JMIR Publications

ABSTRACT

Background:

Continuous monitoring of vital signs has the potential to assist in the recognition of deterioration of patients admitted to the general ward. How toTo explore methods to summarise continuously measured vital sign data, and evaluate their association with respiratory insufficiency in COVID-19 patients at the general ward. efficiently process and use continuously measured vital sign data is still unknown.

Objective:

To explore methods to summarise continuously measured vital sign data, and evaluate their association with respiratory insufficiency in COVID-19 patients at the general ward.

Methods:

In this retrospective cohort study, we included patients admitted to a designated COVID-19 cohort ward equipped with continuous vital sign monitoring. We collected continuously measured data of respiratory rate, heart rate and oxygen saturation. For each patient, 7 methods to summarise vital sign data were calculated: mean, slope, variance, occurrence of a threshold breach, number of episodes, and total duration and area above/under a threshold. These summary measures were calculated over timeframes of either 4 or 8 hours, with a pause between the last data point and the endpoint (the ‘lead’) of 4, 2, 1 or 0 hours, and with 3 predefined thresholds per vital sign. The association between each of the summary measures and the occurrence of respiratory insufficiency was calculated using logistic regression analysis. As a post-hoc analysis, the optimal model using one summary measure per vital sign was determined.

Results:

Of the 429 patients that were monitored, 334 were included for analysis. Of these, 66 patients developed respiratory insufficiency (19.8%). Summarised continuously measured vital sign data in timeframes close to the endpoint showed stronger associations than data measured further in the past (i.e. lead 0 vs. 1, 2 or 4 hours), and summarised estimates over 4 hours of data had stronger associations than estimates over 8 hours of data. The mean was consistently strongly associated with respiratory insufficiency for the three vital signs: in a 4-hour timeframe without a lead, the standardised odds ratio (OR) for heart rate was 2.59 (99%CI 1.74-4.04), for respiratory rate 5.05 (99%CI 2.87-10.03), and for oxygen saturation 3.16 (99%CI 1.78-6.26). The strength of associations of other summary measures varied per vital sign, timeframe and lead. The optimal model consisted of the occurrence of heart rate >90/min (OR 4.82, 99%CI 1.05-28.93), the total duration of respiratory rate >24/min (OR 1.02, 99%CI 1.01-1.03), and the occurrence of oxygen saturation <90% (OR 10.04, 99%CI 2.50-52.28).

Conclusions:

The mean of a vital sign showed a strong association with respiratory insufficiency for the majority of vital signs and timeframes. The type of vital sign, length of the timeframe and length of the lead influence the strength of associations. Highly associated summary measures and combinations could be used in a clinical prediction score or algorithm for an automatic alarm system.