Time series analysis of the in-hospital diagnostic process in suspected pulmonary embolism evaluated by computed tomography: An explorative study
Related Research units
Abstract
Purpose
This retrospective study aims to analyze the distribution of demand and the duration of the diagnostic workup of suspected pulmonary embolism (PE) using computed tomography pulmonary angiography (CTPA).
Methods
Time data from physical examination to report creation were identified for each CTPA in 2013 and 2018 at a tertiary hospital. Multivariable multinomial logistic and linear regression models were used to evaluate differences between 3 time intervals (I1: 6am-2pm, I2: 2pm-10pm, I3: 10pm-6am). A cosinor model was applied to analyze the amount of CTPA per hour.
Results
The relative demand for CTPA from the emergency room was lower in l1 compared to l2 and l3 (I1/I2: odds ratio (OR) 0.84, 95 % confidence interval (CI) 0.78−0.91; I1/I3: OR 0.80, 95 % CI 0.72−0.89; peak 4:23 pm). Requests for in-patients displayed a tendency towards I1 (I1/2: OR 1.15, 95 % CI 1.06–1.24; l1/l3: OR 1.19, 95 % CI 1.07–1.33; peak 1:54 pm). The time from CTPA request to study was shorter in I3 compared to I1 and I2 in 2013 (I1/I3: ratio 5.23, 95 % CI 3.38–8.10; I2/I3: ratio 3.50, 95 % CI 2.24–5.45) and 2018 (I1/I3: ratio 2.27, 95 % CI 1.60–3.22; I2/I3: ratio 2.11, 95 % CI 1.50–2.97). This applied similarly to fatal cases (I1/I3: ratio 2.91, 95 % CI 1.78–4.75; I2/I3: ratio 2.45, 95 % CI1.52−3.95).
Conclusions
The temporal distribution of demand for CTPA depends on the sector of patient care and the processing time differs substantially during the day. Time series analysis can reveal such coherences and may help to optimize workflows in radiology departments.
This retrospective study aims to analyze the distribution of demand and the duration of the diagnostic workup of suspected pulmonary embolism (PE) using computed tomography pulmonary angiography (CTPA).
Methods
Time data from physical examination to report creation were identified for each CTPA in 2013 and 2018 at a tertiary hospital. Multivariable multinomial logistic and linear regression models were used to evaluate differences between 3 time intervals (I1: 6am-2pm, I2: 2pm-10pm, I3: 10pm-6am). A cosinor model was applied to analyze the amount of CTPA per hour.
Results
The relative demand for CTPA from the emergency room was lower in l1 compared to l2 and l3 (I1/I2: odds ratio (OR) 0.84, 95 % confidence interval (CI) 0.78−0.91; I1/I3: OR 0.80, 95 % CI 0.72−0.89; peak 4:23 pm). Requests for in-patients displayed a tendency towards I1 (I1/2: OR 1.15, 95 % CI 1.06–1.24; l1/l3: OR 1.19, 95 % CI 1.07–1.33; peak 1:54 pm). The time from CTPA request to study was shorter in I3 compared to I1 and I2 in 2013 (I1/I3: ratio 5.23, 95 % CI 3.38–8.10; I2/I3: ratio 3.50, 95 % CI 2.24–5.45) and 2018 (I1/I3: ratio 2.27, 95 % CI 1.60–3.22; I2/I3: ratio 2.11, 95 % CI 1.50–2.97). This applied similarly to fatal cases (I1/I3: ratio 2.91, 95 % CI 1.78–4.75; I2/I3: ratio 2.45, 95 % CI1.52−3.95).
Conclusions
The temporal distribution of demand for CTPA depends on the sector of patient care and the processing time differs substantially during the day. Time series analysis can reveal such coherences and may help to optimize workflows in radiology departments.
Bibliographical data
| Original language | English |
|---|---|
| Article number | 109758 |
| ISSN | 0720-048X |
| DOIs | |
| Publication status | Published - 2021 |
