In this prospective observational study, we identified parameters associated with safe, early discharge, among patients presenting to the ED with an infection, by analyzing patient demographics, various clinical scoring systems and vital sign progression during ED stay. Of the 1381 patients included in the analysis, 354 (25.6%), with a median LOS of 3 hours, met the safe, early discharge criteria. Parameters associated with safe, early discharge were younger age, absence of comorbidities, living independently, yellow or green triage color, no arrival by ambulance, no referral by general practitioner, absence of kidney and respiratory failure and low clinical impression, NEWS, SIRS, MEDS, (q)SOFA and PIRO scores at ED admission. Moreover, staying normal of vital signs measured at ED admission and discharge had a stronger association with safe, early discharge than normalization of vital parameters. Of all measured parameters, nine were selected to create a prediction model. This model consists of age, a history of organ transplant, referral by GP and arrival by ambulance, clinical impression score and kidney failure at ED arrival, staying normal of the qSOFA and temperature at ED arrival and discharge and absence of sepsis at ED discharge. The proposed cut-off of 58% for safe, early discharge probability yielded a PPV of 73.4%. Internal validation of the created model resulted in a minimal drop in performance.
Scores predicting safe, early discharge are scarce
To the best of our knowledge, this is the first study that analyzes predictors of safe, early discharge among patients presenting to the ED with an infection. Several studies have focused on predicting early discharge among specific patient populations [35, 36, 53–58] and multiple have proposed prediction models. [36, 55–58] Only two, however, based their prediction model on ED patients. [57, 58] The first included 894 general ED patients and showed an AUC of 0.84 for the prediction of discharge within 48 hours.  The second included 297 general ED patients and demonstrated an AUC of 0.68 for the prediction of discharge within 72 hours.  Neither, however, analyzed the safety of these early discharges. One large prospective observational study did analyze safe, early discharges using a score called Halm’s criteria.  This study, however, is based on patients admitted to the ward with pneumonia and the score they used has not been validated in the ED. Multiple studies analyzed ED discharge safety, using short-term outcomes, like readmission or death, following ED discharge. [59–62] Two of these studies proposed a model for the prediction of safe ED discharge, with AUCs ranging from 0.68 to 0.83. [60, 61]
Both the models predicting early discharge and the models predicting safe ED discharge include patient characteristics, like age and the presence of comorbidities, and most include arrival by ambulance, GP referral and vital signs measured closest to discharge. [36, 57, 58, 60, 61] However, only one incorporated repeated vital sign measurements , and none included biomarkers for organ dysfunction, components of various clinical scoring systems or the clinical impression score. The studies that did analyze the safety of ED discharges used endpoints that differ from study to study, with some utilizing ED readmission [61, 62] and others ward, ICU admission or death [59, 60]. In summary, available studies either analyzed predictors for early discharge or predictors for safe discharge. They utilized varying definitions for unsafe discharge and limited predictor variables. Our study created a prediction model for safe, early discharge among patients presenting to the ED with an infection and included a wide variety of readily available predictor variables.
Repeated vital sign measurements should be used when evaluating ED patients
We found that normal vital signs at ED admission in combination with normal vital signs at ED discharge is associated with safe, early discharge. However, normalization of vital signs generally did not predict safe, early discharge. Multiple studies have analyzed the value of repeated vital sign measurements on patient outcome. Available studies either analyzed individual vital sign measurements  or combined these measurements in scores like the qSOFA or MEWS [11, 35, 64–66]. Three studies compared single with repeated vital sign or score measurements and demonstrated that repeated measurements are superior in predicting clinical course of infectious or septic patients in the ED. [11, 64, 65] Several studies conclude that early changes in vital signs or scores are associated with patient outcomes like mortality or ICU admission. [11, 35, 63–66] These studies show that, compared to patients with deteriorating vital signs, patients with vital sign normalization had a lower risk of mortality. Only one, however, analyzed repeated determination of vital signs in order to predict safe, early discharge.  This prospective observational study used Halm’s criteria, combining normalization and staying normal of vital signs, and showed an AUC of 0.95 for 30-day mortality. Our study shows that patients with normal vital signs upon ED admission and discharge were more likely to be safely discharged compared to those who were admitted with abnormal vital signs which normalized during ED stay. Therefore, patients whose vital signs stayed normal during their ED visit have a better prognosis compared to patients with abnormal vital signs at either ED admission or discharge. Repeated vital sign measurements should therefore be included for risk assessment of ED patients for safe and early discharge.
Clinical impression scores have a wide array of use in the ED
Our study shows that both the clinical impression score of the attending physician and the nurse are strong independent predictors of safe, early discharge. This relationship may not be completely independent, since the attending physician eventually decides if the patient is discharged or admitted. This dependency could have introduced a selection bias, causing overestimation of the performance of the CIS. This bias, however, may be limited due to the fact that the CIS measurement is performed directly after primary assessment of the patient (generally within 30 minutes of the patient arriving), while the median length of stay of our safe, early discharge group was 3 hours. Moreover, the CIS of the nurse, which was independently measured, shows good predictive value as well. And, if we take a look at the arrival mode, since not arriving by ambulance while not being referred by a GP was the strongest independent predictor of safe, early discharge, we speculate that the clinical impression of the GP has also found its way into our prediction model. Several studies have demonstrated the value of the CIS in predicting 28-day mortality, hospital survival and patient disposition in ED and ICU patients. [17, 20, 67, 68] When compared to the PIRO score, clinical impression scores show similar prognostic performance.  Clinical impression scores, as opposed to the PIRO, are easily determined, show good prognostic performance and could therefore be of great value for a wide variety of uses in the ED. Further research in our department will focus on identifying the various aspects of the CIS and its possible causes of bias.
Strengths and limitations
Our study is the first study to analyze predictors of, and create a model for, safe, early discharge in patients presenting to the ED with an infection. This model was created using a ‘simple’ logistic regression analysis and internally validated with stratified k-fold cross-validation. We did not only utilize patient demographics and comorbidities, but also included biochemical organ function, components of individual scoring systems, vital sign and score progression. The parameters used to evaluate biochemical organ function are routinely determined in almost every patient presenting to the ED with an infection. Vital sign measurements are part of the regular ED check-up and thus do not cause an extra burden for the patient. The created score can therefore effortlessly be determined for every patient presenting to the ED with an infection.
Our study has several limitations: first, the generalizability of our study may be limited due to the fact that our study was single center and carried out in an academic tertiary care teaching hospital. Second, given the fact that the CIS, our strongest predictor, is not measured in other hospitals in the Netherlands, the created prediction model was not validated in external patient cohorts. We did, however, validate our model with stratified k-fold cross-validation, by many viewed as the best method for internal model validation.  Third, to analyze the safety of the discharges, we arbitrarily chose a 7-day interval in which readmissions or death could occur. Because of this, and the fact that our patient records do not account for readmissions to other hospitals, the number of patients that were safely discharged may be overestimated. However, most of the studies that analyze the safety of ED discharges use this interval. [59–62] Also, in the Netherlands, if a patient is known in a tertiary care center, like ours, readmission to another hospital is very unlikely due to the complexity of their condition.
It should be noted that our chosen cut-off only reached a PPV of 73.4%, resulting in 31 false positives. However, as shown in Supplementary table 6, prolonged hospitalization accounted for 83.5% of the false positives and none of these patients died within 7 days of hospital discharge. Moreover, half of the patients with a false positive score were admitted on either a Thursday or a Friday. Given the fact that patients are less frequently discharged on weekend days , patients admitted on Thursdays and Fridays are more likely to be hospitalized for a prolonged timeframe. We therefore speculate that these false positives are in fact patients which would benefit from an early discharge.
By choosing a cut-off point with near maximum specificity, the number of false positives were minimized and the number of unsafe discharges resulting in death zero. Our created model is therefore ideal to identify low risk patients likely to benefit from safe, early discharge. In our study population, this resulted in 84 true positives, i.e. patients that could be safely discharged within 24 hours without disease related death or readmission within 7 days. (Supplementary table 5) Meaning that our model can be effectively used as a clinical decision tool to identify a relatively small number of patients that can benefit from safe, early discharge. With the use of our model, the effects of ED overcrowding can be minimized and the costs and adverse events associated with a general ward admission could be prevented. The choice of a cut-off for prospective research should be considered with care, weighing the chance and impact of a false positive score against the benefits of early discharge. Our score, like every scoring system used in medicine, should be used as a tool to guide the treating physician. The created probability of safe, early discharge may augment the physicians’ decision but should not replace the physicians’ evaluation of the individual patient.