Achieving high inter-rater reliability in establishing data labels: a retrospective chart review study.

BACKGROUND: In medical research, the effectiveness of machine learning algorithms depends heavily on the accuracy of labeled data. This study aimed to assess inter-rater reliability (IRR) in a retrospective electronic medical chart review to create high quality labeled data on comorbidities and adverse events (AEs). METHODS: Six registered nurses with diverse clinical backgrounds reviewed patient charts, extracted data on 20 predefined comorbidities and 18 AEs. All reviewers underwent four iterative rounds of training aimed to enhance accuracy and foster consensus. Periodic monitoring was conducted at the beginning, middle, and end of the testing phase to ensure data quality. Weighted Kappa coefficients were calculated with their associated 95% confidence intervals (CIs). RESULTS: Seventy patient charts were reviewed. The overall agreement, measured by Conger's Kappa, was 0.80 (95% CI: 0.78-0.82). IRR scores remained consistently high (ranging from 0.70 to 0.87) throughout each phase. CONCLUSION: Our study suggests the detailed manual for chart review and structured training regimen resulted in a consistently high level of agreement among our reviewers during the chart review process. This establishes a robust foundation for generating high-quality labeled data, thereby enhancing the potential for developing accurate machine learning algorithms.

Effect of health rights accessibility on the urban integration of minority rural migrants in China: a cross-sectional study.

BACKGROUND: Accessing health rights is an integral component of people's aspirations for a better life. Existing discussions and evaluations regarding the accessibility of health rights for minority rural migrants are insufficient. In comparison to objective health conditions, inequalities in health rights lead to chronic and long-term depletion of human capital among minority rural migrants. This study aimed to assess the overall impact, heterogeneity effects, and mechanisms of health rights accessibility on the urban integration of minority rural migrants. METHODS: Based on the 2017 China Migrants Dynamic Survey Data (CMDS), this study employs OLS models, 2SLS models, conditional mixed process (CMP) methods, and omitted variable tests to estimate the impact of health rights accessibility on the urban integration of minority rural migrants. Additionally, from the perspectives of migration scope and illness experience, this study explored the heterogeneity in the relationship between health rights accessibility and urban integration. Finally, using the Karlson-Holm-Breen (KHB) model, this study dissects the mechanisms through which health rights accessibility influences the urban integration of minority rural migrants. RESULTS: Health rights accessibility significantly enhances the urban integration of minority rural migrants. Moreover, compared to minority rural migrants who move across provinces and who have no history of illness, those who migrate within the same province and who have experienced illness are more sensitive to the positive impact of health rights accessibility. However, the enhancing effect of health rights accessibility does not significantly differ between the new and old generations of minority rural migrants. Furthermore, health rights accessibility can indirectly improve the urban integration of minority rural migrants by elevating health levels, improving health habits, and reinforcing health behaviors. Among these, the indirect effects mediated by health habits are more pronounced. CONCLUSION: The research conclusions underscore the issue of health accessibility and urban integration among minority rural migrants, providing a reexamination and clarification of the policy effects of health rights in promoting the urban integration of minority rural migrants. Relevant policy design should commence with improving the health rights of minority rural migrants, enhancing their health integration capabilities, and effectively boosting their ability to integrate into urban life.

How does choice of residential community affect the social integration of rural migrants: insights from China.

The construction of public space is a new and important way to integrate rural migrants into urban society. Existing studies mainly discussed the factors affecting the social integration of rural migrants from the micro-individual and macro-system levels. Still, they seldom analyzed the differences between rural migrants' residential communities and the roles these differences play in their social integration, especially from the perspective of residential space. Based on the data of the 2014 China Migrants Dynamic Monitoring Survey, this paper systematically examines the impact of residential community selection on the social integration of rural migrants and its possible effects using OLS, 2SLS, CMP, omitted variable test method, and KHB mediating effect model. It is found that the choice of residential community has a significant positive impact on the social integration of rural migrants, and the social integration of rural migrants living in formal communities has increased by 2.44%-3.20%. To overcome the potential endogeneity problems and selection bias of the empirical model, the study further adopted an instrumental variable estimation approach, combined with the omitted variable method for robustness check; the results still revealed the positive effect of living in formal communities on the social integration of rural migrants. The heterogeneous results showed that living in formal communities has a greater effect on the social integration of women and older-generation rural migrants. The farther the migration range and the longer the residence time of rural migrants, the greater the effect of living in the formal community on their social integration. Further mechanism testing revealed that living in formal communities not only directly enhances the social integration of rural migrants but also indirectly improves their social integration through public resource allocation, human capital accumulation, social status screening, and social network expansion. The indirect effect of capital accumulation is even greater. Therefore, to accelerate the full integration of rural migrants into urban society and achieve real urbanization and citizenship, the study proposes that the government should scientifically plan the layout of rural migrants' living space and the construction of supporting facilities.

BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study.

BACKGROUND: Inpatient falls are a substantial concern for health care providers and are associated with negative outcomes for patients. Automated detection of falls using machine learning (ML) algorithms may aid in improving patient safety and reducing the occurrence of falls. OBJECTIVE: This study aims to develop and evaluate an ML algorithm for inpatient fall detection using multidisciplinary progress record notes and a pretrained Bidirectional Encoder Representation from Transformers (BERT) language model. METHODS: A cohort of 4323 adult patients admitted to 3 acute care hospitals in Calgary, Alberta, Canada from 2016 to 2021 were randomly sampled. Trained reviewers determined falls from patient charts, which were linked to electronic medical records and administrative data. The BERT-based language model was pretrained on clinical notes, and a fall detection algorithm was developed based on a neural network binary classification architecture. RESULTS: To address various use scenarios, we developed 3 different Alberta hospital notes-specific BERT models: a high sensitivity model (sensitivity 97.7, IQR 87.7-99.9), a high positive predictive value model (positive predictive value 85.7, IQR 57.2-98.2), and the high F1-score model (F1=64.4). Our proposed method outperformed 3 classical ML algorithms and an International Classification of Diseases code-based algorithm for fall detection, showing its potential for improved performance in diverse clinical settings. CONCLUSIONS: The developed algorithm provides an automated and accurate method for inpatient fall detection using multidisciplinary progress record notes and a pretrained BERT language model. This method could be implemented in clinical practice to improve patient safety and reduce the occurrence of falls in hospitals.

High-throughput virtual screening to identify potential small molecule inhibitors of the Zα domain of the adenosine deaminases acting on RNA 1(ADAR1).

Changes in RNA editing are closely associated with diseases such as cancer, viral infections, and autoimmune disorders. Adenosine deaminase (ADAR1), which acts on RNA 1, plays a key role in adenosine to inosine editing and is a potential therapeutic target for these various diseases. The p150 subtype of ADAR1 is the only one that contains a Zα domain that binds to both Z-DNA and Z-RNA. The Zα domain modulates immune responses and may be suitable targets for antiviral therapy and cancer immunotherapy. In this study, we attempted to utilize molecular docking to identify potential inhibitors that bind to the ADAR1 Zα domain. The virtual docking method screened the potential activity of more than 100,000 compounds on the Zα domain of ADAR1 and filtered to obtain the highest scoring results.We identified 71 compounds promising to bind to ADAR1 and confirmed that two of them, lithospermic acid and Regaloside B, interacts with the ADAR1 Zα domain by surface plasmonic resonance technique. The molecular dynamics calculation of the complex of lithospermic acid and ADAR1 also showed that the binding effect of lithospermic acid to ADAR1 was stable.This study provides a new perspective for the search of ADAR1 inhibitors, and further studies on the anti-ADAR11 activity of these compounds have broad prospects.

Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study.

BACKGROUND: Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection. METHODS: This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision-recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated. RESULTS: There were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835-0.978), PR AUC of 0.637 (95% CI 0.528-0.746), and F1 score of 0.79 (0.67-0.90). CONCLUSIONS: Our findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.

The incidence rates of surgical site infections following total hip and knee arthroplasty were 0.5 and 0.52 per 100 surgical procedures. The incidence of SSIs varied significantly between care facilities (ranging from 0.53 to 1.71 per 100 procedures). The optimal machine learning model achieved a ROC AUC of 0.906 (95% CI 0.835­0.978), PR AUC of 0.637 (95% CI 0.528­0.746), and F1 score of 0.79 (0.67­0.90).

A longitudinal seroepidemiology study to evaluate antibody response to SARS-CoV-2 virus infection and vaccination in children in Calgary, Canada from July 2020 to April 2022: Alberta COVID-19 Childhood Cohort (AB3C) Study.

BACKGROUND: Measurement of SARS-CoV-2 antibody seropositivity is important to accurately understand exposure to infection and/or vaccination in specific populations. This study aimed to estimate the serologic response to SARS-CoV-2 virus infection and vaccination in children in Calgary, Alberta over a two-year period. METHODS: Children with or without prior SARS-CoV-2 infections, were enrolled in Calgary, Canada in 2020. Venous blood was sampled 4 times from July 2020 to April 2022 for SARS-CoV-2 nucleocapsid and spike antibodies. Demographic and clinical information was obtained including SARS-CoV-2 testing results and vaccination records. RESULTS: 1035 children were enrolled and 88.9% completed all 4 visits; median age 9 years (IQR: 5,13); 519 (50.1%) female; and 815 (78.7%) Caucasian. Before enrolment, 118 (11.4%) had confirmed or probable SARS-CoV-2. By April 2022, 39.5% of previously uninfected participants had a SARS-CoV-2 infection. Nucleocapsid antibody seropositivity declined to 16.4% of all infected children after more than 200 days post diagnosis. Spike antibodies remained elevated in 93.6% of unvaccinated infected children after more than 200 days post diagnosis. By April 2022, 408 (95.6%) children 12 years and older had received 2 or more vaccine doses, and 241 (61.6%) 5 to 11 year-old children had received 2 vaccine doses. At that time, all 685 vaccinated children had spike antibodies, compared with 94/176 (53.4%) of unvaccinated children. CONCLUSIONS: In our population, after the first peak of Omicron variant infections and introduction of COVID-19 vaccines for children, all vaccinated children, but just over one-half of unvaccinated children, had SARS-CoV-2 spike antibodies indicating infection and/or vaccination, highlighting the benefit of vaccination. It is not yet known whether a high proportion of seropositivity at the present time predicts sustained population-level protection against future SARS-CoV-2 transmission, infection or severe COVID-19 outcomes in children.

10-Gingerol Enhances the Effect of Taxol in Triple-Negative Breast Cancer via Targeting ADRB2 Signaling.

Purpose: Although paclitaxel is widely used in cancer treatment, severe side effects and drug resistance limit its clinical use. 10-gingerol (10-G) is a natural compound isolated from ginger, which displays anti-inflammatory, antioxidant, and antiproliferative properties. However, the chemotherapy-sensitization effect of 10-G on triple-negative breast cancer (TNBC) has not been fully clarified. This study is aimed at investigating the effect of 10-G on the paclitaxel sensitivity in TNBC, and its underlying mechanism. Methods: The study was determined through in vitro and in vivo experiments. Cell viability and proliferation were detected by cell counting kit 8 (CCK-8) and colony formation. To detect cell apoptosis, flow cytometry and TUNEL were used. The expression of proteins was detected by Western blotting and immunohistochemistry. The molecular docking and gene knockout were corroborated by interactions between 10-G and adrenoceptor Beta 2 (ADRB2). The body weight of mice, histopathology and organs (kidney and spleen) coefficients were used to monitor the drug toxicities. Results: In vitro, 10-G increased the sensitivity of TNBC cells to paclitaxel, and could synergistically promote the apoptosis of TNBC cells induced by paclitaxel. In combination with molecular docking and lentivirus knockdown studies, ADRB2 was identified as a 10-G binding protein. 10-G inhibited ADRB2 by binding to the active site of ADRB2. Knockdown of ADRB2 reduces the proliferation activity of TNBC cells but also attenuates the sensitizing effects of 10-G to paclitaxel. Western blotting and immunohistochemistry showed that 10-G played an anti-proliferation and chemotherapy-sensitizing role by inhibiting the ADRB2/ERK signal. Toxicity evaluation showed that 10-G would not increase hepatorenal toxicity with paclitaxel. Conclusion: This data suggests that 10-G may be used as a new chemotherapeutic synergist in combination with paclitaxel to enhance anticancer activity. The potential value of ADRB2 as a target for improving chemotherapy sensitivity was also emphasized.

A New Method of Identifying Pathologic Complete Response After Neoadjuvant Chemotherapy for Breast Cancer Patients Using a Population-Based Electronic Medical Record System.

BACKGROUND: Accurate identification of pathologic complete response (pCR) from population-based electronic narrative data in a timely and cost-efficient manner is critical. This study aimed to derive and validate a set of natural language processing (NLP)-based machine-learning algorithms to capture pCR from surgical pathology reports of breast cancer patients who underwent neoadjuvant chemotherapy (NAC). METHODS: This retrospective cohort study included all invasive breast cancer patients who underwent NAC and subsequent curative-intent surgery during their admission at all four tertiary acute care hospitals in Calgary, Alberta, Canada, between 1 January 2010 and 31 December 2017. Surgical pathology reports were extracted and processed with NLP. Decision tree classifiers were constructed and validated against chart review results. Machine-learning algorithms were evaluated with a performance matrix including sensitivity, specificity, positive predictive value (PPV), negative predictive value [NPV], accuracy, area under the receiver operating characteristic curve [AUC], and F1 score. RESULTS: The study included 351 female patients. Of these patients, 102 (29%) achieved pCR after NAC. The high-sensitivity model achieved a sensitivity of 90.5% (95% confidence interval [CI], 69.6-98.9%), a PPV of 76% (95% CI, 59.6-87.2), an accuracy of 88.6% (95% CI, 78.7-94.9%), an AUC of 0.891 (95% CI, 0.795-0.987), and an F1 score of 82.61. The high-PPV algorithm reached a sensitivity of 85.7% (95% CI, 63.7-97%), a PPV of 81.8% (95% CI, 63.4-92.1%), an accuracy of 90% (95% CI, 80.5-95.9%), an AUC of 0.888 (95% CI, 0.790-0.985), and an F1 score of 83.72. The high-F1 score algorithm obtained a performance equivalent to that of the high-PPV algorithm. CONCLUSION: The developed algorithms demonstrated excellent accuracy in identifying pCR from surgical pathology reports of breast cancer patients who received NAC treatment.

Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis.

Background: Medical researchers and clinicians have shown much interest in developing machine learning (ML) algorithms to detect/predict surgical site infections (SSIs). However, little is known about the overall performance of ML algorithms in predicting SSIs and how to improve the algorithm's robustness. We conducted a systematic review and meta-analysis to summarize the performance of ML algorithms in SSIs case detection and prediction and to describe the impact of using unstructured and textual data in the development of ML algorithms. Methods: MEDLINE, EMBASE, CINAHL, CENTRAL and Web of Science were searched from inception to March 25, 2021. Study characteristics and algorithm development information were extracted. Performance statistics (e.g., sensitivity, area under the receiver operating characteristic curve [AUC]) were pooled using a random effect model. Stratified analysis was applied to different study characteristic levels. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) was followed. Results: Of 945 articles identified, 108 algorithms from 32 articles were included in this review. The overall pooled estimate of the SSI incidence rate was 3.67%, 95% CI: 3.58-3.76. Mixed-use of structured and textual data-based algorithms (pooled estimates of sensitivity 0.83, 95% CI: 0.78-0.87, specificity 0.92, 95% CI: 0.86-0.95, AUC 0.92, 95% CI: 0.89-0.94) outperformed algorithms solely based on structured data (sensitivity 0.56, 95% CI:0.43-0.69, specificity 0.95, 95% CI:0.91-0.97, AUC = 0.90, 95% CI: 0.87-0.92). Conclusions: ML algorithms developed with structured and textual data provided optimal performance. External validation of ML algorithms is needed to translate current knowledge into clinical practice.

Preparing Thick Gradient Surface Layer in Cu-Zn Alloy via Ultrasonic Severe Surface Rolling for Strength-Ductility Balance.

A gradient structure (GS) design is a prominent strategy for strength-ductility balance in metallic materials, including Cu alloys. However, producing a thick GS surface layer without surface damage is still a challenging task limited by the available processing technology. In this work, a gradient structure (GS) surface layer with a thickness at the millimeter scale is produced in the Cu-38 wt.% Zn alloy using ultrasonic severe surface rolling technology at room temperature. The GS surface layer is as thick as 1.1 mm and involves the gradient distribution of grain size and dislocation density. The grain size is refined to 153.5 nm in the topmost surface layer and gradually increases with increasing depth. Tensile tests indicate that the single-sided USSR processed alloy exhibits balanced strength (467.5 MPa in yield strength) and ductility (10.7% in uniform elongation). Tailoring the volume fraction of the GS surface layer can tune the combination of strength and ductility in a certain range. The high strength of GS surface layer mainly stems from the high density of grain boundaries, dislocations and dislocation structures, deformation twins, and GS-induced synergistic strengthening effect. Our study elucidates the effect of the thick GS surface layer on strength and ductility, and provides a novel pathway for optimizing the strength-ductility combination of Cu alloys.

Disrupted gut microbiota aggravates working memory dysfunction induced by high-altitude exposure in mice.

Background: The widely accepted microbiome-gut-brain axis (MGBA) hypothesis may be essential for explaining the impact of high-altitude exposure on the human body, especially brain function. However, studies on this topic are limited, and the underlying mechanism remains unclear. Therefore, this study aimed to determine whether high-altitude-induced working memory dysfunction could be exacerbated with gut microbiota disruption. Methods and results: C57BL/6 mice were randomly divided into three groups: control, high-altitude exposed (HAE), and high-altitude exposed with antibiotic treatment (HAE-A). The HAE and HAE-A groups were exposed to a low-pressure oxygen chamber (60-65 kPa) simulating the altitude of 3,500-4,000 m for 14 days, The air pressure level for the control group was maintained at 94.5 kPa. Antibiotic water (mixed with 0.2 g/L of ciprofloxacin and 1 g/L of metronidazole) was provided to the HAE-A group. Based on the results of the novel object test and P300 in the oddball behavioral paradigm training test, working memory dysfunction was aggravated by antibiotic treatment. We determined the antioxidant capacity in the prefrontal cortex and found a significant negative influence (p < 0.05) of disturbed gut microbiota on the total antioxidant capacity (T-AOC) and malondialdehyde (MDA) content, as well as the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px). The same trend was also observed in the apoptosis-related functional protein content and mRNA expression levels in the prefrontal cortex, especially the levels of bcl-2, Bax, and caspase-3. The high-altitude environment and antibiotic treatment substantially affected the richness and diversity of the colonic microbiota and reorganized the composition and structure of the microbial community. S24-7, Lachnospiraceae, and Lactobacillaceae were the three microbial taxa with the most pronounced differences under the stimulation by external factors in this study. In addition, correlation analysis between colonic microbiota and cognitive function in mice demonstrated that Helicobacteraceae may be closely related to behavioral results. Conclusion: Disrupted gut microbiota could aggravate working memory dysfunction induced by high-altitude exposure in mice, indicating the existence of a link between high-altitude exposure and MGBA.

Developing EMR-based algorithms to Identify hospital adverse events for health system performance evaluation and improvement: Study protocol.

BACKGROUND: Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative data-based adverse event (AE) detection methods are suboptimal due to lack of clinical information. Electronic medical records (EMR) have been widely implemented and contain detailed and comprehensive information regarding all aspects of patient care, offering a valuable complement to administrative data. Harnessing the rich clinical data in EMRs offers a unique opportunity to improve detection, identify possible risk factors of AE and enhance surveillance. However, the methodological tools for detection of AEs within EMR need to be developed and validated. The objectives of this study are to develop EMR-based AE algorithms from hospital EMR data and assess AE algorithm's validity in Canadian EMR data. METHODS: Patient EMR structured and text data from acute care hospitals in Calgary, Alberta, Canada will be linked with discharge abstract data (DAD) between 2010 and 2020 (n~1.5 million). AE algorithms development. First, a comprehensive list of AEs will be generated through a systematic literature review and expert recommendations. Second, these AEs will be mapped to EMR free texts using Natural Language Processing (NLP) technologies. Finally, an expert panel will assess the clinical relevance of the developed NLP algorithms. AE algorithms validation: We will test the newly developed AE algorithms on 10,000 randomly selected EMRs between 2010 to 2020 from Calgary, Alberta. Trained reviewers will review the selected 10,000 EMR charts to identify AEs that had occurred during hospitalization. Performance indicators (e.g., sensitivity, specificity, positive predictive value, negative predictive value, F1 score, etc.) of the developed AE algorithms will be assessed using chart review data as the reference standard. DISCUSSION: The results of this project can be widely implemented in EMR based healthcare system to accurately and timely detect in-hospital AEs.

Research on the Efficiency of Green Agricultural Science and Technology Innovation Resource Allocation Based on a Three-Stage DEA Model-A Case Study of Anhui Province, China.

In order to achieve sustainable development of agriculture, people have gradually begun to attach importance to the development of low-carbon agriculture and to regard green agricultural technology innovation and promotion as increasingly more important. Taking the Anhui Province of China as an example, this study analyzed the impact of green agricultural science and technology innovation resource allocation on rural revitalization by constructing an econometric model. We found that the overall efficiency of the overall allocation of agricultural science and technology innovation resources in Anhui Province increased in the sample period, but the scale efficiency level was relatively low. The key path to improving the overall efficiency of allocation was to improve the scale efficiency level. The allocation of agricultural science and technology innovation resources in 16 cities and prefectures performed well in terms of pure technical efficiency, but there were significant differences in scale efficiency, which further affected the overall allocation efficiency of different regions. Among them, the allocation efficiencies of agricultural science and technology innovation resources in Hefei and Fuyang were at the leading level in Anhui Province. Similar to the overall situation of the province, the improvement path of areas with low comprehensive efficiency lay in the improvement of scale efficiency. In view of this, from the policy level, we need to optimize the relationship between the government and the market, speed up the construction of platforms and carriers, attach importance to the construction of the agricultural science and technology talent training system, and improve the open sharing mechanism.

Validated algorithms for identifying timing of second event of oropharyngeal squamous cell carcinoma using real-world data.

BACKGROUND: Understanding occurrence and timing of second events (recurrence and second primary cancer) is essential for cancer specific survival analysis. However, this information is not readily available in administrative data. METHODS: Alberta Cancer Registry, physician claims, and other administrative data were used. Timing of second event was estimated based on our developed algorithm. For validation, the difference, in days between the algorithm estimated and the chart-reviewed timing of second event. Further, the result of Cox-regression modeling cancer-free survival was compared to chart review data. RESULTS: Majority (74.3%) of the patients had a difference between the chart-reviewed and algorithm-estimated timing of second event falling within the 0-60 days window. Kaplan-Meier curves generated from the estimated data and chart review data were comparable with a 5-year second-event-free survival rate of 75.4% versus 72.5%. CONCLUSION: The algorithm provided an estimated timing of second event similar to that of the chart review.

Exploration of the Mechanism of Lianhua Qingwen in Treating Influenza Virus Pneumonia and New Coronavirus Pneumonia with the Concept of "Different Diseases with the Same Treatment" Based on Network Pharmacology.

The 31 main components of Lianhua Qingwen (LHQW) were obtained through a literature and database search; the components included glycyrrhizic acid, emodin, chlorogenic acid, isophoroside A, forsythia, menthol, luteolin, quercetin, and rutin. Sixty-eight common targets for the treatment of novel coronavirus pneumonia (NCP) and influenza virus pneumonia (IVP) were also obtained. A "component-target-disease" network was constructed with Cytoscape 3.2.1 software, and 20 key targets, such as cyclooxygenase2 (COX2), interleukin-6 (IL-6), mitogen-activated protein kinase14 (Mapk14), and tumor necrosis factor (TNF), were screened from the network. The David database was used to perform a Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment analysis and gene ontology (GO) biological process enrichment. Results showed that the key targets of LHQW in the treatment of NCP and IVP mainly involved biological processes, such as immune system process intervention, cell proliferation, apoptosis and invasion, toxic metabolism, cytokine activity, and regulation of the synthesis process. KEGG enrichment analysis revealed that 115 signalling pathways were related to the treatment of LHQW. Amongst them, IL-17, T cell receptor, Th17 cell differentiation, TNF, toll-like receptor, MAPK, apoptosis, and seven other signalling pathways were closely related to the occurrence and development of NCP and IVP. Molecular docking showed that each component had different degrees of binding with six targets, namely, 3C-like protease (3CL), angiotensin-converting enzyme 2 (ACE2), COX2, hemagglutinin (HA), IL-6, and neuraminidase (NA). Rutin, isoforsythiaside A, hesperidin and isochlorogenic acid B were the best components for docking with the six core targets. The first five components with the best docking results were isoforsythiaside, hesperidin, isochlorogenic acid B, forsythin E, and quercetin. In conclusion, LHQW has many components, targets, and pathways. The findings of this work can provide an important theoretical basis for determining the mechanism of LHQW in treating NCP and IVP.

A Multicenter Cohort Study of Falls Among Patients Admitted to the ICU.

OBJECTIVES: To determine the incidence of falls, risk factors, and adverse outcomes, among patients admitted to the ICU. DESIGN: Retrospective cohort study. SETTING: Seventeen ICUs in Alberta, Canada. PATIENTS: Seventy-three thousand four hundred ninety-five consecutive adult patient admissions between January 1, 2014, and December 31, 2019. MEASUREMENTS AND MAIN RESULTS: A mixed-effects negative binomial regression model was used to examine risk factors associated with falls. Linear and logistic regression models were used to evaluate adverse outcomes. Six hundred forty patients experienced 710 falls over 398,223 patient days (incidence rate of 1.78 falls per 1,000 patient days [95% CI, 1.65-1.91]). The daily incidence of falls increased during the ICU stay (e.g., day 1 vs day 7; 0.51 vs 2.43 falls per 1,000 patient days) and varied significantly between ICUs (range, 0.37-4.64 falls per 1,000 patient days). Male sex (incidence rate ratio [IRR], 1.37; 95% CI, 1.15-1.63), previous invasive mechanical ventilation (IRR, 1.82; 95% CI, 1.40-2.38), previous sedative and analgesic medication infusions (IRR, 1.60; 95% CI, 1.15-2.24), delirium (IRR, 3.85; 95% CI, 3.23-4.58), and patient mobilization (IRR, 1.26; 95% CI, 1.21-1.30) were risk factors for falling. Falls were associated with longer ICU (ratio of means [RM], 3.10; 95% CI, 2.86-3.36) and hospital (RM, 2.21; 95% CI, 2.01-2.42) stays, but lower odds of death in the ICU (odds ratio [OR], 0.09; 95% CI, 0.05-0.17) and hospital (OR, 0.21; 95% CI, 0.14-0.30). CONCLUSIONS: We observed that among ICU patients, falls occur frequently, vary substantially between ICUs, and are associated with modifiable risk factors, longer ICU and hospital stays, and lower risk of death. Our study suggests that fall prevention strategies should be considered for critically ill patients admitted to ICU.

Validity of ICD-10 codes for COVID-19 patients with hospital admissions or ED visits in Canada: a retrospective cohort study.

OBJECTIVE: To evaluate the validity of COVID-19 International Classification of Diseases, 10th Revision (ICD-10) codes and their combinations. DESIGN: Retrospective cohort study. SETTING: Acute care hospitals and emergency departments (EDs) in Alberta, Canada. PARTICIPANTS: Patients who were admitted to hospital or presented to an ED in Alberta, as captured by local administrative databases between 1 March 2020 and 28 February 2021, who had a positive COVID-19 test and/or a COVID-19-related ICD-10 code. MAIN OUTCOME MEASURES: The sensitivity, positive predictive value (PPV) and 95% CIs for ICD-10 codes were computed. Stratified analysis on age group, sex, symptomatic status, mechanical ventilation, hospital type, patient intensive care unit (ICU) admission, discharge status and season of pandemic were conducted. RESULTS: Two overlapping subsets of the study population were considered: those who had a positive COVID-19 test (cohort A, for estimating sensitivity) and those who had a COVID-19-related ICD-10 code (cohort B, for estimating PPV). Cohort A included 17 979 ED patients and 6477 inpatients while cohort B included 33 675 ED patients and 18 746 inpatients. Of inpatients, 9.5% in cohort A and 8.1% in cohort B received mechanical ventilation. Over 13% of inpatients were admitted to ICU. The length of hospital stay was 6 days (IQR: 3-14) for cohort A and 8 days (IQR: 3-19) for cohort B. In-hospital mortality was 15.9% and 38.8% for cohort A and B, respectively. The sensitivity for ICD-10 code U07.1 (COVID-19, virus identified) was 82.5% (81.8%-83.2%) with a PPV of 93.1% (92.6%-93.6%). The combination of U07.1 and U07.3 (multisystem inflammatory syndrome associated with COVID-19) had a sensitivity of 82.5% (81.9%-83.2%) and PPV of 92.9% (92.4%-93.4%). CONCLUSIONS: In Alberta, ICD-10 COVID-19 codes (U07.1 and U07.3) were coded well with high validity. This indicates administrative data can be used for COVID-19 research and pandemic management purposes.