Glen DePalma

Impact of intraoperative hypotension and blood pressure fluctuations on early postoperative delirium after non-cardiac surgery

INTRODUCTION Postoperative delirium is common in older patients. Despite its prognostic significance, the pathophysiology is incompletely understood. Although many risk factors have been identified, no reversible factors, particularly ones potentially modifiable by anaesthetic management, have been identified. The goal of this prospective cohort study was to investigate whether intraoperative hypotension was associated with postoperative delirium in older patients undergoing major non-cardiac surgery. METHODS Study subjects were patients >65 years of age, undergoing major non-cardiac surgery, who were enrolled in an ongoing prospective observational study of the pathophysiology of postoperative delirium. Intraoperative blood pressure was measured and predefined criteria were used to define hypotension. Delirium was measured by the Confusion Assessment Method on the first two postoperative days. Data were analysed using t-tests, two-sample proportion tests and ordered logistic regression multivariable models, including correction for multiple comparisons. RESULTS Data from 594 patients with a mean age of 73.6 years (sd 6.2) were studied. Of these 178 (30%) developed delirium on day 1 and 176 (30%) on day 2. Patients developing delirium were older, more often female, had lower preoperative cognitive scores, and underwent longer operations. Relative hypotension (decreases by 20, 30, or 40%) or absolute hypotension [mean arterial pressure (MAP)<50 mm Hg] were not significantly associated with postoperative delirium, nor was the duration of hypotension (MAP<50 mm Hg). Conversely, intraoperative blood pressure variance was significantly associated with postoperative delirium. DISCUSSION These results showed that increased blood pressure fluctuation, not absolute or relative hypotension, was predictive of postoperative delirium.

Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition

Surface enhanced Raman spectroscopy (SERS) is a rapid and highly sensitive spectroscopic technique that has the potential to measure chemical changes in bacterial cell surface in response to environmental changes. The objective of this study was to determine whether SERS had sufficient resolution to differentiate closely related bacteria within a genus grown on solid and liquid medium, and a single Arthrobacter strain grown in multiple chromate concentrations. Fourteen closely related Arthrobacter strains, based on their 16S rRNA gene sequences, were used in this study. After performing principal component analysis in conjunction with Linear Discriminant Analysis, we used a novel, adapted cross-validation method, which more faithfully models the classification of spectra. All fourteen strains could be classified with up to 97% accuracy. The hierarchical trees comparing SERS spectra from the liquid and solid media datasets were different. Additionally, hierarchical trees created from the Raman data were different from those obtained using 16S rRNA gene sequences (a phylogenetic measure). A single bacterial strain grown on solid media culture with three different chromate levels also showed significant spectral distinction at discrete points identified by the new Elastic Net regularized regression method demonstrating the ability of SERS to detect environmentally induced changes in cell surface composition. This study demonstrates that SERS is effective in distinguishing between a large number of very closely related Arthrobacter strains and could be a valuable tool for rapid monitoring and characterization of phenotypic variations in a single population in response to environmental conditions.

Association Between Intraoperative Blood Transfusions and Early Postoperative Delirium in Older Adults

To determine whether intraoperative blood transfusion, a known trigger and amplifier of inflammation, is an independent risk factor for early postoperative delirium (POD), an acute state of confusion with fluctuating consciousness and inattention after surgery, in older adults undergoing surgery.

Prognostic Significance of Postoperative Subsyndromal Delirium

BACKGROUND Whether postoperative subsyndromal delirium (SSD) is a separate syndrome from delirium and has clinical relevance is not well understood. OBJECTIVES We sought to investigate SSD in older surgical patients and to determine its prognostic significance. METHODS We performed a prospective cohort study of patients who were 65 years or older and were scheduled for noncardiac surgery. Postoperative delirium was determined using the Confusion Assessment Method. SSD was defined as the presence of at least one of the possible 10 symptoms of delirium, as defined by the Confusion Assessment Method, but not meeting the criteria for delirium. RESULTS The number of features of SSD on the first postoperative day was associated with the subsequent development of delirium on the next day, after controlling for other risk factors. When compared with a patient with no SSD features, a patient with 1 SSD feature was 1.07 times more likely to have delirium on the next day (95% CI: 0.42-2.53), with 2 features was 3.32 times more likely to have it (95% CI: 1.42-7.57), and with ≥ 2 features was 8.37 times more likely to have it (95% CI: 4.98-14.53). Furthermore, there was a significant relationship between the number of features of SSD and increased length of hospital stay and worsened functional status at 1 month after surgery. CONCLUSIONS SSD is prevalent in at-risk surgical patients and has prognostic significance. Only a single symptom of SSD was sufficient to cause a significant increase in hospital length of stay and further decline in functional status. These results suggest that monitoring for SSD is indicated in at-risk patients.

Unmet Need for Help With Activities of Daily Living Disabilities and Emergency Department Admissions Among Older Medicare Recipients.

Purpose of the Study This study determined whether self-reports of unmet need for help with activities of daily living (ADL) disabilities are prognostic of emergency department (ED) utilization. Design and Methods This prospective cohort study of 2,194 community-living, ADL-disabled subjects combined 2004 National Long-Term Care Survey responses with linked Medicare data through 2005. A negative binomial count model was computed to assess the association between unmet ADL need and number of subsequent ED admissions while statistically adjusting for predisposing, enabling, and need characteristics associated with ED admissions among older adults. Results The adjusted annual incidence rate (IR) for ED admissions was 19% higher for unmet versus met need (IR = 1.19; 95% confidence interval [CI] = 1.00-1.40; p = .047). The IR for ED admissions for falls and injuries was higher for those with unmet ADL versus met ADL need (IR = 1.43; 95% CI = 1.10-1.86), and trended toward significance for ED admissions for skin breakdown (IR = 2.02; 95% CI = 0.97-2.88), but was not significant for ED admissions for dehydration (IR = 1.13; 95% CI= 0.79-1.63). Implications Unmet ADL need is prognostic of ED admissions, especially for falls and injuries. Future research is needed to determine whether resolution of unmet ADL need reduces ED utilization.

Determination of disk diffusion susceptibility testing interpretive criteria using model-based analysis: development and implementation

The determination of diffusion test breakpoints has become a challenging issue due to the increasing resistance of microorganisms to antibiotics. Currently, the most commonly-used method for determining these breakpoints is the modified error-rate bounded method. Its use has remained widespread despite the introduction of several model-based methods that have been shown superior in terms of precision and accuracy. However, the computational complexities associated with these new approaches has been a significant barrier for clinicians. To remedy this, we developed and examine the utility of a free online software package designed for the determination of diffusion test breakpoints: dBETS (diffusion Breakpoint Estimation Testing Software). This software package allows clinicians to easily analyze data from susceptibility experiments through visualization, error-rate bounded, and model-based approaches. We analyze four publicly available data sets from the Clinical and Laboratory Standards Institute using dBETS.

Vitamin D status and resistance exercise training independently affect glucose tolerance in older adults.

We assessed the influence of serum 25-hydroxyvitamin D (25[OH]D) and parathyroid hormone (PTH) concentrations on oral glucose tolerance, body composition, and muscle strength in older, nondiabetic adults who performed resistance exercise training (RT) while consuming diets with either 0.9 or 1.2 g protein kg(-1) d(-1). We hypothesized that individuals with insufficient 25(OH)D and/or high PTH would have less improvement in glucose tolerance after 12 weeks of RT compared with individuals with sufficient 25(OH)D and lower PTH. Sixteen men and 19 women (aged 61 ± 8 years; range, 50-80 years; body mass index, 26.3 ± 3.6 kg/m(2)) performed RT 3 times/wk for 12 weeks, with oral glucose tolerance tests done at baseline and postintervention. Protein intake did not influence the responses described below. Plasma glucose area under the curve (P = .02) and 2-hour plasma glucose concentration (P = .03) were higher for vitamin D-insufficient subjects (25[OH]D <50 nmol/L, n = 7) vs vitamin D-sufficient subjects (25[OH]D ≥50 nmol/L, n = 28). These differences remained significant after adjustment for age and body mass index. Resistance exercise training reduced fat mass (mean ± SD, -6% ± 7%; P < .001) and increased lean body mass (2% ± 3%, P < .001) and whole-body muscle strength (32% ± 17%, P < .001) in these weight-stable subjects but did not affect 25(OH)D or PTH concentrations. Oral glucose tolerance improved after RT (-10% ± 16% in glucose area under the curve and -21% ± 40% in 2-hour glucose, P = .001), but baseline 25(OH)D and PTH did not influence these RT-induced changes. These findings indicate that vitamin D status and RT independently affect glucose tolerance, and a training-induced improvement in glucose tolerance does not offset the negative effect of insufficient vitamin D status in older, nondiabetic adults.

Bayesian monotonic errors-in-variables models with applications to pathogen susceptibility testing

Drug dilution (MIC) and disk diffusion (DIA) are the 2 most common antimicrobial susceptibility assays used by hospitals and clinics to determine an unknown pathogens susceptibility to various antibiotics. Since only one assay is commonly used, it is important that the 2 assays give similar results. Calibration of the DIA assay to the MIC assay is typically done using the error-rate bounded method, which selects DIA breakpoints that minimize the observed discrepancies between the 2 assays. In 2000, Craig proposed a model-based approach that specifically models the measurement error and rounding processes of each assay, the underlying pathogen distribution, and the true monotonic relationship between the 2 assays. The 2 assays are then calibrated by focusing on matching the probabilities of correct classification (susceptible, indeterminant, and resistant). This approach results in greater precision and accuracy for estimating DIA breakpoints. In this paper, we expand the flexibility of the model-based method by introducing a Bayesian 4-parameter logistic model (extending Craigs original 3-parameter model) as well as a Bayesian nonparametric spline model to describe the relationship between the 2 assays. We propose 2 ways to handle spline knot selection, considering many equally spaced knots but restricting overfitting via a random walk prior and treating the number and location of knots as additional unknown parameters. We demonstrate the 2 approaches via a series of simulation studies and apply the methods to 2 real data sets.

An Asynchronous Distributed Expectation Maximization Algorithm For Massive Data: The DEM Algorithm

ABSTRACT The family of expectation--maximization (EM) algorithms provides a general approach to fitting flexible models for large and complex data. The expectation (E) step of EM-type algorithms is time-consuming in massive data applications because it requires multiple passes through the full data. We address this problem by proposing an asynchronous and distributed generalization of the EM called the distributed EM (DEM). Using DEM, existing EM-type algorithms are easily extended to massive data settings by exploiting the divide-and-conquer technique and widely available computing power, such as grid computing. The DEM algorithm reserves two groups of computing processes called workers and managers for performing the E step and the maximization step (M step), respectively. The samples are randomly partitioned into a large number of disjoint subsets and are stored on the worker processes. The E step of DEM algorithm is performed in parallel on all the workers, and every worker communicates its results to the managers at the end of local E step. The managers perform the M step after they have received results from a γ-fraction of the workers, where γ is a fixed constant in (0, 1]. The sequence of parameter estimates generated by the DEM algorithm retains the attractive properties of EM: convergence of the sequence of parameter estimates to a local mode and linear global rate of convergence. Across diverse simulations focused on linear mixed-effects models, the DEM algorithm is significantly faster than competing EM-type algorithms while having a similar accuracy. The DEM algorithm maintains its superior empirical performance on a movie ratings database consisting of 10 million ratings. Supplementary material for this article is available online.