Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?

BACKGROUND: Preterm birth is a clinical event significant but difficult to predict. Biomarkers such as fetal fibronectin and cervical length are effective, but the often are used only for women with clinically suspected preterm risk. It is unknown whether routinely collected data can be used in early pregnancy to stratify preterm birth risk by identifying asymptomatic women. This paper tries to determine the value of the Victorian Perinatal Data Collection (VPDC) dataset in predicting preterm birth and screening for invasive tests.

METHODS: De-identified VPDC report data from 2009 to 2013 were extracted for patients from Barwon Health in Victoria. Logistic regression models with elastic-net regularization were fitted to predict 37-week preterm, with the VPDC antenatal variables as predictors. The models were also extended with two additional variables not routinely noted in the VPDC: previous preterm birth and partner smoking status, testing the hypothesis that these two factors add prediction accuracy. Prediction performance was evaluated using a number of metrics, including Brier scores, Nagelkerke's R(2), c statistic.

RESULTS: Although the predictive model utilising VPDC data had a low overall prediction performance, it had a reasonable discrimination (c statistic 0.646 [95% CI: 0.596-0.697] for 37-week preterm) and good calibration (goodness-of-fit p = 0.61). On a decision threshold of 0.2, a Positive Predictive Value (PPV) of 0.333 and a negative predictive value (NPV) of 0.941 were achieved. Data on previous preterm and partner smoking did not significantly improve prediction.

CONCLUSIONS: For multiparous women, the routine data contains information comparable to some purposely-collected data for predicting preterm risk. But for nulliparous women, the routine data contains insufficient data related to antenatal complications.

Developing an approach to assess the influence of integrating disaster risk reduction practices into infrastructure reconstruction on socio-economic development

Purpose: Disasters provide physical, social, economic, political and environmental development windows of opportunity particularly through housing and infrastructure reconstruction. The reconstruction process should not be neglected due to the opportunistic nature of facilitating innovation in development. In this respect, post-disaster "infrastructure" reconstruction plays a critical role in development discourse and is often essential to sustain recovery after major disasters. However, reconstruction following a natural disaster is a complicated problem involving social, economic, cultural, environmental, psychological, and technological aspects. There are significant development benefits of well-developed "Disaster Risk Reduction (DRR) Strategies" and, for many reasons, the concept of DRR can be more easily promoted following a disaster. In this respect, a research study was conducted to investigate the effects of integrating DRR strategies into infrastructure reconstruction on enhancing the socio-economic development process from a qualitative stance. The purpose of this paper is to document part of this research study; it proposes an approach that can be used to assess the influence of the application of the DRR concept into infrastructure reconstruction on socio-economic development. Design/methodology/approach: The research methodology included a critical literature review. Findings: This paper suggests that the best way to assess the influence of integrating DRR strategies practices into infrastructure reconstruction on socio-economic development is to assess the level of impact that DRR strategies has on overcoming various factors that form vulnerabilities. Having assessed this, the next step is to assess the influence of overcoming the factors that form vulnerabilities on achieving performance targets of socio-economic development. Originality/value: This paper primarily presents a framework for the concept of socio-economic development and a modelled classification of DRR practices.

Risk stratification of cardiac autonomic neuropathy based on multi-lag tone-entropy

Cardiac autonomic neuropathy (CAN) is an irreversible condition affecting the autonomic nervous system, which leads to abnormal functioning of the visceral organs and affects critical body functions such as blood pressure, heart rate and kidney filtration. This study presents multi-lag Tone-Entropy (T-E) analysis of heart rate variability (HRV) at multiple lags as a screening tool for CAN. A total of 41 ECG recordings were acquired from diabetic subjects with definite CAN (CAN+) and without CAN (CAN-) and analyzed. Tone and entropy values of each patient were calculated for different beat sequence lengths (len: 50-900) and lags (m: 1-8). The CAN- group was found to have a lower mean tone value compared to that of CAN+ group for all m and len, whereas the mean entropy value was higher in CAN- than that in CAN+ group. Leave-one-out (LOO) cross-validation tests using a quadratic discriminant (QD) classifier were applied to investigate the performance of multi-lag T-E features. We obtained 100 % accuracy for tone and entropy with len = 250 and m = {2, 3} settings, which is better than the performance of T-E technique based on lag m = 1. The results demonstrate the usefulness of multi-lag T-E analysis over single lag analysis in CAN diagnosis for risk stratification and highlight the change in autonomic nervous system modulation of the heart rate associated with cardiac autonomic neuropathy.