Mengling Feng

Comments on ‘An empirical comparison of several recent epistatic interaction detection methods’

MOTIVATION Many new methods have recently been proposed for detecting epistatic interactions in GWAS data. There is, however, no in-depth independent comparison of these methods yet. RESULTS Five recent methods-TEAM, BOOST, SNPHarvester, SNPRuler and Screen and Clean (SC)-are evaluated here in terms of power, type-1 error rate, scalability and completeness. In terms of power, TEAM performs best on data with main effect and BOOST performs best on data without main effect. In terms of type-1 error rate, TEAM and BOOST have higher type-1 error rates than SNPRuler and SNPHarvester. SC does not control type-1 error rate well. In terms of scalability, we tested the five methods using a dataset with 100 000 SNPs on a 64 bit Ubuntu system, with Intel (R) Xeon(R) CPU 2.66 GHz, 16 GB memory. TEAM takes ~36 days to finish and SNPRuler reports heap allocation problems. BOOST scales up to 100 000 SNPs and the cost is much lower than that of TEAM. SC and SNPHarvester are the most scalable. In terms of completeness, we study how frequently the pruning techniques employed by these methods incorrectly prune away the most significant epistatic interactions. We find that, on average, 20% of datasets without main effect and 60% of datasets with main effect are pruned incorrectly by BOOST, SNPRuler and SNPHarvester. AVAILABILITY The software for the five methods tested are available from the URLs below. TEAM: http://csbio.unc.edu/epistasis/download.php BOOST: http://ihome.ust.hk/~eeyang/papers.html. SNPHarvester: http://bioinformatics.ust.hk/SNPHarvester.html. SNPRuler: http://bioinformatics.ust.hk/SNPRuler.zip. Screen and Clean: http://wpicr.wpic.pitt.edu/WPICCompGen/. CONTACT wangyue@nus.edu.sg.

Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference

With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines—including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology—gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.

Peripheral Edema, Central Venous Pressure, and Risk of AKI in Critical Illness

BACKGROUND AND OBJECTIVES Although venous congestion has been linked to renal dysfunction in heart failure, its significance in a broader context has not been investigated. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Using an inception cohort of 12,778 critically ill adult patients admitted to an urban tertiary medical center between 2001 and 2008, we examined whether the presence of peripheral edema on admission physical examination was associated with an increased risk of AKI within the first 7 days of critical illness. In addition, in those with admission central venous pressure (CVP) measurements, we examined the association of CVPs with subsequent AKI. AKI was defined using the Kidney Disease Improving Global Outcomes criteria. RESULTS Of the 18% (n=2338) of patients with peripheral edema on admission, 27% (n=631) developed AKI, compared with 16% (n=1713) of those without peripheral edema. In a model that included adjustment for comorbidities, severity of illness, and the presence of pulmonary edema, peripheral edema was associated with a 30% higher risk of AKI (95% confidence interval [95% CI], 1.15 to 1.46; P<0.001), whereas pulmonary edema was not significantly related to risk. Peripheral edema was also associated with a 13% higher adjusted risk of a higher AKI stage (95% CI, 1.07 to 1.20; P<0.001). Furthermore, levels of trace, 1+, 2+, and 3+ edema were associated with 34% (95% CI, 1.10 to 1.65), 17% (95% CI, 0.96 to 1.14), 47% (95% CI, 1.18 to 1.83), and 57% (95% CI, 1.07 to 2.31) higher adjusted risk of AKI, respectively, compared with edema-free patients. In the 4761 patients with admission CVP measurements, each 1 cm H2O higher CVP was associated with a 2% higher adjusted risk of AKI (95% CI, 1.00 to 1.03; P=0.02). CONCLUSIONS Venous congestion, as manifested as either peripheral edema or increased CVP, is directly associated with AKI in critically ill patients. Whether treatment of venous congestion with diuretics can modify this risk will require further study.

Obesity, Acute Kidney Injury, and Mortality in Critical Illness.

Objectives:Although obesity is associated with risk for chronic kidney disease and improved survival, less is known about the associations of obesity with risk of acute kidney injury and post acute kidney injury mortality. Design:In a single-center inception cohort of almost 15,000 critically ill patients, we evaluated the association of obesity with acute kidney injury and acute kidney injury severity, as well as in-hospital and 1-year survival. Acute kidney injury was defined using the Kidney Disease Outcome Quality Initiative criteria. Measurements and Main Results:The acute kidney injury prevalence rates for normal, overweight, class I, II, and III obesity were 18.6%, 20.6%, 22.5%, 24.3%, and 24.0%, respectively, and the adjusted odds ratios of acute kidney injury were 1.18 (95% CI, 1.06–1.31), 1.35 (1.19–1.53), 1.47 (1.25–1.73), and 1.59 (1.31–1.87) when compared with normal weight, respectively. Each 5-kg/m2 increase in body mass index was associated with a 10% risk (95% CI, 1.06–1.24; p < 0.001) of more severe acute kidney injury. Within-hospital and 1-year survival rates associated with the acute kidney injury episodes were similar across body mass index categories. Conclusion:Obesity is a risk factor for acute kidney injury, which is associated with increased short- and long-term mortality.

Adaptive binarization method for document image analysis

This paper proposes an adaptive binarization method, based on the criterion of maximizing local contrast, for document image analysis. The proposed method has overcome, to a large extent, the general problems of poor quality document images, such as non-uniform illumination, undesirable shadows and random noise. It was tested against a variety of challenging images, and the experimental results are presented to show the effectiveness and superiority of the proposed method.

Detection of Atrial fibrillation from non-episodic ECG data: A review of methods

Atrial fibrillation (A-fib) is the most common cardiac arrhythmia. To effectively treat or prevent A-fib, automatic A-fib detection based on Electrocardiograph (ECG) monitoring is highly desirable. This paper reviews recently developed techniques for A-fib detection based on non-episodic surface ECG monitoring data. A-fib detection methods in the literature can be mainly classified into three categories: (1) time domain methods; (2) frequency domain methods; and (3) non-linear methods. In general the performances of these methods were evaluated in terms of sensitivity, specificity and overall detection accuracy on the datasets from the Physionet repository. Based on our survey, we conclude that no promising A-fib detection method that performs consistently well across various scenarios has been proposed yet.

A “datathon” model to support cross-disciplinary collaboration

A “datathon” model combines complementary knowledge and skills to formulate inquiries and drive research that addresses information gaps faced by clinicians. In recent years, there has been a growing focus on the unreliability of published biomedical and clinical research. To introduce effective new scientific contributors to the culture of health care, we propose a “datathon” or “hackathon” model in which participants with disparate, but potentially synergistic and complementary, knowledge and skills effectively combine to address questions faced by clinicians. The continuous peer review intrinsically provided by follow-up datathons, which take up prior uncompleted projects, might produce more reliable research, either by providing a different perspective on the study design and methodology or by replication of prior analyses.

The effect of age and clinical circumstances on the outcome of red blood cell transfusion in critically ill patients

IntroductionWhether red blood cell (RBC) transfusion is beneficial remains controversial. In both retrospective and prospective evaluations, transfusion has been associated with adverse, neutral, or protective effects. These varying results likely stem from a complex interplay between transfusion, patient characteristics, and clinical context. The objective was to test whether age, comorbidities, and clinical context modulate the effect of transfusion on survival.MethodsBy using the multiparameter intelligent monitoring in intensive care II database (v. 2.6), a retrospective analysis of 9,809 critically ill patients, we evaluated the effect of RBC transfusion on 30-day and 1-year mortality. Propensity score modeling and logistic regression adjusted for known confounding and assessed the independent effect of transfusion on 30-day and 1-year mortality. Sensitivity analysis was performed by using 3,164 transfused and non-transfused pairs, matched according the previously validated propensity model for RBC transfusion.ResultsRBC transfusion did not affect 30-day or 1-year mortality in the overall cohort. Patients younger than 55 years had increased odds of mortality (OR, 1.71; P < 0.01) with transfusion. Patients older than 75 years had lower odds of 30-day and 1-year mortality (OR, 0.70; P < 0.01) with transfusion. Transfusion was associated with worse outcome among patients undergoing cardiac surgery (OR, 2.1; P < 0.01). The propensity-matched population corroborated findings identified by regression adjustment.ConclusionA complex relation exists between RBC transfusion and clinical outcome. Our results show that transfusion is associated with improved outcomes in some cohorts and worse outcome in others, depending on comorbidities and patient characteristics. As such, future investigations and clinical decisions evaluating the value of transfusion should account for variations in baseline characteristics and clinical context.

AssocExplorer: an association rule visualization system for exploratory data analysis

We present a system called AssocExplorer to support exploratory data analysis via association rule visualization and exploration. AssocExplorer is designed by following the visual information-seeking mantra: overview first, zoom and filter, then details on demand. It effectively uses coloring to deliver information so that users can easily detect things that are interesting to them. If users find a rule interesting, they can explore related rules for further analysis, which allows users to find interesting phenomenon that are difficult to detect when rules are examined separately. Our system also allows users to compare rules and inspect rules with similar item composition but different statistics so that the key factors that contribute to the difference can be isolated.

The Association Between Indwelling Arterial Catheters and Mortality in Hemodynamically Stable Patients With Respiratory Failure: A Propensity Score Analysis

BACKGROUND Indwelling arterial catheters (IACs) are used extensively in the ICU for hemodynamic monitoring and for blood gas analysis. IAC use also poses potentially serious risks, including bloodstream infections and vascular complications. The purpose of this study was to assess whether IAC use was associated with mortality in patients who are mechanically ventilated and do not require vasopressor support. METHODS This study used the Multiparameter Intelligent Monitoring in Intensive Care II database, consisting of > 24,000 patients admitted to the Beth Israel Deaconess Medical Center ICU between 2001 and 2008. Patients requiring mechanical ventilation who did not require vasopressors or have a diagnosis of sepsis were identified, and the primary outcome was 28-day mortality. A model based on patient demographics, comorbidities, vital signs, and laboratory results was developed to estimate the propensity for IAC placement. Patients were then propensity matched, and McNemar test was used to evaluate the association of IAC with 28-day mortality. RESULTS We identified 1,776 patients who were mechanically ventilated who met inclusion criteria. There were no differences in the covariates included in the final propensity model between the IAC and non-IAC propensity-matched groups. For the matched cohort, there was no difference in 28-day mortality between the IAC group and the non-IAC group (14.7% vs 15.2%; OR, 0.96; 95% CI, 0.62-1.47). CONCLUSIONS In hemodynamically stable patients who are mechanically ventilated, the presence of an IAC is not associated with a difference in 28-day mortality. Validation in other datasets, as well as further analyses in other subgroups, is warranted.