Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram

Red Cell Distribution Width as a Prognostic Indicator in Acute Medical Admissions

The red cell distribution width (RDW) is the coefficient of variation of the mean corpuscular volume (MCV). We sought to evaluate RDW as a predictor of outcomes following acute medical admission. We studied 10 years of acute medical admissions (2002-2011) with subsequent follow-up to 2021. RDW was converted to a categorical variable, Q1 < 12.9 fl, Q2-Q4 ≥ 12.9 and <15.7 fL and Q5 ≥ 15.7 fL. The predictive value of RDW for 30-day in-hospital and long-term mortality was evaluated with logistic and Cox regression modelling. Adjusted odds ratios (aORs) were calculated and loss of life years estimated. There were 62,184 admissions in 35,140 patients. The 30-day in-hospital mortality (n = 3646) occurred in 5.9% of admissions. An additional 15,086 (42.9%) deaths occurred by December 2021. Admission RDW independently predicted 30-day in-hospital mortality aOR 1.93 (95%CI 1.79, 2.07). Admission RDW independently predicted long-term mortality aOR 1.04 (95%CI 1.02, 1.05). Median survival post-admission was 189 months. For those with admission RDW in Q5, observed survival half-life was 133 months-this represents a shortfall of 5.7 life years (33.9%). In conclusion, admission RDW independently predicts 30-day in-hospital and long-term mortality.

Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study

OBJECTIVE

To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients.

METHOD

We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set.

RESULT

For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P < 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P < 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608-0.760), P < 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P < 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set.

CONCLUSION

Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate.

Development of a risk prediction nomogram for disposition of acute toxic exposure patients to intensive care unit

Early risk stratification of acutely poisoned patients is essential to identify patients at high risk of intensive care unit (ICU) admission. We aimed to develop a prognostic model and risk-stratification nomogram based on the readily accessible clinical and laboratory predictors on admission for the probability of ICU admission in acutely poisoned patients. This retrospective cohort study included adult patients with acute toxic exposure to a drug or a chemical substance. Patients' demographic, toxicologic, clinical and laboratory data were collected. Among the 1260 eligible patients, 180 (14.3%) were admitted to the ICU. We developed a generalized prognostic model for predicting ICU admission in patients with acute poisoning. The predictors included the Glasgow coma scale, oxygen saturation, diastolic blood pressure, respiratory rate and blood bicarbonate concentration. The model displayed excellent discrimination and calibration (optimistic-adjusted area under the curve = 0.924 and optimistic-adjusted Hosmer and Lemeshow test = 0.922, respectively) when internally validated. Additionally, we developed prognostic models that determine ICU admission in patients with specific poisonings. Furthermore, we constructed risk-stratification nomograms that rank the probability of ICU admission in these patients. The developed risk-stratification nomograms help decision-making regarding ICU admission in acute poisonings. Future external validation in independent cohorts is necessary before clinical application.

Identifying a Clinical Risk Triage Score for Adult Emergency Department

Emergency triage is crucial for the treatment and prognosis of emergency patients, but its validity needs further improvement. The purpose of this study was to identify a risk score for adult triage. We conducted a regression analysis of physiological and biochemical data from 1,522 adult patients. A 60-point triage scoring model included temperature, pulse, systolic blood pressure, oxygen saturation, consciousness, dyspnea, admission mode, syncope history, chest pain or chest tightness, complexion, hematochezia or hematemesis, hemoptysis, white blood count, creatinine, bicarbonate, platelets, and creatine kinase. The area under curve in predicting ICU admission was 0.929 (95% CI [0.913-0.944]) for the derivation cohort and 0.911 (95% CI [0.884-0.938]) for the validation cohort. Four categories: critical level (≥13 points), severe level (6-12 points), urgency level (1-5 points), and sub-acute level (0 points) were divided, which significantly distinguished the severity of emergency patients.

Predicting 30-day mortality using point-of-care testing; an external validation and derivation study

Background

Early risk stratification for guiding treatment priority in the emergency department (ED) is becoming increasingly important. Existing prediction models typically use demographics, vital signs and laboratory parameters. Laboratory-based models require blood testing, which may cause substantial delay. However, these delays can be prevented by the use of point-of-care testing (POCT), where results are readily available. We aimed to externally validate a laboratory-based model for mortality and subsequently assessed whether a POCT model yields comparable performance.

Methods

All adult patients visiting the ED of a university hospital between January 1^st, 2012 and December 31^st, 2016 were retrospectively reviewed for inclusion. Primary outcome was defined as 30-day mortality after ED presentation. We externally validated one existing prediction model including age, glucose, urea, sodium, haemoglobin, platelet count and white blood cell count. We assessed the predictive performance by discrimination, expressed as Area under the Curve (AUC). We compared the existing model to an equivalent model using predictors that are available with POCT (i.e. glucose, urea, sodium and haemoglobin). Additionally, we internally validated these models with bootstrapping.

Results

We included 34,437 patients of whom 1,942 (5.6%) died within 30 days. The AUC of the laboratory-based model was 0.794. We refitted this model to our ED population and found an AUC of 0.812, which decreased only slightly to 0.790 with only POCT parameters.

Conclusions

Our POCT-model performs similar to existing laboratory-based models in identifying patients at high risk for mortality, with results available within minutes. Although the model needs further validation and evaluation, it shows the potential of POCT for early risk stratification in the ED.

Abnormal routine blood tests as predictors of mortality in acutely admitted patients

BACKGROUND

This study aimed to improve early risk stratification in the emergency department by creating a simple blood test score based on routine biomarkers and assess its predictive ability for 30-day mortality of acutely admitted patients.

METHODS

This was a secondary analysis of data from the TRIAGE II study. It included unselected acutely admitted medical and surgical patients, who had albumin, C-reactive protein, creatinine, haemoglobin, leukocytes, potassium, sodium and thrombocytes levels analysed upon admission. Patients were classified according to the number of biomarker results outside the reference range into four risk groups termed "very low", "low", "intermediate", and "high" with 0-1, 2-3, 4-5 and 6-8 abnormal biomarker results, respectively. Logistic regression was used to calculate odds ratios for 30-day mortality and receiver operating characteristic was used to test the discriminative value. The primary analysis was done in patients triaged with ADAPT (Adaptive Process Triage). Subsequently, we analysed two other cohorts of acutely admitted patients.

RESULTS

The TRIAGE II cohort included 17,058 eligible patients, 30-day mortality was 5.2%. The primary analysis included 7782 patients. Logistic regression adjusted for age and sex showed an OR of 24.1 (95% CI 14.9-41.0) between the very low- and the high-risk group. The area under the curve (AUC) was 0.79 (95% CI 0.76-0.81) for the blood test score in predicting 30-day mortality. The subsequent analyses confirmed the results.

CONCLUSIONS

A blood test score based on number of routine biomarkers with an abnormal result was a predictor of 30-day mortality in acutely admitted patients.

Lung function and outcomes in emergency medical admissions

BACKGROUND

We examine the ability of pre-existing measures of Forced Expiratory Volume in 1 s (FEV1), and Diffusion Capacity for Carbon Monoxide (DLCO) to determine the subsequent 30-day mortality outcome following unselected acute medical admission.

METHODS

Between 2002 and 2017, we studied all emergency medical admissions (106,586 episodes in 54,928 patients) of whom 8071 were classified as respiratory. We employed logisitic multiple variable regression models to evaluate the ability of FEV1 or DLCO to predict the 30-day hospital mortality outcome.

RESULTS

The 30-day hospital episode mortality outcome demonstrated curvilinear relationships to the underlying FEV1 or DLCO values; adjusted for major outcome predictors, a higher FEV1 - OR 0.85 (95% CI: 0.82, 0.89) or DLCO OR 0.76 (95% CI: 0.73, 0.79) values predicted survival. The range of predicted mortalities was from 3.3% (95% CI: 2.5, 4.0) to 23.5% (95% CI: 20.8, 26.2); the FEV1 (Model1) and DLCO (Model2) outcome prediction was essentially equivalent (Chi2 = 2.9: p = 0.08).

CONCLUSION

The 30-day mortality outcome was clearly related to the pre-admission FEV1 and DLCO value. The outcome relationship was curvilinear. Either parameter appears a useful tool to explore hospital outcomes. Previously suggested cut-points are likely an artefact and not supported by these data.

Hyponatraemia during an emergency medical admission as a marker of illness severity & case complexity

BACKGROUND

Altered sodium balance at time of an emergency medical admission adversely impacts on outcome; whether hyponatraemia is independently associated with outcomes or a surrogate of acute illness severity has been debated.

METHODS

All emergency medical admissions between 2002 and 2017 were studied and a risk score calculated. We compared univarate deciles of admission sodium using a multivariable model, adjusting for risk score.

RESULTS

There were 106,586 admissions in 54,928 patients. Patients with lower sodium at admission were older at 66.7 years (IQR 46.7-79.5) compared with 63.3 years (IQR 42.9-78.2) with a longer length of stay (LOS) of 6.8 days (IQR 3.0-14.7) versus 4.9 days (IQR 1.8-10.9). They had a higher 30-day in-hospital mortality at 6.4% vs 4.4% (p < 0.001). Admission sodium predicted survival - OR 0.89 (95%CI 0.88-0.90). We adjusted the model with a Risk Score that is predictive and exponentially related to 30-day in-hospital mortality. When adjusted for Risk Score, the admission sodium value was less predictive - OR 0.95 (95%CI 0.92-0.97). The cumulative percentages within the lowest five deciles fell from 63.3% between 2002 and 2009 to 48.1% from 2010 to 2017. The slope of the prediction line relating admission sodium to mortality did not change over time but a lower mortality rate was predicted at any given sodium level.

CONCLUSION

Hyponatraemia at the time of an emergency medical admission is predictive and probably a marker of Acuity Illness Severity and Case Complexity. Both the frequency of abnormality in admission sodium and mortality have fallen over time.