Early in-hospital clinical deterioration is not predicted by severity of illness, functional status, or comorbidity

Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond

As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.

Associations between patient and system characteristics and MET review within 48 h of admission to a teaching hospital: A retrospective cohort study

The Medical Emergency Team (MET) has enhanced the recognition and response to clinical deterioration in acute healthcare. However, patients reviewed by the MET are at increased risk of in-hospital death. Identifying patients at risk of deterioration may improve patient outcomes. AIM: To identify patient demographic, medical characteristics and healthcare systems and processes at the time of admission (baseline), associated with Medical Emergency Team (MET) review within 48 h (MET-48 h) of admission. METHODS: Single-site, year-long, retrospective cohort comprising patients admitted for at least 24 h, using routinely collected hospital data. A three-stage modelling approach was used to identify baseline factors associated with MET-48 h RESULTS: The study included 15,695 patients with mean age 62.1 years (SD 19.6), male (53.5%), born in Australia or New Zealand (60.9%) and 51.6% held a low-income concession card. A total of 4.3% of patients received a MET review within 48 h of admission. Variables independently associated with MET-48 h in a fully adjusted logistic model included age of 80 years or more (OR = 1.37); ≥3 previous emergency admissions (OR = 1.59); Charlson Comorbidity Index 1 or 2 (OR = 1.47), or ≥ 3 (OR = 1.99); history of alcohol-related behaviour concerns (OR = 2.04), chronic heart failure (OR = 1.48); chronic obstructive pulmonary disease (OR = 1.35); admission for colorectal (OR = 2.66) or upper gastro-intestinal (OR = 1.94) surgery, respiratory or tracheostomy (OR = 2.24); immunology and infections (OR = 1.90); emergency admission (OR = 1.36); admission at night (OR = 1.74), or summer (OR = 1.41) CONCLUSIONS: This is the first study to demonstrate the potential to predict clinical deterioration using data that is readily accessible at the time of admission to hospital.

The value of vital sign trends in predicting and monitoring clinical deterioration: A systematic review

BACKGROUND

Vital signs, i.e. respiratory rate, oxygen saturation, pulse, blood pressure and temperature, are regarded as an essential part of monitoring hospitalized patients. Changes in vital signs prior to clinical deterioration are well documented and early detection of preventable outcomes is key to timely intervention. Despite their role in clinical practice, how to best monitor and interpret them is still unclear.

OBJECTIVE

To evaluate the ability of vital sign trends to predict clinical deterioration in patients hospitalized with acute illness.

DATA SOURCES

PubMed, Embase, Cochrane Library and CINAHL were searched in December 2017.

STUDY SELECTION

Studies examining intermittently monitored vital sign trends in acutely ill adult patients on hospital wards and in emergency departments. Outcomes representing clinical deterioration were of interest.

DATA EXTRACTION

Performed separately by two authors using a preformed extraction sheet.

RESULTS

Of 7,366 references screened, only two were eligible for inclusion. Both were retrospective cohort studies without controls. One examined the accuracy of different vital sign trend models using discrete-time survival analysis in 269,999 admissions. One included 44,531 medical admissions examining trend in Vitalpac Early Warning Score weighted vital signs. They stated that vital sign trends increased detection of clinical deterioration. Critical appraisal was performed using evaluation tools. The studies had moderate risk of bias, and a low certainty of evidence. Additionally, four studies examining trends in early warning scores, otherwise eligible for inclusion, were evaluated.

CONCLUSIONS

This review illustrates a lack of research in intermittently monitored vital sign trends. The included studies, although heterogeneous and imprecise, indicates an added value of trend analysis. This highlights the need for well-controlled trials to thoroughly assess the research question.