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A prediction model for real-time PCR results in blood samples from febrile patients with suspected sepsis
Christian Leli1, Angela Cardaccia1, Francesco D'Alò1
1Microbiology Section, Department of Experimental Medicine and Biochemical Sciences, University of Perugia, Perugia, Italy.
Abstract:
Sepsis, a systemic, deleterious host response to infection that leads to organ dysfunction, is a potentially deadly condition needing prompt identification of the causative organisms and early appropriate antimicrobial therapy. Among non-culture-based diagnostic methods, SeptiFast (SF) can be employed to speed bacterial and fungal DNA detection, but it suffers from poor sensitivity and high cost. The aim of the present study, performed in 285 febrile patients, was to develop a prediction model to restrict the SF assay to clinical cases with a high probability of positive SF results. The prevalence of SF results positive for a pathogen was 17.2 %. Independent predictors of positive results were: blood sampling within 12 h after the onset of fever [odds ratio (OR) 20.03; 95 % confidence interval (CI) 6.87-58.38; P<0.0001]; ≥0.5 ng serum procalcitonin (PCT) ml(-1) (OR 18.52; 95 % CI 5.12-67.02; P<0.0001); body temperature ≥38 °C (OR 3.78; 95 % CI 1.39-10.25; P = 0.009); ≤3 g serum albumin dl(-1) (OR 3.40; 95 % CI 1.27-9.08; P = 0.014); and ≥13 000 white blood cells mm(-3) (OR 2.75; 95 % CI 1.09-7.69; P = 0.05). The model showed good calibration (Hosmer-Lemeshow chi-squared 1.61; P = 0.978). Area under the receiving operating characteristic curve was 0.944 (95 % CI 0.914-0.973; P<0.0001). These results suggest that a prediction model based on PCT and a few other routinely available laboratory and clinical variables could be of help in selecting patients with a high probability of SF-positive results.
Insights
This study developed a prediction model to identify sepsis patients likely to have a positive SeptiFast (SF) test. The model uses procalcitonin (PCT) and other clinical factors to improve diagnostic efficiency for sepsis.
Area of Science:
- Clinical Microbiology
- Infectious Diseases
- Diagnostic Medicine
Background:
- Sepsis is a life-threatening condition requiring rapid diagnosis and treatment.
- SeptiFast (SF) offers rapid DNA detection but has limitations in sensitivity and cost.
- Efficient patient selection for SF testing is crucial.
Purpose of the Study:
- To develop a predictive model for identifying febrile patients with a high probability of positive SeptiFast (SF) results.
- To optimize the use of the SF assay by restricting it to clinically relevant cases.
- To improve the diagnostic workflow for sepsis.
Main Methods:
- A prediction model was developed using data from 285 febrile patients.
- Key predictors identified included time to blood sampling, serum procalcitonin (PCT), body temperature, serum albumin, and white blood cell count.
- Model performance was evaluated using calibration and area under the receiver operating characteristic curve (AUC).
Main Results:
- The prevalence of positive SF results was 17.2%.
- Independent predictors of positive SF results were identified: early blood sampling (<12h), elevated PCT (≥0.5 ng/ml), fever (≥38°C), low albumin (≤3 g/dl), and high WBC count (≥13,000/mm³).
- The prediction model demonstrated good calibration and a high AUC (0.944), indicating strong predictive accuracy.
Conclusions:
- A prediction model incorporating PCT and routine clinical/laboratory variables can effectively identify patients likely to have positive SF results.
- This model can aid in selecting appropriate candidates for the SF assay, potentially reducing costs and improving diagnostic yield.
- The findings support the use of this model to enhance the efficiency of sepsis pathogen detection.
