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Gaceta Sanitaria 2019To analyze the relationship between the type of hospital admission (outlier and non-outlier admissions) and the appearance of clinical complications and the average stay.
OBJECTIVE
To analyze the relationship between the type of hospital admission (outlier and non-outlier admissions) and the appearance of clinical complications and the average stay.
METHODS
From a retrospective epidemiological study of a cohort of patients admitted to the Hospital Complejo Asistencial Universitario de Salamanca (Salamanca, Spain) over a six-month period, outlier and non-outlier patients were identified. This project had access to the admissions department database, the hospital's CMBD (in Spanish, Conjunto Mínimo Básico de Datos) for hospitalisation, the AP-DRG (All Patient-Diagnosis Related Groups) and ALCOR (a clinical-statistics analytics tool). It then proceeded to break down the results by DRG, looking at the five most common DRGs in that period.
RESULTS
8.4% of the total 11,842 admissions were medical outliers. In the overall study, the average stay was longer for outlier patients (8. 11 days) than for other patients (7.15 days). The mortality rate was, likewise, higher for outlier patients, although there was a reduced incidence of complications (7.6% for outlier patients as opposed to 8.4% for others). The analysis by DRG corroborated these results in three of the five cases investigated, showing longer average stays but fewer clinical complications in the case of outlier patients.
CONCLUSIONS
On admission to hospital, a significant proportion of patients were allocated beds on inappropriate wards (outlier patients). It was more common to find medical patients placed on surgical wards than vice versa. The average stay of outlier patients was longer than that of patients admitted to the correct ward. The study found no significant difference between the two groupś in terms of clinical complication rates.
Topics: Cohort Studies; Diagnosis-Related Groups; Epidemiologic Studies; Humans; Length of Stay; Patient Admission; Retrospective Studies
PubMed: 28943019
DOI: 10.1016/j.gaceta.2017.07.012 -
Attention, Perception & Psychophysics Feb 2024Ensemble perception allows our visual system to process large amounts of information efficiently by summarizing its statistical properties. A key aspect of ensemble...
Ensemble perception allows our visual system to process large amounts of information efficiently by summarizing its statistical properties. A key aspect of ensemble perception is the devaluation of outlying elements, which leads to more informative summary statistics with reduced variance and a more representative mean. However, the mechanisms underlying this outlier rejection process are not well understood. One possibility is that outliers are selectively excluded before summarization. To test this, we investigated whether only weaker items were excluded from averaging. We manipulated the encoding strength of items in a display by changing the emotional intensities of faces, the spatial location of emotional outliers, and the spatial distribution of emotional faces. We found that the response to outliers varied depending on their location. Specifically, outliers were more likely to be excluded from averaging when presented in more peripheral regions, while their exclusion was partial in parafoveal regions. In other words, outlier rejection in ensemble processing is more flexible than the supposed rigid designation of weighting against outliers. Alternatively, the results fit well with hierarchically structured pooling, during which outliers are discounted more dynamically without positing any separate selective mechanism before summarization. We propose an explanation for outlier rejection in light of a recently proposed population response model of ensemble processing.
Topics: Humans; Emotions
PubMed: 38191757
DOI: 10.3758/s13414-023-02842-x -
Attention, Perception & Psychophysics Apr 2021It is known that the visual system can efficiently extract mean and variance information, facilitating the detection of outliers. However, no research to date has...
It is known that the visual system can efficiently extract mean and variance information, facilitating the detection of outliers. However, no research to date has directly investigated whether ensemble perception mechanisms contribute to outlier representation precision. We specifically were interested in how the distinctiveness of outliers impacts their precision. Across two experiments, we compared how accurately viewers represented the orientation of spatial outliers that varied in distinctiveness and found that increased outlier distinctiveness resulted in greater precision. Based on comparisons of our data to simulations reflecting particular selective strategies, we eliminated the possibility that participants were selectively processing the outlier, at the expense of the ensemble. Thus, we argued that participants separately represented distinct outliers along with ensemble summaries of the remaining items in a display. We also found that outlier distinctiveness moderated the precision of how the remaining items were summarized. We discuss these findings in relation to computational capacity and constraints of ensemble perception mechanisms.
Topics: Humans; Orientation; Orientation, Spatial; Perception
PubMed: 33728510
DOI: 10.3758/s13414-021-02270-9 -
International Journal of Epidemiology Aug 2019Biobanks increasingly collect, process and store omics with more conventional epidemiologic information necessitating considerable effort in data cleaning. An efficient...
BACKGROUND
Biobanks increasingly collect, process and store omics with more conventional epidemiologic information necessitating considerable effort in data cleaning. An efficient outlier detection method that reduces manual labour is highly desirable.
METHOD
We develop an unsupervised machine-learning method for outlier detection, namely kurPCA, that uses principal component analysis combined with kurtosis to ascertain the existence of outliers. In addition, we propose a novel regression adjustment approach to improve detection, namely the regression adjustment for data by systematic missing patterns (RAMP).
RESULT
Application to epidemiological record data in a large-scale biobank (Tohoku Medical Megabank Organization, Japan) shows that a combination of kurPCA and RAMP effectively detects known errors or inconsistent patterns.
CONCLUSIONS
We confirm through the results of the simulation and the application that our methods showed good performance. The proposed methods are useful for many practical analysis scenarios.
Topics: Algorithms; Humans; Machine Learning; Models, Statistical; Principal Component Analysis; Surveys and Questionnaires
PubMed: 30848787
DOI: 10.1093/ije/dyz012 -
Journal of Experimental Psychology.... Jan 2023According to a growing body of research, human adults are remarkably accurate at extracting intuitive statistics from graphs, such as finding the best-fitting regression...
According to a growing body of research, human adults are remarkably accurate at extracting intuitive statistics from graphs, such as finding the best-fitting regression line through a scatterplot. Here, we ask whether humans can also perform outlier rejection, a nontrivial statistical problem. In three experiments, we investigated human adults' capacity to evaluate the linear trend of a flashed scatterplot comprising 0-4 outlier datapoints. Experiment 1 showed that participants did not spontaneously reject outliers: when outliers were not mentioned, their presence biased the participants' trend judgments and regression line estimates. In Experiment 2, where participants were explicitly asked to exclude outliers, the outlier-induced bias was reduced but remained significant. In Experiment 3, where participants were asked to explicitly detect any outlier before adjusting their regression line, outlier detection was satisfactory, but the detected outliers continued to bias the regression responses, unless they were quite distant from the main regression line. We propose a simple model for outlier detection, based on the computation of a z-score that estimates how far a given datapoint is from the distribution of distances to the regression line, and we show that this model closely approximates human performance. Detection is not rejection, however, and our results suggest that humans can remain biased by outliers that they have detected. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Topics: Humans; Statistics as Topic
PubMed: 36395054
DOI: 10.1037/xhp0001065 -
Identifying Outlier Hospitals in Gastric Cancer Lymph Node Yield Using the National Cancer Database.The Journal of Surgical Research May 2021Lymph node (LN) yield is a key quality indicator that is associated with improved staging in surgically resected gastric cancer. The National Comprehensive Cancer...
BACKGROUND
Lymph node (LN) yield is a key quality indicator that is associated with improved staging in surgically resected gastric cancer. The National Comprehensive Cancer Network recommends a yield of ≥15 LNs for proper staging, yet most facilities in the United States fail to achieve this number. The present study aimed to identify factors that could affect LN yield on a facility level and identify outlier hospitals.
METHODS
This was a retrospective review of adults (aged ≥18 y) with gastric cancer (Tumor-Node-Metastasis Stages I-III) who underwent gastrectomy. Data were analyzed from the National Cancer Database (2004-2016). Multivariate analysis identified patient and tumor characteristics, whereas an observed-to-expected ratio of identified outlier hospitals. Facility factors were compared between high and low outliers.
RESULTS
A total of 26,590 patients were included in this study. Of these patients, only 50.3% had an LN yield ≥15. The multivariate model of patient and tumor characteristics demonstrated a concordance index was 0.684. A total of 1245 facilities were included. There were 198 low outlier LN yield hospitals and 135 high outlier LN yield hospitals (observed-to-expected ratio of 0.42 ± 0.24 versus 1.38 ± 0.19, P < 0.0001). There was a difference in facility type between low and high outliers (P < 0.0001). High LN yield hospitals had a larger surgical volume than low LN yield hospitals (median 8.4 [4.9, 13.5] versus 3.5 [2.4, 5.2]; P < 0.0001).
CONCLUSIONS
Nearly half of the population exhibited low compliance to National Comprehensive Cancer Network recommendations. Facility-level disparities exist as high yearly surgical volume and academic facility status distinguished high-performing outlier hospitals.
Topics: Adenocarcinoma; Aged; Aged, 80 and over; Female; Hospitals; Humans; Lymph Node Excision; Lymph Nodes; Male; Middle Aged; Models, Statistical; Registries; Retrospective Studies; Stomach Neoplasms
PubMed: 33450628
DOI: 10.1016/j.jss.2020.11.046 -
The Journal of Trauma and Acute Care... Aug 2019Expected performance rates for various outcome metrics are a hallmark of hospital quality indicators used by Agency of Healthcare Research and Quality, Center for...
BACKGROUND
Expected performance rates for various outcome metrics are a hallmark of hospital quality indicators used by Agency of Healthcare Research and Quality, Center for Medicare and Medicaid Services, and National Quality Forum. The identification of outlier hospitals with above- and below-expected mortality for emergency general surgery (EGS) operations is therefore of great value for EGS quality improvement initiatives. The aim of this study was to determine hospital variation in mortality after EGS operations, and compare characteristics between outlier hospitals.
METHODS
Using data from the California State Inpatient Database (2010-2011), we identified patients who underwent one of eight common EGS operations. Expected mortality was obtained from a Bayesian model, adjusting for both patient- and hospital-level variables. A hospital-level standardized mortality ratio (SMR) was constructed (ratio of observed to expected deaths). Only hospitals performing three or more of each operation were included. An "outlier" hospital was defined as having an SMR with 80% confidence interval that did not cross 1.0. High- and low-mortality SMR outliers were compared.
RESULTS
There were 140,333 patients included from 220 hospitals. Standardized mortality ratio varied from a high of 2.6 (mortality, 160% higher than expected) to a low of 0.2 (mortality, 80% lower than expected); 12 hospitals were high SMR outliers, and 28 were low SMR outliers. Standardized mortality was over three times worse in the high SMR outliers compared with the low SMR outliers (1.7 vs. 0.5; p < 0.001). Hospital-, patient-, and operative-level characteristics were equivalent in each outlier group.
CONCLUSION
There exists significant hospital variation in standardized mortality after EGS operations. High SMR outliers have significant excess mortality, while low SMR outliers have superior EGS survival. Common hospital-level characteristics do not explain the wide gap between underperforming and overperforming outlier institutions. These findings suggest that SMR can help guide assessment of EGS performance across hospitals; further research is essential to identify and define the hospital processes of care which translate into optimal EGS outcomes.
LEVEL OF EVIDENCE
Epidemiologic Study, level III.
Topics: California; Emergencies; Female; Hospital Mortality; Hospitals; Humans; Male; Middle Aged; Quality Improvement; Quality Indicators, Health Care; Surgical Procedures, Operative
PubMed: 30908450
DOI: 10.1097/TA.0000000000002271 -
Advances in Cancer Research 2019The "CpG Island Methylator Phenotype" (CIMP) has been found to be a useful concept in stratifying several types of human cancer into molecularly and clinically... (Review)
Review
The "CpG Island Methylator Phenotype" (CIMP) has been found to be a useful concept in stratifying several types of human cancer into molecularly and clinically distinguishable subgroups. We have identified an additional epigenetic stratification category, the "Outlier Methylation Phenotype" (OMP). Whereas CIMP is defined on the basis of hyper-methylation in tumor genomes, OMP is defined on the basis of highly variant (either or both hyper- and hypo-methylation) methylation at many sites in normal tissues. OMP was identified and defined, originally, as being more common among low birth weight individuals conceived in vitro but we have also identified OMP individuals among colon cancer patients profiled by us, as well as multiple types of cancer patients in the TCGA database. The cause(s) of OMP are unknown, as is whether these individuals identify a clinically useful subgroup of patients, but both the causes of, and potential consequences to, this epigenetically distinct group are of great interest.
Topics: Biomarkers, Tumor; CpG Islands; DNA Methylation; Epigenomics; Humans; Neoplasms; Phenotype
PubMed: 30885359
DOI: 10.1016/bs.acr.2019.01.006 -
Cureus Mar 2023Objectives Clinical discoveries are heralded by observing unique and unusual clinical cases. The effort of identifying such cases rests on the shoulders of busy...
Objectives Clinical discoveries are heralded by observing unique and unusual clinical cases. The effort of identifying such cases rests on the shoulders of busy clinicians. We assess the feasibility and applicability of an augmented intelligence framework to accelerate the rate of clinical discovery in preeclampsia and hypertensive disorders of pregnancy-an area that has seen little change in its clinical management. Methods We conducted a retrospective exploratory outlier analysis of participants enrolled in the folic acid clinical trial (FACT, N=2,301) and the Ottawa and Kingston birth cohort (OaK, N=8,085). We applied two outlier analysis methods: extreme misclassification contextual outlier and isolation forest point outlier. The extreme misclassification contextual outlier is based on a random forest predictive model for the outcome of preeclampsia in FACT and hypertensive disorder of pregnancy in OaK. We defined outliers in the extreme misclassification approach as mislabelled observations with a confidence level of more than 90%. Within the isolation forest approach, we defined outliers as observations with an average path length z score less or equal to -3, or more or equal to 3. Content experts reviewed the identified outliers and determined if they represented a potential novelty that could conceivably lead to a clinical discovery. Results In the FACT study, we identified 19 outliers using the isolation forest algorithm and 13 outliers using the random forest extreme misclassification approach. We determined that three (15.8%) and 10 (76.9%) were potential novelties, respectively. Out of 8,085 participants in the OaK study, we identified 172 outliers using the isolation forest algorithm and 98 outliers using the random forest extreme misclassification approach; four (2.3%) and 32 (32.7%), respectively, were potential novelties. Overall, the outlier analysis part of the augmented intelligence framework identified a total of 302 outliers. These were subsequently reviewed by content experts, representing the human part of the augmented intelligence framework. The clinical review determined that 49 of the 302 outliers represented potential novelties. Conclusions Augmented intelligence using extreme misclassification outlier analysis is a feasible and applicable approach for accelerating the rate of clinical discoveries. The use of an extreme misclassification contextual outlier analysis approach has resulted in a higher proportion of potential novelties than using the more traditional point outlier isolation forest approach. This finding was consistent in both the clinical trial and real-world cohort study data. Using augmented intelligence through outlier analysis has the potential to speed up the process of identifying potential clinical discoveries. This approach can be replicated across clinical disciplines and could exist within electronic medical records systems to automatically identify outliers within clinical notes to clinical experts.
PubMed: 37009347
DOI: 10.7759/cureus.36909 -
Journal of Computational Biology : a... Jun 2023Detection of omics sample outliers is important for preventing erroneous biological conclusions, developing robust experimental protocols, and discovering rare...
Detection of omics sample outliers is important for preventing erroneous biological conclusions, developing robust experimental protocols, and discovering rare biological states. Two recent publications describe robust algorithms for detecting transcriptomic sample outliers, but neither algorithm had been incorporated into a software tool for scientists. Here we describe Ensemble Methods for Outlier Detection (EnsMOD) which incorporates both algorithms. EnsMOD calculates how closely the quantitation variation follows a normal distribution, plots the density curves of each sample to visualize anomalies, performs hierarchical cluster analyses to calculate how closely the samples cluster with each other, and performs robust principal component analyses to statistically test if any sample is an outlier. The probabilistic threshold parameters can be easily adjusted to tighten or loosen the outlier detection stringency. EnsMOD can be used to analyze any omics dataset with normally distributed variance. Here it was used to analyze a simulated proteomics dataset, a multiomic (proteome and transcriptome) dataset, a single-cell proteomics dataset, and a phosphoproteomics dataset. EnsMOD successfully identified all of the simulated outliers, and subsequent removal of a detected outlier improved data quality for downstream statistical analyses.
Topics: Software; Algorithms; Gene Expression Profiling; Proteomics; Multiomics
PubMed: 37042708
DOI: 10.1089/cmb.2022.0243