| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Kang, JS, et al.
(författare)
-
Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning
- 2020
-
Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1, s. 20140-
-
Tidskriftsartikel (refereegranskat)abstract
- Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diameter, cyst size, mural nodule, and tumour location were factors considered for model development (MD). After the division into a MD set and a test set (2:1), the best ML and LR models were developed by training with the MD set using a tenfold cross validation. The test area under the receiver operating curves (AUCs) of the two models were calculated using an independent test set. A total of 3,708 patients were included. The stacked ensemble algorithm in the ML model and variable combinations containing all variables in the LR model were the most chosen during 200 repetitions. After 200 repetitions, the mean AUCs of the ML and LR models were comparable (0.725 vs. 0.725). The performances of the ML and LR models were comparable. The LR model was more practical than ML counterpart, because of its convenience in clinical use and simple interpretability.
|
|
| 6. |
- Khatri, C, et al.
(författare)
-
Outcomes after perioperative SARS-CoV-2 infection in patients with proximal femoral fractures: an international cohort study
- 2021
-
Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 11:11, s. e050830-
-
Tidskriftsartikel (refereegranskat)abstract
- Studies have demonstrated high rates of mortality in people with proximal femoral fracture and SARS-CoV-2, but there is limited published data on the factors that influence mortality for clinicians to make informed treatment decisions. This study aims to report the 30-day mortality associated with perioperative infection of patients undergoing surgery for proximal femoral fractures and to examine the factors that influence mortality in a multivariate analysis.SettingProspective, international, multicentre, observational cohort study.ParticipantsPatients undergoing any operation for a proximal femoral fracture from 1 February to 30 April 2020 and with perioperative SARS-CoV-2 infection (either 7 days prior or 30-day postoperative).Primary outcome30-day mortality. Multivariate modelling was performed to identify factors associated with 30-day mortality.ResultsThis study reports included 1063 patients from 174 hospitals in 19 countries. Overall 30-day mortality was 29.4% (313/1063). In an adjusted model, 30-day mortality was associated with male gender (OR 2.29, 95% CI 1.68 to 3.13, p<0.001), age >80 years (OR 1.60, 95% CI 1.1 to 2.31, p=0.013), preoperative diagnosis of dementia (OR 1.57, 95% CI 1.15 to 2.16, p=0.005), kidney disease (OR 1.73, 95% CI 1.18 to 2.55, p=0.005) and congestive heart failure (OR 1.62, 95% CI 1.06 to 2.48, p=0.025). Mortality at 30 days was lower in patients with a preoperative diagnosis of SARS-CoV-2 (OR 0.6, 95% CI 0.6 (0.42 to 0.85), p=0.004). There was no difference in mortality in patients with an increase to delay in surgery (p=0.220) or type of anaesthetic given (p=0.787).ConclusionsPatients undergoing surgery for a proximal femoral fracture with a perioperative infection of SARS-CoV-2 have a high rate of mortality. This study would support the need for providing these patients with individualised medical and anaesthetic care, including medical optimisation before theatre. Careful preoperative counselling is needed for those with a proximal femoral fracture and SARS-CoV-2, especially those in the highest risk groups.Trial registration numberNCT04323644
|
|
| 7. |
- Kim, K, et al.
(författare)
-
Variation in the ICAM1-ICAM4-ICAM5 locus is associated with systemic lupus erythematosus susceptibility in multiple ancestries
- 2012
-
Ingår i: Annals of the rheumatic diseases. - : BMJ. - 1468-2060 .- 0003-4967. ; 71:11, s. 1809-1814
-
Tidskriftsartikel (refereegranskat)abstract
- Systemic lupus erythematosus (SLE; OMIM 152700) is a chronic autoimmune disease for which the aetiology includes genetic and environmental factors. ITGAM, integrin αM (complement component 3 receptor 3 subunit) encoding a ligand for intracellular adhesion molecule (ICAM) proteins, is an established SLE susceptibility locus. This study aimed to evaluate the independent and joint effects of genetic variations in the genes that encode ITGAM and ICAM.MethodsThe authors examined several markers in the ICAM1–ICAM4–ICAM5 locus on chromosome 19p13 and the single ITGAM polymorphism (rs1143679) using a large-scale case–control study of 17 481 unrelated participants from four ancestry populations. The single-marker association and gene–gene interaction were analysed for each ancestry, and a meta-analysis across the four ancestries was performed.ResultsThe A-allele of ICAM1–ICAM4–ICAM5 rs3093030, associated with elevated plasma levels of soluble ICAM1, and the A-allele of ITGAM rs1143679 showed the strongest association with increased SLE susceptibility in each of the ancestry populations and the trans-ancestry meta-analysis (ORmeta=1.16, 95% CI 1.11 to 1.22; p=4.88×10−10 and ORmeta=1.67, 95% CI 1.55 to 1.79; p=3.32×10−46, respectively). The effect of the ICAM single-nucleotide polymorphisms (SNPs) was independent of the effect of the ITGAM SNP rs1143679, and carriers of both ICAM rs3093030-AA and ITGAM rs1143679-AA had an OR of 4.08 compared with those with no risk allele in either SNP (95% CI 2.09 to 7.98; p=3.91×10−5).ConclusionThese findings are the first to suggest that an ICAM–integrin-mediated pathway contributes to susceptibility to SLE.
|
|
| 8. |
|
|
| 9. |
|
|
| 10. |
|
|
| 11. |
- Thomas, HS, et al.
(författare)
-
- 2019
-
swepub:Mat__t
|
|
| 12. |
- Glasbey, JC, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 13. |
- Bravo, L, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 14. |
- Niemi, MEK, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 15. |
- Tabiri, S, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 16. |
|
|
| 17. |
|
|
| 18. |
|
|
| 19. |
|
|
| 20. |
|
|
| 21. |
|
|
| 22. |
|
|
| 23. |
|
|
| 24. |
|
|
| 25. |
|
|
