Application of Robotic Stereotactic Assistance (ROSA) for spontaneous intracerebral hematoma aspiration and thrombolytic catheter placement.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) accounts for up to 20% of all strokes and results in 40% mortality at 30 days. Although conservative medical management is still the standard treatment for ICH patients with small hematoma, patients with residual hematoma ≤15 mL after surgery are associated with better functional outcomes and survival rates. This study reported our clinical experience with using Robotic Stereotactic Assistance (ROSA) as a safe and effective approach for stereotactic ICH aspiration and intra-clot catheter placement. METHODS: A retrospective analysis was conducted of patients with spontaneous ICH who underwent ROSA-guided ICH aspiration surgery. ROSA-guided ICH surgical techniques, an aspiration and intra-clot catheter placement protocol, and a specific operative workflow (pre-operative protocol, intraoperative procedure and postoperative management) were employed to aspirate ICH using the ROSA One Brain, and appropriate follow-up care was provided. RESULTS: From September 14, 2021 to May 4, 2022, a total of 7 patients were included in the study. Based on our workflow design, ROSA-guided stereotactic ICH aspiration effectively aspirated more than 50% of hematoma volume (or more than 30 mL for massive hematomas), thereby reducing the residual hematoma to less than 15 mL. The mean operative time of entire surgical procedure was 1.3 ± 0.3 h, with very little perioperative blood loss and no perioperative complications. No patients required catheter replacement and all patients' functional status improved. CONCLUSIONS: Within our clinical practice ROSA-guided ICH aspiration, using our established protocol and workflow, was safe and effective for reducing hematoma volume, with positive functional outcomes.

Renin-Angiotensin-Aldosterone System Inhibitors and Development of Gynecologic Cancers: A 23 Million Individual Population-Based Study.

The chronic receipt of renin-angiotensin-aldosterone system (RAAS) inhibitors including angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been assumed to be associated with a significant decrease in overall gynecologic cancer risks. This study aimed to investigate the associations of long-term RAAS inhibitors use with gynecologic cancer risks. A large population-based case-control study was conducted from claim databases of Taiwan's Health and Welfare Data Science Center (2000-2016) and linked with Taiwan Cancer Registry (1979-2016). Each eligible case was matched with four controls using propensity matching score method for age, sex, month, and year of diagnosis. We applied conditional logistic regression with 95% confidence intervals to identify the associations of RAAS inhibitors use with gynecologic cancer risks. The statistical significance threshold was p < 0.05. A total of 97,736 gynecologic cancer cases were identified and matched with 390,944 controls. The adjusted odds ratio for RAAS inhibitors use and overall gynecologic cancer was 0.87 (95% CI: 0.85-0.89). Cervical cancer risk was found to be significantly decreased in the groups aged 20-39 years (aOR: 0.70, 95% CI: 0.58-0.85), 40-64 years (aOR: 0.77, 95% CI: 0.74-0.81), ≥65 years (aOR: 0.87, 95% CI: 0.83-0.91), and overall (aOR: 0.81, 95% CI: 0.79-0.84). Ovarian cancer risk was significantly lower in the groups aged 40-64 years (aOR: 0.76, 95% CI: 0.69-0.82), ≥65 years (aOR: 0.83, 95% CI: 0.75-092), and overall (aOR: 0.79, 95% CI: 0.74-0.84). However, a significantly increased endometrial cancer risk was observed in users aged 20-39 years (aOR: 2.54, 95% CI: 1.79-3.61), 40-64 years (aOR: 1.08, 95% CI: 1.02-1.14), and overall (aOR: 1.06, 95% CI: 1.01-1.11). There were significantly reduced risks of gynecologic cancers with ACEIs users in the groups aged 40-64 years (aOR: 0.88, 95% CI: 0.84-0.91), ≥65 years (aOR: 0.87, 95% CI: 0.83-0.90), and overall (aOR: 0.88, 95% CI: 0.85-0.80), and ARBs users aged 40-64 years (aOR: 0.91, 95% CI: 0.86-0.95). Our case-control study demonstrated that RAAS inhibitors use was associated with a significant decrease in overall gynecologic cancer risks. RAAS inhibitors exposure had lower associations with cervical and ovarian cancer risks, and increased endometrial cancer risk. ACEIs/ARBs use was found to have a preventive effect against gynecologic cancers. Future clinical research is needed to establish causality.

Development and validation of a new tool: The sarcopenia knowledge questionnaire.

We developed a new questionnaire-the Sarcopenia Knowledge Questionnaire (SKQ)-to evaluate the level of awareness about sarcopenia among older adults and tested the reliability and validity of this tool. A total of 293 older adults completed the questionnaire. The SKQ comprises three domains including 23 items: screening and diagnosis (10 items), sarcopenia outcomes (7 items), and lifestyle factors (6 items). The Cronbach's α value was 0.969, which indicated excellent internal consistency. The SKQ correlated well with the Mandarin Multidimensional Health Literacy Questionnaire (r = 0.511; p < 0.001), confirming its moderate convergent validity. The absolute values of the critical ratio ranged from 9.90 to 25.82 (p < 0.001), indicating satisfactory item discrimination. Thus, the SKQ appears to be a valid and reliable instrument for evaluating the knowledge of older adults about sarcopenia.

Robot-guided versus freehand fluoroscopy-guided minimally invasive transforaminal lumbar interbody fusion: a single-institution, observational, case-control study.

OBJECTIVE: The use of robotics in spinal surgery has gained popularity because of its promising accuracy and safety. ROSA is a commonly used surgical robot system for spinal surgery. The aim of this study was to compare outcomes between robot-guided and freehand fluoroscopy-guided instrumentation in minimally invasive surgery (MIS)-transforaminal lumbar interbody fusion (TLIF). METHODS: This retrospective consecutive series reviewed 224 patients who underwent MIS-TLIF from March 2019 to April 2020 at a single institution. All patients were diagnosed with degenerative pathologies. Of those, 75 patients underwent robot-guided MIS-TLIF, and 149 patients underwent freehand fluoroscopy-guided MIS-TLIF. The incidences of pedicle breach, intraoperative outcomes, postoperative outcomes, and short-term pain control were compared. RESULTS: The patients who underwent robot-guided surgery had a lower incidence of pedicle breach (0.27% vs 1.75%, p = 0.04) and less operative blood loss (313.7 ± 214.1 mL vs 431.6 ± 529.8 mL, p = 0.019). Nonsignificant differences were observed in operative duration (280.7 ± 98.1 minutes vs 251.4 ± 112.0 minutes, p = 0.056), hospital stay (6.6 ± 3.4 days vs 7.3 ± 4.4 days, p = 0.19), complications (intraoperative, 1.3% vs 1.3%, p = 0.45; postoperative surgery-related, 4.0% vs 4.0%, p = 0.99), and short-term pain control (postoperative day 1, 2.1 ± 1.2 vs 1.8 ± 1.2, p = 0.144; postoperative day 30, 1.2 ± 0.5 vs 1.3 ± 0.7, p = 0.610). A shorter operative duration for 4-level spinal surgery was found in the robot-guided surgery group (388.7 ± 107.3 minutes vs 544.0 ± 128.5 minutes, p = 0.047). CONCLUSIONS: This retrospective review revealed that patients who underwent robot-guided MIS-TLIF experienced less operative blood loss. They also benefited from a shorter operative duration with higher-level (> 3 levels) spinal surgery. The postoperative outcomes were similar for both robot-guided and freehand fluoroscopy-guided procedures.

Association between Use of Statin and Risk of Dementia: A Meta-Analysis of Observational Studies.

BACKGROUND AND AIMS: The impact of statin on dementia risk reduction has been a subject of debate over the last decade, but the evidence remains inconclusive. Therefore, we performed a meta-analysis of relevant observational studies to quantify the magnitude of the association between statin therapy and the risk of dementia. METHODS: We systematically searched for relevant studies published from January 2000 to March 2018 using EMBASE, Google, Google Scholar, PubMed, Scopus, and Web of Science. Two authors performed study selection, data abstraction, and risk of bias assessment. We then extracted data from the selected studies and performed meta-analysis of observational studies using a random-effects model. Subgroup and sensitivity analyses were also conducted. RESULTS: A total of 30 observational studies, including 9,162,509 participants (84,101 dementia patients), met the eligibility criteria. Patients with statin had a lower all-caused dementia risk than those without statin (risk ratio [RR] 0.83, 95% CI 0.79-0.87, I2 = 57.73%). The overall pooled reduction of Alzheimer disease in patients with statin use was RR 0.69 (95% CI 0.60-0.80, p < 0.0001), and the overall pooled RR of statin use and vascular dementia risk was RR 0.93 (95% CI 0.74-1.16, p = 0.54). CONCLUSION: This study suggests that the use of statin is significantly associated with a decreased risk of dementia. Future studies measuring such outcomes would provide useful information to patients, clinicians, and policymakers. Until further evidence is established, clinicians need to make sure that statin use should remain restricted to the treatment of cardiovascular disease.

[Broca's area intraoperative mapping with cortico-cortical evoked potentials]. / Intraoperatsionnoe kartirovanie rechevoi zony Broka metodom registratsii kortiko-kortikal'nykh vyzvannykh potentsialov.

OBJECTIVE: Mapping of effective speech connections between the frontal and temporal lobes with cortico-cortical evoked potentials. MATERIAL AND METHODS: There were 3 patients with brain tumors in the left frontoparietal region. The neoplasms were localized in the dominant hemisphere near cortical speech centers and pathways. Cortico-cortical evoked potentials were intraoperatively recorded in response to bipolar stimulation with a direct current delivered through the subdural electrodes (single rectangular biphasic impulses with duration of 300 µs and frequency of 1 Hz). Stimulation intensity was gradually increased from 2 mA within 3-4 mA. Registration was carried out by averaging ECoG (30-50 stimuli in each session) in the 300-ms epoch after stimulus. Direct cortical stimulation was used to validate the results of cortico-cortical speech mapping with cortico-cortical evoked potentials. RESULTS: In our cases, we obtained cortico-cortical evoked potentials from inferior frontal gyrus after stimulation of superior temporal gyrus. In one case, this effective relationship was unidirectional, in the other two patients reciprocal. Mean latency of N1 peak was 65 ms (range 49.6-90 ms), mean amplitude 71 µV (range 50-100 µV). Cortico-cortical mapping data were confirmed by detection of Broca's area in 2 out of 3 cases out during direct cortical stimulation with maximum amplitude of N1 wave. «Awake craniotomy¼ protocol was applied. In one case, Broca's area was not detected during direct stimulation. No postoperative speech impairment was noted. CONCLUSION: Initial results of cortical mapping with cortico-cortical evoked potentials in a small sample confirmed its practical significance for analysis of cortical projections of effective speech communications between the frontal and temporal lobes. Further study of this method in large samples is required.

Risk of Hemorrhagic Stroke in Patients Exposed to Nonsteroidal Anti-Inflammatory Drugs: A Meta-Analysis of Observational Studies.

BACKGROUND AND AIM: 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. METHODS: 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. RESULTS: 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, p = 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). CONCLUSION: 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.

Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis.

BACKGROUND: Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. OBJECTIVE: The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. METHODS: We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. RESULTS: A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). CONCLUSIONS: This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.

Exploring Relationships of Heart Rate Variability, Neurological Function, and Clinical Factors with Mortality and Behavioral Functional Outcome in Patients with Ischemic Stroke.

Ischemic stroke is a leading cause of mortality and disability. The relationships of heart rate variability (HRV) and stroke-related factors with mortality and functional outcome are complex and not fully understood. Understanding these relationships is crucial for providing better insights regarding ischemic stroke prognosis. The objective of this study is to examine the relationship between HRV, neurological function, and clinical factors with mortality and 3-month behavioral functional outcome in ischemic stroke. We prospectively collected the HRV data and monitored the behavioral functional outcome of patients with ischemic stroke. The behavioral functional outcome was represented by a modified Rankin Scale (mRS) score. This study population consisted of 58 ischemic stroke patients (56.9% male; mean age 70) with favorable (mRS score ≤ 2) and unfavorable (mRS score ≥ 3) outcome. The analysis indicated that the median of the mean RR interval (RR mean) showed no statistical difference between mortality groups. Conversely, the median of the RR mean had significant association with unfavorable outcome (OR = 0.989, p = 0.007). Lower hemoglobin levels had significant association with unfavorable outcome (OR = 0.411, p = 0.010). Higher National Institute of Health Stroke Scale (NIHSS) score at admission had significant association with unfavorable outcome (OR = 1.396, p = 0.002). In contrast, age, stroke history, NIHSS score at admission, and hemoglobin showed no significant association with mortality in ischemic stroke. These results imply that HRV, as indicated by the median of RR mean, alongside specific clinical factors and neurological function at admission (measured by NIHSS score), may serve as potential prognostic indicators for 3-month behavioral functional outcome in ischemic stroke.

Artificial intelligence in diabetic retinopathy: Bibliometric analysis.

BACKGROUND: The use of artificial intelligence in diabetic retinopathy has become a popular research focus in the past decade. However, no scientometric report has provided a systematic overview of this scientific area. AIMS: We utilized a bibliometric approach to identify and analyse the academic literature on artificial intelligence in diabetic retinopathy and explore emerging research trends, key authors, co-authorship networks, institutions, countries, and journals. We further captured the diabetic retinopathy conditions and technology commonly used within this area. METHODS: Web of Science was used to collect relevant articles on artificial intelligence use in diabetic retinopathy published between January 1, 2012, and December 31, 2022 . All the retrieved titles were screened for eligibility, with one criterion that they must be in English. All the bibliographic information was extracted and used to perform a descriptive analysis. Bibliometrix (R tool) and VOSviewer (Leiden University) were used to construct and visualize the annual numbers of publications, journals, authors, countries, institutions, collaboration networks, keywords, and references. RESULTS: In total, 931 articles that met the criteria were collected. The number of annual publications showed an increasing trend over the last ten years. Investigative Ophthalmology & Visual Science (58/931), IEEE Access (54/931), and Computers in Biology and Medicine (23/931) were the most journals with most publications. China (211/931), India (143/931, USA (133/931), and South Korea (44/931) were the most productive countries of origin. The National University of Singapore (40/931), Singapore Eye Research Institute (35/931), and Johns Hopkins University (34/931) were the most productive institutions. Ting D. (34/931), Wong T. (28/931), and Tan G. (17/931) were the most productive researchers. CONCLUSION: This study summarizes the recent advances in artificial intelligence technology on diabetic retinopathy research and sheds light on the emerging trends, sources, leading institutions, and hot topics through bibliometric analysis and network visualization. Although this field has already shown great potential in health care, our findings will provide valuable clues relevant to future research directions and clinical practice.

A Meta-Analysis of Proton Pump Inhibitor Use and the Risk of Acute Kidney Injury: Geographical Differences and Associated Factors.

Proton pump inhibitors (PPIs) are widely prescribed in medical practice for the treatment of several gastrointestinal disorders. Previous epidemiology studies have reported the association between PPI use and the risk of AKI, although the magnitude of the association between PPIs and the risk of acute kidney injury (AKI) remains uncertain. Therefore, we conducted a meta-analysis to determine the relationship between PPI therapy and the risk of AKI. We systematically searched for relevant articles published before January 2023 on PubMed, Scopus, and Web of Science. In addition, we conducted a manual search of the bibliographies of potential articles. Two independent reviewers examined the appropriateness of all studies for inclusion. We pooled studies that compared the risk of AKI with PPI against their control using a random effect model. The search criteria based on PRISMA guidelines yielded 568 articles. Twelve observational studies included 2,492,125 individuals. The pooled adjusted RR demonstrated a significant positive association between PPI therapy and the risk of AKI (adjusted RR 1.75, 95% CI: 1.40-2.19, p < 0.001), and it was consistent across subgroups. A visual presentation of the funnel plot and Egger's regression test showed no evidence of publication bias. Our meta-analysis indicated that persons using PPIs exhibited an increased risk of AKI. North American individuals had a higher risk of AKI compared to Asian and European individuals. However, the pooled effect from observational studies cannot clarify whether the observed association is a causal effect or the result of some unmeasured confounding factors. Hence, the biological mechanisms underlying this association are still unclear and require further research.

Applying Deep Learning Model to Predict Diagnosis Code of Medical Records.

The International Classification of Diseases (ICD) code is a diagnostic classification standard that is frequently used as a referencing system in healthcare and insurance. However, it takes time and effort to find and use the right diagnosis code based on a patient's medical records. In response, deep learning (DL) methods have been developed to assist physicians in the ICD coding process. Our findings propose a deep learning model that utilized clinical notes from medical records to predict ICD-10 codes. Our research used text-based medical data from the outpatient department (OPD) of a university hospital from January to December 2016. The dataset used clinical notes from five departments, and a total of 21,953 medical records were collected. Clinical notes consisted of a subjective component, objective component, assessment, plan (SOAP) notes, diagnosis code, and a drug list. The dataset was divided into two groups: 90% for training and 10% for test cases. We applied natural language processing (NLP) technique (word embedding, Word2Vector) to process the data. A deep learning-based convolutional neural network (CNN) model was created based on the information presented above. Three metrics (precision, recall, and F-score) were used to calculate the achievement of the deep learning CNN model. Clinically acceptable results were achieved through the deep learning model for five departments (precision: 0.53-0.96; recall: 0.85-0.99; and F-score: 0.65-0.98). With a precision of 0.95, a recall of 0.99, and an F-score of 0.98, the deep learning model performed the best in the department of cardiology. Our proposed CNN model significantly improved the prediction performance for an automated ICD-10 code prediction system based on prior clinical information. This CNN model could reduce the laborious task of manual coding and could assist physicians in making a better diagnosis.

Benzodiazepine Use and the Risk of Dementia in the Elderly Population: An Umbrella Review of Meta-Analyses.

The prevalence of dementia among the elderly is high, and it is the leading cause of death globally. However, the relationship between benzodiazepine use and dementia risk has produced inconsistent results, necessitating an updated review of the evidence. To address this, we conducted an umbrella review of meta-analyses to summarize the available evidence on the association between benzodiazepine use and dementia risk and evaluate its credibility. We systematically evaluated the meta-analyses of observational studies that examined the connection between benzodiazepine use and dementia risk. For each meta-analysis, we collected the overall effect size, heterogeneity, risk of bias, and year of the most recent article and graded the evidence based on pre-specified criteria. We also used AMSTAR, a measurement tool to evaluate systematic reviews, to assess the methodological quality of each study. Our review included five meta-analyses encompassing 30 studies, and the effect size of the association between benzodiazepine use and dementia risk ranged from 1.38 to 1.78. Nonetheless, the evidence supporting this relationship was weak, and the methodological quality of the studies included was low. In conclusion, our findings revealed limited evidence of a link between benzodiazepine use and dementia risk, and more research is required to determine a causal connection. Physicians should only prescribe benzodiazepine for appropriate indications.

Heterogeneity of Alzheimer's disease identified by neuropsychological test profiling.

Alzheimer's disease (AD) is a highly heterogeneous disorder. Untangling this variability could lead to personalized treatments and improve participant recruitment for clinical trials. We investigated the cognitive subgroups by using a data-driven clustering technique in an AD cohort. People with mild-moderate probable AD from Taiwan was included. Neuropsychological test results from the Cognitive Abilities Screening Instrument were clustered using nonnegative matrix factorization. We identified two clusters in 112 patients with predominant deficits in memory (62.5%) and non-memory (37.5%) cognitive domains, respectively. The memory group performed worse in short-term memory and orientation and better in attention than the non-memory group. At baseline, patients in the memory group had worse global cognitive status and dementia severity. Linear mixed effect model did not reveal difference in disease trajectory within 3 years of follow-up between the two clusters. Our results provide insights into the cognitive heterogeneity in probable AD in an Asian population.

Proton Pump Inhibitors Use and the Risk of Pancreatic Cancer: Evidence from Eleven Epidemiological Studies, Comprising 1.5 Million Individuals.

Previous epidemiological studies have shown that proton pump inhibitor (PPI) may modify the risk of pancreatic cancer. We conducted an updated systematic review and meta-analysis of observational studies assessing the effect of PPI on pancreatic cancer. PubMed, Embase, Scopus, and Web of Science were searched for studies published between 1 January 2000, and 1 May 2022. We only included studies that assessed exposure to PPI, reported pancreatic cancer outcomes, and provided effect sizes (hazard ratio or odds ratio) with 95% confidence intervals (CIs). We calculated an adjusted pooled risk ratio (RR) with 95%CIs using the random-effects model. Eleven studies (eight case-control and three cohorts) that reported 51,629 cases of pancreatic cancer were included. PPI was significantly associated with a 63% increased risk of pancreatic cancer (RRadj. 1.63, 95%CI: 1.19-2.22, p = 0.002). Subgroup analysis showed that the pooled RR for rabeprazole and lansoprazole was 4.08 (95%CI: 0.61-26.92) and 2.25 (95%CI: 0.83-6.07), respectively. Moreover, the risk of pancreatic cancer was established for both the Asian (RRadj. 1.37, 95%CI: 0.98-1.81) and Western populations (RRadj.2.76, 95%CI: 0.79-9.56). The findings of this updated meta-analysis demonstrate that the use of PPI was associated with an increased risk of pancreatic cancer. Future studies are needed to improve the quality of evidence through better verification of PPI status (e.g., patient selection, duration, and dosages), adjusting for possible confounders, and ensuring long-term follow-up.

Deep-ADCA: Development and Validation of Deep Learning Model for Automated Diagnosis Code Assignment Using Clinical Notes in Electronic Medical Records.

Currently, the International Classification of Diseases (ICD) codes are being used to improve clinical, financial, and administrative performance. Inaccurate ICD coding can lower the quality of care, and delay or prevent reimbursement. However, selecting the appropriate ICD code from a patient's clinical history is time-consuming and requires expert knowledge. The rapid spread of electronic medical records (EMRs) has generated a large amount of clinical data and provides an opportunity to predict ICD codes using deep learning models. The main objective of this study was to use a deep learning-based natural language processing (NLP) model to accurately predict ICD-10 codes, which could help providers to make better clinical decisions and improve their level of service. We retrospectively collected clinical notes from five outpatient departments (OPD) from one university teaching hospital between January 2016 and December 2016. We applied NLP techniques, including global vectors, word to vectors, and embedding techniques to process the data. The dataset was split into two independent training and testing datasets consisting of 90% and 10% of the entire dataset, respectively. A convolutional neural network (CNN) model was developed, and the performance was measured using the precision, recall, and F-score. A total of 21,953 medical records were collected from 5016 patients. The performance of the CNN model for the five different departments was clinically satisfactory (Precision: 0.50~0.69 and recall: 0.78~0.91). However, the CNN model achieved the best performance for the cardiology department, with a precision of 69%, a recall of 89% and an F-score of 78%. The CNN model for predicting ICD-10 codes provides an opportunity to improve the quality of care. Implementing this model in real-world clinical settings could reduce the manual coding workload, enhance the efficiency of clinical coding, and support physicians in making better clinical decisions.

Proton Pump Inhibitor Use and Risk of Gastric Cancer: Current Evidence from Epidemiological Studies and Critical Appraisal.

Proton pump inhibitors (PPIs) are used for maintaining or improving gastric problems. Evidence from observational studies indicates that PPI therapy is associated with an increased risk of gastric cancer. However, the evidence for PPIs increasing the risk of gastric cancer is still being debated. Therefore, we aimed to investigate whether long-term PPI use is associated with an increased risk of gastric cancer. We systematically searched the relevant literature in electronic databases, including PubMed, EMBASE, Scopus, and Web of Science. The search and collection of eligible studies was between 1 January 2000 and 1 July 2021. Two independent authors were responsible for the study selection process, and they considered only observational studies that compared the risk of gastric cancer with PPI treatment. We extracted relevant information from selected studies, and assessed the quality using the Newcastle−Ottawa scale (NOS). Finally, we calculated overall risk ratios (RRs) with 95% confidence intervals (CIs) of gastric cancer in the group receiving PPI therapy and the control group. Thirteen observational studies, comprising 10,557 gastric cancer participants, were included. Compared with patients who did not take PPIs, the pooled RR for developing gastric cancer in patients receiving PPIs was 1.80 (95% CI, 1.46−2.22, p < 0.001). The overall risk of gastric cancer also increased in patients with gastroesophageal reflux disease (GERD), H. pylori treatment, and various adjusted factors. The findings were also consistent across several sensitivity analyses. PPI use is associated with an increased risk of gastric cancer in patients compared with those with no PPI treatment. The findings of this updated study could be used in making clinical decisions between physicians and patients about the initiation and continuation of PPI therapy, especially in patients at high risk of gastric cancer. Additionally, large randomized controlled trials are needed to determine whether PPIs are associated with a higher risk of gastric cancer.

Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis.

Esophageal cancer, one of the most common cancers with a poor prognosis, is the sixth leading cause of cancer-related mortality worldwide. Early and accurate diagnosis of esophageal cancer, thus, plays a vital role in choosing the appropriate treatment plan for patients and increasing their survival rate. However, an accurate diagnosis of esophageal cancer requires substantial expertise and experience. Nowadays, the deep learning (DL) model for the diagnosis of esophageal cancer has shown promising performance. Therefore, we conducted an updated meta-analysis to determine the diagnostic accuracy of the DL model for the diagnosis of esophageal cancer. A search of PubMed, EMBASE, Scopus, and Web of Science, between 1 January 2012 and 1 August 2022, was conducted to identify potential studies evaluating the diagnostic performance of the DL model for esophageal cancer using endoscopic images. The study was performed in accordance with PRISMA guidelines. Two reviewers independently assessed potential studies for inclusion and extracted data from retrieved studies. Methodological quality was assessed by using the QUADAS-2 guidelines. The pooled accuracy, sensitivity, specificity, positive and negative predictive value, and the area under the receiver operating curve (AUROC) were calculated using a random effect model. A total of 28 potential studies involving a total of 703,006 images were included. The pooled accuracy, sensitivity, specificity, and positive and negative predictive value of DL for the diagnosis of esophageal cancer were 92.90%, 93.80%, 91.73%, 93.62%, and 91.97%, respectively. The pooled AUROC of DL for the diagnosis of esophageal cancer was 0.96. Furthermore, there was no publication bias among the studies. The findings of our study show that the DL model has great potential to accurately and quickly diagnose esophageal cancer. However, most studies developed their model using endoscopic data from the Asian population. Therefore, we recommend further validation through studies of other populations as well.

Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report.

Duplex ultrasonography (DUS) is a safe, non-invasive, and affordable primary screening tool to identify the vascular risk factors of stroke. The overall process of DUS examination involves a series of complex processes, such as identifying blood vessels, capturing the images of blood vessels, measuring the velocity of blood flow, and then physicians, according to the above information, determining the severity of artery stenosis for generating final ultrasound reports. Generation of transcranial doppler (TCD) and extracranial carotid doppler (ECCD) ultrasound reports involves a lot of manual review processes, which is time-consuming and makes it easy to make errors. Accurate classification of the severity of artery stenosis can provide an early opportunity for decision-making regarding the treatment of artery stenosis. Therefore, machine learning models were developed and validated for classifying artery stenosis severity based on hemodynamic features. This study collected data from all available cases and controlled at one academic teaching hospital in Taiwan between 1 June 2020, and 30 June 2020, from a university teaching hospital and reviewed all patients' medical records. Supervised machine learning models were developed to classify the severity of artery stenosis. The receiver operating characteristic curve, accuracy, sensitivity, specificity, and positive and negative predictive value were used for model performance evaluation. The performance of the random forest model was better compared to the logistic regression model. For ECCD reports, the accuracy of the random forest model to predict stenosis in various sites was between 0.85 and 1. For TCD reports, the overall accuracy of the random forest model to predict stenosis in various sites was between 0.67 and 0.86. The findings of our study suggest that a machine learning-based model accurately classifies artery stenosis, which indicates that the model has enormous potential to facilitate screening for artery stenosis.