Association between self-efficacy, spiritual well-being and the willingness to provide spiritual care among nursing staff in Taiwan: a cross-sectional study.

BACKGROUND: Spiritual care plays a significant role in holistic patient care, addressing not only physical ailments but also attending to patients' emotional and spiritual well-being. While the importance of spiritual care in nursing is widely recognized, there is often a gap in understanding nurses' willingness to provide such care. This cross-sectional study aimed to explore the association between self-efficacy, spiritual well-being, and willingness to provide spiritual care among nursing staff. METHODS: The study conducted a cross-sectional survey of full-time registered nurses at a hospital in Taiwan from January 2019 to December 2019. A sample comprising 168 nurses was selected for participation in the study through a random sampling method. In addition to collecting demographic variables, the assessment tools used in the study include the General Self-Efficacy Scale (GSES) for measuring self-efficacy, the Spiritual Index of Well-Being Chinese Version (SIWB-C) for evaluating spiritual well-being, and the Spiritual Care Needs Inventory (SCNI) to gauge willingness to provide spiritual care. RESULTS: Most participants in the study were female, accounting for 98.2% (n = 165). The mean age of all 168 nurses was 37.1 ± 9.3 years. Additionally, most participants held a Bachelor's degree (79.2%, n = 133) and possessed clinical experience was 10.5 ± 9.3 years. Through logistic regression analysis, it was found that regardless of whether participants have received sufficient spiritual care training, both GSES and SIWB-C remain influential factors in determining the provision of spiritual care. CONCLUSIONS: Collaboration between healthcare management and nursing staff is essential for fostering a healthcare environment that not only appreciates the physical and spiritual dimensions of patient care but also prioritizes the enhancement of nurses ' self-efficacy and well-being.

Sepsis increases the risk of in-hospital cardiac arrest: a population-based analysis.

Sepsis patients have a high risk of developing in-hospital cardiac arrest (IHCA), which portends poor survival. However, little is known about whether the increased incidence of IHCA is due to sepsis itself or to comorbidities harbored by sepsis patients. We conducted a retrospective population-based cohort study comprising 20,022 patients admitted with sepsis to hospitals in Taiwan using the National Health Insurance Research Database (NHIRD). We constructed three non-sepsis comparison cohorts using risk set sampling and propensity score (PS) matching. We used univariate conditional logistic regression to evaluate the risk of IHCA and associated mortality. We identified 12,790 inpatients without infection (matched cohort 1), 12,789 inpatients with infection but without sepsis (matched cohort 2), and 10,536 inpatients with end-organ dysfunction but without sepsis (matched cohort 3). In the three PS-matched cohorts, the odds ratios (OR) for developing ICHA were 21.17 (95% CI 17.19, 26.06), 18.96 (95% CI: 15.56, 23.10), and 1.23 (95% CI: 1.13, 1.33), respectively (p < 0.001 for all ORs). In conclusion, in our study of inpatients across Taiwan, sepsis was independently associated with an increased risk of IHCA. Further studies should focus on identifying the proxy causes of IHCA using real-time monitoring data to further reduce the incidence of cardiopulmonary insufficiency in patients with sepsis.

Comparative Efficacy of Pharmacological Treatments for Acne Vulgaris: A Network Meta-Analysis of 221 Randomized Controlled Trials.

PURPOSE: Acne is an extremely common skin disease with an estimated global prevalence of 9.4%. We aim to provide comprehensive comparisons of the common pharmacological treatments for acne. METHODS: Randomized controlled trials comparing the efficacy of pharmacological therapies for acne vulgaris in patients of any age and sex and with a treatment duration of >2 weeks were included. PubMed and Embase databases were searched from inception until February 2022. Our prespecified primary end points were mean percentage reduction in total, inflammatory, and noninflammatory lesions. Treatment ranking was determined by P values. RESULTS: There were 210 articles describing 221 trials and 37 interventions included in the analysis. Our primary analysis of percentage reduction in total lesion count had 65,601 patients enrolled. Across all trials, the mean age was 20.4 years. The median duration of treatment was 12 weeks. The median total, inflammatory, and noninflammatory lesion counts were 72, 27, and 44, respectively. The most effective treatment was oral isotretinoin (mean difference [MD] = 48.41; P = 1.00), followed by triple therapy containing a topical antibiotic, a topical retinoid, and benzoyl peroxide (BPO) (MD = 38.15; P = .95) and by triple therapy containing an oral antibiotic, a topical retinoid, and BPO (MD = 34.83; P = .90). For monotherapies, oral or topical antibiotics or topical retinoids have comparable efficacy for inflammatory lesions, while oral or topical antibiotics have less effect on noninflammatory lesions. CONCLUSION: The most effective treatment for acne is oral isotretinoin, followed by triple therapies containing a topical retinoid, BPO, and an antibiotic. We present detailed comparisons of each intervention to serve as a practical database.

Genome-phenome wide association study of broadly defined headache.

Until recently, most genetic studies of headache have been conducted on participants with European ancestry. We therefore conducted a large-scale genome-wide association study of self-reported headache in individuals of East Asian ancestry (specifically those who were identified as Han Chinese). In this study, 108 855 participants were enrolled, including 12 026 headache cases from the Taiwan Biobank. For broadly defined headache phenotype, we identified a locus on Chromosome 17, with the lead single-nucleotide polymorphism rs8072917 (odds ratio 1.08, P = 4.49 × 10-8), mapped to two protein-coding genes RNF213 and ENDOV. For severe headache phenotype, we found a strong association on Chromosome 8, with the lead single-nucleotide polymorphism rs13272202 (odds ratio 1.30, P = 1.02 × 10-9), mapped to gene RP11-1101K5.1. We then conducted a conditional analysis and a statistical fine-mapping of the broadly defined headache-associated loci and identified a single credible set of loci with rs8072917 supporting that this lead variant was the true causal variant on RNF213 gene region. RNF213 replicated the result of previous studies and played important roles in the biological mechanism of broadly defined headache. On the basis of the previous results found in the Taiwan Biobank, we conducted phenome-wide association studies for the lead variants using data from the UK Biobank and found that the causal variant (single-nucleotide polymorphism rs8072917) was associated with muscle symptoms, cellulitis and abscess of face and neck, and cardiogenic shock. Our findings foster the genetic architecture of headache in individuals of East Asian ancestry. Our study can be replicated using genomic data linked to electronic health records from a variety of countries, therefore affecting a wide range of ethnicities globally. Our genome-phenome association study may facilitate the development of new genetic tests and novel drug mechanisms.

Serum levels of copeptin predict adverse outcomes and improve risk prediction of TRISS and MGAP scores in patients with multiple trauma: A single-center prospective cohort study.

BACKGROUND: Multiple trauma deserves early prognostication and stratification. Copeptin, a precursor of vasopressin, is produced in response to stress. We examined the association between serum levels of copeptin and mortality risk in patients with multiple trauma. We aimed to also enhance the previously established Trauma-Related Injury Severity Score (TRISS) and Mechanism, GCS, Age, and Arterial Pressure (MGAP) score with the additional consideration of copeptin levels. METHODS: This single-center prospective cohort study enrolled patients who presented to the emergency department with potential major injuries. The serum levels of copeptin were measured, and the correlation to clinical severity in terms of 30-day mortality and requirement of intensive care management was analyzed. By combining copeptin levels with TRISS or MGAP, comparison between performance of the original models with the copeptin-enhanced models was performed via discrimination, calibration, and reclassification analyses. RESULTS: There was a significant increase in copeptin levels in patients who died within 30 days (median 644.4 pg/L, interquartile range [472.5, 785.9]) or were admitted to intensive care units (233.8 pg/L, [105.7, 366.4]), compared with those who survived (37.49 pg/L, [17.88, 77.68]). Adding the natural log of copeptin levels to the established TRISS and MGAP models improved the AUC of TRISS from 0.89 to 0.96, and that of MGAP from 0.82 to 0.95. Both calibrations as measured by Brier's scores and reclassification as measured by net reclassification improvement or integrated discrimination improvement demonstrated significant improvements. A Web-based calculator was built to generate predicted mortality rates of various models for convenient clinical use. CONCLUSION: Admission serum copeptin levels were correlated with clinical severity in multiple trauma. Coupling copeptin with preexisting trauma severity scores improved prediction accuracy. Copeptin shows promise as a novel biomarker for the prediction of trauma outcome. LEVEL OF EVIDENCE: Prognostic and Epidemiologic; Level III.

Use of nicorandil is associated with increased risk of incident atrial fibrillation.

BACKGROUND: Nicorandil will activate ATP-sensitive potassium channel (KATP). However, activation of potassium channels plays an important role in the mechanism of atrial fibrillation (AF) or atrial flutter (AFL). Whether use of nicorandil might contribute to initiation and/or perpetuation of AF/AFL remained unknown. We determined the relationship between use of nicorandil and risk of atrial fibrillation and determined its molecular mechanism. METHODS: We performed a nested case-control study using a cohort from the National Health Insurance Research Database (NHIRD) of Taiwan. The association between nicorandil use and risk of atrial fibrillation/flutter was estimated by logistic regression model. We also performed molecular, cellular and animal studies to explain the association. RESULTS: A total of 715 individuals who experienced AF/atrial flutter were matched to 72,215 controls. New use of nicorandil was found to be associated with increased risk for AF/AFL (odds ratio [OR], 2.34; 95% CI 1.07-5.13) compared to nitrate use. We found the expression of KATP subunits Kir6.2 and SUR2A in human and rat left atrial tissues. Furthermore, nicorandil directly shortened action potential duration (APD) in rat left atrium and shortened the QT interval of cultured human induced pluripotent stem cell (iPSC) derived cardiomyocytes (iPSC-CMs). CONCLUSIONS: Use of nicorandil was found to be associated with increased risk of AF/AFL. We also showed the expression of KATP subunits in human atria, and a possible mechanism that use of nicorandil increases the risk of AF through activation of KATP and shortening of atrial APD.

MALDI-TOF mass spectrometry rapid pathogen identification and outcomes of patients with bloodstream infection: A systematic review and meta-analysis.

There was inconsistent evidence regarding the use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for microorganism identification with/without antibiotic stewardship team (AST) and the clinical outcome of patients with bloodstream infections (BSI). In a systematic review and meta-analysis, we evaluated the effectiveness of rapid microbial identification by MALDI-TOF MS with and without AST on clinical outcomes. We searched PubMed and EMBASE databases from inception to 1 February 2022 to identify pre-post and parallel comparative studies that evaluated the use of MALDI-TOF MS for microorganism identification. Pooled effect estimates were derived using the random-effects model. Twenty-one studies with 14,515 patients were meta-analysed. Compared with conventional phenotypic methods, MALDI-TOF MS was associated with a 23% reduction in mortality (RR = 0.77; 95% CI: 0.66; 0.90; I2  = 35.9%; 13 studies); 5.07-h reduction in time to effective antibiotic therapy (95% CI: -5.83; -4.31; I2  = 95.7%); 22.86-h reduction in time to identify microorganisms (95% CI: -23.99; -21.74; I2  = 91.6%); 0.73-day reduction in hospital stay (95% CI: -1.30; -0.16; I2  = 53.1%); and US$4140 saving in direct hospitalization cost (95% CI: $-8166.75; $-113.60; I2  = 66.1%). No significant heterogeneity sources were found, and no statistical evidence for publication bias was found. Rapid pathogen identification by MALDI-TOF MS with or without AST was associated with reduced mortality and improved outcomes of BSI, and may be cost-effective among patients with BSI.

Dynamic changes in heparin-binding protein as a prognostic biomarker for 30-day mortality in sepsis patients in the intensive care unit.

Heparin-binding protein (HBP) has been shown to be a robust predictor of the progression to organ dysfunction from sepsis, and we hypothesized that dynamic changes in HBP may reflect the severity of sepsis. We therefore aim to investigate the predictive value of baseline HBP, 24-h, and 48-h HBP change for prediction of 30-day mortality in adult patients with sepsis. This is a prospective observational study in an intensive care unit of a tertiary center. Patients aged 20 years or older who met SEPSIS-3 criteria were prospectively enrolled from August 2019 to January 2020. Plasma levels of HBP were measured at admission, 24 h, and 48 h and dynamic changes in HBP were calculated. The Primary endpoint was 30-day mortality. We tested whether the biomarkers could enhance the predictive accuracy of a multivariable predictive model. A total of 206 patients were included in the final analysis. 48-h HBP change (HBPc-48 h) had greater predictive accuracy of area under the curve (AUC: 0.82), followed by baseline HBP (0.79), PCT (0.72), lactate (0.71), and CRP (0.65), and HBPc-24 h (0.62). Incorporation of HBPc-48 h into a clinical prediction model significantly improved the AUC from 0.85 to 0.93. HBPc-48 h may assist clinicians with clinical outcome prediction in critically ill patients with sepsis and can improve the performance of a prediction model including age, SOFA score and Charlson comorbidity index.

Effect of methylprednisolone treatment on COVID-19: An inverse probability of treatment weighting analysis.

OBJECTIVES: While corticosteroids have been hypothesized to exert protective benefits in patients infected with SARS-CoV-2, data remain mixed. This study sought to investigate the outcomes of methylprednisone administration in an Italian cohort of hospitalized patients with confirmed SARS-CoV-2 infection. METHODS: Patients with confirmatory testing for SARS-CoV-2 were retrospectively enrolled from a tertiary university hospital in Milan, Italy from March 1st to April 30th, 2020 and divided into two groups by administration of corticosteroids. Methylprednisolone was administered to patients not responding to pharmacological therapy and ventilatory support at 0.5-1mg/kg/day for 4 to 7 days. Inverse probability of treatment weighting (IPTW) was used to adjust for baseline differences between the steroid and non-steroid cohorts via inverse probability of treatment weight. Primary outcomes included acute respiratory failure (ARF), shock, and 30-day mortality among surviving patients. RESULTS: Among 311 patients enrolled, 71 patients received steroids and 240 did not receive steroids. The mean age was 63.1 years, 35.4% were female, and hypertension, diabetes, heart disease, and chronic pulmonary disease were present in 3.5%, 1.3%, 14.8% and 12.2% respectively. Crude analysis revealed no statistically significant reduction in the incidence of 30-day mortality (36,6% vs 21,7%; OR, 2.09; 95% CI, 1.18-3.70; p = 0.011), shock (2.8% vs 4.6%; OR, 0.60; 95% CI = 0.13-2.79; p = 0.514) or ARF (12.7% vs 15%; OR, 0.82; 95% CI = 0.38-1.80; p = 0.625) between the steroid and non-steroid groups. After IPTW analysis, the steroid-group had lower incidence of shock (0.9% vs 4.1%; OR, 0.21; 95% CI,0.06-0.77; p = 0.010), ARF (6.6% vs 16.0%; OR, 0.37; 95% CI, 0.22-0.64; p<0.001) and 30-day mortality (20.3% vs 22.8%; OR 0.86; 95% CI, 0.59-1.26 p = 0.436); even though, for the latter no statistical significance was reached. Steroid use was also associated with increased length of hospital stay both in crude and IPTW analyses. Subgroup analysis revealed that patients with cardiovascular comorbidities or chronic lung diseases were more likely to be steroid responsive. No significant survival benefit was seen after steroid treatment. CONCLUSIONS: Physicians should avoid routine methylprednisolone use in SARS-CoV-2 patients, since it does not reduce 30-day mortality. However, they must consider its use for severe patients with cardiovascular or respiratory comorbidities in order to reduce the incidence of either shock or acute respiratory failure.

Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach.

BACKGROUND: Although machine learning (ML) algorithms have been applied to point-of-care sepsis prognostication, ML has not been used to predict sepsis mortality in an administrative database. Therefore, we examined the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compared it with that of the conventional context knowledge-based logistic regression approach. OBJECTIVE: The aim of this study is to examine the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compare it with that of the conventional context knowledge-based logistic regression approach. METHODS: We examined inpatient admissions for sepsis in the US National Inpatient Sample using hospitalizations in 2010-2013 as the training data set. We developed four ML models to predict in-hospital mortality: logistic regression with least absolute shrinkage and selection operator regularization, random forest, gradient-boosted decision tree, and deep neural network. To estimate their performance, we compared our models with the Super Learner model. Using hospitalizations in 2014 as the testing data set, we examined the models' area under the receiver operating characteristic curve (AUC), confusion matrix results, and net reclassification improvement. RESULTS: Hospitalizations of 923,759 adults were included in the analysis. Compared with the reference logistic regression (AUC: 0.786, 95% CI 0.783-0.788), all ML models showed superior discriminative ability (P<.001), including logistic regression with least absolute shrinkage and selection operator regularization (AUC: 0.878, 95% CI 0.876-0.879), random forest (AUC: 0.878, 95% CI 0.877-0.880), xgboost (AUC: 0.888, 95% CI 0.886-0.889), and neural network (AUC: 0.893, 95% CI 0.891-0.895). All 4 ML models showed higher sensitivity, specificity, positive predictive value, and negative predictive value compared with the reference logistic regression model (P<.001). We obtained similar results from the Super Learner model (AUC: 0.883, 95% CI 0.881-0.885). CONCLUSIONS: ML approaches can improve sensitivity, specificity, positive predictive value, negative predictive value, discrimination, and calibration in predicting in-hospital mortality in patients hospitalized with sepsis in the United States. These models need further validation and could be applied to develop more accurate models to compare risk-standardized mortality rates across hospitals and geographic regions, paving the way for research and policy initiatives studying disparities in sepsis care.

Association Between Aspirin Use and Sepsis Outcomes: A National Cohort Study.

BACKGROUND: Aspirin has anti-inflammatory and antiplatelet activities and directly inhibits bacterial growth. These effects of aspirin may improve survival in patients with sepsis. We retrospectively reviewed a large national health database to test the relationship between prehospital aspirin use and sepsis outcomes. METHODS: We conducted a retrospective population-based cohort study using the National Health Insurance Research Database of Taiwan from 2001 to 2011 to examine the relationship between aspirin use before hospital admission and sepsis outcomes. The association between aspirin use and 90-day mortality in sepsis patients was determined using logistic regression models and weighting patients by the inverse probability of treatment weighting (IPTW) with the propensity score. Kaplan-Meier survival curves for each IPTW cohort were plotted for 90-day mortality. For sensitivity analyses, restricted mean survival times (RMSTs) were calculated based on Kaplan-Meier curves with 3-way IPTW analysis comparing current use, past use, and nonuse. RESULTS: Of 52,982 patients with sepsis, 12,776 took aspirin before hospital admission (users), while 39,081 did not take any antiplatelet agents including aspirin before hospital admission (nonusers). After IPTW analysis, we found that when compared to nonusers, patients who were taking aspirin within 90 days before sepsis onset had a lower 90-day mortality rate (IPTW odds ratio [OR], 0.90; 95% confidence interval [CI], 0.88-0.93; P < .0001). Based on IPTW RMST analysis, nonusers had an average survival of 71.75 days, while current aspirin users had an average survival of 73.12 days. The difference in mean survival time was 1.37 days (95% CI, 0.50-2.24; P = .002). CONCLUSIONS: Aspirin therapy before hospital admission is associated with a reduced 90-day mortality in sepsis patients.

Risk-standardized sepsis mortality map of the United States.

OBJECTIVE: Sepsis is the leading cause of in-hospital mortality in the United States (US). Quality improvement initiatives for improving sepsis care depend on accurate estimates of sepsis mortality. While hospital 30-day risk-standardized mortality rates have been published for patients hospitalized with acute myocardial infarction, heart failure, and pneumonia, risk-standardized mortality rates for sepsis have not been well characterized. We aimed to construct a sepsis risk-standardized mortality rate map for the United States, to illustrate disparities in sepsis care across the country. METHODS: This cross-sectional study included adults from the US Nationwide Inpatient Sample who were hospitalized with sepsis between 1 January 2010 and 30 December 2011. Hospital-level risk-standardized mortality rates were calculated using hierarchical logistic modelling, and were risk-adjusted with predicted mortality derived from (1) the Sepsis Risk Prediction Score, a logistic regression model, and (2) gradient-boosted decision trees, a supervised machine learning (ML) algorithm. RESULTS: Among 1,739,033 adults hospitalized with sepsis, 50% were female, and the median age was 71 years (interquartile range: 58-81). The national median risk-standardized mortality rate for sepsis was 18.4% (interquartile range: 17.0, 21.0) by the boosted tree model, which had better discrimination than the Sepsis Risk Prediction Score model (C-statistic 0.87 and 0.78, respectively). The highest risk-standardized mortality rates were found in Wyoming, North Dakota, and Mississippi, while the lowest were found in Arizona, Colorado, and Michigan. CONCLUSIONS: Wide variation exists in sepsis risk-standardized mortality rates across states, representing opportunities for improvement in sepsis care. This represents the first map of state-level variation of risk-standardized mortality rates in sepsis.

Temporal Trends in the Microbiological Characteristics of Sepsis in the United States: A Population Based Study.

BACKGROUND: Epidemiologic studies are needed for monitoring population-level trends in sepsis. This study examines sepsis-causing microorganisms from 2006 to 2014 in the United States using data from the Nationwide Inpatient Sample database. METHODS: 7 860 686 adults hospitalized with sepsis were identified using a validated ICD-9 coding approach. Associated microorganisms were identified by ICD-9 code and classified by major groups (Gram-positive, Gram-negative, fungi, anaerobes) and specific species for analysis of their incidence and mortality. RESULTS: The rate of sepsis incidence has increased for all four major categories of pathogens, while the mortality rate decreased. In 2014, Gram-negative pathogens had a higher incidence than Gram-positives. Anaerobes increased the fastest with an average annual increase of 20.17% (p < 0.001). Fungi had the highest mortality (19.28%) and the slowest annual decrease of mortality (-2.31%, p = 0.006) in 2013, while anaerobic sepsis had the highest hazard of mortality (adjusted HR 1.60, 95% CI 1.53-1.66). CONCLUSIONS: Gram-negative pathogens have replaced Gram-positives as the leading cause of sepsis in the United States in 2014 during the study period (2006-2014). The incidence of anaerobic sepsis has an annual increase of 20%, while the mortality of fungal sepsis has not decreased at the same rate as other microorganisms. These findings should inform the diagnosis and management of septic patients, as well as the implementation of public health programs.

Performance of the European Society of Cardiology 0/1-Hour, 0/2-Hour, and 0/3-Hour Algorithms for Rapid Triage of Acute Myocardial Infarction : An International Collaborative Meta-analysis.

BACKGROUND: The 2020 European Society of Cardiology (ESC) guidelines recommend using the 0/1-hour and 0/2-hour algorithms over the 0/3-hour algorithm as the first and second choices of high-sensitivity cardiac troponin (hs-cTn)-based strategies for triage of patients with suspected acute myocardial infarction (AMI). PURPOSE: To evaluate the diagnostic accuracies of the ESC 0/1-hour, 0/2-hour, and 0/3-hour algorithms. DATA SOURCES: PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus from 1 January 2011 to 31 December 2020. (PROSPERO: CRD42020216479). STUDY SELECTION: Prospective studies that evaluated the ESC 0/1-hour, 0/2-hour, or 0/3-hour algorithms in adult patients presenting with suspected AMI. DATA EXTRACTION: The primary outcome was index AMI. Twenty unique cohorts were identified. Primary data were obtained from investigators of 16 cohorts and aggregate data were extracted from 4 cohorts. Two independent authors assessed each study for methodological quality. DATA SYNTHESIS: A total of 32 studies (20 cohorts) with 30 066 patients were analyzed. The 0/1-hour algorithm had a pooled sensitivity of 99.1% (95% CI, 98.5% to 99.5%) and negative predictive value (NPV) of 99.8% (CI, 99.6% to 99.9%) for ruling out AMI. The 0/2-hour algorithm had a pooled sensitivity of 98.6% (CI, 97.2% to 99.3%) and NPV of 99.6% (CI, 99.4% to 99.8%). The 0/3-hour algorithm had a pooled sensitivity of 93.7% (CI, 87.4% to 97.0%) and NPV of 98.7% (CI, 97.7% to 99.3%). Sensitivity of the 0/3-hour algorithm was attenuated in studies that did not use clinical criteria (GRACE score <140 and pain-free) compared with studies that used clinical criteria (90.2% [CI, 82.9 to 94.6] vs. 98.4% [CI, 88.6 to 99.8]). All 3 algorithms had similar specificities and positive predictive values for ruling in AMI, but heterogeneity across studies was substantial. Diagnostic performance was similar across the hs-cTnT (Elecsys; Roche), hs-cTnI (Architect; Abbott), and hs-cTnI (Centaur/Atellica; Siemens) assays. LIMITATION: Diagnostic accuracy, inclusion and exclusion criteria, and cardiac troponin sampling time varied among studies. CONCLUSION: The ESC 0/1-hour and 0/2-hour algorithms have higher sensitivities and NPVs than the 0/3-hour algorithm for index AMI. PRIMARY FUNDING SOURCE: National Taiwan University Hospital.

Fusobacterium Nucleatum-Induced Tumor Mutation Burden Predicts Poor Survival of Gastric Cancer Patients.

Co-infection of Helicobacter pylori and Fusobacterium nucleatum is a microbial biomarker for poor prognosis of gastric cancer patients. Fusobacterium nucleatum is associated with microsatellite instability and the accumulation of mutations in colorectal cancer. Here, we investigated the mutation landscape of Fusobacterium nucleatum-positive resected gastric cancer tissues using Illumina TruSight Oncology 500 comprehensive panel. Sequencing data were processed to identify the small nucleotide variants, small insertions and deletions, and unstable microsatellite sites. The bioinformatic algorithm also calculated copy number gains of preselected genes and tumor mutation burden. The recurrent genetic aberrations were identified in this study cohort. For gene amplification events, ERBB2, cell cycle regulators, and specific FGF ligands and receptors were the most frequently amplified genes. Pathogenic activation mutations of ERBB2, ERBB3, and PIK3CA, as well as loss-of-function of TP53, were identified in multiple patients. Furthermore, Fusobacterium nucleatum infection is positively correlated with a higher tumor mutation burden. Survival analysis showed that the combination of Fusobacterium nucleatum infection and high tumor mutation burden formed an extremely effective biomarker to predict poor prognosis. Our results indicated that the ERBB2-PIK3-AKT-mTOR pathway is frequently activated in gastric cancer and that Fusobacterium nucleatum and high mutation burden are strong biomarkers of poor prognosis for gastric cancer patients.

In-hospital Cardiac Arrest in Patients With Sepsis: A National Cohort Study.

Background: Little is known about the risk of in-hospital cardiac arrest (IHCA) among patients with sepsis. We aimed to characterize the incidence and outcome of IHCA among patients with sepsis in a national database. We then determined the major risk factors associated with IHCA among sepsis patients. Methods: We used data from a population-based cohort study based on the National Health Insurance Research Database of Taiwan (NHRID) between 2000 and 2013. We used Martin's implementation that combined the explicit ICD-9 CM codes for sepsis and six major organ dysfunction categories. IHCA among sepsis patients was identified by the presence of cardiopulmonary resuscitation procedures. The survival impact was analyzed with the Cox proportional-hazards model using inverse probability of treatment weighting (IPTW). The risk factors were identified by logistic regression models with 10-fold cross-validation, adjusting for competing risks. Results: We identified a total of 20,022 patients with sepsis, among whom 2,168 developed in-hospital cardiac arrest. Sepsis patients with a higher burden of comorbidities and organ dysfunction were more likely to develop in-hospital cardiac arrest. Acute respiratory failure, hematological dysfunction, renal dysfunction, and hepatic dysfunction were associated with increased risk of IHCA. Regarding the source of infection, patients with respiratory tract infections were at the highest risk, whereas patients with urinary tract infections and primary bacteremia were less likely to develop IHCA. The risk of IHCA correlated well with age and revised cardiac risk index (RCRI). The final competing risk model concluded that acute respiratory failure, male gender, and diabetes are the three strongest predictors for IHCA. The effect of IHCA on survival can last 1 year after hospital discharge, with an IPTW-weighted hazard ratio of 5.19 (95% CI: 5.06, 5.35) compared to patients who did not develop IHCA. Conclusion: IHCA in sepsis patients had a negative effect on both short- and long-term survival. The risk of IHCA among hospitalized sepsis patients was strongly correlated with age and cardiac risk index. The three identified risk factors can help clinicians to identify patients at higher risk for IHCA.