Normal gait, albumin and d-dimer levels identify low risk emergency department patients: a prospective observational cohort study with 365-day 100% follow-up

Nonspecific stress biomarkers for mortality prediction in older emergency department patients presenting with falls: a prospective multicenter observational study

BACKGROUND

Older patients presenting to the emergency department (ED) after falling are increasingly prevalent. Falls are associated with functional decline and death. Biomarkers predicting short-term mortality might facilitate decisions regarding resource allocation and disposition. D-dimer levels are used to rule out thromboembolic disease, while copeptin and adrenomedullin (MR-proADM) may be used as measures of the patient`s stress level. These nonspecific biomarkers were selected as potential predictors for mortality.

METHODS

Prospective, international, multicenter, cross-sectional observation was performed in two tertiary and two regional hospitals in Germany and Switzerland. Patients aged 65 years or older presenting to the ED after a fall were enrolled. Demographic data, Activities of Daily Living (ADL), and D-dimers were collected upon presentation. Copeptin and MR-proADM levels were determined from frozen samples. Primary outcome was 30-day mortality; and secondary outcomes were mortality at 90, 180, and 365 days.

RESULTS

Five hundred and seventy-two patients were included. Median age was 83 [IQR 78, 89] years, 236 (67.7%) were female. Mortality overall was 3.1% (30 d), 5.4% (90 d), 7.5% (180 d), and 13.8% (365 d), respectively. Non-survivors were older, had a lower ADL index and higher levels of all three biomarkers. Elevated levels of MR-proADM and D-dimer were associated with higher risk of mortality. MR-proADM and D-dimer showed high sensitivity and low negative likelihood ratio regarding short-term mortality, whereas copeptin did not.

CONCLUSION

D-dimer and MR-proADM levels might be useful as prognostic markers in older patients presenting to the ED after a fall, by identifying patients at low risk of short-term mortality.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02244983.

Alerting on mortality among patients discharged from the emergency department: a machine learning model

OBJECTIVES

Physicians continuously make tough decisions when discharging patients. Alerting on poor outcomes may help in this decision. This study evaluates a machine learning model for predicting 30-day mortality in emergency department (ED) discharged patients.

METHODS

We retrospectively analysed visits of adult patients discharged from a single ED (1/2014-12/2018). Data included demographics, evaluation and treatment in the ED, and discharge diagnosis. The data comprised of both structured and free-text fields. A gradient boosting model was trained to predict mortality within 30 days of release from the ED. The model was trained on data from the years 2014-2017 and validated on data from the year 2018. In order to reduce potential end-of-life bias, a subgroup analysis was performed for non-oncological patients.

RESULTS

Overall, 363 635 ED visits of discharged patients were analysed. The 30-day mortality rate was 0.8%. A majority of the mortality cases (65.3%) had a known oncological disease. The model yielded an area under the curve (AUC) of 0.97 (95% CI 0.96 to 0.97) for predicting 30-day mortality. For a sensitivity of 84% (95% CI 0.81 to 0.86), this model had a false positive rate of 1:20. For patients without a known malignancy, the model yielded an AUC of 0.94 (95% CI 0.92 to 0.95).

CONCLUSIONS

Although not frequent, patients may die following ED discharge. Machine learning-based tools may help ED physicians identify patients at risk. An optimised decision for hospitalisation or palliative management may improve patient care and system resource allocation.