In low and middle-income countries, there is a lack of evidence-based diagnostic algorithms for early CDI diagnosis using a PCR test. In 2011, a study showed that the prevalence of in-hospital C. difficile was 10 per 10.000 inpatients in Cali, indicating a high prevalence of this infection and the need to identified better strategies for the control of C. difficile(14). Our study shows a straightforward algorithm that includes clinically relevant variables and may help clinicians in the diagnostic process of patients with suspected CDI. The CDI is an important cause of nosocomial infection, so the implementation of diagnostic strategies, such as decision trees, is an important alternative to guide physicians in making decisions.
Our CART showed that if a patient had a history of antibiotic use, the probability of a positive PCR was 45.7%. Previous studies in this matter showed almost all antibiotics can increase vulnerability to CDI, but cephalosporins, fluoroquinolones, clindamycin, and certain penicillins (e.g., amoxicillin/clavulanic acid) increase risk to the greatest extent (8,14,15). Above supports the results of this study regarding consider this exposure in the decision to perform a PCR.
Also, PPI has been associated with increased odds of CDI and recurrence(8). The 2018 meta-analysis by Oshima et al.(16) concluded that PPI use was associated with CDI in adult (OR 2.30, 95% CI 1.89-2.80; p<0.00001) and pediatric patients (OR 3.00, 95% CI 1.44-6.23; p< 0.00001), and with recurrent CDI (OR 1.73, 95% CI 1.39-2.15; p=0.02). In the CART, PCR performance in patients with a history of use of PPI was 75%. This finding suggests that in a case with a history of antibiotic and PPI use, it could be not necessary to use the PCR test for CDI diagnostic, and the physician will be a big probability to have a positive case of CDI without another diagnostic test.
From our results, we can recommend considering the PCR test when the patients have been exposed to PPI, ranitidine, and antifungal drugs. Instead, if the patient has not a history of antibiotics use, the probability of finding a negative result is higher. This model demonstrated a good capacity to classify healthy patients as healthy (specificity) and a high negative predictive value and thus can be considered as an algorithm to identify conditions that indicate when it is not necessary to perform a PCR test in a patient with symptoms of CDI.
CDI increases patient healthcare costs due to extended hospitalization, re-hospitalization, laboratory tests, and medications. A systematic review found that CDI to be a significant economic healthcare burden in their respective settings, with an increased length of stay and costs(17). That is why our model emerges as a diagnostic alternative for middle and low-income countries, which allows optimizing the indication of PCR for the diagnosis of CDI and thus reduces the economic burden of the disease in health systems.
The CART of this study has the advantage that it is easy to interpret and implement for health workers in their daily practice because the data that contain it is available by performing a routine clinical review to the inpatients. This methodology compared other statistical methods such as regression, which has a good capacity to discriminate, allows identifying directly from the tree, the interactions between the data and the probability of having a PCR with a positive or negative result. Therefore, this tree can be a tool for the decision to perform a PCR in patients with symptoms of CDI in environments where there are restrictions to perform diagnostic tests as PCR.
Limitations. Data were collected retrospectively, which can lead to an information bias due to the absence of data in some variables. The predictive capacity of the variable “history of antibiotics use” is limited by the time of exposure, because the time from the use of antibiotics to the development of the symptoms was not collected. On the other hand, data about the use of certain antibiotics were collected, but there are unidentified if they were administered before or after the PCR. The results of this study can be only generalized to patients treated in a high complexity referral hospital with similar demographics and clinical characteristics of our research. Despite these limitations, the designed algorithm can be a valuable tool in medium and low-income countries where resources and diagnostic tests are routinely limited.