Bayesian pairwise meta-analysis of time-to-event outcomes in the presence of non-proportional hazards: A simulation study of flexible parametric, piecewise exponential and fractional polynomial models.

BACKGROUND: Traditionally, meta-analysis of time-to-event outcomes reports a single pooled hazard ratio assuming proportional hazards (PH). For health technology assessment evaluations, hazard ratios are frequently extrapolated across a lifetime horizon. However, when treatment effects vary over time, an assumption of PH is not always valid. The Royston-Parmar (RP), piecewise exponential (PE), and fractional polynomial (FP) models can accommodate non-PH and provide plausible extrapolations of survival curves beyond observed data. METHODS: Simulation study to assess and compare the performance of RP, PE, and FP models in a Bayesian framework estimating restricted mean survival time difference (RMSTD) at 50 years from a pairwise meta-analysis with evidence of non-PH. Individual patient data were generated from a mixture Weibull distribution. Twelve scenarios were considered varying the amount of follow-up data, number of trials in a meta-analysis, non-PH interaction coefficient, and prior distributions. Performance was assessed through bias and mean squared error. Models were applied to a metastatic breast cancer example. RESULTS: FP models performed best when the non-PH interaction coefficient was 0.2. RP models performed best in scenarios with complete follow-up data. PE models performed well on average across all scenarios. In the metastatic breast cancer example, RMSTD at 50-years ranged from -14.6 to 8.48 months. CONCLUSIONS: Synthesis of time-to-event outcomes and estimation of RMSTD in the presence of non-PH can be challenging and computationally intensive. Different approaches make different assumptions regarding extrapolation and sensitivity analyses varying key assumptions are essential to check the robustness of conclusions to different assumptions for the underlying survival function.

Early transfusion patterns improve the Molecular International Prognostic Scoring System (IPSS-M) prediction in myelodysplastic syndromes.

BACKGROUND: The Molecular International Prognostic Scoring System (IPSS-M) is the new gold standard for diagnostic outcome prediction in patients with myelodysplastic syndromes (MDS). This study was designed to assess the additive prognostic impact of dynamic transfusion parameters during early follow-up. METHODS: We retrieved complete transfusion data from 677 adult Swedish MDS patients included in the IPSS-M cohort. Time-dependent erythrocyte transfusion dependency (E-TD) was added to IPSS-M features and analyzed regarding overall survival and leukemic transformation (acute myeloid leukemia). A multistate Markov model was applied to assess the prognostic value of early changes in transfusion patterns. RESULTS: Specific clinical and genetic features were predicted for diagnostic and time-dependent transfusion patterns. Importantly, transfusion state both at diagnosis and within the first year strongly predicts outcomes in both lower (LR) and higher-risk (HR) MDSs. In multivariable analysis, 8-month landmark E-TD predicted shorter survival independently of IPSS-M (p < 0.001). A predictive model based on IPSS-M and 8-month landmark E-TD performed significantly better than a model including only IPSS-M. Similar trends were observed in an independent validation cohort (n = 218). Early transfusion patterns impacted both future transfusion requirements and outcomes in a multistate Markov model. CONCLUSION: The transfusion requirement is a robust and available clinical parameter incorporating the effects of first-line management. In MDS, it provides dynamic risk information independently of diagnostic IPSS-M and, in particular, clinical guidance to LR MDS patients eligible for potentially curative therapeutic intervention.

Re-expressing coefficients from regression models for inclusion in a meta-analysis.

Meta-analysis poses a challenge when original study results have been expressed in a non-uniform manner, such as when regression results from some original studies were based on a log-transformed key independent variable while in others no transformation was used. Methods of re-expressing regression coefficients to generate comparable results across studies regardless of data transformation have recently been developed. We examined the relative bias of three re-expression methods using simulations and 15 real data examples where the independent variable had a skewed distribution. Regression coefficients from models with log-transformed independent variables were re-expressed as though they were based on an untransformed variable. We compared the re-expressed coefficients to those from a model fit to the untransformed variable. In the simulated and real data, all three re-expression methods usually gave biased results, and the skewness of the independent variable predicted the amount of bias. How best to synthesize the results of the log-transformed and absolute exposure evidence streams remains an open question and may depend on the scientific discipline, scale of the outcome, and other considerations.

Defining measures of kidney function in observational studies using routine health care data: methodological and reporting considerations.

The availability of electronic health records and access to a large number of routine measurements of serum creatinine and urinary albumin enhance the possibilities for epidemiologic research in kidney disease. However, the frequency of health care use and laboratory testing is determined by health status and indication, imposing certain challenges when identifying patients with kidney injury or disease, when using markers of kidney function as covariates, or when evaluating kidney outcomes. Depending on the specific research question, this may influence the interpretation, generalizability, and/or validity of study results. This review illustrates the heterogeneity of working definitions of kidney disease in the scientific literature and discusses advantages and limitations of the most commonly used approaches using 3 examples. We summarize ways to identify and overcome possible biases and conclude by proposing a framework for reporting definitions of exposures and outcomes in studies of kidney disease using routinely collected health care data.

The presence and impact of multimorbidity clusters on adverse outcomes across the spectrum of kidney function.

BACKGROUND: Multimorbidity (the presence of two or more chronic conditions) is common amongst people with chronic kidney disease, but it is unclear which conditions cluster together and if this changes as kidney function declines. We explored which clusters of conditions are associated with different estimated glomerular filtration rates (eGFRs) and studied associations between these clusters and adverse outcomes. METHODS: Two population-based cohort studies were used: the Stockholm Creatinine Measurements project (SCREAM, Sweden, 2006-2018) and the Secure Anonymised Information Linkage Databank (SAIL, Wales, 2006-2021). We studied participants in SCREAM (404,681 adults) and SAIL (533,362) whose eGFR declined lower than thresholds (90, 75, 60, 45, 30 and 15 mL/min/1.73m2). Clusters based on 27 chronic conditions were identified. We described the most common chronic condition(s) in each cluster and studied their association with adverse outcomes using Cox proportional hazards models (all-cause mortality (ACM) and major adverse cardiovascular events (MACE)). RESULTS: Chronic conditions became more common and clustered differently across lower eGFR categories. At eGFR 90, 75, and 60 mL/min/1.73m2, most participants were in large clusters with no prominent conditions. At eGFR 15 and 30 mL/min/1.73m2, clusters involving cardiovascular conditions were larger and were at the highest risk of adverse outcomes. At eGFR 30 mL/min/1.73m2, in the heart failure, peripheral vascular disease and diabetes cluster in SCREAM, ACM hazard ratio (HR) is 2.66 (95% confidence interval (CI) 2.31-3.07) and MACE HR is 4.18 (CI 3.65-4.78); in the heart failure and atrial fibrillation cluster in SAIL, ACM HR is 2.23 (CI 2.04 to 2.44) and MACE HR is 3.43 (CI 3.22-3.64). Chronic pain and depression were common and associated with adverse outcomes when combined with physical conditions. At eGFR 30 mL/min/1.73m2, in the chronic pain, heart failure and myocardial infarction cluster in SCREAM, ACM HR is 2.00 (CI 1.62-2.46) and MACE HR is 4.09 (CI 3.39-4.93); in the depression, chronic pain and stroke cluster in SAIL, ACM HR is 1.38 (CI 1.18-1.61) and MACE HR is 1.58 (CI 1.42-1.76). CONCLUSIONS: Patterns of multimorbidity and corresponding risk of adverse outcomes varied with declining eGFR. While diabetes and cardiovascular disease are known high-risk conditions, chronic pain and depression emerged as important conditions and associated with adverse outcomes when combined with physical conditions.

A natural history and copula-based joint model for regional and distant breast cancer metastasis.

The few existing statistical models of breast cancer recurrence and progression to distant metastasis are predominantly based on multi-state modelling. While useful for summarising the risk of recurrence, these provide limited insight into the underlying biological mechanisms and have limited use for understanding the implications of population-level interventions. We develop an alternative, novel, and parsimonious approach for modelling latent tumour growth and spread to local and distant metastasis, based on a natural history model with biologically inspired components. We include marginal sub-models for local and distant breast cancer metastasis, jointly modelled using a copula function. Different formulations (and correlation shapes) are allowed, thus we can incorporate and directly model the correlation between local and distant metastasis flexibly and efficiently. Submodels for the latent cancer growth, the detection process, and screening sensitivity, together with random effects to account for between-patients heterogeneity, are included. Although relying on several parametric assumptions, the joint copula model can be useful for understanding - potentially latent - disease dynamics, obtaining patient-specific, model-based predictions, and studying interventions at a population level, for example, using microsimulation. We illustrate this approach using data from a Swedish population-based case-control study of postmenopausal breast cancer, including examples of useful model-based predictions.

Use of nephrotoxic medications in adults with chronic kidney disease in Swedish and US routine care.

Background: To characterize the use of nephrotoxic medications in patients with chronic kidney disease (CKD) Stages G3-5 in routine care. Methods: We studied cohorts of adults with confirmed CKD G3-5 undergoing routine care from 1 January 2016 through 31 December 2018 in two health systems [Stockholm CREAtinine Measurements (SCREAM), Stockholm, Sweden (N = 57 880) and Geisinger, PA, USA (N = 16 255)]. We evaluated the proportion of patients receiving nephrotoxic medications within 1 year overall and by baseline kidney function, ranked main contributors and examined the association between receipt of nephrotoxic medication and age, sex, CKD G-stages comorbidities and provider awareness of the patient's CKD using multivariable logistic regression. Results: During a 1-year period, 20% (SCREAM) and 17% (Geisinger) of patients with CKD received at least one nephrotoxic medication. Among the top nephrotoxic medications identified in both cohorts were non-steroidal anti-inflammatory drugs (given to 11% and 9% of patients in SCREAM and Geisinger, respectively), antivirals (2.5% and 2.0%) and immunosuppressants (2.7% and 1.5%). Bisphosphonate use was common in SCREAM (3.3%) and fenofibrates in Geisinger (3.6%). Patients <65 years of age, women and those with CKD G3 were at higher risk of receiving nephrotoxic medications in both cohorts. Notably, provider awareness of a patient's CKD was associated with lower odds of nephrotoxic medication use {odds ratios [OR] 0.85[95% confidence interval (CI) 0.80-0.90] in SCREAM and OR 0.80 [95% CI 0.72-0.89] in Geisinger}. Conclusions: One in five patients with CKD received nephrotoxic medications in two distinct health systems. Strategies to increase physician's awareness of patients' CKD and knowledge of drug nephrotoxicity may reduce prescribing nephrotoxic medications and prevent iatrogenic kidney injury.

Assessing lead time bias due to mammography screening on estimates of loss in life expectancy.

BACKGROUND: An increasingly popular measure for summarising cancer prognosis is the loss in life expectancy (LLE), i.e. the reduction in life expectancy following a cancer diagnosis. The proportion of life lost (PLL) can also be derived, improving comparability across age groups as LLE is highly age-dependent. LLE and PLL are often used to assess the impact of cancer over the remaining lifespan and across groups (e.g. socioeconomic groups). However, in the presence of screening, it is unclear whether part of the differences across population groups could be attributed to lead time bias. Lead time is the extra time added due to early diagnosis, that is, the time from tumour detection through screening to the time that cancer would have been diagnosed symptomatically. It leads to artificially inflated survival estimates even when there are no real survival improvements. METHODS: In this paper, we used a simulation-based approach to assess the impact of lead time due to mammography screening on the estimation of LLE and PLL in breast cancer patients. A natural history model developed in a Swedish setting was used to simulate the growth of breast cancer tumours and age at symptomatic detection. Then, a screening programme similar to current guidelines in Sweden was imposed, with individuals aged 40-74 invited to participate every second year; different scenarios were considered for screening sensitivity and attendance. To isolate the lead time bias of screening, we assumed that screening does not affect the actual time of death. Finally, estimates of LLE and PLL were obtained in the absence and presence of screening, and their difference was used to derive the lead time bias. RESULTS: The largest absolute bias for LLE was 0.61 years for a high screening sensitivity scenario and assuming perfect screening attendance. The absolute bias was reduced to 0.46 years when the perfect attendance assumption was relaxed to allow for imperfect attendance across screening visits. Bias was also present for the PLL estimates. CONCLUSIONS: The results of the analysis suggested that lead time bias influences LLE and PLL metrics, thus requiring special consideration when interpreting comparisons across calendar time or population groups.

Estimating latent, dynamic processes of breast cancer tumour growth and distant metastatic spread from mammography screening data.

We propose a framework for jointly modelling tumour size at diagnosis and time to distant metastatic spread, from diagnosis, based on latent dynamic sub-models of growth of the primary tumour and of distant metastatic detection. The framework also includes a sub-model for screening sensitivity as a function of latent tumour size. Our approach connects post-diagnosis events to the natural history of cancer and, once refined, may prove useful for evaluating new interventions, such as personalised screening regimes. We evaluate our model-fitting procedure using Monte Carlo simulation, showing that the estimation algorithm can retrieve the correct model parameters, that key patterns in the data can be captured by the model even with misspecification of some structural assumptions, and that, still, with enough data it should be possible to detect strong misspecifications. Furthermore, we fit our model to observational data from an extension of a case-control study of post-menopausal breast cancer in Sweden, providing model-based estimates of the probability of being free from detected distant metastasis as a function of tumour size, mode of detection (of the primary tumour), and screening history. For women with screen-detected cancer and two previous negative screens, the probabilities of being free from detected distant metastases 5 years after detection and removal of the primary tumour are 0.97, 0.89 and 0.59 for tumours of diameter 5, 15 and 35 mm, respectively. We also study the probability of having latent/dormant metastases at detection of the primary tumour, estimating that 33% of patients in our study had such metastases.

INTEREST: INteractive Tool for Exploring REsults from Simulation sTudies.

Simulation studies allow us to explore the properties of statistical methods. They provide a powerful tool with a multiplicity of aims; among others: evaluating and comparing new or existing statistical methods, assessing violations of modelling assumptions, helping with the understanding of statistical concepts, and supporting the design of clinical trials. The increased availability of powerful computational tools and usable software has contributed to the rise of simulation studies in the current literature. However, simulation studies involve increasingly complex designs, making it difficult to provide all relevant results clearly. Dissemination of results plays a focal role in simulation studies: it can drive applied analysts to use methods that have been shown to perform well in their settings, guide researchers to develop new methods in a promising direction, and provide insights into less established methods. It is crucial that we can digest relevant results of simulation studies. Therefore, we developed INTEREST: an INteractive Tool for Exploring REsults from Simulation sTudies. The tool has been developed using the Shiny framework in R and is available as a web app or as a standalone package. It requires uploading a tidy format dataset with the results of a simulation study in R, Stata, SAS, SPSS, or comma-separated format. A variety of performance measures are estimated automatically along with Monte Carlo standard errors; results and performance summaries are displayed both in tabular and graphical fashion, with a wide variety of available plots. Consequently, the reader can focus on simulation parameters and estimands of most interest. In conclusion, INTEREST can facilitate the investigation of results from simulation studies and supplement the reporting of results, allowing researchers to share detailed results from their simulations, readers to explore them freely.

Arteriovenous access placement and renal function decline.

BACKGROUND: There is controversial evidence on whether arteriovenous access (AVA) placement may protect renal function and hence should be considered in the timing of access placement. This study aimed to investigate the association between AVA placement and estimated glomerular filtration rate (eGFR) decline as compared with the placement of a peritoneal dialysis catheter (PDC) at a similar time point. METHODS: We studied a cohort of 744 pre-dialysis patients in Stockholm, Sweden, who underwent surgery for AVA or PDC between 2006 and 2012. Data on comorbidity, medication and laboratory measures were collected 100 days before and after surgery. Patients were followed until dialysis start, death or 100 days, whichever came first. The primary outcome was difference in eGFR decline after AVA surgery compared with PDC. Decline in eGFR was estimated through linear mixed models with random intercept and slope, before and after surgery. RESULTS: There were 435 AVA and 309 PDC patients. The AVA patients had higher eGFR (8.1 mL/min/1.73 m2 versus 7.0 mL/min/1.73 m2) and less rapid eGFR decline before surgery (-5.6 mL/min/1.73 m2/year compared with -6.7 mL/min/1.73 m2/year for PDC). We found no difference in eGFR decline after surgery in AVA patients compared with PDC patients [AVA progressed 0.26 (95% confidence interval -0.88 to 0.35) mL/min/1.73 m2/year faster after surgery compared with PDC]. CONCLUSIONS: There was no significant difference in eGFR decline after placement of an AVA compared with a PDC. Both forms of access were associated with reduced eGFR decline in our population. The need for dialysis remains the main determinant for timing of access surgery.

Assessing the Course of Organ Dysfunction Using Joint Longitudinal and Time-to-Event Modeling in the Vasopressin and Septic Shock Trial.

Non-mortality septic shock outcomes (e.g., Sequential Organ Failure Assessment score) are important clinical endpoints in pivotal sepsis trials. However, comparisons of observed longitudinal non-mortality outcomes between study groups can be biased if death is unequal between study groups or is associated with an intervention (i.e., informative censoring). We compared the effects of vasopressin versus norepinephrine on the Sequential Organ Failure Assessment score in the Vasopressin and Septic Shock Trial to illustrate the use of joint modeling to help minimize potential bias from informative censoring. DESIGN: Secondary analysis of the Vasopressin and Septic Shock Trial data. SETTING: Twenty-seven ICUs in Canada, Australia, and United States. SUBJECTS: Seven hundred sixty-three participants with septic shock who received blinded vasopressin (n = 389) or norepinephrine infusions (n = 374). MEASUREMENTS AND MAIN RESULTS: Sequential Organ Failure Assessment scores were calculated daily until discharge, death, or day 28 after randomization. Mortality was numerically higher in the norepinephrine arm (28 d mortality of 39% vs 35%; p = 0.25), and there was a positive association between higher Sequential Organ Failure Assessment scores and patient mortality, characteristics that suggest a potential for bias from informative censoring of Sequential Organ Failure Assessment scores by death. The best-fitting joint longitudinal (i.e., linear mixed-effects model) and survival (i.e., Cox proportional hazards model for the time-to-death) model showed that norepinephrine was associated with a more rapid improvement in the total Sequential Organ Failure Assessment score through day 4, and then the daily Sequential Organ Failure Assessment scores converged and overlapped for the remainder of the study period. CONCLUSIONS: Short-term reversal of organ dysfunction occurred more rapidly with norepinephrine compared with vasopressin, although differences between study arms did not persist after day 4. Joint models are an accessible methodology that could be used in critical care trials to assess the effects of interventions on the longitudinal progression of key outcomes (e.g., organ dysfunction, biomarkers, or quality of life) that may be informatively truncated by death or other censoring events.

Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study.

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.

Impact of model misspecification in shared frailty survival models.

Survival models incorporating random effects to account for unmeasured heterogeneity are being increasingly used in biostatistical and applied research. Specifically, unmeasured covariates whose lack of inclusion in the model would lead to biased, inefficient results are commonly modeled by including a subject-specific (or cluster-specific) frailty term that follows a given distribution (eg, gamma or lognormal). Despite that, in the context of parametric frailty models, little is known about the impact of misspecifying the baseline hazard or the frailty distribution or both. Therefore, our aim is to quantify the impact of such misspecification in a wide variety of clinically plausible scenarios via Monte Carlo simulation, using open-source software readily available to applied researchers. We generate clustered survival data assuming various baseline hazard functions, including mixture distributions with turning points, and assess the impact of sample size, variance of the frailty, baseline hazard function, and frailty distribution. Models compared include standard parametric distributions and more flexible spline-based approaches; we also included semiparametric Cox models. The resulting bias can be clinically relevant. In conclusion, we highlight the importance of fitting models that are flexible enough and the importance of assessing model fit. We illustrate our conclusions with two applications using data on diabetic retinopathy and bladder cancer. Our results show the importance of assessing model fit with respect to the baseline hazard function and the distribution of the frailty: misspecifying the former leads to biased relative and absolute risk estimates, whereas misspecifying the latter affects absolute risk estimates and measures of heterogeneity.

Causes of death across categories of estimated glomerular filtration rate: The Stockholm CREAtinine Measurements (SCREAM) project.

INTRODUCTION: Reduced kidney function increases the risk of death, but there is limited information on causes of death across stages of chronic kidney disease (CKD). We aimed to identify leading causes of death in community-dwelling individuals with differing kidney function. METHODS: Observational analysis from SCREAM, a healthcare utilization cohort of Stockholm, Sweden. We included all individuals who died during 2006-2012 and had one serum creatinine measured in the year prior to death. Using the CKD-EPI formula, we calculated eGFR and stratified individuals according to CKD stages. Causes of death were classified as cardiovascular (CVD), cancer, infection and other, using ICD-10 codes. We compared age- and sex-adjusted differences in the proportions of deaths from each cause. RESULTS: Out of 89,117 registered deaths, 70,547 (79%) had a recent eGFR estimation and were included in this study. Individuals had a median age of 82 (IRE 62-93) years and 52% were women. The proportions of deaths from CVD increased with lower eGFR, along with the proportion of deaths from infections. Deaths due to diabetes and genito-urinary diseases increased. Deaths due to cancer decreased, but other death causes did not vary. Within CVD causes of death, the proportion of arrhythmias and heart failure increased, but ischemic heart disease and cerebrovascular disease remained stable. CONCLUSION: In a region-representative Swedish healthcare extraction, we observe differences regarding specific causes of death across different CKD stages. Increasing patient and provider awareness of this differential pattern of risk may have benefits for patient management, prevention strategies, and health service planning.

Plasma potassium ranges associated with mortality across stages of chronic kidney disease: the Stockholm CREAtinine Measurements (SCREAM) project.

BACKGROUND: Small-scale studies suggest that hyperkalaemia is a less threatening condition in chronic kidney disease (CKD), arguing adaptation/tolerance to potassium (K+) retention. This study formally evaluates this hypothesis by estimating the distribution of plasma K+ and its association with mortality across CKD stages. METHODS: This observational study included all patients undergoing plasma K+ testing in Stockholm during 2006-11. We randomly selected one K+ measurement per patient and constructed a cross-sectional cohort with mortality follow-up. Covariates included demographics, comorbidities, medications and estimated glomerular filtration rate (eGFR). We estimated K+ distribution and defined K+ ranges associated with 90-, 180- and 365-day mortality. RESULTS: Included were 831 760 participants, of which 70 403 (8.5%) had CKD G3 (eGFR <60-30 mL/min) and 8594 (1.1%) had CKD G4-G5 (eGFR <30 mL/min). About 66 317 deaths occurred within a year. Adjusted plasma K+ increased across worse CKD stages: from median 3.98 (95% confidence interval 3.49-4.59) for eGFR >90 to 4.43 (3.22-5.65) mmol/L for eGFR ≤15 mL/min/1.73 m2. The association between K+ and mortality was U-shaped, but it flattened at lower eGFR strata and shifted upwards. For instance, the range where the 90-day mortality risk increased by no more than 100% was 3.45-4.94 mmol/L in eGFR >60 mL/min, but was 3.36-5.18 in G3 and 3.26-5.53 mmol/L in G4-G5. In conclusion, CKD stage modifies K+ distribution and the ranges that predict mortality in the community. CONCLUSION: Although this study supports the view that hyperkalaemia is better tolerated with worse CKD, it challenges the current use of a single optimal K+ range for all patients.

A scheme based on ICD-10 diagnoses and drug prescriptions to stage chronic kidney disease severity in healthcare administrative records.

BACKGROUND: Information about renal function is important for drug safety studies using administrative health databases. However, serum creatinine values are seldom available in these registries. Our aim was to develop and test a simple scheme for stratification of renal function without access to laboratory test results. METHODS: Our scheme uses registry data about diagnoses, contacts, dialysis and drug use. We validated the scheme in the Stockholm CREAtinine Measurements (SCREAM) project using information on approximately 1.1 million individuals residing in the Stockholm County who underwent calibrated creatinine testing during 2006-11, linked with data about health care contacts and filled drug prescriptions. Estimated glomerular filtration rate (eGFR) was calculated with the CKD-EPI formula and used as the gold standard for validation of the scheme. RESULTS: When the scheme classified patients as having eGFR <30 mL/min/1.73 m2, it was correct in 93.5% of cases. The specificity of the scheme was close to 100% in all age groups. The sensitivity was poor, ranging from 68.2% in the youngest age quartile, down to 10.7% in the oldest age quartile. Age-related decline in renal function makes a large proportion of elderly patients fall into the chronic kidney disease (CKD) range without receiving CKD diagnoses, as this often is seen as part of normal ageing. CONCLUSIONS: In the absence of renal function tests, our scheme may be of value for identifying patients with moderate and severe CKD on the basis of diagnostic and prescription data for use in studies of large healthcare databases.

Lower serum calcium is independently associated with CKD progression.

Disturbances in calcium metabolism are common in individuals with chronic kidney disease (CKD), but whether they are associated with subsequent kidney function decline is less clear. In a CKD 3-5 cohort of 15,755 adult citizens of Stockholm with creatinine tests taken during 2006-2011 and concurrent calcium testing at cohort entry, we investigated the association between baseline serum calcium and the subsequent change in estimated glomerular filtration rate (eGFR, by CKD-EPI) decline using linear mixed models. Mean (SD) baseline corrected serum calcium was 9.6 (0.5) mg/dL. Mean (95%-confidence interval [CI]) eGFR decline was -0.82 (-0.90; -0.74) mL/min/1.73 m2/year. In advanced CKD stages, higher baseline serum calcium was associated with less rapid kidney function decline. The adjusted change (95%-CI) in eGFR decline associated with each mg/dL increase in baseline serum calcium was -0.10 (-0.28; 0.26), 0.39 (0.07; 0.71), 0.34 (-0.02; 0.70) and 0.68 (0.36; 1.00) mL/min/1.73 m2/year for individuals in CKD stage 3a, 3b, 4, and 5, respectively. In a subgroup of patients using vitamin D supplements, the association between baseline serum calcium and CKD progression was eliminated, especially in CKD stage 3b and 4. To conclude, in individuals with CKD stage 3b to 5, lower baseline corrected serum calcium, rather than higher baseline serum calcium, associated with a more rapid CKD progression. Lower serum corrected calcium seems to be indicative for vitamin D deficiency.

Cinacalcet use and the risk of cardiovascular events, fractures and mortality in chronic kidney disease patients with secondary hyperparathyroidism.

With the aim to expand the randomized controlled trial evidence of cinacalcet treatment to the unselected, general chronic kidney disease (CKD) population we analysed a large inception cohort of CKD patients in the region of Stockholm, Sweden 2006-2012 (both non-dialysis, dialysis and transplanted) with evidence of secondary hyperparathyroidism (SHPT). We used marginal structural models to account for both confounding by indication and time-dependent confounding. Over 37 months, 435/3,526 (12%) initiated cinacalcet de novo. Before cinacalcet initiation, parathyroid hormone (PTH) had increased progressively to a median of 636ng/L. After cinacalcet initiation, PTH declined, as did serum calcium and phosphate. In total, 42% of patients experienced a fatal/non-fatal cardiovascular event, 32% died and 9% had a new fracture. The unadjusted cardiovascular odds ratio (OR) associated with cinacalcet treatment was 1.01 (95% confidence interval: 0.83, 1.22). In the fully weighted model, the cardiovascular odds was lower in cinacalcet treated patients (OR 0.67: 0.48, 0.93). The adjusted ORs for all-cause mortality and for fractures were 0.79 (0.56, 1.11) and 1.08 (0.59, 1.98) respectively. Our study suggests cinacalcet treatment improves biochemical abnormalities in the wider CKD population, and adds real-world support that treating SHPT with cinacalcet may have beneficial effects on cardiovascular outcomes.

eGFR and the Risk of Community-Acquired Infections.

BACKGROUND AND OBJECTIVES: Community-acquired infections are common, contributing to adverse outcomes and increased health care costs. We hypothesized that, with lower eGFR, the incidence of community-acquired infections increases, whereas the pattern of site-specific infections varies. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Among 1,139,470 health care users (mean age =52±18 years old, 53% women) from the Stockholm CREAtinine Measurements Project, we quantified the associations of eGFR with the risk of infections, overall and major types, over 12 months. RESULTS: A total of 106,807 counts of infections were recorded throughout 1,128,313 person-years. The incidence rate of all infections increased with lower eGFR from 74/1000 person-years for individuals with eGFR=90-104 ml/min per 1.73 m2 to 419/1000 person-years for individuals with eGFR<30 ml/min per 1.73 m2. Compared with eGFR of 90-104 ml/min per 1.73 m2, the adjusted incidence rate ratios of community-acquired infections were 1.08 (95% confidence interval, 1.01 to 1.14) for eGFR of 30-59 ml/min per 1.73 m2 and 1.53 (95% confidence interval, 1.39 to 1.69) for eGFR<30 ml/min per 1.73 m2. The relative proportions of lower respiratory tract infection, urinary tract infection, and sepsis became increasingly higher along with lower eGFR strata (e.g., low respiratory tract infection accounting for 25% versus 15% of community-acquired infections in eGFR<30 versus 90-104 ml/min per 1.73 m2, respectively). Differences in incidence associated with eGFR were in general consistent for most infection types, except for nervous system and upper respiratory tract infections, for which no association was observed. CONCLUSIONS: This region-representative health care study finds an excess community-acquired infections incidence in individuals with mild to severe CKD. Lower respiratory tract infection, urinary tract infection, and sepsis are major infections in CKD.