Predicting 90 day acute heart failure readmission and death using machine learning‐supported decision analysis

Readmission or death soon after heart failure (HF) admission is a significant problem. Traditional analyses for predicting such events often fail to consider the gamut of characteristics that may contribute– tending to focus on 30‐day outcomes even though the window of increased vulnerability may last up to 90 days. Risk assessments incorporating machine learning (ML) methods may be better suited than traditional statistical analyses alone to sort through multitude of data in the electronic health record (EHR) and identify patients at higher risk.HypothesisML‐based decision analysis may better identify patients at increased risk for 90‐day acute HF readmission or death after incident HF admission.Methods and ResultsAmong 3189 patients who underwent index HF hospitalization, 15.2% experienced primary or acute HF readmission and 11.5% died within 90 days. For risk assessment models, 98 variables were considered across nine data categories. ML techniques were used to help select variables for a final logistic regression (LR) model. The final model''s AUC was 0.760 (95% CI 0.752 to 0.767), with sensitivity of 83%. This proved superior to an LR model alone [AUC 0.744 (95% CI 0.732 to 0.755)]. Eighteen variables were identified as risk factors including dilated inferior vena cava, elevated blood pressure, elevated BUN, reduced albumin, abnormal sodium or bicarbonate, and NT pro‐BNP elevation. A risk prediction ML‐based model developed from comprehensive characteristics within the EHR can efficiently identify patients at elevated risk of 90‐day acute HF readmission or death for whom closer follow‐up or further interventions may be considered.     

Man vs. Machine: Comparing Physician vs. Electronic Health Record–Based Model Predictions for 30-Day Hospital Readmissions

BackgroundElectronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. We sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions.MethodsWe conducted a prospective survey of internal medicine clinicians in an urban safety-net hospital. Clinicians prospectively predicted patients’ 30-day readmission risk on 5-point Likert scales, subsequently dichotomized into low- vs. high-risk. We compared human with machine predictions using discrimination, net reclassification, and diagnostic test characteristics. Observed readmissions were ascertained from a regional hospitalization database. We also developed and assessed a “human-plus-machine” logistic regression model incorporating both human and machine predictions.ResultsWe included 1183 hospitalizations from 106 clinicians, with a readmission rate of 20.8%. Both clinicians and the EHR model had similar discrimination (C-statistic 0.66 vs. 0.66, p = 0.91). Clinicians had higher specificity (79.0% vs. 48.9%, p < 0.001) but lower sensitivity (43.9 vs. 75.2%, p < 0.001) than EHR model predictions. Compared with machine, human was better at reclassifying non-readmissions (non-event NRI + 30.1%) but worse at reclassifying readmissions (event NRI − 31.3%). A human-plus-machine approach best optimized discrimination (C-statistic 0.70, 95% CI 0.67–0.74), sensitivity (65.5%), and specificity (66.7%).ConclusionClinicians had similar discrimination but higher specificity and lower sensitivity than EHR model predictions. Human-plus-machine was better than either alone. Readmission risk prediction strategies should incorporate clinician assessments to optimize the accuracy of readmission predictions.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11606-020-06355-3.KEY WORDS: patient readmission, logistic models, electronic health records, safety-net providers, hospitalization     

The Financial Impact of a Partnership Between an Academic Medical Center and a Free Clinic

PurposeThe objective of this study is to examine the association between an academic medical center and free clinic referral partnership and subsequent hospital utilization and costs for uninsured patients discharged from the academic medical center's emergency department (ED) or inpatient hospital.MethodsThis retrospective, cross-sectional study included 6014 uninsured patients age 18 and older who were discharged from the academic medical center's ED or inpatient hospital between July 2016 and June 2017 and were followed for 90 days in the organization's electronic medical record to identify the occurrence and cost of subsequent same-hospital ED visits and hospital admissions. The occurrence of any subsequent ED visits or hospital admissions and the cost of subsequent hospital care were compared by free clinic referral status after inverse probability of treatment weighting.ResultsOverall, 330 (5.5%) of uninsured patients were referred to the free clinic. Compared with patients referred to the free clinic, patients not referred had greater odds of any subsequent ED visits or hospital admissions within 90 days (odds ratio, 1.8; 95% confidence interval: 1.7-2.0). For patients with any subsequent ED visits or hospital admissions, the mean cost of care for those who were not referred to the free clinic was 2.3 times higher (95% confidence interval: 2.0-2.7) compared to referred patients.ConclusionAn academic medical center-free clinic partnership for follow-up care after discharge from the ED or hospital admission is a promising approach for improving access to care for uninsured patients.     

Limited English Proficient Patient Visits and Emergency Department Admission Rates for Ambulatory Care Sensitive Conditions in California: a Retrospective Cohort Study

BackgroundLittle is known about the risk of admission for emergency department (ED) visits for ambulatory care sensitive conditions (ACSCs) by limited English proficient (LEP) patients.ObjectiveEstimate admission rates from ED for ACSCs comparing LEP and English proficient (EP) patients and examine how these rates vary at hospitals with a high versus low proportion of LEP patients.DesignRetrospective cohort study of California’s 2017 inpatient and ED administrative dataParticipantsCommunity-dwelling individuals ≥ 18 years without a primary diagnosis of pregnancy or childbirth. LEP patients had a principal language other than English.Main MeasuresWe used a series of linear probability models with incremental sets of covariates, including patient demographics, primary diagnosis, and Elixhauser comorbidities, to examine admission rate for visits of LEP versus EP patients. We then added an interaction covariate for high versus low LEP-serving hospital. We estimated models with and without hospital-level random effects.Key ResultsThese analyses included 9,641,689 ED visits; 14.7% were for LEP patients. . Observed rate of admission for all ACSC ED visits was higher for LEP than for EP patients (26.2% vs. 25.2; p value < .001). Adjusted rate of admission was not statistically significant (27.3% [95% CI 25.4–29.3%] vs. 26.2% [95% CI 24.3–28.1%]). For COPD, the difference was significant (36.8% [95% CI 35.0–38.6%] vs. 33.3% [95% CI 31.7–34.9%]). Difference in adjusted admission rate for LEP versus EP visits did not differ in high versus low LEP-serving hospitals.ConclusionsIn adjusted analyses, LEP was not a risk factor for admission for most ACSCs. This finding was observed in both high and low LEP-serving hospitals.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11606-020-06523-5.KEY WORDS: limited English proficiency, health disparities, ambulatory care sensitive conditions     

Association between Grit Scales and adherence to regular hospital visits among Japanese patients with type 2 diabetes: Prospective observational study

This study examined the association between Grit Scales and adherence to a schedule of regular hospital visits among Japanese type 2 diabetes patients. Patients with type 2 diabetes who visited the outpatient clinic as new patients comprised the study’s participants. Self‐administered Short Grit Scale data were obtained from 122 patients at the first consultation and were then observed for 1 year. As the results, 21 participants failed to attend the hospital. In a logistic regression analysis, the Grit Scale as a continuous variable was positively associated with adherence to regular clinical visits. Its odds ratio and 95% confidential interval was 9.68 and 2.87–32.65 (P = 0.0003). In conclusion, it is likely that the Grit Scale is closely associated with adherence to regular hospital visits among Japanese type 2 diabetes patients.     

Association between insurance status and in‐hospital outcomes in patients with out‐of‐hospital ventricular fibrillation arrest

BackgroundLack of health insurance is associated with adverse clinical outcomes; however, the association between health insurance status and in‐hospital outcomes after out‐of‐hospital ventricular fibrillation (OHVFA) arrest is unclear.HypothesisLack of health insurance is associated with worse in‐hospital outcomes after out‐of‐hospital ventricular fibrillation arrest.MethodsFrom January 2003 to December 2014, hospitalizations with a primary diagnosis of OHVFA in patients ≥18 years of age were extracted from the Nationwide Inpatient Sample. Patients were categorized into insured and uninsured groups based on their documented health insurance status. Study outcome measures were in‐hospital mortality, utilization of implantable cardioverter defibrillator (ICD), and cost of hospitalization. Inverse probability weighting adjusted binary logistic regression was performed to identify independent predictors of in‐hospital mortality and ICD utilization and linear regression was performed to identify independent predictors of cost of hospitalization.ResultsOf 188 946 patients included in the final analyses, 178 005 (94.2%) patients were insured and 10 941 (5.8%) patients were uninsured. Unadjusted in‐hospital mortality was higher (61.7% vs. 54.7%, p < .001) and ICD utilization was lower (15.3% vs. 18.3%, p < .001) in the uninsured patients. Lack of health insurance was independently associated with higher in‐hospital mortality (O.R = 1.53, 95% C.I. [1.46–1.61]; p < .001) and lower utilization of ICD (O.R = 0.84, 95% C.I [0.79–0.90], p < .001). Cost of hospitalization was significantly higher in uninsured patients (median [interquartile range], p‐value) ($) (39 650 [18 034‐93 399] vs. 35 965 [14 568.50‐96 163], p < .001).ConclusionLack of health insurance is associated with higher in‐hospital mortality, lower utilization of ICD and higher cost of hospitalization after OHVFA.     

Development of an optimized risk score to predict short‐term death among acute myocardial infarction patients in rural China

BackgroundRisk stratification of patients with acute myocardial infarction (AMI) is of great clinical significance.HypothesisThe present study aimed to establish an optimized risk score to predict short‐term (6‐month) death among rural AMI patients from China.MethodsWe enrolled 6581 AMI patients and extracted relevant data. Patients were divided chronologically into a derivation cohort (n = 5539), to establish the multivariable risk prediction model, and a validation cohort (n = 1042), to validate the risk score.ResultsSix variables were identified as independent predictors of short‐term death and were used to establish the risk score: age, Killip class, blood glucose, creatinine, pulmonary artery systolic pressure, and percutaneous coronary intervention treatment. The area under the ROC curve (AUC) of the optimized risk score was 0.82 within the derivation cohort and 0.81 within the validation cohort. The diagnostic performance of the optimized risk score was superior to that of the GRACE risk score (AUC 0.76 and 0.75 in the derivation and validation cohorts, respectively; p < .05).ConclusionThese results indicate that the optimized scoring method developed here is a simple and valuable instrument to accurately predict the risk of short‐term mortality in rural patients with AMI.     

Impact of varying wave periods of COVID‐19 on in‐hospital mortality and length of stay for admission through emergency department: A territory‐wide observational cohort study

BackgroundThe COVID‐19 pandemic has been associated with excess mortality and reduced emergency department attendance. However, the effect of varying wave periods of COVID‐19 on in‐hospital mortality and length of stay (LOS) for non‐COVID disease for non‐COVID diseases remains unexplored.MethodsWe examined a territory‐wide observational cohort of 563,680 emergency admissions between January 1 and November 30, 2020, and 709,583 emergency admissions during the same 2019 period in Hong Kong, China. Differences in 28‐day in‐hospital mortality risk and LOS due to COVID‐19 were evaluated.ResultsThe cumulative incidence of 28‐day in‐hospital mortality increased overall from 2.9% in 2019 to 3.6% in 2020 (adjusted hazard ratio [aHR] = 1.22, 95% CI 1.20 to 1.25). The aHR was higher among patients with lower respiratory tract infection (aHR: 1.30 95% CI 1.26 to 1.34), airway disease (aHR: 1.35 95% CI 1.22 to 1.49), and mental disorders (aHR: 1.26 95% CI 1.15 to 1.37). Mortality risk in the first‐ and third‐wave periods was significantly greater than that in the inter‐wave period (p‐interaction < 0.001). The overall average LOS in the pandemic year was significantly shorter than that in 2019 (Mean difference = −0.40 days; 95% CI −0.43 to −0.36). Patients with mental disorders and cerebrovascular disease in 2020 had a 3.91‐day and 2.78‐day shorter LOS than those in 2019, respectively.ConclusionsIncreased risk of in‐hospital deaths was observed overall and by all major subgroups of disease during the pandemic period. Together with significantly reduced LOS for patients with mental disorders and cerebrovascular disease, this study shows the spillover effect of the COVID‐19 pandemic.     

Outcomes after readmission at the index or nonindex hospital following acute myocardial infarction complicated by cardiogenic shock

Little is known about the prevalence and outcomes of readmission to nonindex hospitals after an admission for acute myocardial infarction complicated by cardiogenic shock (AMI‐CS). We aimed to determine the rate of nonindex readmissions following AMI‐CS and to evaluate its association with clinical factors, hospitalization cost, length of stay (LOS), and in‐hospital mortality rates.HypothesisNonindex readmission may lead to worse in‐hospital outcomes.MethodsWe reviewed the data of inpatients with AMI‐CS between 2010 and 2017 using the National Readmission Database. The survey analytical methods recommended by the Healthcare Cost and Utilization Project were used for national estimates. Multiple regression models were used to evaluate the predictors of nonindex readmission, and its association with hospitalization cost, LOS, and in‐hospital mortality rates.ResultsOf 238 349 patients with AMI‐CS, 28028 (11.76%) had an unplanned readmission within 30 days. Of these patients, 7423 (26.48%) were readmitted to nonindex hospitals. Compared with index readmission, nonindex readmission was associated with higher hospitalization costs (p < .0001), longer LOS (p < .0001), and increased in‐hospital mortality rates (p = .0016). Patients who had a history of percutaneous coronary intervention, received intubation/mechanical ventilation, or left against medical advice during the initial admission had greater odds of a nonindex readmission.ConclusionsOver one‐fourth of readmissions following AMI‐CS were to nonindex hospitals. These admissions were associated with higher hospitalization costs, longer LOS, and higher in‐hospital mortality rates. Further studies are needed to evaluate whether a continuity of care plan in the acute hospital setting can improve outcomes after AMI‐CS.     

Análise de Escores de Risco para Predição de Mortalidade em Pacientes Submetidos à Cirurgia Cardíaca por Endocardite

Background Risk scores are available for use in daily clinical practice, but knowing which one to choose is still fraught with uncertainty.Objectives To assess the logistic EuroSCORE, EuroSCORE II, and the infective endocarditis (IE)-specific scores STS-IE, PALSUSE, AEPEI, EndoSCORE and RISK-E, as predictors of hospital mortality in patients undergoing cardiac surgery for active IE at a tertiary teaching hospital in Southern Brazil.MethodsRetrospective cohort study including all patients aged ≥ 18 years who underwent cardiac surgery for active IE at the study facility from 2007-2016. The scores were assessed by calibration evaluation (observed/expected [O/E] mortality ratio) and discrimination (area under the ROC curve [AUC]). Comparison of AUC was performed by the DeLong test. A p < 0.05 was considered statistically significant.Results A total of 107 patients were included. Overall hospital mortality was 29.0% (95%CI: 20.4-37.6%). The best O/E mortality ratio was achieved by the PALSUSE score (1.01, 95%CI: 0.70-1.42), followed by the logistic EuroSCORE (1.3, 95%CI: 0.92-1.87). The logistic EuroSCORE had the highest discriminatory power (AUC 0.77), which was significantly superior to EuroSCORE II (p = 0.03), STS-IE (p = 0.03), PALSUSE (p = 0.03), AEPEI (p = 0.03), and RISK-E (p = 0.02).Conclusions Despite the availability of recent IE-specific scores, and considering the trade-off between the indexes, the logistic EuroSCORE seemed to be the best predictor of mortality risk in our cohort, taking calibration (O/E mortality ratio: 1.3) and discrimination (AUC 0.77) into account. Local validation of IE-specific scores is needed to better assess preoperative surgical risk. (Arq Bras Cardiol. 2020; 114(3):518-524)     

Association of Race and Neighborhood Disadvantage with Patient Engagement in a Home-Based COVID-19 Remote Monitoring Program

BackgroundCOVID-positive outpatients may benefit from remote monitoring, but such a program often relies on smartphone apps. This may introduce racial and socio-economic barriers to participation. Offering multiple methods for participation may address these barriers.Objectives(1) To examine associations of race and neighborhood disadvantage with patient retention in a monitoring program offering two participation methods. (2) To measure the association of the program with emergency department visits and hospital admissions.DesignRetrospective propensity-matched cohort study.ParticipantsCOVID-positive outpatients at a single university-affiliated healthcare system and propensity-matched controls.InterventionsA home monitoring program providing daily symptom tracking via patient portal app or telephone calls.Main MeasuresAmong program enrollees, retention (until 14 days, symptom resolution, or hospital admission) by race and neighborhood disadvantage, with stratification by program arm. In enrollees versus matched controls, emergency department utilization and hospital admission within 30 days.Key ResultsThere were 7592 enrolled patients and 9710 matched controls. Black enrollees chose the telephone arm more frequently than White enrollees (68% versus 44%, p = 0.009), as did those from more versus less disadvantaged neighborhoods (59% versus 43%, p = 0.02). Retention was similar in Black enrollees and White enrollees (63% versus 62%, p = 0.76) and in more versus less disadvantaged neighborhoods (63% versus 62%, p = 0.44). When stratified by program arm, Black enrollees had lower retention than White enrollees in the app arm (49% versus 55%, p = 0.01), but not in the telephone arm (69% versus 71%, p = 0.12). Compared to controls, enrollees more frequently visited the emergency department (HR 1.71 [95% CI 1.56–1.87]) and were admitted to the hospital (HR 1.16 [95% CI 1.02–1.31]).ConclusionsIn a COVID-19 remote patient monitoring program, Black enrollees preferentially selected, and had higher retention in, telephone- over app-based monitoring. As a result, overall retention was similar between races. Remote monitoring programs with multiple modes may reduce barriers to participation.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11606-021-07207-4.KEY WORDS: ambulatory monitoring, COVID-19, race factors, facilities and services utilization     

Acute upper gastrointestinal bleeding due to portal hypertension in children: What is the best timing of endoscopy?

ObjectiveTo compare initial clinical/laboratory parameters and outcomes of mortality/rebleeding of endoscopy performed <12 h(early UGIE) versus endoscopy performed after 12–24h(late UGIE) of ED admission in children with acute upper gastrointestinal bleeding(AUGIB) due to portal hypertension.MethodsThis is a retrospective cohort study. From January 2010 to July 2017, medical records of all children admitted to a tertiary care hospital with AUGIB due to portal hypertension were reviewed until 60 days after ED admission.ResultsA total of 98 ED admissions occurred from 73 patients. Rebleeding was identified in 8/98(8%) episodes, and 9 deaths were observed. UGIE was performed in 92(94%) episodes, and 53(58%) of them occurred within 12 h of ED admission. Episodes with early UGIE and late UGIE were similar in terms of history/complaints/laboratory data at admission, chronic liver disease associated, AUGIB duration, and initial management. No statistically significant associations were found between early UGIE and the outcomes of death/rebleeding and prevalence of endoscopic hemostatic treatment (band ligation or sclerotherapy) compared to late UGIE.In the multivariable logistic regression model, the endoscopic hemostatic treatment showed a negative association with early UGIE(OR=0.33;95%CI=0.1–0.9;p = 0.04).ConclusionsThis study suggests that in pediatric patients with AUGIB and portal hypertension, UGIE may be performed after 12–24 h without harm to the patient, facilitating better initial clinical stabilization/treatment and optimization of resources.     

A novel study on SARS‐COV‐2 virus associated bradycardia as a predictor of mortality‐retrospective multicenter analysis

BackgroundSARS‐CoV2 has affected more than 73.8 million individuals. While SARS‐CoV2 is considered a predominantly respiratory virus, we report a trend of bradycardia among hospitalized patients, particularly in association with mortality.MethodologyThe multi‐center retrospective analysis consisted of 1053 COVID‐19 positive patients from March to August 2020. A trend of bradycardia was noted in the study population. Absolute bradycardia and profound bradycardia was defined as a sustained heart rate < 60 BPM and < 50 BPM, respectively, on two separate occasions, a minimum of 4 h apart during hospitalization. Each bradycardic event was confirmed by two physicians and exclusion criteria included: less than 18 years old, end of life bradycardia, left AMA, or taking AV Nodal blockers. Data was fetched using a SQL program through the EMR and data was analyzed using SPSS 27.0. A logistic regression was done to study the effect of bradycardia, age, gender, and BMI on mortality in the study group.Results24.9% patients had absolute bradycardia while 13.0% had profound bradycardia. Patients with absolute bradycardia had an odds ratio of 6.59 (95% CI [2.83–15.36]) for mortality compared with individuals with a normal HR response. The logistic regression model explained 19.6% (Nagelkerke R²) of variance in the mortality, correctly classified 88.6% of cases, and was statistically significant X² (5)=47.10, p < .001. For each year of age > 18, the odds of dying increased 1.048 times (95% CI [1.25–5.27]).ConclusionThe incidence of absolute bradycardia was found in 24.9% of the study cohort and these individuals were found to have a significant increase in mortality.     

Effect of Integrated Gastroenterology Specialists in a Primary Care Setting: a Retrospective Cohort Study

BackgroundGastrointestinal (GI) complaints are common in primary care practices. The patient-centered medical home (PCMH) may improve coordination and collaboration by facilitating coordination across healthcare settings and within the community, enhancing communication between providers, and focusing on quality of care delivery.ObjectiveTo investigate the effect of integrated community gastroenterology specialists (ICS-GI) model within a large primary care practice.DesignRetrospective cohort with propensity-matched historic controls.PatientsWe identified 265 patients who had a visit with one of our ICS-GI specialists and matched them (1:2) to 530 similar patients seen prior to the implementation of the ICS-GI model.Main MeasuresFrequency of diagnostic testing for GI indications, visits to our outpatient GI referral practice, emergency department and hospital utilization, and time to access of specialty care for the whole population and by GI condition group.Key ResultsPatients seen in our ICS-GI model had similar outpatient care utilization (OR = 1.0, 95% CI 0.7–1.4, p = 0.90), were more likely to have visits in primary care (OR OR=1.5, 95% CI 1.1–2.2, p = 0.02), and were less likely to have visits to our GI outpatient referral practice (OR = 0.3, 95% CI 0.2–0.7, p < 0.0001). Condition-specific analyses show that all GI conditions experienced decreased visits to the outpatient GI referral practice outside of patients with GI neoplasm. Populations did not differ in emergency department, hospital, or diagnostic utilization.ConclusionsWe observed that an embedded specialist in primary care model is associated with improved care coordination without compromising patient safety. The PCMH could be extended to include subspecialty care.KEY WORDS: patient-centered medical home (PCMH), primary care, gastroenterology, health care utilization, patient-centered care     

Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia

BackgroundTo develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness.MethodsA total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC).ResultsThe mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913–1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757–1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906–1.000).ConclusionsA XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.     

Elevated chronic bronchitis diagnosis risk among women in a local emergency department patient population associated with the 2012 heatwave and drought in Douglas county,NE USA

BackgroundConcerns about global climate change force local public health agencies to assess potential local disease risk.ObjectiveDetermine if risk of an emergency department chronic bronchitis diagnosis in Douglas County, NE, was higher during the 2012 heatwave compared to the same calendar period in 2011.MethodsRetrospective, observational, case-control design selecting subjects from 2011 and 2012 emergency department (ED) admissions. Risk was estimated by conditional logistic regression.ResultsThe odds of an ED chronic bronchitis diagnosis among females was 3.77 (95% CI =1.37-10.21) times higher during the 2012 risk period compared to females admitted to the ED during the 2011 risk period. Chronic bronchitis ED diagnosis odds were 1.05 (95%CI=1.04 – 1.06) times higher for each year of age. ED, gender, and race modified the risk (i.e., effect). The overall chronic bronchitis ED risk estimate was 1.61 (95%CI=0.81 – 3.21) times higher during the 2012 risk period compared to the 2011 risk period. The mean ambient absolute humidity upon admission was 11.44 gr/m³ (95%CI; 10.40 – 12.47) among chronic bronchitis cases and 12.67 gr/m³ (95%CI; 12.63 – 12.71) among controls.ConclusionThe odds of ED chronic bronchitis diagnosis was higher among female subjects admitted during the 2012 risk period compared to females admitted during the 2011 risk period.  Age also increased chronic bronchitis ED diagnosis risk among 2012 risk period admissions compared to 2011 risk period admissions.     

The prognostic significance of electrocardiography findings in patients with coronavirus disease 2019: A retrospective study

BackgroundCoronavirus disease 2019 (COVID‐19) has reached a pandemic level. Cardiac injury is not uncommon among COVID‐19 patients. We sought to describe the electrocardiographic characteristics and to identify the prognostic significance of electrocardiography (ECG) findings of patients with COVID‐19.HypothesisECG abnormality was associated with higher risk of death.MethodsConsecutive patients with laboratory‐confirmed COVID‐19 and definite in‐hospital outcome were retrospectively included. Demographic characteristics and clinical data were extracted from medical record. Initial ECGs at admission or during hospitalization were reviewed. A point‐based scoring system of abnormal ECG findings was formed, in which 1 point each was assigned for the presence of axis deviation, arrhythmias, atrioventricular block, conduction tissue disease, QTc interval prolongation, pathological Q wave, ST‐segment change, and T‐wave change. The association between abnormal ECG scores and in‐hospital mortality was assessed in multivariable Cox regression models.ResultsA total of 306 patients (mean 62.84 ± 14.69 years old, 48.0% male) were included. T‐wave change (31.7%), QTc interval prolongation (30.1%), and arrhythmias (16.3%) were three most common found ECG abnormalities. 30 (9.80%) patients died during hospitalization. Abnormal ECG scores were significantly higher among non‐survivors (median 2 points vs 1 point, p < 0.001). The risk of in‐hospital death increased by a factor of 1.478 (HR 1.478, 95% CI 1.131–1.933, p = 0.004) after adjusted by age, comorbidities, cardiac injury and treatments.ConclusionsECG abnormality was common in patients admitted for COVID‐19 and was associated with adverse in‐hospital outcome. In‐hospital mortality risk increased with increasing abnormal ECG scores.     

A predictive model using left atrial function and B‐type natriuretic peptide level in predicting the recurrence of early persistent atrial fibrillation after radiofrequency ablation

AimA predictive model using left atrial function indexes obtained by real‐time three‐dimensional echocardiography (RT‐3DE) and the blood B‐type natriuretic peptide (BNP) level was constructed, and its value in predicting recurrence in patients with early persistent atrial fibrillation (AF) after radiofrequency ablation was explored.MethodsA total of 228 patients with early persistent AF who were scheduled to receive the first circular pulmonary vein ablation (CPVA) were enrolled. Clinical data of patients were collected: (1) The blood BNP level was measured before radiofrequency ablation; (2) RT‐3DE was used to obtain the left atrial (LA) time‐volume curve; (3) The clinical characteristics, BNP level and LA function parameters were compared, and logistic regression was used to construct a prediction model with combined parameters; (4) The receiver operating characteristic (ROC) curve was used to examine the diagnostic efficacy of the model.Results(1) 215 patients with early persistent AF completed CPVA and the follow‐up. After 3–6 months of follow‐up, the patients were divided into sinus rhythm group (160 cases) and recurrence group (55 cases); (2) The recurrence group showed higher minimum LA volume index, diastolic ejection index, and preoperative BNP (all p ≤ .001), while the sinus rhythm group exhibited higher expansion index (PI) and left atrial appendage peak emptying velocity (p ≤ .001); (3) In univariate analysis, BNP level had the best diagnostic performance in predicting the recurrence of AF(AUC = 0.703). We constructed a model based on LA function and BNP level to predict the recurrence of persistent AF after CPVA. This combined model was better than BNP alone in predicting the recurrence of persistent AF after CPVA (AUC: 0.814 vs. 0.703, z = 2.224, p = .026).ConclusionThe combined model of LA function and blood BNP level has good predictive value for the recurrence of early persistent AF after CPVA.     

Hospital outcomes of community-acquired COVID-19 versus influenza: Insights from the Swiss hospital-based surveillance of influenza and COVID-19

BackgroundSince the onset of the COVID-19 pandemic, the disease has frequently been compared with seasonal influenza, but this comparison is based on little empirical data.AimThis study compares in-hospital outcomes for patients with community-acquired COVID-19 and patients with community-acquired influenza in Switzerland.MethodsThis retrospective multi-centre cohort study includes patients > 18 years admitted for COVID-19 or influenza A/B infection determined by RT-PCR. Primary and secondary outcomes were in-hospital mortality and intensive care unit (ICU) admission for patients with COVID-19 or influenza. We used Cox regression (cause-specific and Fine-Gray subdistribution hazard models) to account for time-dependency and competing events with inverse probability weighting to adjust for confounders.ResultsIn 2020, 2,843 patients with COVID-19 from 14 centres were included. Between 2018 and 2020, 1,381 patients with influenza from seven centres were included; 1,722 (61%) of the patients with COVID-19 and 666 (48%) of the patients with influenza were male (p < 0.001). The patients with COVID-19 were younger (median 67 years; interquartile range (IQR): 54–78) than the patients with influenza (median 74 years; IQR: 61–84) (p < 0.001). A larger percentage of patients with COVID-19 (12.8%) than patients with influenza (4.4%) died in hospital (p < 0.001). The final adjusted subdistribution hazard ratio for mortality was 3.01 (95% CI: 2.22–4.09; p < 0.001) for COVID-19 compared with influenza and 2.44 (95% CI: 2.00–3.00, p < 0.001) for ICU admission.ConclusionCommunity-acquired COVID-19 was associated with worse outcomes compared with community-acquired influenza, as the hazards of ICU admission and in-hospital death were about two-fold to three-fold higher.     

Contemporary use of guideline‐based higher potency P2Y12 receptor inhibitor therapy in patients with moderate‐to‐high risk non‐ST‐segment elevation myocardial infarction: Results from the Canadian ACS reflective II cross‐sectional study

BackgroundAfter myocardial infarction, guidelines recommend higher‐potency P2Y12 receptor inhibitors, namely ticagrelor and prasugrel, over clopidogrel.HypothesisWe aimed to determine the contemporary use of higher‐potency antiplatelet therapy in Canadian patients with non‐ST‐elevation myocardial infarction (NSTEMI).MethodsA total of 684 moderate‐to‐high risk NSTEMI patients were enrolled in the prospective Canadian ACS Reflective II registry at 12 Canadian hospitals and three clinics in five provinces between July 2016 and May 2018. Multivariable logistic regression modeling was performed to assess factors independently associated with higher‐potency P2Y12 receptor inhibitor use at discharge.ResultsAt hospital discharge, 78.3% of patients were treated with a P2Y12 receptor inhibitor. Among patients discharged on a P2Y12 receptor inhibitor, use of higher‐potency P2Y12 receptor inhibitor was 61.4%. After adjustment, treatment in‐hospital with PCI (OR 4.48, 95%CI 3.34–6.03, p < .0001) was most strongly associated with higher use of higher‐potency P2Y12 receptor inhibitor, while oral anticoagulant use at discharge (OR 0.03, 95%CI 0.01–0.12, p < .0001), and atrial fibrillation (OR 0.40, 95%CI 0.17–0.98, p = .046) were most strongly associated with lower use of higher‐potency P2Y12 receptor inhibitor. Use of higher‐potency P2Y12 receptor inhibitor varied across provinces (range, 21.6%–78.9%).DiscussionIn contemporary Canadian practice, approximately 60% of moderate‐to‐high risk NSTEMI patients discharged on a P2Y12 receptor inhibitor are treated with a higher‐potency P2Y12 receptor inhibitor. In addition to factors that increase risk of bleeding, interprovincial differences in practice patterns were associated with use of higher‐potency P2Y12 receptor inhibitor at discharge. Opportunities remain for further optimization of evidence‐based, guideline‐recommended antiplatelet therapy use.