Machine Learning Approaches for Predicting Psoriatic Arthritis Risk Using Electronic Medical Records: Population-Based Study.

BACKGROUND: Psoriasis (PsO) is a chronic, systemic, immune-mediated disease with multiorgan involvement. Psoriatic arthritis (PsA) is an inflammatory arthritis that is present in 6%-42% of patients with PsO. Approximately 15% of patients with PsO have undiagnosed PsA. Predicting patients with a risk of PsA is crucial for providing them with early examination and treatment that can prevent irreversible disease progression and function loss. OBJECTIVE: The aim of this study was to develop and validate a prediction model for PsA based on chronological large-scale and multidimensional electronic medical records using a machine learning algorithm. METHODS: This case-control study used Taiwan's National Health Insurance Research Database from January 1, 1999, to December 31, 2013. The original data set was split into training and holdout data sets in an 80:20 ratio. A convolutional neural network was used to develop a prediction model. This model used 2.5-year diagnostic and medical records (inpatient and outpatient) with temporal-sequential information to predict the risk of PsA for a given patient within the next 6 months. The model was developed and cross-validated using the training data and was tested using the holdout data. An occlusion sensitivity analysis was performed to identify the important features of the model. RESULTS: The prediction model included a total of 443 patients with PsA with earlier diagnosis of PsO and 1772 patients with PsO without PsA for the control group. The 6-month PsA risk prediction model that uses sequential diagnostic and drug prescription information as a temporal phenomic map yielded an area under the receiver operating characteristic curve of 0.70 (95% CI 0.559-0.833), a mean sensitivity of 0.80 (SD 0.11), a mean specificity of 0.60 (SD 0.04), and a mean negative predictive value of 0.93 (SD 0.04). CONCLUSIONS: The findings of this study suggest that the risk prediction model can identify patients with PsO at a high risk of PsA. This model may help health care professionals to prioritize treatment for target high-risk populations and prevent irreversible disease progression and functional loss.

How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?

We propose the idea of using an open data set of doctor-patient interactions to develop artificial empathy based on facial emotion recognition. Facial emotion recognition allows a doctor to analyze patients' emotions, so that they can reach out to their patients through empathic care. However, face recognition data sets are often difficult to acquire; many researchers struggle with small samples of face recognition data sets. Further, sharing medical images or videos has not been possible, as this approach may violate patient privacy. The use of deepfake technology is a promising approach to deidentifying video recordings of patients' clinical encounters. Such technology can revolutionize the implementation of facial emotion recognition by replacing a patient's face in an image or video with an unrecognizable face-one with a facial expression that is similar to that of the original. This technology will further enhance the potential use of artificial empathy in helping doctors provide empathic care to achieve good doctor-patient therapeutic relationships, and this may result in better patient satisfaction and adherence to treatment.

Pilot Report for Intracranial Hemorrhage Detection with Deep Learning Implanted Head Computed Tomography Images at Emergency Department.

Hemorrhagic stroke is a serious clinical condition that requires timely diagnosis. An artificial intelligence algorithm system called DeepCT can identify hemorrhagic lesions rapidly from non-contrast head computed tomography (NCCT) images and has received regulatory clearance. A non-controlled retrospective pilot clinical trial was conducted. Patients who received NCCT at the emergency department (ED) of Kaohsiung Veteran General Hospital were collected. From 2020 January-1st to April-30th, the physicians read NCCT images without DeepCT. From 2020May-1st to August-31st, the physicians were assisted by DeepCT. The length of ED stays (LOS) for the patients was collected. 2,999 patients were included (188 and 2811 with and without ICH). For patients with a final diagnosis of ICH, implementing DeepCT significantly shortened their LOS (560.67 ± 604.93 min with DeepCT vs. 780.83 ± 710.27 min without DeepCT; p = 0.0232). For patients with a non-ICH diagnosis, the LOS did not significantly differ (705.90 ± 760.86 min with DeepCT vs. 679.45 ± 681.97 min without DeepCT; p = 0.3362). For patients with ICH, those assisted with DeepCT had a significantly shorter LOS than those without DeepCT. For patients with a non-ICH diagnosis, implementing DeepCT did not affect the LOS, because emergency physicians need same efforts to identify the underlying problem(s) with or without DeepCT. In summary, implementing DeepCT system in the ED will save costs, decrease LOS, and accelerate patient flow; most importantly, it will improve the quality of care and increase the confidence and shorten the response time of the physicians and radiologists.

Risk of cancer in long-term levothyroxine users: Retrospective population-based study.

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.

Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach.

BACKGROUND: Artificial intelligence approaches can integrate complex features and can be used to predict a patient's risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. OBJECTIVE: The aim of this study was to use electronic medical records to prescreen patients who are at risk of developing lung cancer. METHODS: We randomly selected 2 million participants from the Taiwan National Health Insurance Research Database who received care between 1999 and 2013. We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data, and we tested the model prospectively on post-2012 data. An age- and gender-matched subgroup that was 10 times larger than the original lung cancer group was used to assess the predictive power of the electronic medical record. Discrimination (area under the receiver operating characteristic curve [AUC]) and calibration analyses were performed. RESULTS: The analysis included 11,617 patients with lung cancer and 1,423,154 control patients. The model achieved AUCs of 0.90 for the overall population and 0.87 in patients ≥55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among people aged ≥55 years with a pre-existing history of lung disease. CONCLUSIONS: Our model achieved excellent performance in predicting lung cancer within 1 year and has potential to be deployed for digital patient screening. Convolution neural networks facilitate the effective use of EMRs to identify individuals at high risk for developing lung cancer.

Correction: Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach.

[This corrects the article DOI: 10.2196/26256.].

Statins use and its impact in EGFR-TKIs resistance to prolong the survival of lung cancer patients: A Cancer registry cohort study in Taiwan.

Statins have been shown to be a beneficial treatment as chemotherapy and target therapy for lung cancer. This study aimed to investigate the effectiveness of statins in combination with epidermal growth factor receptor-tyrosine kinase inhibitor therapy for the resistance and mortality of lung cancer patients. A population-based cohort study was conducted using the Taiwan Cancer Registry database. From January 1, 2007, to December 31, 2012, in total 792 non-statins and 41 statins users who had undergone EGFR-TKIs treatment were included in this study. All patients were monitored until the event of death or when changed to another therapy. Kaplan-Meier estimators and Cox proportional hazards regression models were used to calculate overall survival. We found that the mortality was significantly lower in patients in the statins group compared with patients in the non-statins group (4-y cumulative mortality, 77.3%; 95% confidence interval (CI), 36.6%-81.4% vs. 85.5%; 95% CI, 78.5%-98%; P = .004). Statin use was associated with a reduced risk of death in patients the group who had tumor sizes <3 cm (hazard ratio [HR], 0.51, 95% CI, 0.29-0.89) and for patients in the group who had CCI scores <3 (HR, 0.6; 95% CI, 0.41-0.88; P = .009). In our study, statins were found to be associated with prolonged survival time in patients with lung cancer who were treated with EGFR-TKIs and played a synergistic anticancer role.

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.

Correction: How Can Artificial Intelligence Make Medicine More Preemptive?

[This corrects the article DOI: .].

How Can Artificial Intelligence Make Medicine More Preemptive?

In this paper we propose the idea that Artificial intelligence (AI) is ushering in a new era of "Earlier Medicine," which is a predictive approach for disease prevention based on AI modeling and big data. The flourishing health care technological landscape is showing great potential-from diagnosis and prescription automation to the early detection of disease through efficient and cost-effective patient data screening tools that benefit from the predictive capabilities of AI. Monitoring the trajectories of both in- and outpatients has proven to be a task AI can perform to a reliable degree. Predictions can be a significant advantage to health care if they are accurate, prompt, and can be personalized and acted upon efficiently. This is where AI plays a crucial role in "Earlier Medicine" implementation.

Non-steroidal anti-inflammatory drugs and risk of Parkinson's disease in the elderly population: a meta-analysis.

PURPOSE: Several studies have explored the impact of non-steroidal anti-inflammatory drugs (NSAIDs) and the risk of Parkinson disease (PD). However, the extent to which NSAIDs may increase or decrease the risk of PD remains unresolved. We, therefore, performed a meta-analysis of relevant studies to quantify the magnitude of the association between NSAID use and PD risk in the elderly population. METHODS: The electronic databases such as PubMed, EMBASE, Scopus, Google Scholar, and Web of Science were used to search the relevant articles published between January 1990 and December 2017. Large (n ≥ 1000) observational design studies with a follow-up at least 1 year were considered. Two authors independently extracted information from the included studies. Random effect model was used to calculate risk ratios (RRs) with 95% confidence interval (Cl). RESULTS: A total of 17 studies with 2,498,258 participants and nearly 14,713 PD patients were included in the final analysis. The overall pooled RR of PD was 0.95 (95%CI 0.860-1.048) with significant heterogeneity (I2 = 63.093, Q = 43.352, p < 0.0001). In the subgroup analysis, the overall pooled RR of PD was 0.90 (95%CI 0.738-1.109), 0.96 (95%CI 0.882-1.055), and 0.99 (95%CI 0.841-0.982) from the studies of North America, Europe, and Asia. Additionally, long-term use, study design, individual NSAID use, and risk of PD were also evaluated. CONCLUSION: Despite the neuroprotective potential of NSAIDs demonstrated in some experimental studies, our findings suggest that there is no association between NSAIDs and the risk of Parkinson disease at the population level. Until further evidence is established, clinicians need to be vigilant ensuring that the use of NSAIDs remains restricted to their approved anti-inflammatory and analgesic effect.

Virtual reality among the elderly: a usefulness and acceptance study from Taiwan.

BACKGROUND: Virtual reality (VR) has several applications in the medical domain and also generates a secure environment to carry out activities. Evaluation of the effectiveness of VR among older populations revealed positive effects of VR as a tool to reduce risks of falls and also improve the social and emotional well-being of older adults. The decline in physical and mental health, the loss of functional capabilities, and a weakening of social ties represent obstacles towards active aging among older adults and indicate a need for support. Existing research focused on the effects of VR among older populations, and its uses and benefits. Our study investigated the acceptance and use of VR by the elderly. METHODS: This pilot study was conducted on 30 older adults who voluntarily participated during March to May 2018. Nine VR applications that promote physical activities, motivate users, and provide entertainment were chosen for this study. Participants were asked to use any one of the applications of their choice for 15 min twice a week for 6 weeks. At the end of 6 weeks, participants were asked to fill out a questionnaire based on the Technology Acceptance Model and a literature review, to evaluate their acceptance of VR technology. Cronbach's alpha reliability analysis was used to test the internal consistency of the questionnaire items. Pearson's product moment correlation was used to examine the validity of the questionnaire. A linear regression and mediation analysis were utilized to identify relationships among the variables of the questionnaire. RESULTS: In total, six male and 24 female participants aged 60~95 years volunteered to participate in the study. Perceived usefulness, perceived ease of use, social norms, and perceived enjoyment were seen to have had significant effects on the intention to use VR. Participants agreed to a large extent regarding the perceived usefulness, perceived enjoyment, and their experience of using VR. Thus, VR was seen to have high acceptance among this elderly population. CONCLUSIONS: Older people have positive perceptions towards accepting and using VR to support active aging. They perceived VR to be useful, easy to use, and an enjoyable experience, implying positive attitudes toward adopting this new technology.

Opportunities and challenges in Taiwan for implementing the learning health system.

Due to the low ratio of medical decisions made upon solid scientific evidence (4%) and the low efficiency of deploying knowledge in practice (17 years), the concept of a learning health system (LHS) was initiated to speed up knowledge generation and adoption and systematically approach continuous improvement in clinical practice. This concept can be illustrated by a so-called learning health cycle. This cycle, the first version as well as its variants, provides a framework for discussion on a common basis and has been well-accepted by the medical communities. Though the idea attracted major attention widely, very little has been done in way of actual adoption in real practices in the past 10 years. Nevertheless, as one of the pioneers in Taiwan, we have been involved in the effort to implement the LHS locally since 2016. In this article, we systematically summarize the evolution of the learning health cycle, review cases of its applications and briefly introduce the work we have done for promoting LHSs in Taiwan. Based on the experience we have gained, we try to identify the challenges and opportunities in Taiwan. While full-scale electronic medical records powered by the National Health Insurance system give Taiwan a special advantage in achieving a nationwide LHS, the medical community is not yet ready for a dramatic change. The lack of infrastructure for this use and motivation to take action right away makes the implementation of a LHS in Taiwan challenging.

A positive legacy of trauma? A study on the impact of natural disasters on medical utilization.

OBJECTIVE: The impact of natural disasters on medical utilization is largely unknown and often overlooked how it affects bereaving and non-bereaving survivors. The aim of this study is to determine the medical utilization between both survivor groups and long-term effects. STUDY DESIGN: A 10-year 1999-2009 population-based retrospective study by using the National Health Insurance claim database and the Household Registration database from the Department of Health, Executive Yuan, Taiwan. SETTINGS: Taiwan 1999 Chi-Chi earthquake-affected areas. PARTICIPANTS: A total of 49 834 individuals which included 1183 bereaving survivors and 48 651 non-bereaving earthquake survivors. INTERVENTION(S): None. MAIN OUTCOME MEASURES: Medical utilization of bereaving and non-bereaving survivors. RESULTS: The results showed that bereaving survivors had significantly more outpatient visits before the earthquake, within 3-month period and 1 year after earthquake (odds ratio (OR) = 1.11, 1.16 and 1.08). However, after 1 year after earthquake their outpatient visits were not significantly different from non-bereaving, and even significantly less in some years. Inpatient visits of bereaving survivors had similar trend to outpatient visits, i.e. visits were more both before earthquake and within 3-month period after earthquake (OR = 1.59 and 1.89), however, they were not significantly higher than non-bereaving survivors for the following years of the study. CONCLUSION: Our study reveals that compared to non-bereaving survivors, bereaving survivors slightly had higher medical utilization in the beginning stage of earthquake, i.e. for the first 3-month period or 1 year after earthquake. However, there were no differences between these two groups in medical utilization including outpatient and inpatient visits in long run.

Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data.

Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for a period of 23 weeks during their HD sessions. During these 23 weeks, a total number of 3237 HD sessions were observed. Out of 109 patients enrolled in the study, 78 patients reported clinical events such as muscle spasms, inpatient stays, emergency visits or even death during the study period. We analyzed the sensor data of these two groups of patients, namely an event and no-event group. We found a statistically significant difference in the heart rates, respiration rates, and some heart rate variability parameters among the two groups of patients when their means were compared using an independent sample t-test. We further developed a supervised machine-learning-based prediction model to predict event or no-event based on the sensor data and demographic information. A mean area under curve (ROC AUC) of 90.16% with 96.21% mean precision, and 88.47% mean recall was achieved. Our findings point towards the novel use of non-contact sensors in clinical settings to monitor the vital parameters of patients and the further development of early warning solutions using artificial intelligence (AI) for the prediction of clinical events. These models could assist healthcare professionals in taking decisions and designing better care plans for patients by early detecting changes to vital parameters.

Exploring the Association between Statin Use and the Risk of Parkinson's Disease: A Meta-Analysis of Observational Studies.

BACKGROUND: Parkinson's disease (PD) is a progressive disorder of the central nervous system. The prevalence of PD varies considerably by age group; it has a higher prevalence in patients aged 60 years and more. Several studies have shown that statin, a cholesterol-lowering medication, reduces the risk of developing PD, but evidence for this is so far inconclusive. The objective of this study is to evaluate the association between statin use and the risk of developing PD. METHODS: PubMed, EMBASE, and the bibliographies of articles were searched for studies published between January 1, 1990, and January 1, 2017, which reported on the association between statin use and PD. Articles were included if they (1) were published in English, (2) reported patients treated with statin, and the outcome of interest was PD, (3) provided OR/HR with 95% CI or sufficient information to calculate the 95% CI. All abstracts, full-text articles, and sources were reviewed, with duplicate data excluded. Summary relative risk (RRs) with 95% CI was pooled using a random-effects model. Subgroup and sensitivity analyses were also conducted. RESULTS: We selected 16 out of 529 unique abstracts for full-text review using our selection criteria, and 13 out of these 16 studies, comprising 4,877,059 persons, met all of our inclusion criteria. The overall pooled RR of PD was 0.70 (95% CI 0.58-0.84) with significant heterogeneity between estimates (I2 = 93.41%, p = 0.000) for the random-effects model. In subgroup analysis, the greater decreased risk was found in studies from Asia (RR 0.62 95% CI 0.51-0.76), whereas a moderate reduction was observed in studies from North America (RR 0.69 95% CI 0.47-1.00), but less reduction was observed in studies from Europe (RR 0.86 95% CI 0.80-0.92). Also, long-term statin use, simvastatin, and atorvastatin showed a higher rate of reduction with significance heterogeneity. CONCLUSION: Our results showed that statin use is significantly associated with a lower risk of developing PD. Physicians should consider statin drug therapy, monitor its outcomes, and empower their patients to improve their knowledge, therapeutic outcomes, and quality of life. However, preventive measures and their associated mechanisms must be further assessed and explored.

Benzodiazepines use and breast cancer risk: A population-based study and gene expression profiling evidence.

The aim of this study was to investigate whether long-term use of Benzodiazepines (BZDs) is associated with breast cancer risk through the combination of population-based observational and gene expression profiling evidence. We conducted a population-based case-control study by using 1998 to 2009year Taiwan National Health Insurance Research Database and investigated the association between BZDs use and breast cancer risk. We selected subjects age of >20years old and six eligible controls matched for age, sex and the index date (i.e., free of any cancer at the case diagnosis date) by using propensity scores. A bioinformatics analysis approach was also performed for the identification of oncogenesis effects of BZDs on breast cancer. We used breast cancer gene expression data from the Cancer Genome Atlas and perturbagen signatures of BZDs from the Library of Integrated Cellular Signatures database in order to identify the oncogenesis effects of BZDs on breast cancer. We found evidence of increased breast cancer risk for diazepam (OR, 1.16; 95%CI, 0.95-1.42; connectivity score [CS], 0.3016), zolpidem (OR, 1.11; 95%CI, 0.95-1.30; CS, 0.2738), but not for lorazepam (OR, 1.04; 95%CI, 0.89-1.23; CS, -0.2952) consistently in both methods. The finding for alparazolam was contradictory from the two methods. Diazepam and zolpidem trends showed association, although not statistically significant, with breast cancer risk in both epidemiological and bioinformatics analyses outcomes. The methodological value of our study is in introducing the way of combining epidemiological and bioinformatics approaches in order to answer a common scientific question. Combining the two approaches would be a substantial step towards uncovering, validation and further application of previously unknown scientific knowledge to the emerging field of precision medicine informatics.

Gender-based personalized pharmacotherapy: a systematic review.

PURPOSE: In general, male and female are prescribed the same amount of dosage even if most of the cases female required less dosage than male. Physicians are often facing problem on appropriate drug dosing, efficient treatment, and drug safety for a female in general. To identify and synthesize evidence about the effectiveness of gender-based therapy; provide the information to patients, providers, and health system intervention to ensure safety treatment; and minimize adverse effects. METHODS: We performed a systematic review to evaluate the effect of gender difference on pharmacotherapy. Published articles from January 1990 to December 2015 were identified using specific term in MEDLINE (PubMed), EMBASE, and the Cochrane library according to search strategies that strengthen the reporting of observational and clinical studies. RESULTS: Twenty-six studies fulfilled the inclusion criteria for this systematic review, yielding a total of 6309 subjects. We observed that female generally has a lower the gastric emptying time, gastric PH, lean body mass, and higher plasma volume, BMI, body fat, as well as reduce hepatic clearance, difference in activity of Cytochrome P450 enzyme, and metabolize drugs at different rate compared with male. Other significant factors such as conjugation, protein binding, absorption, and the renal elimination could not be ignored. However, these differences can lead to adverse effects in female especially for the pregnant, post-menopausal, and elderly women. CONCLUSION: This systematic review provides an evidence for the effectiveness of dosage difference to ensure safety and efficient treatment. Future studies on the current topic are, therefore, recommended to reduce the adverse effect of therapy.

Exploring association between statin use and breast cancer risk: an updated meta-analysis.

PURPOSE: The benefits of statin treatment for preventing cardiac disease are well established. However, preclinical studies suggested that statins may influence mammary cancer growth, but the clinical evidence is still inconsistent. We, therefore, performed an updated meta-analysis to provide a precise estimate of the risk of breast cancer in individuals undergoing statin therapy. METHODS: For this meta-analysis, we searched PubMed, the Cochrane Library, Web of Science, Embase, and CINAHL for published studies up to January 31, 2017. Articles were included if they (1) were published in English; (2) had an observational study design with individual-level exposure and outcome data, examined the effect of statin therapy, and reported the incidence of breast cancer; and (3) reported estimates of either the relative risk, odds ratios, or hazard ratios with 95% confidence intervals (CIs). We used random-effect models to pool the estimates. RESULTS: Of 2754 unique abstracts, 39 were selected for full-text review, and 36 studies reporting on 121,399 patients met all inclusion criteria. The overall pooled risks of breast cancer in patients using statins were 0.94 (95% CI 0.86-1.03) in random-effect models with significant heterogeneity between estimates (I 2 = 83.79%, p = 0.0001). However, we also stratified by region, the duration of statin therapy, methodological design, statin properties, and individual stain use. CONCLUSIONS: Our results suggest that there is no association between statin use and breast cancer risk. However, observational studies cannot clarify whether the observed epidemiologic association is a causal effect or the result of some unmeasured confounding variable. Therefore, more research is needed.

Benzodiazepine Use and Risk of Dementia in the Elderly Population: A Systematic Review and Meta-Analysis.

BACKGROUND: Benzodiazepines are a widely used medication in developed countries, particularly among elderly patients. However, benzodiazepines are known to affect memory and cognition and might thus enhance the risk of dementia. The objective of this review is to synthesize evidence from observational studies that evaluated the association between benzodiazepines use and dementia risk. SUMMARY: We performed a systematic review and meta-analysis of controlled observational studies to evaluate the risk of benzodiazepines use on dementia outcome. All control observational studies that compared dementia outcome in patients with benzodiazepine use with a control group were included. We calculated pooled ORs using a random-effects model. Ten studies (of 3,696 studies identified) were included in the systematic review, of which 8 studies were included in random-effects meta-analysis and sensitivity analyses. Odds of dementia were 78% higher in those who used benzodiazepines compared with those who did not use benzodiazepines (OR 1.78; 95% CI 1.33-2.38). In subgroup analysis, the higher association was still found in the studies from Asia (OR 2.40; 95% CI 1.66-3.47) whereas a moderate association was observed in the studies from North America and Europe (OR 1.49; 95% CI 1.34-1.65 and OR 1.43; 95% CI 1.16-1.75). Also, diabetics, hypertension, cardiac disease, and statin drugs were associated with increased risk of dementia but negative association was observed in the case of body mass index. There was significant statistical and clinical heterogeneity among studies for the main analysis and most of the sensitivity analyses. There was significant statistical and clinical heterogeneity among the studies for the main analysis and most of the sensitivity analyses. Key Messages: Our results suggest that benzodiazepine use is significantly associated with dementia risk. However, observational studies cannot clarify whether the observed epidemiologic association is a causal effect or the result of some unmeasured confounding variable. Therefore, more research is needed.