| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font
First page > Show document

Show document Help

Title:Razvoj modela za ugotavljanje podhranjenosti pri hospitaliziranih pediatričnih pacientih z metodami podatkovnega rudarjenja in neinvazivnimi kazalniki
Authors:ID Klanjšek, Petra (Author)
ID Pajnkihar, Majda (Mentor) More about this mentor... New window
ID Povalej Bržan, Petra (Comentor)
ID Marčun Varda, Nataša (Comentor)
Files:.pdf DOK_Klanjsek_Petra_2024.pdf (8,53 MB)
MD5: 75CF718554F37FC9D8172FA09B954C57
 
Language:Slovenian
Work type:Dissertation
Typology:2.08 - Doctoral Dissertation
Organization:MF - Faculty of Medicine
Abstract:Izhodišča: Neprepoznana podhranjenost pri hospitaliziranih otrocih in mladostnikih lahko vodi v kronično podhranjenost, otežuje zdravljenje osnovne bolezni ter poslabša klinične izide. Z rutinskim presejanjem tveganja za podhranjenost ob hospitalizaciji se olajša pravočasno prepoznavanje podhranjenosti, z ustreznimi prehranskimi intervencijami se preprečijo trajne posledice podhranjenosti, zmanjšajo se stroški zdravljenja in skrajša se hospitalizacija otrok in mladostnikov. Priporoča se uporaba presejalnega orodja, razvitega v kliničnem okolju za točno določeno populacijo hospitaliziranih otrok in mladostnikov ter kliniko. Namen doktorske disertacije je bil razviti model za ugotavljanje tveganja podhranjenosti pri hospitaliziranih otrocih in mladostnikih z metodami podatkovnega rudarjenja in neinvazivnimi kazalniki. Metode: Izvedli smo presečno opazovalno raziskavo z uporabo zaporednega eksplorativnega načrta mešanih metod na populaciji hospitaliziranih otrok in mladostnikov, starih od 1 meseca do 18 let. V kvalitativnem delu smo podatke zbrali s pregledom, analizo in sintezo literature ter jih analizirali z induktivnim generiranjem kategorij spremenljivk, ki so bile vključene v obrazec z vprašanji. V kvantitativnem delu smo podatke zbrali z obrazcem z vprašanji, zdravnikovo poglobljeno oceno prehranskega tveganja, klasifikacijo prehranskega stanja Svetovne zdravstvene organizacije, antropometričnimi meritvami in anketiranjem staršev otrok oz. mladostnikov. Podatke smo analizirali z uporabo deskriptivne in inferenčne statistike ter inteligentnimi metodami podatkovnega rudarjenja. Rezultati: Od 180 otrok in mladostnikov jih je v učni skupini sodelovalo 142 in v testni 38. Od 277 zbranih spremenljivk, vključenih v zbiranje podatkov, smo v razvoj modelov vključili 30 statistično značilnih, kot so: izguba telesne mase, izguba mišične ali maščobne mase, prehranski vnos, gastrointestinalni simptomi. Razvili smo 3 statistične modele in 10 modelov podatkovnega rudarjenja. Najboljše rezultate testiranja ima model GP (AUC = 1, 95 % IZ 1, 1), med statističnimi pa model Logistična regresija (AUC = 0,977, 95 % IZ 0,922, 1). Ujemanje modela GP s poglobljeno oceno prehranskega tveganja je popolno (κ = 1, 95 % IZ 1, 1). Ujemanje modela Logistična regresija s poglobljeno oceno prehranskega tveganja je prav tako skoraj popolno (κ = 0, 837, 95 % IZ 0,659, 1,014) s Se 93,3 %, Sp 91,3 %, PPV 95,5 % in NPV 87,5 %. Ujemanje s SZO klasifikacijo prehranskega stanja je pri obeh modelih precejšnje (κ = med 0,73 in 0,78). Nobeden od razvitih modelov se ne razlikuje statistično značilno od poglobljene ocene prehranskega tveganja in SZO klasifikacije prehranskega stanja. Model, razvit z inteligentnimi metodami, je v primerjavi s statističnim modelom uspešnejše ugotavljal podhranjenost pri hospitaliziranih otrocih in mladostnikih, prav tako v primerjavi s SZO klasifikacijo prehranskega stanja. Razprava in zaključek: Vseh 13 razvitih modelov presejanja je dokazano veljavnih z visoko napovedno vrednostjo ugotavljanja tveganja za podhranjenost. Priporočamo testiranje modelov na večji populaciji hospitaliziranih otrok in mladostnikov v ostalih pediatričnih zdravstvenih institucijah v Sloveniji. S tem bi modele modificirali, dopolnjevali in prilagodili kliniki, v kateri bi jih uporabljali z namenom zagotavljanja kakovosti celostne zdravstvene obravnave otrok in mladostnikov. Uvajanje rutinskega prehranskega presejanja z razvitimi modeli predstavlja temelj sistematične obravnave klinične poti prehranskega presejanja.
Keywords:tveganje za podhranjenost, nedohranjenost, prehransko presejalno orodje, otrok, prehransko stanje
Place of publishing:Maribor
Publisher:[P. Klanjšek]
Year of publishing:2024
PID:20.500.12556/DKUM-81979 New window
UDC:616-056.25-053.2:004.6/.9(043.3)
COBISS.SI-ID:209381891 New window
Publication date in DKUM:30.09.2024
Views:0
Downloads:39
Metadata:XML DC-XML DC-RDF
Categories:MF
:
Copy citation
  
Average score:(0 votes)
Your score:Voting is allowed only for logged in users.
Share:Bookmark and Share


Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:24.06.2022

Secondary language

Language:English
Title:Development of a model for identifying malnutrition in hospitalized pediatric patients with data mining methods and non-invasive indicators
Abstract:Background: Unrecognized malnutrition in hospitalized children and adolescents can lead to chronic malnutrition, complicate treatment of the underlying disease and worsen clinical outcomes. Routine screening for the risk of malnutrition at admission facilitates the timely identification of malnutrition and appropriate dietary interventions, prevents permanent consequences of malnutrition, reduces treatment costs, and shorten the hospitalization. The application of a screening tool developed in a clinical setting for specific population of hospitalized children and clinic is recommended. Therefore, the aim was to develop a screening model for identifying malnutrition in hospitalized children and adolescents with data mining methods and non-invasive indicators. Methods: A cross-sectional observational study using mixed methods of exploratory sequential design was conducted on a population of hospitalized children and adolescents aged from 1 month till 18 years. Data within the qualitative strand were gathered through the review, analysis, and synthesis of literature, afterward analysed by inductively generating categories of variables that were included in the question form. Data within the quantitative strand were gathered with a question form, a doctor's an in-depth nutritional risk assessment and the World Health Organisation's classification of nutritional status, anthropometric measurements, and survey of parents with children and adolescents. Data were analysed using descriptive and inferential statistics and intelligent data mining methods. Results: A total of 180 recruited hospitalized children and adolescents, 142 participated in the study group and 38 in the test group. Of total 277 collected variables included in the data gathering, 30 statistically significant ones were included in the development of the models, such as: weight loss, loss of muscle or fat mass, dietary intake, gastrointestinal symptoms. There were 3 statistical models and 10 data mining models developed. The GP model has the best test results (AUC = 1, 95 % CI 1, 1) and among the statistical ones, the Logistic Regression model (AUC = 0,977, 95 % CI 0,922, 1). The agreement between GP model and the in-depth nutritional risk assessment was perfect (κ = 1, 95 % CI 1, 1). The agreement between Logistic regression model and the in-depth nutritional risk assessment was also almost perfect (κ = 0.837, 95 % CI 0,659, 1,014) with Se 93,3 %, Sp 91,3 %, PPV 95,5 % and NPV 87,5 %. Both models agreed with the WHO classification of nutritional status substantially (κ = between 0,73 and 0,78). None of the developed models did not differ statistically significantly from the in-depth nutritional risk assessment or from WHO classification of nutritional status. The model developed with intelligent methods compared to the statistical model more successfully determined malnutrition in hospitalized children, equally so compared to the WHO classification of nutritional status. Conclusions: All 13 developed screening models have been shown to be valid, with a high predictive value for determining the risk of malnutrition. We recommend further testing of the models on a larger population of hospitalized children in other pediatric health institutions in Slovenia. This would allow models to be modified, supplemented, and adapted for the clinic in which they would be used to ensure the quality of integrated health care for children. The introduction of routine nutritional screening by the developed models represents the foundation of the systematic approach to the clinical pathway of nutritional screening.
Keywords:risk of malnutrition, undernutrition, nutritional screening tool, child, adolescent, nutritional status


Comments

Leave comment

You must log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica