COMPO SYS, V36, P513, DOI 10.1080-15325000701735389; Swarnkar A, 2011, ELECTR POW SYST RES, V81, P1619, DOI 10.1016-j.epsr.2011.03.020; Yu Y., 2002, IEEE T POWER SYST, V3, P172953
Superconducting properties of Tl-2223 phase substituted by iron
No full textBulk superconducting samples of type Tl2Ba2Ca 2Cu3-xFexO10-δ; with 0 x 0.4, have been prepared using a single step of solid-state reaction. The prepared samples have been characterized using X-ray powder diffraction (XRD), scanning electron microscope (SEM) and microprobe analysis (MPA). The tetragonal structure of Tl-2223 did not change with the partial replacement of Cu 2+ by Fe3+ ions, whereas the lattice parameters were found to vary as function of Fe-content. The superconducting transition temperature Tc determined from electrical resistivity and ac magnetic susceptibility measurements shows suppression in its value as Fecontent increases. The suppression in Tc was attributed to the magnetic disorder and Cooperpairs breaking. The critical current density Jc and field irreversibility Bir were calculated as function of Fe-content. © 2006 IOP Publishing Ltd.Abou-Aly A. I., 2002, INT C RES TRENDS SCI, P91; Awad R, 2000, PHYSICA C, V341, P685, DOI 10.1016-S0921-4534(00)00650-X; Awad R, 2001, PHYSICA B, V307, P72, DOI 10.1016-S0921-4526(01)00971-1; BEAN CP, 1964, REV MOD PHYS, V36, P31, DOI 10.1103-RevModPhys.36.31; ESKES H, 1988, PHYS REV LETT, V61, P1415, DOI 10.1103-PhysRevLett.61.1415; GOTO T, 1997, PHYSICA C, V263, P8750; Isber S, 2005, SUPERCOND SCI TECH, V18, P311, DOI 10.1088-0953-2048-18-3-018; Koo JH, 2003, J PHYS-CONDENS MAT, V15, pL729, DOI 10.1088-0953-8984-15-46-L03; Li Y, 1999, PHYSICA C, V315, P129, DOI 10.1016-S0921-4534(99)00209-9; SIEGAD MP, 1997, J MATER RES, V12, P1421; WESTERHOLT K, 1989, PHYS REV B, V39, P11680, DOI 10.1103-PhysRevB.39.1168022
Mixed-integer quadratic programming based rounding technique for power system state estimation with discrete and continuous variables
No full textAs a basic energy management system function that processes real-time measurements, state estimation deals with both continuous and discrete variables to estimate states in a power system. One possible source for the discrete parameters is the transformer taps, whose positions should be estimated with high confidence. Given that the tap estimation error causes a network topological modeling inaccuracy, its range should be minimized. Motivated to accurately estimate a state vector including transformer taps, this research presents an estimator based on mixed-integer quadratic programming and a comparison to a recently proposed ordinal optimization formulation based on a sensitivity analysis. Experimental results on the IEEE 30-, 57-, and 118-bus benchmarks reveal a slight superiority of the mixed-integer quadratic programming algorithm in terms of overall accuracy and motivate follow-up research. Copyright © Taylor and Francis Group, LLC.Abur A., 2004, POWER SYSTEM STATE E; Atmaca E, 2008, ELECTR POW SYST RES, V78, P694, DOI 10.1016-j.epsr.2007.05.012; Capitanescu F, 2010, IEEE T POWER SYST, V25, P1780, DOI 10.1109-TPWRS.2010.2044426; CHRISTENSEN GS, 1990, AUTOMATICA, V26, P389, DOI 10.1016-0005-1098(90)90134-4; FLETCHER DL, 1983, IEEE T POWER AP SYST, V102, P3680, DOI 10.1109-TPAS.1983.317732; HANDSCHIN E, 1995, IEEE T POWER SYST, V10, P810, DOI 10.1109-59.387921; IRVING MR, 1978, P I ELECTR ENG, V125, P879; Katsikas P., 2003, IEEE BOL POW TECH C; KOTIUGA WW, 1982, IEEE T POWER AP SYST, V101, P844, DOI 10.1109-TPAS.1982.317150; Lin S. S., 2008, COMP OO BASED METHOD; Lin SS, 2008, IET GENER TRANSM DIS, V2, P576, DOI 10.1049-iet-gtd:20070446; Lin SS, 2010, IEEE T POWER SYST, V25, P234, DOI 10.1109-TPWRS.2009.2030368; Meliopoulos A., 2001, IEEE POW ENG SOC SUM, V1, P419; MILI L, 1994, IEEE T CIRCUITS-I, V41, P349, DOI 10.1109-81.296336; Mosek ApS, HIGH PERF OPT SOFTW; MUKHERJEE BK, 1984, IEEE T POWER AP SYST, V103, P1454, DOI 10.1109-TPAS.1984.318483; PSerc, MATPOWER MATLAB POW; TEIXEIRA PA, 1992, IEEE T POWER SYST, V7, P1386, DOI 10.1109-59.207358; VANCUTSEM T, 1988, IEE PROC-C, V135, P31; Winker P, 2004, COMPUT STAT DATA AN, V47, P211, DOI 10.1016-j.csda.2003.11.026; Wolsey LA, 1998, INTEGER PROGRAMMING; Wu F. F., 1988, ELECTRICAL POWER ENE, V10, P83; WU FF, 1990, INT J ELEC POWER, V12, P80, DOI 10.1016-0142-0615(90)90003-T0
Entropy-based and weighted selective sift clustering as an energy aware framework for supervised visual recognition of man-made structures
No full textUsing local invariant features has been proven by published literature to be powerful for image processing and pattern recognition tasks. However, in energy aware environments, these invariant features would not scale easily because of their computational requirements. Motivated to find an efficient building recognition algorithm based on scale invariant feature transform (SIFT) keypoints, we present in this paper uSee, a supervised learning framework which exploits the symmetrical and repetitive structural patterns in buildings to identify subsets of relevant clusters formed by these keypoints. Once an image is captured by a smart phone, uSee preprocesses it using variations in gradient angle- and entropy-based measures before extracting the building signature and comparing its representative SIFT keypoints against a repository of building images. Experimental results on 2 different databases confirm the effectiveness of uSee in delivering, at a greatly reduced computational cost, the high matching scores for building recognition that local descriptors can achieve. With only 14.3percent of image SIFT keypoints, uSee exceeded prior literature results by achieving an accuracy of 99.1percent on the Zurich Building Database with no manual rotation; thus saving significantly on the computational requirements of the task at hand. © 2013 Ayman El Mobacher et al.Awad M., 2009, P 5 INT C SOFT COMP; Bonaiuto J. J., 2005, P 3 INT WORKSH ATT P; Gao K, 2008, 7TH IEEE-ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE IN CONJUNCTION WITH 2ND IEEE-ACIS INTERNATIONAL WORKSHOP ON E-ACTIVITY, PROCEEDINGS, P191, DOI 10.1109-ICIS.2008.24; Kennedy Lyndon S., 2008, P 17 INT C WORLD WID, P297, DOI 10.1145-1367497.1367539; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023-B:VISI.0000029664.99615.94; Pass G, 1999, MULTIMEDIA SYST, V7, P234, DOI 10.1007-s005300050125; Quack T., 2008, P INT C CONT BAS IM, P47, DOI DOI 10.1145-1386352.1386363; Shao H, 2003, LECT NOTES COMPUT SC, V2728, P71; Shao H, 2003, 260 SWISS FED I TECH; Zhang W., 2005, IEEE COMP SOC C COMP, P21; Zhang W., 2004, GMUCSTR20043; Zheng YT, 2009, PROC CVPR IEEE, P10850
Effect of Black Mustard (Brassica nigra) on the Interaction between Immune and Biotransformation Systems of Nile Tilapia (Oreochromis niloticus) Exposed to Benzo-a-Pyrene
No full textEnergy-Aware Discrete Probabilistic Localization of Wireless Sensor Networks
No full textLocalizing sensor nodes is critical in the context of wireless sensor network applications. It has been shown that, for some applications, low-overhead discrete localization achieves results comparable to costly fine localization. This research presents a hybrid energy-aware discrete localization method that requires no transmission overhead from the sensor nodes. The proposed method, E-KalmaNN, is a combination of a Kalman-inspired localization and Artificial Neural Networks estimation that updates the position of a node with respect to a mobile reference. E-KalmaNN runs on the sensor nodes and supports different listening-wakeup frequencies for different nodes to balance power requirements with localization accuracy for each node. Simulation results show that the method converges to the correct position of the node in a relatively short time with high average location accuracy. Compared to the localization methods found in the literature, E-KalmaNN localizes with comparable accuracy, lower transmission costs and-or fewer motion restrictions. © 2013 Copyright TSI® Press.Amro A., 2008, IEEE ASME INT C ADV; Bulusu N, 2000, IEEE PERS COMMUN, V7, P28, DOI 10.1109-98.878533; Elhajj I.H., 2006, IEEE RSJ INT C INT R; Galstyan A., 2004, P 3 INT S INF PROC S, P61, DOI 10.1145-984622.984632; Gorski J., 2005, IEEE ASME INT C ADV, P735; Hu L., 2004, P 10 ANN INT C MOB C; Karl H, 2005, PROTOCOLS AND ARCHITECTURES FOR WIRELESS SENSOR NETWORKS, P1, DOI 10.1002-0470095121; Kecman V., 2001, LEARNING SOFT COMPUT; Khan Haseebulla M., 2006, P 2 IEEE WORKSH DEP; Moore D., 2004, P 2 ACM C EMB NETW S; Priyantha N. B., 2003, 892 MIT LAB COMP SCI; Priyantha Nissanka B., 2000, P 6 ACM MOBICOM BOST; Ramadurai V., 2007, P ACM SIGMOBILE MOB, V11, P53; Reichenbach F., 2006, P 9 EUROMICRO C DIG; Savarese C., 2001, P INT C AC SPEECH SI; Want R., 1992, ACM T INFORM SYSTEMS, V10; Welch G., 2001, INTRO KALMAN FILTER; Xiao B., 2007, P IEEE INT C ICC 070
An educational intervention to increase advance directive completion in the intensive care unit
No full textAdvance care planning (ACP) enables and empowers individuals to drive their medical care, especially once they become incapacitated by disease or terminal illness. Advance directives (ADs) have proven to decrease end-of-life expenditures by Medicare. In addition, completion of an AD increases the probability of medical care that coincides with the patient’s wishes and decreases decision-making burden for the family and physicians.
Purpose of Project
The purpose of this quality improvement project is to assess the effectiveness in increasing advance directive completion rates by offering an educational intervention for healthcare providers about advance care planning.
Methodology
This quality improvement project was designed with implementation of a pre- and post-test survey design to measure healthcare provider knowledge on proper ACP approaches. Thirty healthcare providers (internal medicine residents and advanced nurse practitioners) that rotate through the intensive care unit (ICU) were offered education on advance care planning utilizing conversation guides and a presentation developed by the head of palliative care at Johns Hopkins Hospital. The number of ADs on file in the ICU pre-implementation and post-implementation was evaluated by counting the number of ADs on file 3 months prior to implementation compared to the number of ADs on file over a 3-month period after implementation.
Results
A paired-samples t-test was used to compare the pre- and post-test survey results in order to determine the association between reinforced healthcare education and AD completion over a 6-month period. The findings revealed a statistically significant difference in the pre- and post-test scores, with an overall increase in scores post educational sessions. The paired-samples t-test indicated that scores were significantly higher post-implementation (M = 39.5, SD = 7.5) than pre-implementation (M = 30.5, SD = 5.8), t(29) = -6.8, p < .05, d = 1.34. In addition, a clinical significance was noted in the number of ADs on file prior to and after implementation. The number of ADs on file increased from 3 to 7 after implementing this quality improvement project.
Implications for Practice
Based on the findings of this project, interventions to inform healthcare providers about the ACP process should be considered as part of clinical practice. It is strongly suggested that healthcare providers receive periodic updates regarding the ACP process in addition to receiving conversation tools for guidance. If the findings of this intervention could be applied to the overall hospital population, then this simple intervention would likely result in a significant increase in completed ADs overall. Thus, the objective of providing medical care that coincides with ones’ wishes and improved outcomes at the end of life would be a closer reality.DNPIncludes bibliographical reference
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