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Selected Hematological Biomarkers to Predict Acute Mortality in Emergency Department Patients. Recent Polish Hospital Statistics.

ABSTRACT: Background:Complete blood count (CBC), red cell distribution width (RDW), mean platelet volume (MPV), mean corpuscular volume (MCV), mean cell hemoglobin (MCH), mean cell hemoglobin concentration (MCHC), or platelet (PLT) count are referred as predictors of adverse clinical outcomes in patients. The aim of the research was to identify potential factors of acute mortality in Polish emergency department (ED) patients by using selected hematological biomarkers and routine statistical tools. Methods:The study presents statistical results on patients who were recently discharged from inpatient facilities within one month prior to the index ED visit. In total, the analysis comprised 14,881 patients with the first RDW, MPV, MCV, MCH, MCHC, or PLT biomarkers' measurements recorded in the emergency department within the years 2016-2019 with a subsequent one month of all-cause mortality observation. The patients were classified with the codes of the International Statistical Classification of Diseases and Related Health Problems after 10th Revision (ICD10). Results:Based on the analysis of RDW, MPV, MCV, MCH, MCHC, and PLT on acute deaths in patients, we establish strong linear and quadratic relationships between the risk factors under study and the clinical response (P < 0.05), however, with different mortality courses and threats. In our statistical analysis, (1) gradient linear relationships were found for RDW and MPV along an entire range of the analyzed biomarkers' measurements, (2) following the quadratic modeling, an increasing risk of death above 95?fL was determined for MCV, and (3) no relation to excess death in ED patients was calculated for MCH, MCHC, and PLT. Conclusion:The study shows that there are likely relationships between blood counts and expected patient mortality at some time interval from measurements. Up to 1 month of observation since the first measurement of an hematological biomarker, RDW and MPV stand for a strong relationship with acute mortality of patients, whereas MCV, MCH, MCHC, and PLT give the U-shaped association, RDW and MPV can be established as the stronger predictors of early deaths of patients, MCV only in the highest levels (>95?fL), whereas MCH, MCHC, and PLT have no impact on the excess acute mortality in ED patients.

SUBMITTER: Brzezniakiewicz-Janus K

PROVIDER: S-EPMC7381964 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Selected Hematological Biomarkers to Predict Acute Mortality in Emergency Department Patients. Recent Polish Hospital Statistics.

Brzeźniakiewicz-Janus Katarzyna K Lancé Marcus Daniel MD Tukiendorf Andrzej A Janus Tomasz T Franków Mirosław M Rupa-Matysek Joanna J Walkowiak Zuzanna Z Gil Lidia L

Disease markers 20200716

<h4>Background</h4>Complete blood count (CBC), red cell distribution width (RDW), mean platelet volume (MPV), mean corpuscular volume (MCV), mean cell hemoglobin (MCH), mean cell hemoglobin concentration (MCHC), or platelet (PLT) count are referred as predictors of adverse clinical outcomes in patients. The aim of the research was to identify potential factors of acute mortality in Polish emergency department (ED) patients by using selected hematological biomarkers and routine statistical tools. ...[more]

PMID: 32724484

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Project description:BackgroundLarge-scale war-related migration to Switzerland and other European countries is currently challenging European health systems. Little is known about recent patterns and trends in Emergency Department (ED) consultations by Asylum Seekers (AS).MethodsA retrospective single-centre analysis was performed of the data from all adult patients with the official status of "Asylum Seeker" or "Refugee" who consulted the ED of Bern University Hospital, Switzerland, between June 2012 and June 2015. Patient characteristics and clinical information, such as triage category, type of referral and discharge, violence-related injury and diagnostic group on discharge, were extracted from the computerised database or determined from the medical reports. Changes in categorical variables between the three studied years were described.ResultsA total of 1,653 eligible adult patients were identified in the 3-year period. Between the first (06/12-06/13) and third periods (06/14-06/15), the number of presentations per year increased by about 45%. The AS came from 62 different nations, the most common countries being Eritrea (13%), Somalia (13%) and Syria (11%). The mean age was 33.3 years (SD 12.3) and two thirds (65.7%) were male. The proportion of women increased over time. Moreover the relative proportions shifted from patients between 20 and 50 years to patients of under 20 or over 60 years. Nearly two thirds of the patients were walk-in emergencies and this proportion increased over time. The mean triage score was 2.9 (SD 0.7), with more than 90% presenting as "urgent consultation". About half of the patients were treated for trauma (17.2%), infections (16.8%) or psychiatric problems (14.2%). Trauma was seen in a higher proportion of male than female patients. About 25% of the patients were admitted for in-hospital treatment.ConclusionsThe recent rise in AS in the population has lead to an increase in AS presenting to EDs. This changes the composition of ED patients and should raise awareness that changes in procedures may be needed. Infectious diseases and psychiatric problems remain a heavy burden for AS presenting in the ED. A trend towards an increasing proportion of walk-in patients to the ED could not be explained by this study. Further studies and surveillance are needed to investigate this trend.

Project description:Background Identification of stroke patients at increased risk of emergency department (ED) visits or hospital admissions allows implementation of mitigation strategies. We evaluated the ability of the Patient-Reported Outcomes Information Measurement System (PROMIS) patient-reported outcomes (PROs) collected as part of routine care to predict 1-year emergency department (ED) visits and admissions when added to other readily available clinical variables. Methods and Results This was a cohort study of 1696 patients with ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage, or transient ischemic attack seen in a cerebrovascular clinic from February 17, 2015, to June 11, 2018, who completed the following PROs at the visit: Patient Health Questionnaire-9, Quality of Life in Neurological Disorders cognitive function, PROMIS Global Health, sleep disturbance, fatigue, anxiety, social role satisfaction, physical function, and pain interference. A series of logistic regression models was constructed to determine the ability of models that include PRO scores to predict 1-year ED visits and all-cause and unplanned admissions. In the 1 year following the PRO encounter date, 1046 ED visits occurred in 548 patients; 751 admissions occurred in 453 patients. All PROs were significantly associated with future ED visits and admissions except PROMIS sleep. Models predicting unplanned admissions had highest optimism-corrected area under the curve (range, 0.684-0.724), followed by ED visits (range, 0.674-0.691) and then all-cause admissions (range, 0.628-0.671). PROs measuring domains of mental health had stronger associations with ED visits; PROs measuring domains of physical health had stronger associations with admissions. Conclusions PROMIS scales improve the ability to predict ED visits and admissions in patients with stroke. The differences in model performance and the most influential PROs in the prediction models suggest differences in factors influencing future hospital admissions and ED visits.

Project description:ObjectiveWe sought to find a bedside prognosis prediction model based on clinical and image parameters to determine the in-hospital outcomes of acute aortic dissection (AAD) in the emergency department.MethodsPatients who presented with AAD from January 2010 to December 2019 were retrospectively recruited in our derivation cohort. Then we prospectively collected patients with AAD from January 2020 to December 2021 as the validation cohort. We collected the demographics, medical history, treatment options, and in-hospital outcomes. All enrolled patients underwent computed tomography angiography. The image data were systematically reviewed for anatomic criteria in a retrospective fashion by three professional radiologists. A series of radiological parameters, including the extent of dissection, the site of the intimal tear, entry tear diameter, aortic diameter at each level, maximum false lumen diameter, and presence of pericardial effusion were collected.ResultsOf the 449 patients in the derivation cohort, 345 (76.8%) were male, the mean age was 61 years, and 298 (66.4%) had a history of hypertension. Surgical repair was performed in 327 (72.8%) cases in the derivation cohort, and the overall crude in-hospital mortality of AAD was 10.9%. Multivariate logistic regression analysis showed that predictors of in-hospital mortality in AAD included age, Marfan syndrome, type A aortic dissection, surgical repair, and maximum false lumen diameter. A final prognostic model incorporating these five predictors showed good calibration and discrimination in the derivation and validation cohorts. As for type A aortic dissection, 3-level type A aortic dissection clinical prognosis score (3ADPS) including 5 clinical and image variables scored from -2 to 5 was established: (1) moderate risk of death if 3ADPS is <0; (2) high risk of death if 3ADPS is 1-2; (3) very high risk of death if 3ADPS is more than 3. The area under the receiver operator characteristic curves in the validation cohorts was 0.833 (95% CI, 0.700-0.967).ConclusionAge, Marfan syndrome, type A aortic dissection, surgical repair, and maximum false lumen diameter can significantly affect the in-hospital outcomes of AAD. And 3ADPS contributes to the prediction of in-hospital prognosis of type A aortic dissection rapidly and effectively. As multivariable risk prediction tools, the risk models were readily available for emergency doctors to predict in-hospital mortality of patients with AAD in extreme clinical risk.

Project description:BACKGROUND: Clinical governance (CG) is among the different frameworks proposed to improve the quality of healthcare. Iran, like many other countries, has put healthcare quality improvement in its top health policy priorities. In November 2009, implementation of CG became a task for all hospitals across the country. However, it has been a challenge to clarify the notion of CG and the way to implement it in Iran. The purpose of this action research study is to understand how CG can be defined and implemented in a selected teaching emergency department (ED). METHODS/DESIGN: We will use Soft Systems Methodology for both designing the study and inquiring into its content. As we considered a complex problem situation regarding the quality of care in the selected ED, we initially conceptualized CG as a cyclic set of purposeful activities designed to explore the situation and find relevant changes to improve the quality of care. Then, implementation of CG will conceptually be to carry out that set of purposeful activities. The activities will be about: understanding the situation and finding out relevant issues concerning the quality of care; exploring different stakeholders' views and ideas about the situation and how it can be improved; and defining actions to improve the quality of care through structured debates and development of accommodations among stakeholders. We will flexibly use qualitative methods of data collection and analysis in the course of the study. To ensure the study rigor, we will use different strategies. DISCUSSION: Successful implementation of CG, like other quality improvement frameworks, requires special consideration of underlying complexities. We believe that addressing the complex situation and reflections on involvement in this action research will make it possible to understand the concept of CG and its implementation in the selected setting. By describing the context and executed flexible methods of implementation, the results of this study would contribute to the development of implementation science and be employed by boards and executives governing other clinical settings to facilitate CG implementation.

Project description:BACKGROUND:Whether types of hospitals with high readmission rates also have high overall postdischarge acute care utilization (including emergency department and observation care) is unknown. DESIGN:Cross-sectional analysis. SUBJECTS:Nonfederal United States acute care hospitals. MEASURES:Using methodology established by the Centers for Medicare & Medicaid Services, we calculated each hospital's "excess days in acute care" for fee-for-service (FFS) Medicare beneficiaries aged over 65 years discharged after hospitalization for acute myocardial infarction, heart failure (HF), or pneumonia, representing the mean difference between predicted and expected total days of acute care utilization in the 30 days following hospital discharge, per 100 discharges. We assessed the multivariable association of 8 hospital characteristics with excess days in acute care and the proportion of hospitals with each characteristic that were statistical outliers (95% credible interval estimate does not include 0). RESULTS:We included 2184 hospitals for acute myocardial infarction [228 (10.4%) better than expected, 549 (25.1%) worse than expected], 3720 hospitals for HF [484 (13.0%) better and 840 (22.6%) worse], and 4195 hospitals for pneumonia [673 (16.0%) better, 1005 (24.0%) worse]. Results for all conditions were similar. Worse than expected outliers for pneumonia included: 18.8% of safety net hospitals versus 26.1% of nonsafety net hospitals; 16.7% of public hospitals versus 33.1% of for-profit hospitals; 19.5% of nonteaching hospitals versus 52.2% of major teaching hospitals; 7.9% of rural hospitals versus 42.1% of large urban hospitals; 5.9% of hospitals with 24-<50 beds versus 58% of hospitals with >500 beds; and 29.0% of hospitals with nurse-to-bed ratios >1.0-1.5 versus 21.7% of hospitals with ratios >2.0. CONCLUSIONS:Including emergency department and observation stays in measures of postdischarge utilization produces similar results as measuring only readmissions in that major teaching, urban and for-profit hospitals still perform disproportionately poorly versus nonteaching or public hospitals. However, it enables identification of more outliers and a more granular assessment of the association of hospital factors and outcomes.

Project description:BackgroundEmergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors.MethodsData on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016-2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital's catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit.ResultsThe number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE's were 8.7%).ConclusionsBecause of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

Dataset Information

Selected Hematological Biomarkers to Predict Acute Mortality in Emergency Department Patients. Recent Polish Hospital Statistics.

Publications

Selected Hematological Biomarkers to Predict Acute Mortality in Emergency Department Patients. Recent Polish Hospital Statistics.

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