| 1. |
- Avdic, Tarik, et al.
(författare)
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Non-coronary arterial outcomes in people with type 1 diabetes mellitus: a Swedish retrospective cohort study.
- 2024
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Ingår i: The Lancet regional health - Europe. - 2666-7762. ; 39
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Tidskriftsartikel (refereegranskat)abstract
- Observational studies on long-term trends, risk factor association and importance are scarce for type 1 diabetes mellitus and peripheral arterial outcomes. We set out to investigate trends in non-coronary complications and their relationships with cardiovascular risk factors in persons with type 1 diabetes mellitus compared to matched controls.34,263 persons with type 1 diabetes mellitus from the Swedish National Diabetes Register and 164,063 matched controls were included. Incidence rates of extracranial large artery disease, aortic aneurysm, aortic dissection, lower extremity artery disease, and diabetic foot syndrome were analyzed using standardized incidence rates and Cox regression.Between 2001 and 2019, type 1 diabetes mellitus incidence rates per 100,000 person-years were as follows: extracranial large artery disease 296.5-84.3, aortic aneurysm 0-9.2, aortic dissection remained at 0, lower extremity artery disease 456.6-311.1, and diabetic foot disease 814.7-77.6. Persons with type 1 diabetes mellitus with cardiometabolic risk factors at target range did not exhibit excess risk of extracranial large artery disease [HR 0.83 (95% CI, 0.20-3.36)] or lower extremity artery disease [HR 0.94 (95% CI, 0.30-2.93)], compared to controls. Persons with type 1 diabetes with all risk factors at baseline, had substantially elevated risk for diabetic foot disease [HR 29.44 (95% CI, 3.83-226.04)], compared to persons with type 1 diabetes with no risk factors. Persons with type 1 diabetes mellitus continued to display a lower risk for aortic aneurysm, even with three cardiovascular risk factors at baseline [HR 0.31 (95% CI, 0.15-0.67)]. Relative importance analyses demonstrated that education, glycated hemoglobin (HbA1c), duration of diabetes and lipids explained 54% of extracranial large artery disease, while HbA1c, smoking and systolic blood pressure explained 50% of lower extremity artery disease and HbA1c alone contributed to 41% of diabetic foot disease. Income, duration of diabetes and body mass index explained 66% of the contribution to aortic aneurysm.Peripheral arterial complications decreased in persons with type 1 diabetes mellitus, except for aortic aneurysm which remained low. Besides glycemic control, traditional cardiovascular risk factors were associated with incident outcomes. Risk of these outcomes increased with additional risk factors present. Persons with type 1 diabetes mellitus exhibited a lower risk of aortic aneurysm compared to controls, despite presence of cardiovascular risk factors.Swedish Governmental and the county support of research and education of doctors, the Swedish Heart and Lung Foundation, Sweden and Åke-Wibergs grant.
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| 2. |
- Berglund, Sara, et al.
(författare)
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Cardiorenal function and survival in in-hospital cardiac arrest : A nationwide study of 22,819 cases
- 2022
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Ingår i: Resuscitation. - : Elsevier BV. - 0300-9572 .- 1873-1570. ; 172, s. 9-16
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Tidskriftsartikel (refereegranskat)abstract
- Background: We studied the association between cardiorenal function and survival, neurological outcome and trends in survival after in-hospital Methods: We included cases aged 18 years in the Swedish Cardiopulmonary Resuscitation Registry during 2008 to 2020. The CKD-EPI equation was used to calculate estimated glomerular filtration rate (eGFR). A history of heart failure was defined according to contemporary guideline criteria. Logistic regression was used to study survival. Neurological outcome was assessed using cerebral performance category (CPC). Results: We studied 22,819 patients with IHCA. The 30-day survival was 19.3%, 16.6%, 22.5%, 28.8%, 39.3%, 44.8% and 38.4% in cases with eGFR < 15, 15-29, 30-44, 45-59, 60-89, 90-130 and 130-150 ml/min/1.73 m2, respectively. All eGFR levels below and above 90 ml/min/1.73 m2 were associated with increased mortality. Probability of survival at 30 days was 62% lower in cases with eGFR < 15 ml/min/1.73 m2, compared with normal kidney function. At every level of eGFR, presence of heart failure increased mortality markedly; patients without heart failure displayed higher mortality only at eGFR below 30 ml/min/1.73 m2. Among survivors with eGFR < 15 ml/min/1.73 m2, good neurological outcome was noted in 87.2%. Survival increased in most groups over time, but most for those with eGFR < 15 ml/min/1.73 m2, and least for those with normal eGFR. Conclusions: All eGFR levels below and above normal range are associated with increased mortality and this association is modified by the presence of heart failure. Neurological outcome is good in the majority of cases, across kidney function levels and survival is increasing.
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| 3. |
- Bjornsdottir, H. H., et al.
(författare)
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A national observation study of cancer incidence and mortality risks in type 2 diabetes compared to the background population over time
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- We examined changing patterns in cancer incidence and deaths in diabetes compared to the background population. A total of 457,473 patients with type 2 diabetes, included between 1998 and 2014, were matched on age, sex, and county to five controls from the population. Incidence, trends in incidence and post-cancer mortality for cancer were estimated with Cox regression and standardised incidence rates. Causes of death were estimated using logistic regression. Relative importance of risk factors was estimated using Heller's relative importance model. Type 2 diabetes had a higher risk for all cancer, HR 1.10 (95% CI 1.09-1.12), with highest HRs for liver (3.31), pancreas (2.19) and uterine cancer (1.78). There were lesser increases in risk for breast (1.05) and colorectal cancers (1.20). Type 2 diabetes patients experienced a higher HR 1.23 (1.21-1.25) of overall post-cancer mortality and mortality from prostate, breast, and colorectal cancers. By the year 2030 cancer could become the most common cause of death in type 2 diabetes. Persons with type 2 diabetes are at greater risk of developing cancer and lower chance of surviving it. Notably, hazards for specific cancers (e.g. liver, pancreas) in type 2 patients cannot be explained by obesity alone.
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| 4. |
- Dejby, Ellen, et al.
(författare)
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Left-sided valvular heart disease and survival in out-of-hospital cardiac arrest: a nationwide registry-based study.
- 2023
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Ingår i: Scientific reports. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Survival in left-sided valvular heart disease (VHD; aortic stenosis [AS], aortic regurgitation [AR], mitral stenosis [MS], mitral regurgitation [MR]) in out-of-hospital cardiac arrest (OHCA) is unknown. We studied all cases of OHCA in the Swedish Registry for Cardiopulmonary Resuscitation. All degrees of VHD, diagnosed prior to OHCA, were included. Association between VHD and survival was studied using logistic regression, gradient boosting and Cox regression. We studied time to cardiac arrest, comorbidities, survival, and cerebral performance category (CPC) score. We included 55,615 patients; 1948 with AS (3,5%), 384 AR (0,7%), 17 MS (0,03%), and 704 with MR (1,3%). Patients with MS were not described due to low case number. Time from VHD diagnosis to cardiac arrest was 3.7years in AS, 4.5years in AR and 4.1years in MR. ROSC occurred in 28% with AS, 33% with AR, 36% with MR and 35% without VHD. Survival at 30days was 5.2%, 10.4%, 9.2%, 11.4% in AS, AR, MR and without VHD, respectively. There were no survivors in people with AS presenting with asystole or PEA. CPC scores did not differ in those with VHD compared with no VHD. Odds ratio (OR) for MR and AR showed no difference in survival, while AS displayed OR 0.58 (95% CI 0.46-0.72), vs no VHD. AS is associated with halved survival in OHCA, while AR and MR do not affect survival. Survivors with AS have neurological outcomes comparable to patients without VHD.
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| 5. |
- Edqvist, Jon, 1988, et al.
(författare)
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Trajectories in HbA1c and other risk factors among adults with type 1 diabetes by age at onset
- 2021
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Ingår i: BMJ Open Diabetes Research and Care. - : BMJ. - 2052-4897. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- In type 1 diabetes, potential loss of life-years is greatest in those who are youngest at the time of onset. Using data from a nationwide cohort of patients with type 1 diabetes, we aimed to study risk factor trajectories by age at diagnosis. We stratified 30005 patients with type 1 diabetes aged 18–75 years into categories based on age at onset: 0–10, 11–15, 16–20, 21–25, and 26–30 years. HbA1c, albuminuria, estimated glomerular filtration rate (eGFR), body mass index (BMI), low-denisty lipoprotein (LDL)-cholesterol, systolic blood pressure (SBP), and diastolic blood pressure trends were analyzed using mixed models. Variable importance for baseline HbA1c was analyzed using conditional random forest and gradient boosting machine approaches. Individuals aged ≥16 years at onset displayed a relatively low mean HbA1c level (~55–57mmol/mol) that gradually increased. In contrast, individuals diagnosed at ≤15 years old entered adulthood with a mean HbA1c of approximately 70mmol/mol. For all groups, HbA1c levels stabilized at a mean of approximately 65mmol/mol by about 40 years old. In patients who were young at the time of onset, albuminuria appeared at an earlier age, suggesting a more rapid decrease in eGFR, while there were no distinct differences in BMI, SBP, and LDL-cholesterol trajectories between groups. Low education, higher age, and poor risk factor control were associated with higher HbA1c levels. Young age at the diabetes onset plays a substantial role in subsequent glycemic control and the presence of albuminuria, where patients with early onset may accrue a substantial glycemic load during this period. © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.
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| 6. |
- Gellerstedt, Martin, 1966-, et al.
(författare)
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Could prioritisation by emergency medicine dispatchers be improved by using computer-based decision support? : A cohort of patients with chest pain
- 2016
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Ingår i: International Journal of Cardiology. - : Elsevier BV. - 0167-5273 .- 1874-1754. ; 220, s. 734-738
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Tidskriftsartikel (refereegranskat)abstract
- Background: To evaluate whether a computer-based decision support system could improve the allocation of patients with acute coronary syndrome (ACS) or a life-threatening condition (LTC). We hypothesised that a system of this kind would improve sensitivity without compromising specificity. Methods: A total of 2285 consecutive patients who dialed 112 due to chest pain were asked 10 specific questions and a prediction model was constructed based on the answers. We compared the sensitivity of the dispatchers' decisions with that of the model-based decision support model. Results: A total of 2048 patients answered all 10 questions. Among the 235 patients with ACS, 194 were allocated the highest prioritisation by dispatchers (sensitivity 82.6%) and 41 patients were given a lower prioritisation (17.4% false negatives). The allocation suggested by the model used the highest prioritisation in 212 of the patients with ACS (sensitivity of 90.2%), while 23 patients were underprioritised (9.8% false negatives). The results were similar when the two systems were compared with regard to LTC and 30-day mortality. This indicates that computer-based decision support could be used either for increasing sensitivity or for saving resources. Three questions proved to be most important in terms of predicting ACS/LTC, [1] the intensity of pain, [2] the localisation of pain and [3] a history of ACS. Conclusion: Among patients with acute chest pain, computer-based decision support with a model based on a few fundamental questions could improve sensitivity and reduce the number of cases with the highest prioritisation without endangering the patients.
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| 7. |
- Halminen, Janita, 1998, et al.
(författare)
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Range of Risk Factor Levels, Risk Control, and Temporal Trends for Nephropathy and End-stage Kidney Disease in Patients With Type 1 and Type 2 Diabetes
- 2022
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Ingår i: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 45:10, s. 2326-2335
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To investigate trends, optimal levels for cardiometabolic risk factors, and multifactorial risk control in diabetic nephropathy and end-stage kidney disease (ESKD) in patients with diabetes and matched control subjects. RESEARCH DESIGN AND METHODS: This study included 701,622 patients with diabetes from the Swedish National Diabetes Register and 2,738,137 control subjects. Trends were analyzed with standardized incidence rates. Cox regression was used to assess excess risk, optimal risk factor levels, and risk according to the number of risk factors, in diabetes. RESULTS: ESKD incidence among patients with and without diabetes initially declined until 2007 and increased thereafter, whereas diabetic nephropathy decreased throughout follow-up. In patients with diabetes, baseline values for glycated hemoglobin, systolic blood pressure (SBP), triglycerides, and BMI were associated with outcomes. Hazard ratio (HR) for ESKD for patients with type 2 diabetes who had all included risk factors at target was 1.60 (95% CI 1.49-1.71) compared with control subjects and for patients with type 1 diabetes 6.10 (95% CI 4.69-7.93). Risk for outcomes increased in a stepwise fashion for each risk factor not at target. Excess risk for ESKD in type 2 diabetes showed a HR of 2.32 (95% CI 2.30-2.35) and in type 1 diabetes 10.92 (95% CI 10.15-11.75), compared with control. CONCLUSIONS: Incidence of diabetic nephropathy has declined substantially, whereas ESKD incidence has increased. Traditional and modifiable risk factors below target levels were associated with lower risks for outcomes, particularly notable for the causal risk factors of SBP and HbA1c, with potential implications for care. © 2022 by the American Diabetes Association.
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| 8. |
- Helleryd, Edvin, 1997, et al.
(författare)
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Association between exercise load, resting heart rate, and maximum heart rate and risk of future ST-segment elevation myocardial infarction (STEMI).
- 2023
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Ingår i: Open heart. - 2053-3624. ; 10:2
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Tidskriftsartikel (refereegranskat)abstract
- This study aimed to examine the association between exercise workload, resting heart rate (RHR), maximum heart rate and the risk of developing ST-segment elevation myocardial infarction (STEMI).The study included all participants from the UK Biobank who had undergone submaximal exercise stress testing. Patients with a history of STEMI were excluded. The allowed exercise load for each participant was calculated based on clinical characteristics and risk categories. We studied the participants who exercised to reach 50% or 35% of their expected maximum exercise tolerance. STEMI was adjudicated by the UK Biobank. We used Cox regression analysis to study how exercise tolerance and RHR were related to the risk of STEMI.A total of 66 949 participants were studied, of whom 274 developed STEMI during a median follow-up of 7.7 years. After adjusting for age, sex, blood pressure, smoking, forced vital capacity, forced expiratory volume in 1 s, peak expiratory flow and diabetes, we noted a significant association between RHR and the risk of STEMI (p=0.015). The HR for STEMI in the highest RHR quartile (>90 beats/min) compared with that in the lowest quartile was 2.92 (95% CI 1.26 to 6.77). Neither the maximum achieved exercise load nor the ratio of the maximum heart rate to the maximum load was significantly associated with the risk of STEMI. However, a non-significant but stepwise inverse association was noted between the maximum load and the risk of STEMI.RHR is an independent predictor of future STEMI. An RHR of >90 beats/min is associated with an almost threefold increase in the risk of STEMI.
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| 9. |
- Hellsén, Gustaf, et al.
(författare)
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Predicting recurrent cardiac arrest in individuals surviving Out-of-Hospital cardiac arrest
- 2023
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Ingår i: Resuscitation. - : Elsevier BV. - 0300-9572 .- 1873-1570. ; 184
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Tidskriftsartikel (refereegranskat)abstract
- Background: Despite improvements in short-term survival for Out-of-Hospital Cardiac Arrest (OHCA) in the past two decades, long-term survival is still not well studied. Furthermore, the contribution of different variables on long-term survival have not been fully investigated. Aim: Examine the 1-year prognosis of patients discharged from hospital after an OHCA. Furthermore, identify factors predicting re-arrest and/or death during 1-year follow-up. Methods: All patients 18 years or older surviving an OHCA and discharged from the hospital were identified from the Swedish Register for Car-diopulmonary Resuscitation (SRCR). Data on diagnoses, medications and socioeconomic factors was gathered from other Swedish registers. A machine learning model was constructed with 886 variables and evaluated for its predictive capabilities. Variable importance was gathered from the model and new models with the most important variables were created. Results: Out of the 5098 patients included, 902 (-18%) suffered a recurrent cardiac arrest or death within a year. For the outcome death or re-arrest within 1 year from discharge the model achieved an ROC (receiver operating characteristics) AUC (area under the curve) of 0.73. A model with the 15 most important variables achieved an AUC of 0.69. Conclusions: Survivors of an OHCA have a high risk of suffering a re-arrest or death within 1 year from hospital discharge. A machine learning model with 15 different variables, among which age, socioeconomic factors and neurofunctional status at hospital discharge, achieved almost the same predictive capabilities with reasonable precision as the full model with 886 variables.
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| 10. |
- Hessulf, Fredrik, 1986, et al.
(författare)
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Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning: the SCARS model
- 2023
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Ingår i: eBioMedicine. - : Elsevier BV. - 2352-3964. ; 89
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Tidskriftsartikel (refereegranskat)abstract
- Background: A prediction model that estimates survival and neurological outcome in out-of-hospital cardiac arrest patients has the potential to improve clinical management in emergency rooms.Methods: We used the Swedish Registry for Cardiopulmonary Resuscitation to study all out-of-hospital cardiac arrest (OHCA) cases in Sweden from 2010 to 2020. We had 393 candidate predictors describing the circumstances at cardiac arrest, critical time intervals, patient demographics, initial presentation, spatiotemporal data, socioeconomic status, medications, and comorbidities before arrest. To develop, evaluate and test an array of prediction models, we created stratified (on the outcome measure) random samples of our study population. We created a training set (60% of data), evaluation set (20% of data), and test set (20% of data). We assessed the 30-day survival and cerebral performance category (CPC) score at discharge using several machine learning frameworks with hyperparameter tuning. Parsimonious models with the top 1 to 20 strongest predictors were tested. We calibrated the decision threshold to assess the cut-off yielding 95% sensitivity for survival. The final model was deployed as a web application.Findings: We included 55,615 cases of OHCA. Initial presentation, prehospital interventions, and critical time intervals variables were the most important. At a sensitivity of 95%, specificity was 89%, positive predictive value 52%, and negative predictive value 99% in test data to predict 30-day survival. The area under the receiver characteristic curve was 0.97 in test data using all 393 predictors or only the ten most important predictors. The final model showed excellent calibration. The web application allowed for near-instantaneous survival calculations.Interpretation: Thirty-day survival and neurological outcome in OHCA can rapidly and reliably be estimated during ongoing cardiopulmonary resuscitation in the emergency room using a machine learning model incorporating widely available variables.
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