Real-world NUDT15 genotyping and thiopurine treatment optimization in inflammatory bowel disease: a multicenter study.

BACKGROUND: This study evaluated the effectiveness of NUDT15 codon 139 genotyping in optimizing thiopurine treatment for inflammatory bowel disease (IBD) in Japan, using real-world data, and aimed to establish genotype-based treatment strategies. METHODS: A retrospective analysis of 4628 IBD patients who underwent NUDT15 codon 139 genotyping was conducted. This study assessed the purpose of the genotyping test and subsequent prescriptions following the obtained results. Outcomes were compared between the Genotyping group (thiopurine with genotyping test) and Non-genotyping group (thiopurine without genotyping test). Risk factors for adverse events (AEs) were analyzed by genotype and prior genotyping status. RESULTS: Genotyping test for medical purposes showed no significant difference in thiopurine induction rates between Arg/Arg and Arg/Cys genotypes, but nine Arg/Cys patients opted out of thiopurine treatment. In the Genotyping group, Arg/Arg patients received higher initial doses than the Non-genotyping group, while Arg/Cys patients received lower ones (median 25 mg/day). Fewer AEs occurred in the Genotyping group because of their lower incidence in Arg/Cys cases. Starting with < 25 mg/day of AZA reduced AEs in Arg/Cys patients, while Arg/Arg patients had better retention rates when maintaining ≥ 75 mg AZA. Nausea and liver injury correlated with thiopurine formulation but not dosage. pH-dependent mesalamine reduced leukopenia risk in mesalamine users. CONCLUSIONS: NUDT15 codon 139 genotyping effectively reduces thiopurine-induced AEs and improves treatment retention rates in IBD patients after genotype-based dose adjustments. This study provides data-driven treatment strategies based on genotype and identifies risk factors for specific AEs, contributing to a refined thiopurine treatment approach.

Artificial intelligence-assisted colonoscopy to identify histologic remission and predict the outcomes of patients with ulcerative colitis: A systematic review.

This systematic review evaluated the current status of AI-assisted colonoscopy to identify histologic remission and predict the clinical outcomes of patients with ulcerative colitis. The use of artificial intelligence (AI) has increased substantially across several medical fields, including gastrointestinal endoscopy. Evidence suggests that it may be helpful to predict histologic remission and relapse, which would be beneficial because current histological diagnosis is limited by the inconvenience of obtaining biopsies and the high cost and time-intensiveness of pathological diagnosis. MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 1, 2000, and October 31, 2023. Nine studies fulfilled the selection criteria and were included; five evaluated the prediction of histologic remission, two assessed the prediction of clinical outcomes, and two evaluated both. Seven were prospective observational or cohort studies, while two were retrospective observational studies. No randomized controlled trials were identified. AI-assisted colonoscopy demonstrated sensitivity between 65 %-98 % and specificity values of 80 %-97 % for identifying histologic remission. Furthermore, it was able to predict future relapse in patients with ulcerative colitis. However, several challenges and barriers still exist to its routine clinical application, which should be overcome before the true potential of AI-assisted colonoscopy can be fully realized.

A novel artificial intelligence-assisted "vascular-healing" diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study.

BACKGROUND AND AIMS: Image-enhanced endoscopy (IEE) has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelligence (AI)-assisted IEE may help non-experts to provide objective accurate predictions using optical imaging. We aimed to develop a novel AI-based system using 8853 images from 167 patients with UC to diagnose "vascular-healing" and establish the role of AI-based vascular-healing for predicting the outcomes of patients with UC. METHODS: This open-label, prospective cohort study analyzed data for 104 patients with UC in clinical remission. Endoscopists performed colonoscopy using the AI system, which identified the target mucosa as AI-based vascular-active or vascular-healing. Mayo endoscopic subscore (MES), AI outputs, and histological assessment were recorded for six colorectal segments from each patient. Patients were followed-up for 12 months. Clinical relapse was defined as a partial Mayo score >2 RESULTS: The clinical relapse rate was significantly higher in the AI-based vascular-active group [23.9% (16/67)] compared with the AI-based vascular-healing group [3.0% (1/33)] (P=0.01). In a sub-analysis predicting clinical relapse in patients with MES ≤1, the area under the curve for the combination of complete endoscopic remission and vascular-healing (0.70) was increased compared with that for complete endoscopic remission alone (0.65). CONCLUSIONS: AI-based vascular healing diagnosis system may potentially be used to provide more confidence to physicians to accurately identify patients in remission of UC who would likely relapse rather than remain stable.

Impact of computer-aided characterization for diagnosis of colorectal lesions, including sessile serrated lesions: Multireader, multicase study.

OBJECTIVES: Computer-aided characterization (CADx) may be used to implement optical biopsy strategies into colonoscopy practice; however, its impact on endoscopic diagnosis remains unknown. We aimed to evaluate the additional diagnostic value of CADx when used by endoscopists for assessing colorectal polyps. METHODS: This was a single-center, multicase, multireader, image-reading study using randomly extracted images of pathologically confirmed polyps resected between July 2021 and January 2022. Approved CADx that could predict two-tier classification (neoplastic or nonneoplastic) by analyzing narrow-band images of the polyps was used to obtain a CADx diagnosis. Participating endoscopists determined if the polyps were neoplastic or not and noted their confidence level using a computer-based, image-reading test. The test was conducted twice with a 4-week interval: the first test was conducted without CADx prediction and the second test with CADx prediction. Diagnostic performances for neoplasms were calculated using the pathological diagnosis as reference and performances with and without CADx prediction were compared. RESULTS: Five hundred polyps were randomly extracted from 385 patients and diagnosed by 14 endoscopists (including seven experts). The sensitivity for neoplasia was significantly improved by referring to CADx (89.4% vs. 95.6%). CADx also had incremental effects on the negative predictive value (69.3% vs. 84.3%), overall accuracy (87.2% vs. 91.8%), and high-confidence diagnosis rate (77.4% vs. 85.8%). However, there was no significant difference in specificity (80.1% vs. 78.9%). CONCLUSIONS: Computer-aided characterization has added diagnostic value for differentiating colorectal neoplasms and may improve the high-confidence diagnosis rate.

Differentiation grade as a risk factor for lymph node metastasis in T1 colorectal cancer.

Objectives: Japanese guidelines include high-grade (poorly differentiated) tumors as a risk factor for lymph node metastasis (LNM) in T1 colorectal cancer (CRC). However, whether the grading is based on the least or most predominant component when the lesion consists of two or more levels of differentiation varies among institutions. This study aimed to investigate which method is optimal for assessing the risk of LNM in T1 CRC. Methods: We retrospectively evaluated 971 consecutive patients with T1 CRC who underwent initial or additional surgical resection from 2001 to 2021 at our institution. Tumor grading was divided into low-grade (well- to moderately differentiated) and high-grade based on the least or predominant differentiation analyses. We investigated the correlations between LNM and these two grading analyses. Results: LNM was present in 9.8% of patients. High-grade tumors, as determined by least differentiation analysis, accounted for 17.0%, compared to 0.8% identified by predominant differentiation analysis. A significant association with LNM was noted for the least differentiation method (p < 0.05), while no such association was found for predominant differentiation (p = 0.18). In multivariate logistic regression, grading based on least differentiation was an independent predictor of LNM (p = 0.04, odds ratio 1.68, 95% confidence interval 1.00-2.83). Sensitivity and specificity for detecting LNM were 27.4% and 84.1% for least differentiation, and 2.1% and 99.3% for predominant differentiation, respectively. Conclusions: Tumor grading via least differentiation analysis proved to be a more reliable measure for assessing LNM risk in T1 CRC compared to grading by predominant differentiation.

Deep learning-based automated quantification of goblet cell mucus using histological images as a predictor of clinical relapse of ulcerative colitis with endoscopic remission.

BACKGROUND: Mucin depletion is one of the histological indicators of clinical relapse among patients with ulcerative colitis (UC). Mucin depletion is evaluated semiquantitatively by pathologists using histological images. Therefore, the interobserver concordance is not extremely high, and an objective evaluation method is needed. This study was conducted to demonstrate that our automated quantitative method using a deep learning-based model is useful in predicting the prognosis of patients with UC. METHODS: Deep learning-based models were trained to detect goblet cell mucus area from whole slide images of biopsy specimens. This study involved 114 patients with UC in endoscopic remission with a partial Mayo score of ≤ 1. Biopsy specimens were collected during colonoscopy, and the ratio of goblet cell mucus area to the epithelial cell and goblet cell mucus area was calculated as goblet cell ratio (GCR). The follow-up time was 12 months, and the primary outcome was the relapse rate. Clinical relapse was defined as partial Mayo score of ≥ 3. RESULTS: Sixteen patients (14%) experienced clinical relapse. In the relapsed group, the GCRs of specimens obtained from the cecum, ascending colon, and rectum were significantly lower than those of specimens in the relapse-free group (p = 0.010, p = 0.027, p < 0.01). In the rectum, patients with a GCR of ≤ 12% had a significantly higher relapse rate than those with a GCR of > 12% (45% [10/22] vs. 6.5% [6/92]; p < 0.01). CONCLUSIONS: Quantifying goblet cell mucus areas using a deep learning-based model is useful in predicting the clinical relapse in patients with UC in clinical and endoscopic remission.

Use of advanced endoscopic technology for optical characterization of neoplasia in patients with ulcerative colitis: Systematic review.

OBJECTIVES: Advances in endoscopic technology, including magnifying and image-enhanced techniques, have been attracting increasing attention for the optical characterization of colorectal lesions. These techniques are being implemented into clinical practice as cost-effective and real-time approaches. Additionally, with the recent progress in endoscopic interventions, endoscopic resection is gaining acceptance as a treatment option in patients with ulcerative colitis (UC). Therefore, accurate preoperative characterization of lesions is now required. However, lesion characterization in patients with UC may be difficult because UC is often affected by inflammation, and it may be characterized by a distinct "bottom-up" growth pattern, and even expert endoscopists have relatively little experience with such cases. In this systematic review, we assessed the current status and limitations of the use of optical characterization of lesions in patients with UC. METHODS: A literature search of online databases (MEDLINE via PubMed and CENTRAL via the Cochrane Library) was performed from 1 January 2000 to 30 November 2021. RESULTS: The database search initially identified 748 unique articles. Finally, 25 studies were included in the systematic review: 23 focused on differentiation of neoplasia from non-neoplasia, one focused on differentiation of UC-associated neoplasia from sporadic neoplasia, and one focused on differentiation of low-grade dysplasia from high-grade dysplasia and cancer. CONCLUSIONS: Optical characterization of neoplasia in patients with UC, even using advanced endoscopic technology, is still challenging and several issues remain to be addressed. We believe that the information revealed in this review will encourage researchers to commit to the improvement of optical diagnostics for UC-associated lesions.

Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study.

BACKGROUND AND AIMS: The use of artificial intelligence (AI) during colonoscopy is attracting attention as an endoscopist-independent tool to predict histologic disease activity of ulcerative colitis (UC). However, no study has evaluated the real-time use of AI to directly predict clinical relapse of UC. Hence, it is unclear whether the real-time use of AI during colonoscopy helps clinicians make real-time decisions regarding treatment interventions for patients with UC. This study aimed to establish the role of real-time AI in stratifying the relapse risk of patients with UC in clinical remission. METHODS: This open-label, prospective, cohort study was conducted in a referral center. The cohort comprised 145 consecutive patients with UC in clinical remission who underwent AI-assisted colonoscopy with a contact-microscopy function. We classified patients into either the Healing group or Active group based on the AI outputs during colonoscopy. The primary outcome measure was clinical relapse of UC (defined as a partial Mayo score >2) during 12 months of follow-up after colonoscopy. RESULTS: Overall, 135 patients completed the 12-month follow-up after AI-assisted colonoscopy. AI-assisted colonoscopy classified 61 patients as the Healing group and 74 as the Active group. The relapse rate was significantly higher in the AI-Active group (28.4% [21/74]; 95% confidence interval, 18.5%-40.1%) than in the AI-Healing group (4.9% [3/61]; 95% confidence interval, 1.0%-13.7%; P < .001). CONCLUSIONS: Real-time use of AI predicts the risk of clinical relapse in patients with UC in clinical remission, which helps clinicians make real-time decisions regarding treatment interventions. (Clinical trial registration number: UMIN000036650.).

Combined endocytoscopy with pit pattern diagnosis in ulcerative colitis-associated neoplasia: Pilot study.

OBJECTIVES: Ulcerative colitis-associated neoplasias (UCAN) are often flat with an indistinct boundary from surrounding tissues, which makes differentiating UCAN from non-neoplasias difficult. Pit pattern (PIT) has been reported as one of the most effective indicators to identify UCAN. However, regenerated mucosa is also often diagnosed as a neoplastic PIT. Endocytoscopy (EC) allows visualization of cell nuclei. The aim of this retrospective study was to demonstrate the diagnostic ability of combined EC irregularly-formed nuclei with PIT (EC-IN-PIT) diagnosis to identify UCAN. METHODS: This study involved patients with ulcerative colitis whose lesions were observed by EC. Each lesion was diagnosed by two independent expert endoscopists, using two types of diagnostic strategies: PIT alone and EC-IN-PIT. We evaluated and compared the diagnostic abilities of PIT alone and EC-IN-PIT. We also examined the difference in the diagnostic abilities of an EC-IN-PIT diagnosis according to endoscopic inflammation severity. RESULTS: We analyzed 103 lesions from 62 patients; 23 lesions were UCAN and 80 were non-neoplastic. EC-IN-PIT diagnosis had a significantly higher specificity and accuracy compared with PIT alone: 84% versus 58% (P < 0.001), and 88% versus 67% (P < 0.01), respectively. The specificity and accuracy were significantly higher for Mayo endoscopic score (MES) 0-1 than MES 2-3: 93% versus 68% (P < 0.001) and 95% versus 74% (P < 0.001), respectively. CONCLUSIONS: Our novel EC-IN-PIT strategy had a better diagnostic ability than PIT alone to predict UCAN from suspected and initially detected lesions using conventional colonoscopy. UMIN clinical trial (UMIN000040698).

Beyond complete endoscopic healing: Goblet appearance using an endocytoscope to predict future sustained clinical remission in ulcerative colitis.

OBJECTIVES: Complete endoscopic healing, defined as Mayo endoscopic score (MES) = 0, is an optimal target in the treatment of ulcerative colitis (UC). However, some patients with MES = 0 show clinical relapse within 12 months. Histologic goblet mucin depletion has emerged as a predictor of clinical relapse in patients with MES = 0. We observed goblet depletion in vivo using an endocytoscope, and analyzed the association between goblet appearance and future prognosis in UC patients. METHODS: In this retrospective cohort study, all enrolled UC patients had MES = 0 and confirmed clinical remission between October 2016 and March 2020. We classified the patients into two groups according to the goblet appearance status: preserved-goblet and depleted-goblet groups. We followed the patients until March 2021 and evaluated the difference in cumulative clinical relapse rates between the two groups. RESULTS: We identified 125 patients with MES = 0 as the study subjects. Five patients were subsequently excluded. Thus, we analyzed the data for 120 patients, of whom 39 were classified as the preserved-goblet group and 81 as the depleted-goblet group. The patients were followed-up for a median of 549 days. During follow-up, the depleted-goblet group had a significantly higher cumulative clinical relapse rate than the preserved-goblet group (19% [15/81] vs. 5% [2/39], respectively; P = 0.02). CONCLUSIONS: Observing goblet appearance in vivo allowed us to better predict the future prognosis of UC patients with MES = 0. This approach may assist clinicians with onsite decision-making regarding treatment interventions without a biopsy.

Clinicopathological features of small T1 colorectal cancers.

BACKGROUND: Although small colorectal neoplasms (< 10 mm) are often easily resected endoscopically and are considered to have less malignant potential compared with large neoplasms (≥ 10 mm), some are invasive to the submucosa. AIM: To clarify the clinicopathological features of small T1 colorectal cancers. METHODS: Of 32025 colorectal lesions between April 2001 and March 2018, a total of 1152 T1 colorectal cancers resected endoscopically or surgically were included in this study and were divided into two groups by tumor size: a small group (< 10 mm) and a large group (≥ 10 mm). We compared clinicopathological factors including lymph node metastasis (LNM) between the two groups. RESULTS: The incidence of small T1 cancers was 10.1% (116/1152). The percentage of initial endoscopic treatment in small group was significantly higher than in large group (< 10 mm 74.1% vs ≥ 10 mm 60.2%, P < 0.01). In the surgical resection cohort (n = 798), the rate of LNM did not significantly differ between the two groups (small 12.3% vs large 10.9%, P = 0.70). In addition, there were also no significant differences between the two groups in pathological factors such as histological grade, vascular invasion, or lymphatic invasion. CONCLUSION: Because there was no significant difference in the rate of LNM between small and large T1 colorectal cancers, the requirement for additional surgical resection should be determined according to pathological findings, regardless of tumor size.

Image-Enhanced Capsule Endoscopy Improves the Identification of Small Intestinal Lesions.

Image-enhanced endoscopy is useful for diagnosing and identifying lesions in the gastrointestinal tract. Recently, image-enhanced endoscopy has become a breakthrough technology that has attracted significant attention. This image enhancing technology is available for capsule endoscopy, which is an effective tool for small intestinal lesions and has been applied in flexible spectral color enhancement technology and in contrast capsule like narrow-band imaging. In this field, most researchers focus on improving the visibility and detection of small intestinal lesions. This review summarizes previous studies on image-enhanced capsule endoscopy and aims to evaluate the efficacy of this technology.

Long-Term Follow-Up of Targeted Biopsy Yield (LOFTY Study) in Ulcerative Colitis Surveillance Colonoscopy.

We previously performed a randomized controlled trial (RCT) comparing targeted and random biopsy in neoplasia detection in patients with ulcerative colitis (UC), which showed the short-term effectiveness of targeted biopsy with one-time colonoscopy. In this retrospective cohort study, we investigated the long-term effectiveness of targeted biopsy in tertiary care hospitals, using the follow-up data from patients with UC for ≥ 8 years who had enrolled in the initial RCT. The primary outcome was death from colorectal cancer (CRC). Secondary outcomes were advanced neoplasia (CRC or high-grade dysplasia) and colectomy due to neoplasia after the RCT. We compared these outcomes between target and random groups. Data on 195 of the 221 patients (88.2%) enrolled in the previous RCT were collected from 28 institutions between 2008 and 2019. No patients died of CRC in either group, with a median 8.8-year follow-up demonstrating a robustness for targeted biopsy in terms of CRC death prevention. Advanced neoplasia was detected in four and three patients in the target and random groups, respectively. Colectomy was required due to neoplasia in three patients in each group. The chance of developing CRC in patients with a negative colonoscopy was low, and the targeted biopsy appeared effective in this population. Conversely, patients found with low-grade dysplasia at initial RCT have 10-fold higher risk of progression to high-grade dysplasia and/or CRC. Ten extracolonic malignancies were observed during the follow-up, resulting in four deaths. Panchromoendoscopy was used only in 4.6% and targeted biopsy was only performed in 59.1% of colonoscopies. We recommend targeted biopsy rather than > 33 random biopsies in real-world settings under adequate observation by specialists.

Left-sided location is a risk factor for lymph node metastasis of T1 colorectal cancer: a single-center retrospective study.

PURPOSE: Although some studies have reported differences in clinicopathological features between left- and right-sided advanced colorectal cancer (CRC), there are few reports regarding early-stage disease. In this study, we aimed to compare the clinicopathological features of left- and right-sided T1 CRC. METHODS: Subjects were 1142 cases with T1 CRC undergoing surgical or endoscopic resection between 2001 and 2018 at Showa University Northern Yokohama Hospital. Of these, 776 cases were left-sided (descending colon to rectum) and 366 cases were right-sided (cecum to transverse colon). We compared clinical (patients age, sex, tumor size, morphology, initial treatment) and pathological features (invasion depth, histological grade, lymphatic invasion, vascular invasion, tumor budding) including lymph node metastasis (LNM). RESULTS: Left-sided T1 CRC showed significantly higher rates of LNM (left-sided 12.0% vs. right-sided 5.4%, P < 0.05) and lymphatic invasion (left-sided 32.7% vs. right-sided 23.2%, P < 0.05). Especially, the sigmoid colon and rectum showed higher rates of LNM (12.4% and 12.1%, respectively) than other locations. Patients with left-sided T1 CRC were younger than those with right-sided T1 CRC (64.9 years ±11.5 years vs. 68.7 ± 11.6 years, P < 0.05), as well as significantly lower rates of poorly differentiated carcinoma/mucinous carcinoma than right-sided T1 CRC (11.6% vs. 16.1%, P < 0.05). CONCLUSION: Left-sided T1 CRC, especially in the sigmoid colon and rectum, exhibited higher rates of LNM than right-sided T1 CRC, followed by higher rates of lymphatic invasion. These results suggest that tumor location should be considered in decisions regarding additional surgery after endoscopic resection. TRIAL REGISTRATION: This study was registered with the University Hospital Medical Network Clinical Trials Registry ( UMIN 000032733 ).

Endocytoscopy with NBI has the potential to correctly diagnose diminutive colorectal polyps that are difficult to diagnose using conventional NBI.

Background and study aims Real-time diagnosis of colorectal polyps is needed to prevent unnecessary resection of benign polyps. The vessels in hyperplastic polyps sometimes mimic the characteristic meshed capillary network of neoplastic lesions on non-magnified narrow-band imaging (NBI). Endocytoscopy in conjunction with NBI (EC-NBI) enables more detailed vessel observation. The current study evaluated whether EC-NBI can accurately diagnose small colorectal lesions with visible vessels on non-magnified NBI. Patients and methods This retrospective study was conducted from January to December 2016. During colonoscopy, lesion images were obtained using NBI and EC-NBI. On EC-NBI, lesions were classified as having "clear," "unclear," or "invisible" blood vessel margins. All specimens were resected and pathologically examined, and the association between vessel margin findings and pathological diagnosis was assessed. The lesion surface to vessel depth was measured in clear, unclear, and invisible lesions. Results Among 114 adenomas, 108 were clear, while six were unclear. Among 36 hyperplastic polyps, eight were clear, while 28 were unclear. A micro-network (MN) pattern was seen in 106 of 114 adenomas, and four of 36 hyperplastic polyps. The sensitivity, specificity, correct diagnostic rate, and positive and negative predictive values of clear blood vessel margins or a MN pattern as an adenoma index were 98.2 %, 69.4 %, 91.3 %, 91.1 %, and 92.6 %, respectively. EC-NBI correctly diagnosed 69.4 % (25/36) of hyperplastic polyps. The lesion surface-blood vessel distance was greater in unclear versus clear lesions ( P  < 0.001), and invisible versus unclear lesions ( P  < 0.001). Conclusions EC-NBI may effectively differentiate hyperplastic polyps with visible vessels from adenomas. Blood vessel depth affects visibility.

Endocytoscopic intramucosal capillary network changes and crypt architecture abnormalities can predict relapse in patients with an ulcerative colitis Mayo endoscopic score of 1.

OBJECTIVES: Recent studies have suggested the necessity of therapeutic intervention for patients with ulcerative colitis at high risk of clinical relapse with a Mayo endoscopic score (MES) of 1. The aim of this retrospective cohort study was to demonstrate the impact of intramucosal capillary network changes and crypt architecture abnormalities to stratify the risk of relapse in patients with an MES of 1. METHODS: All included patients had an MES of ≤1 and confirmed sustained clinical remission between October 2016 and April 2019. We classified patients with an MES of 1 as "intramucosal capillary/crypt (ICC)-active" or "ICC-inactive" using endocytoscopic evaluation. We followed patients until October 2019 or until relapse; the main outcome measure was the difference in clinical relapse-free rates between ICC-active and ICC-inactive patients with an MES of 1. RESULTS: We included 224 patients and analyzed data for 218 (82 ICC-active and 54 ICC-active with an MES of 1 and 82 with an MES of 0). During follow-up, among the patients with an MES of 1, 30.5% (95% confidence interval 20.8-41.6; 25/82) of the patients relapsed in the ICC-active group and 5.6% (95% confidence interval 1.2-15.4; 3/54) of the patients relapsed in the ICC-inactive group. The ICC-inactive group had a significantly higher clinical relapse-free rate compared with the ICC-active group (P < 0.01). CONCLUSIONS: In vivo intramucosal capillary network and crypt architecture patterns stratified the risk of clinical relapse in patients with an MES of 1 (UMIN 000032580; UMIN 000036359).

Risk Factors for Mortality in Pneumocystis jirovecii Pneumonia in Patients with Inflammatory Bowel Disease.

BACKGROUND: Pneumocystis jirovecii pneumonia (PJP) is highly fatal once infection is established. In this study, we investigated the risk of PJP mortality in patients with inflammatory bowel disease (IBD). METHODS: We conducted a retrospective observational study of case data from IBD patients who developed PJP, compiled from 17 collaborating institutions. Parameters such as age, sex, medications used, and blood test results were analyzed to identify risk factors for mortality. RESULTS: The mortality rate among the 28 IBD patients who developed PJP was 17.9%. A low serum albumin level at the start of IBD treatment was identified as a risk factor for mortality and showed the following association with probability of death (P): P = 1/[1 + exp(-5.5 + 2.4 × Alb). The probability of death exceeded 0.5 when serum albumin was 2.2 g/dL or lower. CONCLUSION: Patients with IBD who develop PJP have a high mortality rate and often cannot continue treatment with medication alone. Therefore, it is necessary to pay attention to albumin levels at the start of immunosuppressive therapy when creating a treatment plan.