Drainage effects on carbon budgets of degraded peatlands in the north of the Netherlands.

Peatlands store vast amounts of carbon (C). However, land-use-driven drainage causes peat oxidation, resulting in CO2 emission. There is a growing need for ground-truthing CO2 emission and its potential drivers to better quantify long-term emission trends in peatlands. This will help improve National Inventory Reporting and ultimately aid the design and verification of mitigation measures. To investigate regional drivers of CO2 emission, we estimated C budgets using custom-made automated chamber systems measuring CO2 concentrations corrected for carbon export and import. Chamber systems were rotated among thirteen degraded peatland pastures in Friesland (the Netherlands). These peatlands varied in water table depth (WTD), drainage-irrigation management (fixed regulated ditch water level (DWL), subsurface irrigation, furrow irrigation, or dynamic regulated DWL), and soil moisture. We investigated (1) whether drainage-irrigation management and related hydrological drivers could explain variation in C budgets, (2) how nighttime ecosystem respiration (Reconight) related to hydrological drivers, and (3) how C budgets compared with estimates from Tier 1 and Tier 2 models regularly used in National Inventory Reporting. Deep-drained peatlands largely overlapped with C budgets from shallow-drained peatlands. The variation in C budgets could not be explained with drainage-irrigation measures or annual WTD, likely because of high variation between sites. Reconightincreased from 85 to 250 kg CO2 ha-1 day-1 as the WTD dropped from 0 to 50 cm across all sites. A deeper WTD had no apparent effect on Reconight, which could be explained by the unimodal relationship we found between Reconight and soil moisture. Finally, C budgets estimated by Tier 1 emission factors and Tier 2 national models mismatched the between-site and between-year variation found in chamber-based estimated NECBs. To conclude, our study showed that shallow WTDs greatly determine C budgets and that regional C budgets, which can be accurately measure with periodic automated chamber measurements, are instrumental for model validation.

Association Between Treatment Adherence and Continuous Glucose Monitoring Outcomes in People With Diabetes Using Smart Insulin Pens in a Real-World Setting.

OBJECTIVE: To evaluate the association of insulin injection adherence, smart insulin pen engagement, and glycemic control using real-world data from 16 countries from adults self-administering basal insulin degludec and bolus insulin with a smart insulin pen (NovoPen 6 or NovoPen Echo Plus) alongside continuous glucose monitoring (CGM). RESEARCH DESIGN AND METHODS: Data were aggregated over 14-day periods. Treatment adherence was defined according to the number of missed basal and missed bolus insulin doses and smart pen engagement according to the number of days with data uploads. RESULTS: Data from 3,945 adults, including 25,157 14-day periods with ≥70% CGM coverage, were analyzed. On average, 0.2 basal and 6.0 bolus insulin doses were missed over 14 days. The estimated probability of missing at least one basal insulin dose over a 14-day period was 17.6% (95% CI 16.5, 18.7). Missing one basal or bolus insulin dose per 14 days was associated with a significant decrease in percentage of time with glucose levels in range (TIR) (3.9-10.0 mmol/L), of -2.8% (95% CI -3.7, -1.8) and -1.7% (-1.8, -1.6), respectively; therefore, missing two basal or four bolus doses would decrease TIR by >5%. Smart pen engagement was associated positively with glycemic outcomes. CONCLUSIONS: This combined analysis of real-world smart pen and CGM data showed that missing two basal or four bolus insulin doses over a 14-day period would be associated with a clinically relevant decrease in TIR. Smart insulin pens provide valuable insights into treatment injection behaviors.

Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines.

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.

Fe(II)Cl2 amendment suppresses pond methane emissions by stimulating iron-dependent anaerobic oxidation of methane.

Aquatic ecosystems are large contributors to global methane (CH4) emissions. Eutrophication significantly enhances CH4-production as it stimulates methanogenesis. Mitigation measures aimed at reducing eutrophication, such as the addition of metal salts to immobilize phosphate (PO43-), are now common practice. However, the effects of such remedies on methanogenic and methanotrophic communities-and therefore on CH4-cycling-remain largely unexplored. Here, we demonstrate that Fe(II)Cl2 addition, used as PO43- binder, differentially affected microbial CH4 cycling-processes in field experiments and batch incubations. In the field experiments, carried out in enclosures in a eutrophic pond, Fe(II)Cl2 application lowered in-situ CH4 emissions by lowering net CH4-production, while sediment aerobic CH4-oxidation rates-as found in batch incubations of sediment from the enclosures-did not differ from control. In Fe(II)Cl2-treated sediments, a decrease in net CH4-production rates could be attributed to the stimulation of iron-dependent anaerobic CH4-oxidation (Fe-AOM). In batch incubations, anaerobic CH4-oxidation and Fe(II)-production started immediately after CH4 addition, indicating Fe-AOM, likely enabled by favorable indigenous iron cycling conditions and the present methanotroph community in the pond sediment. 16S rRNA sequencing data confirmed the presence of anaerobic CH4-oxidizing archaea and both iron-reducing and iron-oxidizing bacteria in the tested sediments. Thus, besides combatting eutrophication, Fe(II)Cl2 application can mitigate CH4 emissions by reducing microbial net CH4-production and stimulating Fe-AOM.

Acute clinical evaluation for the diagnosis of lateral ankle ligament injuries is useful: A comparison between the acute and delayed settings.

PURPOSE: To determine the diagnostic value of seven injury history variables, nine clinical tests (including the combination thereof) and overall clinical suspicion for complete discontinuity of the lateral ankle ligaments in the acute (0-2 days post-injury) and delayed setting (5-8 days post-injury). METHODS: All acute ankle injuries in adult athletes (≥18 years) presenting up to 2 days post-injury were assessed for eligibility. Athletes were excluded if imaging studies demonstrated a frank fracture or 3 T MRI could not be acquired within 10 days post-injury. Using standardized history variables and clinical tests, acute clinical evaluation was performed within 2 days post-injury. Delayed clinical evaluation was performed 5-8 days post-injury. Overall, clinical suspicion was recorded after clinical evaluation. MRI was used as the reference standard. RESULTS: Between February 2018 and February 2020, a total of 117 acute ankle injuries were screened for eligibility, of which 43 were included in this study. Complete discontinuity of lateral ankle ligaments was observed in 23 (53%) acute ankle injuries. In the acute setting, lateral swelling had 100% (95% confidence interval [CI]: 82-100) sensitivity, haematoma had 85% (95% CI: 61-96) specificity and the anterior drawer test had 100% (95% CI: 77-100) specificity. In the delayed setting, sensitivity for the presence of haematoma improved from 43% (95% CI: 24-65) to 91% (95% CI: 70-98; p < 0.01) and the sensitivity of the anterior drawer test improved from 21% (95% CI: 7-46) to 61% (95% CI: 39-80; p = 0.02). Clinical suspicion had a positive likelihood ratio (LR) of 4.35 (95% CI: 0.55-34.17) in the acute setting and a positive LR of 6.09 (95% CI: 1.57-23.60) in the delayed setting. CONCLUSIONS: In the acute setting, clinical evaluation can exclude complete discontinuity (e.g., absent lateral swelling) and identify athletes with a high probability of complete discontinuity (e.g., positive anterior drawer test) of the lateral ankle ligaments. In the delayed setting, the sensitivity of common clinical findings increases resulting in an improved diagnostic accuracy. In clinical practice, this study underlines the importance of meticulous clinical evaluation in the acute setting. LEVEL OF EVIDENCE: Level III.

Multiple imputation of incomplete multilevel data using Heckman selection models.

Missing data is a common problem in medical research, and is commonly addressed using multiple imputation. Although traditional imputation methods allow for valid statistical inference when data are missing at random (MAR), their implementation is problematic when the presence of missingness depends on unobserved variables, that is, the data are missing not at random (MNAR). Unfortunately, this MNAR situation is rather common, in observational studies, registries and other sources of real-world data. While several imputation methods have been proposed for addressing individual studies when data are MNAR, their application and validity in large datasets with multilevel structure remains unclear. We therefore explored the consequence of MNAR data in hierarchical data in-depth, and proposed a novel multilevel imputation method for common missing patterns in clustered datasets. This method is based on the principles of Heckman selection models and adopts a two-stage meta-analysis approach to impute binary and continuous variables that may be outcomes or predictors and that are systematically or sporadically missing. After evaluating the proposed imputation model in simulated scenarios, we illustrate it use in a cross-sectional community survey to estimate the prevalence of malaria parasitemia in children aged 2-10 years in five regions in Uganda.

Confounder Adjustment Using the Disease Risk Score: A Proposal for Weighting Methods.

Propensity score analysis is a common approach to addressing confounding in nonrandomized studies. Its implementation, however, requires important assumptions (e.g., positivity). The disease risk score (DRS) is an alternative confounding score that can relax some of these assumptions. Like the propensity score, the DRS summarizes multiple confounders into a single score, on which conditioning by matching allows the estimation of causal effects. However, matching relies on arbitrary choices for pruning out data (e.g., matching ratio, algorithm, and caliper width) and may be computationally demanding. Alternatively, weighting methods, common in propensity score analysis, are easy to implement and may entail fewer choices, yet none have been developed for the DRS. Here we present 2 weighting approaches: One derives directly from inverse probability weighting; the other, named target distribution weighting, relates to importance sampling. We empirically show that inverse probability weighting and target distribution weighting display performance comparable to matching techniques in terms of bias but outperform them in terms of efficiency (mean squared error) and computational speed (up to >870 times faster in an illustrative study). We illustrate implementation of the methods in 2 case studies where we investigate placebo treatments for multiple sclerosis and administration of aspirin in stroke patients.

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed.

OBJECTIVES: Risk of bias assessments are important in meta-analyses of both aggregate and individual participant data (IPD). There is limited evidence on whether and how risk of bias of included studies or datasets in IPD meta-analyses (IPDMAs) is assessed. We review how risk of bias is currently assessed, reported, and incorporated in IPDMAs of test accuracy and clinical prediction model studies and provide recommendations for improvement. STUDY DESIGN AND SETTING: We searched PubMed (January 2018-May 2020) to identify IPDMAs of test accuracy and prediction models, then elicited whether each IPDMA assessed risk of bias of included studies and, if so, how assessments were reported and subsequently incorporated into the IPDMAs. RESULTS: Forty-nine IPDMAs were included. Nineteen of 27 (70%) test accuracy IPDMAs assessed risk of bias, compared to 5 of 22 (23%) prediction model IPDMAs. Seventeen of 19 (89%) test accuracy IPDMAs used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), but no tool was used consistently among prediction model IPDMAs. Of IPDMAs assessing risk of bias, 7 (37%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided details on the information sources (e.g., the original manuscript, IPD, primary investigators) used to inform judgments, and 4 (21%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided information or whether assessments were done before or after obtaining the IPD of the included studies or datasets. Of all included IPDMAs, only seven test accuracy IPDMAs (26%) and one prediction model IPDMA (5%) incorporated risk of bias assessments into their meta-analyses. For future IPDMA projects, we provide guidance on how to adapt tools such as Prediction model Risk Of Bias ASsessment Tool (for prediction models) and QUADAS-2 (for test accuracy) to assess risk of bias of included primary studies and their IPD. CONCLUSION: Risk of bias assessments and their reporting need to be improved in IPDMAs of test accuracy and, especially, prediction model studies. Using recommended tools, both before and after IPD are obtained, will address this.

Application of Causal Inference Methods to Pooled Longitudinal Non- Randomized Studies: A Methodological Systematic Review.

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.

Propensity-based standardization to enhance the validation and interpretation of prediction model discrimination for a target population.

External validation of the discriminative ability of prediction models is of key importance. However, the interpretation of such evaluations is challenging, as the ability to discriminate depends on both the sample characteristics (ie, case-mix) and the generalizability of predictor coefficients, but most discrimination indices do not provide any insight into their respective contributions. To disentangle differences in discriminative ability across external validation samples due to a lack of model generalizability from differences in sample characteristics, we propose propensity-weighted measures of discrimination. These weighted metrics, which are derived from propensity scores for sample membership, are standardized for case-mix differences between the model development and validation samples, allowing for a fair comparison of discriminative ability in terms of model characteristics in a target population of interest. We illustrate our methods with the validation of eight prediction models for deep vein thrombosis in 12 external validation data sets and assess our methods in a simulation study. In the illustrative example, propensity score standardization reduced between-study heterogeneity of discrimination, indicating that between-study variability was partially attributable to case-mix. The simulation study showed that only flexible propensity-score methods (allowing for non-linear effects) produced unbiased estimates of model discrimination in the target population, and only when the positivity assumption was met. Propensity score-based standardization may facilitate the interpretation of (heterogeneity in) discriminative ability of a prediction model as observed across multiple studies, and may guide model updating strategies for a particular target population. Careful propensity score modeling with attention for non-linear relations is recommended.

Controlled donation after circulatory death lung transplantation: Results of the French protocol including in situ abdominal normothermic regional perfusion and ex vivo lung perfusion.

BACKGROUND: The French national protocol for controlled donation after circulatory determination of death (cDCD) includes normothermic regional perfusion (NRP) in case of abdominal organ procurement and additional ex-vivo lung perfusion (EVLP) before considering lung transplantation (LT). METHODS: We made a retrospective study of a prospective registry that included all donors considered for cDCD LT from the beginning of the program in May 2016 to November 2021. RESULTS: One hundred grafts from 14 donor hospitals were accepted by 6 LT centers. The median duration of the agonal phase was 20 minutes [2-166]. The median duration from circulatory arrest to pulmonary flush was 62 minutes [20-90]. Ten lung grafts were not retrieved due to prolonged agonal phases (n = 3), failure of NRP insertion (n = 5), or poor in situ evaluation (n = 2). The remaining 90 lung grafts were all evaluated on EVLP, with a conversion rate of 84% and a cDCD transplantation rate of 76%. The median total preservation time was 707 minutes [543-1038]. Seventy-one bilateral LTs and 5 single LTs were performed for chronic obstructive pulmonary disease (n = 29), pulmonary fibrosis (n = 21), cystic fibrosis (n = 15), pulmonary hypertension (n = 8), graft-versus-host disease (n = 2), and adenosquamous carcinoma (n = 1). The rate of PGD3 was 9% (n = 5). The 1-year survival rate was 93.4%. CONCLUSION: After initial acceptance, cDCD lung grafts led to LT in 76% of cases, with outcomes similar to those already reported in the literature. The relative impacts of NRP and EVLP on the outcome following cDCD LT should be assessed prospectively in the context of comparative studies.

Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis.

BACKGROUND: Many children with pulmonary tuberculosis remain undiagnosed and untreated with related high morbidity and mortality. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies so far have been small and localised, with limited generalisability. We aimed to evaluate the performance of currently used diagnostic algorithms and to use prediction modelling to develop evidence-based algorithms to assist in tuberculosis treatment decision making for children presenting to primary health-care centres. METHODS: For this meta-analysis, we identified individual participant data from a WHO public call for data on the management of tuberculosis in children and adolescents and referral from childhood tuberculosis experts. We included studies that prospectively recruited consecutive participants younger than 10 years attending health-care centres in countries with a high tuberculosis incidence for clinical evaluation of pulmonary tuberculosis. We collated individual participant data including clinical, bacteriological, and radiological information and a standardised reference classification of pulmonary tuberculosis. Using this dataset, we first retrospectively evaluated the performance of several existing treatment-decision algorithms. We then used the data to develop two multivariable prediction models that included features used in clinical evaluation of pulmonary tuberculosis-one with chest x-ray features and one without-and we investigated each model's generalisability using internal-external cross-validation. The parameter coefficient estimates of the two models were scaled into two scoring systems to classify tuberculosis with a prespecified sensitivity target. The two scoring systems were used to develop two pragmatic, treatment-decision algorithms for use in primary health-care settings. FINDINGS: Of 4718 children from 13 studies from 12 countries, 1811 (38·4%) were classified as having pulmonary tuberculosis: 541 (29·9%) bacteriologically confirmed and 1270 (70·1%) unconfirmed. Existing treatment-decision algorithms had highly variable diagnostic performance. The scoring system derived from the prediction model that included clinical features and features from chest x-ray had a combined sensitivity of 0·86 [95% CI 0·68-0·94] and specificity of 0·37 [0·15-0·66] against a composite reference standard. The scoring system derived from the model that included only clinical features had a combined sensitivity of 0·84 [95% CI 0·66-0·93] and specificity of 0·30 [0·13-0·56] against a composite reference standard. The scoring system from each model was placed after triage steps, including assessment of illness acuity and risk of poor tuberculosis-related outcomes, to develop treatment-decision algorithms. INTERPRETATION: We adopted an evidence-based approach to develop pragmatic algorithms to guide tuberculosis treatment decisions in children, irrespective of the resources locally available. This approach will empower health workers in primary health-care settings with high tuberculosis incidence and limited resources to initiate tuberculosis treatment in children to improve access to care and reduce tuberculosis-related mortality. These algorithms have been included in the operational handbook accompanying the latest WHO guidelines on the management of tuberculosis in children and adolescents. Future prospective evaluation of algorithms, including those developed in this work, is necessary to investigate clinical performance. FUNDING: WHO, US National Institutes of Health.

Measuring the performance of prediction models to personalize treatment choice.

When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration for benefit. We then amalgamate these two dimensions into an additional concept, decision accuracy, which quantifies the model's ability to identify patients for whom the benefit from treatment exceeds a given threshold. Subsequently, we propose a series of performance measures related to these dimensions and discuss estimating procedures, focusing on randomized data. Our methods are applicable for continuous or binary outcomes, for any type of prediction model, as long as it uses baseline covariates to predict outcomes under treatment and control. We illustrate all methods using two simulated datasets and a real dataset from a trial in depression. We implement all methods in the R package predieval. Results suggest that the proposed measures can be useful in evaluating and comparing the performance of competing models in predicting individualized treatment effect.