scholarly journals Predicting outcomes in emergency medical admissions – role of laboratory data and co-morbidity

2012 ◽  
Vol 11 (2) ◽  
pp. 59-65
Author(s):  
Esther O’Sullivan ◽  
◽  
Elizabeth Callely ◽  
Kathleen Bennett ◽  
Deirdre O’Riordan ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Laboratory Data ◽  
Medical Admission ◽  
Emergency Admissions ◽  
Fractional Polynomial ◽  
Emergency Medical ◽  
Co Morbidity ◽  
Validation Set ◽  
Patient Administration System

Background: The utility of risk stratification following an emergency medical admission has been debated. We have examined the predictability of outcomes, from a database of all emergency admissions to St James’ Hospital, Dublin, over a six year period (2005-2010). Methods: Analysis was performed using the hospital in-patient enquiry system, linked to the patient administration system and laboratory data. The utility of a fractional polynomial laboratory only model to predict 30-day in-hospital mortality was determined. Results: The AUROC for the laboratory parameters to predict a 30 day death was 0.90 (95% CI 0.89, 0.90) in the 2002 – 2010 derivation dataset and was 0.88 (95% CI 0.86, 0.90) in the 2011 validation set. The addition of co-morbidity measures did not improve the model prediction (0.89 : 95% CI 0.88 – 0.89). Conclusion: A fractional polynomial laboratory only model can reliably predict 30-day hospital mortality following an emergency medical admission, potentially allowing resources to be risk focused and patients to be prioritised.

scholarly journals The Problems with Risk Prediction during an Emergency Medical Admission Using Laboratory Data – Evidence from Potassium

2019 ◽  
Vol 18 (1) ◽  
pp. 16-22
Author(s):  
Richard Conway ◽  
◽  
Declan Byrne ◽  
Seán Cournane ◽  
Deirdre O’Riordan ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Risk Prediction ◽  
Biochemical Markers ◽  
Laboratory Data ◽  
Progressive Increase ◽  
Medical Admission ◽  
Emergency Admissions ◽  
Fractional Polynomial ◽  
Relationship Of ◽  
Over Time

Background: The prediction of clinical outcomes using biochemical markers is an important tool. Methods: We calculated a risk score for all emergency admissions 2002-2017. We related potassium and mortality in a multivariable fractional polynomial model. We investigated the potassium distribution and relationship of potassium to mortality over time. Results: There were 106,586 admissions in 54,928 patients. Mortality was higher for those with an admission potassium above the median – 6.1% vs 4.6% (p<0.001), OR 1.07 (95%CI: 1.06, 1.09). There was a progressive increase in mortality from the lowest – 8.9% (95%CI: 8.3%, 9.4%) to highest potassium decile – 14.2% (95%CI: 13.5%, 14.8%). The frequency of admission hypokalaemia and the mortality at any given potassium decreased over time. Conclusion: Admission potassium predicts mortality.

scholarly journals Excess Out-of-Hospital Mortality and Declining Oxygen Saturation: The Sentinel Role of Emergency Medical Services Data in the COVID-19 Crisis in Tijuana, Mexico

2020 ◽  
Vol 76 (4) ◽  
pp. 413-426 ◽  
Author(s):  
Joseph Friedman ◽  
Alhelí Calderón-Villarreal ◽  
Ietza Bojorquez ◽  
Carlos Vera Hernández ◽  
David L. Schriger ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Oxygen Saturation ◽  
Emergency Medical Services ◽  
Medical Services ◽  
Emergency Medical

scholarly journals Development and validation of a novel computer-aided score to predict the risk of in-hospital mortality for acutely ill medical admissions in two acute hospitals using their first electronically recorded blood test results and vital signs: a cross-sectional study

BMJ Open ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. e022939 ◽  
Author(s):  
Muhammad Faisal ◽  
Andrew J Scally ◽  
Natalie Jackson ◽  
Donald Richardson ◽  
Kevin Beatson ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Blood Test ◽  
External Validation ◽  
Model Development ◽  
Vital Signs ◽  
Test Results ◽  
Medical Admission ◽  
Emergency Medical ◽  
Computer Aided ◽  
Acute Hospitals

ObjectivesThere are no established mortality risk equations specifically for emergency medical patients who are admitted to a general hospital ward. Such risk equations may be useful in supporting the clinical decision-making process. We aim to develop and externally validate a computer-aided risk of mortality (CARM) score by combining the first electronically recorded vital signs and blood test results for emergency medical admissions.DesignLogistic regression model development and external validation study.SettingTwo acute hospitals (Northern Lincolnshire and Goole NHS Foundation Trust Hospital (NH)—model development data; York Hospital (YH)—external validation data).ParticipantsAdult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic National Early Warning Score(s) and blood test results recorded on admission.ResultsThe risk of in-hospital mortality following emergency medical admission was 5.7% (NH: 1766/30 996) and 6.5% (YH: 1703/26 247). The C-statistic for the CARM score in NH was 0.87 (95% CI 0.86 to 0.88) and was similar in an external hospital setting YH (0.86, 95% CI 0.85 to 0.87) and the calibration slope included 1 (0.97, 95% CI 0.94 to 1.00).ConclusionsWe have developed a novel, externally validated CARM score with good performance characteristics for estimating the risk of in-hospital mortality following an emergency medical admission using the patient’s first, electronically recorded, vital signs and blood test results. Since the CARM score places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

scholarly journals Development and validation of automated computer aided-risk score for predicting the risk of in-hospital mortality using first electronically recorded blood test results and vital signs for COVID-19 hospital admissions: a retrospective development and validation study

2020 ◽  
Author(s):  
Muhammad Faisal ◽  
Mohammed A Mohammed ◽  
Donald Richardson ◽  
Massimo Fiori ◽  
Kevin Beatson
Keyword(s):  
Hospital Mortality ◽  
Blood Test ◽  
Model Development ◽  
Vital Signs ◽  
Validation Dataset ◽  
Test Results ◽  
Medical Admission ◽  
Emergency Medical ◽  
Computer Aided ◽  
Development And Validation

AbstractObjectivesThere are no established mortality risk equations specifically for unplanned emergency medical admissions which include patients with the novel coronavirus SARS-19 (COVID-19). We aim to develop and validate a computer-aided risk score (CARMc19) for predicting mortality risk by combining COVID-19 status, the first electronically recorded blood test results and latest version of the National Early Warning Score (NEWS2).DesignLogistic regression model development and validation study using a cohort of unplanned emergency medical admissions to hospital.SettingYork Hospital (YH) as model development dataset and Scarborough Hospital (SH) as model validation dataset.ParticipantsUnplanned adult medical admissions discharged over three months (11 March 2020 to 13 June 2020) from two hospitals (YH for model development; SH for external model validation) based on admission NEWS2 electronically recorded within ±24 hours and/or blood test results within ±96 hours of admission. We used logistic regression modelling to predict the risk of in-hospital mortality using two models: 1) CARMc19_N: age + sex + NEWS2 including subcomponents + COVID19; 2) CARMc19_NB: CARMc19_N in conjunction with seven blood test results and acute kidney injury score. Model performance was evaluated according to discrimination (c-statistic), calibration (graphically), and clinical usefulness at NEWS2 thresholds of 4+, 5+, 6+.ResultsThe risk of in-hospital mortality following emergency medical admission was similar in development and validation datasets (8.4% vs 8.2%). The c-statistics for predicting mortality for Model CARMc19_NB is better than CARMc19_N in the validation dataset (CARMc19_NB = 0.88 (95%CI 0.86 to 0.90) vs CARMc19_N = 0.86 (95%CI 0.83 to 0.88)). Both models had good internal and external calibration (CARMc19_NB: 1.01 (95%CI 0.88 vs 1.14) & CARMc19_N: 0.95 (95%CI 0.83 to 1.06)). At all NEWS2 thresholds (4+, 5+, 6+) model CARMc19_NB had better sensitivity and similar specificity.ConclusionsWe have developed a validated CARMc19 score with good performance characteristics for predicting the risk of in-hospital mortality following an emergency medical admission using the patient’s first, electronically recorded vital signs and blood tests results. Since the CARMc19 scores place no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

scholarly journals Hyponatraemia as a Predictor of Mortality in Medical Admissions in Ghana: A Comparative Study

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Elliot Koranteng Tannor ◽  
Martin Agyei ◽  
Abena Y. Tannor ◽  
Afua Ofori ◽  
Emmanuel Akumiah ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Hospital Mortality ◽  
Serum Albumin ◽  
Hospital Stay ◽  
Serum Sodium ◽  
Laboratory Data ◽  
Multiple Logistic Regression ◽  
Medical Admission ◽  
Medical Diagnoses ◽  
Low Serum

Background. Hyponatraemia is the most common electrolyte abnormality in hospital admissions. It occurs in a quarter of medical admissions in Ghana, and it is associated with high mortality. Mortality has been suggested to be due to the underlying medical condition and not necessarily the hyponatraemia. We set out to compare the outcomes of patients with documented hyponatraemia as compared to those with normonatraemia in terms of mortality and length of hospital stay. Methods. We conducted a comparative analysis of patients with hyponatraemia and those with normonatraemia on the medical ward at the Komfo Anokye Teaching Hospital between May 2018 and December 2018. The medical diagnoses, demographics, and laboratory data of the patients were recorded. Participants’ age and gender were matched. Student’s t-test was used to test for differences in continuous variables when parametric and Wilcoxon signed-rank test for nonparametric variables. Multiple logistic regression was used to identify predictors of in-hospital mortality. A p value of <0.05 was considered statistically significant. Results. Within the study period, 846 patients with documented serum sodium were included in the study. The study involved 406 patients with hyponatraemia and 440 patients with normonatraemia. Serum albumin and protein were significantly lower in the hyponatraemia patients as compared to those with normonatraemia. The mortality rate in patients with hyponatraemia was significantly higher than those with normonatraemia (129 (31.8%) vs. 9 (22.3%); OR 1.62 (95% CI: 1.19–2.22), p = 0.002 ). In-hospital stay was longer in patients with hyponatraemia than normonatraemia (7 (4–10) vs. 6 (3–10) days) but not statistically significant p = 0.09 . Multiple logistic regression showed that low serum sodium p < 0.001 and low serum albumin p = 0.009 were the predictors of in-hospital mortality. Conclusion. Hyponatraemia is associated with significantly higher mortality than normonatraemia and predicts worse prognosis in patients on medical admission. Low serum albumin is also a predictor of mortality in medical admission.

The outcomes of emergency hospital admissions with non-malignant upper gastrointestinal bleeding in England between 2003 and 2015

Endoscopy ◽  
2021 ◽  
Author(s):  
James Rees ◽  
Felicity Evison ◽  
Jemma Mytton ◽  
Prashant Patel ◽  
Nigel Trudgill
Keyword(s):  
Gastrointestinal Bleeding ◽  
Variceal Bleeding ◽  
Key Management ◽  
Hospital Admissions ◽  
Upper Gastrointestinal Bleeding ◽  
Medical Emergency ◽  
Emergency Admissions ◽  
Co Morbidity ◽  
Standardized Mortality Rate ◽  
Upper Gastrointestinal

Abstract Background Upper gastrointestinal bleeding (UGIB) is a common medical emergency with significant mortality. Despite developments in endoscopic and clinical management, only minor improvements in outcomes have been reported. Methods This was a retrospective cohort study of patients with non-malignant UGIB emergency admissions in England between 2003 and 2015, using Hospital Episode Statistics. Multilevel logistic regression analysis examined the associations with mortality. Results 242 796 patients with an UGIB admission were identified (58.8 % men; median age 70 [interquartile range (IQR) 53 – 81]). Between 2003 and 2015, falls occurred in both 30-day mortality (7.5 % to 7.0 %; P < 0.001) and age-standardized mortality (odds ratio (OR) 0.74, 95 % confidence interval [CI] 0.69 – 0.80; P < 0.001), including from variceal bleeding (OR 0.63, 95 %CI 0.45 – 0.87; P < 0.005). Increasing co-morbidity (Charlson score > 5, OR 2.94, 95 %CI 2.85 – 3.04; P < 0.001), older age (> 83 years, OR 6.50, 95 %CI 6.09 – 6.94; P < 0.001), variceal bleeding (OR 2.03, 95 %CI 1.89 – 2.18; P < 0.001), and a weekend admission (Sunday, OR 1.18, 95 %CI 1.12 – 1.23; P < 0.001) were associated with 30-day mortality. Of deaths at 30 days, 8.9 % were from ischemic heart disease (IHD) and the cardiovascular age-standardized mortality rate following UGIB was high (IHD deaths within 1 year, 1188.4 [95 %CI 1036.8 – 1353.8] per 100 000 men in 2003). Conclusions Between 2003 and 2015, 30-day mortality among emergency admissions with non-malignant UGIB fell by 0.5 % to 7.0 %. Mortality was higher among UGIB admissions at the weekend, with important implications for service provision. Patients with UGIB had a much greater risk of subsequently dying from cardiovascular disease and addressing this risk is a key management step in UGIB.

scholarly journals Baseline T-lymphocyte subset absolute counts can predict both outcome and severity in SARS-CoV-2 infected patients: a single center study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marco Iannetta ◽  
Francesco Buccisano ◽  
Daniela Fraboni ◽  
Vincenzo Malagnino ◽  
Laura Campogiani ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
T Lymphocyte ◽  
Lymphocyte Subsets ◽  
Severe Disease ◽  
Laboratory Data ◽  
Lymphocyte Subset ◽  
Multivariable Logistic Regression Analysis ◽  
Double Positive ◽  
Increased Risk ◽  
T Lymphocyte Subset

AbstractThe aim of this study was to evaluate the role of baseline lymphocyte subset counts in predicting the outcome and severity of COVID-19 patients. Hospitalized patients confirmed to be infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) were included and classified according to in-hospital mortality (survivors/nonsurvivors) and the maximal oxygen support/ventilation supply required (nonsevere/severe). Demographics, clinical and laboratory data, and peripheral blood lymphocyte subsets were retrospectively analyzed. Overall, 160 patients were retrospectively included in the study. T-lymphocyte subset (total CD3+, CD3+ CD4+, CD3+ CD8+, CD3+ CD4+ CD8+ double positive [DP] and CD3+ CD4− CD8− double negative [DN]) absolute counts were decreased in nonsurvivors and in patients with severe disease compared to survivors and nonsevere patients (p < 0.001). Multivariable logistic regression analysis showed that absolute counts of CD3+ T-lymphocytes < 524 cells/µl, CD3+ CD4+ < 369 cells/µl, and the number of T-lymphocyte subsets below the cutoff (T-lymphocyte subset index [TLSI]) were independent predictors of in-hospital mortality. Baseline T-lymphocyte subset counts and TLSI were also predictive of disease severity (CD3+  < 733 cells/µl; CD3+ CD4+ < 426 cells/µl; CD3+ CD8+ < 262 cells/µl; CD3+ DP < 4.5 cells/µl; CD3+ DN < 18.5 cells/µl). The evaluation of peripheral T-lymphocyte absolute counts in the early stages of COVID-19 might represent a useful tool for identifying patients at increased risk of unfavorable outcomes.

scholarly journals Role of Chest Imaging in Viral Lung Diseases

2021 ◽  
Vol 18 (12) ◽  
pp. 6434
Author(s):  
Diletta Cozzi ◽  
Eleonora Bicci ◽  
Alessandra Bindi ◽  
Edoardo Cavigli ◽  
Ginevra Danti ◽  
...  
Keyword(s):  
Lung Diseases ◽  
Respiratory Tract Infections ◽  
Laboratory Data ◽  
World Health ◽  
Pulmonary Manifestations ◽  
Radiological Patterns ◽  
Health Organization ◽  
Tract Infections ◽  
Combining Information

The infection caused by novel beta-coronavirus (SARS-CoV-2) was officially declared a pandemic by the World Health Organization in March 2020. However, in the last 20 years, this has not been the only viral infection to cause respiratory tract infections leading to hundreds of thousands of deaths worldwide, referring in particular to severe acute respiratory syndrome (SARS), influenza H1N1 and Middle East respiratory syndrome (MERS). Although in this pandemic period SARS-CoV-2 infection should be the first diagnosis to exclude, many other viruses can cause pulmonary manifestations and have to be recognized. Through the description of the main radiological patterns, radiologists can suggest the diagnosis of viral pneumonia, also combining information from clinical and laboratory data.

scholarly journals Frailty is associated with in-hospital mortality in older hospitalised COVID-19 patients in the Netherlands: the COVID-OLD study

2021 ◽  
Author(s):  
Laura C Blomaard ◽  
Carolien M J van der Linden ◽  
Jessica M van der Bol ◽  
Steffy W M Jansen ◽  
Harmke A Polinder-Bos ◽  
...  
Keyword(s):  
The Netherlands ◽  
Hospital Mortality ◽  
Oxygen Demand ◽  
Symptom Duration ◽  
Clinical Frailty Scale ◽  
Reactive Protein ◽  
Increased Risk ◽  
Co Morbidity ◽  
Lower Oxygen ◽  
Independent Association

Abstract Background During the first wave of the coronavirus disease 2019 (COVID-19) pandemic, older patients had an increased risk of hospitalisation and death. Reports on the association of frailty with poor outcome have been conflicting. Objective The aim of the present study was to investigate the independent association between frailty and in-hospital mortality in older hospitalised COVID-19 patients in the Netherlands. Methods This was a multicentre retrospective cohort study in 15 hospitals in the Netherlands, including all patients aged ≥70 years, who were hospitalised with clinically confirmed COVID-19 between February and May 2020. Data were collected on demographics, co-morbidity, disease severity and Clinical Frailty Scale (CFS). Primary outcome was in-hospital mortality. Results A total of 1,376 patients were included (median age 78 years (interquartile range 74–84), 60% male). In total, 499 (38%) patients died during hospital admission. Parameters indicating presence of frailty (CFS 6–9) were associated with more co-morbidities, shorter symptom duration upon presentation (median 4 versus 7 days), lower oxygen demand and lower levels of C-reactive protein. In multivariable analyses, the CFS was independently associated with in-hospital mortality: compared with patients with CFS 1–3, patients with CFS 4–5 had a two times higher risk (odds ratio (OR) 2.0 (95% confidence interval (CI) 1.3–3.0)) and patients with CFS 6–9 had a three times higher risk of in-hospital mortality (OR 2.8 (95% CI 1.8–4.3)). Conclusions The in-hospital mortality of older hospitalised COVID-19 patients in the Netherlands was 38%. Frailty was independently associated with higher in-hospital mortality, even though COVID-19 patients with frailty presented earlier to the hospital with less severe symptoms.

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