Logical Observation Identifiers Names and Codes (LOINC®) Applied to Microbiology: A National Laboratory Mapping Experience in Taiwan.

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%.

Deep Learning for Accurate Diagnosis of Glaucomatous Optic Neuropathy Using Digital Fundus Image: A Meta-Analysis.

We conducted a study to evaluate the algorithms based on deep learning to automatically diagnosis of GON from digital fundus images. A systematic articles search was conducted in PubMed, EMBASE, Google Scholar for the study that investigated the performance of deep learning algorithms for the detection of GON. A total of eight studies were included in this study, of which 5 studies were used to conduct our meta-analysis. The pooled AUROC for detecting GON was 0.98. However, the sensitivity and specificity of deep learning to detect GON were 0.90 (95% CI: 0.90-0.91), and 0.94 (95%CI: 0.93-0.94), respectively.

Deep Learning Approach for the Development of a Novel Predictive Model for Prostate Cancer.

We developed a deep learning approach for accurate prediction of PCA patients one year earlier with minimal features from electronic health records. The area under the receiver operating curve for prediction of PCA was 0.94. Moreover, the sensitivity and specificity of CNN were 0.87 and 0.88, respectively.

Meta-analysis of proton pump inhibitors induced risk of community-acquired pneumonia.

PURPOSE: Proton pump inhibitors (PPIs), one of the most widely used medications, are commonly used to suppress several acid-related upper gastrointestinal disorders. Acid-suppressing medication use could be associated with increased risk of community-acquired pneumonia (CAP), although the results of clinical studies have been conflicting. DATA SOURCES: A comprehensive search of MEDLINE, EMBASE and Cochrane library and Database of Systematic Reviews from the earliest available online year of indexing up to October 2018. STUDY SELECTION: We performed a systematic review and meta-analysis of observational studies to evaluate the risk of PPI use on CAP outcomes. DATA EXTRACTION: Included study location, design, population, the prevalence of CAP, comparison group and other confounders. We calculated pooled odds ratio (OR) using a random-effects meta-analysis. RESULTS OF DATA SYNTHESIS: Of the 2577 studies screening, 11 papers were included in the systematic review and 7 studies with 65 590 CAP cases were included in the random-effects meta-analysis. In current PPI users, pooled OR for CAP was 1.86 (95% confidence interval (CI), 1.30-2.66), and in the case of recent users, OR for CAP was 1.66 (95% CI, 1.22-2.25). In the subgroup analysis of CAP, significance association is also observed in both high-dose and low-dose PPI therapy. When stratified by duration of exposure, 3-6 months PPIs users group was associated with increased risk of developing CAP (OR, 2.05; 95% CI, 1.22-3.45). There was a statistically significant association between the PPI users and the rate of hospitalization (OR, 2.59; 95% CI, 1.83-3.66). CONCLUSION: We found possible evidence linking PPI use to an increased risk of CAP. More randomized controlled studies are warranted to clarify an understanding of the association between PPI use and risk of CAP because observational studies cannot clarify whether the observed epidemiologic association is a causal effect or a result of unmeasured/residual confounding.

Gout drugs use and risk of cancer: A case-control study.

OBJECTIVE: Firm conclusion about whether short and long-term gout medications use has an impact on cancer risk remain inconclusive. The aim of this study was to investigate the association between gout drugs use and risk of cancer. METHODS: We conducted a retrospective longitudinal population-based case-control study in Taiwan. Cases were identified all patients who were aged 20years or above, and had a first time diagnosis of cancers for the period between 2001 and 2011. Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) were calculated by using conditional logistic regression. RESULTS: We examined 601,733 cases and 2,406,932 matched controls. The adjusted odd ratio for any gout drugs use and overall cancer risk was 1.007 (95% CI: 0.994-1.020). There was a significant risk of leukemia (AOR: 1.34, 95% CI: 1.20-1.50), endometrial cancer (AOR: 1.33, 95% CI: 1.12-1.57), non-Hodgkin's (AOR: 1.24, 95% CI: 1.13-1.35), female breast cancer (AOR: 1.21, 95% CI: 1.13-1.29), cervical cancer (AOR: 1.21, 95% CI: 1.07-1.37). However, no association was observed in male group (AOR: 0.97, 95% CI: 0.95-0.98) but female showed a significantly increased risk of cancer at any site (AOR: 1.107, 95% CI: 1.08-1.13). CONCLUSION: In summary, our results suggest that gout drugs increase risk of the most common cancers, particularly in leukemia, non-Hodgkin's, endometrial, breast and cervical cancer.

Psychotropic medications prescribing trends in adolescents: A nationwide population-based study in Taiwan.

OBJECTIVE: To describe psychotropic medications prescription patterns among adolescents in Taiwan; focusing on age, gender, duration of treatments and various classes of psychotropic medications. DESIGN: A retrospective description analysis. SETTING: Taiwan National Health Insurance Database. PARTICIPANTS: Twelve to seventeen years' patients treated with psychotropic medications. INTERVENTION: None. MAIN OUTCOME MEASURE(S): Percentage and duration of treatment with psychotropic medications during the study periods by medication classes and age groups were calculated. In addition, top three prescribed psychotropic medications were also determined. RESULTS: A total of 3,120 patients were prescribed psychotropic drugs. The percentage of adolescent patients that received anxiolytics and antidepressants in 2002-2012 were 2.89% and 2.15%, respectively. Also, 851 patients (1.21%) were prescribed hypnotics and 638 (0.91%) were given sedatives. The prevalence rate of the prescription of psychotropic drugs increased steadily with age and females were more treated than males except antipsychotic. Among psychotropic drugs, antidepressants (mean: 8.6 times) were refilled more but antipsychotics (mean 188 days) were the long-term treatment drugs. Additionally, the trend of hospital visits fluctuated over the year while May and December showed a higher rate of visits. CONCLUSIONS: These findings show that the prevalence of psychotropic drug prescriptions in Taiwanese adolescents is even low but increasing trends in the prescription of these medications raises some concern. As the evidence of psychotropic drug safety and effectiveness in adolescents is still inadequate; we recommend that healthcare providers should consider psychotropic drugs therapy, continuously monitor for outcomes and empower their patients to improve their knowledge, therapeutic outcomes and quality of life.