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.

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.

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.

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.

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.

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.

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.

Association Between Proton Pump Inhibitor Use and Risk of Progression of Chronic Kidney Disease.

BACKGROUND & AIMS: Proton pump inhibitors (PPI) have been associated with acute kidney injury and recent studies suggest that they may be associated with the risk of chronic kidney disease (CKD). METHODS: We performed a retrospective analysis using the Stockholm creatinine measurements database, which contains information on diagnoses, dispensation claims, and laboratory test results for all citizens in the Stockholm region from 2007 through 2010. We identified new users of PPIs (n = 105,305) and new users of H2 blockers (H2B; n = 9578); data on renal outcomes were collected for a median 2.7 years. The primary outcome was progression CKD, defined as doubling of creatinine or decrease in estimated glomerular filtration rate of 30% or more. Secondary outcomes were end-stage renal disease and acute kidney injury. Complete collection of repeated PPI and H2B dispensations at pharmacies in Sweden allowed modeling the time-dependent risk associated with cumulative PPI exposure. RESULTS: Users of PPIs, compared with users of H2Bs, had an increased risk for doubled levels of creatinine (1985 events; adjusted hazard ratio [HR], 1.26; 95% CI, 1.05-1.51) and decrease in estimated glomerular filtration rate of 30% or more (11,045 events; 1.26; 95% CI, 1.16-1.36). PPI use also associated with development of end-stage renal disease (HR, 2.40; 95% CI, 0.76-7.58) and acute kidney injury (HR, 1.30; 95% CI, 1.00-1.69). There was a graded association between cumulative exposure to PPIs and risk of CKD progression. This was not the case for cumulative H2B use. CONCLUSIONS: Initiation of PPI therapy and cumulative PPI exposure is associate with increased risk of CKD progression in a large, North European healthcare system. Although consistent, the association was modest in magnitude, and cannot exclude residual confounding.

Albuminuria changes are associated with subsequent risk of end-stage renal disease and mortality.

Current guidelines for chronic kidney disease (CKD) recommend using albuminuria as well as estimated glomerular filtration rate (eGFR) to stage CKD. However, CKD progression is solely defined by change in eGFR with little regard to the risk implications of change in albuminuria. This is an observational study from the Stockholm CREAtinine Measurements (SCREAM) project, a health care utilization cohort from Stockholm, Sweden, with laboratory measures from 2006-2011 and follow-up through December 2012. Included were 31,732 individuals with two or more ambulatory urine albumin to creatinine ratio (ACR) tests. We assessed the association between change in ACR during a baseline period of 1, 2, or 3 years and end-stage renal disease (ESRD) or death. Using a 2-year baseline period, there were 378 ESRD events and 1712 deaths during a median of 3 years of follow-up. Compared to stable ACR, a 4-fold increase in ACR was associated with a 3.08-times (95% confidence interval 2.59 to 3.67) higher risk of ESRD while a 4-fold decrease in ACR was associated with a 0.34-times (0.26 to 0.45) lower risk of ESRD. Similar associations were found in people with and without diabetes mellitus, with and without hypertension, and also when adjusted for the change in eGFR during the same period. The association between change in ACR and mortality was weaker: ACR increase was associated with mortality, but the relationship was largely flat for ACR decline. Results were consistent for 1-, 2-, and 3-year ACR changes. Thus, changes in albuminuria are strongly and consistently associated with the risk of ESRD and death.

Routinely measured iohexol glomerular filtration rate versus creatinine-based estimated glomerular filtration rate as predictors of mortality in patients with advanced chronic kidney disease: a Swedish Chronic Kidney Disease Registry cohort study.

Background: Estimated glomerular filtration rate (eGFR) becomes less reliable in patients with advanced chronic kidney disease (CKD). Methods: Using the Swedish CKD Registry (2005-11), linked to the national inpatient, dialysis and death registers, we compared the performance of plasma-iohexol measured GFR (mGFR) and urinary clearance measures versus eGFR to predict death in adults with CKD stages 4/5. Performance was assessed using survival and prognostic models. Results: Of the 2705 patients, 1517 had mGFR performed, with the remainder providing 24-h urine clearances. Median eGFR (CKD-EPI creatinine ) was 20 mL/min/1.73 m 2 [interquartile range (IQR) 14-26], mGFR 18 mL/min/1.73 m 2 (IQR 13-23) and creatinine clearance 23 mL/min (IQR 15-31). Median follow-up was 45 months (IQR 26-59), registering 968 deaths (36%). In fully adjusted Cox models, a rise in mGFR of 1 mL/min/1.73 m 2 was associated with a 5.3% fall in all-cause mortality compared with a 1.7% corresponding fall for eGFR [adjusted hazard ratio (aHR) 0.947 (95% CI, 0.930-0.964) versus aHR 0.983 (95% CI, 0.970-0.996)]. mGFR was also statistically superior in prognostic models (discrimination using logistic regression and integrated discrimination improvement). Urinary clearance measures showed a stronger aetiological relationship with death than eGFR, but were not statistically superior in the prognostic models. Conclusions: The performance of mGFR was superior to eGFR, in both aetiological and prognostic models, in predicting mortality in adults with CKD stage 4/5, demonstrating the importance of GFR per se versus non-GFR determinants of outcome. However, the relatively modest enhancement suggests that eGFR may be sufficient to use in everyday clinical practice while mGFR adds important prognostic information for those where eGFR is believed to be biased.

Low renal replacement therapy incidence among slowly progressing elderly chronic kidney disease patients referred to nephrology care: an observational study.

BACKGROUND: Elderly patients with advanced chronic kidney disease (CKD) have a high risk of death before reaching end-stage kidney disease. In order to allocate resources, such as advanced care nephrology where it is most needed, it is essential to know which patients have the highest absolute risk of advancing to renal replacement therapy (RRT). METHODS: We included all nephrology-referred CKD stage 3b-5 patients in Sweden 2005-2011 included in the Swedish renal registry (SRR-CKD) who had at least two serum creatinine measurements one year apart (+/- 6 months). We followed these patients to either initiation of RRT, death, or September 30, 2013. Decline in estimated glomerular filtration rate (eGFR) (%) was estimated during the one-year baseline period. The patients in the highest tertile of progression (>18.7% decline in eGFR) during the initial year of follow-up were classified as "fast progressors". We estimated the cumulative incidence of RRT and death before RRT by age, eGFR and progression status using competing risk models. RESULTS: There were 2119 RRT initiations (24.2%) and 2060 deaths (23.5%) before RRT started. The median progression rate estimated during the initial year was -8.8% (Interquartile range [IQR] - 24.5-6.5%). A fast initial progression rate was associated with a higher risk of RRT initiation (Sub Hazard Ratio [SHR] 2.24 (95% confidence interval [CI] 2.00-2.51) and also a higher risk of death before RRT initiation (SHR 1.27 (95% CI 1.13-1.43). The five year probability of RRT was highest in younger patients (<65 years) with fast initial progression rate (51% in CKD stage 4 and 76% in stage 5), low overall in patients >75 years with a slow progression rate (7, 13, and 25% for CKD stages 3b, 4 and 5 respectively), and slightly higher in elderly patients with a fast initial progression rate (28% in CKD stage 4 and 47% in CKD stage 5) or with diabetic kidney disease. CONCLUSIONS: The 5-year probability of RRT was low among referred slowly progressing CKD patients >75 years of age because of the competing risk of death.

Prevalence and recognition of chronic kidney disease in Stockholm healthcare.

BACKGROUND: Chronic kidney disease (CKD) is common, but the frequency of albuminuria testing and referral to nephrology care has been difficult to measure. We here characterize CKD prevalence and recognition in a complete healthcare utilization cohort of the Stockholm region, in Sweden. METHODS: We included all adult individuals (n = 1 128 058) with at least one outpatient measurement of IDMS-calibrated serum creatinine during 2006-11. Estimated glomerular filtration rate (eGFR) was calculated via the CKD-EPI equation and CKD was solely defined as eGFR <60 mL/min/1.73 m2. We also assessed the performance of diagnostic testing (albuminuria), nephrology consultations, and utilization of ICD-10 diagnoses. RESULTS: A total of 68 894 individuals had CKD, with a crude CKD prevalence of 6.11% [95% confidence interval (CI): 6.07-6.16%] and a prevalence standardized to the European population of 5.38% (5.33-5.42%). CKD was more prevalent among the elderly (28% prevalence >75 years old), women (6.85 versus 5.24% in men), and individuals with diabetes (17%), hypertension (17%) or cardiovascular disease (31%). The frequency of albuminuria monitoring was low, with 38% of diabetics and 27% of CKD individuals undergoing albuminuria testing over 2 years. Twenty-three per cent of the 16 383 individuals satisfying selected KDIGO criteria for nephrology referral visited a nephrologist. Twelve per cent of CKD patients carried an ICD-10 diagnostic code of CKD. CONCLUSIONS: An estimated 6% of the adult Stockholm population accessing healthcare has CKD, but the frequency of albuminuria testing, nephrology consultations and registration of CKD diagnoses was suboptimal despite universal care. Improving provider awareness and treatment of CKD could have a significant public health impact.

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.

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.

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.

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.

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.