Patient-clinician collaboration in making care fit:A qualitative analysis of clinical consultations in diabetes care

Objective: To confirm described dimensions of making care fit and explore how patients and clinicians collaborate to make care fit in clinical practice. Methods: As part of an ongoing study, we audiotaped and transcribed patient-clinician consultations in diabetes care. We purposively selected consultations based on participants’ demographical, biomedical and biographical characteristics. We analysed transcripts using reflexive thematic analysis. We combined a deductive and inductive approach, using the pre-described dimensions of making care fit and adding new (sub-)dimensions when pertinent. Results: We analysed 24 clinical consultations. Our data confirmed eight previously described dimensions and provided new sub-dimensions of making care fit with examples from clinical practice (problematic situation, influence of devices, sense of options, shared agenda setting, clinician context, adapting to changing organization of care, and possibility to reconsider). Conclusion: Our study confirmed, specified and enriched the conceptualization of making care fit through practice examples. We observed patient-clinician collaboration in exploration of patients’ context, and by responsively changing, adapting or maintaining care plans. Practice implications: Our findings support clinicians and researchers with insights in important aspects of patient-clinician collaboration. Ultimately, this would lead to optimal design of care plans that fit well in each patient life.</p

Sex differences in cardiometabolic risk factors, pharmacological treatment and risk factor control in type 2 diabetes:findings from the Dutch Diabetes Pearl cohort

Introduction Sex differences in cardiometabolic risk factors and their management in type 2 diabetes (T2D) have not been fully identified. Therefore, we aimed to examine differences in cardiometabolic risk factor levels, pharmacological treatment and achievement of risk factor control between women and men with T2D. Research design and methods Cross-sectional data from the Dutch Diabetes Pearl cohort were used (n=6637, 40% women). Linear and Poisson regression analyses were used to examine sex differences in cardiometabolic risk factor levels, treatment, and control. Results Compared with men, women had a significantly higher body mass index (BMI) (mean difference 1.79 kg/m 2 (95% CI 1.49 to 2.08)), while no differences were found in hemoglobin A 1c (HbA 1c) and systolic blood pressure (SBP). Women had lower diastolic blood pressure (-1.94 mm Hg (95% CI -2.44 to -1.43)), higher total cholesterol (TC) (0.44 mmol/L (95% CI 0.38 to 0.51)), low-density lipoprotein cholesterol (LDL-c) (0.26 mmol/L (95% CI 0.22 to 0.31)), and high-density lipoprotein cholesterol (HDL-c) sex-standardized (0.02 mmol/L (95% CI 0.00 to 0.04)), and lower TC:HDL ratio (-0.29 (95% CI -0.36 to -0.23)) and triglycerides (geometric mean ratio 0.91 (95% CI 0.85 to 0.98)). Women had a 16% higher probability of being treated with antihypertensive medication in the presence of high cardiovascular disease (CVD) risk and elevated SBP than men (relative risk 0.84 (95% CI 0.73 to 0.98)), whereas no sex differences were found for glucose-lowering medication and lipid-modifying medication. Among those treated, women were less likely to achieve treatment targets of HbA 1c (0.92 (95% CI 0.87 to 0.98)) and LDL-c (0.89 (95% CI 0.85 to 0.92)) than men, while no differences for SBP were found. Conclusions In this Dutch T2D population, women had a slightly different cardiometabolic risk profile compared with men and a substantially higher BMI. Women had a higher probability of being treated with antihypertensive medication in the presence of high CVD risk and elevated SBP than men, and were less likely than men to achieve treatment targets for HbA 1c and LDL levels

Survival in dialysis patients is not different between patients with diabetes as primary renal disease and patients with diabetes as a co-morbid condition

On dialysis, survival among patients with diabetes mellitus is inferior to survival of non-diabetic patients. We hypothesized that patients with diabetes as primary renal disease have worse survival compared to patients with diabetes as a co-morbid condition and aimed to compare all-cause mortality between these patient groups. Data were collected from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD), a multicenter, prospective cohort study in which new patients with end stage renal disease (ESRD) were monitored until transplantation or death. Patients with diabetes as primary cause of ESRD were compared with patients with diabetes as co-morbid condition and both of these patient groups were compared to patients without diabetes. Analysis was performed using Kaplan-Meier and Cox regression. Fifteen % of the patients had diabetic nephropathy as primary renal disease (N = 281); 6% had diabetes as co-morbid condition (N = 107) and 79% had no diabetes (N = 1465). During follow-up 42% of patients (N = 787) died. Compared to non-diabetic patients, mortality risk was increased for both patients with diabetes as primary renal disease HR: 1.9 (95% CI 1.6, 2.3) and for patients with diabetes as co-morbid condition HR: 1.7 (95% CI 1.3, 2.2). Mortality was not significantly higher in patients with diabetes as primary renal disease compared to patients with diabetes as co-morbid condition (HR 1.06; 95% CI 0.79, 1.43). This study in patients with ESRD showed no survival difference between patients with diabetes as primary renal disease and patients with diabetes as a co-morbid condition. Both conditions were associated with increased mortality risk compared to non-diabetic patient

Survival in dialysis patients is not different between patients with diabetes as primary renal disease and patients with diabetes as a co-morbid condition

Abstract Background On dialysis, survival among patients with diabetes mellitus is inferior to survival of non-diabetic patients. We hypothesized that patients with diabetes as primary renal disease have worse survival compared to patients with diabetes as a co-morbid condition and aimed to compare all-cause mortality between these patient groups. Methods Data were collected from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD), a multicenter, prospective cohort study in which new patients with end stage renal disease (ESRD) were monitored until transplantation or death. Patients with diabetes as primary cause of ESRD were compared with patients with diabetes as co-morbid condition and both of these patient groups were compared to patients without diabetes. Analysis was performed using Kaplan-Meier and Cox regression. Results Fifteen % of the patients had diabetic nephropathy as primary renal disease (N = 281); 6% had diabetes as co-morbid condition (N = 107) and 79% had no diabetes (N = 1465). During follow-up 42% of patients (N = 787) died. Compared to non-diabetic patients, mortality risk was increased for both patients with diabetes as primary renal disease HR: 1.9 (95% CI 1.6, 2.3) and for patients with diabetes as co-morbid condition HR: 1.7 (95% CI 1.3, 2.2). Mortality was not significantly higher in patients with diabetes as primary renal disease compared to patients with diabetes as co-morbid condition (HR 1.06; 95% CI 0.79, 1.43). Conclusions This study in patients with ESRD showed no survival difference between patients with diabetes as primary renal disease and patients with diabetes as a co-morbid condition. Both conditions were associated with increased mortality risk compared to non-diabetic patients.</p

Peripheral Neuropathy, Episodic Rhabdomyolysis, and Hypoparathyroidism in a Patient with Mitochondrial Trifunctional Protein Deficiency

A combination of unexplained peripheral neuropathy, hypoparathyroidism, and the inability to cope with metabolic stress could point to a rare inborn error of metabolism, such as mitochondrial trifunctional protein (MTP) deficiency.Here, we describe a 20-year-old woman who was known since childhood with axonal motor sensory polyneuropathy of unknown origin. She presented with progressive dyspnoea, and increased muscle weakness, preceded by 6 days of fever, vomiting, and diarrhoea. Laboratory testing showed rhabdomyolysis, and hypocalcaemia with low parathyroid levels. The patient was intubated because of respiratory insufficiency and a viral and bacterial pneumonia was diagnosed. She was discharged after 16 days of admission. Metabolic screening, performed at the time of rhabdomyolysis, showed increased concentrations of long-chain 3-hydroxyacyl carnitine species, together with elevated urinary excretion of 3-hydroxy dicarboxylic acids. Decreased activity of long-chain 3-hydroxyacyl-CoA dehydrogenase and long-chain 3-ketoacyl-CoA thiolase in peripheral lymphocytes and fibroblasts confirmed a MTP deficiency. Sequence analysis of the HADHB gene showed two heterozygous variants: c.209+1G>C (splicing defect) and c.980T>C (p.Leu327Leu). When the acylcarnitine profile was repeated after the episode of rhabdomyolysis had resolved it showed no abnormalities.Our case illustrates a cluster of peripheral neuropathy, episodic rhabdomyolysis, and hypoparathyroidism in a patient with MTP deficiency caused by mutations in the HADHB gene. It stresses the importance of performing metabolic screening when patients are most symptomatic, as normal results can be found at times when no metabolic stress is present. Screening is relatively easy and timely diagnosis has important implications for treatmen

Baseline characteristics of the study population.

<p>Age and duration of DM are presented as median (interquartile range). Other continuous predictors are presented as means (SD); categorical variables are presented as %.</p><p>Abbreviations: BMI, body mass index; BP, blood pressure; DM, diabetes mellitus; HD, hemodialysis; rGFR, residual glomerular filtration rate.</p

Predicting Mortality in Patients with Diabetes Starting Dialysis

<div><p>Background</p><p>While some prediction models have been developed for diabetic populations, prediction rules for mortality in diabetic dialysis patients are still lacking. Therefore, the objective of this study was to identify predictors for 1-year mortality in diabetic dialysis patients and use these results to develop a prediction model.</p><p>Methods</p><p>Data were used from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD), a multicenter, prospective cohort study in which incident patients with end stage renal disease (ESRD) were monitored until transplantation or death. For the present analysis, patients with DM at baseline were included. A prediction algorithm for 1-year all-cause mortality was developed through multivariate logistic regression. Candidate predictors were selected based on literature and clinical expertise. The final model was constructed through backward selection. The model's predictive performance, measured by calibration and discrimination, was assessed and internally validated through bootstrapping.</p><p>Results</p><p>A total of 394 patients were available for statistical analysis; 82 (21%) patients died within one year after baseline (3 months after starting dialysis therapy). The final prediction model contained seven predictors; age, smoking, history of macrovascular complications, duration of diabetes mellitus, Karnofsky scale, serum albumin and hemoglobin level. Predictive performance was good, as shown by the c-statistic of 0.810. Internal validation showed a slightly lower, but still adequate performance. Sensitivity analyses showed stability of results.</p><p>Conclusions</p><p>A prediction model containing seven predictors has been identified in order to predict 1-year mortality for diabetic incident dialysis patients. Predictive performance of the model was good. Before implementing the model in clinical practice, for example for counseling patients regarding their prognosis, external validation is necessary.</p></div

One-year mortality according to risk quartiles.

<p>Grey bars represent predicted 1-year mortality risk and black bars represent observed 1-year mortality risk.</p

Predictive variables for 1-year mortality based on multivariate regression analysis.

<p>Abbreviations: B, estimated coefficient; S.E., standard error of estimate; B_adj, estimated coefficient adjusted for overfitting.</p><p>The intercept of the model, which is necessary for computing predicted mortality risks, was 1.692 (1.610), and 1.427 when adjusted for overfitting.</p

Receiver operating characteristic curve for the logistic regression model.

<p>The c-statistic was 0.810.</p