BackgroundThe interest in prognostic reviews is increasing, but to properly review existing evidence an accurate search filer for finding prediction research is needed. The aim of this paper was to validate and update two previously introduced search filters for finding prediction research in Medline: the Ingui filter and the Haynes Broad filter.Methodology/Principal FindingsBased on a hand search of 6 general journals in 2008 we constructed two sets of papers. Set 1 consisted of prediction research papers (n = 71), and set 2 consisted of the remaining papers (n = 1133). Both search filters were validated in two ways, using diagnostic accuracy measures as performance measures. First, we compared studies in set 1 (reference) with studies retrieved by the search strategies as applied in Medline. Second, we compared studies from 4 published systematic reviews (reference) with studies retrieved by the search filter as applied in Medline. Next – using word frequency methods – we constructed an additional search string for finding prediction research. Both search filters were good in identifying clinical prediction models: sensitivity ranged from 0.94 to 1.0 using our hand search as reference, and 0.78 to 0.89 using the systematic reviews as reference. This latter performance measure even increased to around 0.95 (range 0.90 to 0.97) when either search filter was combined with the additional string that we developed. Retrieval rate of explorative prediction research was poor, both using our hand search or our systematic review as reference, and even combined with our additional search string: sensitivity ranged from 0.44 to 0.85.Conclusions/SignificanceExplorative prediction research is difficult to find in Medline, using any of the currently available search filters. Yet, application of either the Ingui filter or the Haynes broad filter results in a very low number missed clinical prediction model studies.

Abstract. Many diabetic patients in general practice do not achieve good glycaemic control. The aim of this study was to assess which characteristics of type 2 diabetes patients treated in primary care predict poor glycaemic control (HbA 1c P 7%). Data were collected from the medical records. 1641 patients were included who had mean HbA 1c 7.1(SD 1.7)% , and 42% had HbA 1c P 7%. On univariate analysis younger age; longer duration of diabetes; higher levels of blood glucose at diagnosis; most recent fasting blood glucose (FBG), total cholesterol, and triglyceride; higher body mass index (BMI); treatment with oral hypoglycaemic agents (OHA); treatment with insulin; more GP-visits for diabetes in the last year; and lower educational level were associated with poor control. Both in multiple linear regression and in multiple logistic regression higher levels of FBG (odds ratio (OR): = 1.6, 95% confidence interval (CI): 1.49, 1.70), treatment with OHA (OR: 2.1, 95% CI: 1.41, 3.04), treatment with insulin (OR: 7.2, 95% CI: 4.18, 12.52), lower educational level (OR: 1.26, 95% CI: 1.01, 1.56) were independently associated with poor levels of HbA 1c . When FBG levels were excluded from the model, higher blood glucose at diagnosis, higher values for triglyceride and total cholesterol, and younger age predicted poor glycaemic control, but these variables explained only 15% of the variation in HbA 1c . In conclusion prediction of poor glycaemic control from patient characteristics in diabetic patients in general practice is hardly possible. FBG appeared to be a strong predictor of HbA 1c , which underlines the usefulness of this simple test in daily diabetes care. The worse metabolic control in those treated with either OHA or insulin suggests that current treatment regimes might be not sufficiently applied to reach the targets of care. Providers of diabetes care should be attentive to patients with lower educational level.

In patients with IBD, a VAS seems the most appropriate tool for quantifying medication adherence in clinical practice. The MMAS-8 may be used additionally to provide insight in specific reasons for non-adherence.

Persistence of the virus, as well as viral load in the URT, may not be associated with the induction and/or persistence of asthmatic symptoms.

Objectives: To evaluate if pre-pregnancy body mass index (BMI) determines blood pressure throughout pregnancy and to explore the role of gestational weight gain in this association. In addition, the effects of prepregnancy BMI and gestational weight gain on the occurrence of gestational hypertension and preeclampsia were investigated.Design: Prospective cohort study. Setting: Maternal and child health primary care referral centre, Jakarta, Indonesia.Population and measurements: 2252 pregnant women visiting Budi Kemuliaan Hospital and its branch for regular antenatal care visits from July 2012 to April 2015. Pre-pregnancy BMI (kg/m 2 ) was based on selfreported pre-pregnancy weight and measured height at first visit. Gestational weight gain was calculated as weight at the day of delivery minus the pre-pregnancy weight. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured during pregnancy at every visit. Linear mixed models were used to analyse this relation with repeated blood pressure measures as the outcome and pre-pregnancy BMI as the predictor. When looking at gestational hypertension and pre-eclampsia as outcomes, (multiple) logistic regression was used in the analysis.Results: Independent of pre-pregnancy BMI, SBP and DBP increased by 0.99 mm Hg/month and 0.46 mm Hg/month, respectively. Higher pre-pregnancy BMI was associated with higher pregnancy SBP (0.25 mm Hg/kg/m 2 ; 95% CI 0.17 to 0.34; p<0.01) and DBP (0.18 mm Hg/kg/m 2 ; 0.13 to 0.24; p<0.01) in adjusted analysis. Every 1 kg/m 2 higher pre-pregnancy BMI was associated with 6% and 9% higher odds for gestational hypertension (adjusted OR (aOR) 1.06; 95% CI 1.03 to 1.09; p<0.01) and pre-eclampsia (aOR 1.09; 1.04 to 1.14; p<0.01). Accounting for gestational weight gain did not attenuate these associations.Conclusions: Pre-pregnancy BMI determines the level, but not the change, of blood pressure in pregnancy and is linked to higher odds for gestational hypertension and pre-eclampsia, independent of gestational weight gain.

Background: Alfredson isolated eccentric loading and Silbernagel concentric–eccentric loading have both shown beneficial effects on clinical symptoms in midportion Achilles tendinopathy (AT), but they have never been compared directly. Purpose: To test for differences in clinical effects at 1-year follow-up between Alfredson and Silbernagel loading in midportion AT. Study Design: Randomized controlled trial; Level of evidence, 2. Methods: A total of 40 recreational athletes were allocated to the Alfredson group (AG) or the Silbernagel group (SG). The primary outcome was the difference in the Victorian Institute of Sports Assessment–Achilles (VISA-A) at 1-year follow-up. Secondary outcomes were the visual analog scale for pain during activities of daily living (VAS-ADL) and sports activities (VAS–sports), the EuroQol 5 Dimensions instrument (EQ-5D), and global perceived effect score. Measurements were performed at baseline and 12-week, 26-week, and 1-year follow-up. Analysis was performed using a linear mixed-regression model with intervention (AG vs SG), time (12 weeks, 26 weeks, and 1 year postoperatively), and intervention-by-time interaction. Results: The VISA-A score improved for both AG and SG, from 60.7 ± 17.1 at baseline to 89.4 ± 13.0 at 1-year follow-up and from 59.8 ± 22.2 to 83.2 ± 22.4, respectively ( P < .001 for both). Because the interaction term did not significantly improve the model, we reported a treatment effect without interaction term, indicating a constant difference at each follow-up. The linear mixed model with correction for baseline VISA-A and confounders revealed a nonsignificant treatment effect (2.4 [95% CI, –8.5 to 13.3]; P = .656). In addition, after adjustment for the respective baseline values and confounders, nonsignificant treatment effects were found for the VAS-ADL (–2.0 [95% CI, –11.3 to 7.3]; P = .665) and VAS-sports (1.3 [95% CI, –12.8 to 15.3], P = .858). The EQ-5D subscales improved in both groups. After 1 year, significantly more SG participants considered themselves improved (77.3% [SG] vs 50.0% [AG]; P = .04). Conclusion: No differences in clinical effects were found between Alfredson and Silbernagel loading at up to 1-year follow-up. Both programs significantly improved clinical symptoms, and given their high adherence rates, offering either of them as a home-based program with limited supervision appears to be an effective treatment strategy for midportion AT. Registration: NTR5638 (Netherlands Trial Register number).

Background:The interest in prognostic reviews is increasing, but to properly review existing evidence an accurate search filer for finding prediction research is needed. The aim of this paper was to validate and update two previously introduced search filters for finding prediction research in Medline: the Ingui filter and the Haynes Broad filter.Methodology/Principal Findings: Based on a hand search of 6 general journals in 2008 we constructed two sets of papers. Set 1 consisted of prediction research papers (n = 71), and set 2 consisted of the remaining papers (n = 1133). Both search filters were validated in two ways, using diagnostic accuracy measures as performance measures. First, we compared studies in set 1 (reference) with studies retrieved by the search strategies as applied in Medline. Second, we compared studies from 4 published systematic reviews (reference) with studies retrieved by the search filter as applied in Medline. Next -using word frequency methods -we constructed an additional search string for finding prediction research. Both search filters were good in identifying clinical prediction models: sensitivity ranged from 0.94 to 1.0 using our hand search as reference, and 0.78 to 0.89 using the systematic reviews as reference. This latter performance measure even increased to around 0.95 (range 0.90 to 0.97) when either search filter was combined with the additional string that we developed. Retrieval rate of explorative prediction research was poor, both using our hand search or our systematic review as reference, and even combined with our additional search string: sensitivity ranged from 0.44 to 0.85.Conclusions/Significance: Explorative prediction research is difficult to find in Medline, using any of the currently available search filters. Yet, application of either the Ingui filter or the Haynes broad filter results in a very low number missed clinical prediction model studies.

BackgroundHIV infection and antiretroviral treatment are associated with changes in lipid levels, insulin resistance and risk of cardiovascular disease (CVD). We investigated these changes in the first 96 weeks of treatment with low-dose stavudine or tenofovir regimens.MethodsThis is a secondary analysis of a double blind, randomised controlled trial performed in South-Africa, Uganda and India comparing low-dose stavudine (20 mg twice daily) with tenofovir in combination with efavirenz and lamivudine in antiretroviral-naïve adults (n = 1067) (Clinicaltrials.gov, NCT02670772). Over 96 weeks, data were collected on fasting lipids, glucose and insulin. Insulin resistance was assessed with the HOMA-IR index and 10-year CVD risk with the Framingham risk score (FRS). A generalized linear mixed model was used to estimate trends over time.ResultsParticipants were on average 35.3 years old, 57.6% female and 91.8% Black African. All lipid levels increased following treatment initiation, with the sharpest increase in the first 24 weeks of treatment. The increase in all lipid subcomponents over 96 weeks was higher among those in the stavudine than the tenofovir group. Insulin resistance increased steadily with no difference detected between study groups. FRS rose from 1.90% (1.84–1.98%) at baseline to 2.06 (1.98–2.15%) at week 96 for the total group, with no difference between treatment arms (p = 0.144). Lipid changes were more marked in Indian than African participants.ConclusionLipid levels increased in both groups, with low-dose stavudine resulting in a worse lipid profile compared to tenofovir. Insulin resistance increased, with no difference between regimens. CVD risk increased over time and tended to increase more in the group on stavudine. The low CVD risk across both arms argues against routine lipid and glucose monitoring in the absence of other CVD risk factors. In high risk patients, monitoring may only be appropriate at least a year after treatment initiation.Electronic supplementary materialThe online version of this article (10.1186/s12977-018-0460-z) contains supplementary material, which is available to authorized users.