| 1. |
- 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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| 2. |
- 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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| 3. |
- Hjalmarsson, Alfred, et al.
(författare)
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No obesity paradox in out-of-hospital cardiac arrest: Data from the Swedish registry of cardiopulmonary resuscitation.
- 2023
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Ingår i: Resuscitation plus. - 2666-5204. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Although an "obesity paradox", which states an increased chance of survival for patients with obesity after myocardial infarction has been proposed, it is less clear whether this phenomenon even exists in patients suffering out-of-hospital cardiac arrest (OHCA) and if diabetes, which is often associated with obesity, implies an additional risk.To investigate if and how obesity, with or without diabetes, affects the survival of patients with OHCA.This study included 55,483 patients with OHCA reported to the Swedish Registry of Cardiopulmonary Resuscitation between 2010 and 2020. Patients were classified in five groups: obesity only (Ob), type 1 diabetes only (T1D), type 2 diabetes only (T2D), obesity and any diabetes (ObD), or belonging to the group other (OTH). Patient characteristics and outcomes were studied using descriptive statistics, logistic, and Cox proportional regression.Obesity only was found in 2.7% of the study cohort, while 3.2% had obesity and any type of diabetes. Ob patients were significantly younger than all other patients (p≤0.001); the 30day-survival was 9.6% in Ob, and 10.6%, 7.3%, 6.9%, and 12.7% in T1D, T2D, ObD, and OTH, respectively, with OR (95% CI) of 0.69 (0.57-0.82), 0.78 (0.56-1.05), 0.65 (0.59-0.71), and 0.55 (0.45-0.66) for Ob, T1D, T2D, and ObD, respectively (reference group OTH). No time-related trends in 30-days survival were found.Obesity was present in 6% of the population and was associated with younger age and a 30% reduction in survival; a combination of obesity and diabetes further reduced the survival rate.
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| 4. |
- Al-Dury, Nooraldeen, 1986, et al.
(författare)
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Characteristics and outcome among 14,933 adult cases of in-hospital cardiac arrest : A nationwide study with the emphasis on gender and age.
- 2017
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Ingår i: American Journal of Emergency Medicine. - : Elsevier. - 0735-6757 .- 1532-8171. ; 35:12, s. 1839-1844
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Tidskriftsartikel (refereegranskat)abstract
- AIM: To investigate characteristics and outcome among patients suffering in-hospital cardiac arrest (IHCA) with the emphasis on gender and age.METHODS: Using the Swedish Register of Cardiopulmonary Resuscitation, we analyzed associations between gender, age and co-morbidities, etiology, management, 30-day survival and cerebral function among survivors in 14,933 cases of IHCA. Age was divided into three ordered categories: young (18-49years), middle-aged (50-64years) and older (65years and above). Comparisons between men and women were age adjusted.RESULTS: The mean age was 72.7years and women were significantly older than men. Renal dysfunction was the most prevalent co-morbidity. Myocardial infarction/ischemia was the most common condition preceding IHCA, with men having 27% higher odds of having MI as the underlying etiology. A shockable rhythm was found in 31.8% of patients, with men having 52% higher odds of being found in VT/VF. After adjusting for various confounders, it was found that men had a 10% lower chance than women of surviving to 30days. Older individuals were managed less aggressively than younger patients. Increasing age was associated with lower 30-day survival but not with poorer cerebral function among survivors.CONCLUSION: When adjusting for various confounders, it was found that men had a 10% lower chance than women of surviving to 30days after in-hospital cardiac arrest. Older individuals were managed less aggressively than younger patients, despite a lower chance of survival. Higher age was, however, not associated with poorer cerebral function among survivors.
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| 5. |
- Al-Dury, Nooraldeen, 1986, et al.
(författare)
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Identifying the relative importance of predictors of survival in out of hospital cardiac arrest : a machine learning study
- 2020
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Ingår i: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. - : BioMed Central. - 1757-7241. ; 28:1, s. 1-8
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Studies examining the factors linked to survival after out of hospital cardiac arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in different parts of the world, or focused on certain factors and whether they were associated with survival. Unfortunately, this approach does not measure how strong each factor is in predicting survival after OHCA. Aim: To investigate the relative importance of 16 well-recognized factors in OHCA at the time point of ambulance arrival, and before any interventions or medications were given, by using a machine learning approach that implies building models directly from the data, and arranging those factors in order of importance in predicting survival. Methods: Using a data-driven approach with a machine learning algorithm, we studied the relative importance of 16 factors assessed during the pre-hospital phase of OHCA We examined 45,000 cases of OHCA between 2008 and 2016. Results: Overall, the top five factors to predict survival in order of importance were: initial rhythm, age, early Cardiopulmonary Resuscitation (CPR, time to CPR and CPR before arrival of EMS), time from EMS dispatch until EMS arrival, and place of cardiac arrest The largest difference in importance was noted between initial rhythm and the remaining predictors. A number of factors, including time of arrest and sex were of little importance. Conclusion: Using machine learning, we confirm that the most important predictor of survival in OHCA is initial rhythm, followed by age, time to start of CPR, EMS response time and place of OHCA. Several factors traditionally viewed as important e.g. sex, were of little importance.
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| 6. |
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| 7. |
- 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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| 8. |
- 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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| 9. |
- 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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| 10. |
- 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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