Healthcare processes of laboratory tests for the prediction of mortality in the intensive care unit: a retrospective study based on electronic healthcare records in the USA

Abstract

ObjectivesHealthcare process carries important prognostic information for patients, but the healthcare processes of laboratory tests have not yet been investigated for patients in the intensive care unit (ICU). The study aimed to investigate the effect of healthcare processes of laboratory tests on hospital mortality, with the hypothesis that the addition of healthcare processes could improve the discrimination for mortality outcome.DesignThe study included 12 laboratory tests. There were two dimensions for each laboratory test. One was the pathophysiology value; and the other was the healthcare process variables including the clock hour, the number of measurements and the measurement time from ICU admission. Generalised additive model was employed to investigate the effect of continuous variables on mortality. Generalised linear models with and without healthcare process variables were compared for their discrimination power.SettingICUs in an US-based hospital.ParticipantsAdult patients included in the critical care big data Medical Information Mart for Intensive Care.Primary and secondary outcome measuresThe hospital mortality was the primary outcome.ResultsA total of 52 963 adult patients with complete ICU stay information were included for analysis. The mortality rate was 12.3%. Lower number of tests such as 1–3 times were associated with the lowest mortality for most laboratory tests. However, the hematocrit, glucose and potassium required 6–10 measurements for the first 24 hours to reach the lowest mortality rate. In n of the 12 prediction models involving laboratory tests, the addition of healthcare process variables was associated with significantly increased area under receiver operating characteristics.ConclusionsThe study showed that healthcare processes of laboratory tests were independently associated with hospital mortality. The addition of healthcare processes to the pathophysiology value could increase the discrimination for mortality outcome.

Identification of risk factors for mortality associated with COVID-19

“…Missing values per se may carry important predictive information. For example, our previous studies have shown that missing values on blood gas in hospitalized patients have better clinical outcomes (Zhang et al, 2019). In the study, we assumed that the missing values were produced at random, and imputation with random sampling was performed.…”

Informative presence and observation in routine health data: A review of methodology for clinical risk prediction

“… 15 , 24 , 31–44 Examples include a count of the number of measurements (eg, throughout a critical care admission), 37 weighted counts, 42 combined missing indicators, 31 missingness rates over time, 32 time intervals between measures, 33–35 embedding vectors that represent missing values, 36 or information relating to hospital processes. 38 , 39 …”

“…The association between a chosen summary measure and the outcome might lack generalizability where measurement processes vary across locations. 23 , 39 Simple summary measures such as counts may also fail to capture the complex relationship between the observation process and outcome.…”

Peer Review #1 of "Identification of risk factors for mortality associated with COVID-19 (v0.2)"

“…Missing values per se may carry important predictive information. For example, our previous studies have shown that missing values on blood gas in hospitalized patients have better clinical outcomes (Zhang et al, 2019). In the study, we assumed that the missing values were produced at random, and PeerJ reviewing PDF | (2020:05:49272:2:0:NEW 10 Aug 2020)…”