Predicting ICU survival: A meta-level approach

<p>Abstract</p> <p>Background</p> <p>The performance of separate Intensive Care Unit (ICU) status scoring systems vis-à-vis prediction of outcome is not satisfactory. Computer-based predictive modeling techniques may yield good results but their performance has seldom been extensively compared to that of other mature or emerging predictive models. The objective of the present study was twofold: to propose a prototype meta-level predicting approach concerning Intensive Care Unit (ICU) survival and to evaluate the effectiveness of typical mining models in this context.</p> <p>Methods</p> <p>Data on 158 men and 46 women, were used retrospectively (75% of the patients survived). We used Glasgow Coma Scale (GCS), Acute Physiology And Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA) and Injury Severity Score (ISS) values to structure a decision tree (DTM), a neural network (NNM) and a logistic regression (LRM) model and we evaluated the assessment indicators implementing Receiver Operating Characteristics (ROC) plot analysis.</p> <p>Results</p> <p>Our findings indicate that regarding the assessment of indicators' capacity there are specific discrete limits that should be taken into account. The Az score ± SE was 0.8773± 0.0376 for the DTM, 0.8061± 0.0427 for the NNM and 0.8204± 0.0376 for the LRM, suggesting that the proposed DTM achieved a near optimal Az score.</p> <p>Conclusion</p> <p>The predicting processes of ICU survival may go "one step forward", by using classic composite assessment indicators as variables.</p

Genetic Overlap Profiles of Cognitive Ability in Psychotic and Affective Illnesses::A Multi-Site Study of Multiplex Pedigrees

BACKGROUND: Cognitive impairment is a key feature of psychiatric illness, making cognition an important tool for exploring of the genetics of illness risk. It remains unclear which measures should be prioritized in pleiotropy-guided research. Here, we generate profiles of genetic overlap between psychotic and affective disorders and cognitive measures in Caucasian and Hispanic groups. METHODS: Data were from four samples of extended pedigrees (N = 3046). Coefficient of relationship analyses were used to estimate genetic overlap between illness risk and cognitive ability. Results were meta-analyzed. FINDINGS: Psychosis was characterized by cognitive impairments on all measures with a generalized profile of genetic overlap. General cognitive ability shared greatest genetic overlap with psychosis risk (average Endophenotype Ranking Value (ERV) across samples from a random-effects meta-analysis = 0.32) followed by Verbal Memory (ERV = 0.24), Executive Function (ERV = 0.22), and Working Memory (ERV = 0.21). For bipolar disorder, there was genetic overlap with Processing Speed (ERV = 0.05) and Verbal Memory (ERV = 0.11), but these were confined to select samples. Major depression was characterized by enhanced Working and Face Memory performance, as reflected in significant genetic overlap in two samples. INTERPRETATION: There is substantial genetic overlap between risk for psychosis and a range of cognitive abilities (including general intelligence). Most of these effects are largely stable across of ascertainment strategy and ethnicity. Genetic overlap between affective disorders and cognition, on the other hand, tend to be specific to ascertainment strategy, ethnicity, and cognitive test battery

Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations

BACKGROUND: Microarrays measure the binding of nucleotide sequences to a set of sequence specific probes. This information is combined with annotation specifying the relationship between probes and targets and used to make inferences about transcript- and, ultimately, gene expression. In some situations, a probe is capable of hybridizing to more than one transcript, in others, multiple probes can target a single sequence. These 'multiply targeted' probes can result in non-independence between measured expression levels. RESULTS: An analysis of these relationships for Affymetrix arrays considered both the extent and influence of exact matches between probe and transcript sequences. For the popular HGU133A array, approximately half of the probesets were found to interact in this way. Both real and simulated expression datasets were used to examine how these effects influenced the expression signal. It was found not only to lead to increased signal strength for the affected probesets, but the major effect is to significantly increase their correlation, even in situations when only a single probe from a probeset was involved. By building a network of probe-probeset-transcript relationships, it is possible to identify families of interacting probesets. More than 10% of the families contain members annotated to different genes or even different Unigene clusters. Within a family, a mixture of genuine biological and artefactual correlations can occur. CONCLUSION: Multiple targeting is not only prevalent, but also significant. The ability of probesets to hybridize to more than one gene product can lead to false positives when analysing gene expression. Comprehensive annotation describing multiple targeting is required when interpreting array data

Reduced Fertility in Patients' Families Is Consistent with the Sexual Selection Model of Schizophrenia and Schizotypy

BACKGROUND: Schizophrenia is a mental disorder marked by an evolutionarily puzzling combination of high heritability, reduced reproductive success, and a remarkably stable prevalence. Recently, it has been proposed that sexual selection may be crucially involved in the evolution of schizophrenia. In the sexual selection model (SSM) of schizophrenia and schizotypy, schizophrenia represents the negative extreme of a sexually selected indicator of genetic fitness and condition. Schizotypal personality traits are hypothesized to increase the sensitivity of the fitness indicator, thus conferring mating advantages on high-fitness individuals but increasing the risk of schizophrenia in low-fitness individuals; the advantages of successful schzotypy would be mediated by enhanced courtship-related traits such as verbal creativity. Thus, schizotypy-increasing alleles would be maintained by sexual selection, and could be selectively neutral or even beneficial, at least in some populations. However, most empirical studies find that the reduction in fertility experienced by schizophrenic patients is not compensated for by increased fertility in their unaffected relatives. This finding has been interpreted as indicating strong negative selection on schizotypy-increasing alleles, and providing evidence against sexual selection on schizotypy. METHODOLOGY: A simple mathematical model is presented, showing that reduced fertility in the families of schizophrenic patients can coexist with selective neutrality of schizotypy-increasing alleles, or even with positive selection on schizotypy in the general population. If the SSM is correct, studies of patients' families can be expected to underestimate the true fertility associated with schizotypy. SIGNIFICANCE: This paper formally demonstrates that reduced fertility in the families of schizophrenic patients does not constitute evidence against sexual selection on schizotypy-increasing alleles. Futhermore, it suggests that the fertility estimates derived from extant studies may be biased to an unknown extent. These results have important implications for the evolutionary genetics of psychosis