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  • 1
    Online Resource
    Online Resource
    Risk of Hemorrhagic Stroke in Patients Exposed to Nonsteroidal Anti-Inflammatory Drugs: A Meta-Analysis of Observational Studies
    S. Karger AG ; 2018
    In:  Neuroepidemiology Vol. 51, No. 3-4 ( 2018), p. 166-176
    Details
    In: Neuroepidemiology, S. Karger AG, Vol. 51, No. 3-4 ( 2018), p. 166-176
    Abstract: 〈 b 〉 〈 i 〉 Background and Aim: 〈 /i 〉 〈 /b 〉 Nonsteroidal anti-inflammatory drugs (NSAIDs) are one of the most common pain relief medications, but the risk of hemorrhagic stroke in patients taking these medications is unclear. In this study, our aim was to systematically review, synthesize, and critique the epidemiological studies that evaluate the association between NSAIDs and hemorrhagic stroke risk. We therefore assessed the current state of knowledge, filling the gaps in our existing concern, and make a recommendation for future research. 〈 b 〉 〈 i 〉 Methods: 〈 /i 〉 〈 /b 〉 We searched for articles in PubMed, EMBASE, Scopus, and Web of Science between January 1, 1990, and July 30, 2017, which reported on the association between the use of NSAIDs and hemorrhagic stroke. The search was limited to studies published in English. The quality of the included studies was assessed in accordance with the Cochrane guidelines and the Newcastle-Ottawa criteria. Summary risk ratios (RRs) with 95% CI were pooled using a random-effects model. Subgroup and sensitivity analyses were also conducted. 〈 b 〉 〈 i 〉 Results: 〈 /i 〉 〈 /b 〉 We selected 15 out of the 785 unique abstracts for full-text review using our selection criteria, and 13 out of these 15 studies met all of our inclusion criteria. The overall pooled RR of hemorrhagic stroke was 1.332 (95% CI 1.105–1.605, 〈 i 〉 p 〈 /i 〉 = 0.003) for the random effect model. In the subgroup analysis, a significant risk was observed among meloxicam, diclofenac, and indomethacin users (RR 1.48; 95% CI 1.149–1.912, RR 1.392; 95% CI 1.107–1.751, and RR 1.363; 95% CI 1.088–1.706). In addition, a greater risk was found in studies from Asia (RR 1.490, 95% CI 1.226–1.811) followed by Europe (RR 1.393, 95% CI 1.104–1.757) and Australia (RR 1.361, 95% CI 0.755–2.452). 〈 b 〉 〈 i 〉 Conclusion: 〈 /i 〉 〈 /b 〉 Our results indicated that the use of NSAIDs is significantly associated with a higher risk of developing hemorrhagic stroke. These results should be interpreted with caution because they may be confounded owing to the observational design of the individual studies. Nevertheless, we recommend that NSAIDs should be used judiciously, and their efficacy and safety should be monitored proactively.
    Type of Medium: Online Resource
    ISSN: 0251-5350 , 1423-0208
    URL: Article
    DOI: 10.1159/000490741
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2018
    detail.hit.zdb_id: 1483032-2
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  • 2
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    Predicting Hepatocellular Carcinoma With Minimal Features From Electronic Health Records: Development of a Deep Learning Model
    JMIR Publications Inc. ; 2021
    In:  JMIR Cancer Vol. 7, No. 4 ( 2021-10-28), p. e19812-
    Details
    In: JMIR Cancer, JMIR Publications Inc., Vol. 7, No. 4 ( 2021-10-28), p. e19812-
    Abstract: Hepatocellular carcinoma (HCC), usually known as hepatoma, is the third leading cause of cancer mortality globally. Early detection of HCC helps in its treatment and increases survival rates. Objective The aim of this study is to develop a deep learning model, using the trend and severity of each medical event from the electronic health record to accurately predict the patients who will be diagnosed with HCC in 1 year. Methods Patients with HCC were screened out from the National Health Insurance Research Database of Taiwan between 1999 and 2013. To be included, the patients with HCC had to register as patients with cancer in the catastrophic illness file and had to be diagnosed as a patient with HCC in an inpatient admission. The control cases (non-HCC patients) were randomly sampled from the same database. We used age, gender, diagnosis code, drug code, and time information as the input variables of a convolution neural network model to predict those patients with HCC. We also inspected the highly weighted variables in the model and compared them to their odds ratio at HCC to understand how the predictive model works Results We included 47,945 individuals, 9553 of whom were patients with HCC. The area under the receiver operating curve (AUROC) of the model for predicting HCC risk 1 year in advance was 0.94 (95% CI 0.937-0.943), with a sensitivity of 0.869 and a specificity 0.865. The AUROC for predicting HCC patients 7 days, 6 months, 1 year, 2 years, and 3 years early were 0.96, 0.94, 0.94, 0.91, and 0.91, respectively. Conclusions The findings of this study show that the convolutional neural network model has immense potential to predict the risk of HCC 1 year in advance with minimal features available in the electronic health records.
    Type of Medium: Online Resource
    ISSN: 2369-1999
    URL: Article
    DOI: 10.2196/19812
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2021
    detail.hit.zdb_id: 2928105-2
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  • 3
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    Association between Anemia and Risk of Parkinson Disease
    Hindawi Limited ; 2021
    In:  Behavioural Neurology Vol. 2021 ( 2021-7-7), p. 1-8
    Details
    In: Behavioural Neurology, Hindawi Limited, Vol. 2021 ( 2021-7-7), p. 1-8
    Abstract: Background and Objective. People with anemia have higher rates of developing Parkinson disease (PD) than the general population. Previous epidemiological studies have invested the risk of PD in patients with anemia. However, the findings are still inconclusive. Therefore, we did a systematic review with meta-analysis to clarify the association between anemia and risk of PD. Methods. We systematically searched articles on electronic databases such as PubMed, Embase, Scopus, and Google Scholar between January 1, 2000 and July 30, 2020. Articles were independently evaluated by two authors. We included observational studies (case-control and cohort) and calculated the risk ratios (RRs) for associated with anemia and PD. Heterogeneity among the studies was assessed using the Q and I 2 statistic. We utilized the random-effect model to calculate the overall RR with 95% CI. Results. A total of 342 articles were identified in the initial searches, and 7 full-text articles were evaluated for eligibility. Three articles were further excluded for prespecified reasons including insufficient data and duplications, and 4 articles were included in our systematic review and meta-analysis. A random effect model meta-analysis of all 4 studies showed no increased risk of PD in patients with anemia ( N = 4 , R R adjusted = 1.17 (95% CI: 0.94-1.45, p = 0.15 ). However, heterogeneity among the studies was significant ( I 2 = 92.60 , p = 〈 0.0001 ). The pooled relative risk of PD in female patients with anemia was higher ( N = 3 , R R adjusted = 1.14 (95% CI: 0.83-1.57, p = 0.40 ) as compared to male patients with anemia ( N = 3 , R R adjusted = 1.09 (95% CI: 0.83-1.42, p = 0.51 ). Conclusion. This is the first meta-analysis that shows that anemia is associated with higher risk of PD when compared with patients without anemia. However, more studies are warranted to evaluate the risk of PD among patients with anemia.
    Type of Medium: Online Resource
    ISSN: 1875-8584 , 0953-4180
    URL: Article
    DOI: 10.1155/2021/8360627
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2035544-0
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  • 4
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    Increased Risk of Dementia in Patients with Antidepressants: A Meta-Analysis of Observational Studies
    Hindawi Limited ; 2018
    In:  Behavioural Neurology Vol. 2018 ( 2018-07-10), p. 1-8
    Details
    In: Behavioural Neurology, Hindawi Limited, Vol. 2018 ( 2018-07-10), p. 1-8
    Abstract: Antidepressants are the most commonly and widely used medication for its effectiveness in the treatment of anxiety and depression. A few epidemiological studies have documented that antidepressant is associated with increased risk of dementia so far. Here, our aim is to assess the association between antidepressant use and risk of dementia in elderly patients. We searched articles through MEDLINE, EMBASE, Google, and Google Scholar from inception to December 1, 2017, that reported on the association between antidepressant use and dementia risk. Data were collected from each study independently, and study duplication was checked by at least three senior researchers based on a standardized protocol. Summary relative risk (RR) with 95% CI was calculated by using a random-effects model. We selected 9 out of 754 unique abstracts for full-text review using our predetermined selection criteria, and 5 out of these 9 studies, comprising 53,955 participants, met all of our inclusion criteria. The overall pooled RR of dementia was 1.75 (95% CI: 1.033–2.964) for SSRIs whereas the overall pooled RR of dementia was 2.131 (95% CI: 1.427–3.184) for tricyclic use. Also, MAOIs showed a high rate of increase with significant heterogeneity. Our findings indicate that antidepressant use is significantly associated with an increased risk of developing dementia. Therefore, we suggest physicians to carefully prescribe antidepressants, especially in elder patients. Additionally, treatment should be stopped if any symptoms related to dementia are to be noticed.
    Type of Medium: Online Resource
    ISSN: 0953-4180 , 1875-8584
    URL: Article
    DOI: 10.1155/2018/5315098
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2018
    detail.hit.zdb_id: 2035544-0
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  • 5
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    Gender-based personalized pharmacotherapy: a systematic review
    Springer Science and Business Media LLC ; 2017
    In:  Archives of Gynecology and Obstetrics Vol. 295, No. 6 ( 2017-6), p. 1305-1317
    Details
    In: Archives of Gynecology and Obstetrics, Springer Science and Business Media LLC, Vol. 295, No. 6 ( 2017-6), p. 1305-1317
    Type of Medium: Online Resource
    ISSN: 0932-0067 , 1432-0711
    URL: Article
    DOI: 10.1007/s00404-017-4363-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 1458450-5
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  • 6
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    Risk of cancer in long‐term levothyroxine users: Retrospective population‐based study
    Wiley ; 2021
    In:  Cancer Science Vol. 112, No. 6 ( 2021-06), p. 2533-2541
    Details
    In: Cancer Science, Wiley, Vol. 112, No. 6 ( 2021-06), p. 2533-2541
    Abstract: Levothyroxine is a widely prescribed medication for the treatment of an underactive thyroid. The relationship between levothyroxine use and cancer risk is largely underdetermined. To investigate the magnitude of the possible association between levothyroxine use and cancer risk, this retrospective case‐control study was conducted using Taiwan’s Health and Welfare Data Science Center database. Cases were defined as all patients who were aged ≥20 years and had a first‐time diagnosis for cancer at any site for the period between 2001 and 2011. Multivariable conditional logistic regression models were used to calculate an adjusted odds ratio (AOR) to reduce potential confounding factors. A total of 601 733 cases and 2 406 932 controls were included in the current study. Levothyroxine users showed a 50% higher risk of cancer at any site (AOR: 1.50, 95% CI: 1.46‐1.54; P   〈  .0001) compared with non–users. Significant increased risks were also observed for brain cancer (AOR: 1.90, 95% CI: 1.48‐2.44; P   〈  .0001), skin cancer (AOR: 1.42, 95% CI: 1.17‐1.72; P   〈  .0001), pancreatic cancer (AOR: 1.27, 95% CI: 1.01‐1.60; P  = .03), and female breast cancer (AOR: 1.24, 95% CI: 1.15‐1.33; P   〈  .0001). Our study results showed that levothyroxine use was significantly associated with an increased risk of cancer, particularly brain, skin, pancreatic, and female breast cancers. Levothyroxine remains a highly effective therapy for hypothyroidism; therefore, physicians should carefully consider levothyroxine therapy and monitor patients’ condition to avoid negative outcomes. Additional studies are needed to confirm these findings and to evaluate the potential biological mechanisms.
    Type of Medium: Online Resource
    ISSN: 1347-9032 , 1349-7006
    URL: Issue
    URL: Article
    DOI: 10.1111/cas.v112.6
    DOI: 10.1111/cas.14908
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2115647-5
    detail.hit.zdb_id: 2111204-6
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  • 7
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    Logical Observation Identifiers Names and Codes (LOINC®) Applied to Microbiology: A National Laboratory Mapping Experience in Taiwan
    MDPI AG ; 2021
    In:  Diagnostics Vol. 11, No. 9 ( 2021-08-28), p. 1564-
    Details
    In: Diagnostics, MDPI AG, Vol. 11, No. 9 ( 2021-08-28), p. 1564-
    Abstract: Background and Objective: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-intensive, and requires substantial domain expertise. Our objective was to provide training and evaluate the performance of LOINC mapping of 20 pathogens from 53 hospitals participating in the National Notifiable Disease Surveillance System (NNDSS). Methods: Complete mapping codes for 20 pathogens (nine bacteria and 11 viruses) were requested from all participating hospitals to review between January 2014 and December 2016. Participating hospitals mapped those pathogens to LOINC terminology, utilizing the Regenstrief LOINC mapping assistant (RELMA) and reported to the NNDSS, beginning in January 2014. The mapping problems were identified by expert panels that classified frequently asked questionnaires (FAQs) into seven LOINC categories. Finally, proper and meaningful suggestions were provided based on the error pattern in the FAQs. A general meeting was organized if the error pattern proved to be difficult to resolve. If the experts did not conclude the local issue’s error pattern, a request was sent to the LOINC committee for resolution. Results: A total of 53 hospitals participated in our study. Of these, 26 (49.05%) used homegrown and 27 (50.95%) used outsourced LOINC mapping. Hospitals who participated in 2015 had a greater improvement in LOINC mapping than those of 2016 (26.5% vs. 3.9%). Most FAQs were related to notification principles (47%), LOINC system (42%), and LOINC property (26%) in 2014, 2015, and 2016, respectively. Conclusions: The findings of our study show that multiple stage approaches improved LOINC mapping by up to 26.5%.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics11091564
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662336-5
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  • 8
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    Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation
    MDPI AG ; 2020
    In:  Journal of Clinical Medicine Vol. 9, No. 4 ( 2020-04-03), p. 1018-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 9, No. 4 ( 2020-04-03), p. 1018-
    Abstract: Background and Objective: Accurate retinal vessel segmentation is often considered to be a reliable biomarker of diagnosis and screening of various diseases, including cardiovascular diseases, diabetic, and ophthalmologic diseases. Recently, deep learning (DL) algorithms have demonstrated high performance in segmenting retinal images that may enable fast and lifesaving diagnoses. To our knowledge, there is no systematic review of the current work in this research area. Therefore, we performed a systematic review with a meta-analysis of relevant studies to quantify the performance of the DL algorithms in retinal vessel segmentation. Methods: A systematic search on EMBASE, PubMed, Google Scholar, Scopus, and Web of Science was conducted for studies that were published between 1 January 2000 and 15 January 2020. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) procedure. The DL-based study design was mandatory for a study’s inclusion. Two authors independently screened all titles and abstracts against predefined inclusion and exclusion criteria. We used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for assessing the risk of bias and applicability. Results: Thirty-one studies were included in the systematic review; however, only 23 studies met the inclusion criteria for the meta-analysis. DL showed high performance for four publicly available databases, achieving an average area under the ROC of 0.96, 0.97, 0.96, and 0.94 on the DRIVE, STARE, CHASE_DB1, and HRF databases, respectively. The pooled sensitivity for the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.77, 0.79, 0.78, and 0.81, respectively. Moreover, the pooled specificity of the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.97, 0.97, 0.97, and 0.92, respectively. Conclusion: The findings of our study showed the DL algorithms had high sensitivity and specificity for segmenting the retinal vessels from digital fundus images. The future role of DL algorithms in retinal vessel segmentation is promising, especially for those countries with limited access to healthcare. More compressive studies and global efforts are mandatory for evaluating the cost-effectiveness of DL-based tools for retinal disease screening worldwide.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm9041018
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662592-1
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  • 9
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    Development of an Artificial Intelligence–Based Automated Recommendation System for Clinical Laboratory Tests: Retrospective Analysis of the National Health Insurance Database
    JMIR Publications Inc. ; 2020
    In:  JMIR Medical Informatics Vol. 8, No. 11 ( 2020-11-18), p. e24163-
    Details
    In: JMIR Medical Informatics, JMIR Publications Inc., Vol. 8, No. 11 ( 2020-11-18), p. e24163-
    Abstract: Laboratory tests are considered an essential part of patient safety as patients’ screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains underestimated by policymakers and clinicians. Nowadays, artificial intelligence methods such as machine learning and deep learning (DL) have been extensively used as powerful tools for pattern recognition in large data sets. Therefore, developing an automated laboratory test recommendation tool using available data from electronic health records (EHRs) could support current clinical practice. Objective The objective of this study was to develop an artificial intelligence–based automated model that can provide laboratory tests recommendation based on simple variables available in EHRs. Methods A retrospective analysis of the National Health Insurance database between January 1, 2013, and December 31, 2013, was performed. We reviewed the record of all patients who visited the cardiology department at least once and were prescribed laboratory tests. The data set was split into training and testing sets (80:20) to develop the DL model. In the internal validation, 25% of data were randomly selected from the training set to evaluate the performance of this model. Results We used the area under the receiver operating characteristic curve, precision, recall, and hamming loss as comparative measures. A total of 129,938 prescriptions were used in our model. The DL-based automated recommendation system for laboratory tests achieved a significantly higher area under the receiver operating characteristic curve (AUROCmacro and AUROCmicro of 0.76 and 0.87, respectively). Using a low cutoff, the model identified appropriate laboratory tests with 99% sensitivity. Conclusions The developed artificial intelligence model based on DL exhibited good discriminative capability for predicting laboratory tests using routinely collected EHR data. Utilization of DL approaches can facilitate optimal laboratory test selection for patients, which may in turn improve patient safety. However, future study is recommended to assess the cost-effectiveness for implementing this model in real-world clinical settings.
    Type of Medium: Online Resource
    ISSN: 2291-9694
    URL: Article
    DOI: 10.2196/24163
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2020
    detail.hit.zdb_id: 2798261-0
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  • 10
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    Use of Mobile Phone App Interventions to Promote Weight Loss: Meta-Analysis
    JMIR Publications Inc. ; 2020
    In:  JMIR mHealth and uHealth Vol. 8, No. 7 ( 2020-7-22), p. e17039-
    Details
    In: JMIR mHealth and uHealth, JMIR Publications Inc., Vol. 8, No. 7 ( 2020-7-22), p. e17039-
    Abstract: Obesity and lack of physical activity are major health risk factors for many life-threatening diseases, such as cardiovascular diseases, type 2 diabetes, and cancer. The use of mobile app interventions to promote weight loss and boost physical activity among children and adults is fascinating owing to the demand for cutting-edge and more efficient interventions. Previously published studies have examined different types of technology-based interventions and their impact on weight loss and increase in physical activity, but evidence regarding the impact of only a mobile phone app on weight loss and increase in physical activity is still lacking. Objective The main objective of this study was to assess the efficacy of a mobile phone app intervention for reducing body weight and increasing physical activity among children and adults. Methods PubMed, Google Scholar, Scopus, EMBASE, and the Web of Science electronic databases were searched for studies published between January 1, 2000, and April 30, 2019, without language restrictions. Two experts independently screened all the titles and abstracts to find the most appropriate studies. To be included, studies had to be either a randomized controlled trial or a case-control study that assessed a mobile phone app intervention with body weight loss and physical activity outcomes. The Cochrane Collaboration Risk of Bias tool was used to examine the risk of publication bias. Results A total of 12 studies involving a mobile phone app intervention were included in this meta-analysis. Compared with the control group, the use of a mobile phone app was associated with significant changes in body weight (−1.07 kg, 95% CI −1.92 to −0.21, P=.01) and body mass index (−0.45 kg/m2, 95% CI −0.78 to −0.12, P=.008). Moreover, a nonsignificant increase in physical activity was observed (0.17, 95% CI −2.21 to 2.55, P=.88). Conclusions The findings of this study demonstrate the promising and emerging efficacy of using mobile phone app interventions for weight loss. Future studies are needed to explore the long-term efficacy of mobile app interventions in larger samples.
    Type of Medium: Online Resource
    ISSN: 2291-5222
    URL: Article
    DOI: 10.2196/17039
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2020
    detail.hit.zdb_id: 2719220-9
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