The fifth vital sign? Nurse worry predicts inpatient deterioration within 24 hours

Abstract

Introduction Identification of hospitalized patients with suddenly unfavorable clinical course remains challenging. Models using objective data elements from the electronic health record may miss important sources of information available to nurses. Methods We recorded nurses’ perception of patient potential for deterioration in 2 medical and 2 surgical adult hospital units using a 5-point score at the start of the shift (the Worry Factor [WF]), and any time a change or an increase was noted by the nurse. Cases were evaluated by three reviewers. Intensive care unit (ICU) transfers were also tracked. Results 31 159 patient-shifts were recorded for 3185 unique patients during 3551 hospitalizations, with 169 total outcome events. Out of 492 potential deterioration events identified, 380 (77%) were confirmed by reviewers as true deterioration events. Likelihood ratios for ICU transfer were 17.8 (15.2–20.9) in the 24 hours following a WF > 2, and 40.4 (27.1–60.1) following a WF > 3. Accuracy rates were significantly higher in nurses with over a year of experience (68% vs 79%, P = 0.04). The area under the receiver operator characteristic curve (AUROC) was 0.92 for the prediction of ICU transfer within 24 hours. Discussion This is a higher accuracy than most published early warning scores. Conclusion Nurses’ pattern recognition and sense of worry can provide important information for the detection of acute physiological deterioration and should be included in the electronic medical record.

Patterns of behaviour in nursing staff actioning the afferent limb of the rapid response system (RRS): A focused ethnography

“…Arguably, it also denies the RN a further opportunity to interact with the patient and capture additional clinical information (Cardona‐Morrell et al., 2015). In the context of patient deterioration, there is evidence that “nurse worry” is important in predicting adverse patient outcomes (Douw, Huisman‐de Waal, van Zanten, van der Hoeven, & Schoonhoven, 2016; Romero‐Brufau et al., 2019). While “nurse worry” has been linked to tacit knowledge, it may also arise from a more comprehensive assessment and the collection of additional clinical cues (e.g., patient appearing agitated or skin clammy to touch) (Douw et al., 2015).…”

Dynamic individual vital sign trajectory early warning score (DyniEWS) versus snapshot national early warning score (NEWS) for predicting postoperative deterioration

“…A very weak correlation between frequency of ‘records in the previous 6 h’ and ‘time out of ICU’ (0.092) provides circumstantial evidence for the inclusion of nursing concern in future EWS models. 23 , 24 …”

“…A very weak correlation between frequency of 'records in the previous 6 h' and 'time out of ICU' (0.092) provides circumstantial evidence for the inclusion of nursing concern in future EWS models. 23,24 Dynamic individual patient trajectory prediction is an advanced, highly interpretable and computationally efficient statistical method. Instead of leaving one centre out for external validation, we believe that it is essential to develop the model using data from all available centres to better capture patient heterogeneity and case-mix, maximise the use of data and reduce optimism in the final model's predictive performance.…”

Nurses’ intuitive worrying predicts physiological changes in patients’ vital signs

“…Romero-Brufau et al 5 conducted 10 focus groups with 150 staff from two medical and two surgical units to identify a ‘worry factor’ measurement developed on a 5-point Likert scale (0–4). Scores ranged from no concern (0) up to extreme concern (4).…”