Study design
The study was conducted as a retrospective register study, including all visits to the ED of a 420-bed emergency hospital in southern Sweden between 1 January 2011 and 31 December 2012, not resulting in admission, death, or transfer to another hospital. In order to avoid selection bias, no further selection was made.
Setting
The ED of Helsingborg Hospital serves a population of around 250,000. Due to tourism, the population expands to nearly 300,000 during summer. It is one of the four emergency hospitals in the region of Skåne in southern Sweden. The annual ED census is around 60,000, with approximately 15% of patients arriving by ambulance. Patients are registered in the information system Patientliggaren® by a secretary upon arrival. Patients who arrive by ambulance or are referred by a physician gain access to the ED directly after registration. Other patients gain access to the ED in accordance with predefined guidelines, or are further evaluated by a nurse in primary triage. Patients could be referred elsewhere from primary triage (e.g., to primary care). Patients who gain access to the ED undergo secondary triage, which is performed by a nurse. The following is controlled upon secondary triage: Airway, respiratory rate, and SpO
2 (pulse oximetry), heart rate, and blood pressure (non-invasive), alertness (Reaction Level Scale (RLS)), and body temperature. The four-level triage system ‘Medical emergency triage and treatment system’ (METTS) was used for secondary triage during the study period. The triage priority is registered in Patientliggaren® directly after the secondary triage. Only physicians may down-prioritize patients (Table
1).
Table 1
Overview of the four category triage system METTS
| Airway obstruction | | | |
| Stridor | | | |
Oxygen saturation | SpO2 < 90% with oxygen supply | SpO2 < 90% without oxygen supply | SpO2 90% to 95% without oxygen supply | SpO2 > 95% without oxygen supply |
Respiratory rate (breaths/min) | >30 or <8 | >25 | | 8 to 25 |
Pulse (beats/min) | Regular >130 or irregular >150 | >120 or <40 | >110 or <50 | 50 to 110 |
Systolic bp (mmHg) | <90 | | | |
Consciousness | Unconscious | RLS 2 to 3/somnolence | Disoriented | Alert |
| Seizures | Temperature >41°C or <35°C | Temperature >38.5°C | Temperature 35°C to 38.5°C |
Patients are directed to separate units for Surgery, Orthopedics, Medicine, and Otolaryngology in a triage-to-specialty model after the secondary triage. A complementary unit staffed by emergency physicians capable of handling various complaints except for psychiatric, otolaryngologic, ophthalmologic, and pediatric (medicine) complaints was introduced in 2010 and operates from 8 am to 11 pm daily. There are separate EDs for children with medical conditions (<18 years of age) and for patients with obstetric/gynecologic, psychiatric, or ophthalmologic complaints. Visits to these EDs were not included in the study. Patients with suspected hip fractures or ST elevation myocardial infarction diagnosed in the ambulance bypass the ED in fast tracks and were not included either. Hand surgery, neurosurgery, and thoracic surgery are not available in the hospital. The availability of endovascular surgery and percutaneous coronary intervention is limited from 5 pm to 08 am. Patients with such needs are referred to Skåne University Hospital and were not included in the study.
Physical ED records for patients who are advised to revisit the ED are stored at each specialty desk. Nurses indicate whether a visit is a planned revisit in Patientliggaren® upon patient arrival. Swedish national reimbursement systems are tied to a goal of 80% of visits with ED LOS ≤4 h. At in-hospital bed occupancy close to 100%, the hospital utilizes full-capacity protocols.
Statistics
Occupancy was defined as the overall proportion of occupied beds in the hospital at whole-hour intervals. All patients registered in Patientliggaren® during an interval were assigned the same occupancy.
The proportion of visits resulting in an unplanned 72-h revisit was computed for in-hospital occupancy levels of <85%, 85% to 90%, 90% to 95%, 95% to 100%, 100% to 105%, and ≥105%. Subgroup analysis was performed for each specialty unit. Computations were repeated for unplanned 72-h revisits resulting in admission.
Adjusted analysis was performed in an attempt to take confounding factors into account, using binary logistic regression models. Perceived clinical significance governed the decision of screened predictors but was inevitably tainted by data availability. Screened variables were the following: specialty unit, presenting complaint at index, referral status at index, triage priority at index, age group, sex, index presentation on an intense shift, index presentation on a night shift and during weekends, leaving without being seen (LWBS) at index, entering ED via primary triage at index, time to physician at index, and in-hospital occupancy at index. The variable indicating presentation on an intense shift was constructed as a dichotomous variable indicating presentation on one of the 25% of shifts subject to most visits (adjusted for shift type and unit). Night shift was set from 12:00 mn to 08:00 am. Presenting complaint was constructed as a nominal variable indicating the ten most common complaints, using the remainder as reference.
The medicine unit was used as reference among the specialty units. Age was grouped into intervals 0 to 18 years, 18 to 40 years, 40 to 65 years, and ≥65 years. Age ≥65 years was used for reference. Youths in Sweden become of age at 18 and pension age is 65 years.
For the multivariate models, in-hospital occupancy was categorized as <85%, 85% to 90%, 90% to 95%, 95% to 100%, 100% to 105%, and ≥105%. The reference interval was set to <85%. Sensitivity analysis was performed using occupancy <95% as reference.
Predictors were tested for crude association with the outcome before entering the preliminary primary effects model. Associations weaker than
P = 0.25, but of clinical importance were still included [[
18]]. Multicollinearity testing was performed using Spearman correlation [[
19]]. Selection of interaction terms screened for inclusion in the final models was governed by perceived clinical significance and made
a priori to analysis. Variables were manually added to the models, rather than stepwise [[
18]]. Missing data was indicated by a separate category and included in the models [[
20]].
Model fit was evaluated through Nagelkerke’s
R2. The association between each predictor and the outcome was addressed by the −2LL and the Wald statistics. The final models were the models with the highest explanatory value and the fewest number of predictors [[
19]]. Additionally, the models were screened for influential cases by addressing standardized residuals and Cook’s distance.
Statistical analyses were performed in IBM® SPSS® Statistics 19. Data was anonymized before analysis. The Regional Ethical Review Board in Lund granted ethical approval for the study.