60 (95.2%) of the 63 patients ultimately diagnose with CMV esophagitis gave positive results for IHC. Of 63 patients with confirmed CMV esophagitis, 45 (71%) underwent CMV antigenemia tests. Of these 45 patients, 34 (76%) showed positive CMV antigenemia. Of 63 patients with confirmed CMV esophagitis, 46 (73%) and 12 (19%) underwent tissue CMV PCR tests and blood CMV PCR tests, respectively. Of the 46 patients who underwent tissue CMV PCR tests, 41 (91%) revealed positive tissue CMV PCR results. Of the 12 patients who underwent tissue CMV PCR tests, 11 (92%) revealed positive blood CMV PCR results. Before definitive diagnosis, 6 patients (7.1%) ultimately diagnosed with HSV esophagitis and 37 (58.7%) with CMV esophagitis received empirical antiviral therapy, and the remaining patients were treated conservatively. After the pathological diagnosis, 47.1% of the patients ultimately diagnosed with HSV esophagitis received acyclovir, and 81.0% of those with CMV esophagitis received ganciclovir.
3.2 Clinical experience of endoscopy and diagnostic accuracy
Eight experienced endoscopists and 5 less-experienced ones were asked to make a set of diagnoses of esophagitis based on endoscopic pictures. Their average diagnostic accuracy was 74.7% for the experienced endoscopists, and 74.3% for the less-experienced endoscopists (Table 2). Thus, the accuracy of diagnosis of viral esophagitis did not differ between the experienced and less-experienced endoscopists (P = .935) (Table 2).
3.3 Endoscopic features of esophagitis
The typical endoscopic findings for HSV esophagitis and CMV esophagitis are shown in Figures 2 and 3, respectively. Endoscopic findings of discrete ulcers, bullae or vesicles, pseudomembranes, and shouldered margins were significantly more common in patients ultimately diagnosed with HSV esophagitis than in those ultimately diagnosed with CMV esophagitis (Table 3). In addition, coalescent features and geographic ulcers were more frequent in HSV esophagitis. In contrast, deep or punched-out ulcers, serpiginous ulcers, healing ulcers, ulcers with an uneven base or yellowish exudate, and with circumferential involvement, were significantly more common in CMV esophagitis than in HSV esophagitis.
3.4 Development of predictive models for differentiating between HSV and CMV esophagitis
To develop a predictive model, candidate scoring components were selected from the list of variables differentiating CMV esophagitis from HSV esophagitis identified by logistic regression analysis. In addition, the endoscopic features were classified into four categories as follows: category 1, discrete ulcers or ulcers with vesicles, bullae, or pseudomembranes, category 2, coalescent or geographic ulcers, category 3, ulcers with an uneven base, friability, or a circumferential distribution, category 4, punched-out, serpiginous, or healing ulcers with yellowish exudates. In addition, previous history of transplantation was included in the model as a discriminating clinical feature. Using the above categories and the single clinical variable, β-coefficients were calculated by logistic regression analysis, and each component of the predictive model was scored from −3 to +2 (Supplemental Table 1, http://links.lww.com/MD/D15).
When the sum of the five scores was used, the optimal cutoff score was −0.5, where a positive score favors CMV esophagitis (Supplemental Figure 1, http://links.lww.com/MD/D15). However, we chose a cut-off of 0 for clinical convenience because it is more intuitive and easy for distinguishing HSV esophagitis from CMV esophagitis without sacrificing sensitivity. A ROC analysis of the scoring system revealed good discriminatory power, with an area under the ROC curve of 0.967 (Supplemental Fig. 2, http://links.lww.com/MD/D15). Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 96.8%, 89.4%, 92.6%, 87.3%, and 97.5% respectively (Supplemental Table 2, http://links.lww.com/MD/D15).
Clinical suspicion and precise diagnosis of esophagitis are important for the correct timing of treatment and for avoiding administering an inappropriate antiviral agent. However, making a presumptive diagnosis of CMV vs HSV esophagitis based on the endoscopic findings alone is challenging because many of their endoscopic features overlap. In fact, we found that about a quarter of the patients who were presumptively diagnosed as HSV or CMV esophagitis based on the endoscopic findings were incorrectly assigned regardless of the endoscopists’ expertise. It means that diagnosis of CMV or HSV esophagitis on the endoscopic findings alone might be insufficient because underlying disease is substantially overlapping between two diseases and some findings are can be seen both HSV esophagitis and CMV esophagitis. To help endoscopists’ tentative gross findings and guide appropriate empirical antiviral therapy until definitive diagnostic results are available, the clinical characteristics and endoscopic features of esophagitis were analyzed, and a predictive model for differentiating CMV esophagitis from HSV esophagitis was developed. The predictive model consists of endoscopic features divided into 4 categories plus clinical factor. The model had good powers of discrimination, suggesting that it is useful for differential diagnosis of esophagitis.
A diagnosis of HSV or CMV esophagitis is made on the basis of endoscopic findings and histopathological examination of the lesions. A diagnosis of HSV infection is generally based on the Tzank smear test, tissue culture, or IHC using tissue specimens. For CMV esophagitis, hematoxylin and eosin (H&E) staining reveals hypertrophic cells containing large eosinophilic cytoplasmic inclusions surrounded by a clear halo, described as an “owl's eye”.[12,13] IHC increases the diagnostic sensitivity to 93% with specificity approaching 100%. However, histological evaluation or PCR may take several days, thus delaying confirmation of the diagnosis and the initiation of antiviral therapy. As misdiagnosis and inappropriate management may expose patients to unnecessary drug toxicity and increase medical costs, differential diagnosis of HSV and CMV esophagitis based on the endoscopic features is crucial in clinical practice. However, we found in the present study that about one quarter of presumptive diagnoses of esophagitis were incorrect, regardless of the expertise of the endoscopist.
Certain endoscopic and clinical features are known to be helpful in discriminating CMV esophagitis from HSV esophagitis. Endoscopic findings of discrete ulcers, presence of vesicles or bullae, shouldered margins, and coalescent or geographic ulcers were more frequent in patients with HSV esophagitis, whereas punch-out ulcers, serpiginous ulcers, ulcers with an uneven base, friability, and with a circumferential distribution were more frequent in CMV esophagitis in this study. These findings are consistent with previous observations.[15,16] To generate a prediction model, we divided the endoscopic findings into four categories, and a previous history of transplantation was added as a clinical factor for discriminating CMV esophagitis from HSV esophagitis. The sensitivity, specificity, and accuracy of the diagnosis of CMV esophagitis were 96.8%, 89.4%, and 92.6%, respectively. Our endoscopic classification and its incorporation into an objective prediction model may improve diagnostic accuracy in patients with esophagitis.
Recently, artificial intelligence (AI) with deep learning of digital imaging has yielded promising results for diagnosing diabetic retinopathy and detecting lymph node metastasis in breast cancer. It has also been demonstrated that convolutional neural network-aided diagnosis using upper gastrointestinal endoscopy images is useful for identifying H. pylori infection. In this context, our prediction model based mainly on endoscopic images may be useful for developing AI-aided diagnosis of viral esophagitis. Further studies are needed in this area.
Our study has several limitations. As it was a retrospective study of patients who visited a tertiary-care hospital with a high volume of transplantation, the prevalence of HSV and CMV esophagitis may not reflect those in the general population. Another limitation is that we could not perform internal or external validation because of the small number of cases. Further studies are warranted to validate this prediction model. Third, patient population in this study was not homogenous in terms of underlying diseases. Underlying disease or immunosuppression may affect the endoscopic findings as well as the incidence of HSV or CMV esophagitis. So, further studies are needed about the effect of immunosuppression on the endoscopic findings in HSV or CMV esophagitis. Finally, the specificity of 89% for our clinical prediction model is still suboptimal for clinical use in real clinical practice to confirm HSV or CMV esophagitis. So, further diagnostic tests such as immunohistochemical staining or molecular tests are needed to confirm the diagnosis. However, it takes a few days. Therefore, our predictive model may help to reduce inappropriate use of acyclovir or unnecessary exposure to ganciclovir toxicity until these confirmative test results are available. It is worth to note that the “possible” category of CMV GI disease including blood by nuclear acid test (e.g., PCR) or antigenemia or CMV documented by PCR from tissue biopsies according to the recent IDSA guidelines may open a new way to suspect GI CMV disease and/or decide empirical antiviral agent. So, further studies are needed on the clinical usefulness of our clinical prediction model or new diagnostic category for the early management in patients with suspected HSV or CMV esophagitis.
In conclusion, the endoscopic findings were helpful in the differential diagnosis of CMV and HSV esophagitis. A prediction model based on the endoscopic findings and a clinical factor seems to be reliable for differentiating CMV esophagitis from HSV esophagitis, and may be useful for guiding empirical antiviral therapy until a definitive diagnosis can be made.
Kyung Hwa Jung: drafting of the manuscript; statistical analysis
Jonggi Choi: data analysis and interpretation; statistical analysis
Eun Jeong Gong, Jeong Hoon Lee, Kee Don Choi, Ho June Song, Gin Hyug Lee, Hwoon-Yong Jung, Yong Pil Chong, Sang-Oh Lee, Sang-Ho Choi, Yang Soo Kim, Jun Hee Woo:
Do Hoon Kim: study concept and design; data acquisition; critical revision of the manuscript for important intellectual content, study supervision
Sung-Han Kim: study concept and design; critical revision of the manuscript for important intellectual content, study supervision
Conceptualization: Sung-Han Kim, Jonggi Choi, Do Hoon Kim.
Data curation: Kyung Hwa Jung, Jonggi Choi, Do Hoon Kim.
Formal analysis: Kyung Hwa Jung, Jonggi Choi, Do Hoon Kim.
Investigation: Do Hoon Kim.
Methodology: Jonggi Choi, Do Hoon Kim.
Supervision: Sung-Han Kim, Eun Jeong Gong, Jeong Hoon Lee, Kee Don Choi, Ho June Song, Gin Hyug Lee, Hwoon-Yong Jung, Yong Pil Chong, Sang-Oh Lee, Sang-Ho Choi, Yang Soo Kim, Jun Hee Woo, Do Hoon Kim.
Visualization: Do Hoon Kim.
Writing – original draft: Kyung Hwa Jung.
Writing – review & editing: Sung-Han Kim, Jonggi Choi, Do Hoon Kim.
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