Objective: Familial hypercholesterolemia (FH) is traditionally defined as a monogenic disease characterized by severely elevated LDL-C (low-density lipoprotein cholesterol) levels. In practice, FH is ...commonly a clinical diagnosis without confirmation of a causative mutation. In this study, we sought to characterize and compare monogenic and clinically defined FH in a large sample of Icelanders.
Approach and Results: We whole-genome sequenced 49 962 Icelanders and imputed the identified variants into an overall sample of 166 281 chip-genotyped Icelanders. We identified 20 FH mutations in LDLR, APOB, and PCSK9 with combined prevalence of 1 in 836. Monogenic FH was associated with severely elevated LDL-C levels and increased risk of premature coronary disease, aortic valve stenosis, and high burden of coronary atherosclerosis. We used a modified version of the Dutch Lipid Clinic Network criteria to screen for the clinical FH phenotype among living adult participants (N=79 058). Clinical FH was found in 2.2% of participants, of whom only 5.2% had monogenic FH. Mutation-negative clinical FH has a strong polygenic basis. Both individuals with monogenic FH and individuals with mutation-negative clinical FH were markedly undertreated with cholesterol-lowering medications and only a minority attained an LDL-C target of <2.6 mmol/L (<100 mg/dL; 11.0% and 24.9%, respectively) or <1.8 mmol/L (<70 mg/dL; 0.0% and 5.2%, respectively), as recommended for primary prevention by European Society of Cardiology/European Atherosclerosis Society cholesterol guidelines.
Conclusions: Clinically defined FH is a relatively common phenotype that is explained by monogenic FH in only a minority of cases. Both monogenic and clinical FH confer high cardiovascular risk but are markedly undertreated.
The aim of this study was to identify the prevalence of modifiable risk factors of surgical site infections (SSI) in patients undergoing primary elective total joint arthroplasty (TJA) receiving ...conventional preoperative preparation, and to explore their association with infectious outcomes.
Information regarding modifiable risk factors (anemia, diabetes, obesity, nutritional status, smoking, physical activity) was prospectively gathered in patients undergoing primary TJA of hip or knee in 2018-2020 at a single institution with 6 weeks' follow-up time.
738 patients (median age 68 years IQR 61-73, women 57%) underwent TJA (knee 64%, hip 36%). Anemia was detected in 8%, diabetes was present in 9%, an additional 2% had undiagnosed diabetes (HbA1c > 47 mmol/mol), and 8% dysglycemia (HbA1c 42-47 mmol/mol). Obesity (BMI ≥ 30) was observed in 52%. Serum albumin, total lymphocyte count, and vitamin D below normal limits was identified in 0.1%, 18%, and 16%, respectively. Current smokers were 7%. Surgical site complications occurred in 116 (16%), superficial SSI in 57 (8%), progressing to periprosthetic joint infection in 7 cases. Univariate analysis identified higher odds of superficial SSI for BMI ≥ 30 (OR 2.1, 95%CI 1.2-3.8) and HbA1c ≥ 42 mmol/mol (OR 2.2, CI 1.1-4.2), but no association was found with other factors.
In a general population undergoing primary TJA an association was found between obesity (52%) and dysglycemia/diabetes (19%) and superficial SSI (8%), which progressed to PJI in 12% of cases, generating a 1% total rate of PJI. Modification of these risk factors might mitigate infectious adverse outcomes.
Celotno besedilo
Dostopno za:
DOBA, FSPLJ, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Machine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models ...compared to general practitioners (GPs). The aim of this study was to evaluate the diagnostic accuracy of an ML classifier on primary headache diagnoses in PHC, compare its performance to GPs, and examine the most impactful signs and symptoms when making a prediction.
A retrospective study on diagnostic accuracy, using electronic health records from the database of the Primary Health Care Service of the Capital Area (PHCCA) in Iceland.
Fifteen primary health care centers of the PHCCA.
All patients that consulted a physician, from 1 January 2006 to 30 April 2020, and received one of the selected diagnoses.
Sensitivity, Specificity, Positive Predictive Value, Matthews Correlation Coefficient, Receiver Operating Characteristic (ROC) curve, and Area under the ROC curve (AUROC) score for primary headache diagnoses, as well as Shapley Additive Explanations (SHAP) values of the ML classifier.
The classifier outperformed the GPs on all metrics except specificity. The SHAP values indicate that the classifier uses the same signs and symptoms (features) as a physician would, when distinguishing between headache diagnoses.
In a retrospective comparison, the diagnostic accuracy of the ML classifier for primary headache diagnoses is superior to GPs. According to SHAP values, the ML classifier relies on the same signs and symptoms as a physician when making a diagnostic prediction.
Keypoints
Little is known about the diagnostic accuracy of machine learning (ML) in the context of primary health care, despite its considerable potential to aid in clinical work. This novel research sheds light on the diagnostic accuracy of ML in a clinical context, as well as the interpretation of its predictions. If the vast potential of ML is to be utilized in primary health care, its performance, safety, and inner workings need to be understood by clinicians.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Respiratory symptoms are the most common presenting complaint in primary care. Often these symptoms are self resolving, but they can indicate a severe illness. With increasing physician workload and ...health care costs, triaging patients before in-person consultations would be helpful, possibly offering low-risk patients other means of communication. The objective of this study was to train a machine learning model to triage patients with respiratory symptoms before visiting a primary care clinic and examine patient outcomes in the context of the triage.
We trained a machine learning model, using clinical features only available before a medical visit. Clinical text notes were extracted from 1,500 records for patients that received 1 of 7
codes (J00, J10, JII, J15, J20, J44, J45). All primary care clinics in the Reykjavík area of Iceland were included. The model scored patients in 2 extrinsic data sets and divided them into 10 risk groups (higher values having greater risk). We analyzed selected outcomes in each group.
Risk groups 1 through 5 consisted of younger patients with lower C-reactive protein values, re-evaluation rates in primary and emergency care, antibiotic prescription rates, chest x-ray (CXR) referrals, and CXRs with signs of pneumonia, compared with groups 6 through 10. Groups 1 through 5 had no CXRs with signs of pneumonia or diagnosis of pneumonia by a physician.
The model triaged patients in line with expected outcomes. The model can reduce the number of CXR referrals by eliminating them in risk groups 1 through 5, thus decreasing clinically insignificant incidentaloma findings without input from clinicians.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
INTRODUCTION: Interest in the use of psychedelics has increased following reports of their possible therapeutic potential. However, little is known about the knowledge of and attitudes towards the ...substances among health care professional who provide treatment for mental disorders in Iceland. An online survey was therefore conducted among members of the Icelandic associations of psychiatrists, general practitioners and psychologists. METHODS: Respondents were 256 in total, including 177 psychologists, 38 psychiatrists and 41 general practitioners that provided information on their background, type of work, knowledge of and attitude towards different types of psychedelic substances and their views on optimal service delivery if psychedelics were approved by licencing authorities and used for treatment. RESULTS: Around half of psychiatrists reported having received questions about treatment with psychedelics in their clinical work, compared to only 14,6% of general practitioners and 17,5% of psychologists. The majority of respondents had little, or no knowledge of the substances targeted in the survey. A majority also expressed negative attitudes towards treatment with psilocybin mushrooms, but was positive towards ongoing scientific research and felt that such a treatment should be prescribed and provided by psychiatrists. Moreover, the majority view was that psilocybin treatment should be provided in specialised clinics or psychiatric units in a hospital setting. Scientific articles on the topic, discussions with colleagues and information in the media were identified as having had most influence on respondents´ attitudes towards psychedelics. Most respondents were interested in further education on psychedelics. CONCLUSIONS: Respondents among these three professions felt that the time has not yet come to use psychedelics in the treatment of mental disorders in Iceland but thought more education on psychedelics, their potential efficacy and adverse health effects is important given the increased interest in psychedelics.
Features of the QRS complex of the electrocardiogram, reflecting ventricular depolarisation, associate with various physiologic functions and several pathologic conditions. We test 32.5 million ...variants for association with ten measures of the QRS complex in 12 leads, using 405,732 electrocardiograms from 81,192 Icelanders. We identify 190 associations at 130 loci, the majority of which have not been reported before, including associations with 21 rare or low-frequency coding variants. Assessment of genes expressed in the heart yields an additional 13 rare QRS coding variants at 12 loci. We find 51 unreported associations between the QRS variants and echocardiographic traits and cardiovascular diseases, including atrial fibrillation, complete AV block, heart failure and supraventricular tachycardia. We demonstrate the advantage of in-depth analysis of the QRS complex in conjunction with other cardiovascular phenotypes to enhance our understanding of the genetic basis of myocardial mass, cardiac conduction and disease.
To describe antibiotic prescriptions in out-of-hour (OOH) service in primary care setting in Iceland and to study the indications for prescriptions.
A population based retrospective study, using ...electronic data from the OOH registration system.
OOH primary care setting in Reykjavik capital area in Iceland.
All patients that received a prescription for oral antibiotic drug at an OOH service in Reykjavik capital area over a one-year period.
Number of oral antibiotic prescriptions and diagnosis connected to the prescriptions according to age and sex.
There were 75,582 contacts with the OOH primary care of which 25,059 contacts resulted in prescription of an oral antibiotic (33%). The most common antibiotic prescribed in total, and for the diagnosis studied, was amoxicillin with clavulanic acid. It was most often prescribed for acute otitis media. Of those diagnosed with otitis media 50% were treated with amoxicillin with clavulanic acid and 40% of those diagnosed with pneumonia received that treatment. The second most prescribed antibiotic was amoxicillin. Most often it was prescribed for sinusitis, in 47% of cases with that diagnosis.
Antibiotics are often prescribed in OOH primary care in Iceland and a substantial number of the patients diagnosed in OOH primary care with acute otitis media or pneumonia are prescribed broad-spectrum antibiotics.
Key points
Antibiotic prescription rate is high and broad-spectrum drugs often prescribed in OOH primary care service in Iceland.
The results should encourage general practitioners in Iceland to review antibiotic prescriptions in OOH service.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
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