Study of surgical wound infections after sternotomy at patients with surgical revascularization of the heart

Uvod: Okužbe kirurških ran po srčnih operacijah se v svetu in v Sloveniji kljub antibiotični preventivi še vedno pojavljajo in negativno vplivajo na pacienta ter bremenijo ekonomski zdravstveni sistem. Koronarna bolezen je eden najpogostejših vzrokov smrti v svetu, ki se jo zdravi tudi z metodo kirurške revaskularizacije srca. Uporaba negativnega tlaka je metoda, ki se v kirurgiji že nekaj časa izvaja za zdravljenje okuženih kirurških ran, vse bolj pa se jo izvaja tudi kot preventivo kirurških okužb na zašitih kirurških ranah. Namen: Želeli smo raziskati in predstaviti vpliv posameznih faktorjev na razvoj okužbe kirurške rane po sternotomiji po posegu kirurške revaskularizacije srca ter preučiti vpliv uporabe negativnega tlaka pri preprečevanju okužbe kirurške rane po sternotomiji. Metode dela: Za potrebe empiričnega dela so bili analizirani primarni in sekundarni viri. Izvedena je bila retrospektivna študija 592 pacientov, pri katerih smo preverjali in z metodo logistične regresije napovedovali vpliv faktorjev na okužbo kirurške rane po sternotomiji po posegu kirurške revaskularizacije srca. V sklopu večje prospektivne študije je bila izvedena pilotna študija 53 pacientov, pri katerih smo preverjali vpliv uporabe negativnega tlaka na preprečevanje razvoja okužbe kirurške rane po sternotomiji po posegu kirurške revaskularizacije srca. Rezultati: Od opazovanih faktorjev sta se kot statistično značilno pomembna za razvoj okužbe kirurške rane po sternotomiji pokazala indeks telesne mase (p = 0,043) in trajanje operacije (p = 0,007). Smrtnost in pojav infekta nista bila statistično značilno povezana (p = 0,211). Pri pilotni študiji sta si bila vzorca med seboj statistično značilno podobna, razlikovala sta se samo v prisotnosti diabetesa (p = 0,034), rezultati preliminarne pilotne študije pa nakazujejo na to, da naj vrsta oskrbe kirurške rane ne bi vplivala na pogostost pojava okužbe kirurške rane po sternotomiji (p = 0,745). Razprava in sklep: Pri pacientih, ki imajo opravljeno večje število obvodov in pri katerih operacija traja dalj časa, ter pacientih z višjim indeksom telesne mase bi bilo smiselno preventivno uporabljati obliže z negativnim tlakom za kirurško rano, saj bi oskrba rane v primerjavi z ekonomskim bremenom ob pojavu okužbe predstavljala minimalen strošek.Introduction: Sternal wound infections after cardiac surgery still occur worldwide and in Slovenia, despite the preventive use of antibiotics, and negatively affect the patient outcome and also the economics of the health system. Coronary artery disease is among the most common causes of death in the world, and it is commonly treated with the method of surgical revascularization of the heart. The method of negative pressure wound therapy is used in surgery for a while, mostly for the treatment of infected surgical wounds. Increasingly the negative pressure is used as a prevention of closed surgical wound infections. Purpose: We wanted to explore and represent the effect of different risk factors on development of surgical wound infections after sternotomy after surgical revascularization of the heart, and to examine the effect of the negative pressure method on prevention of closed sternal surgical wound infections. Methods: Primary and secondary sources were analyzed for the empirical part. Retrospective study was performed on 592 patient, in which we have explored and with the method of logistic regression predicted the influence of factors on the development of wound infection after sternotomy after surgical revascularization of the heart. In the context of larger prospective study the pilot study on 53 patients was done, in which we examined the influence of negative wound pressure method on prevention of development of wound infection after sternotomy after surgical revascularization of the heart. Results: From all observed factors, body mass index (p = 0,043) and the operative time (p = 0,007) statistically significantly influenced on development of sternal surgical infections. Mortality and the occurrence of infection were not significantly associated (p = 0,211). In a pilot study, the two groups were almost statistically similar, the only difference between groups was in the presence of diabetes (p = 0,034). Results of a preliminary pilot study suggest that the type of surgical wound care does not affect the occurrence of wound infection after sternotomy (p = 0,745). Discussion and conclusion: In patients with higher number of grafts and consequently longer operative time and patients with increased body mass index, it would be reasonable to use a dressing with negative pressure for the prevention of surgical wound infection. The cost of negative pressure dressing would be minimal compared to the economic burden of the wound infections after sternotomy

Uporaba virtualnih simulacij ali virtualnih pacientov pri izobraževanju študentov v zdravstveni negi

Introduction: The Covid-19 epidemic has significantly compromised the practical training of nursing students. While in nursing, the use of simulation is not new, virtual simulation or virtual patients represent relatively new educational modalities. The aim of this literature review was to examine the most recent empirical evidence on the efficacy or effectiveness of using virtual simulation or virtual patients in nursing education around the world.Methods: Scholarly articles published between 2016 and 2021 in the CINAHL, MEDLINE, ERIC and COBIB bibliographic databases were reviewed. The review included articles which focused on student nurses using virtual simulation or virtual patients as a method of learning rather than as a way of assessing students\u27 knowledge acquired through a different learning method. A thematic analysis was used to synthesise the results. Results: Twelve studies were included in the review, most of which were conducted in developed countries. The results showed that the use of virtual simulation or virtual patients has a positive effect on the acquisition of cognitive and affective knowledge, practical implementation of interventions, assessment of self-efficacy and competence, and student satisfaction.Discussion and conclusion: In situations where clinical training is not possible for nursing students, the use of virtual simulation or virtual patients can replace the clinical setting for the purposes of practising clinical decisions, but it cannot replace the clinical education and experience students obtain when working with actual patients.Uvod: Epidemija covida-19 je omejila praktično usposabljanje študentov zdravstvene nege. V zdravstveni negi simulacije niso novost, razmeroma novo področje izobraževanja pa so virtualne simulacije ali virtualni pacienti. Namen pregleda je bil preučiti najnovejše empirične ugotovitve o učinkovitosti oziroma uspešnosti uporabe virtualnih simulacij ali virtualnih pacientov pri študiju zdravstvene nege po svetu.Metode: Pregledani so bili znanstveni članki, objavljeni med letoma 2016 in 2021 v bibliografskih bazah CINAHL, MEDLINE, ERIC ter v kataložno-bibliografski bazi podatkov COBIB. Proučevani so bili članki, ki so obravnavali študente zdravstvene nege, pri katerih sta bila virtualna simulacija ali virtualni pacient uporabljena za način učenja in ne kot način preverjanja usvojenega znanja druge učne metode. Sinteza rezultatov je bila narejena s tematsko analizo. Rezultati: Vključenih je bilo 12 raziskav, ki so bile večinoma opravljene v razvitih državah. Rezultati so pokazali pozitiven vpliv virtualnih simulacij ali virtualnih pacientov na kognitivno in afektivno domeno znanja, praktično izvedbo intervencij, oceno samoučinkovitosti, kompetentnosti in zadovoljstva študentov.Diskusija in zaključek: Kadar kliničnega usposabljanja za študente zdravstvene nege ni mogoče zagotoviti, so virtualne simulacije ali virtualni pacienti lahko eno izmed možnih okolij za trening kliničnih odločitev, ki bi jih študent sicer pridobil v klinični praksi, ne morejo pa nadomestiti kliničnega usposabljanja in izkušenj, ki jih študent pridobi ob pacientu

Ključni izzivi pri modeliranju epidemije - dosedanje izkušnje pri modeliranju epidemije COVID-19

Mathematical modelling can be useful for predicting how infectious diseases progress, enabling us to show the likely outcome of an epidemic and help inform public health interventions. Different modelling techniques have been used to predict and simulate the spread of COVID-19, but they have not always been useful for epidemiologists and decision-makers. To improve the reliability of the modelling results, it is very important to critically evaluate the data used and to check whether or not due regard has been paid to the different ways in which the disease spreads through the population. As building an epidemiological model that is reliable enough and suits the current epidemiological situation within a country or region, certain criteria must be met in the modelling process. It might be necessary to use a combination of two or more different types of models in order to cover all aspects of epidemic modelling. If we want epidemiological models to be a useful tool in combating the epidemic, we need to engage experts from epidemiology, data science and statistics.Matematično modeliranje je lahko koristno za napovedovanje razvoja nalezljivih bolezni, saj s prikazom možnih izidov epidemije pomaga oblikovati javnozdravstvene ukrepe. Za napovedovanje in simulacijo širjenja v času epidemije COVID-19 so bile uporabljene različne tehnike modeliranja, vendar vse niso bile vedno koristne za epidemiologe in odločevalce. Da bi bili rezultati modeliranja zanesljivejši, je zelo pomembno kritično ovrednotiti uporabljene podatke ter preveriti, ali so bili upoštevani različni načini širjenja bolezni v populaciji ali ne. Izdelava dobrega epidemiološkega modela, ki je dovolj zanesljiv in ustreza trenutnim epidemiološkim razmeram v državi ali regiji, je zahtevna, zato je treba pri modeliranju slediti določenim kriterijem. Smiselno bi bilo tudi kombinirati dve različni vrsti modelov. Modeliranje bi bilo tako zanesljivejše, saj bi upoštevalo različne predpostavke. Če želimo, da bodo epidemiološki modeli koristno orodje v boju proti epidemiji, morajo pri modeliranju sodelovati strokovnjaki z različnih področij, predvsem epidemiologije, podatkovne znanosti in statistik

Extended compartmental model for modeling COVID‑19 epidemic in Slovenia

In the absence of a systematic approach to epidemiological modeling in Slovenia, various isolated mathematical epidemiological models emerged shortly after the outbreak of the COVID-19 epidemic. We present an epidemiological model adapted to the COVID-19 situation in Slovenia. The standard SEIR model was extended to distinguish between age groups, symptomatic or asymptomatic disease progression, and vaccinated or unvaccinated populations. Evaluation of the model forecasts for 2021 showed the expected behavior of epidemiological modeling: our model adequately predicts the situation up to 4 weeks in advancethe changes in epidemiologic dynamics due to the emergence of a new viral variant in the population or the introduction of new interventions cannot be predicted by the model, but when the new situation is incorporated into the model, the forecasts are again reliable. Comparison with ensemble forecasts for 2022 within the European Covid-19 Forecast Hub showed better performance of our model, which can be explained by a model architecture better adapted to the situation in Slovenia, in particular a refined structure for vaccination, and better parameter tuning enabled by the more comprehensive data for Slovenia. Our model proved to be flexible, agile, and, despite the limitations of its compartmental structure, heterogeneous enough to provide reasonable and prompt short-term forecasts and possible scenarios for various public health strategies. The model has been fully operational on a daily basis since April 2020, served as one of the models for decision making during the COVID-19 epidemic in Slovenia, and is part of the European Covid-19 Forecast Hub

Key Challenges in Modelling an Epidemic – What Have we Learned from the COVID-19 Epidemic so far

Mathematical modelling can be useful for predicting how infectious diseases progress, enabling us to show the likely outcome of an epidemic and help inform public health interventions. Different modelling techniques have been used to predict and simulate the spread of COVID-19, but they have not always been useful for epidemiologists and decision-makers. To improve the reliability of the modelling results, it is very important to critically evaluate the data used and to check whether or not due regard has been paid to the different ways in which the disease spreads through the population. As building an epidemiological model that is reliable enough and suits the current epidemiological situation within a country or region, certain criteria must be met in the modelling process. It might be necessary to use a combination of two or more different types of models in order to cover all aspects of epidemic modelling. If we want epidemiological models to be a useful tool in combating the epidemic, we need to engage experts from epidemiology, data science and statistics

Parallel sense-annotated corpus ELEXIS-WSD 1.1

ELEXIS-WSD is a parallel sense-annotated corpus in which content words (nouns, adjectives, verbs, and adverbs) have been assigned senses. Version 1.1 contains sentences for 10 languages: Bulgarian, Danish, English, Spanish, Estonian, Hungarian, Italian, Dutch, Portuguese, and Slovene. The corpus was compiled by automatically extracting a set of sentences from WikiMatrix (Schwenk et al., 2019), a large open-access collection of parallel sentences derived from Wikipedia, using an automatic approach based on multilingual sentence embeddings. The sentences were manually validated according to specific formal, lexical and semantic criteria (e.g. by removing incorrect punctuation, morphological errors, notes in square brackets and etymological information typically provided in Wikipedia pages). To obtain a satisfying semantic coverage, we filtered out sentences with less than 5 words and less than 2 polysemous words were filtered out. Subsequently, in order to obtain datasets in the other nine target languages, for each selected sentence in English, the corresponding WikiMatrix translation into each of the other languages was retrieved. If no translation was available, the English sentence was translated manually. The resulting corpus is comprised of 2,024 sentences for each language. The sentences were tokenized, lemmatized, and tagged with POS tags using UDPipe v2.6 (https://lindat.mff.cuni.cz/services/udpipe/). Senses were annotated using LexTag (https://elexis.babelscape.com/): each content word (noun, verb, adjective, and adverb) was assigned a sense from among the available senses from the sense inventory selected for the language (see below) or BabelNet. Sense inventories were also updated with new senses during annotation. List of sense inventories BG: Dictionary of Bulgarian DA: DanNet – The Danish WordNet EN: Open English WordNet ES: Spanish Wiktionary ET: The EKI Combined Dictionary of Estonian HU: The Explanatory Dictionary of the Hungarian Language IT: PSC + Italian WordNet NL: Open Dutch WordNet PT: Portuguese Academy Dictionary (DACL) SL: Digital Dictionary Database of Slovene The corpus is available in the CoNLL-U tab-separated format. In order, the columns contain the token ID, its form, its lemma, its UPOS-tag, five empty columns (reserved for e.g. dependency parsing, which is absent from this version), and the final MISC column containing the following: the token's whitespace information (whether the token is followed by a whitespace or not), the ID of the sense assigned to the token, and the index of the multiword expression (if the token is part of an annotated multiword expression). Each language has a separate sense inventory containing all the senses (and their definitions) used for annotation in the corpus. Not all the senses from the sense inventory are necessarily included in the corpus annotations: for instance, all occurrences of the English noun "bank" in the corpus might be annotated with the sense of "financial institution", but the sense inventory also contains the sense "edge of a river" as well as all other possible senses to disambiguate between. For more information, please refer to 00README.txt. Differences to version 1.0: - Several minor errors were fixed (e.g. a typo in one of the Slovene sense IDs). - The corpus was converted to the true CoNLL-U format (as opposed to the CoNLL-U-like format used in v1.0). - An error was fixed that resulted in missing UPOS tags in version 1.0. - The sentences in all corpora now follow the same order (from 1 to 2024)