Arvid Sjölander’s research while affiliated with University of Gothenburg and other places

Background Chronic kidney disease (CKD) is a global health problem affected by under-recognition and under-treatment in primary care settings. Electronic clinical decision support (CDS) triggering systems have the potential to improve detection and management of people with CKD by assisting clinicians in adhering to guideline recommendations. We aimed to test whether an electronic CDS triggering system would improve the detection, recognition, and management of patients with CKD in primary care. Method/Design This is a pragmatic cluster-randomized controlled trial where 66 primary healthcare centers from the Stockholm Region, Sweden were randomized 1:1 to receive either a new expanded CDS-triggering system offering kidney-specific advice or to continue with their current CDS-triggering system. The expanded CDS system reminds and provides practical facilitators of the processes of CKD screening, recognition with a diagnosis, management and referral to specialist care. The trial duration is 24 months and it is embedded into the Stockholm CREAtinine measurements (SCREAM) project, a repository of healthcare data from the region, which minimizes disturbances with healthcare praxis due to the trial and makes it fully pragmatic. The primary outcomes are the number of eligible patients screened for creatinine and albuminuria once annually and the re-testing of these labs within 6 months in patients with abnormal eGFR or albuminuria. Secondary outcomes are the proportions of issued clinical diagnoses among those fulfilling criteria, proportions of patients with significant albuminuria receiving prescribed nephroprotective medications, proportions of accepted referrals to nephrologist care among those fulfilling criteria and proportion of referrals for ultrasound of the kidneys. Discussion Prior pragmatic trials of CDS-systems in CKD has shown an improvement in quality indicators primarily in patients already diagnosed with CKD. This study expands this evidence by focusing on the process of screening, identification, monitoring and diagnostic work-up. Conclusion This pragmatic trial will assess the value of CDS for improved adherence to CKD guidelines in primary care. Clinicaltrials.gov registration: NCT06386172, submitted 2024-04-23.

Background Knowledge of which medications may lead to acute kidney injury (AKI) is limited, relying mostly on spontaneous reporting in pharmacovigilance systems. We here conducted an exploratory drug-wide association study (DWAS) to screen for associations between dispensed drugs and AKI risk. Methods Using two large Danish and Swedish data linkages, we identified AKI hospitalizations occurring between April 1997 to December 2021 in Denmark and between March 2007 to December 2021 in Sweden. We used a case-time control design comparing drug dispensing in the three months prior to the AKI with earlier periods for the same patient. Odds Ratios (ORs) for the association between each drug and AKI were estimated using conditional logistic regression and adjusting for the presence of comorbidities. We sought replication of signals in both health systems and explored the plausibility of findings through pharmacovigilance system analysis in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database, appearance in the RESCUE list of medications that report AKI as a side effect, PUBMED evidence review and causality assessment through direct acyclic graphs. Results We included 20 622 adults in Denmark and 13 852 in Sweden hospitalized for AKI. In total, 16 unique medications were identified in both cohorts as associated to increased AKI occurrence. Of these, 10 medications had higher reporting odd ratios in the FAERS database, and 9 were listed by RESCUE or appeared in PUBMED. This analysis identified some medications with known AKI risks (i.e. likely true positives such as furosemide, penicillin, spironolactone, and omeprazole), medications that may have initiated in response to conditions that lead to AKI (i.e. false positives like metoclopramide provided to treat nausea/vomiting) and other candidates (e.g. opioids) that warrant further evaluation in subsequent studies. Conclusions This hypothesis-generating study identifies medications with potential involvement in AKI that require confirmation and validation.

In instrumental variable (IV) settings, such as imperfect randomized trials and observational studies with Mendelian randomization, one may encounter a continuous exposure, the causal effect of which is not of true interest. Instead, scientific interest may lie in a coarsened version of this exposure. Although there is a lengthy literature on the impact of coarsening of an exposure with several works focusing specifically on IV settings, all methods proposed in this literature require parametric assumptions. Instead, just as in the standard IV setting, one can consider partial identification via bounds making no parametric assumptions. This was first pointed out in Alexander Balke’s PhD dissertation. We extend and clarify his work and derive novel bounds in several settings, including for a three-level IV, which will most likely be the case in Mendelian randomization. We demonstrate our findings in two real data examples, a randomized trial for peanut allergy in infants and a Mendelian randomization setting investigating the effect of homocysteine on cardiovascular disease.

Aims About 10%–30% of individuals with obesity are metabolically healthy, but the specific characteristics of the metabolically healthy obesity (MHO) phenotype remain unclear. We aimed to examine how physical activity, education, depressive symptoms and genetic predisposition to obesity differ between individuals with MHO and those with metabolically unhealthy obesity (MUO), and whether these factors predict stability in MHO or conversion to a metabolically unhealthy state. Materials and Methods We retrieved data on 9809 individuals with obesity from the Health and Retirement Study collected between 2006 and 2016. We compared how physical activity, education, depressive symptoms and a polygenic score for higher body mass index (BMI) (PGS BMI ) differed cross‐sectionally between MHO and MUO using logistic regression. We then examined if the same factors predict conversion to a metabolically unhealthy state over 4 years in individuals with MHO. Results Individuals with MHO had higher physical activity (odds ratio [OR] = 0.81), higher education (OR = 0.83) and lower depressive symptoms (OR = 1.14) compared to those with MUO but did not differ in the PGS BMI . The associations were slightly attenuated in mutually adjusted models. None of the factors were associated with conversion from MHO to a metabolically unhealthy state. However, a higher PGS BMI indicated 24% lower risk of conversion to a metabolically unhealthy state ( p = 0.07). Conclusions Physical activity, education and depressive symptoms differed between MHO and MUO, even when mutually adjusted for, but did not predict conversion from a metabolically healthy to unhealthy state. Although not statistically significant, the results indicated that those with genetically predicted high BMI are more likely to maintain MHO and not convert to a metabolically unhealthy state.

The number needed to treat (NNT) is an efficacy and effect size measure commonly used in epidemiological studies and meta-analyses. The NNT was originally defined as the average number of patients needed to be treated to observe one less adverse effect. In this study, we introduce the novel direct and indirect number needed to treat (DNNT and INNT, respectively). The DNNT and the INNT are efficacy measures defined as the average number of patients that need to be treated to benefit from the treatment’s direct and indirect effects, respectively. We start by formally defining these measures using nested potential outcomes. Next, we formulate the conditions for the identification of the DNNT and INNT, as well as for the direct and indirect number needed to expose (DNNE and INNE, respectively) and the direct and indirect exposure impact number (DEIN and IEIN, respectively) in observational studies. Next, we present an estimation method with two analytical examples. A corresponding simulation study follows the examples. The simulation study illustrates that the estimators of the novel indices are consistent, and their confidence intervals meet the nominal coverage rates.

Introduction Children with parents with psychiatric diagnoses have an increased probability for not only the same condition as their parent, but also for other conditions and behavioral and psychosocial problems. Whereas many studies have focused on parental severe mental illness due to their significant impairment, less attention has been paid to more common disorders despite their higher prevalence. In addition, because most past research only included one exposure or one outcome at a time, it remains difficult to examine and compare broad patterns of intergenerational transmission. Objectives To examine associations between six parental psychiatric diagnoses in parents, and a broad range of psychiatric diagnoses, psychotropic medications, criminality, suicide, violent victimization, accidents, and school and labor performance in their offspring. Methods Based on Swedish national registers, we linked all individuals born in Sweden between 1970 and 2000 to their biological parents ( N = 3 286 293). We used a matched cohort design, analyzed with stratified Cox regression and conditional logistic regressions to examine associations between six psychiatric diagnoses in the parents, and 32 outcomes in their offspring. All exposed and unexposed children were followed from their date of birth to the date of emigration from Sweden, the death, or 31 December 2013 when the offspring were 14-44 years old. Results In terms of absolute risk, most children who had parents with psychiatric diagnoses were not diagnosed in specialist care themselves, as the proportion of having any of the 16 types of psychiatric conditions ranged from 22.17% (exposed to parental depression) to 25.05% (exposed to parental drug-related disorders) at the end of follow-up. Nevertheless, in terms of relative risk, all six parental psychiatric diagnoses increased the probability of all 32 outcomes in their offspring, with the Hazard Ratio ranging from 1.04 to 8.91 for time-to-event outcomes, and the Odds Ratio ranging from 1.29 to 3.36 for binary outcomes. Some specificities were observed for parental psychotic and substance misuse diagnoses, which strongly predicted offspring psychotic-like and externalizing-related outcomes, respectively. Conclusions The intergenerational transmission of parental psychiatric conditions appeared largely transdiagnostic, even for non-psychiatric outcomes in offspring. Given the broad spectrum of associations with the outcomes, service providers (e.g., psychiatrists, teachers, and social workers) should consider clients’ broader psychiatric family history when predicting prognosis and planning interventions/treatment. Disclosure of Interest None Declared

Objectives: The Number Needed to Treat (NNT) is an efficacy index defined as the average number of patients needed to treat to attain one additional treatment benefit. In observational studies, specifically in epidemiology, the adequacy of the populationwise NNT is questionable since the exposed group characteristics may substantially differ from the unexposed. To address this issue, groupwise efficacy indices were defined: the Exposure Impact Number (EIN) for the exposed group and the Number Needed to be Exposed (NNE) for the unexposed. Each defined index answers a unique research question since it targets a unique sub-population. In observational studies, the group allocation is typically affected by confounders that might be unmeasured. The available estimation methods that rely either on randomization or the sufficiency of the measured covariates for confounding control result in statistically inconsistent estimators of the true EIN, NNE, and NNT. This study presents a theoretical framework for statistically consistent point and interval estimation of the NNE, EIN and NNE in observational studies with unmeasured confounders. Methods: Using Rubin's potential outcomes framework, this study explicitly defines the NNT and its derived indices, EIN and NNE, as causal measures. Then, we use instrumental variables to introduce a novel method to estimate the three aforementioned indices in observational studies where the omission of unmeasured con-founders cannot be ruled out. To illustrate the novel methods, we present two analytical examples-double logit and double probit models. Next, a corresponding simulation study and a real-world data example are presented. Results: This study provides an explicit causal formulation of the EIN, NNE, and NNT indices and a comprehensive theoretical framework for their point and interval estimation using the G-estimators in observational studies with unmeasured confounders. The analytical proofs and the corresponding simulation study illustrate the improved performance of the new estimation method compared to the available methods in terms of consistency and the confidence intervals empirical coverage rates. Conclusions: In observational studies, traditional estimation methods to estimate the EIN, NNE, or NNT result in statistically inconsistent estimators. We introduce a novel estimation method that overcomes this pitfall. The novel method produces consistent estimators and reliable CIs for the true EIN, NNE, and NNT. Such a method may facilitate more accurate clinical decision-making and the development of efficient public health policies.

Objectives The Number Needed to Treat (NNT) is an efficacy index defined as the average number of patients needed to treat to attain one additional treatment benefit. In observational studies, specifically in epidemiology, the adequacy of the populationwise NNT is questionable since the exposed group characteristics may substantially differ from the unexposed. To address this issue, groupwise efficacy indices were defined: the Exposure Impact Number (EIN) for the exposed group and the Number Needed to be Exposed (NNE) for the unexposed. Each defined index answers a unique research question since it targets a unique sub-population. In observational studies, the group allocation is typically affected by confounders that might be unmeasured. The available estimation methods that rely either on randomization or the sufficiency of the measured covariates for confounding control result in statistically inconsistent estimators of the true EIN, NNE, and NNT. This study presents a theoretical framework for statistically consistent point and interval estimation of the NNE, EIN and NNE in observational studies with unmeasured confounders. Methods Using Rubin’s potential outcomes framework, this study explicitly defines the NNT and its derived indices, EIN and NNE, as causal measures. Then, we use instrumental variables to introduce a novel method to estimate the three aforementioned indices in observational studies where the omission of unmeasured confounders cannot be ruled out. To illustrate the novel methods, we present two analytical examples – double logit and double probit models. Next, a corresponding simulation study and a real-world data example are presented. Results This study provides an explicit causal formulation of the EIN, NNE, and NNT indices and a comprehensive theoretical framework for their point and interval estimation using the G-estimators in observational studies with unmeasured confounders. The analytical proofs and the corresponding simulation study illustrate the improved performance of the new estimation method compared to the available methods in terms of consistency and the confidence intervals empirical coverage rates. Conclusions In observational studies, traditional estimation methods to estimate the EIN, NNE, or NNT result in statistically inconsistent estimators. We introduce a novel estimation method that overcomes this pitfall. The novel method produces consistent estimators and reliable CIs for the true EIN, NNE, and NNT. Such a method may facilitate more accurate clinical decision-making and the development of efficient public health policies.

Importance Hyperkalemia is a common complication in people with type 2 diabetes (T2D) that may limit the use of guideline-recommended renin-angiotensin system inhibitors (RASis). Emerging evidence suggests that glucagon-like peptide-1 receptor agonists (GLP-1RAs) increase urinary potassium excretion, which may translate into reduced hyperkalemia risk. Objective To compare rates of hyperkalemia and RASi persistence among new users of GLP-1RAs vs dipeptidyl peptidase-4 inhibitors (DPP-4is). Design, Setting, and Participants This cohort study included all adults with T2D in the region of Stockholm, Sweden, who initiated GLP-1RA or DPP-4i treatment between January 1, 2008, and December 31, 2021. Analyses were conducted between October 1, 2023, and April 29, 2024. Exposures GLP-1RAs or DPP-4is. Main Outcomes and Measures The primary study outcome was time to any hyperkalemia (potassium level >5.0 mEq/L) and moderate to severe (potassium level >5.5 mEq/L) hyperkalemia. Time to discontinuation of RASi use among individuals using RASis at baseline was assessed. Inverse probability of treatment weights served to balance more than 70 identified confounders. Marginal structure models were used to estimate per-protocol hazard ratios (HRs). Results A total of 33 280 individuals (13 633 using GLP-1RAs and 19 647 using DPP-4is; mean [SD] age, 63.7 [12.6] years; 19 853 [59.7%] male) were included. The median (IQR) time receiving treatment was 3.9 (1.0-10.9) months. Compared with DPP-4i use, GLP-1RA use was associated with a lower rate of any hyperkalemia (HR, 0.61; 95% CI, 0.50-0.76) and moderate to severe (HR, 0.52; 95% CI, 0.28-0.84) hyperkalemia. Of 21 751 participants who were using RASis, 1381 discontinued this therapy. The use of GLP-1RAs vs DPP-4is was associated with a lower rate of RASi discontinuation (HR, 0.89; 95% CI, 0.82-0.97). Results were consistent in intention-to-treat analyses and across strata of age, sex, cardiovascular comorbidity, and baseline kidney function. Conclusions In this study of patients with T2D managed in routine clinical care, the use of GLP-1RAs was associated with lower rates of hyperkalemia and sustained RASi use compared with DPP-4i use. These findings suggest that GLP-1RA treatment may enable wider use of guideline-recommended medications and contribute to clinical outcomes in this population.