Beyond linear methods of data analysis: time series analysis and its applications in renal research

Visual Computing of Causality in Personalized Depression

Major depressive disorder or clinical depression is a mental disorder characterized by daily low moods, which occur across many situations. Individuals suffering from depression are typically treated with counseling and antidepressant medication. This paper presents a computing approach for visualizing the dynamics of pairwise interactions of moods in personalized depression under and without medication. The methods of fuzzy cross recurrence plots of time series and their tensor decomposition offer a new way for gaining insight into the causality of the complex behavior of depression and its treatment.

Renal autoregulation and blood pressure management in circulatory shock

The importance of personalized blood pressure management is well recognized. Because renal pressure–flow relationships may vary among patients, understanding how renal autoregulation may influence blood pressure control is essential. However, much remains uncertain regarding the determinants of renal autoregulation in circulatory shock, including the influence of comorbidities and the effects of vasopressor treatment. We review published studies on renal autoregulation relevant to the management of acutely ill patients with shock. We delineate the main signaling pathways of renal autoregulation, discuss how it can be assessed, and describe the renal autoregulatory alterations associated with chronic disease and with shock.

Heart-Rate Variability-More than Heart Beats?

Heart-rate variability (HRV) is frequently introduced as mirroring imbalances within the autonomous nerve system. Many investigations are based on the paradigm that increased sympathetic tone is associated with decreased parasympathetic tone and vice versa. But HRV is probably more than an indicator for probable disturbances in the autonomous system. Some perturbations trigger not reciprocal, but parallel changes of vagal and sympathetic nerve activity. HRV has also been considered as a surrogate parameter of the complex interaction between brain and cardiovascular system. Systems biology is an inter-disciplinary field of study focusing on complex interactions within biological systems like the cardiovascular system, with the help of computational models and time series analysis, beyond others. Time series are considered surrogates of the particular system, reflecting robustness or fragility. Increased variability is usually seen as associated with a good health condition, whereas lowered variability might signify pathological changes. This might explain why lower HRV parameters were related to decreased life expectancy in several studies. Newer integrating theories have been proposed. According to them, HRV reflects as much the state of the heart as the state of the brain. The polyvagal theory suggests that the physiological state dictates the range of behavior and psychological experience. Stressful events perpetuate the rhythms of autonomic states, and subsequently, behaviors. Reduced variability will according to this theory not only be a surrogate but represent a fundamental homeostasis mechanism in a pathological state. The neurovisceral integration model proposes that cardiac vagal tone, described in HRV beyond others as HF-index, can mirror the functional balance of the neural networks implicated in emotion-cognition interactions. Both recent models represent a more holistic approach to understanding the significance of HRV.

Impact of the Publication of Appropriate Use Criteria on Utilization Rates of Myocardial Perfusion Imaging Studies in Ontario, Canada: A Population-Based Study

BACKGROUND

Concern regarding overutilization of cardiac imaging has led to the development of appropriate use criteria (AUC). Myocardial perfusion imaging (MPI) is one of the most commonly used cardiac imaging modalities worldwide. Despite multiple iterations of AUC, there is currently no evidence regarding their real-world impact on population-based utilization rates of MPI. Our goal was to assess the impact of the AUC on rates of MPI in Ontario, Canada. We hypothesized that publication of the AUC would be associated with a significant reduction in MPI rates.

METHODS AND RESULTS

We conducted a retrospective cohort study of the adult population of Ontario from January 1, 2000, to December 31, 2015. Age- and sex-standardized rates were compared from 4 different periods intersected by 3 published iterations of the AUC. Overall, 3 072 611 MPI scans were performed in Ontario during our study period. The mean monthly rate increased from 14.1/10 000 in the period from January 2000 to October 2005 to 18.2/10 000 between November 2005 and June 2009. After this point in time, there was a reduction in rates, falling to a mean monthly rate of 17.1/10 000 between March 2014 and December 2015. Time series analysis revealed that publication of the 2009 AUC was associated with a significant reduction in MPI rates (P<0.001). This translated into ≈88 849 fewer MPI scans at a cost savings of ≈72 million Canadian dollars.

CONCLUSIONS

Our results reflect a potential real-world impact of the 2009 MPI AUC by demonstrating evidence of a significant effect on population-based rates of MPI.

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis

BACKGROUND

Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical.

METHOD

In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes.

RESULTS

This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted.

CONCLUSION

This tool will allow anyone with historic test volume data to model future demand.

Association between publication of appropriate use criteria and the temporal trends in diagnostic angiography in stable coronary artery disease: A population-based study

BACKGROUND

The appropriate use criteria (AUC) were developed to aid clinicians in making clinical decisions regarding coronary angiography. The aim of the present study was to evaluate the association between the publication of the AUC criteria in diagnostic angiography and rates of angiography in Ontario.

METHODS

Our cohort consisted of all patients who underwent coronary angiography in Ontario from 1st October 2008 to 31st October 2013 for the indication of suspected stable coronary artery disease. We determined monthly age- and sex-standardized rates of angiography per 100,000 adults. To determine the association between the publication of the AUC for diagnostic angiograms and the rates of angiography, we conducted a time series analysis using an autoregressive integrated moving average model. As a sensitivity analyses, we evaluated the impact of the AUC on the rates of percutaneous coronary intervention (PCI) per 100 angiograms.

RESULTS

We included 114,551 angiograms for stable coronary artery disease. In the period prior to the publication of the AUC, the average monthly age- and sex-standardized rate of angiography was 18.7 per 100,000; post-AUC, the average monthly rate decreased to 17.6 per 100,000 adults (P = .037). In contrast, in the 29,358 PCIs included in the analysis, the monthly PCI rates per 100 angiograms were unchanged (25.2 pre-AUC; 26.8 post-AUC; P = .29). In the sensitivity analysis, the rate of appropriate/inappropriate and uncertain indication did not significantly change over the study period.

CONCLUSIONS

The publication of the diagnostic angiography AUC criteria was associated with a decrease in the population rates of diagnostic angiography. This suggests that the AUC potentially was associated with an increased threshold to pursue invasive diagnostic testing.