| 1. |
- Agvall, B., et al.
(författare)
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Characteristics, management and outcomes in patients with CKD in a healthcare region in Sweden: a population-based, observational study
- 2023
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Ingår i: Bmj Open. - London : BMJ Publishing Group Ltd. - 2044-6055. ; 13:7
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Tidskriftsartikel (refereegranskat)abstract
- ObjectivesTo describe chronic kidney disease (CKD) regarding treatment rates, comorbidities, usage of CKD International Classification of Diseases (ICD) diagnosis, mortality, hospitalisation, evaluate healthcare utilisation and screening for CKD in relation to new nationwide CKD guidelines. DesignPopulation-based observational study. SettingHealthcare registry data of patients in Southwest Sweden. ParticipantsA total cohort of 65 959 individuals aged >18 years of which 20 488 met the criteria for CKD (cohort 1) and 45 470 at risk of CKD (cohort 2). Primary and secondary outcome measuresData were analysed with regards to prevalence, screening rates of blood pressure, glucose, estimated glomerular filtration rate (eGFR), Urinary-albumin-creatinine ratio (UACR) and usage of ICD-codes for CKD. Mortality and hospitalisation were analysed with logistic regression models. ResultsOf the CKD cohort, 18% had CKD ICD-diagnosis and were followed annually for blood pressure (79%), glucose testing (76%), eGFR (65%), UACR (24%). UACR follow-up was two times as common in hypertensive and cardiovascular versus diabetes patients with CKD with a similar pattern in those at risk of CKD. Statin and renin-angiotensin-aldosterone inhibitor appeared in 34% and 43%, respectively. Mortality OR at CKD stage 5 was 1.23 (CI 0.68 to 0.87), diabetes 1.20 (CI 1.04 to 1.38), hypertension 1.63 (CI 1.42 to 1.88), atherosclerotic cardiovascular disease (ASCVD) 1.84 (CI 1.62 to 2.09) associated with highest mortality risk. Hospitalisation OR in CKD stage 5 was 1.96 (CI 1.40 to 2.76), diabetes 1.15 (CI 1.06 to 1.25), hypertension 1.23 (CI 1.13 to 1.33) and ASCVD 1.52 (CI 1.41 to 1.64). ConclusionsThe gap between patients with CKD by definition versus those diagnosed as such was large. Compared with recommendations patients with CKD have suboptimal follow-up and treatment with renin-angiotensin-aldosterone system inhibitor and statins. Hypertension, diabetes and ASCVD were associated with increased mortality and hospitalisation. Improved screening and diagnosis of CKD, identification and management of risk factors and kidney protective treatment could affect clinical and economic outcomes.
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| 2. |
- Ashfaq, Awais, 1990-, et al.
(författare)
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Data resource profile : Regional healthcare information platform in Halland, Sweden
- 2020
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Ingår i: International Journal of Epidemiology. - Oxford : Oxford University Press. - 0300-5771 .- 1464-3685. ; 49:3, s. 738-739f
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Tidskriftsartikel (refereegranskat)abstract
- Accurate and comprehensive healthcare data coupled with modern analytical tools can play a vital role in enabling care providers to make better-informed decisions, leading to effective and cost-efficient care delivery. This paper describes a novel strategic healthcare analysis and research platform that encapsulates 360-degree pseudo-anonymized data covering clinical, operational capacity and financial data on over 500,000 patients treated since 2009 across all care delivery units in the county of Halland, Sweden. The over-arching goal is to develop a comprehensive healthcare data infrastructure that captures complete care processes at individual, organizational and population levels. These longitudinal linked healthcare data are a valuable tool for research in a broad range of areas including health economy and process development using real world evidence.Key messagesStructured and standardized variables have been linked from different regional healthcare sources into a research information platform including all healthcare visits in the county of Halland in Sweden, from 2009 to date.Since 2015, the regional information platform integrates a cost component to each healthcare visit: thus being able to quantify patient level value, safety and cost efficiency across the continuum of care. © The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
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| 3. |
- Ashfaq, Awais, 1990-, et al.
(författare)
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DEED : DEep Evidential Doctor
- 2023
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Ingår i: Artificial Intelligence. - Amsterdam : Elsevier. - 0004-3702 .- 1872-7921. ; 325
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Tidskriftsartikel (refereegranskat)abstract
- As Deep Neural Networks (DNN) make their way into safety-critical decision processes, it becomes imperative to have robust and reliable uncertainty estimates for their predictions for both in-distribution and out-of-distribution (OOD) examples. This is particularly important in real-life high-risk settings such as healthcare, where OOD examples (e.g., patients with previously unseen or rare labels, i.e., diagnoses) are frequent, and an incorrect clinical decision might put human life in danger, in addition to having severe ethical and financial costs. While evidential uncertainty estimates for deep learning have been studied for multi-class problems, research in multi-label settings remains untapped. In this paper, we propose a DEep Evidential Doctor (DEED), which is a novel deterministic approach to estimate multi-label targets along with uncertainty. We achieve this by placing evidential priors over the original likelihood functions and directly estimating the parameters of the evidential distribution using a novel loss function. Additionally, we build a redundancy layer (particularly for high uncertainty and OOD examples) to minimize the risk associated with erroneous decisions based on dubious predictions. We achieve this by learning the mapping between the evidential space and a continuous semantic label embedding space via a recurrent decoder. Thereby inferring, even in the case of OOD examples, reasonably close predictions to avoid catastrophic consequences. We demonstrate the effectiveness of DEED on a digit classification task based on a modified multi-label MNIST dataset, and further evaluate it on a diagnosis prediction task from a real-life electronic health record dataset. We highlight that in terms of prediction scores, our approach is on par with the existing state-of-the-art having a clear advantage of generating reliable, memory and time-efficient uncertainty estimates with minimal changes to any multi-label DNN classifier. © 2023 The Author(s)
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| 4. |
- Ashfaq, Awais, 1990-, et al.
(författare)
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KAFE : Knowledge and Frequency Adapted Embeddings
- 2022
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Ingår i: Machine Learning, Optimization, and Data Science. - Cham : Springer. - 9783030954697 - 9783030954703 ; , s. 132-146
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Konferensbidrag (refereegranskat)abstract
- Word embeddings are widely used in several Natural Language Processing (NLP) applications. The training process typically involves iterative gradient updates of each word vector. This makes word frequency a major factor in the quality of embedding, and in general the embedding of words with few training occurrences end up being of poor quality. This is problematic since rare and frequent words, albeit semantically similar, might end up far from each other in the embedding space.In this study, we develop KAFE (Knowledge And Frequency adapted Embeddings) which combines adversarial principles and knowledge graph to efficiently represent both frequent and rare words. The goal of adversarial training in KAFE is to minimize the spatial distinguishability (separability) of frequent and rare words in the embedding space. The knowledge graph encourages the embedding to follow the structure of the domain-specific hierarchy, providing an informative prior that is particularly important for words with low amount of training data. We demonstrate the performance of KAFE in representing clinical diagnoses using real-world Electronic Health Records (EHR) data coupled with a knowledge graph. EHRs are notorious for including ever-increasing numbers of rare concepts that are important to consider when defining the state of the patient for various downstream applications. Our experiments demonstrate better intelligibility through visualisation, as well as higher prediction and stability scores of KAFE over state-of-the-art. © Springer Nature Switzerland AG 2022
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| 5. |
- Ashfaq, Awais, 1990-, et al.
(författare)
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Readmission prediction using deep learning on electronic health records
- 2019
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Ingår i: Journal of Biomedical Informatics. - Maryland Heights, MO : Elsevier BV. - 1532-0464 .- 1532-0480. ; 97
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Tidskriftsartikel (refereegranskat)abstract
- Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted intervention programs for patients at risk of readmission. This requires identifying high-risk patients at the time of discharge from hospital. Here, using real data from over 7500 CHF patients hospitalized between 2012 and 2016 in Sweden, we built and tested a deep learning framework to predict 30-day unscheduled readmission. We present a cost-sensitive formulation of Long Short-Term Memory (LSTM) neural network using expert features and contextual embedding of clinical concepts. This study targets key elements of an Electronic Health Record (EHR) driven prediction model in a single framework: using both expert and machine derived features, incorporating sequential patterns and addressing the class imbalance problem. We evaluate the contribution of each element towards prediction performance (ROC-AUC, F1-measure) and cost-savings. We show that the model with all key elements achieves higher discrimination ability (AUC: 0.77; F1: 0.51; Cost: 22% of maximum possible savings) outperforming the reduced models in at least two evaluation metrics. Additionally, we present a simple financial analysis to estimate annual savings if targeted interventions are offered to high risk patients. © 2019 The Authors
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| 6. |
- Bergfeldt, Lennart, 1950, et al.
(författare)
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Spatial peak and mean QRS-T angles: A comparison of similar but different emerging risk factors for cardiac death.
- 2020
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Ingår i: Journal of electrocardiology. - : Elsevier BV. - 1532-8430 .- 0022-0736. ; 61, s. 112-120
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Tidskriftsartikel (refereegranskat)abstract
- The spatial peak and mean QRS-T angles are scientifically but not clinically established risk factors for cardiovascular events including cardiac death. The study aims were to compare these angles, assess their association with hypertension (HT) and diabetes mellitus (DM), and explore the relation between the mean QRS-T angle and the ventricular gradient (VG; reflecting electrical heterogeneity), which both are derived from the QRSarea and Tarea vectors.Altogether 1094 participants (aged 50-65years, 550 women) from the pilot of the population-based Swedish CArdioPulmonary bioImage Study with Frank vectorcardiographic recordings were included and divided into 5 subgroups: apparently healthy n=320; HT n=311; DM n=33; DM+HT n=53; miscellaneous conditions n=377. Abnormal peak and mean QRS-T angles were defined as >95th percentile.Peak QRS-T angles were generally narrower than the mean QRS-T angles; both were narrower in women than in men. Abnormal peak (>124°) and/or mean (>119°) QRS-T angles were found in 73 participants (6.7%). The concordance regarding abnormal versus normal-borderline QRS-T angles was good (Cohen's kappa 0.61). The prevalence of abnormal angles varied from 2.5% in healthy to 21.2% in DM. There was an inverse logarithmical relation between the mean QRS-T angle and the VG.The peak and mean QRS-T angles are not interchangeable but complementary. DM, HT, sex and absence of disease are important determinants of both QRS-T angles. The mean QRS-T angle and the VG relationship is complex. All three VCG derived measures reflect related but differing electrophysiological properties and have potential prognostic value vis-à-vis cardiovascular events.
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| 7. |
- Blom, Mathias Carl, et al.
(författare)
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Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: A retrospective, population-based registry study
- 2019
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Ingår i: BMJ Open. - London : BMJ. - 2044-6055. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Objectives The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy. Design Retrospective, population-based registry study. Setting Swedish health services. Primary and secondary outcome measures All cause 30-day mortality. Methods Electronic health records (EHRs) and administrative data were used to train six supervised machine learning models to predict all-cause mortality within 30 days in patients discharged from EDs in southern Sweden, Europe. Participants The models were trained using 65 776 ED visits and validated on 55 164 visits from a separate ED to which the models were not exposed during training. Results The outcome occurred in 136 visits (0.21%) in the development set and in 83 visits (0.15%) in the validation set. The model with highest discrimination attained ROC-AUC 0.95 (95% CI 0.93 to 0.96), with sensitivity 0.87 (95% CI 0.80 to 0.93) and specificity 0.86 (0.86 to 0.86) on the validation set. Conclusions Multiple models displayed excellent discrimination on the validation set and outperformed available indexes for short-term mortality prediction interms of ROC-AUC (by indirect comparison). The practical utility of the models increases as the data they were trained on did not require costly de novo collection but were real-world data generated as a by-product of routine care delivery.
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| 8. |
- Galozy, Alexander, 1991-, et al.
(författare)
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Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation
- 2020
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Ingår i: International Journal of Medical Informatics. - Shannon : Elsevier BV. - 1386-5056 .- 1872-8243. ; 136
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Tidskriftsartikel (refereegranskat)abstract
- Background and purpose: Patients' adherence to medication is a complex, multidimensional phenomenon. Dispensation data and electronic health records are used to approximate medication-taking through refill adherence. In-depth discussions on the adverse effects of data quality and computational differences are rare. The purpose of this article is to evaluate the impact of common pitfalls when computing medication adherence using electronic health records. Procedures: We point out common pitfalls associated with the data and operationalization of adherence measures. We provide operational definitions of refill adherence and conduct experiments to determine the effect of the pitfalls on adherence estimations. We performed statistical significance testing on the impact of common pitfalls using a baseline scenario as reference. Findings: Slight changes in definition can significantly skew refill adherence estimates. Pickup patterns cause significant disagreement between measures and the commonly used proportion of days covered. Common data related issues had a small but statistically significant (p < 0.05) impact on population-level and significant effect on individual cases. Conclusion: Data-related issues encountered in real-world administrative databases, which affect various operational definitions of refill adherence differently, can significantly skew refill adherence values, leading to false conclusions about adherence, particularly when estimating adherence for individuals.
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| 9. |
- Heyman, Ellen Tolestam, et al.
(författare)
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Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths
- 2021
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Ingår i: Journal of Emergency Medicine. - Philadelphia, PA : Elsevier BV. - 0736-4679 .- 1090-1280. ; 61:6, s. 763-773
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit.OBJECTIVES: To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge.METHODS: In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either "surprising" or "unsurprising" by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM).RESULTS: Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models.CONCLUSION: In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly.
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| 10. |
- Jernberg, T., et al.
(författare)
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Long-Term Effects of Oxygen Therapy on Death or Hospitalization for Heart Failure in Patients With Suspected Acute Myocardial Infarction
- 2018
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Ingår i: Circulation. - : Ovid Technologies (Wolters Kluwer Health). - 0009-7322 .- 1524-4539. ; 138:24, s. 2754-2762
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: In the DETO2X-AMI trial (Determination of the Role of Oxygen in Suspected Acute Myocardial Infarction), we compared supplemental oxygen with ambient air in normoxemic patients presenting with suspected myocardial infarction and found no significant survival benefit at 1 year. However, important secondary end points were not yet available. We now report the prespecified secondary end points cardiovascular death and the composite of all-cause death and hospitalization for heart failure. METHODS: In this pragmatic, registry-based randomized clinical trial, we used a nationwide quality registry for coronary care for trial procedures and evaluated end points through the Swedish population registry (mortality), the Swedish inpatient registry (heart failure), and cause of death registry (cardiovascular death). Patients with suspected acute myocardial infarction and oxygen saturation of >= 90% were randomly assigned to receive either supplemental oxygen at 6 L/min for 6 to 12 hours delivered by open face mask or ambient air. RESULTS: A total of 6629 patients were enrolled. Acute heart failure treatment, left ventricular systolic function assessed by echocardiography, and infarct size measured by high-sensitive cardiac troponin T were similar in the 2 groups during the hospitalization period. All-cause death or hospitalization for heart failure within 1 year after randomization occurred in 8.0% of patients assigned to oxygen and in 7.9% of patients assigned to ambient air (hazard ratio, 0.99; 95% CI, 0.84-1.18; P=0.92). During long-term follow-up (median [range], 2.1 [1.0-3.7] years), the composite end point occurred in 11.2% of patients assigned to oxygen and in 10.8% of patients assigned to ambient air (hazard ratio, 1.02; 95% CI, 0.88-1.17; P=0.84), and cardiovascular death occurred in 5.2% of patients assigned to oxygen and in 4.8% assigned to ambient air (hazard ratio, 1.07; 95% CI, 0.87-1.33; P=0.52). The results were consistent across all predefined subgroups. CONCLUSIONS: Routine use of supplemental oxygen in normoxemic patients with suspected myocardial infarction was not found to reduce the composite of all-cause mortality and hospitalization for heart failure, or cardiovascular death within 1 year or during long-term follow-up.
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