Introduction to the Analysis of Survival Data in the Presence of Competing Risks

Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk. When estimating the crude incidence of outcomes, analysts should use the cumulative incidence function, rather than the complement of the Kaplan-Meier survival function. The use of the Kaplan-Meier survival function results in estimates of incidence that are biased upward, regardless of whether the competing events are independent of one another. When fitting regression models in the presence of competing risks, researchers can choose from 2 different families of models: modeling the effect of covariates on the cause-specific hazard of the outcome or modeling the effect of covariates on the cumulative incidence function. The former allows one to estimate the effect of the covariates on the rate of occurrence of the outcome in those subjects who are currently event free. The latter allows one to estimate the effect of covariates on the absolute risk of the outcome over time. The former family of models may be better suited for addressing etiologic questions, whereas the latter model may be better suited for estimating a patient’s clinical prognosis. We illustrate the application of these methods by examining cause-specific mortality in patients hospitalized with heart failure. Statistical software code in both R and SAS is provided

The Challenges of Multimorbidity from the Patient Perspective

BACKGROUND Although multiple co-occurring chronic illnesses within the same individual are increasingly common, few studies have examined the challenges of multimorbidity from the patient perspective. OBJECTIVE The aim of this study is to examine the self-management learning needs and willingness to see non-physician providers of patients with multimorbidity compared to patients with single chronic illnesses. DESIGN. This research is designed as a cross-sectional survey. PARTICIPANTS Based upon ICD-9 codes, patients from a single VHA healthcare system were stratified into multimorbidity clusters or groups with a single chronic illness from the corresponding cluster. Nonproportional sampling was used to randomly select 720 patients. MEASUREMENTS Demographic characteristics, functional status, number of contacts with healthcare providers, components of primary care, self-management learning needs, and willingness to see nonphysician providers. RESULTS Four hundred twenty-two patients returned surveys. A higher percentage of multimorbidity patients compared to single morbidity patients were "definitely" willing to learn all 22 self-management skills, of these only 2 were not significant. Compared to patients with single morbidity, a significantly higher percentage of patients with multimorbidity also reported that they were "definitely" willing to see 6 of 11 non-physician healthcare providers. CONCLUSIONS Self-management learning needs of multimorbidity patients are extensive, and their preferences are consistent with team-based primary care. Alternative methods of providing support and chronic illness care may be needed to meet the needs of these complex patients.US Department of Veterans Affairs (01-110, 02-197); Agency for Healthcare Research and Quality (K08 HS013008-02

Second surface: multi-user spatial collaboration system based on augmented reality

An environment for creative collaboration is significant for enhancing human communication and expressive activities, and many researchers have explored different collaborative spatial interaction technologies. However, most of these systems require special equipment and cannot adapt to everyday environment. We introduce Second Surface, a novel multi-user Augmented reality system that fosters a real-time interaction for user-generated contents on top of the physical environment. This interaction takes place in the physical surroundings of everyday objects such as trees or houses. Our system allows users to place three dimensional drawings, texts, and photos relative to such objects and share this expression with any other person who uses the same software at the same spot. Second Surface explores a vision that integrates collaborative virtual spaces into the physical space. Our system can provide an alternate reality that generates a playful and natural interaction in an everyday setup

Genome sequencing and annotation of Cellulomonas sp. HZM

We report the draft genome sequence of Cellulomonas sp. HZM, isolated from a tropical peat swamp forest. The draft genome size is 3,559,280 bp with a G + C content of 73% and contains 3 rRNA sequences (single copies of 5S, 16S and 23S rRNA)

Developing points-based risk-scoring systems in the presence of competing risks.

Predicting the occurrence of an adverse event over time is an important issue in clinical medicine. Clinical prediction models and associated points-based risk-scoring systems are popular statistical methods for summarizing the relationship between a multivariable set of patient risk factors and the risk of the occurrence of an adverse event. Points-based risk-scoring systems are popular amongst physicians as they permit a rapid assessment of patient risk without the use of computers or other electronic devices. The use of such points-based risk-scoring systems facilitates evidence-based clinical decision making. There is a growing interest in cause-specific mortality and in non-fatal outcomes. However, when considering these types of outcomes, one must account for competing risks whose occurrence precludes the occurrence of the event of interest. We describe how points-based risk-scoring systems can be developed in the presence of competing events. We illustrate the application of these methods by developing risk-scoring systems for predicting cardiovascular mortality in patients hospitalized with acute myocardial infarction. Code in the R statistical programming language is provided for the implementation of the described methods. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd

Recruitment of faster motor units is associated with greater rates of fascicle strain and rapid changes in muscle force during locomotion

Animals modulate the power output needed for different locomotor tasks through changes in muscle force production and fascicle strain rate. To generate sufficient force, appropriate motor unit recruitment must occur. Given that faster motor units contract with faster strain rates and have faster activation-deactivation rates, it is therefore likely that faster motor units are recruited for more rapid movements. The goals of this study were to 1) describe changes in motor unit recruitment patterns that occur with changes in locomotor dynamics and 2) test whether motor unit recruitment can be directly related to in vivo measures of muscle force and fascicle strain and strain rate, and thus mechanical work. Myoelectric, sonomicrometric, and muscle-tendon force data were collected from the lateral and medial gastrocnemius muscles of the goat hind limb during level and incline walking and trotting, and level galloping. Myoelectric signals were analyzed using wavelet and principal component analysis in order to quantify changes to the myoelectric frequency spectra across locomotor conditions. Fascicle strain and strain rate were calculated from the sonomicrometric data, and force rate was calculated from the tendon force data. The results of this study demonstrate that, under certain locomotor conditions such as level galloping and incline walking, where EMG activity were similar but had different frequency components, faster and slower motor units are recruited in patterns that were task-specific. The study also shows that the recruitment patterns of different motor unit types are related to in vivo fascicle strain rates in addition to myoelectric intensity and force. Together, these data provide evidence that changes in motor unit recruitment have an underlying mechanical basis, at least for certain locomotor tasks.Organismic and Evolutionary Biolog

A Muscle’s Force Depends on the Recruitment Patterns of Its Fibers

Biomechanical models of whole muscles commonly used in simulations of musculoskeletal function and movement typically assume that the muscle generates force as a scaled-up muscle fiber. However, muscles are comprised of motor units that have different intrinsic properties and that can be activated at different times. This study tested whether a muscle model comprised of motor units that could be independently activated resulted in more accurate predictions of force than traditional Hill-type models. Forces predicted by the models were evaluated by direct comparison with the muscle forces measured in situ from the gastrocnemii in goats. The muscle was stimulated tetanically at a range of frequencies, muscle fiber strains were measured using sonomicrometry, and the activation patterns of the different types of motor unit were calculated from electromyographic recordings. Activation patterns were input into five different muscle models. Four models were traditional Hill-type models with different intrinsic speeds and fiber-type properties. The fifth model incorporated differential groups of fast and slow motor units. For all goats, muscles and stimulation frequencies the differential model resulted in the best predictions of muscle force. The in situ muscle output was shown to depend on the recruitment of different motor units within the muscle.Organismic and Evolutionary Biolog

Long-term cardiovascular outcomes after pregnancy in women with heart disease

BACKGROUND: Women with heart disease are at risk for pregnancy complications, but their long-term cardiovascular outcomes after pregnancy are not known. METHODS AND RESULTS: We examined long-term cardiovascular outcomes after pregnancy in 1014 consecutive women with heart disease and a matched group of 2028 women without heart disease. The primary outcome was a composite of mortality, heart failure, atrial fibrillation, stroke, myocardial infarction, or arrhythmia. Secondary outcomes included cardiac procedures and new hypertension or diabetes mellitus. We compared the rates of these outcomes between women with and without heart disease and adjusted for maternal and pregnancy characteristics. We also determined if pregnancy risk prediction tools (CARPREG [Canadian Cardiac Disease in Pregnancy] and World Health Organization) could stratify long-term risks. At 20-year follow-up, a primary outcome occurred in 33.1% of women with heart disease, compared with 2.1% of women without heart disease. Thirty-one percent of women with heart disease required a cardiac procedure. The primary outcome (adjusted hazard ratio, 19.6; 95% CI, 13.8–29.0; P\u3c0.0001) and new hypertension or diabetes mellitus (adjusted hazard ratio, 1.6; 95% CI, 1.4–2.0; P\u3c0.0001) were more frequent in women with heart disease compared with those without. Pregnancy risk prediction tools further stratified the late cardiovascular risks in women with heart disease, a primary outcome occurring in up to 54% of women in the highest pregnancy risk category. CONCLUSIONS: Following pregnancy, women with heart disease are at high risk for adverse long-term cardiovascular outcomes. Current pregnancy risk prediction tools can identify women at highest risk for long-term cardiovascular events

Geographic and temporal validity of prediction models: different approaches were useful to examine model performance

AbstractObjectiveValidation of clinical prediction models traditionally refers to the assessment of model performance in new patients. We studied different approaches to geographic and temporal validation in the setting of multicenter data from two time periods.Study Design and SettingWe illustrated different analytic methods for validation using a sample of 14,857 patients hospitalized with heart failure at 90 hospitals in two distinct time periods. Bootstrap resampling was used to assess internal validity. Meta-analytic methods were used to assess geographic transportability. Each hospital was used once as a validation sample, with the remaining hospitals used for model derivation. Hospital-specific estimates of discrimination (c-statistic) and calibration (calibration intercepts and slopes) were pooled using random-effects meta-analysis methods. I2 statistics and prediction interval width quantified geographic transportability. Temporal transportability was assessed using patients from the earlier period for model derivation and patients from the later period for model validation.ResultsEstimates of reproducibility, pooled hospital-specific performance, and temporal transportability were on average very similar, with c-statistics of 0.75. Between-hospital variation was moderate according to I2 statistics and prediction intervals for c-statistics.ConclusionThis study illustrates how performance of prediction models can be assessed in settings with multicenter data at different time periods