Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data

BACKGROUND: In recent years, outcome prediction models using artificial neural network and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages. In this study we have compared the performance of artificial neural network and multivariable logistic regression models, in prediction of outcomes in head trauma and studied the reproducibility of the findings. METHODS: 1000 Logistic regression and ANN models based on initial clinical data related to the GCS, tracheal intubation status, age, systolic blood pressure, respiratory rate, pulse rate, injury severity score and the outcome of 1271 mainly head injured patients were compared in this study. For each of one thousand pairs of ANN and logistic models, the area under the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow (HL) statistics and accuracy rate were calculated and compared using paired T-tests. RESULTS: ANN significantly outperformed logistic models in both fields of discrimination and calibration but under performed in accuracy. In 77.8% of cases the area under the ROC curves and in 56.4% of cases the HL statistics for the neural network model were superior to that for the logistic model. In 68% of cases the accuracy of the logistic model was superior to the neural network model. CONCLUSIONS: ANN significantly outperformed the logistic models in both fields of discrimination and calibration but lagged behind in accuracy. This study clearly showed that any single comparison between these two models might not reliably represent the true end results. External validation of the designed models, using larger databases with different rates of outcomes is necessary to get an accurate measure of performance outside the development population

Placental determinants of fetal growth: identification of key factors in the insulin-like growth factor and cytokine systems using artificial neural networks

<p>Abstract</p> <p>Background</p> <p>Changes and relationships of components of the cytokine and IGF systems have been shown in placenta and cord serum of fetal growth restricted (FGR) compared with normal newborns (AGA). This study aimed to analyse a data set of clinical and biochemical data in FGR and AGA newborns to assess if a mathematical model existed and was capable of identifying these two different conditions in order to identify the variables which had a mathematically consistent biological relevance to fetal growth.</p> <p>Methods</p> <p>Whole villous tissue was collected at birth from FGR (N = 20) and AGA neonates (N = 28). Total RNA was extracted, reverse transcribed and then real-time quantitative (TaqMan) RT-PCR was performed to quantify cDNA for IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IL-6. The corresponding proteins with TNF-α in addition were assayed in placental lysates using specific kits. The data were analysed using Artificial Neural Networks (supervised networks), and principal component analysis and connectivity map.</p> <p>Results</p> <p>The IGF system and IL-6 allowed to predict FGR in approximately 92% of the cases and AGA in 85% of the cases with a low number of errors. IGF-II, IGFBP-2, and IL-6 content in the placental lysates were the most important factors connected with FGR. The condition of being FGR was connected mainly with the IGF-II placental content, and the latter with IL-6 and IGFBP-2 concentrations in placental lysates.</p> <p>Conclusion</p> <p>These results suggest that further research in humans should focus on these biochemical data. Furthermore, this study offered a critical revision of previous studies. The understanding of this system biology is relevant to the development of future therapeutical interventions possibly aiming at reducing IL-6 and IGFBP-2 concentrations preserving IGF bioactivity in both placenta and fetus.</p

Modulation of Transcriptional and Inflammatory Responses in Murine Macrophages by the Mycobacterium tuberculosis Mammalian Cell Entry (Mce) 1 Complex

The outcome of many infections depends on the initial interactions between agent and host. Aiming at elucidating the effect of the M. tuberculosis Mce1 protein complex on host transcriptional and immunological responses to infection with M. tuberculosis, RNA from murine macrophages at 15, 30, 60 min, 4 and 10 hrs post-infection with M. tuberculosis H37Rv or Δ-mce1 H37Rv was analyzed by whole-genome microarrays and RT-QPCR. Immunological responses were measured using a 23-plex cytokine assay. Compared to uninfected controls, 524 versus 64 genes were up-regulated by 15 min post H37Rv- and Δ-mce1 H37Rv-infection, respectively. By 15 min post-H37Rv infection, a decline of 17 cytokines combined with up-regulation of Ccl24 (26.5-fold), Clec4a2 (23.2-fold) and Pparγ (10.5-fold) indicated an anti-inflammatory response initiated by IL-13. Down-regulation of Il13ra1 combined with up-regulation of Il12b (30.2-fold), suggested switch to a pro-inflammatory response by 4 hrs post H37Rv-infection. Whereas no significant change in cytokine concentration or transcription was observed during the first hour post Δ-mce1 H37Rv-infection, a significant decline of IL-1b, IL-9, IL-13, Eotaxin and GM-CSF combined with increased transcription of Il12b (25.1-fold) and Inb1 (17.9-fold) by 4 hrs, indicated a pro-inflammatory response. The balance between pro-and anti-inflammatory responses during the early stages of infection may have significant bearing on outcome

Symptomatic late-onset sialadenitis after radioiodine therapy in thyroid cancer

OBJECTIVE: The aim of this study was to document the subjective and objective findings of symptomatic late-onset sialadenitis after radioiodine therapy in patients with differentiated thyroid cancer. METHODS: Subjective symptoms related to sialadenitis and Tc-99m pertechnetate salivary gland scintigram findings were assessed in 118 patients (26 males, 92 females) both before and during the late phase (mean 338 days) after the administration of radioiodine. RESULTS: Twelve of the 118 patients (10.2 %) complained of symptomatic sialadenitis in the late phase without symptoms during the early phase (within 7 days of radioiodine administration). Significant associations were found between subjective symptoms and visual scintigram findings during the late phase (p = 0.023). Furthermore, uptake and excretion by both parotid glands were significantly affected by radioiodine therapy. CONCLUSIONS: Symptomatic late-onset sialadenitis occurred at an incidence of 10.2 %, and salivary gland function was affected in both parotids in most patients