Prognostic factors for critically ill surgical patients with unplanned intensive care unit readmission: Developing a novel predictive scoring model for predicting readmission.

BACKGROUND: Unplanned readmission to the surgical intensive care unit has been demonstrated to worsen patient outcomes. Our objective was to identify risk factors and outcomes associated with unplanned surgical intensive care unit readmission and to develop a predictive scoring model to identify patients at high risk of readmission. METHODS: We retrospectively analyzed patients admitted to the surgical intensive care unit (2020-2021) and categorized them as either with or without unplanned readmission. RESULTS: Of 1,112 patients in the derivation cohort, 76 (6.8%) experienced unplanned surgical intensive care unit readmission, with sepsis being the leading cause of readmission (35.5%). Patients who were readmitted had significantly higher in-hospital mortality rates than those who were not. Multivariate analysis identified congestive heart failure, high Sequential Organ Failure Assessment-Hepatic score, use of carbapenem during surgical intensive care unit stay, as well as factors before surgical intensive care unit discharge such as inadequate glycemic control, positive fluid balance, low partial pressure of oxygen in arterial blood/fraction of inspired oxygen ratio, and receipt of total parenteral nutrition as independent predictors for unplanned readmission. The scoring model developed using these predictors exhibited good discrimination between readmitted and non-readmitted patients, with an area under the curve of 0.74. The observed rates of unplanned readmission for scores of <4 points and ≥4 points were 4% and 20.2% (P < .001), respectively. The model also demonstrated good performance in the validation cohort, with an area under the curve of 0.74 and 19% observed unplanned readmission rate for scores ≥4 points. CONCLUSION: Besides congestive heart failure, clinicians should meticulously re-evaluate critical variables such as the Sequential Organ Failure Assessment-Hepatic score, partial pressure of oxygen in arterial blood/fraction of inspired oxygen ratio, glycemic control, and fluid status before releasing the patient from the surgical intensive care unit. It is crucial to determine the reasons for using carbapenems during surgical intensive care unit stay and the causes for the inability to discontinue total parenteral nutrition before discharging the patient from the surgical intensive care unit.

Multi-ancestry meta-analysis identifies 5 novel loci for ischemic stroke and reveals heterogeneity of effects between sexes and ancestries.

Stroke is the second leading cause of death and disability worldwide. Stroke prevalence varies by sex and ancestry, possibly due to genetic heterogeneity between subgroups. We performed a genome-wide meta-analysis of 16 biobanks across multiple ancestries to study the genetics of ischemic stroke (60,176 cases, 1,310,725 controls) as part of the Global Biobank Meta-analysis Initiative (GBMI) and further combined the results with previously published MegaStroke. Five novel loci for ischemic stroke (LAMC1, CALCRL, PLSCR1, CDKN1A, and SWAP70) were identified after replication in four additional datasets. One previously reported locus showed significant ancestry heterogeneity (ABO), and one showed significant sex heterogeneity (ALDH2). The ALDH2 association was male specific (males p = 1.67e-24, females p = 0.126) and was additionally observed only in the East Asian ancestry (male) samples. These findings emphasize the need for more diverse datasets with large sample sizes to further understand the genetic predisposition of stroke in different ancestry and sex groups.

Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts.

Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

Diagnostic metabolomic profiling of Parkinson's disease biospecimens.

BACKGROUND: Reliable and sensitive biomarkers are needed for enhancing and predicting Parkinson's disease (PD) diagnosis. OBJECTIVE: To investigate comprehensive metabolomic profiling of biochemicals in CSF and serum for determining diagnostic biomarkers of PD. METHODS: Fifty subjects, symptomatic with PD for ≥5 years, were matched to 50 healthy controls (HCs). We used ultrahigh-performance liquid chromatography linked to tandem mass spectrometry (UHPLC-MS/MS) for measuring relative concentrations of ≤1.5 kDalton biochemicals. A reference library created from authentic standards facilitated chemical identifications. Analytes underwent univariate analysis for PD association, with false discovery rate-adjusted p-value (≤0.05) determinations. Multivariate analysis (for identifying a panel of biochemicals discriminating PD from HCs) used several biostatistical methods, including logistic LASSO regression. RESULTS: Comparing PD and HCs, strong differentiation was achieved from CSF but not serum specimens. With univariate analysis, 21 CSF compounds exhibited significant differential concentrations. Logistic LASSO regression led to selection of 23 biochemicals (11 shared with those determined by the univariate analysis). The selected compounds, as a group, distinguished PD from HCs, with Area-Under-the-Receiver-Operating-Characteristic (ROC) curve of 0.897. With optimal cutoff, logistic LASSO achieved 100% sensitivity and 96% specificity (and positive and negative predictive values of 96% and 100%). Ten-fold cross-validation gave 84% sensitivity and 82% specificity (and 82% positive and 84% negative predictive values). From the logistic LASSO-chosen regression model, 2 polyamine metabolites (N-acetylcadaverine and N-acetylputrescine) were chosen and had the highest fold-changes in comparing PD to HCs. Another chosen biochemical, acisoga (N-(3-acetamidopropyl)pyrrolidine-2-one), also is a polyamine metabolism derivative. CONCLUSIONS: UHPLC-MS/MS assays provided a metabolomic signature highly predictive of PD. These findings provide further evidence for involvement of polyamine pathways in the neurodegeneration of PD.

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Genome-wide meta-analysis of iron status biomarkers and the effect of iron on all-cause mortality in HUNT.

Iron is essential for many biological processes, but iron levels must be tightly regulated to avoid harmful effects of both iron deficiency and overload. Here, we perform genome-wide association studies on four iron-related biomarkers (serum iron, serum ferritin, transferrin saturation, total iron-binding capacity) in the Trøndelag Health Study (HUNT), the Michigan Genomics Initiative (MGI), and the SardiNIA study, followed by their meta-analysis with publicly available summary statistics, analyzing up to 257,953 individuals. We identify 123 genetic loci associated with iron traits. Among 19 novel protein-altering variants, we observe a rare missense variant (rs367731784) in HUNT, which suggests a role for DNAJC13 in transferrin recycling. We further validate recently published results using genetic risk scores for each biomarker in HUNT (6% variance in serum iron explained) and present linear and non-linear Mendelian randomization analyses of the traits on all-cause mortality. We find evidence of a harmful effect of increased serum iron and transferrin saturation in linear analyses that estimate population-averaged effects. However, there was weak evidence of a protective effect of increasing serum iron at the very low end of its distribution. Our findings contribute to our understanding of the genes affecting iron status and its consequences on human health.

Dynamic Risk Prediction of Response to Ursodeoxycholic Acid Among Patients with Primary Biliary Cholangitis in the USA.

BACKGROUND: Ursodeoxycholic acid (UDCA) remains the first-line therapy for primary biliary cholangitis (PBC); however, inadequate treatment response (ITR) is common. The UK-PBC Consortium developed the modified UDCA Response Score (m-URS) to predict ITR (using alkaline phosphatase [ALP] > 1.67 times the upper limit of normal [*ULN]) at 12 months post-UDCA initiation). Using data from the US-based Fibrotic Liver Disease Consortium, we assessed the m-URS in our multi-racial cohort. We then used a dynamic modeling approach to improve prediction accuracy. METHODS: Using data collected at the time of UDCA initiation, we assessed the m-URS using the original formula; then, by calibrating coefficients to our data, we also assessed whether it remained accurate when using Paris II criteria for ITR. Next, we developed and validated a dynamic risk prediction model that included post-UDCA initiation laboratory data. RESULTS: Among 1578 patients (13% men; 8% African American, 9% Asian American/American Indian/Pacific Islander; 25% Hispanic), the rate of ITR was 27% using ALP > 1.67*ULN and 45% using Paris II criteria. M-URS accuracy was "very good" (AUROC = 0.87, sensitivity = 0.62, and specificity = 0.82) for ALP > 1.67*ULN and "moderate" (AUROC = 0.74, sensitivity = 0.57, and specificity = 0.70) for Paris II. Our dynamic model significantly improved accuracy for both definitions of ITR (ALP > 1.67*ULN: AUROC = 0.91; Paris II: AUROC = 0.81); specificity approached 100%. Roughly 9% of patients in our cohort were at the highest risk of ITR. CONCLUSIONS: Early identification of patients who will not respond to UDCA treatment using a dynamic prediction model based on longitudinal, repeated risk factor measurements may facilitate earlier introduction of adjuvant treatment.

Cardiorespiratory Fitness After Open Repair for Acute Type A Aortic Dissection - A Prospective Study.

Cardiorespiratory fitness (as measured by peak oxygen consumption [VO2peak]) is an independent predictor of cardiovascular disease and all-cause mortality. Limited data exist on VO2peak following repair for an acute type A aortic dissection (ATAAD) or proximal thoracic aortic aneurysm (pTAA). This study prospectively evaluated VO2peak, functional capacity, and health-related quality of life (HR-QOL) following open repair. Participants with a history of an ATAAD (n = 21) or pTAA (n = 43) performed cardiopulmonary exercise testing (CPX), 6-minute walk testing, and HR-QOL at 3 (early) and 15 (late) months following open repair. The median age at time of surgery was 55-years-old and 60-years-old in the ATAAD and pTAA groups, respectively. Body mass index significantly increased between early and late timepoints for both ATAAD (p = 0.0245, 56% obese) and pTAA groups (p = 0.0045, 54% obese). VO2peak modestly increased by 0.8 mLO2·kg-1·min-1 within the ATAAD group (p = 0.2312) while VO2peak significantly increased by 2.2 mLO2·kg-1·min-1 within the pTAA group (p = 0.0003). Anxiety significantly decreased in the ATAAD group whereas functional capacity and HR-QOL metrics (social roles and activities, physical function) significantly improved in the pTAA group (p values < 0.05). There were no serious adverse events during CPX. Cardiorespiratory fitness among the ATAAD group remained 36% below predicted normative values >1 year after repair. CPX should be considered post-operatively to evaluate exercise tolerance and blood pressure response to determine whether mild-to-moderate aerobic exercise should be recommended to reduce future risk of morbidity and mortality.

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis.

Genomics-driven drug discovery is indispensable for accelerating the development of novel therapeutic targets. However, the drug discovery framework based on evidence from genome-wide association studies (GWASs) has not been established, especially for cross-population GWAS meta-analysis. Here, we introduce a practical guideline for genomics-driven drug discovery for cross-population meta-analysis, as lessons from the Global Biobank Meta-analysis Initiative (GBMI). Our drug discovery framework encompassed three methodologies and was applied to the 13 common diseases targeted by GBMI (N mean = 1,329,242). Individual methodologies complementarily prioritized drugs and drug targets, which were systematically validated by referring previously known drug-disease relationships. Integration of the three methodologies provided a comprehensive catalog of candidate drugs for repositioning, nominating promising drug candidates targeting the genes involved in the coagulation process for venous thromboembolism and the interleukin-4 and interleukin-13 signaling pathway for gout. Our study highlighted key factors for successful genomics-driven drug discovery using cross-population meta-analyses.

Comparison of Different Risk Scores for Prediction of In-Hospital Mortality in STEMI Patients Treated with PPCI.

Background: Several risk scores have been developed to predict and analyze in-hospital mortality and short- and long-term outcomes of ST-elevation myocardial infarction (STEMI) patients after primary percutaneous coronary intervention (PPCI); these can classify patients as having a high or low risk of death or complications. Objective: To compare the prognostic precision of four risk scores for predicting in-hospital mortality in patients with STEMI treated with PPCI. Methods: We performed a retrospective cohort analysis of patients with STEMI who underwent PPCI between 2012 and 2019 (N = 1346). GRACE (Global Registry of Acute Cardiac Events), CADILLAC (Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications), Zwolle, and TIMI (Thrombolysis in Myocardial Infarction) risk scores were calculated for each patient according to different variables. We evaluated the predictive accuracy of these scores for in-hospital mortality using the C statistic, which was obtained using logistic regression and receiver operating characteristic curves. Results: The GRACE, CADILLAC, Zwolle, and TIMI risk scores all had good predictive precision for in-hospital mortality, with C statistics ranging from 0.842 to 0.923. The GRACE and CADILLAC risk scores were found to be superior. Conclusions: All GRACE, CADILLAC, Zwolle, and TIMI risk scores showed a high predictive value for in-hospital mortality due to all causes in patients with STEMI treated with PPCI. The GRACE and CADILLAC risk scores revealed a better accuracy for predicting in-hospital mortality than the Zwolle and TIMI risk scores.

The power of genetic diversity in genome-wide association studies of lipids.

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.

Pre-hospital Prognostic Factors of Out-of-Hospital Cardiac Arrest: The Difference Between Pediatric and Adult.

The prognosis of out-of-hospital cardiac arrest (OHCA) is very poor. Although several pre-hospital factors are associated with survival, the different association of pre-hospital factors with OHCA outcomes in pediatric and adult groups remain unclear. To assess the association of pre-hospital factors with OHCA outcomes among pediatric and adult groups, a retrospective observational study was conducted using the emergency medical service (EMS) database in Kaohsiung from January 2015 to December 2019. Pre-hospital factors, underlying diseases, and OHCA outcomes were collected for the pediatric (Age ≤ 20) and adult groups. Kaplan-Meier type plots and multivariable logistic regression were used to analyze the association between pre-hospital factors and outcomes. In total, 7,461 OHCAs were analyzed. After adjusting for EMS response time, bystander CPR, attended by EMT-P, witness, and pre-hospital defibrillation, we found that age [odds ratio (OR) = 0.877, 95% confidence interval (CI): 0.764-0.990, p = 0.033], public location (OR = 7.681, 95% CI: 1.975-33.428, p = 0.003), and advanced airway management (AAM) (OR = 8.952; 95% CI, 1.414-66.081; p = 0.02) were significantly associated with survival till hospital discharge in pediatric OHCAs. The results of Kaplan-Meier type plots with log-rank test showed a significant difference between the pediatric and adult groups in survival for 2 h (p < 0.001), 24 h (p < 0.001), hospital discharge (p < 0.001), and favorable neurologic outcome (p < 0.001). AAM was associated with improved survival for 2 h (p = 0.015), 24 h (p = 0.023), and neurologic outcome (p = 0.018) only in the pediatric group. There were variations in prognostic factors between pediatric and adult patients with OHCA. The prognosis of the pediatric group was better than that of the adult group. Furthermore, AAM was independently associated with outcomes in pediatric patients, but not in adult patients. Age and public location of OHCA were independently associated with survival till hospital discharge in both pediatric and adult patients.

Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach.

BACKGROUND: A feasible and accurate risk prediction systems for emergency department (ED) patients is urgently required. The Modified Early Warning Score (MEWS) is a wide-used tool to predict clinical outcomes in ED. Literatures showed that machine learning (ML) had better predictability in specific patient population than traditional scoring system. By analyzing a large multicenter dataset, we aim to develop a ML model to predict in-hospital morality of the adult non traumatic ED patients for different time stages, and comparing performance with other ML models and MEWS. METHODS: A retrospective observational cohort study was conducted in five Taiwan EDs including two tertiary medical centers and three regional hospitals. All consecutively adult (>17 years old) non-traumatic patients admit to ED during a 9-year period (January first, 2008 to December 31th, 2016) were included. Exclusion criteria including patients with (1) out-of-hospital cardiac arrest and (2) discharge against medical advice and transferred to other hospital (3) missing collect variables. The primary outcome was in-hospital mortality and were categorized into 6, 24, 72, 168 hours mortality. MEWS was calculated by systolic blood pressure, pulse rate, respiratory rate, body temperature, and level of consciousness. An ensemble supervised stacking ML model was developed and compared to sensitive and unsensitive Xgboost, Random Forest, and Adaboost. We conducted a performance test and examine both the area under the receiver operating characteristic (AUROC) and the area under the precision and recall curve (AUPRC) as the comparative measures. RESULT: After excluding 182,001 visits (7.46%), study group was consisted of 24,37,326 ED visits. The dataset was split into 67% training data and 33% test data for ML model development. There was no statistically difference found in the characteristics between two groups. For the prediction of 6, 24, 72, 168 hours in-hospital mortality, the AUROC of MEW and ML mode was 0.897, 0.865, 0.841, 0.816 and 0.939, 0.928, 0.913, 0.902 respectively. The stacking ML model outperform other ML model as well. For the prediction of in-hospital mortality over 48-hours, AUPRC performance of MEWS drop below 0.1, while the AUPRC of ML mode was 0.317 in 6 hours and 0.2150 in 168 hours. For each time frame, ML model achieved statistically significant higher AUROC and AUPRC than MEWS (all P < 0.001). Both models showed decreasing prediction ability as time elapse, but there was a trend that the gap of AUROC values between two model increases gradually (P < 0.001). Three MEWS thresholds (score >3, >4, and >5) were determined as baselines for comparison, ML mode consistently showed improved or equally performance in sensitivity, PPV, NPV, but not in specific. CONCLUSION: Stacking ML methods improve predicted in-hospital mortality than MEWS in adult non-traumatic ED patients, especially in the prediction of delayed mortality.

Outcomes for Patients with Urinary Tract Infection After an Initial Intravenous Antibiotics Dose Before Emergency Department Discharge.

INTRODUCTION: To investigate the effect of single-dose intravenous antibiotics before emergency department (ED) discharge on the outcomes of patients with urinary tract infections (UTIs). METHODS: This is a retrospective study conducted at the EDs of three medical centers. Patients aged over 18 years who presented to the ED with UTI and were discharged without admission between January 1, 2016 and December 31, 2017 were evaluated. The study group received a single dose of effective intravenous antibiotics on the basis of urine culture during the index ED visit following oral antibiotics, while the comparison group received oral antibiotics only. The primary outcomes were ED revisit within 72 h and admission following the return visit. RESULTS: A total of 8168 patients were included. Of these, 20.9% received intravenous antibiotics before ED discharge. Patients who received effective intravenous antibiotics before ED discharge were associated with less than 72-h ED revisit (adjusted odds ratio [OR] 0.791, 95% confidence interval [CI] 0.640-0.979]), but not decreased admission following the return visit (adjusted OR 0.921, 95% CI [0.731-1.153]). In subgroup analysis, parenteral antibiotic use during the index ED visit was associated with decreased admission following ED revisit in patients who presented with fever (adjusted OR 0.605; 95% CI 0.443-0.932). CONCLUSION: For patients with UTI and clinically well to be discharged from the ED, a single dose of effective intravenous antibiotics before ED discharge was associated with decreased 72-h ED revisit. In patients with febrile UTI, initial intravenous antibiotics were associated with decreased revisit leading to admissions.

Influence of the Coronavirus Disease 2019 Pandemic on Patients with ST-Segment Elevation Myocardial Infarction in Taiwan.

The outbreak of the new coronavirus disease 2019 (COVID-19) has notably affected the medical system worldwide and influenced the health-seeking behavior of people while depleting medical resources, causing a delay in ST-elevation myocardial infarction (STEMI) management. In this single-center, retrospective cohort study, we compared the clinical pictures of nontransfer patients who presented to the emergency department directly and received primary percutaneous cardiovascular intervention (PPCI) from February 1 to April 30, 2020 (group 2, N = 28), with patients who received PPCI from February 1 to April 30, 2016-2019 (group 1, N = 130). A total of 158 patients with STEMI who received PPCI were included in the study. A decrease in the percentage of patients with door-to-balloon time <90 minutes was found in group 2 (64.3% vs. 81.5%, p = 0.044). The adjusted odds ratio was calculated using logistic regression, according to potential confounding factors such as age, sex, off-hours, and Killip class. An adjusted odds ratio of 2.45 (95% confidence interval, 1.1-6.0, p = 0.048) was reported for group 2. A decrease in the percentage of patients meeting the criteria of door-to-balloon time <90 minutes was demonstrated, and differences were revealed in the clinical pictures of patients with STEMI after the pandemic. While systemic factors contributed the most, improvements and adjustments in the protocols for managing patients with STEMI for better outcomes in the COVID-19 era have yet to be studied.

In-Hospital Outcomes in Patients Admitted to the Intensive Care Unit after a Return Visit to the Emergency Department.

BACKGROUND: Intensive care unit (ICU) admission following a short-term emergency department (ED) revisit has been considered a particularly undesirable outcome among return-visit patients, although their in-hospital prognosis has not been discussed. We aimed to compare clinical outcomes between adult patients admitted to the ICU after unscheduled ED revisits and those admitted during index ED visits. METHOD: This retrospective study was conducted at two tertiary medical centers in Taiwan from 1 January 2016 to 31 December 2017. All adult non-trauma patients admitted to the ICU directly via the ED during the study period were included and divided into two comparison groups: patients admitted to the ICU during index ED visits and those admitted to the ICU during return ED visits. The outcomes of interest included in-hospital mortality, mechanical ventilation (MV) support, profound shock, hospital length of stay (HLOS), and total medical cost. RESULTS: Altogether, 12,075 patients with a mean (standard deviation) age of 64.6 (15.7) years were included. Among these, 5.3% were admitted to the ICU following a return ED visit within 14 days and 3.1% were admitted following a return ED visit within 7 days. After adjusting for confounding factors for multivariate regression analysis, ICU admission following an ED revisit within 14 days was not associated with an increased mortality rate (adjusted odds ratio (aOR): 1.08, 95% confidence interval (CI): 0.89 to 1.32), MV support (aOR: 1.06, 95% CI: 0.89 to 1.26), profound shock (aOR: 0.99, 95% CI: 0.84 to 1.18), prolonged HLOS (difference: 0.04 days, 95% CI: -1.02 to 1.09), and increased total medical cost (difference: USD 361, 95% CI: -303 to 1025). Similar results were observed after the regression analysis in patients that had a 7-day return visit. CONCLUSION: ICU admission following a return ED visit was not associated with major in-hospital outcomes including mortality, MV support, shock, increased HLOS, or medical cost. Although ICU admissions following ED revisits are considered serious adverse events, they may not indicate poor prognosis in ED practice.

Exposure and risk factors for COVID-19 and the impact of staying home on Michigan residents.

COVID-19 has had a substantial impact on clinical care and lifestyles globally. The State of Michigan reports over 80,000 positive COVID-19 tests between March 1, 2020 and July 29, 2020. We surveyed 8,041 Michigan Medicine biorepository participants in late June 2020. We found that 55% of COVID-19 cases reported no known exposure to family members or to someone outside the house diagnosed with COVID-19. A significantly higher rate of COVID-19 cases were employed as essential workers (45% vs 19%, p = 9x10-12). COVID-19 cases reporting a fever were more likely to require hospitalization (categorized as severe; OR = 4.4 [95% CI: 1.6-12.5, p = 0.005]) whereas respondents reporting rhinorrhea was less likely to require hospitalization (categorized as mild-to-moderate; OR = 0.16 [95% CI: 0.04-0.73, p = 0.018]). African-Americans reported higher rates of being diagnosed with COVID-19 (OR = 4.0 [95% CI: 2.2-7.2, p = 5x10-6]), as well as higher rates of exposure to family or someone outside the household diagnosed with COVID-19, an annual household income < $40,000, living in rental housing, and chronic diseases. During the Executive Order in Michigan, African Americans, women, and the lowest income group reported worsening health behaviors and higher overall concern for the potential detrimental effects of the pandemic. The higher risk of contracting COVID-19 observed among African Americans may be due to the increased rates of working as essential employees, lower socioeconomic status, and exposure to known positive cases. Continued efforts should focus on COVID-19 prevention and mitigation strategies, as well as address the inequality gaps that result in higher risks for both short-term and long-term health outcomes.