The most important finding of our study was that the timely escalation of antibiotics after the PCT reached an “alert” level was beneficial for those in the nPIS group, which contradicts previous conclusions. In addition, this is the first real-world PCT research in China.
PCT has been widely recognized as an indicator of bacterial infection for a long time, and many high-quality studies have demonstrated its significance in guiding antibiotic applications. Most studies focused on when to initiate or stop antimicrobial drugs in patients with infection. However, in the real-world clinical environment, physicians tend to be more cautious, especially regarding critically ill patients or patients at high risk for drug resistance.
Andreas Hohn et al.[5] reported low adherence to a PCT-guided antibiotic treatment protocol in real-world clinical situations involving high-risk sepsis patients if they had insufficiently decreasing or even increasing PCT. This result might reflect the significance of PCT in the real world, which is why we performed this study in a real-world clinical environment, unlike previous studies[9]; we found that the timely escalation of antibiotics when the PCT level does not decrease as expected is a negative factor for PISs (28-day ICU-free days ≤ 20 days). This important finding leads us to hypothesize that a “PCT alert” might remind medical staff of the high possibility of multidrug resistant germs and that escalating drug grades might improve the outcomes of patients.
Jensen et al.[9] proved that antimicrobial spectrum escalation guided by PCT prolonged the patients’ ICU stays and increased the possibility of organ-related harm. However, there was an important inherent design flaw in this study; the physicians were not double-blinded to the PCT values, which may have led to bias because physicians tend to be “overcautious” if they are alerted to an abnormal PCT level. This means that physicians’ decisions regarding transfer or extubation may have been unfairly influenced in the “PCT intervention” group[15].
In our study, PCT tests were performed, and the results were informed, which eliminated bias. To rectify the influence of a patients’ baseline condition, we included their baseline information (sex; age; surgery; and the SOFA score, PCT level, Tmax, lactate level, and the P/F ratio on the 1st day after ICU admission) in the binary logistic regression model. The two designs and data analysis methods made the results more reliable.
In the real world, doctors tend to be more scrupulous in adjusting antibacterial drugs, especially for patients in the ICU. It is not only because of the illness but also because of the increased possibility of bacterial drug resistance in ICUs. Peking Union Medical College Hospital is a very renowned hospital in China, with the responsibility of treating complicated and serious diseases, especially those requiring ICU admission. Many of our patients are transferred from other hospitals and have long-term antimicrobial application histories. Therefore, the proportion of multidrug resistant bacteria is increased. Understandably, our physicians tend to escalate antibiotics when patients with infection do not present obvious recovery or present characteristics such as a “PCT alert” after the application of broad-spectrum antibiotics. However, it should be noted that our conclusion might be biased because it is suited to only a certain group of patients. Applying this conclusion practically and scientifically requires further investigation. Despite the bias, a “PCT alert” is still important for informing doctors of uncontrolled infection and the potential necessity of antimicrobial escalation.
In addition to the above findings, we also found that surgical patients tended to have longer ICU stays than patients without surgery, but PIS patients had a lower proportion of surgery than nPIS patients. Our department is a surgical ICU ward, and patients admitted to our department for surgery usually have less severe health conditions than patients who are admitted for nonsurgical reasons. Nonsurgical patients tend to stay longer in the ICU than surgical patients. Surgical site infections (SSIs) account for a large proportion of healthcare-associated infections, which means that surgery could be a risk factor for infection[16–18]. Surgical patients who are transferred to the ICU are more likely to have nosocomial infections than surgical patients in non-ICU wards[19]. All of the above factors might influence patients’ ICU stays. Therefore, we analyzed the influential variables by building a binary logistic regression model and found that surgery was an independent risk factor for delayed ICU discharge.
Our study is the first to focus on escalating antimicrobials in a real-world environment, and the results are very novel. To determine what to do in the face of a “PCT alert”, we analyzed only the indicators on the alert day along with baseline and demographic characteristics. There were some limitations to our study. First, it was a single-center retrospective study, and considering our hospital’s specificity, our conclusion on escalating antibiotics according to a “PCT alert” might be restricted to serious patients receiving long-term treatment or hospitals with a high proportion of multi-resistant bacteria. Despite this possible limitation, an unexpected decrease in the PCT level should be regarded as a warning signal for doctors. Second, the patients were very heterogeneous. To reflect real clinical conditions, we did not establish very strict inclusion criteria. This may have led to some deviations and counteracted some of the significance. Therefore, it would be better to further divide the groups into subgroups to analyze the data in depth. However, because of the limited sample size, the number of patients in some of the subgroups would have been very small, affecting the analysis. We should collect more data to perform subgroup analyses in the future. Third, the definition of nPIS and PIS may have been a too arbitrary. However, for most ICU patients, an ICU stay longer than 7 days increases the possibility of complications and indicates poor outcomes. Therefore, we used “28-day ICU-free days > 20” as the cutoff value to distinguish patients with relatively short ICU-free days and thus worse prognoses. This value might be slightly increased for all ICU patients because this particular group of patients accounted for less than 30% of the patients in our department. Fourth, despite our large total sample size, as a retrospective study, missing data was inevitable.