Introduction:

a time series is the product of the observation of a variable in time. It is a mathematical tool frequently applied in health. No temporal models have been developed to predict patients’ behavior during their staying in the Intensive Care Unit.

Objectives:

to create a time series allowing to predict the behavior of seriously-ill patients due to COVID-19, during their staying in the Intensive Care Unit in the region of Lombardy, Italy.

Materials and methods:

analytic, longitudinal prospective study with a group of critical patients who were admitted from April 1st to May 1st, with COVID-19 diagnosis, to Ospedale Maggiore di Crema, in the Lombardy region, Italy. The universe was formed by 28 patients and all of them were worked on.

Results:

48% of patients were male. Average age: 83 years; Time series: Model 1 holding PO2/FiO2 p = 0.251; Model 2 (ARIMA) SatO2/FiO2 p = 0.674 (in the two first models the result increased with the days, following a predictable behavior=; Model 3 (ARIMA) p = 0.406 (in this case the expected result decreased as time passed). The obtained functions allow to calculate the expected value according to the day from the admission.

Conclusions:

predicting patient's evolution in the Intensive Care Unit allowed early detecting those with unexpected curves and targeting more aggressive therapies toward them.

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