Unsupervised Feature Selection to Identify Important ICD-10 and ATC Codes for Machine Learning on a Cohort of Patients With Coronary Heart Disease: Retrospective Study.

Background: The application of machine learning in health care often necessitates the use of hierarchical codes such as the International Classification of Diseases (ICD) and Anatomical Therapeutic Chemical (ATC) systems. These codes classify diseases and medications, respectively, thereby forming extensive data dimensions. Unsupervised feature selection tackles the "curse of dimensionality" and helps to improve the accuracy and performance of supervised learning models by reducing the number of irrelevant or redundant features and avoiding overfitting. Techniques for unsupervised feature selection, such as filter, wrapper, and embedded methods, are implemented to select the most important features with the most intrinsic information. However, they face challenges due to the sheer volume of ICD and ATC codes and the hierarchical structures of these systems. Objective: The objective of this study was to compare several unsupervised feature selection methods for ICD and ATC code databases of patients with coronary artery disease in different aspects of performance and complexity and select the best set of features representing these patients. Methods: We compared several unsupervised feature selection methods for 2 ICD and 1 ATC code databases of 51,506 patients with coronary artery disease in Alberta, Canada. Specifically, we used the Laplacian score, unsupervised feature selection for multicluster data, autoencoder-inspired unsupervised feature selection, principal feature analysis, and concrete autoencoders with and without ICD or ATC tree weight adjustment to select the 100 best features from over 9000 ICD and 2000 ATC codes. We assessed the selected features based on their ability to reconstruct the initial feature space and predict 90-day mortality following discharge. We also compared the complexity of the selected features by mean code level in the ICD or ATC tree and the interpretability of the features in the mortality prediction task using Shapley analysis. Results: In feature space reconstruction and mortality prediction, the concrete autoencoder-based methods outperformed other techniques. Particularly, a weight-adjusted concrete autoencoder variant demonstrated improved reconstruction accuracy and significant predictive performance enhancement, confirmed by DeLong and McNemar tests (P<.05). Concrete autoencoders preferred more general codes, and they consistently reconstructed all features accurately. Additionally, features selected by weight-adjusted concrete autoencoders yielded higher Shapley values in mortality prediction than most alternatives. Conclusions: This study scrutinized 5 feature selection methods in ICD and ATC code data sets in an unsupervised context. Our findings underscore the superiority of the concrete autoencoder method in selecting salient features that represent the entire data set, offering a potential asset for subsequent machine learning research. We also present a novel weight adjustment approach for the concrete autoencoders specifically tailored for ICD and ATC code data sets to enhance the generalizability and interpretability of the selected features.

Linking physiology and demographics, non-ocular pathology and pharmaceutical drug use to standard OCT measures of the inner retina: The PPP project.

PURPOSE: To assess the associations between physiology and demographics, non-ocular pathology and pharmaceutical drug use against peri-papillary retinal nerve fibre layer thickness (pRNFL T) and other optical coherence tomography (OCT) inner retinal measures in normal, healthy eyes. METHODS: A retrospective, cross-sectional study of 705 consecutive participants with bilateral normal, healthy optic nerves and maculae. PRNFL Ts, vertical cup/disc ratio (CDR), cup volume and macular ganglion cell layer-inner plexiform layer (GCL-IPL) Ts were extracted from Cirrus OCT scans, then regressed against predictor variables of participants' physiology and demographics (eye laterality, refraction, intraocular pressure [IOP], age, sex, race/ethnicity, etc.) and non-ocular pathology and pharmaceutical drug use according to the World Health Organisation classifications. Associations were assessed for statistical significance (p < 0.05) and clinical significance (|ß| > 95% limits of agreement for repeated measures). RESULTS: A multitude of non-ocular pathology and pharmaceutical drug use were statistically and clinically significantly associated with deviations in standard OCT inner retinal measures, exceeding the magnitude of other factors such as age, IOP and race/ethnicity. Thinner inner retina and larger optic nerve cup measures were linked to use of systemic corticosteroids, sex hormones/modulators, presence of vasomotor/allergic rhinitis and other diseases and drugs (up to -29.3 [-49.88, -8.72] µm pRNFL T, 0.31 [0.07, 0.54] vertical CDR, 0.29 [0.03, 0.54] mm3 cup volume and -10.18 [-16.62, -3.74] µm macular GCL-IPL T; all p < 0.05). Thicker inner retina and smaller optic nerve cup measures were diffusely associated with use of antineoplastic agents, presence of liver or urinary diseases and other diseases and drugs (up to 67.12 [64.92, 69.31] µm pRNFL T, -0.31 [-0.53, -0.09] vertical CDR, -0.06 [-0.11, 0] mm3 cup volume and 28.84 [14.51, 43.17] µm macular GCL-IPL T; all p < 0.05). CONCLUSION: There are a multitude of systemic diseases and drugs associated with altered OCT inner retinal measures, with magnitudes far exceeding those of other factors such as age, IOP and race/ethnicity. These systemic factors should at least be considered during OCT assessments to ensure precise interpretation of normal versus pathological inner retinal health.

Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost.

Introduction: Regulatory agencies generate a vast amount of textual data in the review process. For example, drug labeling serves as a valuable resource for regulatory agencies, such as U.S. Food and Drug Administration (FDA) and Europe Medical Agency (EMA), to communicate drug safety and effectiveness information to healthcare professionals and patients. Drug labeling also serves as a resource for pharmacovigilance and drug safety research. Automated text classification would significantly improve the analysis of drug labeling documents and conserve reviewer resources. Methods: We utilized artificial intelligence in this study to classify drug-induced liver injury (DILI)-related content from drug labeling documents based on FDA's DILIrank dataset. We employed text mining and XGBoost models and utilized the Preferred Terms of Medical queries for adverse event standards to simplify the elimination of common words and phrases while retaining medical standard terms for FDA and EMA drug label datasets. Then, we constructed a document term matrix using weights computed by Term Frequency-Inverse Document Frequency (TF-IDF) for each included word/term/token. Results: The automatic text classification model exhibited robust performance in predicting DILI, achieving cross-validation AUC scores exceeding 0.90 for both drug labels from FDA and EMA and literature abstracts from the Critical Assessment of Massive Data Analysis (CAMDA). Discussion: Moreover, the text mining and XGBoost functions demonstrated in this study can be applied to other text processing and classification tasks.

PDATC-NCPMKL: Predicting drug's Anatomical Therapeutic Chemical (ATC) codes based on network consistency projection and multiple kernel learning.

The development and discovery of new drugs is time-consuming and needs lots of human and material resources. Therefore, discovery of novel effects of existing drugs is an important alternative way, which can accelerate the process of designing "new" drugs. The anatomical Therapeutic Chemical (ATC) classification system recommended by World Health Organization (WHO) is a basic research area in this regard. A novel ATC code of an existing drug suggests its novel effects. Some computational models have been proposed, which can predict the drug-ATC code associations. However, their performance is not very high. There still exist spaces for improvement. In this study, a new recommendation system (named PDATC-NCPMKL), which incorporated network consistency projection and multi-kernel learning, was designed to identify drug-ATC code associations. For drugs or ATC codes, several kernels were constructed, which were fused by a multiple kernel learning method and an additional kernel integration scheme. To enhance the performance, the drug-ATC code association adjacency matrix was reformulated by a variant of weighted K nearest known neighbors (WKNKN). The reformulated adjacency matrix, drug and ATC code kernels were fed into network consistency projection to generate the association score matrix. The proposed recommendation system was tested on the ATC codes at the second, third and fourth levels in drug ATC classification system using ten-fold cross-validation. The results indicated that all AUROC and AUPR values were close to or exceeded 0.96. Such performance was higher than some existing computational models. Some additional tests were conducted to prove the utility of adjacency matrix reformulation and to analyze the importance of drug and ATC code kernels.

Evaluating the effectiveness of the Ministry of Health restriction policy on seasonal antibiotic consumption trends in Saudi Arabia, 2016-2020.

Background: Understanding antibiotic consumption patterns over time is essential to optimize prescribing practices and minimizing antimicrobial resistance. This study aimed to determine whether the antibiotics restriction policy launched by the Saudi Ministry of Health in April 2018 has impacted antibiotic use by assessing changes and seasonal variations following policy enforcement. Methods: Quarterly sales data of J01 antibacterial for systemic use in standard units were obtained from the IQVIA-MIDAS database, spanning from the first quarter of 2016 to the last quarter of 2020. Antibiotics consumption was measured in defined daily doses per 1,000 inhabitant per day- in a quarter (DDDdq). A comparative analysis of antibiotic consumption pre- and post-policy periods introduction was conducted by computing the average consumption values for each period. Statistical comparison of the mean differences between the two periods were then made using independent samples t-test, Mann-Whitney U Test where needed. Time series analysis was employed to estimate the projected antibiotic consumption in the post-policy period if the restriction policy had not been implemented, which was then compared to actual consumption values to evaluate the effectiveness of the restriction policy. Results: During the pre-policy, there were seasonal trends of the total and oral antibiotic consumption through quarters, with higher consumption observed in the first and fourth quarters. In contrast, parenteral antibiotic consumption did not appear to follow a clear seasonal pattern. Following the restriction policy, there was a significant reduction in total and oral antibiotic use, with mean reductions of -96.9 DDDdq (p-value = 0.002) and -98 DDDdq (p-value = 0.002), respectively. Conversely, a significant increase in parenteral antibiotic consumption was observed with a mean increase of +1.4 DDDdq (p-value < 0.0001). The comparison between the forecasted and actual models showed that the actual antibiotics consumption for total, oral, and parenteral were lower than the corresponding forecasted values by 30%, 31%, and 34%, respectively. Conclusion: Overall, our analysis of antibiotics consumption from 2016 to 2020 displays great success for the policy implemented by the Saudi Ministry of Health in significantly reducing the total and oral use of antibiotics. However, future studies are needed to explore the increased consumption of the parenteral antibiotics as well as the persistent high consumption patterns during the fall and winter months even after the implementation of the restriction policy.

The association of medications and supplements with human male reproductive health: a systematic review.

Some medications used to treat comorbidities and conditions in reproductive-aged individuals could have a negative impact on fertility. This may occur through hormonal disruption, toxicity to germ cells and spermatozoa, functional impact on the sperm, teratogenicity potential, or ejaculatory abnormalities. Having knowledge of these potential interactions between medications and reproductive potential is important for clinicians to be aware of and guide the patient, along with their treating clinicians, to reproductively favorable alternatives when available. This review aims to summarize the state of the literature regarding medication interactions with human male reproduction using the Anatomical Therapeutic Chemical Classification System of medications.

Effect of the chronic medication use on outcome measures of hospitalized COVID-19 patients: Evidence from big data.

Background: Concerns about the role of chronically used medications in the clinical outcomes of the coronavirus disease 2019 (COVID-19) have remarkable potential for the breakdown of non-communicable diseases (NCDs) management by imposing ambivalence toward medication continuation. This study aimed to investigate the association of single or combinations of chronically used medications in NCDs with clinical outcomes of COVID-19. Methods: This retrospective study was conducted on the intersection of two databases, the Iranian COVID-19 registry and Iran Health Insurance Organization. The primary outcome was death due to COVID-19 hospitalization, and secondary outcomes included length of hospital stay, Intensive Care Unit (ICU) admission, and ventilation therapy. The Anatomical Therapeutic Chemical (ATC) classification system was used for medication grouping. The frequent pattern growth algorithm was utilized to investigate the effect of medication combinations on COVID-19 outcomes. Findings: Aspirin with chronic use in 10.8% of hospitalized COVID-19 patients was the most frequently used medication, followed by Atorvastatin (9.2%) and Losartan (8.0%). Adrenergics in combination with corticosteroids inhalants (ACIs) with an odds ratio (OR) of 0.79 (95% confidence interval: 0.68-0.92) were the most associated medications with less chance of ventilation therapy. Oxicams had the least OR of 0.80 (0.73-0.87) for COVID-19 death, followed by ACIs [0.85 (0.77-0.95)] and Biguanides [0.86 (0.82-0.91)]. Conclusion: The chronic use of most frequently used medications for NCDs management was not associated with poor COVID-19 outcomes. Thus, when indicated, physicians need to discourage patients with NCDs from discontinuing their medications for fear of possible adverse effects on COVID-19 prognosis.

Assessment and Improvement of Drug Data Structuredness From Electronic Health Records: Algorithm Development and Validation.

BACKGROUND: Digitization offers a multitude of opportunities to gain insights into current diagnostics and therapies from retrospective data. In this context, real-world data and their accessibility are of increasing importance to support unbiased and reliable research on big data. However, routinely collected data are not readily usable for research owing to the unstructured nature of health care systems and a lack of interoperability between these systems. This challenge is evident in drug data. OBJECTIVE: This study aimed to present an approach that identifies and increases the structuredness of drug data while ensuring standardization according to Anatomical Therapeutic Chemical (ATC) classification. METHODS: Our approach was based on available drug prescriptions and a drug catalog and consisted of 4 steps. First, we performed an initial analysis of the structuredness of local drug data to define a point of comparison for the effectiveness of the overall approach. Second, we applied 3 algorithms to unstructured data that translated text into ATC codes based on string comparisons in terms of ingredients and product names and performed similarity comparisons based on Levenshtein distance. Third, we validated the results of the 3 algorithms with expert knowledge based on the 1000 most frequently used prescription texts. Fourth, we performed a final validation to determine the increased degree of structuredness. RESULTS: Initially, 47.73% (n=843,980) of 1,768,153 drug prescriptions were classified as structured. With the application of the 3 algorithms, we were able to increase the degree of structuredness to 85.18% (n=1,506,059) based on the 1000 most frequent medication prescriptions. In this regard, the combination of algorithms 1, 2, and 3 resulted in a correctness level of 100% (with 57,264 ATC codes identified), algorithms 1 and 3 resulted in 99.6% (with 152,404 codes identified), and algorithms 1 and 2 resulted in 95.9% (with 39,472 codes identified). CONCLUSIONS: As shown in the first analysis steps of our approach, the availability of a product catalog to select during the documentation process is not sufficient to generate structured data. Our 4-step approach reduces the problems and reliably increases the structuredness automatically. Similarity matching shows promising results, particularly for entries with no connection to a product catalog. However, further enhancement of the correctness of such a similarity matching algorithm needs to be investigated in future work.

Drug purchases prior to conception and the risk of gestational diabetes mellitus.

OBJECTIVE: Some drugs have adverse effects on glucose metabolism, but it is unknown whether prescription drugs used prior to conception influence the future risk of gestational diabetes mellitus (GDM). Our study evaluated whether the purchase of prescription drugs 6 months prior to conception was associated with the occurrence of GDM. METHODS: This cohort study enrolled women with a Finnish background who delivered between 2009 and 2015 in the city of Vantaa, Finland (N = 10,455). Data on maternal characteristics and prescription drug purchases were obtained from national health registers. The use of a unique personal identification number enabled us to combine the register data on an individual level. RESULTS: Six months prior to conception, women who had pregnancies complicated by GDM purchased more prescription drugs than women without GDM (1.38 ± 2.04 vs. 1.11 ± 1.80). The GDM risk was higher in women with higher numbers of prescription purchases and those with more than three deliveries. CONCLUSIONS: Multiparous women who purchase several prescription drugs should be given personalized counseling to prevent GDM.

Antibiotic consumption in the public sector of the Limpopo province, South Africa, 2014-2018.

Background: Antibiotic consumption (ABC) surveillance is a critical component of the strategic priority response to the increasing antibiotic resistance threat. Levels of ABC at the national and provincial levels in South Africa are unknown because of inadequate ABC surveillance systems and literature. Antibiotic consumption in the public sector of Limpopo province, South Africa, 2014-2018. Methods: This retrospective study used sales data retrieved from a pharmaceutical warehouse distribution database to quantify ABC. Antibiotic consumption was measured by the defined daily dose (DDD) per 1000 inhabitants per day (DID) and 75% drug utilisation index (DU75%). Change in consumption was measured by the compound annual growth rate (CAGR). Results: Between 2014 and 2018, the mean ABC was 4.6 ± 1.0 DID, with an overall decrease of 1.6% in the CAGR. Penicillins (2.3 ± 0.8; 50.0%), sulphonamide and trimethoprim combinations (1.4 ± 0.3 DID; 30.4%) were the most consumed antibiotics. Macrolides had the highest relative increase in consumption during the study period, with a CAGR of 18.5%. In contrast, tetracyclines had the highest relative decrease in consumption, with a CAGR of 100.0%. The CAGR ratio for broad- to narrow-spectrum increased by 39.3%, from 0.4 in 2014 to 2.1 in 2018. The DU75% comprised amoxicillin (28.4%), sulphamethoxazole and trimethoprim (SMX-TMP) (27.2%), doxycycline (12.3%) and azithromycin (9.2%). Conclusion: While ABC remained relatively stable throughout the study, there was an increase in broad-spectrum ABC that requires further investigation. Contribution: This study contributes ABC surveillance data in Southern Africa, described by ATC classification, which is essential for monitoring and evaluating antibiotic stewardship programmes.

Rationalising Antipsychotic Prescribing in Dementia (RAPID) complex intervention: A mixed-methods feasibility intervention study.

Background: To help address the issue of inappropriate antipsychotic prescribing to nursing home residents with dementia, the 'Rationalising Antipsychotic Prescribing in Dementia' (RAPID) complex intervention was developed, comprising staff education and training, academic detailing and a novel resident assessment tool. Objectives: The primary objective was to assess the feasibility and acceptability of the RAPID complex intervention in a nursing home setting. The secondary objective was to describe associated trends in psychotropic prescribing, falls, and behavioural symptoms. Methods: A mixed-methods feasibility intervention study in one large nursing home in Ireland was undertaken between 07/2017 and 01/2018. Focus groups and semi-structured interviews were conducted with nursing home staff and GPs at the end of the 3-month follow up period to assess participants' experience of the intervention. Quantitative measurements included pre- and post-course evaluation and psychotropic prescribing rates. Results: Sixteen nursing home staff members attended the two education and training days (21% attendance rate), and four GPs participated in the academic detailing sessions (100% attendance rate). Participants of the focus groups and interviews (n = 18) found the education and training beneficial for their work and expressed a desire to continue educating new staff after the study's completion. However, there was limited usage of the resident assessment tool. Participants also offered recommendations to enhance the intervention.The proportion of dementia residents prescribed at least one regular antipsychotic was stable over the 3-months pre-intervention at 45% (n = 18), and at baseline at 44% (n = 19) but decreased slightly to 36% (n = 14) at 3-months post-intervention. At the same time the absolute number of 'PRN' psychotropics administered monthly to dementia residents decreased substantially from 90 at baseline to 69 at 3-months post-intervention. Conclusion: The RAPID complex intervention was broadly feasible to conduct and may be acceptable to stakeholders. However, before it can be evaluated in larger scale studies, certain protocol modifications and further exploratory work are required to improve implementation.

Identifying the kind behind SMILES-anatomical therapeutic chemical classification using structure-only representations.

Anatomical Therapeutic Chemical (ATC) classification for compounds/drugs plays an important role in drug development and basic research. However, previous methods depend on interactions extracted from STITCH dataset which may make it depend on lab experiments. We present a pilot study to explore the possibility of conducting the ATC prediction solely based on the molecular structures. The motivation is to eliminate the reliance on the costly lab experiments so that the characteristics of a drug can be pre-assessed for better decision-making and effort-saving before the actual development. To this end, we construct a new benchmark consisting of 4545 compounds which is with larger scale than the one used in previous study. A light-weight prediction model is proposed. The model is with better explainability in the sense that it is consists of a straightforward tokenization that extracts and embeds statistically and physicochemically meaningful tokens, and a deep network backed by a set of pyramid kernels to capture multi-resolution chemical structural characteristics. Its efficacy has been validated in the experiments where it outperforms the state-of-the-art methods by 15.53% in accuracy and by 69.66% in terms of efficiency. We make the benchmark dataset, source code and web server open to ease the reproduction of this study.

Hormonal contraception and risk of breast cancer and breast cancer in situ among Swedish women 15-34 years of age: A nationwide register-based study.

Background: Evidence on a possible association between newer hormonal contraceptives (HC) and risk of breast cancer remains inconclusive, especially as concerns progestogen-only methods. Methods: In this nationwide prospective cohort study, all Swedish women aged 15-34 at study start on January 1st 2005, or who turned 15 years during the study period, were followed until December 31st 2017. Using information from seven National Registers, we assessed the risk ratio of developing breast cancer and breast cancer in situ in relation to different HC using Poisson regression. We adjusted the analyses for several known confounders of breast cancer. Findings: This cohort included 1.5 million women providing more than 14 million person-years. During the study period, 3842 women were diagnosed with breast cancer. Compared with never users of any HC, we found no increased risk of developing breast cancer among current users of any combined HC, IRR 1.03 (0.91-1.16), whereas current users of progestogen-only methods had an increased risk of developing breast cancer, IRR 1.32 (1.20-1.45). Across all types of HC, the risk of developing breast cancer appeared to be highest the first five years of use (combined HC IRR 1.39 (1.14-1.69); progestogen-only methods IRR 1.74 (1.44-2.10). The risk disappeared ten years after the women stopped using HC. The absolute risk of breast cancer per 100,000 women-years was 22.4 for never users, 10.9 for current users of combined HC, and 29.8 for current users of progestogen-only methods. Interpretation: Current use of progestogen-only methods is associated with a small increased risk of developing breast cancer, whereas we could only detect an increased risk among users of combined HC during the first five years of use. This may partly be explained by a selective prescription of progestogen-only methods to women with risk factors for breast cancer, like smoking or obesity. As the absolute risk of breast cancer was small, the many health benefits associated with HC must also be taken into account in contraceptive counselling. Funding: This study was funded by the Swedish Cancer Society and by the Uppsala County Council, the Faculty of Medicine at Uppsala University.

Use of medication data alone to identify diagnoses and related contraindications: Application of algorithms to close a common documentation gap.

AIMS: Automated checks for medication-related problems have become a cornerstone of medication safety. In many clinical settings medication checks remain confined to drug-drug interactions because only medication data are available in an adequately coded form, leaving possible contraindicated drug-disease combinations unaccounted for. Therefore, we devised algorithms that identify frequently contraindicated diagnoses based on medication patterns related to these diagnoses. METHODS: We identified drugs that are associated with diseases constituting common contraindications based on their exclusive use for these conditions (such as allopurinol for gout or salbutamol for bronchial obstruction). Expert-based and machine learning algorithms were developed to identify diagnoses based on highly specific medication patterns. The applicability, sensitivity and specificity of the approach were assessed by using an anonymized real-life sample of medication and diagnosis data excerpts from 3506 discharge records of geriatric patients. RESULTS: Depending on the algorithm, the desired focus (i.e., sensitivity vs. specificity) and the disease, we were able to identify the diagnoses gout, epilepsy, coronary artery disease, congestive heart failure and bronchial obstruction with a specificity of 44.0-99.8% (95% CI 41.7-100.0%) and a sensitivity of 3.8-83.1% (95% CI 1.0-86.1%). Using only medication data, we were able to identify 123 (51.3%) of 240 contraindications identified by experts with access to medication data and diagnoses. CONCLUSION: This study provides a proof of principle that some key diagnosis-related contraindications can be identified based on a patient's medication data alone, while others cannot be identified. This approach offers new opportunities to analyse drug-disease contraindications in community pharmacy or clinical routine data.

Cancer Incidence and Mortality According to Pre-Existing Heart Failure in a Community-Based Cohort.

Background: Studies assessing whether heart failure (HF) is associated with cancer and cancer-related mortality have yielded conflicting results. Objectives: This study assessed cancer incidence and mortality according to pre-existing HF in a community-based cohort. Methods: Among individuals ≥50 years of age from the Puglia region in Italy with administrative health data from 2002 to 2018, no cancer within 3 years before the baseline evaluation, and ≥5-year follow-up, the study matched 104,020 subjects with HF at baseline with 104,020 control subjects according to age, sex, drug-derived complexity index, Charlson comorbidity index, and follow-up duration. Cancer incidence and mortality were defined based on International Classification of Diseases-Ninth Revision codes in hospitalization records or death certificates. Results: The incidence rate of cancer in HF patients and control subjects was 21.36 (95% CI: 20.98-21.74) and 12.42 (95% CI: 12.14-12.72) per 1000 person-years, respectively, with the HR being 1.76 (95% CI: 1.71-1.81). Cancer mortality was also higher in HF patients than control subjects (HR: 4.11; 95% CI: 3.86-4.38), especially in those <70 years of age (HR: 7.54; 95% CI: 6.33-8.98 vs HR: 3.80; 95% CI: 3.44-4.19 for 70-79 years of age; and HR: 3.10; 95% CI: 2.81-3.43 for ≥80 years of age). The association between HF and cancer mortality was confirmed in a competing risk analysis (subdistribution HR: 3.48; 95% CI: 3.27-3.72). The HF-related excess risk applied to the majority of cancer types. Among HF patients, prescription of high-dose loop diuretic was associated with higher cancer incidence (HR: 1.11; 95% CI: 1.03-1.21) and mortality (HR: 1.35; 95% CI: 1.19-1.53). Conclusions: HF is associated with an increased risk of cancer and cancer-related mortality, which may be heightened in decompensated states.

Utilization of healthcare and prescription medicines after non-pharmacological interventions for depression - A 3-year register follow-up of an RCT in primary care.

Depression is a common, recurrent disorder. There is a need for readily available treatments with few negative side effects, that demands little resources and that are effective both in the short- and long term. Our aim was to investigate the long-term effectiveness of two different interventions; physical exercise and internet-based cognitive behavioural therapy (internet-CBT), compared to usual care in patients with mild to moderate depression in a Swedish primary care setting. We performed a register-based 3-year follow-up study of participants in the randomized controlled trial REGASSA (n = 940) using healthcare utilization and dispensed medicines as outcomes. We found no difference between the three groups regarding proportion of participants consulting healthcare due to mental illness or pain during follow-up. Regarding number of consultations, there was no difference between the groups, except for consultations related to pain. For this outcome both treatment arms had significantly fewer consultations compared to usual care, during year 2-3, the risk ratio (RR) for physical exercise and internet-CBT was 0.64 (95% CI = 0.43-0.95) and 0.61 (95% CI = 0.41-0.90), respectively. A significantly lower proportion of patients in both treatment arms were dispensed hypnotics and sedatives year 2-3 compared to the usual care arm, RR for both physical exercise and internet-CBT was 0.72 (95% CI = 0.53-0.98). No other differences between the groups were found. In conclusion, considering long-term effects, both physical exercise and internet-CBT, being resource-efficient treatments, could be considered as appropriate additions for patients with mild to moderate depression in primary care settings. Trial registration: The original RCT was registered with the German Clinical Trial Register (DRKS study ID: DRKS00008745).

Consumption of Antibacterials for Systemic Use in Slovakia: A National Study and the Quality Indicators for Outpatient Antibiotic Use.

This paper aims to analyse the consumption of antibiotics in the Slovak health care system from 2011 to 2020. The data source on the consumption of antibiotics is sales data from SUKL and NCZI. The study employed the ATC/DDD Index and focused on the consumption of antibiotics in the primary care sector. Total antibiotic consumption decreased from 19.21 DID in 2011 to 13.16 DID in 2020. Consumption of beta-lactamase-sensitive penicillins, expressed as a percentage of the total consumption of antibiotics, decreased from 8.4% in 2011 to 4.2% in 2020. Consumption of the combination of penicillins, including beta-lactamase inhibitor, expressed as a percentage of the total consumption of antibiotics, increased from 16.2% in 2011 to 17.9% in 2020. Consumption of third- and fourth-generation cephalosporins, expressed as the percentage of the total consumption of antibiotics, increased from 2.0% in 2011 to 4.6% in 2020. Consumption of fluoroquinolones, expressed as the percentage of the total consumption of antibiotics, decreased from 10.7% in 2011 to 8.6% in 2020. Overall, antibiotic consumption significantly changed in Slovakia from 2011 to 2020. The ratio of the consumption of broad-spectrum to the consumption of narrow-spectrum penicillins, cephalosporins and macrolides decreased from 14.98 in 2011 to 13.38 in 2020.

GLP-1 based therapies and disease course of inflammatory bowel disease.

BACKGROUND: The disease course of inflammatory bowel disease (IBD) following treatment with glucagon-like peptide (GLP)-1 based therapies is unclear. The aim of this study was to examine the disease course of IBD in patients treated with GLP-1 based therapies compared with treatment with other antidiabetics. METHODS: Using nationwide Danish registries, we identified patients with IBD and type 2 diabetes who received antidiabetic treatment between 1 January 2007 and 31 March 2019. The primary outcome was a composite of the need for oral corticosteroids, tumour necrosis factor-α inhibitors, IBD-related hospitalisation, or IBD-related surgery. In the setting of a new-user active comparator design, we used Poisson regression to estimate incidence rate ratios (IRR) comparing treatment with GLP-1 receptor agonists and dipeptidyl peptidase (DPP)-4 inhibitors with other antidiabetic therapies. The analyses were adjusted for age, sex, calendar year, IBD severity, and metformin use. FINDINGS: We identified 3751 patients with a diagnosis of IBD and type 2 diabetes and with a prescription of an antidiabetic drug (GLP-1 receptor agonists/DPP-4 inhibitors: 982 patients; other antidiabetic treatment: 2769 patients). The adjusted IRR of the composite outcome was 0·52 (95% CI: 0·42-0·65) for patients exposed to GLP-1 receptor agonists/DPP-4 inhibitors compared with patients exposed to other antidiabetics. INTERPRETATION: In patients with IBD and type 2 diabetes, we observed a lower risk of adverse clinical events amongst patients treated with GLP-1 based therapies compared with treatment with other antidiabetics. These findings suggest that treatment with GLP-1 based therapies may improve the disease course of IBD.

Drug repositioning by prediction of drug's anatomical therapeutic chemical code via network-based inference approaches.

Drug discovery and development is a time-consuming and costly process. Therefore, drug repositioning has become an effective approach to address the issues by identifying new therapeutic or pharmacological actions for existing drugs. The drug's anatomical therapeutic chemical (ATC) code is a hierarchical classification system categorized as five levels according to the organs or systems that drugs act and the pharmacology, therapeutic and chemical properties of drugs. The 2nd-, 3rd- and 4th-level ATC codes reserved the therapeutic and pharmacological information of drugs. With the hypothesis that drugs with similar structures or targets would possess similar ATC codes, we exploited a network-based approach to predict the 2nd-, 3rd- and 4th-level ATC codes by constructing substructure drug-ATC (SD-ATC), target drug-ATC (TD-ATC) and Substructure&Target drug-ATC (STD-ATC) networks. After 10-fold cross validation and two external validations, the STD-ATC models outperformed the SD-ATC and TD-ATC ones. Furthermore, with KR as fingerprint, the STD-ATC model was identified as the optimal model with AUC values at 0.899 ± 0.015, 0.916 and 0.893 for 10-fold cross validation, external validation set 1 and external validation set 2, respectively. To illustrate the predictive capability of the STD-ATC model with KR fingerprint, as a case study, we predicted 25 FDA-approved drugs (22 drugs were actually purchased) to have potential activities on heart failure using that model. Experiments in vitro confirmed that 8 of the 22 old drugs have shown mild to potent cardioprotective activities on both hypoxia model and oxygen-glucose deprivation model, which demonstrated that our STD-ATC prediction model would be an effective tool for drug repositioning.

Utilization of the Parenteral Morphine in Emergency Department using the Anatomical Therapeutic Chemical Classification/Defined Daily Doses (ATC/DDD) System.

OBJECTIVE: To evaluate the utilization of the parenteral morphine in Emergency Department (ED) using the Anatomical Therapeutic Chemical Classification/Defined Daily Doses (ATC/DDD) system. METHODS: In this retrospective cross-sectional study, morphine administration was recorded in 4-year time period from January 2013 to December 2016 in the ED of a referral center. The dose of the administered morphine was evaluated using the ATC/DDD system. The ATC/DDD of the parenteral morphine was calculated based on the world health organization (WHO). The data was evaluated based on the different diagnosis and conditions using the ATC/DDD protocol. RESULTS: In this study, 500 patients referred to ED with mean age of 48.29 ± 10.10 years were included. There were 306 (61.2%) men and 194 (38.8%) women among the patients. The lowest and highest DDD of parenteral morphine were 0.1 and 0.43, respectively. The utilization of parenteral morphine was significantly higher in men when compared to women (p<0.001). Those with history of tricyclic anti-depressant (TCA) consumption (p<0.001) and opium addiction (p<0.001) had significantly higher parenteral morphine utilization. Those with pain in the extremities and chest pain had significantly higher parenteral morphine utilization (p<0.001). CONCLUSION: The utilization of parenteral morphine in the ED of our center was higher than the WHO standard dosage. The morphine utilization was associated with male gender, opium addiction and TCA consumption.