Measurement of Spinal Sagittal Curvatures using the Laser Triangulation Method

The purpose of the first part of the study was to establish the variability of repeated measurements in different measuring conditions. In the second part, we performed in a large number of patients, a measurement of thoracic kyphosis and lumbar lordosis and compared them to age, gender, and level of nourishment. In the first part, measurements were performed on a plastic model of the back of a patient with a rigid and a normal spine. In the second part, 250 patients participated in the study (126 men and 124 women). For measuring spinal curvatures we used an apparatus for laser triangulation constructed at the Faculty of Mechanical Engineering, University of Ljubljana. A comparison of 30 repeated measurements was shown as the average value±2 SD which included 95% of the results. Thirty repeated readings of one 3D measurement: thoracic kyphosis 41.2°±0.6°, lumbar lordosis 4.4°±1.2°; 30 measurements on a plastic model: thoracic kyphosis 36.8°±1.2°, lumbar lordosis 30.9°±2.0°; 30 measurements on a patient with a rigid spine: thoracic kyphosis 41.5°±2.4°, lumbar lordosis 4.0°±1.8°; 30 measurements on a patient with a normal spine: thoracic kyphosis 48.8°±7.4°, lumbar lordosis 21.1°±4.4°. The average size of thoracic kyphosis in 250 patients was 46.8° (SD 10.1°) and lumbar lordosis 31.7° (SD 12.5°). The angle size was statistically significantly correlated to gender (increased thoracic kyphosis and lumbar lordosis in women) and body mass index (increased thoracic kyphosis and lumbar lordosis in more nourished patients). Age was not significantly correlated to the observed angles. During measurements of the spinal angles it was important to pay attention to relaxation and the patient’s position as well as to perform more measurements providing the average value. The age and the level of nourishment influence the size of the sagittal spinal angles. In the observed sample the effect of age was not confirmed

Missing values imputation using a rotation regression forest

Manjkajoče vrednosti predstavljajo pogosto težavo, ki spremlja ustvarjanje podatkovnih baz, bodisi če se podatki zbirajo s pomočjo anket bodisi če so pridobljeni iz načrtovanih eksperimentov. Ne glede na to, koliko truda je vloženo za zagotavljanje popolne izpolnjenosti vprašalnikov ali v skrbno načrtovanje znanstvenega poskusa, se manjkajočim vrednostim pogosto ni možno izogniti. Nepopolni podatki so, odvisno od razmerja v katerem se pojavljajo manjkajoče vrednosti, lahko neustrezni za nadaljnjo analizo, medtem ko je brisanje vzorcev z manjkajočimi vrednostmi, posebno ko njihov odstotek ni dovolj majhen in ti vzorci predstavljajo pomembne informacije, lahko zelo neustrezno. Za reševanje tega problema se tako na področju statistične analize uporabljajo različne metode za nadomeščanje manjkajočih vrednosti. Z namenom zapolnitve vrzeli, ki obstaja med obstoječimi metodami enkratnega vstavljanja manjkajočih vrednosti in modeli, ki temeljijo na večkratnem vstavljanju in pri katerih je za vsak cikel vstavljanja potrebna ločena statistična analiza, smo v okviru disertacije razvili nov postopek nadomeščanja manjkajočih vrednosti, ki temelji na ansambelskem pristopu nadzorovanega strojnega učenja. Uporabili smo ansambel, imenovan rotacijski regresijski gozd, ki predstavlja varianto rotacijskega gozda (Rotation forest), kot so ga razvili Rodríguez, Kuncheva in Alonso (Rodríguez, Kuncheva, & Alonso, 2006), pri katerem smo namesto osnovne metode, namenjene reševanju klasifikacijskih problemov, uporabili modelno regresijsko drevo. Našo metodo za nadomeščanje manjkajočih vrednosti smo primerjali z 9 drugimi popularnimi metodami, pri čemer smo merili natančnost metod in njihovo sposobnost ohranjanja variance po vstavljanju različnih deležev manjkajočih vrednosti. Meritve smo izvedli na 14 javno dostopnih podatkovnih množicah in eni umetno ustvarjeni množici, tako da smo obravnavali vse mehanizme nastanka manjkajočih vrednosti, kot jih je definiral Rubin (Rubin, 1976). Na podlagi poizkusov smo ugotovili, da naša metoda v povprečju natančneje napoveduje manjkajoče vrednosti v izbranih podatkovnih množicah, ne glede na mehanizem nastanka manjkajočih vrednosti. Prav tako smo pokazali, da z uvedbo dodatne stohastične metode za ohranjanje variance naš rotacijski regresijski gozd bolje ohranja varianco od vseh preostalih metod, ki izvajajo enkratno vstavljanje, pri čemer po svoji natančnosti še vedno prekaša vse metode. V disertaciji smo v uvodnih, teoretičnih poglavjih podrobneje opisali problematiko manjkajočih vrednosti ter obstoječe metode, ki se najpogosteje uporabljajo za njihovo nadomeščanje. Predstavili smo rotacijski regresijski gozd in stohastično metodo za ohranjanje variance. Največjo pozornost smo posvetili rezultatom poizkusov, na podlagi katerih smo v zaključku izoblikovali priporočila za uporabo rotacijskega regresijskega gozda za nadomeščanje manjkajočih vrednosti ter predstavili izhodišča za nadaljnje delo.Missing values represent a common problem, plaguing many databaseseither based on surveys and questionnaires or designed experiments. No matter how carefully the surveys are taken, or how well the experiments are designed, missing values can occur. Incomplete data can, depending on the amount of missing values, be unsuitable for further statistical analysis, while case deletion, especially when dealing with considerable amounts of missing values, can be very inappropriate. Therefore different methods were developed which can be used to impute missing data. The main goal of this dissertation was to develop a new imputation method, which would narrow the gap between single-impute methods and multiple-imputation models, which require standard statistical analysis to be carried out on multiple imputed data sets. For this purpose we used an ensemble-based approach to supervised machine learning. We relied on a variation of rotation forest ensemble, developed by Rodríguez, Kuncheva and Alonso (Rodríguez, Kuncheva, & Alonso, 2006) which we named “rotation regression forest”, since we used a model regression tree as a base method instead of a method used for classification purposes. We selected 9 other popular imputation methods for comparison with our ensemble where we measured their accuracy as well as their ability to preserve the variance structure within data when dealing with different amounts of missing values. Measurements were carried out on 14 different public access datasets and one artificial dataset to account for each of the three missingness mechanisms, as described by Rubin (Rubin, 1976). Based on results of these tests we concluded that, on average, our method is the most accurate among the selected methods, no matter which misingness mechanism is responsible for missing values. When an additional stochastic method for preservation of variance was used, our rotation regression forest was able to preserve the variance structure within data better than any other single-impute method, while still besting them all in accuracy. The introductory, more theoretical chapters of this dissertation deal with supervised machine learning, missing values and commonly used imputation methods. Rotation regression forest ensemble was introduced, as well as our stochastic method for preservation of variance. The bulk of our work is focused on results, gained through empirical experiments, which were used to model our recommendations concerning the use of rotation regression forest ensemble for imputation of missing values and to form starting points for possible future work

Comparison of point-of-care and laboratory troponin I assays

BACKGROUND In recent years a number of pointof- care troponin assays have emerged. There have been reports of discrepancies between the results of point-of-care and laboratory assays. We sought to compare the results of point-ofcare and laboratory troponin I assays in patients with suspected acute coronary syndromes. METHODS A retrospective study was performed comparing the results of point-of-care (i-STAT cardiac troponin I test, Abbott Point of Care) and laboratory troponin I analysis in patients with suspected acute coronary syndrome treated in the Internal Medicine Emergency Department, University Medical Centre Maribor, between 23 November and 21 December 2010, who had blood samples drawn simultaneously for pointof- care and laboratory troponin I analysis. RESULTS 112 patients were included in the analysis. There was an agreement between the results of point-of-care and laboratory troponin analysis in 105 (93.8 %) patients. If we consider the laboratory results as »gold standard« (diagnosis was based on laboratory troponin results), then 6 (5.4 %) false negative results and 1 (0.9 %) false positive result were found (sensitivity 81.2 %, specificity 98.7 %). However, there was no statistically significant difference between point-of-care and laboratory troponin I analysis (p = 0.125). CONCLUSIONS We detected lower sensitivity of point-of-care assay, but there was no statistically significant difference between point-of-care and laboratory troponin I analysis. We adopted a strategy of using point-of-care troponin assay primarily in patients at high-risk for acute coronary syndrome without ST elevation

Urinary incontinence and overactive bladder in patients attending the family practice physicians office

Izhodišča: V tej presečni vseslovenski raziskavi smo želeli ugotoviti, kako pogosto se zdravniki družinske medicine srečujejo v ambulanti s problemom urinske inkontinence (UI) in prekomerne aktivnosti sečnega mehurja (PASM) in kako poteka ambulantna obravnava teh bolnikov. Metode: V raziskavo na podlagi vprašalnikov smo vključili 100 naključno izbranih zdravnikov družinske medicine in njihovih 50 bolnikov, starih od 40 do 70 let, ki so zaporedoma prihajali na pregled v ambulanto. Pri anketiranju smo zagotovili intimnost in anonimnost. Podatke iz vprašalnikov smo nato obdelali s statističnim programom SPSS. Rezultati: V raziskavo je privolilo 68 % zdravnikov in 3057 njihovih bolnikov (88,9 %). Zdravniki pri UI ali PAM najpogosteje bolnike napotijo na nadaljnjo obravnavo, izključijo uroinfekt, jim razložijo trening mišic medeničnega dna (TMMD) in jim predpišejo hlačne predloge. Prevalenca UI v ambulanti zdravnika družinske medicine znaša 30,6 % in je značilno pogostejša pri ženskah (39,3 % proti 14,1 %, p<0,001). Med bolniki z UI prevladuje mešana UI (69,6 %), sledi stresna UI (16,8 %) in urgentna UI (13,6 %). Prevalenca PASM (urgenca) znaša 35,2 %, pogostejša je pri ženskah (40,6 % proti 24,8 %, p<0,001). 51,4 % žensk in 24,8 % moških točno ve, kaj TMMD pomeni, poznavanje treninga mehurja pa je pri bolnikih še slabše (17,9 % žensk in 7,7 % moških). Zaključki: UI in PASM sta statistično značilna problema med bolniki, ki obiskujejo ambulanto družinskega zdravnika. Zdi se, da je poznavanje obeh motenj med zdravniki zadovoljivo. Večina bi svoje težave zaupala zdravniku in pri njem dobila ustrezna navodila glede treninga mehurja in TMMD, ki sta pomembni metodi za preprečevanje in zdravljenje obeh motenj.Background: The aim of this pan-Slovene crossover survey was to assess how often the family practice physicians are dealing with urinary incontinence (UI) and overactive bladder (OAB) at their offices and to assess how are their patients with these disorders managed. Methods: In this questionnaire-based study we randomly selected 100 family practice physicians and their 50 patients, aged between 40-70 years, who had come consecutively to their offices. They all filled out questionnaires in a way that enabled their privacy and anonymity. Data from questionnaires was managed by statistical software program SPSS. Results: 68% of physicians and 3057 of their patients (88.9%) agreed to participate in the study. In case of UI or OAB, physicians most commonly refer patients to other specialists, rule out uroinfection, explain them the pelvic floor muscle training (PFMT) and prescribe pads. The UI prevalence in patients was 30.6%, and was more common in women than in men (39.3% vs. 14.1%, respectively, p<0.001). Most patients were diagnosed with mixed UI (69.6%), followed by stress UI (16.8%) and urgency UI (13.6%). OAB (urgency) was found in 35.2% of patients and was more common in women than in men (40.6% vs. 24.8%, respectively, p<0.001). Only 51.4% of women and 24.8% of men exactly knew what PFMT meant, however, the knowledge of bladder training was even worse (17.9% of women and7.7% of men). Conclusions: Both, UI and OAB represent a significant problem among patients attending the family practice physician office. It seems that the knowledge of both dysfunctions is satisfactory among physicians. The majority of patients would tell their doctors about UI and OAB and would also receive appropriate instructions regarding the bladder training and PFMT, both methods being very important for the prevention and treatment of these dysfunctions