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    Gut and Liver is an international journal of gastroenterology, focusing on the gastrointestinal tract, liver, biliary tree, pancreas, motility, and neurogastroenterology. Gut atnd Liver delivers up-to-date, authoritative papers on both clinical and research-based topics in gastroenterology. The Journal publishes original articles, case reports, brief communications, letters to the editor and invited review articles in the field of gastroenterology. The Journal is operated by internationally renowned editorial boards and designed to provide a global opportunity to promote academic developments in the field of gastroenterology and hepatology. +MORE

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A New Risk Prediction Model for Detecting Endoscopic Activity of Ulcerative Colitis

Guoyu Guan1 , Sangdan Zhuoga1 , Songbai Zheng1 , Kangqiao Xu2 , Tingwen Weng3 , Wensi Qian4 , Danian Ji5 , Xiaofeng Yu6

1Department of Gastroenterology, Huadong Hospital, Shanghai Medical College Fudan University, Shanghai, China; 2Department of Respiration, Jiading Branch of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Departments of 3Cardiology, 4Hematology, 5Endoscopy Center, and 6General Practice, Huadong Hospital, Shanghai Medical College Fudan University, Shanghai, China

Correspondence to: Songbai Zheng
ORCID https://orcid.org/0000-0003-4453-4664
E-mail songbai1009@163.com

Received: September 19, 2023; Revised: December 13, 2023; Accepted: January 11, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Gut Liver.

Published online April 16, 2024

Copyright © Gut and Liver.

Background/Aims: Ulcerative colitis (UC) is an incurable, relapsing-remitting inflammatory disease that increases steadily. Mucosal healing has become the primary therapeutic objective for UC. Nevertheless, endoscopic assessments are invasive, expensive, time-consuming, and inconvenient. Therefore, it is crucial to develop a noninvasive predictive model to monitor endoscopic activity in patients with UC.
Methods: Clinical data of 198 adult patients with UC were collected from January 2016 to August 2022 at Huadong Hospital, China.
Results: Patients with UC were randomly divided into the training cohort (70%, n=138) and the validation cohort (30%, n=60). The receiver operating characteristic curve value for the training group was 0.858 (95% confidence interval [CI], 0.781 to 0.936), whereas it was 0.845 (95% CI, 0.731 to 0.960) for the validation group. The calibration curve employed the Hosmer-Lemeshow test (p>0.05) to demonstrate the consistency between the predicted and the actual probabilities in the nomogram of these two groups. The decision curve analysis validated that the nomogram had clinical usefulness.
Conclusions: The nomogram, which incorporated activated partial thromboplastin time, fecal occult blood test, β2-globulin level, and fibrinogen degradation products, served as a prospective tool for evaluating UC activity in clinical practices.

Keywords: Ulcerative colitis, Fecal marker, Blood marker, Endoscopy, Nomograms

Ulcerative colitis (UC) is an inflammatory disease characterized by recurring episodes of inflammation that is incurable. The frequency of UC has been continuously increasing worldwide, particularly in developing nations.1 To date, the pathogenesis and etiology of UC remain unknown; however, they may be related to immunological, inflammatory, environmental, or genetic factors.2 Bloody purulent stool is the main clinical manifestation of UC, usually accompanied by abdominal pain and diarrhea.3 Currently, immunosuppressants, biological agents, and 5-aminosalicylic are the main treatment options for UC.4,5 Most patients with UC require long-term treatment, surveillance, and management, which can even be life-long. Endoscopy is a common, efficient, and preferable method used for monitoring the activity of inflammatory diseases.6 Mucosal healing has become the main therapeutic objective for UC since it is strongly associated with long-lasting clinical remission and reduced incidence of colectomy.7-10 Nevertheless, endoscopy is associated with several limitations, including invasiveness, expensive healthcare costs, and intricate preoperative preparations.11 Additionally, there are potential hazards associated with conducting a colonoscopy, such as the risk of perforation and bleeding. This risk is particularly heightened among older people and persons with a history of inflammatory bowel disease.12

It is important to promote the use of non-endoscopic indicators of disease activity to predict the severity of the disease and assess the clinical prognosis.13 Over the past decades, a growing number of studies have shown that biological disease markers, including C-reactive protein (CRP), erythrocyte sedimentation rate, hemoglobin, platelets (PLT), blood leukocytes, vitamin D, fecal calprotectin test (FCT), and fecal immunochemical test have been proposed as the indicators for estimating the activity of the disease.14-17 An ideal biomarker should include attributes that make it readily applicable for routine clinical use, and it should have the ability to detect and distinguish specific diseases in individuals accurately. Unfortunately, such a biomarker has not yet been determined for UC. The current blood markers lack specificity and sensitivity in assessing endoscopic activity due to the potential influence of inflammatory, autoimmune, and infectious diseases beyond the digestive system, which elevates the levels of these markers in the blood.18

Currently, fecal indicators have demonstrated greater specificity in comparison to blood markers, although some other conditions may also elevate the level of such fecal markers, including colonic tumors and treatment with anti-inflammation or nonsteroidal drugs.19 Emerging data suggested that the prediction model with the combined use of blood and fecal markers for detecting endoscopic activity in patients with UC tends to be more specific and sensitive compared to their separate detection.20 However, the process of testing these biomarkers, such as FCT, is inaccessible, time-consuming, and expensive in clinical practice. It is necessary to develop a new risk prediction model that incorporates universal blood and fecal markers. This approach would replace the need for endoscopic inspections and alleviate the physical and financial constraints faced by patients with UC.

A nomogram is a graphical tool that is frequently used to condense complex statistical prediction models into a single numerical estimate of the likelihood of a clinical occurrence. It is known for its simplicity, intuitiveness, and widespread usage.21 The present retrospective study aimed to develop a new risk prediction model based on noninvasive parameters of blood and stool for well-detecting endoscopic activity in patients with UC. Moreover, the clinical results provided another choice for monitoring and guiding the medicating of patients with UC, circumventing the need for routine colonoscopy exams.

1. Study patients and design

The study was approved by the Biomedical Research Ethics Committee at Huadong Hospital (Number: 2021K174). A total of 493 adult patients with a diagnosed UC, as recorded in the Electronic Medical Record System, were included in this study and referred to Huadong Hospital, affiliated with Fudan University, between January 2016 and August 2022. The inclusion criteria were as follows: (1) patients clearly diagnosed with UC according to clinical consensus;22 (2) patients aged ≥18 years; and (3) patients who had undergone the overall examinations of blood and stool within 7 days before or after undergoing colonoscopy. The key exclusion criteria were as follows: (1) patients who had not been hospitalized previously (n=290); (2) patients lacking information regarding colonoscopy examinations (n=5); (3) pregnant women (n=0); or (4) patients with autoimmune illnesses, severe underlying conditions, persistent infectious diseases, or malignant tumors (n=0). Finally, a total of 198 patients with UC were found to be eligible for the analysis in this study. These patients were randomly divided into the training cohort (70%, n=138) and the validation cohort (30%, n=60), as Fig. 1 shows. The Mayo endoscopic subscore was used to evaluate disease activity based on endoscopic assessments conducted by skilled endoscopists.23 Mayo endoscopic subscore=0 was defined as "endoscopic remission," while Mayo endoscopic subscore ≥1 was defined as "endoscopic activity." The extension of endoscopic activity was categorized into three distinct types (proctitis, left-sided, and extensive) based on the Montreal classification.24

Figure 1.Flowchart of this study. UC, ulcerative colitis.

2. Clinical data collection

Predominantly relevant parameters of the patients were collected according to the literature review.14-17 Demographic variables and clinical characteristics were recorded, including age, gender, Montreal classification, medications, and comorbidities. Serum biomarkers were monitored as well, involving white blood cell (×109/L), neutrophils (%), lymphocyte (%), monocyte (%), red blood cell (×1012/L), hemoglobin (g/L), red blood cell distribution width (%), PLT (×1012/L), platelet distribution width (fL), mean platelet volume (fL), alanine transaminase (U/L), aspartate transaminase (U/L), γ-glutamyl transpeptidase (U/L), alkaline phosphatase (U/L), albumin (g/L), total bilirubin (μmol/L), total cholesterol (mmol/L), triglyceride (mmol/L), high-density lipoprotein (mmol/L), low-density lipoprotein (mmol/L), blood urea nitrogen (mmol/L), serum creatinine (μmol/L), uric acid (μmol/dL), estimated glomerular filtration rate (mL/min), blood glucose (fasting, mmol/L), amylase (U/L), erythrocyte sedimentation rate (mm/hr), CRP (mg/L), procalcitonin (ng/mL), prothrombin time (sec), activated partial thromboplastin time (APTT, sec), international normalized ratio, fibrinogen (g/L), fibrinogen degradation products (FDPs, mg/L), D-dimer (DD, mg/L), immunoglobulin (Ig) A (g/L), IgG (g/L), IgM (g/L), prealbumin (g/L), α1-globulin (%), α2-globulin (%), β1-globulin (%), β2-globulin (%), peripheral antineutrophil cytoplasmic antibodies, myeloperoxidase-antineutrophil cytoplasmic antibodies, vitamin A (μmol/L), vitamin B1 (nmol/L), vitamin B2 (mg/mL), vitamin B6 (nmol/L), vitamin B9 (nmol/L), vitamin C (μmol/L), 1,25-(OH)2-vitamin D3 (nmol/L), vitamin E (μg/mL), procollagen type I C-terminal propeptide (pg/μL), procollagen type I N-terminal propeptide (ng/mL), osteocalcin (ng/mL), and parathyroid hormone (pg/mL). The fecal marker exhibited a fecal occult blood test (FOBT). In the current study, the above biomarkers were detected within 7 days before or after colonoscopy.

3. Statistical analysis

This study used the Kolmogorov-Smirnov test to assess the normality of data distribution for continuous variables. The data on normal distribution was represented by the mean and standard deviation and was compared using the independent samples t-test. Data that did not follow a normal distribution were presented using the median and interquartile range and were compared using the Mann-Whitney U test. Descriptive statistics was conducted with frequencies for categorical variables presented as percentages (%) and using the chi-square or Fisher exact tests for comparison.

Statistical analysis employing univariate logistic regression was performed to assess the significance of variables and to screen for potential predictors of endoscopic activity in patients with UC. A p-value less than 0.05 was considered statistically significant and included in the multivariable logistic regression analysis. The nomogram model was constructed using variables that had a p-value less than 0.05, which were then further filtered by multivariable analysis. These predictors were presented as odds ratio and 95% confidence interval (CI).

The performance of the prediction model was evaluated with respect to discriminatory capacity, calibration ability, and clinical effectiveness. The area under the curve (AUC) or receiver operating characteristic displayed the discriminatory capacity, which equaled the concordance index (c-index). The calibration accuracy was assessed by the utilization of a visual calibration plot and the Hosmer-Lemeshow test. The decision curve analysis was utilized to evaluate the clinical efficacy of the prediction model. Two-tailed p<0.05 was regarded as significant. The R version 4.2.0 was used for all statistical analysis (R Foundation for Statistical Computing, Vienna, Austria).

1. Baseline characteristics of patients with UC

Of the 493 patients with UC, 198 were entered into the study. In the training cohort, patients were classified as endoscopic activity (n=102) and endoscopic remission (n=36). In the validation cohort, patients were sorted as endoscopic activity (n=43) and endoscopic remission (n=17). Out of the individuals that were excluded, 290 were not the first hospitalization, and five did not have data on colonoscopy examinations. The baseline characteristics of the individuals with UC are shown in Table 1. The median age of included patients was 56.0 years (interquartile range, 41.2 to 64.0 years), and 52.0% of patients (103/198) were male. Statistically significant differences (p<0.05) were seen in six variables between the two groups–red blood cell, hemoglobin, red blood cell distribution width, serum creatinine, DD, and IgM. According to Fig. 2, patients with active UC had greater odds ratio for FDP, α1-globulin, β1-globulin, FOBT, PLT, prothrombin time, APTT, fibrinogen, DD, and CRP. They also had lower odds ratio for vitamin B1, β2-globulin, and mean platelet volume compared to inactive individuals.

Figure 2.Univariate logistic regression analysis of endoscopic activity in patients with ulcerative colitis. OR, odds ratio; CI, confidence interval; FDP, fibrinogen degradation product; FOBT, fecal occult blood test; PLT, platelet; PT, prothrombin time; APTT, activated partial thromboplastin time; FBG, fibrinogen; DD, D-dimer; CRP, C-reactive protein; MPV, mean platelet volume.

Table 1. Baseline Characteristics of Patients with Ulcerative Colitis

VariableValidation cohort (n=60)Training cohort (n=138)p-value
Age, yr57.00 (41.20–66.00)55.50 (41.20–63.00)0.231
Sex0.251
Male27 (45.0)76 (55.1)
Female33 (55.0)62 (44.9)
Montreal classification0.612
E131 (51.7)69 (50.0)
E214 (23.3)26 (18.8)
E315 (25.0)43 (31.2)
Medications0.332
Thiopurine1 (1.7)0
Biologics01 (0.7)
Corticosteroid23 (38.3)63 (45.7)
5-ASA36 (60.0)74 (53.6)
Hematological variable
WBC, ×109/L6.05 (4.85–7.30)6.40 (5.10–7.77)0.150
NEUT, %59.90±10.0061.90±10.700.219
LYM, %30.30±8.9328.90±9.580.335
MONO, %6.16 (5.10–6.90)6.00 (5.00–7.00)0.995
RBC, ×1012/L4.26 (3.93–4.53)4.46 (4.10–4.81)0.039
Hb, g/dL12.80 (11.60–13.60)13.30 (12.00–14.50)0.047
RDW, %12.90 (12.70–13.50)12.70 (12.30–13.30)0.028
PLT, ×1012/L0.23 (0.18–0.28)0.22 (0.18–0.27)0.381
PDW, fL15.90 (15.40–16.20)16.00 (15.50–16.40)0.280
MPV, fL9.90 (9.00–10.60)9.97 (9.00–11.00)0.368
Biochemical variable
ALT, U/L14.00 (11.00–22.20)14.00 (9.93–20.00)0.397
AST, U/L17.00 (14.00–22.00)16.10 (13.00–20.00)0.205
γ-GGT, U/L18.60 (13.10–25.60)16.10 (11.10–23.80)0.168
AKP, U/L67.40 (60.50–79.00)67.00 (55.00–78.50)0.435
ALB, g/L42.80 (39.80–45.00)43.00 (40.00–45.90)0.606
TBIL, μmol/L9.45 (6.83–13.80)9.85 (7.50–13.00)0.715
TCHO, mmol/L4.31±0.974.44±0.940.407
TG, mmol/L1.00 (0.79–1.25)1.21 (0.86–1.57)0.053
HDL, mmol/L1.37 (1.29–1.43)1.35 (1.24–1.49)0.691
LDL, mmol/L2.66 (2.26–2.99)2.70 (2.28–2.99)0.907
BUN, mmol/L4.45 (3.30–5.27)4.60 (3.50–5.30)0.715
SCR, μmol/L67.20 (56.60–75.50)72.00 (60.50–83.10)0.016
UA, μmol/dL27.10 (22.10–35.00)29.80 (24.50–36.00)0.148
eGFR, mL/min99.20 (90.80–108.00)96.10 (85.00–108.00)0.143
BG, mmol/L4.95 (4.38–5.30)4.90 (4.40–5.38)0.887
AMY, U/L63.10 (58.60–75.00)67.50 (56.40–86.00)0.268
ESR, mm/hr12.00 (6.00–21.00)12.00 (6.00–22.00)0.749
CRP, mg/L6.55 (2.36–8.54)4.64 (2.10–8.49)0.677
PCT, ng/mL0.22 (0.18–0.27)0.22 (0.18–0.26)0.619
Coagulation variable
PT, sec11.80 (11.20–12.90)11.50 (11.10–12.30)0.320
APTT, sec29.20 (27.00–35.40)29.90 (27.40–33.30)0.760
INR0.98 (0.96–1.03)0.98 (0.95–1.03)0.862
FBG, g/L3.16 (2.48–3.48)3.07 (2.57–3.56)0.959
FDP, mg/L1.46 (0.89–2.37)1.40 (0.93–2.07)0.392
DD, mg/L0.34 (0.23–0.75)0.29 (0.18–0.55)0.014
Immunological variable
IgA, g/L2.07 (1.89–2.65)2.15 (1.88–2.55)0.671
IgG, g/L12.20 (11.70–13.40)12.30 (11.60–13.30)0.732
IgM, g/L0.91 (0.76–1.13)1.06 (0.86–1.36)0.004
PA, mg/L232.00 (211.00–241.00)230.00 (203.00–245.00)0.815
α1-Globulin, %3.70 (3.47–4.64)3.71 (3.45–4.41)0.616
α2-Globulin, %7.51 (7.11–8.71)7.40 (6.98–8.13)0.189
β1-Globulin, %6.29 (5.94–6.69)6.26 (5.93–6.58)0.364
β2-Globulin, %4.08 (3.72–4.76)4.21 (3.86–4.80)0.526
pANCA, n (%)0.222
No50 (83.3)125 (90.6)
Yes10 (16.7)13 (9.4)
MPO-ANCA, n (%)0.515
No59 (98.3)137 (99.3)
Yes1 (1.7)1 (0.7)
Vitamin variable
Vitamin A, μmol/L0.59 (0.54–0.70)0.61 (0.55–0.69)0.605
Vitamin B1, nmol/L61.60 (58.20–68.20)60.90 (56.60–67.20)0.673
Vitamin B2, mg/mL0.24 (0.21–0.25)0.24 (0.21–0.26)0.522
Vitamin B6, nmol/L26.70 (25.40–29.80)26.60 (24.10–29.70)0.420
Vitamin B9, nmol/L12.30 (11.60–13.10)12.30 (11.40–13.50)0.995
Vitamin C, μmol/L38.10 (36.70–41.10)38.70 (36.50–41.50)0.662
1,25-(OH)2- Vitamin D3, nmol/L17.80 (14.90–20.50)18.70 (13.00–21.80)0.796
Vitamin E, μg/mL11.00 (10.30–11.40)10.90 (10.40–11.60)0.754
Bone metabolism
PICP, pg/μL0.66 (0.53–0.76)0.69 (0.60–0.78)0.367
PINP, ng/mL50.60 (39.60–57.90)53.40 (40.20–64.30)0.284
OC, ng/mL21.50 (16.20–24.70)22.60 (18.70–24.70)0.256
PTH, pg/mL40.00 (35.60–44.10)39.90 (36.20–44.40)0.990
FOBT0.899
No30 (50.0)72 (52.2)
Yes30 (50.0)66 (47.8)
Comorbidity
Diabetes0.558
No57 (95.0)127 (92.0)
Yes3 (5.0)11 (8.0)
Hypertension0.595
No46 (76.7)112 (81.2)
Yes14 (23.3)26 (18.8)
Thyroid nodule0.898
No43 (71.7)96 (69.6)
Yes17 (28.3)42 (30.4)

Data are presented as median (interquartile range), number (%), or mean±SD.

E1, proctitis; E2, left-sided; E3, extensive; 5-ASA, 5-amino salicylic acid; WBC, white blood cell; NEUT, neutrophils; LYM, lymphocyte; MONO, monocyte; RBC, red blood cell; Hb, hemoglobin; RDW, red blood cell distribution width; PLT, platelet; PDW, platelet distribution width; MPV, mean platelet volume; ALT, alanine transaminase; AST, aspartate transaminase; γ-GGT, γ-glutamyl transpeptidase; AKP, alkaline phosphatase; ALB, albumin; TBIL, total bilirubin; TCHO, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; BUN, blood urea nitrogen; SCR, serum creatinine; UA, uric acid; eGFR, estimated glomerular filtration rate; BG, blood glucose (fasting); AMY, amylase; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; PCT, procalcitonin; prothrombin time; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, international normalized ratio; FBG, fibrinogen; FDP, fibrinogen degradation product; DD, D-dimer; PA, prealbumin; p-ANCA, peripheral antineutrophil cytoplasmic antibodies; MPO-ANCA, myeloperoxidase-antineutrophil cytoplasmic antibodies; PICP, procollagen type I C-terminal propeptide; PINP, procollagen type I N-terminal propeptide; OC, osteocalcin; PTH, parathyroid hormone; FOBT, fecal occult blood test.



2. Predictive model development

The findings of univariate logistic regression analysis are displayed in Fig. 2. Thirteen variables with statistical significance (p<0.05) were initially assessed, including vitamin B1, FDP, α1-globulin, β1-globulin, β2-globulin, FOBT, PLT, PT, APTT, fibrinogen, DD, CRP, and mean platelet volume. Then, the 13 parameters were analyzed by multivariate logistic regression for further selection. Four variables (APTT, FOBT, β2-globulin, and FDP) were identified as the independent risk variables and used to construct the final model (Fig. 3) based on multivariate logistic regression analysis (Table 2). The specific steps for interpreting the nomogram are as follows. Firstly, the initial data of each individual was intuitively exhibited in the form of a figure on the horizontal axis of the four predictive variables, respectively. Secondly, each figure on the horizontal axis vertically corresponds to the "Points Line" at the top of the nomogram, resulting in a new score ranging from 0 to 100. Third, the "Total Points" were obtained by summation of each new score of the four predictors, which ranges from 0 to 160. Finally, the "Risk" is read by drawing a vertical line from the "Total Points Line" downward the "Risk Line," which ranges from 0 to 1. The value of the "Risk" represents the likelihood of endoscopic activity in individuals with UC. As the "Total Points" amount rises, the "Risk" value likewise climbs proportionally.

Figure 3.Nomogram developed for predicting endoscopic activity in patients with ulcerative colitis. APTT, activated partial thromboplastin time; FOBT, fecal occult blood test; FDP, fibrinogen degradation product.

Table 2. Multivariate Logistic Regression Analysis of Endoscopic Activity in Patients with Ulcerative Colitis

VariableOR (95% CI)p-value
APTT1.16 (1.04–1.31)0.011
FOBT9.53 (3.35–32.71)<0.001
β2-Globulin0.47 (0.24–0.85)0.018
FDP3.15 (1.54–7.14)0.003

OR, odds ratio; CI, confidence interval; APTT, activated partial thromboplastin time; FOBT, fecal occult blood test; FDP, fibrinogen degradation product.



3. The performance and validation of the nomogram

The AUC of the nomogram in the training group (Fig. 4A) was 0.858 (95% CI, 0.781 to 0.936), and in the validation group (Fig. 4B) was 0.845 (95% CI, 0.731 to 0.960), indicating that this model had good discriminatory ability. To assess the consistency between anticipated and actual probabilities in these two groups, a calibration plot and the Hosmer-Lemeshow test was employed. The results were χ2=3.121 (p=0.959) in the training group (Fig. 5A) and χ2=6.032 (p=0.737) in the validation group (Fig. 5B), which exhibited good predicting accuracy of the nomogram. The decision curve analysis of this model was performed to evaluate the clinical validity in the training group (Fig. 6A) and validation group (Fig. 6B), showing that the net benefits of these two groups were significantly higher than the two extreme cases, where no patients had endoscopic activity, and all patients had endoscopic activity. In other words, the produced nomogram can guide therapeutic decisions as a prospective tool.

Figure 4.Receiver operating characteristic (ROC) curve of the nomogram for predicting the endoscopic activity in patients with ulcerative colitis. (A) ROC curve in the training group; (B) ROC curve in the validation group. AUC, area under the ROC.

Figure 5.Calibration curve of the nomogram for predicting the endoscopic activity in patients with ulcerative colitis. (A) Calibration curve in the training group. (B) Calibration curve in the validation group.

Figure 6.Decision curve of the nomogram for predicting the endoscopic activity in patients with ulcerative colitis. (A) Decision curve in the training group. (B) Decision curve in the validation group.

The prevalence of UC has significantly grown across various regions of the world over the past several decades, leading to a noticeable rise in the social and economic burden on the global healthcare system and healthcare professionals.25 The government must effectively address the management of this intricate and costly ailment by implementing early prevention strategies for UC and introducing innovative measures to enhance existing healthcare systems. According to the second European evidence-based consensus for the management of UC, endoscopic remission was regarded as the ideal therapeutic goal.26 Previous studies highlighted the association between one or several parameters and the endoscopic activity of UC, paying less attention to the development of a prediction model.16,17,27 Therefore, in the present study, a more acceptable and less costly nomogram model was developed for monitoring the disease activity of UC based on noninvasive biomarkers (blood and fecal samples). This model would enable the early detection of individuals with endoscopic activity and guide clinical practice.

Through multivariate logistic regression analysis, four variables were considered as the independent risk factors and selected for the construction of the final model: APTT, FOBT, β2-globulin, and FDP. This nomogram was a reliable prediction tool for monitoring the endoscopic activity of patients with UC not only in the training group but also in the validation group. This study specifically examined the ability of combined biomarkers to predict disease activity in patients with UC, comparing it to the use of FCT. The study included a small sample size of 27 UC patients, which was due to the limited use of FCT and its high cost (Supplementary Fig. 1). The results showed that employing combined biomarkers (APTT, FOBT, β2-globulin, and FDP) was more effective than FCT (p=0.03).

It has long been seen that autoimmune disorders play an important role in the etiopathogenesis of UC.2 β2-globulin is a small protein with a low molecular weight. Its levels in the blood rise during immunological activation and autoimmune disorders because of its structural similarity to immunoglobulin domains. Macrophages and T-lymphocytes release this protein.28 The immunity in the severe activity or steroid-resistant UC is usually suppressed, which leads to a decrease in the number of lymphocytes. This suggests that the decrease in β2-globulin can be considered a reliable indicator for assessing the activity of UC. In addition, clinical evidence showed that β2-globulin is related to the activity of inflammatory bowel disease as well. Zissis et al.29 conducted a study including 74 cases of Crohn's disease and 68 cases of control individuals. The study revealed a correlation between the levels of β2-globulin in the blood serum and the disease activity in patients with Crohn's disease. Yamamoto-Furusho et al.30 also found a correlation between β2-globulin serum levels and the histological activity of patients with UC (p=0.02).

Patients with UC who experienced intestinal damage were shown to have a blood coagulation disease characterized by symptoms such as bleeding, hypercoagulability, and hyperfunction of the fibrinolytic system.31,32 The prolongation of APTT in UC patients may be related to the consumption of coagulation factors. Patients with UC frequently have a hypercoagulable condition, commonly resulting from intestinal inflammation. When UC disease is active, inflammatory intestinal endothelial cell damage exposes negatively charged collagen fibers beneath the endothelium, activating factor XII and thus initiating the endogenous coagulation pathway. The compromised intestinal mucosal cells secrete tissue factors, which then bind to factor VIIa to form a complex, ultimately initiating the exogenous coagulation pathway.33 In addition, Stadnicki revealed that patients with UC had a considerably longer APTT compared to control participants. This suggests that APTT can be used as a dependable indicator for assessing the disease activity of UC.34

A crosstalk between inflammation and coagulation exists in UC patients.33 Inflammation can promote the activation of coagulation and cause hyperfunction of the fibrinolytic system via various pathways. Inflammation can stimulate the initiation of blood clotting and result in excessive activity of the system that breaks down blood clots through different mechanisms. These mechanisms include reducing the effectiveness of anticoagulants and the fibrinolytic activities, elevating the levels of homocysteine, and contributing to the series of reactions that lead to a coagulation cascade. This can potentially lead to an increase in the levels of FDP, which are markers associated with the breakdown of blood clots.35 Furthermore, basic research has shown that FDP can produce inflammatory effects by upregulating the expression of interleukin-6 and tumor necrosis factor, which may exacerbate the degree of intestinal inflammation in active UC patients.36 Currently, the efficacy of preventive heparin medication is uncertain. However, it is considered safe to administer anticoagulant therapy to high-risk patients with UC.37 Additionally, high serum FDP levels are easily presented in patients with UC who have experienced an active phase and have failed to respond to medicinal intervention.38,39 Several investigators have proposed that FDP might emerge as a potential predictor for detecting endoscopic activity in patients with UC.40 The clinical evidence was consistent with the results obtained from the present study.

Fecal biomarkers are also the common noninvasive parameters for monitoring the disease activity of patients with UC.14 Despite initial indications that FCT could serve as a reliable predictor, subsequent research has revealed that this fecal marker is not able to distinguish between UC and Crohn's disease effectively. It is also susceptible to the influence of medications such as nonsteroidal anti-inflammatory drugs, have inconsistent cutoff levels, and is unable to determine the extent of colonic involvement accurately.14,19,41,42 FOBT, also called fecal immunochemical test, is generally a fast and inexpensive technique that could be easily used in routine facilities that indicated bleeding from inflammation in the intestines.43 Takashima et al.44 reported that FOBT was a more sensitive method for determining the remission of patients with UC than FCT (AUC 0.833 vs 0.581, p=0.025). A recent study suggests that FOBT was a specific (AUC, 0.800; 95% CI, 0.670 to 0.890) and sensitive (AUC, 0.720; 95% CI, 0.570 to 0.840) predictor for detecting disease activity in UC.45 These findings encourage the use of FOBT as a preferred technique in medical practice for evaluating the clinical status of patients with UC.

There are some limitations to this study as well. First, the present study was conducted at a single center, which is consistent with the majority of research on correlation analysis and prediction models of UC lacking support from multicenter data.46-48 However, it needed the support of data from multicenter. Therefore, further research should be performed to validate these results among a larger study patient. Secondly, the study primarily focused on endoscopic mucosal healing and did not assess histological mucosal healing. However, this aspect will be investigated in future research.

In conclusion, this nomogram based on noninvasive parameters (APTT, FOBT, β2-globulin, and FDP) was a highly accurate tool for detecting the endoscopic activity of UC, which simultaneously avoids repeated, costly, time-consuming, and painful endoscopic examinations. From a clinical viewpoint, the present model was convenient for doctors and healthcare professionals in treating and managing these patients in any medical institute, such as a hospital, community, nursing home, etc. Further prospective studies with large samples of UC patients are required to determine the role of these risk factors as useful treatment targets.

This study was supported by the Jiading District Health Commission of Shanghai (grant number: 2023KY10; Kangqiao Xu) and the National Key R&D Program of China (grant numbers: 2020YFC2009000 and 2020YFC2009001; Songbai Zheng).

Study concept and design: Sangdan Zhuoga. Data acquisition: Sangdan Zhuoga, W.Q. Data analysis and interpretation: G.G., K.X. Drafting of the manuscript; critical revision of the manuscript for important intellectual content: G.G. Statistical analysis: G.G., K.X. Obtained funding: K.X., S.Z. Administrative, technical, or material support; study supervision: Songbai Zheng, T.W., X.Y. Endoscopic evaluation: D.J. Approval of final manuscript: all authors.

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Article

ahead

Gut and Liver

Published online April 16, 2024

Copyright © Gut and Liver.

A New Risk Prediction Model for Detecting Endoscopic Activity of Ulcerative Colitis

Guoyu Guan1 , Sangdan Zhuoga1 , Songbai Zheng1 , Kangqiao Xu2 , Tingwen Weng3 , Wensi Qian4 , Danian Ji5 , Xiaofeng Yu6

1Department of Gastroenterology, Huadong Hospital, Shanghai Medical College Fudan University, Shanghai, China; 2Department of Respiration, Jiading Branch of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Departments of 3Cardiology, 4Hematology, 5Endoscopy Center, and 6General Practice, Huadong Hospital, Shanghai Medical College Fudan University, Shanghai, China

Correspondence to:Songbai Zheng
ORCID https://orcid.org/0000-0003-4453-4664
E-mail songbai1009@163.com

Received: September 19, 2023; Revised: December 13, 2023; Accepted: January 11, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background/Aims: Ulcerative colitis (UC) is an incurable, relapsing-remitting inflammatory disease that increases steadily. Mucosal healing has become the primary therapeutic objective for UC. Nevertheless, endoscopic assessments are invasive, expensive, time-consuming, and inconvenient. Therefore, it is crucial to develop a noninvasive predictive model to monitor endoscopic activity in patients with UC.
Methods: Clinical data of 198 adult patients with UC were collected from January 2016 to August 2022 at Huadong Hospital, China.
Results: Patients with UC were randomly divided into the training cohort (70%, n=138) and the validation cohort (30%, n=60). The receiver operating characteristic curve value for the training group was 0.858 (95% confidence interval [CI], 0.781 to 0.936), whereas it was 0.845 (95% CI, 0.731 to 0.960) for the validation group. The calibration curve employed the Hosmer-Lemeshow test (p>0.05) to demonstrate the consistency between the predicted and the actual probabilities in the nomogram of these two groups. The decision curve analysis validated that the nomogram had clinical usefulness.
Conclusions: The nomogram, which incorporated activated partial thromboplastin time, fecal occult blood test, β2-globulin level, and fibrinogen degradation products, served as a prospective tool for evaluating UC activity in clinical practices.

Keywords: Ulcerative colitis, Fecal marker, Blood marker, Endoscopy, Nomograms

INTRODUCTION

Ulcerative colitis (UC) is an inflammatory disease characterized by recurring episodes of inflammation that is incurable. The frequency of UC has been continuously increasing worldwide, particularly in developing nations.1 To date, the pathogenesis and etiology of UC remain unknown; however, they may be related to immunological, inflammatory, environmental, or genetic factors.2 Bloody purulent stool is the main clinical manifestation of UC, usually accompanied by abdominal pain and diarrhea.3 Currently, immunosuppressants, biological agents, and 5-aminosalicylic are the main treatment options for UC.4,5 Most patients with UC require long-term treatment, surveillance, and management, which can even be life-long. Endoscopy is a common, efficient, and preferable method used for monitoring the activity of inflammatory diseases.6 Mucosal healing has become the main therapeutic objective for UC since it is strongly associated with long-lasting clinical remission and reduced incidence of colectomy.7-10 Nevertheless, endoscopy is associated with several limitations, including invasiveness, expensive healthcare costs, and intricate preoperative preparations.11 Additionally, there are potential hazards associated with conducting a colonoscopy, such as the risk of perforation and bleeding. This risk is particularly heightened among older people and persons with a history of inflammatory bowel disease.12

It is important to promote the use of non-endoscopic indicators of disease activity to predict the severity of the disease and assess the clinical prognosis.13 Over the past decades, a growing number of studies have shown that biological disease markers, including C-reactive protein (CRP), erythrocyte sedimentation rate, hemoglobin, platelets (PLT), blood leukocytes, vitamin D, fecal calprotectin test (FCT), and fecal immunochemical test have been proposed as the indicators for estimating the activity of the disease.14-17 An ideal biomarker should include attributes that make it readily applicable for routine clinical use, and it should have the ability to detect and distinguish specific diseases in individuals accurately. Unfortunately, such a biomarker has not yet been determined for UC. The current blood markers lack specificity and sensitivity in assessing endoscopic activity due to the potential influence of inflammatory, autoimmune, and infectious diseases beyond the digestive system, which elevates the levels of these markers in the blood.18

Currently, fecal indicators have demonstrated greater specificity in comparison to blood markers, although some other conditions may also elevate the level of such fecal markers, including colonic tumors and treatment with anti-inflammation or nonsteroidal drugs.19 Emerging data suggested that the prediction model with the combined use of blood and fecal markers for detecting endoscopic activity in patients with UC tends to be more specific and sensitive compared to their separate detection.20 However, the process of testing these biomarkers, such as FCT, is inaccessible, time-consuming, and expensive in clinical practice. It is necessary to develop a new risk prediction model that incorporates universal blood and fecal markers. This approach would replace the need for endoscopic inspections and alleviate the physical and financial constraints faced by patients with UC.

A nomogram is a graphical tool that is frequently used to condense complex statistical prediction models into a single numerical estimate of the likelihood of a clinical occurrence. It is known for its simplicity, intuitiveness, and widespread usage.21 The present retrospective study aimed to develop a new risk prediction model based on noninvasive parameters of blood and stool for well-detecting endoscopic activity in patients with UC. Moreover, the clinical results provided another choice for monitoring and guiding the medicating of patients with UC, circumventing the need for routine colonoscopy exams.

MATERIALS AND METHODS

1. Study patients and design

The study was approved by the Biomedical Research Ethics Committee at Huadong Hospital (Number: 2021K174). A total of 493 adult patients with a diagnosed UC, as recorded in the Electronic Medical Record System, were included in this study and referred to Huadong Hospital, affiliated with Fudan University, between January 2016 and August 2022. The inclusion criteria were as follows: (1) patients clearly diagnosed with UC according to clinical consensus;22 (2) patients aged ≥18 years; and (3) patients who had undergone the overall examinations of blood and stool within 7 days before or after undergoing colonoscopy. The key exclusion criteria were as follows: (1) patients who had not been hospitalized previously (n=290); (2) patients lacking information regarding colonoscopy examinations (n=5); (3) pregnant women (n=0); or (4) patients with autoimmune illnesses, severe underlying conditions, persistent infectious diseases, or malignant tumors (n=0). Finally, a total of 198 patients with UC were found to be eligible for the analysis in this study. These patients were randomly divided into the training cohort (70%, n=138) and the validation cohort (30%, n=60), as Fig. 1 shows. The Mayo endoscopic subscore was used to evaluate disease activity based on endoscopic assessments conducted by skilled endoscopists.23 Mayo endoscopic subscore=0 was defined as "endoscopic remission," while Mayo endoscopic subscore ≥1 was defined as "endoscopic activity." The extension of endoscopic activity was categorized into three distinct types (proctitis, left-sided, and extensive) based on the Montreal classification.24

Figure 1. Flowchart of this study. UC, ulcerative colitis.

2. Clinical data collection

Predominantly relevant parameters of the patients were collected according to the literature review.14-17 Demographic variables and clinical characteristics were recorded, including age, gender, Montreal classification, medications, and comorbidities. Serum biomarkers were monitored as well, involving white blood cell (×109/L), neutrophils (%), lymphocyte (%), monocyte (%), red blood cell (×1012/L), hemoglobin (g/L), red blood cell distribution width (%), PLT (×1012/L), platelet distribution width (fL), mean platelet volume (fL), alanine transaminase (U/L), aspartate transaminase (U/L), γ-glutamyl transpeptidase (U/L), alkaline phosphatase (U/L), albumin (g/L), total bilirubin (μmol/L), total cholesterol (mmol/L), triglyceride (mmol/L), high-density lipoprotein (mmol/L), low-density lipoprotein (mmol/L), blood urea nitrogen (mmol/L), serum creatinine (μmol/L), uric acid (μmol/dL), estimated glomerular filtration rate (mL/min), blood glucose (fasting, mmol/L), amylase (U/L), erythrocyte sedimentation rate (mm/hr), CRP (mg/L), procalcitonin (ng/mL), prothrombin time (sec), activated partial thromboplastin time (APTT, sec), international normalized ratio, fibrinogen (g/L), fibrinogen degradation products (FDPs, mg/L), D-dimer (DD, mg/L), immunoglobulin (Ig) A (g/L), IgG (g/L), IgM (g/L), prealbumin (g/L), α1-globulin (%), α2-globulin (%), β1-globulin (%), β2-globulin (%), peripheral antineutrophil cytoplasmic antibodies, myeloperoxidase-antineutrophil cytoplasmic antibodies, vitamin A (μmol/L), vitamin B1 (nmol/L), vitamin B2 (mg/mL), vitamin B6 (nmol/L), vitamin B9 (nmol/L), vitamin C (μmol/L), 1,25-(OH)2-vitamin D3 (nmol/L), vitamin E (μg/mL), procollagen type I C-terminal propeptide (pg/μL), procollagen type I N-terminal propeptide (ng/mL), osteocalcin (ng/mL), and parathyroid hormone (pg/mL). The fecal marker exhibited a fecal occult blood test (FOBT). In the current study, the above biomarkers were detected within 7 days before or after colonoscopy.

3. Statistical analysis

This study used the Kolmogorov-Smirnov test to assess the normality of data distribution for continuous variables. The data on normal distribution was represented by the mean and standard deviation and was compared using the independent samples t-test. Data that did not follow a normal distribution were presented using the median and interquartile range and were compared using the Mann-Whitney U test. Descriptive statistics was conducted with frequencies for categorical variables presented as percentages (%) and using the chi-square or Fisher exact tests for comparison.

Statistical analysis employing univariate logistic regression was performed to assess the significance of variables and to screen for potential predictors of endoscopic activity in patients with UC. A p-value less than 0.05 was considered statistically significant and included in the multivariable logistic regression analysis. The nomogram model was constructed using variables that had a p-value less than 0.05, which were then further filtered by multivariable analysis. These predictors were presented as odds ratio and 95% confidence interval (CI).

The performance of the prediction model was evaluated with respect to discriminatory capacity, calibration ability, and clinical effectiveness. The area under the curve (AUC) or receiver operating characteristic displayed the discriminatory capacity, which equaled the concordance index (c-index). The calibration accuracy was assessed by the utilization of a visual calibration plot and the Hosmer-Lemeshow test. The decision curve analysis was utilized to evaluate the clinical efficacy of the prediction model. Two-tailed p<0.05 was regarded as significant. The R version 4.2.0 was used for all statistical analysis (R Foundation for Statistical Computing, Vienna, Austria).

RESULTS

1. Baseline characteristics of patients with UC

Of the 493 patients with UC, 198 were entered into the study. In the training cohort, patients were classified as endoscopic activity (n=102) and endoscopic remission (n=36). In the validation cohort, patients were sorted as endoscopic activity (n=43) and endoscopic remission (n=17). Out of the individuals that were excluded, 290 were not the first hospitalization, and five did not have data on colonoscopy examinations. The baseline characteristics of the individuals with UC are shown in Table 1. The median age of included patients was 56.0 years (interquartile range, 41.2 to 64.0 years), and 52.0% of patients (103/198) were male. Statistically significant differences (p<0.05) were seen in six variables between the two groups–red blood cell, hemoglobin, red blood cell distribution width, serum creatinine, DD, and IgM. According to Fig. 2, patients with active UC had greater odds ratio for FDP, α1-globulin, β1-globulin, FOBT, PLT, prothrombin time, APTT, fibrinogen, DD, and CRP. They also had lower odds ratio for vitamin B1, β2-globulin, and mean platelet volume compared to inactive individuals.

Figure 2. Univariate logistic regression analysis of endoscopic activity in patients with ulcerative colitis. OR, odds ratio; CI, confidence interval; FDP, fibrinogen degradation product; FOBT, fecal occult blood test; PLT, platelet; PT, prothrombin time; APTT, activated partial thromboplastin time; FBG, fibrinogen; DD, D-dimer; CRP, C-reactive protein; MPV, mean platelet volume.

Table 1 . Baseline Characteristics of Patients with Ulcerative Colitis.

VariableValidation cohort (n=60)Training cohort (n=138)p-value
Age, yr57.00 (41.20–66.00)55.50 (41.20–63.00)0.231
Sex0.251
Male27 (45.0)76 (55.1)
Female33 (55.0)62 (44.9)
Montreal classification0.612
E131 (51.7)69 (50.0)
E214 (23.3)26 (18.8)
E315 (25.0)43 (31.2)
Medications0.332
Thiopurine1 (1.7)0
Biologics01 (0.7)
Corticosteroid23 (38.3)63 (45.7)
5-ASA36 (60.0)74 (53.6)
Hematological variable
WBC, ×109/L6.05 (4.85–7.30)6.40 (5.10–7.77)0.150
NEUT, %59.90±10.0061.90±10.700.219
LYM, %30.30±8.9328.90±9.580.335
MONO, %6.16 (5.10–6.90)6.00 (5.00–7.00)0.995
RBC, ×1012/L4.26 (3.93–4.53)4.46 (4.10–4.81)0.039
Hb, g/dL12.80 (11.60–13.60)13.30 (12.00–14.50)0.047
RDW, %12.90 (12.70–13.50)12.70 (12.30–13.30)0.028
PLT, ×1012/L0.23 (0.18–0.28)0.22 (0.18–0.27)0.381
PDW, fL15.90 (15.40–16.20)16.00 (15.50–16.40)0.280
MPV, fL9.90 (9.00–10.60)9.97 (9.00–11.00)0.368
Biochemical variable
ALT, U/L14.00 (11.00–22.20)14.00 (9.93–20.00)0.397
AST, U/L17.00 (14.00–22.00)16.10 (13.00–20.00)0.205
γ-GGT, U/L18.60 (13.10–25.60)16.10 (11.10–23.80)0.168
AKP, U/L67.40 (60.50–79.00)67.00 (55.00–78.50)0.435
ALB, g/L42.80 (39.80–45.00)43.00 (40.00–45.90)0.606
TBIL, μmol/L9.45 (6.83–13.80)9.85 (7.50–13.00)0.715
TCHO, mmol/L4.31±0.974.44±0.940.407
TG, mmol/L1.00 (0.79–1.25)1.21 (0.86–1.57)0.053
HDL, mmol/L1.37 (1.29–1.43)1.35 (1.24–1.49)0.691
LDL, mmol/L2.66 (2.26–2.99)2.70 (2.28–2.99)0.907
BUN, mmol/L4.45 (3.30–5.27)4.60 (3.50–5.30)0.715
SCR, μmol/L67.20 (56.60–75.50)72.00 (60.50–83.10)0.016
UA, μmol/dL27.10 (22.10–35.00)29.80 (24.50–36.00)0.148
eGFR, mL/min99.20 (90.80–108.00)96.10 (85.00–108.00)0.143
BG, mmol/L4.95 (4.38–5.30)4.90 (4.40–5.38)0.887
AMY, U/L63.10 (58.60–75.00)67.50 (56.40–86.00)0.268
ESR, mm/hr12.00 (6.00–21.00)12.00 (6.00–22.00)0.749
CRP, mg/L6.55 (2.36–8.54)4.64 (2.10–8.49)0.677
PCT, ng/mL0.22 (0.18–0.27)0.22 (0.18–0.26)0.619
Coagulation variable
PT, sec11.80 (11.20–12.90)11.50 (11.10–12.30)0.320
APTT, sec29.20 (27.00–35.40)29.90 (27.40–33.30)0.760
INR0.98 (0.96–1.03)0.98 (0.95–1.03)0.862
FBG, g/L3.16 (2.48–3.48)3.07 (2.57–3.56)0.959
FDP, mg/L1.46 (0.89–2.37)1.40 (0.93–2.07)0.392
DD, mg/L0.34 (0.23–0.75)0.29 (0.18–0.55)0.014
Immunological variable
IgA, g/L2.07 (1.89–2.65)2.15 (1.88–2.55)0.671
IgG, g/L12.20 (11.70–13.40)12.30 (11.60–13.30)0.732
IgM, g/L0.91 (0.76–1.13)1.06 (0.86–1.36)0.004
PA, mg/L232.00 (211.00–241.00)230.00 (203.00–245.00)0.815
α1-Globulin, %3.70 (3.47–4.64)3.71 (3.45–4.41)0.616
α2-Globulin, %7.51 (7.11–8.71)7.40 (6.98–8.13)0.189
β1-Globulin, %6.29 (5.94–6.69)6.26 (5.93–6.58)0.364
β2-Globulin, %4.08 (3.72–4.76)4.21 (3.86–4.80)0.526
pANCA, n (%)0.222
No50 (83.3)125 (90.6)
Yes10 (16.7)13 (9.4)
MPO-ANCA, n (%)0.515
No59 (98.3)137 (99.3)
Yes1 (1.7)1 (0.7)
Vitamin variable
Vitamin A, μmol/L0.59 (0.54–0.70)0.61 (0.55–0.69)0.605
Vitamin B1, nmol/L61.60 (58.20–68.20)60.90 (56.60–67.20)0.673
Vitamin B2, mg/mL0.24 (0.21–0.25)0.24 (0.21–0.26)0.522
Vitamin B6, nmol/L26.70 (25.40–29.80)26.60 (24.10–29.70)0.420
Vitamin B9, nmol/L12.30 (11.60–13.10)12.30 (11.40–13.50)0.995
Vitamin C, μmol/L38.10 (36.70–41.10)38.70 (36.50–41.50)0.662
1,25-(OH)2- Vitamin D3, nmol/L17.80 (14.90–20.50)18.70 (13.00–21.80)0.796
Vitamin E, μg/mL11.00 (10.30–11.40)10.90 (10.40–11.60)0.754
Bone metabolism
PICP, pg/μL0.66 (0.53–0.76)0.69 (0.60–0.78)0.367
PINP, ng/mL50.60 (39.60–57.90)53.40 (40.20–64.30)0.284
OC, ng/mL21.50 (16.20–24.70)22.60 (18.70–24.70)0.256
PTH, pg/mL40.00 (35.60–44.10)39.90 (36.20–44.40)0.990
FOBT0.899
No30 (50.0)72 (52.2)
Yes30 (50.0)66 (47.8)
Comorbidity
Diabetes0.558
No57 (95.0)127 (92.0)
Yes3 (5.0)11 (8.0)
Hypertension0.595
No46 (76.7)112 (81.2)
Yes14 (23.3)26 (18.8)
Thyroid nodule0.898
No43 (71.7)96 (69.6)
Yes17 (28.3)42 (30.4)

Data are presented as median (interquartile range), number (%), or mean±SD..

E1, proctitis; E2, left-sided; E3, extensive; 5-ASA, 5-amino salicylic acid; WBC, white blood cell; NEUT, neutrophils; LYM, lymphocyte; MONO, monocyte; RBC, red blood cell; Hb, hemoglobin; RDW, red blood cell distribution width; PLT, platelet; PDW, platelet distribution width; MPV, mean platelet volume; ALT, alanine transaminase; AST, aspartate transaminase; γ-GGT, γ-glutamyl transpeptidase; AKP, alkaline phosphatase; ALB, albumin; TBIL, total bilirubin; TCHO, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; BUN, blood urea nitrogen; SCR, serum creatinine; UA, uric acid; eGFR, estimated glomerular filtration rate; BG, blood glucose (fasting); AMY, amylase; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; PCT, procalcitonin; prothrombin time; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, international normalized ratio; FBG, fibrinogen; FDP, fibrinogen degradation product; DD, D-dimer; PA, prealbumin; p-ANCA, peripheral antineutrophil cytoplasmic antibodies; MPO-ANCA, myeloperoxidase-antineutrophil cytoplasmic antibodies; PICP, procollagen type I C-terminal propeptide; PINP, procollagen type I N-terminal propeptide; OC, osteocalcin; PTH, parathyroid hormone; FOBT, fecal occult blood test..



2. Predictive model development

The findings of univariate logistic regression analysis are displayed in Fig. 2. Thirteen variables with statistical significance (p<0.05) were initially assessed, including vitamin B1, FDP, α1-globulin, β1-globulin, β2-globulin, FOBT, PLT, PT, APTT, fibrinogen, DD, CRP, and mean platelet volume. Then, the 13 parameters were analyzed by multivariate logistic regression for further selection. Four variables (APTT, FOBT, β2-globulin, and FDP) were identified as the independent risk variables and used to construct the final model (Fig. 3) based on multivariate logistic regression analysis (Table 2). The specific steps for interpreting the nomogram are as follows. Firstly, the initial data of each individual was intuitively exhibited in the form of a figure on the horizontal axis of the four predictive variables, respectively. Secondly, each figure on the horizontal axis vertically corresponds to the "Points Line" at the top of the nomogram, resulting in a new score ranging from 0 to 100. Third, the "Total Points" were obtained by summation of each new score of the four predictors, which ranges from 0 to 160. Finally, the "Risk" is read by drawing a vertical line from the "Total Points Line" downward the "Risk Line," which ranges from 0 to 1. The value of the "Risk" represents the likelihood of endoscopic activity in individuals with UC. As the "Total Points" amount rises, the "Risk" value likewise climbs proportionally.

Figure 3. Nomogram developed for predicting endoscopic activity in patients with ulcerative colitis. APTT, activated partial thromboplastin time; FOBT, fecal occult blood test; FDP, fibrinogen degradation product.

Table 2 . Multivariate Logistic Regression Analysis of Endoscopic Activity in Patients with Ulcerative Colitis.

VariableOR (95% CI)p-value
APTT1.16 (1.04–1.31)0.011
FOBT9.53 (3.35–32.71)<0.001
β2-Globulin0.47 (0.24–0.85)0.018
FDP3.15 (1.54–7.14)0.003

OR, odds ratio; CI, confidence interval; APTT, activated partial thromboplastin time; FOBT, fecal occult blood test; FDP, fibrinogen degradation product..



3. The performance and validation of the nomogram

The AUC of the nomogram in the training group (Fig. 4A) was 0.858 (95% CI, 0.781 to 0.936), and in the validation group (Fig. 4B) was 0.845 (95% CI, 0.731 to 0.960), indicating that this model had good discriminatory ability. To assess the consistency between anticipated and actual probabilities in these two groups, a calibration plot and the Hosmer-Lemeshow test was employed. The results were χ2=3.121 (p=0.959) in the training group (Fig. 5A) and χ2=6.032 (p=0.737) in the validation group (Fig. 5B), which exhibited good predicting accuracy of the nomogram. The decision curve analysis of this model was performed to evaluate the clinical validity in the training group (Fig. 6A) and validation group (Fig. 6B), showing that the net benefits of these two groups were significantly higher than the two extreme cases, where no patients had endoscopic activity, and all patients had endoscopic activity. In other words, the produced nomogram can guide therapeutic decisions as a prospective tool.

Figure 4. Receiver operating characteristic (ROC) curve of the nomogram for predicting the endoscopic activity in patients with ulcerative colitis. (A) ROC curve in the training group; (B) ROC curve in the validation group. AUC, area under the ROC.

Figure 5. Calibration curve of the nomogram for predicting the endoscopic activity in patients with ulcerative colitis. (A) Calibration curve in the training group. (B) Calibration curve in the validation group.

Figure 6. Decision curve of the nomogram for predicting the endoscopic activity in patients with ulcerative colitis. (A) Decision curve in the training group. (B) Decision curve in the validation group.

DISCUSSION

The prevalence of UC has significantly grown across various regions of the world over the past several decades, leading to a noticeable rise in the social and economic burden on the global healthcare system and healthcare professionals.25 The government must effectively address the management of this intricate and costly ailment by implementing early prevention strategies for UC and introducing innovative measures to enhance existing healthcare systems. According to the second European evidence-based consensus for the management of UC, endoscopic remission was regarded as the ideal therapeutic goal.26 Previous studies highlighted the association between one or several parameters and the endoscopic activity of UC, paying less attention to the development of a prediction model.16,17,27 Therefore, in the present study, a more acceptable and less costly nomogram model was developed for monitoring the disease activity of UC based on noninvasive biomarkers (blood and fecal samples). This model would enable the early detection of individuals with endoscopic activity and guide clinical practice.

Through multivariate logistic regression analysis, four variables were considered as the independent risk factors and selected for the construction of the final model: APTT, FOBT, β2-globulin, and FDP. This nomogram was a reliable prediction tool for monitoring the endoscopic activity of patients with UC not only in the training group but also in the validation group. This study specifically examined the ability of combined biomarkers to predict disease activity in patients with UC, comparing it to the use of FCT. The study included a small sample size of 27 UC patients, which was due to the limited use of FCT and its high cost (Supplementary Fig. 1). The results showed that employing combined biomarkers (APTT, FOBT, β2-globulin, and FDP) was more effective than FCT (p=0.03).

It has long been seen that autoimmune disorders play an important role in the etiopathogenesis of UC.2 β2-globulin is a small protein with a low molecular weight. Its levels in the blood rise during immunological activation and autoimmune disorders because of its structural similarity to immunoglobulin domains. Macrophages and T-lymphocytes release this protein.28 The immunity in the severe activity or steroid-resistant UC is usually suppressed, which leads to a decrease in the number of lymphocytes. This suggests that the decrease in β2-globulin can be considered a reliable indicator for assessing the activity of UC. In addition, clinical evidence showed that β2-globulin is related to the activity of inflammatory bowel disease as well. Zissis et al.29 conducted a study including 74 cases of Crohn's disease and 68 cases of control individuals. The study revealed a correlation between the levels of β2-globulin in the blood serum and the disease activity in patients with Crohn's disease. Yamamoto-Furusho et al.30 also found a correlation between β2-globulin serum levels and the histological activity of patients with UC (p=0.02).

Patients with UC who experienced intestinal damage were shown to have a blood coagulation disease characterized by symptoms such as bleeding, hypercoagulability, and hyperfunction of the fibrinolytic system.31,32 The prolongation of APTT in UC patients may be related to the consumption of coagulation factors. Patients with UC frequently have a hypercoagulable condition, commonly resulting from intestinal inflammation. When UC disease is active, inflammatory intestinal endothelial cell damage exposes negatively charged collagen fibers beneath the endothelium, activating factor XII and thus initiating the endogenous coagulation pathway. The compromised intestinal mucosal cells secrete tissue factors, which then bind to factor VIIa to form a complex, ultimately initiating the exogenous coagulation pathway.33 In addition, Stadnicki revealed that patients with UC had a considerably longer APTT compared to control participants. This suggests that APTT can be used as a dependable indicator for assessing the disease activity of UC.34

A crosstalk between inflammation and coagulation exists in UC patients.33 Inflammation can promote the activation of coagulation and cause hyperfunction of the fibrinolytic system via various pathways. Inflammation can stimulate the initiation of blood clotting and result in excessive activity of the system that breaks down blood clots through different mechanisms. These mechanisms include reducing the effectiveness of anticoagulants and the fibrinolytic activities, elevating the levels of homocysteine, and contributing to the series of reactions that lead to a coagulation cascade. This can potentially lead to an increase in the levels of FDP, which are markers associated with the breakdown of blood clots.35 Furthermore, basic research has shown that FDP can produce inflammatory effects by upregulating the expression of interleukin-6 and tumor necrosis factor, which may exacerbate the degree of intestinal inflammation in active UC patients.36 Currently, the efficacy of preventive heparin medication is uncertain. However, it is considered safe to administer anticoagulant therapy to high-risk patients with UC.37 Additionally, high serum FDP levels are easily presented in patients with UC who have experienced an active phase and have failed to respond to medicinal intervention.38,39 Several investigators have proposed that FDP might emerge as a potential predictor for detecting endoscopic activity in patients with UC.40 The clinical evidence was consistent with the results obtained from the present study.

Fecal biomarkers are also the common noninvasive parameters for monitoring the disease activity of patients with UC.14 Despite initial indications that FCT could serve as a reliable predictor, subsequent research has revealed that this fecal marker is not able to distinguish between UC and Crohn's disease effectively. It is also susceptible to the influence of medications such as nonsteroidal anti-inflammatory drugs, have inconsistent cutoff levels, and is unable to determine the extent of colonic involvement accurately.14,19,41,42 FOBT, also called fecal immunochemical test, is generally a fast and inexpensive technique that could be easily used in routine facilities that indicated bleeding from inflammation in the intestines.43 Takashima et al.44 reported that FOBT was a more sensitive method for determining the remission of patients with UC than FCT (AUC 0.833 vs 0.581, p=0.025). A recent study suggests that FOBT was a specific (AUC, 0.800; 95% CI, 0.670 to 0.890) and sensitive (AUC, 0.720; 95% CI, 0.570 to 0.840) predictor for detecting disease activity in UC.45 These findings encourage the use of FOBT as a preferred technique in medical practice for evaluating the clinical status of patients with UC.

There are some limitations to this study as well. First, the present study was conducted at a single center, which is consistent with the majority of research on correlation analysis and prediction models of UC lacking support from multicenter data.46-48 However, it needed the support of data from multicenter. Therefore, further research should be performed to validate these results among a larger study patient. Secondly, the study primarily focused on endoscopic mucosal healing and did not assess histological mucosal healing. However, this aspect will be investigated in future research.

In conclusion, this nomogram based on noninvasive parameters (APTT, FOBT, β2-globulin, and FDP) was a highly accurate tool for detecting the endoscopic activity of UC, which simultaneously avoids repeated, costly, time-consuming, and painful endoscopic examinations. From a clinical viewpoint, the present model was convenient for doctors and healthcare professionals in treating and managing these patients in any medical institute, such as a hospital, community, nursing home, etc. Further prospective studies with large samples of UC patients are required to determine the role of these risk factors as useful treatment targets.

ACKNOWLEDGEMENTS

This study was supported by the Jiading District Health Commission of Shanghai (grant number: 2023KY10; Kangqiao Xu) and the National Key R&D Program of China (grant numbers: 2020YFC2009000 and 2020YFC2009001; Songbai Zheng).

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Study concept and design: Sangdan Zhuoga. Data acquisition: Sangdan Zhuoga, W.Q. Data analysis and interpretation: G.G., K.X. Drafting of the manuscript; critical revision of the manuscript for important intellectual content: G.G. Statistical analysis: G.G., K.X. Obtained funding: K.X., S.Z. Administrative, technical, or material support; study supervision: Songbai Zheng, T.W., X.Y. Endoscopic evaluation: D.J. Approval of final manuscript: all authors.

SUPPLEMENTARY MATERIALS

Supplementary materials can be accessed at https://doi.org/10.5009/gnl230370.

Fig 1.

Figure 1.Flowchart of this study. UC, ulcerative colitis.
Gut and Liver 2024; :

Fig 2.

Figure 2.Univariate logistic regression analysis of endoscopic activity in patients with ulcerative colitis. OR, odds ratio; CI, confidence interval; FDP, fibrinogen degradation product; FOBT, fecal occult blood test; PLT, platelet; PT, prothrombin time; APTT, activated partial thromboplastin time; FBG, fibrinogen; DD, D-dimer; CRP, C-reactive protein; MPV, mean platelet volume.
Gut and Liver 2024; :

Fig 3.

Figure 3.Nomogram developed for predicting endoscopic activity in patients with ulcerative colitis. APTT, activated partial thromboplastin time; FOBT, fecal occult blood test; FDP, fibrinogen degradation product.
Gut and Liver 2024; :

Fig 4.

Figure 4.Receiver operating characteristic (ROC) curve of the nomogram for predicting the endoscopic activity in patients with ulcerative colitis. (A) ROC curve in the training group; (B) ROC curve in the validation group. AUC, area under the ROC.
Gut and Liver 2024; :

Fig 5.

Figure 5.Calibration curve of the nomogram for predicting the endoscopic activity in patients with ulcerative colitis. (A) Calibration curve in the training group. (B) Calibration curve in the validation group.
Gut and Liver 2024; :

Fig 6.

Figure 6.Decision curve of the nomogram for predicting the endoscopic activity in patients with ulcerative colitis. (A) Decision curve in the training group. (B) Decision curve in the validation group.
Gut and Liver 2024; :

Table 1 Baseline Characteristics of Patients with Ulcerative Colitis

VariableValidation cohort (n=60)Training cohort (n=138)p-value
Age, yr57.00 (41.20–66.00)55.50 (41.20–63.00)0.231
Sex0.251
Male27 (45.0)76 (55.1)
Female33 (55.0)62 (44.9)
Montreal classification0.612
E131 (51.7)69 (50.0)
E214 (23.3)26 (18.8)
E315 (25.0)43 (31.2)
Medications0.332
Thiopurine1 (1.7)0
Biologics01 (0.7)
Corticosteroid23 (38.3)63 (45.7)
5-ASA36 (60.0)74 (53.6)
Hematological variable
WBC, ×109/L6.05 (4.85–7.30)6.40 (5.10–7.77)0.150
NEUT, %59.90±10.0061.90±10.700.219
LYM, %30.30±8.9328.90±9.580.335
MONO, %6.16 (5.10–6.90)6.00 (5.00–7.00)0.995
RBC, ×1012/L4.26 (3.93–4.53)4.46 (4.10–4.81)0.039
Hb, g/dL12.80 (11.60–13.60)13.30 (12.00–14.50)0.047
RDW, %12.90 (12.70–13.50)12.70 (12.30–13.30)0.028
PLT, ×1012/L0.23 (0.18–0.28)0.22 (0.18–0.27)0.381
PDW, fL15.90 (15.40–16.20)16.00 (15.50–16.40)0.280
MPV, fL9.90 (9.00–10.60)9.97 (9.00–11.00)0.368
Biochemical variable
ALT, U/L14.00 (11.00–22.20)14.00 (9.93–20.00)0.397
AST, U/L17.00 (14.00–22.00)16.10 (13.00–20.00)0.205
γ-GGT, U/L18.60 (13.10–25.60)16.10 (11.10–23.80)0.168
AKP, U/L67.40 (60.50–79.00)67.00 (55.00–78.50)0.435
ALB, g/L42.80 (39.80–45.00)43.00 (40.00–45.90)0.606
TBIL, μmol/L9.45 (6.83–13.80)9.85 (7.50–13.00)0.715
TCHO, mmol/L4.31±0.974.44±0.940.407
TG, mmol/L1.00 (0.79–1.25)1.21 (0.86–1.57)0.053
HDL, mmol/L1.37 (1.29–1.43)1.35 (1.24–1.49)0.691
LDL, mmol/L2.66 (2.26–2.99)2.70 (2.28–2.99)0.907
BUN, mmol/L4.45 (3.30–5.27)4.60 (3.50–5.30)0.715
SCR, μmol/L67.20 (56.60–75.50)72.00 (60.50–83.10)0.016
UA, μmol/dL27.10 (22.10–35.00)29.80 (24.50–36.00)0.148
eGFR, mL/min99.20 (90.80–108.00)96.10 (85.00–108.00)0.143
BG, mmol/L4.95 (4.38–5.30)4.90 (4.40–5.38)0.887
AMY, U/L63.10 (58.60–75.00)67.50 (56.40–86.00)0.268
ESR, mm/hr12.00 (6.00–21.00)12.00 (6.00–22.00)0.749
CRP, mg/L6.55 (2.36–8.54)4.64 (2.10–8.49)0.677
PCT, ng/mL0.22 (0.18–0.27)0.22 (0.18–0.26)0.619
Coagulation variable
PT, sec11.80 (11.20–12.90)11.50 (11.10–12.30)0.320
APTT, sec29.20 (27.00–35.40)29.90 (27.40–33.30)0.760
INR0.98 (0.96–1.03)0.98 (0.95–1.03)0.862
FBG, g/L3.16 (2.48–3.48)3.07 (2.57–3.56)0.959
FDP, mg/L1.46 (0.89–2.37)1.40 (0.93–2.07)0.392
DD, mg/L0.34 (0.23–0.75)0.29 (0.18–0.55)0.014
Immunological variable
IgA, g/L2.07 (1.89–2.65)2.15 (1.88–2.55)0.671
IgG, g/L12.20 (11.70–13.40)12.30 (11.60–13.30)0.732
IgM, g/L0.91 (0.76–1.13)1.06 (0.86–1.36)0.004
PA, mg/L232.00 (211.00–241.00)230.00 (203.00–245.00)0.815
α1-Globulin, %3.70 (3.47–4.64)3.71 (3.45–4.41)0.616
α2-Globulin, %7.51 (7.11–8.71)7.40 (6.98–8.13)0.189
β1-Globulin, %6.29 (5.94–6.69)6.26 (5.93–6.58)0.364
β2-Globulin, %4.08 (3.72–4.76)4.21 (3.86–4.80)0.526
pANCA, n (%)0.222
No50 (83.3)125 (90.6)
Yes10 (16.7)13 (9.4)
MPO-ANCA, n (%)0.515
No59 (98.3)137 (99.3)
Yes1 (1.7)1 (0.7)
Vitamin variable
Vitamin A, μmol/L0.59 (0.54–0.70)0.61 (0.55–0.69)0.605
Vitamin B1, nmol/L61.60 (58.20–68.20)60.90 (56.60–67.20)0.673
Vitamin B2, mg/mL0.24 (0.21–0.25)0.24 (0.21–0.26)0.522
Vitamin B6, nmol/L26.70 (25.40–29.80)26.60 (24.10–29.70)0.420
Vitamin B9, nmol/L12.30 (11.60–13.10)12.30 (11.40–13.50)0.995
Vitamin C, μmol/L38.10 (36.70–41.10)38.70 (36.50–41.50)0.662
1,25-(OH)2- Vitamin D3, nmol/L17.80 (14.90–20.50)18.70 (13.00–21.80)0.796
Vitamin E, μg/mL11.00 (10.30–11.40)10.90 (10.40–11.60)0.754
Bone metabolism
PICP, pg/μL0.66 (0.53–0.76)0.69 (0.60–0.78)0.367
PINP, ng/mL50.60 (39.60–57.90)53.40 (40.20–64.30)0.284
OC, ng/mL21.50 (16.20–24.70)22.60 (18.70–24.70)0.256
PTH, pg/mL40.00 (35.60–44.10)39.90 (36.20–44.40)0.990
FOBT0.899
No30 (50.0)72 (52.2)
Yes30 (50.0)66 (47.8)
Comorbidity
Diabetes0.558
No57 (95.0)127 (92.0)
Yes3 (5.0)11 (8.0)
Hypertension0.595
No46 (76.7)112 (81.2)
Yes14 (23.3)26 (18.8)
Thyroid nodule0.898
No43 (71.7)96 (69.6)
Yes17 (28.3)42 (30.4)

Data are presented as median (interquartile range), number (%), or mean±SD.

E1, proctitis; E2, left-sided; E3, extensive; 5-ASA, 5-amino salicylic acid; WBC, white blood cell; NEUT, neutrophils; LYM, lymphocyte; MONO, monocyte; RBC, red blood cell; Hb, hemoglobin; RDW, red blood cell distribution width; PLT, platelet; PDW, platelet distribution width; MPV, mean platelet volume; ALT, alanine transaminase; AST, aspartate transaminase; γ-GGT, γ-glutamyl transpeptidase; AKP, alkaline phosphatase; ALB, albumin; TBIL, total bilirubin; TCHO, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; BUN, blood urea nitrogen; SCR, serum creatinine; UA, uric acid; eGFR, estimated glomerular filtration rate; BG, blood glucose (fasting); AMY, amylase; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; PCT, procalcitonin; prothrombin time; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, international normalized ratio; FBG, fibrinogen; FDP, fibrinogen degradation product; DD, D-dimer; PA, prealbumin; p-ANCA, peripheral antineutrophil cytoplasmic antibodies; MPO-ANCA, myeloperoxidase-antineutrophil cytoplasmic antibodies; PICP, procollagen type I C-terminal propeptide; PINP, procollagen type I N-terminal propeptide; OC, osteocalcin; PTH, parathyroid hormone; FOBT, fecal occult blood test.


Table 2 Multivariate Logistic Regression Analysis of Endoscopic Activity in Patients with Ulcerative Colitis

VariableOR (95% CI)p-value
APTT1.16 (1.04–1.31)0.011
FOBT9.53 (3.35–32.71)<0.001
β2-Globulin0.47 (0.24–0.85)0.018
FDP3.15 (1.54–7.14)0.003

OR, odds ratio; CI, confidence interval; APTT, activated partial thromboplastin time; FOBT, fecal occult blood test; FDP, fibrinogen degradation product.


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Gut and Liver

Vol.18 No.3
May, 2024

pISSN 1976-2283
eISSN 2005-1212

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