Prospective adverse event risk evaluation in clinical trials.

Proactive and objective regulatory risk management of ongoing clinical trials is limited, especially when it involves the safety of the trial. We seek to prospectively evaluate the risk of facing adverse outcomes from standardized and routinely collected protocol data. We conducted a retrospective cohort study of 2860 Phase 2 and Phase 3 trials that were started and completed between 1993 and 2017 and documented in ClinicalTrials.gov. Adverse outcomes considered in our work include Serious or Non-Serious as per the ClinicalTrials.gov definition. Random-forest-based prediction models were created to determine a trial's risk of adverse outcomes based on protocol data that is available before the start of a trial enrollment. A trial's risk is defined by dichotomic (classification) and continuous (log-odds) risk scores. The classification-based prediction models had an area under the curve (AUC) ranging from 0.865 to 0.971 and the continuous-score based models indicate a rank correlation of 0.6-0.66 (with p-values < 0.001), thereby demonstrating improved identification of risk of adverse outcomes. Whereas related frameworks highlight the prediction benefits of incorporating data that is highly context-specific, our results indicate that Adverse Event (AE) risks can be reliably predicted through a framework of mild data requirements. We propose three potential applications in leading regulatory remits, highlighting opportunities to support regulatory oversight and informed consent decisions.

A simulation study on the association of HRO communication patterns and surgical team performance.

BACKGROUND: The hallmarks of high-reliability organizations (HROs) have been broadly embraced by health-care organizations as a path to achieve greater reliability in patient care. A simulation study was conducted to investigate the hypothesis that surgical teams whose intraoperative communication displayed the HRO hallmarks had a greater capacity to detect and resolve surgical complications in less time. METHODS: The study consisted of presenting four simulations to five surgical teams using a within-subject experimental design. In each simulation, the patient would manifest a complication in which the detection and/or resolution was either obscure or obvious. Communication patterns related to the frequency and sustained duration of HRO content were extracted from coded transcripts using recurrence quantification analysis (RQA), which were paired with the teams' elapsed time to detect or resolve a surgical complication. Spearman's rank-order statistics was then used to test for a monotonically decreasing association between these variables. RESULTS: Data consisting of the RQA metrics and elapsed times are reported for each surgical team in each simulation in addition to statistical tests for association between these variables and inter-rater reliability statistics of the coded communication. CONCLUSIONS: The study suggests that surgical teams whose communication espouses the HRO hallmarks of commitment to resilience and deference to expertise in both frequency and duration are able to resolve surgical complications with an obscure corrective action in less time. The study did not provide confirming evidence that these are associated with their ability to resolve a complication with an obvious corrective action in less time or that patterns of sensitivity to operations are associated with their ability to detect an obscure surgical complication in less time.

Computing exact bundle compliance control charts via probability generating functions.

Compliance to evidenced-base practices, individually and in 'bundles', remains an important focus of healthcare quality improvement for many clinical conditions. The exact probability distribution of composite bundle compliance measures used to develop corresponding control charts and other statistical tests is based on a fairly large convolution whose direct calculation can be computationally prohibitive. Various series expansions and other approximation approaches have been proposed, each with computational and accuracy tradeoffs, especially in the tails. This same probability distribution also arises in other important healthcare applications, such as for risk-adjusted outcomes and bed demand prediction, with the same computational difficulties. As an alternative, we use probability generating functions to rapidly obtain exact results and illustrate the improved accuracy and detection over other methods. Numerical testing across a wide range of applications demonstrates the computational efficiency and accuracy of this approach.

Assessing the state of lean and six sigma practices in healthcare in Mexico.

PURPOSE: The purpose of this study is to assess the state at which lean and six sigma (LSS) are used as a management system to improve the national health system national health system of Mexico. DESIGN/METHODOLOGY/APPROACH: Cross-sectional survey-research. The survey was administered at 30 different hospitals across six states in Mexico. These were selected using convenience sampling and participants (N = 258) were selected through random/snowball sampling procedures, including from top managers down to front-line staff. FINDINGS: Only 16 per cent of respondents reported participation in LSS projects. Still, these implementations are limited to using isolated tools, mainly 5s, failure mode and effects analysis (FMEA) and Fishbone diagram, with the lack of training/knowledge and financial resources as the top disabling factors. Overall, LSS has not become systematic in daily management and operations. RESEARCH LIMITATIONS/IMPLICATIONS: The sampling procedure was by convenience; however, every attempt was made to ensure a lack of bias in the individual responses. If still there was a bias, it is conjectured that this would likely be in overestimating the penetration of LSS. PRACTICAL IMPLICATIONS: The penetration of LSS management practices into the Mexican health system is in its infancy, and the sustainability of current projects is jeopardized given the lack of systematic integration. Hence, LSS should be better spread and communicated across healthcare organizations in Mexico. ORIGINALITY/VALUE: This is the first research work that evaluates the use of LSS management practices in a Latin American country, and the first journal paper that focuses on LSS in healthcare in Mexico.

Generalized aphid population growth models with immigration and cumulative-size dependent dynamics.

Mechanistic models in which the per-capita death rate of a population is proportional to cumulative past size have been shown to describe adequately the population size curves for a number of aphid species. Such previous cumulative-sized based models have not included immigration. The inclusion of immigration is suggested biologically as local aphid populations are initiated by migration of winged aphids and as reproduction is temperature-dependent. This paper investigates two models with constant immigration, one with continuous immigration and the other with restricted immigration. Cases of the latter are relatively simple to fit to data. The results from these two immigration models are compared for data sets on the mustard aphid in India.

Stochastic modeling of aphid population growth with nonlinear, power-law dynamics.

This paper develops a deterministic and a stochastic population size model based on power-law kinetics for the black-margined pecan aphid. The deterministic model in current use incorporates cumulative-size dependency, but its solution is symmetric. The analogous stochastic model incorporates the prolific reproductive capacity of the aphid. These models are generalized in this paper to include a delayed feedback mechanism for aphid death. Whereas the per capita aphid death rate in the current model is proportional to cumulative size, delayed feedback is implemented by assuming that the per capita rate is proportional to some power of cumulative size, leading to so-called power-law dynamics. The solution to the resulting differential equations model is a left-skewed abundance curve. Such skewness is characteristic of observed aphid data, and the generalized model fits data well. The assumed stochastic model is solved using Kolmogrov equations, and differential equations are given for low order cumulants. Moment closure approximations, which are simple to apply, are shown to give accurate predictions of the two endpoints of practical interest, namely (1) a point estimate of peak aphid count and (2) an interval estimate of final cumulative aphid count. The new models should be widely applicable to other aphid species, as they are based on three fundamental properties of aphid population biology.

Nonlinear stochastic modeling of aphid population growth.

This paper develops a stochastic population size model for the black-margined pecan aphid. Prajneshu [Prajneshu, A nonlinear statistical model for aphid population growth. J. Indian Soc. Agric. Statist. 51 (1998), p. 73] proposes a novel nonlinear deterministic model for aphid abundance. The per capita death rate in his model is proportional to the cumulative population size, and the solution is a symmetric analytical function. This paper fits Prajneshu's deterministic model to data. An analogous stochastic model, in which both the current and the cumulative aphid counts are state variables, is then proposed. The bivariate solution of the model, with parameter values suggested by the data, is obtained by solving a large system of Kolmogorov equations. Differential equations are derived for the first and second order cumulants, and moment closure approximations are obtained for the means and variances by solving the set of only five equations. These approximations, which are simple for ecologists to calculate, are shown to give accurate predictions of the two endpoints of applied interest, namely (1) the peak aphid count and (2) the final cumulative aphid count.

Healthcare Engineering Defined: A White Paper.

Engineering has been playing an important role in serving and advancing healthcare. The term "Healthcare Engineering" has been used by professional societies, universities, scientific authors, and the healthcare industry for decades. However, the definition of "Healthcare Engineering" remains ambiguous. The purpose of this position paper is to present a definition of Healthcare Engineering as an academic discipline, an area of research, a field of specialty, and a profession. Healthcare Engineering is defined in terms of what it is, who performs it, where it is performed, and how it is performed, including its purpose, scope, topics, synergy, education/training, contributions, and prospects.