Metformin treatment is associated with reduced risk of hypoglycaemia, major adverse cardiovascular events, and all-cause mortality in patients with post-pancreatitis diabetes mellitus: a nationwide cohort study.

OBJECTIVE: Post-pancreatitis diabetes mellitus (PPDM) is a frequent complication of pancreatitis and is associated with an increased risk of adverse outcomes. Metformin is recommended for the treatment of PPDM, but evidence of its risk-benefit profile is limited. In a pharmaco-epidemiologic study, we investigated the association between metformin treatment and adverse outcomes in patients with PPDM. DESIGN AND METHODS: In a Danish nationwide population-based cohort study, we included adults (≥18 years) with incident PPDM or type 2 diabetes between 2009 and 2018. Post-pancreatitis diabetes mellitus was categorised into acute and chronic subtypes (PPDM-A and PPDM-C). Associations between metformin treatment and severe hypoglycaemia, major adverse cardiovascular events (MACE), and all-cause mortality were examined across the diabetes subgroups using Cox regression analysis. Treatments with metformin, insulin, and other glucose-lowering therapies were handled as time-varying exposures. RESULTS: We included 222 337 individuals with new-onset type 2 diabetes and 3781 with PPDM, of whom 2305 (61%) were classified as PPDM-A and 1476 (39%) as PPDM-C. Treatment with metformin was associated with a lower risk of severe hypoglycaemia (adjusted hazard ratio [HR] 0.41, 95% CI 0.27-0.62, P < .0001), MACE (HR 0.74, 95% CI 0.60-0.92, P = .0071), and all-cause mortality (HR 0.56, 95% CI 0.49-0.64, P < .0001) in patients with PPDM. In sensitivity analyses and among individuals with type 2 diabetes, metformin treatment exhibited comparable trends of risk reduction. CONCLUSIONS: Metformin is associated with a lower risk of adverse outcomes, including all-cause mortality in patients with PPDM, supporting the use of metformin as a glucose-lowering therapy for these patients.

Using random forest machine learning on data from a large, representative cohort of the general population improves clinical spirometry references.

INTRODUCTION: Spirometry is associated with several diagnostic difficulties, and as a result, misdiagnosis of chronic obstructive pulmonary disease (COPD) occurs. This study aims to investigate how random forest (RF) can be used to improve the existing clinical FVC and FEV1 reference values in a large and representative cohort of the general population of the US without known lung disease. MATERIALS AND METHODS: FVC, FEV1, body measures, and demographic data from 23 433 people were extracted from NHANES. RF was used to develop different prediction models. The accuracy of RF was compared with the existing Danish clinical references, an improved multiple linear regression (MLR) model, and a model from the literature. RESULTS: The correlation between actual and predicted FVC and FEV1 and the 95% confidence interval for RF were found to be FVC = 0.85 (0.85; 0.86) (p < 0.001), FEV1 = 0.92 (0.92; 0.93) (p < 0.001), and existing clinical references were FVC = 0.66 (0.64; 0.68) (p < 0.001) and FEV1 = 0.69 (0.67; 0.70) (p < 0.001). Slope and intercept for the RF models predicting FVC and FEV1 were FVC 1.06 and -238.04 (mL), FEV1: 0.86 and 455.36 (mL), and for the MLR models, slope and intercept were FVC: 0.99 and 38.56 39 (mL), and FEV1: 1.01 and -56.57-57 (mL). CONCLUSIONS: The results point toward machine learning models such as RF have the potential to improve the prediction of estimated lung function for individual patients. These predictions are used as reference values and are an important part of assessing spirometry measurements in clinical practice. Further work is necessary in order to reduce the size of the intercepts obtained through these results.

The incidence of fractures at various sites in newly treated patients with type 2 diabetes mellitus.

Purpose: In this descriptive study, we examined the incidence of fractures in patients with newly treated type 2 diabetes mellitus (T2D) compared to matched reference population. Methods: Participants from the UK Clinical Practice research datalink (CPRD) GOLD (1987-2017), aged ≥30 years, with a T2D diagnosis code and a first prescription for a non-insulin anti-diabetic drug (n = 124,328) were included. Cases with T2D were matched by year of birth, sex and practice to a reference population (n = 124,328), the mean follow-up was 7.7 years. Crude fracture incidence rates (IRs) and incidence rate ratios (IRRs) were calculated. Analyses were stratified by fracture site and sex and additionally adjusted for BMI, smoking status, alcohol use and history of any fracture at index date. Results: The IR of all fractures and major osteoporotic fractures was lower in T2D compared to the reference population (IRR 0.97; 95%CI 0.94-0.99). The IRs were lower for clavicle (IRR 0.67; 0.56-0.80), radius/ulna (IRR 0.81; 0.75-0.86) and vertebral fractures (0.83; 0.75-0.92) and higher for ankle (IRR 1.16; 95%CI 1.06-1.28), foot (1.11; 1.01-1.22), tibia/fibula (1.17; 1.03-1.32) and humerus fractures (1.11; 1.03-1.20). Differences in IRs at various fracture sites between T2D and the reference population were more pronounced in women than in men. In contrast, BMI adjusted IRs for all fractures (IRR 1.07; 1.04-1.10) and most individual fracture sites were significantly higher in T2D, especially in women. Conclusion: The crude incidence of all fractures was marginally lower in patients with newly treated T2D compared to the matched reference population but differed according to fracture site, especially in women. BMI adjusted analyses resulted in higher incidence rates in T2D at almost all fracture sites compared to crude incidence rates and this was more pronounced in women than in men. This implies that BMI may have a protective impact on the crude incidence of fractures, especially in women with newly treated T2D.

Risk of Major Adverse Cardiovascular Events, Severe Hypoglycemia, and All-Cause Mortality in Postpancreatitis Diabetes Mellitus Versus Type 2 Diabetes: A Nationwide Population-Based Cohort Study.

OBJECTIVE: Postpancreatitis diabetes mellitus (PPDM) is a frequent complication of pancreatitis and associates with poor glycemic control. We investigated the risk of adverse diabetes-related outcomes in PPDM compared with type 2 diabetes. RESEARCH DESIGN AND METHODS: In this Danish population-based cohort study, we included adults (>18 years) with incident PPDM or type 2 diabetes between 1998 and 2018 through national health registries. PPDM was further divided into acute (PPDM-A) and chronic (PPDM-C) subtypes. We ascertained risk of major adverse cardiovascular events (MACE), severe hypoglycemia, and all-cause mortality as well as incidence rates of severe hypoglycemia. We compared risk and incidence rates across diabetes subgroups using multivariate Cox and Poisson regression analyses. RESULTS: We identified 383,325 people with incident type 2 diabetes, 3,418 with PPDM-A, and 2,461 with PPDM-C. Compared with type 2 diabetes, PPDM-C was associated with increased risks of severe hypoglycemia (hazard ratio [HR] 5.27, 95% CI 4.62-6.00, P < 0.001) and all-cause mortality (HR 1.54, 95% CI 1.45-1.64, P < 0.001). Similar patterns were observed for people with PPDM-A. Incidence rate ratios (IRRs) for severe hypoglycemia were increased in both PPDM-C (IRR 7.38, 95% CI 6.75-8.08, P < 0.001) and PPDM-A (IRR 3.76, 95% CI 3.36-4.21, P < 0.001) compared with type 2 diabetes. Findings were consistent in an analysis restricted to people on insulin and in an analysis including pancreatitis patients without diabetes as comparator group. CONCLUSIONS: Compared with type 2 diabetes, PPDM is associated with excess risk of adverse diabetes-related outcomes. This has important implications for management.

Epidemiology of hypoglycaemic episodes leading to hospitalisations in Denmark in 1998-2018.

AIMS/HYPOTHESIS: We aimed to investigate the nationwide trends in incidence and associated risk factors, with focus on blood glucose-lowering medication, for the first hypoglycaemic episode leading to hospitalisation in Denmark among people with type 1 and 2 diabetes mellitus. METHODS: A cohort study of all people with diabetes from 1977 to 2018 experiencing hypoglycaemic episodes leading to hospitalisation in 1998-2018 was established. Data were extracted from the Danish National Patient Registry. Trends in incidence rates were investigated with Poisson regression models and linear regressions, and risk factors were investigated with Cox proportional hazards models. RESULTS: A total of 66,438 hypoglycaemic episodes leading to hospitalisation in 1998-2018 was investigated among 641,402 people with type 1 (mean ± SD age 37 ± 22 years) and type 2 diabetes (mean ± SD age 61 ± 17 years). Between 2003 and 2018, the incidence rate fell by 66% for type 1 diabetes (incidence rate ratio [IRR] 0.34 [95% CI 0.31, 0.36], p < 0.0001) and 61% for type 2 diabetes (IRR 0.39 [95% CI 0.36, 0.42], p < 0.0001). With respect to hypoglycaemic episodes, insulin glargine (HR 1.20 [95% CI 1.05, 1.36], p = 0.0059), insulin detemir (HR 1.18 [95% CI 1.04, 1.32], p = 0.0077) and insulin degludec (HR 1.04 [95% CI 0.81,1.33], p = 0.7706) seemed safer than human insulin (long-acting insulin HR 1.38 [95% CI 1.25, 1.52], p < 0.0001; combination insulins HR 1.84 [95% CI 1.65, 2.05], p < 0.0001) and, especially, sodium-glucose cotransporter 2 inhibitors (HR 0.43 [95% CI 0.33, 0.56], p < 0.0001), glucagon-like peptide 1 receptor agonists (HR 0.51 [95% CI 0.44, 0.58], p < 0.0001) and dipeptidyl peptidase 4 inhibitors (HR 0.44 [95% CI 0.38, 0.49], p < 0.0001) seemed safer than sulfonylureas (HR 2.27 [95% CI 2.18, 2.37], p < 0.0001). CONCLUSIONS/INTERPRETATION: Incidence rates of hypoglycaemic episodes leading to hospitalisation are declining in Denmark, and the advent of new treatment alternatives may play a significant role in this decline. From a safety perspective, these findings are important and should be considered by clinicians when assessing treatment options for patients.

Efficacy of the TMPRSS2 inhibitor camostat mesilate in patients hospitalized with Covid-19-a double-blind randomized controlled trial.

BACKGROUND: The trans-membrane protease serine 2 (TMPRSS2) is essential for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cell entry and infection. Efficacy and safety of TMPRSS2 inhibitors in patients with coronavirus disease 2019 (Covid-19) have not been evaluated in randomized trials. METHODS: We conducted an investigator-initiated, double-blind, randomized, placebo-controlled multicenter trial in patients hospitalized with confirmed SARS-CoV-2 infection from April 4, to December 31, 2020. Within 48 h of admission, participants were randomly assigned in a 2:1 ratio to receive the TMPRSS2 inhibitor camostat mesilate 200 mg three times daily for 5 days or placebo. The primary outcome was time to discharge or clinical improvement measured as ≥2 points improvement on a 7-point ordinal scale. Other outcomes included 30-day mortality, safety and change in oropharyngeal viral load. FINDINGS: 137 patients were assigned to receive camostat mesilate and 68 to placebo. Median time to clinical improvement was 5 days (interquartile range [IQR], 3 to 7) in the camostat group and 5 days (IQR, 2 to 10) in the placebo group (P = 0·31). The hazard ratio for 30-day mortality in the camostat compared with the placebo group was 0·82 (95% confidence interval [CI], 0·24 to 2·79; P = 0·75). The frequency of adverse events was similar in the two groups. Median change in viral load from baseline to day 5 in the camostat group was -0·22 log10 copies/mL (p <0·05) and -0·82 log10 in the placebo group (P <0·05). INTERPRETATION: Under this protocol, camostat mesilate treatment was not associated with increased adverse events during hospitalization for Covid-19 and did not affect time to clinical improvement, progression to ICU admission or mortality. ClinicalTrials.gov Identifier: NCT04321096. EudraCT Number: 2020-001200-42.

Prediction of Nocturnal Hypoglycemia From Continuous Glucose Monitoring Data in People With Type 1 Diabetes: A Proof-of-Concept Study.

BACKGROUND: Intensive insulin therapy has documented benefits but may also come at the expense of a higher risk of hypoglycemia. Hypoglycemia is associated with higher all-cause mortality and nocturnal hypoglycemia has been associated with the sudden dead-in-bed syndrome. This proof-of-concept study sought to investigate if nocturnal hypoglycemia can be predicted. METHOD: Continuous glucose monitoring, meal, insulin, and demographics data from 463 people with type 1 diabetes were obtained from a clinical trial. A total of 4721 nights without or with hypoglycemia (429) were available including data from three consecutive days before the night. Thirty-two features were calculated based on these data. Data were split into 20% participants for evaluation and 80% for training. The optimal feature subset was found from forward selection of the 80% participants with linear discriminant analysis as basis for the classifier. RESULTS: The forward selection resulted in a feature subset of four features. The evaluation resulted in an area under the receiver operating characteristics curve (ROC-AUC) of 0.79 leading to a sensitivity and a specificity of, e.g., 75% and 70%. CONCLUSIONS: It was possible to predict nocturnal hypoglycemic episodes with a ROC-AUC of 0.79. A warning at bedtime about nocturnal hypoglycemia could be of great help for people with diabetes to enable preventive actions. Further development of the proposed algorithm is needed for implementation in everyday practice.

Age at hip fracture and life expectancy in Denmark - Secular trends over two decades.

BACKGROUND: Recent improvements in the health of the oldest old coexist with a decline in hip fracture rates, in particular in women. We speculated that increased longevity with decreasing hip fracture rates would result in a delay in hip fracture. We conducted an analysis of time trends in the age at hip fracture, by type and gender, for the past two decades using national data. STUDY POPULATION AND METHODS: We used data from the Danish Hospital Discharge Register (1996-2017) to analyse the age distribution of femoral neck (FN) and pertrochanteric fractures (PT), allowing only the first fracture at each of these two sites to contribute to the analysis in each calendar year. Demographics for the background population at risk including life expectancy tabulations, were also obtained. RESULTS: The average age at FN fracture in women increased slowly but significantly by 0.035years - or 12.8 days - per calendar year [0.035, 95% CI (0.016; 0.054), p<0.001], resulting in an increase from 79.6 to 80.4 years. There were no significant changes in the age at FN fracture in men or the age at PT fracture in women and men. Further, increases in life expectancy were considerably faster than any change observed in the age at hip fracture. In 1996, the average age at FN or PT fracture exceeded the average life expectancy in both men and women whereas the opposite was the case from 2009 and onwards in men and 2015 and onwards in women. CONCLUSION: This study demonstrates a significant change in the demographics of hip fractures in Denmark over the past two decades. We observed a significant increase in the age at FN fracture in women but not in men, with no significant increase in the age at IT fracture and PT fracture. This developed much more slowly, however, than the increase in life expectancy in both sexes observed over the same period of time. Taken together, these changes resulted in a large decrease in the female to male incidence rate ratio from 2.6 and 2.5 (FN and PT, respectively) to 1.9 and 1.7.Additional effort is required to prevent hip fractures to ensure that the increasing life expectancy is matched by a similar increase in hip-fracture free life expectancy.

Prediction of exacerbation onset in chronic obstructive pulmonary disease patients.

The objective of this study was to develop an algorithm for prediction of exacerbation onset in Chronic Obstructive Pulmonary Disease (COPD) patients based on continuous self-monitoring of physiological parameters from telehome-care monitoring. 151 physiological parameters of COPD patients were monitored on a daily/weekly basis for up to 2 years. Data were segmented in 30-day periods leading up to an exacerbation (exacerbation episode) and starting from a 14-day recovery period post-exacerbation (control episode) and tested in 6 intervals to predict exacerbation onset using k-nearest neighbour (k = 1, 3, 5). A classifier with sensitivity of 73%, specificity of 74%, positive predictive value of 69%, negative predictive value of 78% and an accuracy of 74% was achieved using data intervals consisting of 5 days. Intelligent processing of physiological recordings have potential for predicting exacerbation onset.

Distribution volume assessment compartment modelling: theoretic phosphate kinetics in steady state hemodialys patients.

PURPOSE: Hyperphosphatemia constitutes a major problem in end-stage renal disease patients. At this stage, dialysis efficacy usually plays an important role in obtaining phosphate levels within the normal range. Currently, no practical tool capable of making individualized predictions about phosphate changes during and after hemodialysis (HD) have gained clinical acceptance. As a result, optimal dialysis prescriptions seem to be difficult to achieve. The objective of the present study was to develop and test a quantitative tool to predict intradialytic and postdialytic (2 hours) phosphate kinetics in HD therapy. This included distribution volume assessment. METHODS: The approach included compartment modeling. Various model attempts were produced and tested using experimental data that included 2 treatment regimens; conventional and nocturnal HD, with 2-hour postdialysis rebound. Graphical comparison and determination of R(2) was applied to determine the best model variation. RESULTS: 1-, 2- and 3-compartment simulations were produced. Both 2- and 3-compartment model variations showed a close fit with the experimental data. However, a 3-compartment model showed the best graphical fit. This was supported by R(2) values in the 0.951-0.979 range. CONCLUSION: The 3-compartment model seems promising for prediction about plasma phosphate and holds the potential to be employed as a decision support tool and to enhance optimal dialysis prescriptions. Furthermore, the results provide specific suggestions about the distribution of phosphate in the body. Despite the promising results, further data and testing are necessary to validate the initial results.

A diet management information and communication system to help chronic kidney patients cope with diet restrictions.

Hyperphosphatemia, hyperkalemia, and fluid overload are frequently observed and pose major physiological concerns in chronic kidney patients. The problems are closely related to inadequate diet and phosphate binder intake, which are considerable challenges for many patients. The objective of this study was to develop and test an educational decision support system to help kidney patients cope with diet restrictions and phosphate binder dosage. A prototype was designed including three main functions: 1) information and education, 2) food analyser database and diet registration, and 3) model-based decision support to phosphate binder dosage. The functions and the usability of the prototype were evaluated through user testing and qualitative interviews including five kidney patients. The decision support function was modified and tested using experimental data. In conclusion, the system was evaluated to be a relevant, and potentially beneficial tool to cope with kidney diet restrictions. Further data are necessary to validate the correct phosphate binder dosage and assess the ability of the system to decrease the incidence of fluid and electrolyte disorders in kidney patients.

Moving prediction of exacerbation in chronic obstructive pulmonary disease for patients in telecare.

We investigated whether physiological data can be used for predicting chronic obstructive pulmonary disease (COPD) exacerbations. Home measurements from 57 patients were analysed, during which 10 exacerbations occurred in nine patients. A total of 273 different features were evaluated for their discrimination abilities between periods with and without exacerbations. The analysis showed that if a sensitivity level of 70% is considered to be acceptable, then the specificity was 95% and the AUC was 0.73, i.e. it is possible to discriminate between periods of exacerbation and periods without. A system capable of predicting risk could provide support to COPD patients in their tele-rehabilitation.