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Introduction: Pre‐invasive squamous lesions of the central airways can progress into invasive lung cancers. Identifying these high‐risk patients could enable detection of invasive lung cancers at an early stage. In this study, we investigated the value of 18F‐fluorodeoxyglucose (18F‐FDG) positron emission tomography (PET) scans in predicting progression in patients with pre‐invasive squamous endobronchial lesions. Methods: In this retrospective study, patients with pre‐invasive endobronchial lesions, who underwent an 18F‐FDG PET scan at the VU University Medical Center Amsterdam, between January 2000 and December 2016, were included. Autofluorescence bronchoscopy (AFB) was used for tissue sampling and was repeated every 3 months. The minimum and median follow‐up was 3 and 46.5 months. Study endpoints were the occurrence of biopsy proven invasive carcinoma, time‐to‐progression and overall survival (OS). Results: A total number of 40 of 225 patients met the inclusion criteria of which 17 (42.5%) patients had a positive baseline 18F‐FDG PET scan. A total of 13 of 17 (76.5%) developed invasive lung carcinoma during follow‐up, with a median time to progression of 5.0 months (range, 3.0–25.0). In 23 (57.5%) patients with a negative 18F‐FDG PET scan at baseline, 6 (26%) developed lung cancer, with a median time to progression of 34.0 months (range, 14.0–42.0 months, p < 0.002). With a median OS of 56.0 months (range, 9.0–60.0 months) versus 49.0 months (range, 6.0–60.0 months) (p = 0.876) for the 18F‐FDG PET positive and negative groups, respectively. Conclusions: Patients with pre‐invasive endobronchial squamous lesions and a positive baseline 18F‐FDG PET scan were at high‐risk for developing lung carcinoma, highlighting that this patient group requires early radical treatment. [ABSTRACT FROM AUTHOR]

Background: Structured primary diabetes care within a collectively supported setting is associated with better monitoring of biomedical and lifestyle-related target indicators amongst people with type 2 diabetes and with better HbA1c levels. Whether socioeconomic status affects the delivery of care in terms of monitoring and its association with HbA1c levels within this approach, is unclear. This study aims to understand whether, within a structured care approach, (1) socioeconomic categories differ concerning diabetes monitoring as recommended; (2) socioeconomic status modifies the association between monitoring as recommended and HbA1c. Methods: Observational real-life cohort study with primary care registry data from general practitioners within diverse socioeconomic areas, who are supported with the implementation of structured diabetes care. People with type 2 diabetes mellitus were offered quarterly diabetes consultations. “Monitoring as recommended” by professional guidelines implied minimally one annual registration of HbA1c, systolic blood pressure, LDL, BMI, smoking behaviour and physical activity. Regarding socioeconomic status, deprived, advantageous urban and advantageous suburban categories were compared to the intermediate category concerning (a) recommended monitoring; (b) association between recommended monitoring and HbA1c. Results: Aim 1 (n = 13 601 people): Compared to the intermediate socioeconomic category, no significant differences in odds of being monitored as recommended were found in the deprived (OR 0.45 (95% CI 0.19-1.08)), advantageous urban (OR 1.27 (95% CI 0.46-3.54)) and advantageous suburban (OR 2.32 (95% CI 0.88-6.08)) categories. Aim 2 (n = 11 164 people): People with recommended monitoring had significantly lower HbA1c levels than incompletely monitored people (−2.4 (95% CI −2.9; −1.8) mmol/mol). SES modified monitoring-related HbA1c differences, which were significantly higher in the deprived (−3.3 (95% CI −4.3; −2.4) mmol/mol) than the intermediate category (−1.3 (95% CI −2.2; −0.4) mmol/mol). Conclusions: Within a structured diabetes care setting, socioeconomic status is not associated with recommended monitoring. Socioeconomic differences in the association between recommended monitoring and HbA1c levels advocate further exploration of practice and patient-related factors contributing to appropriate monitoring and for care adjustment to population needs. [ABSTRACT FROM AUTHOR]

Background: Most risk assessment models for type 2 diabetes (T2DM) have been developed in Caucasians and Asians; little is known about their performance in other ethnic groups. Objective(s): We aimed to identify existing models for the risk of prevalent or undiagnosed T2DM and externally validate them in a multi-ethnic population currently living in the Netherlands. Methods: A literature search to identify risk assessment models for prevalent or undiagnosed T2DM was performed in PubMed until December 2017. We validated these models in 4,547 Dutch, 3,035 South Asian Surinamese, 4,119 African Surinamese, 2,326 Ghanaian, 3,598 Turkish, and 3,894 Moroccan origin participants from the HELIUS (Healthy LIfe in an Urban Setting) cohort study performed in Amsterdam. Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). We identified 25 studies containing 29 models for prevalent or undiagnosed T2DM. C-statistics varied between 0.77-0.92 in Dutch, 0.66-0.83 in South Asian Surinamese, 0.70-0.82 in African Surinamese, 0.61-0.81 in Ghanaian, 0.69-0.86 in Turkish, and 0.69-0.87 in the Moroccan populations. The C-statistics were generally lower among the South Asian Surinamese, African Surinamese, and Ghanaian populations and highest among the Dutch. Calibration was poor (Hosmer-Lemeshow p < 0.05) for all models except one. Conclusions: Generally, risk models for prevalent or undiagnosed T2DM show moderate to good discriminatory ability in different ethnic populations living in the Netherlands, but poor calibration. Therefore, these models should be recalibrated before use in clinical practice and should be adapted to the situation of the population they are intended to be used in. [ABSTRACT FROM AUTHOR]

Objective Whether care group participation by general practitioners improves delivery of diabetes care is unknown. Using ‘monitoring of biomedical and lifestyle target indicators as recommended by professional guidelines’ as an operationalisation for quality of care, we explored whether (1) in new practices monitoring as recommended improved a year after initial care group participation (aim 1); (2) new practices and experienced practices differed regarding monitoring (aim 2). Design Observational, real-life cohort study. Setting Primary care registry data from Eerstelijns Zorggroep Haaglanden (ELZHA) care group. Participants Aim 1: From six new practices (n=538 people with diabetes) that joined care group ELZHA in January 2014, two practices (n=211 people) were excluded because of missing baseline data; four practices (n=182 people) were included. Aim 2: From all six new practices (n=538 people), 295 individuals were included. From 145 experienced practices (n=21 465 people), 13 744 individuals were included. Exposure Care group participation includes support by staff nurses on protocolised diabetes care implementation and availability of a system providing individual monitoring information. ‘Monitoring as recommended’ represented minimally one annual registration of each biomedical (HbA1c, systolic blood pressure, low-density lipoprotein) and lifestyle-related target indicator (body mass index, smoking behaviour, physical exercise). Primary outcome measures Aim 1: In new practices, odds of people being monitored as recommended in 2014 were compared with baseline (2013). Aim 2: Odds of monitoring as recommended in new and experienced practices in 2014 were compared. Results Aim 1: After 1-year care group participation, odds of being monitored as recommended increased threefold (OR 3.00, 95% CI 1.84 to 4.88, p<0.001). Aim 2: Compared with new practices, no significant differences in the odds of monitoring as recommended were found in experienced practices (OR 1.21, 95% CI 0.18 to 8.37, p=0.844). Conclusions We observed a sharp increase concerning biomedical and lifestyle monitoring as recommended after 1-year care group participation, and subsequently no significant difference between new and experienced practices—indicating that providing diabetes care within a collective approach rapidly improves registration of care. [ABSTRACT FROM AUTHOR]

Background: We aimed to find the best predictive model for 14-day mortality in antibiotic-treated nursing home residents with dementia and pneumonia by first applying an existing model to the recent PneuMonitor study. Second, we evaluated whether model performance improved by revising variables or adding variables related to recent changes in the care for older people.Methods: The original prognostic model included gender, respiratory rate, respiratory difficulty, pulse rate, decreased alertness, fluid intake, eating dependency, and pressure sores. This model was applied to 380 recent pneumonia episodes in nursing home residents with dementia, updated by considering revising and/or adding variables, internally validated using bootstrapping, and transformed into a simplified risk score that can be used in clinical practice. Model performance was evaluated by Hosmer-Lemeshow statistics and calibration graphs to assess calibration; and area under the receiver operating characteristic curve (AUC) to assess discrimination.Results: The newer cohort had lower 14-day mortality and was less often dehydrated or malnourished. Median AUC of the original model over the imputed datasets was 0.76 (interquartile range: 0.76-0.77), compared to 0.80 in the old cohort. Extending the model with dehydration, bowel incontinence, increase in eating dependency and cardiovascular history, while removing pressure sores, improved AUC: 0.80 (interquartile range: 0.80-0.81) after internal validation. Calibration remained adequate (Hosmer-Lemeshow statistic: p = .67).Conclusions: In the newer cohort with less severe illness, model performance of the existing model was adequate, but a new extended model distinguished better between residents at low and high mortality risk. [ABSTRACT FROM AUTHOR]

Objective: Gastrointestinal adverse effects occur in 20-30% of patients with metformin-treated type 2 diabetes, leading to premature discontinuation in 5-10% of the cases. Gastrointestinal intolerance may reflect localized high concentrations of metformin in the gut. We hypothesized that reduced transport of metformin via the plasma membrane monoamine transporter (PMAT) and organic cation transporter 1 (OCT1) could increase the risk of severe gastrointestinal adverse effects.Research Design and Methods: The study included 286 severe metformin-intolerant and 1,128 metformin-tolerant individuals from the IMI DIRECT (Innovative Medicines Initiative: DIabetes REsearCh on patient straTification) consortium. We assessed the association of patient characteristics, concomitant medication, and the burden of mutations in the SLC29A4 and SLC22A1 genes on odds of intolerance.Results: Women (P < 0.001) and older people (P < 0.001) were more likely to develop metformin intolerance. Concomitant use of transporter-inhibiting drugs increased the odds of intolerance (odds ratio [OR] 1.72, P < 0.001). In an adjusted logistic regression model, the G allele at rs3889348 (SLC29A4) was associated with gastrointestinal intolerance (OR 1.34, P = 0.005). rs3889348 is the top cis-expression quantitative trait locus for SLC29A4 in gut tissue where carriers of the G allele had reduced expression. Homozygous carriers of the G allele treated with transporter-inhibiting drugs had more than three times higher odds of intolerance compared with carriers of no G allele and not treated with inhibiting drugs (OR 3.23, P < 0.001). Use of a genetic risk score derived from rs3889348 and SLC22A1 variants found that the odds of intolerance were more than twice as high in individuals who carry three or more risk alleles compared with those carrying none (OR 2.15, P = 0.01).Conclusions: These results suggest that intestinal metformin transporters and concomitant medications play an important role in the gastrointestinal adverse effects of metformin. [ABSTRACT FROM AUTHOR]

Objective Management of type 2 diabetes mellitus (T2DM) requires frequent monitoring of patients. Within a collective care group setting, doubts on the clinical effects of registration are a barrier for full adoption of T2DM registration in general practice. We explored whether full monitoring of biomedical and lifestyle-related target indicators within a care group approach is associated with lower HbA1c levels. Design Observational, real-life cohort study. Setting Primary care data registry from the Hadoks (EerstelijnsZorggroepHaaglanden) care group. Exposure The care group provides general practitioners collectively with organisational support to facilitate structured T2DM primary care. Patients are offered quarterly medical and lifestyle-related consultation. Main outcome measure Full monitoring of each target indicator in patients with T2DM which includes minimally one measure of HbA1c level, systolic blood pressure, LDL, BMI, smoking behaviour and physical exercise between January and December 2014; otherwise, patients were defined as 'incompletely monitored'. HbA1c levels of 8137 fully monitored and 3958 incompletely monitored patients were compared, adjusted for the confounders diabetes duration, age and gender. Since recommended HbA1c values depend on age, medication use and diabetes duration, analyses were stratified into three HbA1c profile groups. Linear multilevel analyses enabled adjustment for general practice. Results Compared with incompletely monitored patients, fully monitored patients had significantly lower HbA1c levels (95% CI) in the first (-2.03 [-2.53 to -1.52] mmol/mol) (-0.19% [-0.23% to -0.14%]), second (-3.36 [-5.28 to -1.43] mmol/mol) (-0.31% [-0.48% to -0.13%]) and third HbA1c profile group (-1.89 [-3.76 to -0.01] mmol/mol) (-0.17% [-0.34% to 0.00%]). Conclusions/interpretation This study shows that in a care group setting, fully monitored patients had significantly lower HbA1c levels compared with incompletely monitored patients. Since this difference might have considerable clinical impact in terms of T2DM-related risks, this might help general practices in care group settings to overcome barriers on adequate registration and thus improve structured T2DM primary care. From population health management perspective, we recommend a systematic approach to adjust the structured care protocol for incompletely monitored subgroups. [ABSTRACT FROM AUTHOR]

Objectives/hypothesis: To develop a prediction model that can accurately predict the chance of success following revision stapes surgery in patients with recurrent or persistent otosclerosis at 2- to 6-months follow-up and to validate this model internally.Study Design: A retrospective cohort study of prospectively gathered data in a tertiary referral center.Methods: The associations of 11 prognostic factors with treatment success were tested in 705 cases using multivariable logistic regression analysis with backward selection. Success was defined as a mean air-bone gap closure to 10 dB or less. The most relevant predictors were used to derive a clinical prediction rule to determine the probability of success. Internal validation by means of bootstrapping was performed. Model performance indices, including the Hosmer-Lemeshow test, the area under the receiver operating characteristics curve (AUC), and the explained variance were calculated.Results: Success was achieved in 57.7% of cases at 2- to 6-months follow-up. Certain previous surgical techniques, primary causes of failure leading up to revision stapes surgery, and positions of the prosthesis placed during revision surgery were associated with higher success percentages. The clinical prediction rule performed moderately well in the original dataset (Hosmer-Lemeshow P = .78; AUC = 0.73; explained variance = 22%), which slightly decreased following internal validation by means of bootstrapping (AUC = 0.69; explained variance = 13%).Conclusions: Our study established the importance of previous surgical technique, primary cause of failure, and type of the prosthesis placed during the revision surgery in predicting the probability of success following stapes surgery at 2- to 6-months follow-up.Level Of Evidence: 2b. Laryngoscope, 128:2390-2396, 2018. [ABSTRACT FROM AUTHOR]

Background: Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), share many modifiable risk factors and can be prevented using combined prevention programs. Valid risk prediction tools are needed to accurately identify individuals at risk.Objective: We aimed to validate a previously developed non-invasive risk prediction tool for predicting the combined 7-year-risk for chronic cardiometabolic diseases.Design: The previously developed tool is stratified for sex and contains the predictors age, BMI, waist circumference, use of antihypertensives, smoking, family history of myocardial infarction/stroke, and family history of diabetes. This tool was externally validated, evaluating model performance using area under the receiver operating characteristic curve (AUC)-assessing discrimination-and Hosmer-Lemeshow goodness-of-fit (HL) statistics-assessing calibration. The intercept was recalibrated to improve calibration performance.Participants: The risk prediction tool was validated in 3544 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab).Key Results: Discrimination was acceptable, with an AUC of 0.78 (95% CI 0.75-0.81) in men and 0.78 (95% CI 0.74-0.81) in women. Calibration was poor (HL statistic: p < 0.001), but improved considerably after intercept recalibration. Examination of individual outcomes showed that in men, AUC was highest for CKD (0.85 [95% CI 0.78-0.91]) and lowest for T2D (0.69 [95% CI 0.65-0.74]). In women, AUC was highest for CVD (0.88 [95% CI 0.83-0.94)]) and lowest for T2D (0.71 [95% CI 0.66-0.75]).Conclusions: Validation of our previously developed tool showed robust discriminative performance across populations. Model recalibration is recommended to account for different disease rates. Our risk prediction tool can be useful in large-scale prevention programs for identifying those in need of further risk profiling because of their increased risk for chronic cardiometabolic diseases. [ABSTRACT FROM AUTHOR]

Only a few studies have investigated the metabolic consequences of social jetlag. Therefore, we examined the association of social jetlag with the metabolic syndrome and type 2 diabetes mellitus in a population-based cohort. We used cross-sectional data from the New Hoorn Study cohort (n = 1585, 47% men, age 60.8 ± 6 years). Social jetlag was calculated as the difference in midpoint sleep (in hours) between weekdays and weekend days. Poisson and linear regression models were used to study the associations, and age was regarded as a possible effect modifier. We adjusted for sex, employment status, education, smoking, physical activity, sleep duration, and body mass index. In the total population, we only observed an association between social jetlag and the metabolic syndrome, with prevalence ratios adjusted for sex, employment status, and educational levels of 1.64 (95% CI 1.1-2.4), for participants with >2 h social jetlag, compared with participants with <1 h social jetlag. However, we observed an interaction effect of median age (<61 years). In older participants (≥61 years), no significant associations were observed between social jetlag status, the metabolic syndrome, and diabetes or prediabetes. In the younger group (<61 years), the adjusted prevalence ratios were 1.29 (95% CI 0.9-1.9) and 2.13 (95% CI 1.3-3.4) for the metabolic syndrome and 1.39 (95% CI 1.1-1.9) and 1.75 (95% CI 1.2-2.5) for diabetes/prediabetes, for participants with 1-2 h and >2 h social jetlag, compared with participants with <1 h social jetlag. In conclusion, in our population-based cohort, social jetlag was associated with a 2-fold increased risk of the metabolic syndrome and diabetes/prediabetes, especially in younger (<61 years) participants. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. Methods: Data from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, Inter99, KORA S4/F4) were combined to predict HbA1c levels at six year follow-up. Using backward selection, age, BMI, waist circumference, use of anti-hypertensive medication, current smoking and parental history of diabetes remained in sex-specific linear regression models. To minimize overfitting of coefficients, we performed internal validation using bootstrapping techniques. Explained variance, discrimination and calibration were assessed using R2, classification tables (comparing highest/lowest 50% HbA1c levels) and calibration graphs. The model was externally validated in 2,765 non-diabetic subjects of the population-based cohort METSIM. Results: At baseline, mean HbA1c level was 5.6% (38 mmol/mol). After a mean follow-up of six years, mean HbA1c level was 5.7% (39 mmol/mol). Calibration graphs showed that predicted HbA1c levels were somewhat underestimated in the Inter99 cohort and overestimated in the Hoorn and KORA cohorts, indicating that the model’s intercept should be adjusted for each cohort to improve predictions. Sensitivity and specificity (95% CI) were 55.7% (53.9, 57.5) and 56.9% (55.1, 58.7) respectively, for women, and 54.6% (52.7, 56.5) and 54.3% (52.4, 56.2) for men. External validation showed similar performance in the METSIM cohort. Conclusions/interpretation: In the non-diabetic population, our DIRECT-DETECT prediction model, including readily available predictors, has a relatively low explained variance and moderate discriminative performance, but can help to distinguish between future highest and lowest HbA1c levels. Absolute HbA1c values are cohort-dependent. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to assess the time to insulin initiation in type 2 diabetes mellitus (T2DM) patients treated with oral glucose-lowering agents and to determine the baseline characteristics associated with time to insulin initiation. This was evaluated in T2DM patients with HbA1c levels consistently ≥7.0% during total follow up and in those with fluctuating HbA1c levels around 7.0%. Design and methods: Prospective, observational study was performed, comprising 2418 persons with T2DM aged ≥40 years who entered the Diabetes Care System between 1998 and 2012 with a minimum follow up of at least 3 years, following the first HbA1c level ≥7.0%. Cox regression analyses were performed to assess the determinants of time to insulin initiation. Data related to long-term effects ofinsulin initiation were studied at baseline and at the end of follow up using descriptive summary statistics. Results: Two-thirds of the patients initiated insulin during follow up. The time to insulin varied from 1.2 years (range 0.3-3.1) in patients with HbA1c levels consistently≥7.0% to 5.4 years (range 3.0-7.5) in patients with fluctuating HbA1c levels around 7.0%. Longer diabetes duration (hazard ratio (HR) 1.04 95% CI 1.03-1.05) and lower age (HR 1.00 95% CI 0.99-1.00) at baseline were associated with a shorter time to initiation. More insulin initiators had retinopathy compared with patients that remained on oral glucose-lowering agents during follow up. Conclusion: The time to insulin initiation was short, and most of the patients with HbA1c levels consistently ≥7.0% were initiating insulin. Longer diabetes duration and younger age shortened the time to insulin. [ABSTRACT FROM AUTHOR]

Stressful life events are associated with the metabolic syndrome in cross-sectional studies, but prospective studies addressing this issue are rare and limited. We therefore evaluated whether the number of stressful life events is associated with incident metabolic syndrome. We assessed the association between the number of stressful life events experienced in the 5 years up until baseline and incident metabolic syndrome after 6.5 years at follow-up in the Hoorn study, a middle-aged and elderly population-based cohort. Participants with prevalent metabolic syndrome at baseline were excluded. Metabolic syndrome was defined according to the Adult Treatment Panel III, including fasting plasma glucose levels, HDL-C levels, triglyceride levels, waist circumference and hypertension. We included 1099 participants (47% male; age 60 ± 7 years). During 6.5 years of follow-up, 238 participants (22%) developed the metabolic syndrome. Logistic regression adjusted for age, sex, education level and follow-up duration showed a positive association between the number of stressful life events at baseline and incident metabolic syndrome [OR 1.13 (1.01–1.27) per event,p = 0.049]. In addition, a Poisson model showed a significant positive association between the number of stressful life events at baseline and the number of metabolic syndrome factors at follow-up [OR 1.05 (1.01–1.11) per event,p = 0.018]. Finally, we observed a significant association between the number of stressful life events at baseline and waist circumference at follow-up [adjusted for confoundersβ0.86 (0.39–1.34) cm per event,p < 0.001]. Overall, we concluded that persons who reported more stressful life events at baseline had a significantly increased risk for developing metabolic syndrome during 6.5 years of follow-up, in a middle-aged and elderly population-based cohort. [ABSTRACT FROM PUBLISHER]