Predicting Acute Kidney Injury at Hospital Re-entry Using High-dimensional Electronic Health Record Data

Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes preventable, there is great interest in prediction. Most existing studies consider all patients and therefore restrict to features available in the first hours of hospitalization. Here, the focus is instead on rehospitalized patients, a cohort in which rich longitudinal features from prior hospitalizations can be analyzed. Our objective is to provide a risk score directly at hospital re-entry. Gradient boosting, penalized logistic regression (with and without stability selection), and a recurrent neural network are trained on two years of adult inpatient EHR data (3,387 attributes for 34,505 patients who generated 90,013 training samples with 5,618 cases and 84,395 controls). Predictions are internally evaluated with 50 iterations of 5-fold grouped cross-validation with special emphasis on calibration, an analysis of which is performed at the patient as well as hospitalization level. Error is assessed with respect to diagnosis, race, age, gender, AKI identification method, and hospital utilization. In an additional experiment, the regularization penalty is severely increased to induce parsimony and interpretability. Predictors identified for rehospitalized patients are also reported with a special analysis of medications that might be modifiable risk factors. Insights from this study might be used to construct a predictive tool for AKI in rehospitalized patients. An accurate estimate of AKI risk at hospital entry might serve as a prior for an admitting provider or another predictive algorithm.Comment: In revisio

Properties of Healthcare Teaming Networks as a Function of Network Construction Algorithms

Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other. Most healthcare service network models have been constructed from patient claims data, using billing claims to link patients with providers. The data sets can be quite large, making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks. To address this issue, we compared the properties of healthcare networks constructed using different algorithms and the 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We found that each algorithm produced networks with substantially different topological properties. Provider networks adhered to a power law, and organization networks to a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and greatly altered measures of vertex prominence such as the betweenness centrality. We identified patterns in the distance patients travel between network providers, and most strikingly between providers in the Northeast United States and Florida. We conclude that the choice of network construction algorithm is critical for healthcare network analysis, and discuss the implications for selecting the algorithm best suited to the type of analysis to be performed.Comment: With links to comprehensive, high resolution figures and networks via figshare.co

Reward management in the Irish civil service.

This dissertation set out to examine the Reward Management System which is currently operating within the Irish Civil Service. In conjunction with this, the area of performance management was looked at, and the link between both reward management and performance management was outlined. A review of literature provided the author with a view to what the subject entailed and Michael Armstrong 2002 provided the main source of information regarding an effective reward system. Based on this information, primary research was conducted in four local government offices in the Kerry area, namely, the Department of Social Welfare, Tralee, the Department of the Revenue Commissioners, the Department of Agriculture and the Department of Social Welfare, Listowel. The hypothesis for testing was: 'The current reward system in the Irish Civil Service and its effect on performance'. Both quantitative and qualitative data was gathered using a questionnaire, the result of which was negative overall. The research highlighted the discontentment felt towards the current reward system along with the dissatisfaction felt towards the new Performance Management and Development System (PMDS) which has been recently introduced into the service. Little research has been carried out in the area of Reward Management in the Irish Civil Service and the author hopes that her research will be useful in the future

Pain perception in disorders of consciousness: Neuroscience, clinical care, and ethics in dialogue

peer reviewedPain, suffering and positive emotions in patients in vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious states (MCS) pose clinical and ethical challenges. Clinically, we evaluate behavioural responses after painful stimulation and also emotionally-contingent behaviours (e.g., smiling). Using stimuli with emotional valence, neuroimaging and electrophysiology technologies can detect subclinical remnants of preserved capacities for pain which might influence decisions about treatment limitation. To date, no data exist as to how healthcare providers think about end-of-life options (e.g., withdrawal of artificial nutrition and hydration) in the presence or absence of pain in non-communicative patients. Here, we aimed to better clarify this issue by re-analyzing previously published data on pain perception (Prog Brain Res 2009 177, 329–38) and end-of-life decisions (J Neurol 2010 258, 1058–65) in patients with disorders of consciousness. In a sample of 2259 European healthcare professionals we found that, for VS/UWS more respondents agreed with treatment withdrawal when they considered that VS/UWS patients did not feel pain (77%) as compared to those who thought VS/UWS did feel pain (59%). This interaction was influenced by religiosity and professional background. For MCS, end-of-life attitudes were not influenced by opinions on pain perception. Within a contemporary ethical context we discuss (1) the evolving scientific understandings of pain perception and their relationship to existing clinical and ethical guidelines; (2) the discrepancies of attitudes within (and between) healthcare providers and their consequences for treatment approaches, and (3) the implicit but complex relationship between pain perception and attitudes toward life-sustaining treatments

Network vertex degree distribution by algorithm for <i>τ</i> = 365 days.

<p>Network vertex degree distribution by algorithm for <i>τ</i> = 365 days.</p

Variation in provider community identification.

<p>We analyzed undirected Provider-Provider networks constructed with the trace-route, sliding frame and binning algorithms for <i>τ</i> = 365 days, and censored for edge weights ≤ 11. Provider-Provider community teams identified for providers within NY State from each network using the Girvan-Newman modularity community finding algorithm [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175876#pone.0175876.ref026" target="_blank">26</a>] implemented in <i>Mathematica</i>. Each provider was assigned to only one community. (A) Provider densities. Hexagonal bins show the counts of providers that were members of any community within each geographic region color coded by range. Note the different geographic density patterns for each method. (B) Histogram of number of providers per community. Note the large number of communities (<i>n</i>) in each histogram, with the majority having only 2 providers. Community sizes, compositions and number differed between all 3 methods. (C) Shows the five largest communities identified in each network.</p

Betweenness centrality <i>C</i>′<i>_β</i> of healthcare networks by algorithm for <i>τ</i> = 365 days betweenness centrality was calculated for all networks using the Oracle PGX algorithm.

<p>Results are displayed with algorithmic binning of <i>C</i>′<i>_β</i> = <i>C^β</i> / (<i>N</i> − 1)(<i>N</i> − 2) for directed graphs produced by the sliding frame and trace-route algorithms, and <i>C</i>′<i>_β</i> = 2<i>C^β</i> / (<i>N</i> − 1)(<i>N</i> − 2) for undirected networks produced by the binning algorithm. All plots are scaled in the y-axis to frequency, allowing direct comparison of centralities. Note that edge-weight censoring (excluding edges with Ω_{<i>v</i>_<i>j</i> → <i>v</i>_<i>k</i>} ≤ 11) markedly changes the centrality distribution of all networks.</p