The role of angiogenic and antiangiogenic factors in the second trimester in the prediction of preeclampsia in pregnant women with type 1 diabetes

OBJECTIVE To assess the association between circulating angiogenic and antiangiogenic factors in the second trimester and risk of preeclampsia in women with type 1 diabetes.

RESEARCH DESIGN AND METHODS Maternal plasma concentrations of placental growth factor (PlGF), soluble fms-like tyrosine kinase 1 (sFlt-1), and soluble endoglin (sEng) were available at 26 weeks of gestation in 540 women with type 1 diabetes enrolled in the Diabetes and Preeclampsia Intervention Trial.

RESULTS Preeclampsia developed in 17% of pregnancies (n = 94). At 26 weeks of gestation, women in whom preeclampsia developed later had significantly lower PlGF (median [interquartile range]: 231 pg/mL [120–423] vs. 365 pg/mL [237–582]; P < 0.001), higher sFlt-1 (1,522 pg/mL [1,108–3,393] vs. 1,193 pg/mL [844–1,630] P < 0.001), and higher sEng (6.2 ng/mL [4.9–7.9] vs. 5.1 ng/mL[(4.3–6.2]; P < 0.001) compared with women who did not have preeclampsia. In addition, the ratio of PlGF to sEng was significantly lower (40 [17–71] vs. 71 [44–114]; P < 0.001) and the ratio of sFlt-1 to PlGF was significantly higher (6.3 [3.4–15.7] vs. 3.1 [1.8–5.8]; P < 0.001) in women who later developed preeclampsia. The addition of the ratio of PlGF to sEng or the ratio of sFlt-1 to PlGF to a logistic model containing established risk factors (area under the curve [AUC], 0.813) significantly improved the predictive value (AUC, 0.850 and 0.846, respectively; P < 0.01) and significantly improved reclassification according to the integrated discrimination improvement index (IDI) (IDI scores 0.086 and 0.065, respectively; P < 0.001).

CONCLUSIONS These data suggest that angiogenic and antiangiogenic factors measured during the second trimester are predictive of preeclampsia in women with type 1 diabetes. The addition of the ratio of PlGF to sEng or the ratio of sFlt-1 to PlGF to established clinical risk factors significantly improves the prediction of preeclampsia in women with type 1 diabetes.

Preeclampsia is characterized by the development of hypertension and new-onset proteinuria during the second half of pregnancy (1,2), leading to increased maternal morbidity and mortality (3). Women with type 1 diabetes are at increased risk for development of preeclampsia during pregnancy, with rates being two-times to four-times higher than that of the background maternity population (4,5). Small advances have come from preventive measures, such as low-dose aspirin in women at high risk (6); however, delivery remains the only effective intervention, and preeclampsia is responsible for up to 15% of preterm births and a consequent increase in infant mortality and morbidity (7).

Although the etiology of preeclampsia remains unclear, abnormal placental vascular remodeling and placental ischemia, together with maternal endothelial dysfunction, hemodynamic changes, and renal pathology, contribute to its pathogenesis (8). In addition, over the past decade accumulating evidence has suggested that an imbalance between angiogenic factors, such as placental growth factor (PlGF), and antiangiogenic factors, such as soluble fms-like tyrosine kinase 1 (sFlt-1) and soluble endoglin (sEng), plays a key role in the pathogenesis of preeclampsia (8,9). In women at low risk (10–13) and women at high risk (14,15), concentrations of angiogenic and antiangiogenic factors are significantly different between women who later develop preeclampsia (lower PlGF, higher sFlt-1, and higher sEng levels) compared with women who do not.

Few studies have specifically focused on circulating angiogenic factors and risk of preeclampsia in women with diabetes, and the results have been conflicting. In a small study, higher sFlt-1 and lower PlGF were reported at the time of delivery in women with diabetes who developed preeclampsia (16). In a longitudinal prospective cohort of pregnant women with diabetes, Yu et al. (17) reported increased sFlt-1 and reduced PlGF in the early third trimester as potential predictors of preeclampsia in women with type 1 diabetes, but they did not show any difference in sEng levels in women with preeclampsia compared with women without preeclampsia. By contrast, Powers et al. (18) reported only increased sEng in the second trimester in women with pregestational diabetes who developed preeclampsia.

The aim of this study, which was significantly larger than the previous studies highlighted, was to assess the association between circulating angiogenic (PlGF) and antiangiogenic (sFlt-1 and sEng) factors and the risk of preeclampsia in women with type 1 diabetes. A further aim was to evaluate the added predictive ability and clinical usefulness of angiogenic factors and established risk factors for preeclampsia risk prediction in women with type 1 diabetes.

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms

Rationale, aims and objectives: This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. 

Methods: Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. 

Results: Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). 

Conclusions: Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized.

Predictive Value of Depressive Symptoms for All-Cause Mortality: Findings From the PRIME Belfast Study Examining the Role of Inflammation and Cardiovascular Risk Markers

OBJECTIVES: To improve understanding about the potential underlying biological mechanisms in the link between depression and all-cause mortality and to investigate the role that inflammatory and other cardiovascular risk factors may play in the relationship between depressive symptoms and mortality.

METHODS: Depression and blood-based biological markers were assessed in the Belfast PRIME prospective cohort study (N = 2389 men, aged 50-59 years) in which participants were followed up for 18 years. Depression was measured using the 10-item Welsh Pure Depression Inventory. Inflammation markers (C-reactive protein [CRP], neopterin, interleukin [IL]-1 receptor antagonist [IL-1Ra], and IL-18) and cardiovascular-specific risk factors (N-terminal pro-b-type natriuretic peptide, midregion pro-atrial natriuretic peptide, midregion pro-adrenomedullin, C-terminal pro-endothelin-1 [CT-proET]) were obtained at baseline. We used Cox proportional hazards modeling to examine the association between depression and biological measures in relation to all-cause mortality and explore the mediating effects.

RESULTS: During follow-up, 418 participants died. Higher levels of depressive symptoms were associated with higher levels of CRP, IL-1Ra, and CT-proET. After adjustment for socioeconomic and life-style risk factors, depressive symptoms were significantly associated with all-cause mortality (hazard ratio = 1.10 per scale unit, 95% confidence interval = 1.04-1.16). This association was partly explained by CRP (7.3%) suggesting a minimal mediation effect. IL-1Ra, N-terminal pro-b-type natriuretic peptide, midregion pro-atrial natriuretic peptide, midregion pro-adrenomedullin, and CT-proET contributed marginally to the association between depression and subsequent mortality.

CONCLUSIONS: Inflammatory and cardiovascular risk markers are associated with depression and with increased mortality. However, depression and biological measures show additive effects rather than a pattern of meditation of biological factors in the association between depression and mortality.

Modelling the influence of biotic factors on species distribution patterns

Biotic interactions can have large effects on species distributions yet their role in shaping species ranges is seldom explored due to historical difficulties in incorporating biotic factors into models without a priori knowledge on interspecific interactions. Improved SDMs, which account for biotic factors and do not require a priori knowledge on species interactions, are needed to fully understand species distributions. Here, we model the influence of abiotic and biotic factors on species distribution patterns and explore the robustness of distributions under future climate change. We fit hierarchical spatial models using Integrated Nested Laplace Approximation (INLA) for lagomorph species throughout Europe and test the predictive ability of models containing only abiotic factors against models containing abiotic and biotic factors. We account for residual spatial autocorrelation using a conditional autoregressive (CAR) model. Model outputs are used to estimate areas in which abiotic and biotic factors determine species’ ranges. INLA models containing both abiotic and biotic factors had substantially better predictive ability than models containing abiotic factors only, for all but one of the four species. In models containing abiotic and biotic factors, both appeared equally important as determinants of lagomorph ranges, but the influences were spatially heterogeneous. Parts of widespread lagomorph ranges highly influenced by biotic factors will be less robust to future changes in climate, whereas parts of more localised species ranges highly influenced by the environment may be less robust to future climate. SDMs that do not explicitly include biotic factors are potentially misleading and omit a very important source of variation. For the field of species distribution modelling to advance, biotic factors must be taken into account in order to improve the reliability of predicting species distribution patterns both presently and under future climate change.

PROGNOSTIC FACTORS PREDICTING FUNCTIONAL OUTCOME AT FOUR MONTHS FOLLOWING ACUTE ANKLE SPRAIN

PROGNOSTIC FACTORS PREDICTING FUNCTIONAL OUTCOME AT FOUR MONTHS FOLLOWING ACUTE ANKLE SPRAINBleakley C.M.1, O'Connor S.R.1, Tully M.A.2, Rocke L.G.3, MacAuley D.C.1, Bradbury I.4, Keegan S.4, McDonough S.M.11University of Ulster, Health & Rehabilitation Sciences Research Institute, Newtownabbey, United Kingdom, 2Queen's University, UKCRC Centre of Excellence for Public Health (NI), Belfast, United Kingdom, 3Royal Victoria Hospital, Department of Emergency Medicine, Belfast, United Kingdom, 4Frontier Science (Scotland), Kincraig, Inverness-shire, United KingdomPurpose: To identify clinically relevant factors assessed following acute ankle sprain that predict functional recovery at four months post-injury.Relevance: Ankle sprains are one of the most common musculoskeletal injuries with an estimated 5000 new cases occurring each day in the United Kingdom. In the acute phase, ankle sprains may be associated with pain and loss of function. In the longer-term there is a risk of residual problems including chronic pain or reinjury. Few studies have sought to examine factors associated with a poor long-term prognosis.Participants: 101 patients (Age: Mean (SD) 25.9 (7.9) years; Body Mass Index (BMI): 25.3 (3.5) kg/m2) with an acute grade 1 or 2 ankle sprain attending an accident and emergency department or sports injury clinic. Exclusion criteria included complete (grade 3) rupture of the ankle ligament complex, bony ankle injury or multiple injuries.Methods: Participants were allocated as part of a randomised controlled trial to an accelerated intervention incorporating intermittent ice and early therapeutic exercise or a standard protection, rest, ice, compression, and elevation intervention for one week. Treatment was then standardised in both groups and consisted of ankle rehabilitation exercises focusing on muscle strengthening, neuromuscular training, and sports specific functional exercises for a period of approximately four to six weeks. On initial assessment age, gender, mechanism of injury, presence of an audible pop or snap and the presence of contact during the injury were recorded. The following factors were also recorded at baseline and at one and four weeks post-injury: weight-bearing dorsi-flexion test, lateral hop test, presence of medial pain on palpation and a positive impingement sign. Functional status was assessed using the Karlsson score at baseline, at week four and at four months. Reinjury rates were recorded throughout the intervention phase and at four months.Analysis: A mixed between-within subjects analysis of variance (ANOVA) was used to determine the effect of each factor on functional status at week four and at four months. Significance was set at a Bonferroni adjusted level of 0.0125 (0.05/4).Results: Eighty-five participants (84%) were available at final follow-up assessment. Pain on weight-bearing dorsi-flexion and lateral hop tests at week four were both associated with a lower functional score at four months post-injury (P = 0.011 and P = 0.001). No other significant interactions were observed at any other timepoint (baseline or week one). There were only two reinjuries within the four month follow-up period with a further two reported at approximately six months post-injury. We were therefore unable to determine whether any factors were associated with an increased risk of reinjury.Conclusions: Potential prognostic factors on initial or early examination after acute ankle sprain did not help predict functional recovery at four months post-injury. However, pain on weight-bearing dorsi-flexion and lateral hop tests observed at four weeks were associated with a slower rate of recovery.Implications: Some clinical tests may help identify patients at risk of poor functional recovery after acute ankle sprain. However, further work is required to examine factors which may be predictive on initial assessment.Key-words: 1. Prognostic factors 2. Recovery 3. Ankle sprainFunding acknowledgements: Physiotherapy Research Foundation, Chartered Society of Physiotherapy, Strategic Priority Fund; Department of Employment and Learning, Northern Ireland.Ethics approval: Office for Research Ethics Committee (UK).