| 1. |
- Bergman, Lina, 1982, et al.
(author)
-
Study for Improving Maternal Pregnancy And Child ouTcomes (IMPACT): a study protocol for a Swedish prospective multicentre cohort study
- 2020
-
In: BMJ Open. - : BMJ. - 2044-6055 .- 2044-6055. ; 10:9, s. e033851-e033851
-
Journal article (peer-reviewed)abstract
- Introduction First-trimester pregnancy risk evaluation facilitates individualised antenatal care, as well as application of preventive strategies for pre-eclampsia or birth of a small for gestational age infant. A range of early intervention strategies in pregnancies identified as high risk at the end of the first trimester has been shown to decrease the risk of preterm pre-eclampsia (<37 gestational weeks). The aim of this project is to create the Improving Maternal Pregnancy And Child ouTcomes (IMPACT) database; a nationwide database with individual patient data, including predictors recorded at the end of the first trimester and later pregnancy outcomes, to identify women at high risk of pre-eclampsia. A second aim is to link the IMPACT database to a biobank with first-trimester blood samples. Methods and analysis This is a Swedish prospective multicentre cohort study. Women are included between the 11th and 14th weeks of pregnancy. At inclusion, pre-identified predictors are retrieved by interviews and medical examinations. Blood samples are collected and stored in a biobank. Additional predictors and pregnancy outcomes are retrieved from the Swedish Pregnancy Register. Inclusion in the study began in November 2018 with a targeted sample size of 45 000 pregnancies by end of 2021. Creation of a new risk prediction model will then be developed, validated and implemented. The database and biobank will enable future research on prediction of various pregnancy-related complications. Ethics and dissemination Confidentiality aspects such as data encryption and storage comply with the General Data Protection Regulation and with ethical committee requirements. This study has been granted national ethical approval by the Swedish Ethical Review Authority (Uppsala 2018-231) and national biobank approval at Uppsala Biobank (18237 2 2018 231). Results from the current as well as future studies using information from the IMPACT database will be published in peer-reviewed journals.
|
|
| 2. |
- Carlsson, Ylva, 1975, et al.
(author)
-
Comparing the results from a Swedish pregnancy cohort using data from three automated placental growth factor immunoassay platforms intended for first-trimester preeclampsia prediction.
- 2023
-
In: Acta Obstetricia et Gynecologica Scandinavica. - : Wiley. - 0001-6349 .- 1600-0412. ; :8, s. 1084-1091
-
Journal article (peer-reviewed)abstract
- INTRODUCTION: Risk evaluation for preeclampsia in early pregnancy allows identification of women at high risk. Prediction models for preeclampsia often include circulating concentrations of placental growth factor (PlGF); however, the models are usually limited to a specific PlGF method of analysis. The aim of this study was to compare three different PlGF methods of analysis in a Swedish cohort to assess their convergent validity and appropriateness for use in preeclampsia risk prediction models in the first trimester of pregnancy.MATERIAL AND METHODS: First-trimester blood samples were collected in gestational week 11+0 to 13+6 from 150 pregnant women at Uppsala University Hospital during November 2018 until November 2020. These samples were analyzed using the different PlGF methods from Perkin Elmer, Roche Diagnostics, and Thermo Fisher Scientific.RESULTS: There were strong correlations between the PlGF results obtained with the three methods, but the slopes of the correlations clearly differed from 1.0: PlGFPerkinElmer = 0.553 (95% confidence interval [CI] 0.518-0.588) * PlGFRoche -1.112 (95% CI -2.773 to 0.550); r = 0.966, mean difference -24.6 (95% CI -26.4 to -22.8). PlGFPerkinElmer = 0.673 (95% CI 0.618-0.729) * PlGFThermoFisher -0.199 (95% CI -2.292 to 1.894); r = 0.945, mean difference -13.8 (95% CI -15.1 to -12.6). PlGFRoche = 1.809 (95% CI 1.694-1.923) * PlGFPerkinElmer +2.010 (95% CI -0.877 to 4.897); r = 0.966, mean difference 24.6 (95% CI 22.8-26.4). PlGFRoche = 1.237 (95% CI 1.113-1.361) * PlGFThermoFisher +0.840 (95% CI -3.684 to 5.363); r = 0.937, mean difference 10.8 (95% CI 9.4-12.1). PlGFThermoFisher = 1.485 (95% CI 1.363-1.607) * PlGFPerkinElmer +0.296 (95% CI -2.784 to 3.375); r = 0.945, mean difference 13.8 (95% CI 12.6-15.1). PlGFThermoFisher = 0.808 (95% CI 0.726-0.891) * PlGFRoche -0.679 (95% CI -4.456 to 3.099); r = 0.937, mean difference -10.8 (95% CI -12.1 to -9.4).CONCLUSION: The three PlGF methods have different calibrations. This is most likely due to the lack of an internationally accepted reference material for PlGF. Despite different calibrations, the Deming regression analysis indicated good agreement between the three methods, which suggests that results from one method may be converted to the others and hence used in first-trimester prediction models for preeclampsia.
|
|
| 3. |
|
|
| 4. |
- Göteson, Andreas, 1991, et al.
(author)
-
Cerebrospinal fluid proteomics targeted for central nervous system processes in bipolar disorder
- 2021
-
In: Molecular Psychiatry. - : Springer Science and Business Media LLC. - 1359-4184 .- 1476-5578. ; 26, s. 7446-53
-
Journal article (peer-reviewed)abstract
- The etiopathology of bipolar disorder is largely unknown. We collected cerebrospinal fluid (CSF) samples from two independent case-control cohorts (total n = 351) to identify proteins associated with bipolar disorder. A panel of 92 proteins targeted towards central nervous system processes identified two proteins that replicated across the cohorts: the CSF concentrations of testican-1 were lower, and the CSF concentrations of C-type lectin domain family 1 member B (CLEC1B) were higher, in cases than controls. In a restricted subgroup analysis, we compared only bipolar type 1 with controls and identified two additional proteins that replicated in both cohorts: draxin and tumor necrosis factor receptor superfamily member 21 (TNFRSF21), both lower in cases than controls. This analysis additionally revealed several proteins significantly associated with bipolar type 1 in one cohort, falling just short of replicated statistical significance in the other (tenascin-R, disintegrin and metalloproteinase domain-containing protein 23, cell adhesion molecule 3, RGM domain family member B, plexin-B1, and brorin). Next, we conducted genome-wide association analyses of the case-control-associated proteins. In these analyses, we found associations with the voltage-gated calcium channel subunit CACNG4, and the lipid-droplet-associated gene PLIN5 with CSF concentrations of TNFRSF21 and CLEC1B, respectively. The reported proteins are involved in neuronal cell-cell and cell-matrix interactions, particularly in the developing brain, and in pathways of importance for lithium's mechanism of action. In summary, we report four novel CSF protein associations with bipolar disorder that replicated in two independent case-control cohorts, shedding new light on the central nervous system processes implicated in bipolar disorder.
|
|
| 5. |
- Nilsen, Per, 1960-, et al.
(author)
-
Realizing the potential of artificial intelligence in healthcare : Learning from intervention, innovation, implementation and improvement sciences
- 2022
-
In: Frontiers in Health Services. - Lausanne : Frontiers Media S.A.. - 2813-0146. ; 2
-
Journal article (peer-reviewed)abstract
- Introduction: Artificial intelligence (AI) is widely seen as critical for tackling fundamental challenges faced by health systems. However, research is scant on the factors that influence the implementation and routine use of AI in healthcare, how AI may interact with the context in which it is implemented, and how it can contribute to wider health system goals. We propose that AI development can benefit from knowledge generated in four scientific fields: intervention, innovation, implementation and improvement sciences.Aim: The aim of this paper is to briefly describe the four fields and to identify potentially relevant knowledge from these fields that can be utilized for understanding and/or facilitating the use of AI in healthcare. The paper is based on the authors' experience and expertise in intervention, innovation, implementation, and improvement sciences, and a selective literature review.Utilizing knowledge from the four fields: The four fields have generated a wealth of often-overlapping knowledge, some of which we propose has considerable relevance for understanding and/or facilitating the use of AI in healthcare.Conclusion: Knowledge derived from intervention, innovation, implementation, and improvement sciences provides a head start for research on the use of AI in healthcare, yet the extent to which this knowledge can be repurposed in AI studies cannot be taken for granted. Thus, when taking advantage of insights in the four fields, it is important to also be explorative and use inductive research approaches to generate knowledge that can contribute toward realizing the potential of AI in healthcare. © 2022 Nilsen, Reed, Nair, Savage, Macrae, Barlow, Svedberg, Larsson, Lundgren and Nygren.
|
|
| 6. |
- Severinsson, Yvonne, 1957, et al.
(author)
-
Components of primary care multimodal rehabilitation and their association with changes in sick leave: An observational study.
- 2023
-
In: Work (Reading, Mass.). - 1875-9270. ; 74:3, s. 907-917
-
Journal article (peer-reviewed)abstract
- To address the increase in sick leave for nonspecific chronic pain and mental illness, the Swedish government and the Swedish Association of Local Authorities and Regions entered into an agreement on a "Rehabilitation Guarantee" to carry out multimodal rehabilitation (MMR).To investigate whether components of primary care MMR are associated with changes in sick leave.A web-based survey was conducted in conjunction with a retrospective cross-sectional observational study of 53 MMR units. Sick leave data for the years before and after MMR completion was collected for 846 individuals.There was great disparity in how MMR was delivered. The average duration of rehabilitation was 4-8 weeks, and 74% of the MMR teams reported having fewer patients than recommended (≥20/year). Only 58% of the teams met the competence requirements. In-depth competence in pain relief and rehabilitation was reported by 45% of the teams and was significantly associated with fewer sick leave days after MMR (26.53, 95% CI: 3.65; 49.42), as were pain duration (17.83, 95% CI: -9.20; 44.87) and geographic proximity (23.75, 95% CI: -5.25; 52.75) of the health care professionals included in the MMR unit.In-depth competence and knowledge about the complex health care needs of patients seem essential to MMR teams' success in reducing sickness benefits for patients with nonspecific chronic pain and mental illness. Further research is needed to elucidate the optimal combination of primary care MMR components for increasing the return-to work rate and to determine whether involvement of the Social Insurance Agency or employers could support and further contribute to recuperation and help patients regain their previous work capacity.
|
|
