Introduction
Acute pancreatitis (AP) carries an overall mortality rate of 4.2%, but in cases of severe acute pancreatitis (SAP), mortality dramatically increases to over 50%. Despite the use of several scoring systems to predict SAP in the early stages of the disease, their clinical value is limited as their performance does not significantly influence decision-making due to their low post-test probability. Although early prediction upon admission would be ideal, the development of a marker that accurately predicts severity at this stage seems unlikely. Therefore, a dynamic assessment based on early identification of changes in clinical and laboratory parameters within the first 48 hours could prove valuable in detecting unfavorable outcomes.
Aims & Methods
The aim of our study was to evaluate the clinical utility of changes in routine clinical and laboratory parameters within the first 48 hours of admission in a Spanish cohort, based on likelihood ratios and post-test probability of severe acute pancreatitis (SAP). Patients admitted to our tertiary hospital with a diagnosis of acute pancreatitis were included. Demographic information, comorbidities, and the Bedside Index for Severity in Acute Pancreatitis (BISAP) score were collected upon admission, while vital signs, routine laboratory parameters typically used in acute pancreatitis, and the Systemic Inflammatory Response Syndrome (SIRS) criteria were collected upon admission and after 48 hours. Patients were followed until death or discharge and categorized into mild to moderately severe and severe acute pancreatitis groups.
Results
227 patients were included in the study. The mean age was 65.3 years (standard deviation 18.9), and 115 patients were women (50.7%). Among them, 22 (10.7%) developed severe acute pancreatitis, and 13 (5.7%) died. BISAP≥2, rising BUN, increasing heart rate over 20 beats per minute, and persistent SIRS were identified as independent risk factors for severe acute pancreatitis. The positive likelihood ratio and post-test probability of SAP are shown in Table 1. When combining the parameters independently related to SAP, we found that BISAP ≥2 combined with persistent SIRS after 48 hours and any rise in BUN combined with persistent SIRS showed the highest post-test probabilities (82% and 80%, respectively).
Predictor
| + LR (CI 95%)
| + Post-test probability of SAP
|
Rise in heart rate > 20 bpm
| 3.27 (1.44-7.43)
| 26% (13-44%)
|
Rising BUN
| 3.57 (2.52- 5.04)
| 28% (21-35%)
|
Rising Hematocrit
| 1.59 (0.75-3.36)
| 15% (8-28%)
|
BISAP ≥ 2
| 2.81 (2.16-3.66)
| 23% (19-28%)
|
SIRS on admission
| 2.80 (1.74-4.48)
| 23% (16-32%)
|
SIRS after 48h
| 12 (5.11-26)
| 56% (36-74%)
|
BISAP ≥ 2 and rising BUN
| 8.22 (4.80-14)
| 47% (34-60%)
|
BISAP ≥ 2 and SIRS after 48h
| 42 (9.62-181)
| 82% (51-95%)
|
Table 1. Positive likelihood ratio of clinical and laboratory parameters related to SAP with a pre-test probability of 10.7%.
Conclusion
Monitoring the evolution of the disease in the first hours is crucial, especially in patients who develop organ failure. For those who do not, dynamic assessment of known risk factors for SAP can be valuable in clinical practice. In our sample, the combination of BISAP and dynamic assessment of BUN, heart rate, and SIRS raised the probability of SAP up to 80%. Ideally, assessing the specific incidence of SAP in multicenter cohorts within a specific area and the impact of these and other factors that aid in monitoring patient evolution would provide valuable insights.
