Development and validation of a nomogram for estimation of inadequate bowel preparation before colonoscopy in adults: an observational study

Development and validation of a nomogram for estimation of inadequate bowel preparation before colonoscopy in adults: an observational study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of a nomogram for estimation of inadequate bowel preparation before colonoscopy in adults: an observational study Xin Yuan, Hui Gao, Shuhao Zheng, Weihong Wang, Jiarong Xie, Zhixin Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6187293/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Adequate bowel preparation rate is not satisfactory at present. External verification for inadequate bowel preparation is needed. Methods A total of 1,216 patients undergoing colonoscopy in a tertiary hospital in China were included. Inadequate bowel preparation was defined by the Boston Bowel Preparation Scale. 13 risk factors were included into consideration. A logistic regression model derived from the derivation cohort was used to develop the nomogram. Measures of discrimination and calibration were determined in the internal and external validation cohorts separately. Decision curve analyses (DCA) was also used for assessing the clinical efficacy. Results There was a total of 224 patients with inadequate bowel preparation (24.5%) in the derivation cohort. Only 5 risk factors were considered in the final model, including hospitalization, diabetes, constipation, history of colonoscopy and indication for colonoscopy. The model had high levels of discrimination (the areas under the receiver operating characteristic curves, AUC = 0.76) and excellent calibration in both cohorts. The Hosmer–Lemeshow test showed no statistical significance, and DCA performed preferable net benefit in the nomogram. Conclusion An updated inadequate bowel preparation nomogram was well developed and validated. The new model can help doctors easily identify those at most risk of inadequate bowel preparation. Trial registration: Clinical Trials.gov, NCT04290715. Registered on February 25, 2020. Bowel preparation Colonoscopy Prediction Nomogram Figures Figure 1 Figure 2 Figure 3 Background Adequate bowel preparation for colonoscopy has essential consequences for the quality and safety of colonoscopy[ 1 ]. Despite these importance, recent studies report adequate bowel preparation is below the recommended ideal threshold of 90%[ 2 ]. Several factors have been found to be related to inadequate bowel cleansing. The risk factors are mainly divided into two categories: one is related to the clinical characteristics of the patients, including constipation, diabetes, and so on; the other is the nonclinical risk factors related to the patients' compliance with the regimens, including indications for colonoscopy, examination time, low level of education and so on[ 3 ]. Based on these risk factors, the first model to estimate the risk of inadequate bowel preparation before colonoscopy was published in 2012, but no clinically useful model became available[ 4 ]. A validated, easy-to-use prediction score was then developed by Dik in 2015[ 5 ]. It was followed by an updated model developed by Antonio in 2017, which included fewer risk factors[ 6 ]. Both of them were targeting outpatients. More recently, a model targeting hospitalized patients was developed by Fuccio in 2021 and was presented in the form of application available online[ 7 ]. Although there are several predictive models, the European Society of Gastrointestinal Endoscopy (ESGE) guidelines suggest that no sufficient evidence to recommend the use of specialized inadequate bowel preparation predictive models for clinical practice[ 2 ]. However, the models currently available cannot be applied in the clinic. The main reason for this is that it is difficult to carry out external verification when these models are established or after publication, limiting their robustness and validity. We therefore derived and validated a new nomogram-CH 2 ID to improve the capability of estimating the risk of inadequate bowel preparation for these patients. Methods Study design and data source We undertook a prospective, observational study in a tertiary hospital in China. Taking account of an inadequate bowel preparation rate of 20% based on previous studies, the necessary sample size was calculated to be at least 650 participants in the derivation dataset with 13 variables included in the analysis. Adults scheduled for colonoscopy who agreed to participate in this study were included. In addition, when the same patient underwent repeated colonoscopy within 1 month, only the first procedure was included. The bowel preparation regimen was the most widely used protocol in our hospital, that is, consuming a low-residue diet for one day and then drinking 2L polyethylene glycol (PEG) 4–6 hours before the colonoscopy [ 8 ]. Patients who did not attempt to follow the regimen were excluded. Outcome Our outcome was inadequate bowel preparation, defined by the Boston Bowel Preparation Score (BBPS) of total score less than 6 or if any colon segment less than 2[ 9 ]. All of the trained and qualified clinical data collectors in this study had previously passed the BBPS Educational Program ( http://www.cori.org/bbps/ ). For confirmation, all of the results were reviewed by another researcher based on the endoscopic video. All authors had access to the study data and reviewed and approved the final manuscript. Predictor variables We examined the predictor variables using a questionnaire based on the established risk factors incorporated into the previous models and also selected candidate variables according to our clinical practice. Before the patient entered the endoscopic room, a specified data collector applied the questionnaire to collect the patients’ data, including basic demographics and clinical features. The 13 predictor variables presented in Table 2 . For demographic factors, illiteracy was considered to be equated with a primary education or less, and body mass index (BMI) was the most recent value recorded before the baseline date. For clinical factors, diabetes or hypertension was deemed clinically diagnosed, regardless of whether it was under drug control. All types of invasive abdominal operations were considered to be surgeries, including mini-laparotomy tubal ligation. Constipation was based on defecation during the past week (< 3 bowel movements/week and at least one of the following: straining, hard stools defined as Bristol scale 1 or 2, and incomplete evacuation)[ 10 ]. For patients with a previous history of colonoscopy, we traced whether their records of bowel preparation condition were available, and if not, they were classified as no history of colonoscopy. All indications for an abnormal tumor index, a positive stool occult blood test, and treatment for polyps or other abdominal discomfort symptoms were defined as a diagnosis, and patients with previous colorectal surgery or polypectomy were defined as surveillance. The medication factor only focused on whether they did or did not take any medications on a regular basis. Derivation and validation of the models We allocated the data from March 1, 2020 to December 31, 2020 as the derivation dataset and the data from January 1, 2021 to April 30, 2021 as the validation cohort. Comparing the baseline characteristics, t-tests or Mann-Whitney U tests was conducted for continuous variables and Fisher’s exact test or the Chi-square test for categorical variables as appropriate. Variable conversion was carried out only when the nonlinearity value of the continuous variable was < 0.05. Logistic regression analysis was performed to identify predictors independently associated with inadequate bowel preparation, estimating odds ratios (ORs) and 95% confidence intervals (95% CIs). The variables with P < 0.1 in univariate regression or with clinical significance were incorporate in the multivariate regression analysis. Then, a backward elimination procedure was performed to select the significant variables which was finally presented in the nomogram. The discrimination of the new model was assessed by drawing the receiver operating characteristic curve (ROC) and computing the area under the curve (AUC) in the derivation and validation cohorts. The calibration of the predictive model was assessed by drawing calibration curves and computing the Hosmer-Lemeshow test. The internal validation was performed by enhanced bootstrapping with 1000 bootstrapping resamples. To evaluate the clinical effectiveness of the model, we drew a decision curve analysis (DCA) curve. This study abided by the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement for reporting, and the analyses were conducted using R software (version 4.0.3, http://www.Rproject.org ). A two-sided p value<0.05 was defined as statistical significance. Results Study population Overall, 1,644 patients participated in our study (Fig. 1 ). We excluded 365 patients who did not follow the 2 L PEG regimen, 37 patients who exceeded the 6-hour interval between the end of preparation and the time of colonoscopy, 15 patients who did not receive their first colonoscopy within 1 month, and 11 patients who failed the colonoscopy due to inadequate bowel preparation. Finally, 1,216 patients undergoing colonoscopy in China met our criteria. Among these, the derivation cohort aged 18–87 years included 916 patients, and the validation dataset age 22–83 years included 300 patients. The characteristics of the patients undergoing colonoscopy in the development and validation cohorts are listed in Table 1 . Among the people with inadequate bowel preparation, nearly half of the patients were hospitalized (50.9% in the development cohort and 52.4% in the validation cohort). Common diseases such as diabetes or hypertension were correlated with 70% of cases of inadequate bowel preparation, while constipation occurred in 90%. For the indication of colonoscopy, the most common was a general physical examination, followed by diagnosis and surveillance. Risk factors selection For the continuous variables in the dataset, we tested their linearity initially, and both the nonlinearity values of age and BMI were 0.20 and 0.16, respectively. Therefore, we did not convert them into another form. After univariate analysis, the participants’ baseline characteristics, including older age, low education, hospitalization, high BMI, diabetes, constipation, a history of inadequate bowel preparation, and physical examination for colonoscopy, were significantly associated with inadequate bowel preparation. The multivariate logistic regression analyses showed that hospitalization, diabetes, constipation, a history of inadequate bowel preparation, and an indication for colonoscopy were independent risk factors for inadequate bowel preparation (Table 2 ). Nomogram construction and validation The predictive nomogram for adequate bowel preparation was established based on the previous five prognostic factors selected by backward analysis (Fig. 2 a). To use the nomogram, first, locate various indicators of subjects on the relevant axis. Then the fraction of the corresponding point was obtained on the top abscissa. Add the scores and find the corresponding point on the total points axis, and then find the corresponding percentage on the predicted value to represent the possibility of adequate bowel preparation. For the internal validation, the ROC curve showed that the resulting model had a fairly good discriminatory ability with an AUC of 0.76 (0.72–0.80). The Hosmer–Lemeshow test showed no statistical significance (P = 0.97), the slope was 1, and the intercept was 0, indicating that the model fit well. In addition, the calibration plan was graphically displayed, and the predicted and observed data agreed well in the validation cohort (Fig. 2 b). In the external validation cohort, the model demonstrated good discriminatory ability with an AUC of 0.77 (0.70–0.84). As shown in the calibration curve (Fig. 2 c), nonstatistical significance (P = 0.35) was obtained in the Hosmer–Lemeshow test, the slope was 0.97, and the intercept was 0.17, which also indicated good calibration. Clinical use Figure 3 shows the DCA of the nomogram. The decision curve from the development cohort demonstrated that the capacity of the model to predict the occurrence of adequate bowel preparation was superior to a ‘treat-all-patients’ or ‘treat-no-patient’ management approach. We found that the threshold probability for clinical decisions was less than 80%. Finally, we derived a web-based application ( https://nbdy.shinyapps.io/abppapp/ ) as an easy-to-use tool for physicians to assess the probability of adequate colon cleansing among adults. Discussion In this observational, prospective study, we found that clinical factors, including hospitalization, diabetes, constipation, a history of inadequate bowel preparation, and the indication for colonoscopy, were independent risk factors for inadequate bowel preparation. Different from previous studies targeting inpatients or outpatients only, based on these risk factors, we built an easy-to-use, brief, and individualized nomogram applicable-CH 2 ID to all adults to identify those at risk for inadequate bowel preparation before colonoscopy. Different from most previous models, our study included fewer predictors and confirmed in a large cohort of patients the critical role of simple clinical factors such as hospitalization, diabetes, constipation and previous colonoscopy in predicting inadequate bowel preparation. Consistent with existing models and other previous studies, we found that diabetes and constipation are independent risk factors[ 4 – 7 , 11 ]. For the indications, Hassan et al. suggested a correlation between occult blood positivity and bowel preparation[ 4 ]. We detailed the indications for patients undergoing colonoscopy, considered it an independent risk factor for inadequate bowel preparation ( P <0.05), and used it as a predictive factor[ 10 ]. We speculate that this may be caused by the protopathy itself and the consequent importance of the colonoscopy to the patient. In our model, hospitalization status was also identified as an important influencing factor. A specific model was developed for this population in 2020, but they used a different bowel preparation method than in previous studies. Larger doses of laxative may not make it cleaner. Regarding hospitalization and a history of inadequate bowel preparation, Dik also believed these were high-risk factors[ 5 ]. Kumar et al. evaluated the relationship between functional status and bowel preparation and thought it might be a predictive factor[ 12 ]. A history of inadequate bowel preparation showed a strong correlation with current inadequate bowel preparation (OR = 45.41, P <0.001). In clinical practice, we found that patients could not clearly describe whether there was a history of inadequate bowel preparation. Compared with the existing imaging evidence, 5% of the patients misdescribed their previous bowel preparation condition. Only the relationship between bowel preparation and the previous colonoscopy results in patients with available imaging evidence was analyzed, and the results showed that this factor was a strong independent risk factor, as mentioned above. However, the current colonoscopy examination report shows few bowel preparation images and lacks a description or score of the status of the colon cleanliness, such as the BBPS[ 13 ]. Therefore, we consider it necessary to standardize the endoscopy report, ensuring it includes the results for the bowel preparation conditions[ 14 ]. This change may help improve the model in the future. Compared to the previous models, our nomogram is based on both outpatients and inpatients to improve the generalizability of the model, and the predictors are fewer and easier to obtain. In addition, we validated the model internally and externally, showing excellent discrimination and calibration. At present, there are many bowel preparation regimens, and the dosages vary from 1 L to 4 L[ 15 ]. Individualized bowel preparation for patients with different risks would help to improve the adequate bowel preparation rate and patient compliance. For example, for low-risk patients, using 2 L or less medication regimens can not only achieve a considerable bowel preparation quality but also achieve higher patient satisfaction[ 16 ], and for high-risk patients, high-dose medications, a combination of laxative drugs or a split-dose may be used to obtain higher quality bowel preparation[ 17 – 19 ]. Our study excluded patients with inadequate bowel preparation due to a lack of compliance. This is different from the uncleanliness caused by patients' physiological factors, and we can improve the cleanliness of these patients by improving their education[ 20 ]. Therefore, for the high-risk patients predicted by the model, a personalized intensive bowel preparation regime may be helpful. Moreover, as in many previous studies, we also used the BBPS score as an outcome indicator. The purpose of our study was to predict colon cleanliness based on individual conditions that patients cannot change; however, some of the BBPS scores can reach a satisfactory level after endoscopic irrigation and other operations even if the Ottawa Bowel Preparation Scale (OBPS) score is evaluated as disappointing when entering the colon. This type of patient is not the focus of the prediction. In addition, in clinical practice, we found that some endoscopes have more stringent requirements for bowel preparation cleanliness. As reported in some previous studies, they believe that a BBPS ≥ 8 means adequate bowel cleaning, which is different from the current widely used cutoff value of 6[ 21 ]. However, more research is necessary to prove the relationship between different BBPS scores and the detection rate of polyps and adenomas. The model may be improved and updated in this respect. There are limitations of the present study. First, our model lacks a detailed analysis of the influencing factors of routine medication use. Most outpatients are unable to describe the specific drugs they are taking. Although detailed information can be obtained for inpatients, the use of medications is complex, and different drugs have different effects on intestinal motility[ 22 , 23 ]. Additional studies may be needed to determine the effects of various drugs on bowel preparation. Second, we only analyzed the significance of diabetes and hypertension. They are the most common types of chronic diseases worldwide and the patients’ status is easier to obtain than for other diseases, which makes the model have better clinical practicability[ 24 ]. Third, in our study involving more than 1200 patients in total, we found a rate of inadequate bowel preparation as high as 24%. The reason for this might be the unified bowel preparation regime that was adopted in this study. This unsatisfactory rate is consistent with previous studies, underlining the need for new and more effective models to identify patients at high risk of inadequate bowel preparation. Finally, the source of these data is discontinuous data. We did not include all patients who underwent colonoscopy during the study period, but no participants were specifically selected during the data collection, and there were no significant differences in their baseline data such as age and sex. Conclusions Our study identified patient-related unchangeable factors influencing the adequacy of colon cleansing among adults undergoing colonoscopy. After validation internally and externally, the inclusion of a few easily evaluated variables in the new model can help doctors easily identify those at most risk of inadequate bowel preparation. Overall, the nomogram is feasible and could be used to make reasonable predictions. Future work could help improve the accuracy of the nomogram. Declarations Availability of data and materials All data generated or analysed during this study are included in this published article. Acknowledgments The authors would like to thank all participants without whom this study was impossible. The authors thank AJE for the language editing. Author contributions: conception and design: XY and LX; analysis and interpretation of the data: HG, CL, SZ and WW; drafting of the article: XY; critical revision of the article for important intellectual content: JX and ZZ; final approval of the article: XY, HG, SZ, CL, WW, JX, ZZ and LX. Funding The Zhejiang Provincial Health Commission had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Ethics approval and consent to participate All participants provided their written informed consent to participate in this study. The study protocol was reviewed and approved by the medical ethics committee of Ningbo first hospital (2019-R066). The study was registered with Clinical Trials.gov (NCT04290715) and adhered to the Declaration of Helsinki and its later amendments. Consent for publication Not applicable. Conflict of interest: The authors declare no conflicts of interest. References Sharma P, Burke CA, Johnson DA, Cash BD. The importance of colonoscopy bowel preparation for the detection of colorectal lesions and colorectal cancer prevention. Endoscopy Int open. 2020;8(5):E673–83. Hassan C, East J, Radaelli F, Spada C, Benamouzig R, Bisschops R, et al. Bowel preparation for colonoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Guideline - Update 2019. Endoscopy. 2019;51(8):775–94. Martel M, Ménard C, Restellini S, Kherad O, Almadi M, Bouchard M, et al. Which Patient-Related Factors Determine Optimal Bowel Preparation? Curr Treat Options Gastroenterol. 2018;16(4):406–16. Hassan C, Fuccio L, Bruno M, Pagano N, Spada C, Carrara S, et al. 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Participant Characteristics Characteristic Development cohort Validation cohort IBP , N = 224 1 ABP , N = 692 1 P Value IBP , N = 63 1 ABP , N = 237 1 P Value age a 60 (50, 68) 54 (45, 62) <0.001 64 (52, 71) 52 (43, 63) 0.001 gender female 105 (46.9%) 317 (45.8%) 0.781 24 (38.1%) 97 (40.9%) 0.684 male 119 (53.1%) 375 (54.2%) 39 (61.9%) 140 (59.1%) education illiteracy or primary 92 (41.1%) 231 (33.4%) 0.043 25 (39.7%) 63 (26.6%) 0.145 junior high 64 (28.6%) 179 (25.9%) 20 (31.7%) 73 (30.8%) high 27 (12.1%) 117 (16.9%) 6 (9.5%) 33 (13.9%) college or higher 41 (18.3%) 165 (23.8%) 12 (19.0%) 68 (28.7%) hospitalization no 110 (49.1%) 493 (71.2%) <0.001 33 (52.4%) 81 (34.2%) <0.001 yes 114 (50.9%) 199 (28.8%) 13 (20.6%) 45 (19.0%) BMI b 23.37 (4.03) 22.87 (3.46) 0.094 23.99 (3.29) 23.06 (3.25) 0.046 comorbidity no 158 (70.5%) 626 (90.5%) <0.001 32 (50.8%) 174 (73.4%) 0.001 yes 66 (29.5%) 66 (9.5%) 31 (49.2%) 63 (26.6%) diabetes no 158 (70.5%) 632 (91.3%) <0.001 43 (68.3%) 224 (94.5%) <0.001 yes 66 (29.5%) 60 (8.7%) 20 (31.7%) 13 (5.5%) hypertension no 153 (68.3%) 530 (76.6%) 0.013 46 (73.0%) 181 (76.4%) 0.581 yes 71 (31.7%) 162 (23.4%) 17 (27.0%) 56 (23.6%) ASA score≥3 no 114 (50.9%) 484 (69.9%) <0.001 32 (50.8%) 174 (73.4%) 0.001 yes 110 (49.1%) 208 (30.1%) 31 (49.2%) 63 (26.6%) surgery colorectal surgery 14 (6.2%) 20 (2.9%) 0.025 1 (1.6%) 8 (3.4%) 0.127 other abdominal/pelvis surgery 79 (35.3%) 217 (31.4%) 28 (44.4%) 74 (31.2%) no surgery 131 (58.5%) 455 (65.8%) 34 (54.0%) 155 (65.4%) constipation no 203 (90.6%) 680 (98.3%) <0.001 57 (90.5%) 233 (98.3%) 0.007 yes 21 (9.4%) 12 (1.7%) 6 (9.5%) 4 (1.7%) examination time morning 45 (20.1%) 149 (21.5%) 0.646 12 (19.0%) 47 (19.8%) 0.746 afternoon 179 (79.9%) 543 (78.5%) 51 (81.0%) 190 (80.2%) history of colonoscopy history of ABP 5 (2.2%) 80 (11.6%) <0.001 4 (6.3%) 24 (10.1%) 0.020 history of IBP 28 (12.5%) 11 (1.6%) 3 (4.8%) 1 (0.4%) no 191 (85.3%) 601 (86.8%) 56 (88.9%) 212 (89.5%) indication diagnosis 88 (39.3%) 217 (31.4%) 0.008 24 (38.1%) 72 (30.4%) 0.013 physical examination 106 (47.3%) 410 (59.2%) 29 (46.0%) 151 (63.7%) surveillance 30 (13.4%) 65 (9.4%) 10 (15.9%) 13 (5.5%) medication use no 46 (20.5%) 263 (38.0%) <0.001 29 (46.0%) 123 (51.9%) 0.514 yes 178 (79.5%) 429 (62.0%) 34 (54.0%) 112 (48.1%) TCA use no 219 (97.8%) 687 (99.3%) 0.129 62 (98.4%) 236 (99.6%) 0.889 yes 5 (2.2%) 5 (0.7%) 1 (1.6%) 1 (0.4%) Opioid use no 224 (100%) 692 (100%) - 63 (100%) 237 (100%) - yes 0 0 0 0 1 n (%); a Median (IQR); b Median (SD). ABP, adequate bowel preparation; BMI, body mass index; IBP, inadequate bowel preparation; IQR, inter quartile range; SD, standard deviation; TCA, tricyclic antidepressant. Table 2. Univariate and Multivariate Logistic Regression Analysis of Adequate Bowel Preparation Risk Factors Based on Preoperative Data in the Development Cohort Characteristic Univariate analysis Multivariate analysis OR 1 95% CI 1 p-value OR 1 95% CI 1 p-value age 0.98 0.97, 1.00 0.041 0.99 0.97, 1.00 0.069 gender male — — female 0.98 0.68, 1.43 0.928 education illiteracy or primary — — junior high 1.16 0.68, 1.98 0.597 high 1.86 0.98, 3.50 0.056 college or higher 1.65 0.94, 2.91 0.083 hospitalization yes — — — — no 2.58 1.61, 4.14 <0.001 2.35 1.63, 3.39 <0.001 BMI 0.96 0.91, 1.00 0.056 0.96 0.92, 1.00 0.066 diabetes yes — — — — no 3.12 1.99, 4.90 <0.001 3.24 2.08, 5.03 <0.001 hypertension yes — — no 1.05 0.70, 1.58 0.812 surgery other abdominal/pelvis surgery — — colorectal surgery 0.71 0.26, 1.95 0.510 no surgery 0.93 0.63, 1.38 0.732 constipation yes — — — — no 7.47 3.18, 17.55 <0.001 7.25 3.16, 16.66 <0.001 examination time afternoon — — morning 1.50 0.92, 2.43 0.101 history of colonoscopy no — — — — history of ABP 10.16 3.48, 29.64 <0.001 9.91 3.43, 28.61 <0.001 history of IBP 0.19 0.08, 0.45 <0.001 0.20 0.09, 0.46 <0.001 indication surveillance — — — — physical examination 3.08 1.37, 6.90 0.006 3.52 1.72, 7.23 0.001 diagnosis 1.91 0.84, 4.34 0.121 2.32 1.11, 4.85 0.025 medication yes — — no 1.34 0.83, 2.16 0.238 1 OR, odds ratio; CI, confidence interval; ABP, adequate bowel preparation; BMI, body mass index; IBP, inadequate bowel preparation. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 17 Apr, 2025 Editor assigned by journal 20 Mar, 2025 Editor invited by journal 18 Mar, 2025 Submission checks completed at journal 18 Mar, 2025 First submitted to journal 18 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6187293","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444610066,"identity":"bb285fba-d6b1-4408-a7a7-41734ff7207f","order_by":0,"name":"Xin Yuan","email":"","orcid":"","institution":"The First affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Yuan","suffix":""},{"id":444610067,"identity":"8f7a84ac-c00e-4eb1-92bb-1d1eb63c17b3","order_by":1,"name":"Hui Gao","email":"","orcid":"","institution":"The First affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Gao","suffix":""},{"id":444610068,"identity":"52cd6c78-f0fc-4ed4-aae4-db2f21965df1","order_by":2,"name":"Shuhao Zheng","email":"","orcid":"","institution":"The First affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Shuhao","middleName":"","lastName":"Zheng","suffix":""},{"id":444610069,"identity":"ceac62e4-cc78-494a-a273-e4348e1ae003","order_by":3,"name":"Weihong Wang","email":"","orcid":"","institution":"The First affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Weihong","middleName":"","lastName":"Wang","suffix":""},{"id":444610070,"identity":"47b425b3-6471-48f7-8626-41d74707c2da","order_by":4,"name":"Jiarong Xie","email":"","orcid":"","institution":"The First affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Jiarong","middleName":"","lastName":"Xie","suffix":""},{"id":444610071,"identity":"7f6d2c4b-3daf-4ec2-ba0c-a0085ce5fcf4","order_by":5,"name":"Zhixin Zhang","email":"","orcid":"","institution":"The First affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Zhixin","middleName":"","lastName":"Zhang","suffix":""},{"id":444610072,"identity":"cf8471b0-d7bc-4634-88ad-c2fe0a1b71e8","order_by":6,"name":"Lei Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYFAC5gaGhAobOTb29gPEamEEajmTZszHcyaBBC2MbYcT50k4GBCnweBGYuODB2zM6W0SDAkMPyq2EdYiOSOx2SCBhy23TbrxAGPPmduEtfBLJLZJJEjw5LbJHEhgZmwjQgubRGL7jwQDiXQ2iQQD4rSAbGFISDBIIF6LZM/DZomEAwmGbcBAPkiUXwyOJx/8+PPff3n59vaDD35UEKEFBRwgUf0oGAWjYBSMAlwAAGQ+Oy3hQngFAAAAAElFTkSuQmCC","orcid":"","institution":"The First affiliated Hospital of Ningbo University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-03-09 07:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6187293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6187293/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81011931,"identity":"e45c6442-34e5-4962-ae75-ffcdcb9d1847","added_by":"auto","created_at":"2025-04-21 08:25:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the patients’ enrollment. PEG, polyethylene glycol.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6187293/v1/f05c0e2def8db2f1a5a9b35c.png"},{"id":81011928,"identity":"f41b93d0-15b2-4048-9fb1-3d23a6e51cc7","added_by":"auto","created_at":"2025-04-21 08:25:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for preoperative estimation of inadequate bowel preparation risk and its predictive performance. \u003c/strong\u003e(A) Nomogram to estimate the probability of adequate bowel preparation. (B) Calibration curves of the nomogram in the development cohort. (C) Calibration curves of the nomogram in the validation cohort.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6187293/v1/2ca5be91c4ea44511e2196e2.png"},{"id":81011930,"identity":"e0e09871-35bf-46de-9d85-c29b8df9f1a2","added_by":"auto","created_at":"2025-04-21 08:25:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of the decision curve analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6187293/v1/8f0c680b684e463c8dc8281c.png"},{"id":81016406,"identity":"2596b417-4f6f-4d13-8f95-7af052a46bcf","added_by":"auto","created_at":"2025-04-21 08:57:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1280695,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6187293/v1/e8480c7f-5465-469c-b417-03f9876d456c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a nomogram for estimation of inadequate bowel preparation before colonoscopy in adults: an observational study","fulltext":[{"header":"Background","content":"\u003cp\u003eAdequate bowel preparation for colonoscopy has essential consequences for the quality and safety of colonoscopy[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite these importance, recent studies report adequate bowel preparation is below the recommended ideal threshold of 90%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral factors have been found to be related to inadequate bowel cleansing. The risk factors are mainly divided into two categories: one is related to the clinical characteristics of the patients, including constipation, diabetes, and so on; the other is the nonclinical risk factors related to the patients' compliance with the regimens, including indications for colonoscopy, examination time, low level of education and so on[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Based on these risk factors, the first model to estimate the risk of inadequate bowel preparation before colonoscopy was published in 2012, but no clinically useful model became available[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A validated, easy-to-use prediction score was then developed by Dik in 2015[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It was followed by an updated model developed by Antonio in 2017, which included fewer risk factors[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Both of them were targeting outpatients. More recently, a model targeting hospitalized patients was developed by Fuccio in 2021 and was presented in the form of application available online[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough there are several predictive models, the European Society of Gastrointestinal Endoscopy (ESGE) guidelines suggest that no sufficient evidence to recommend the use of specialized inadequate bowel preparation predictive models for clinical practice[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the models currently available cannot be applied in the clinic. The main reason for this is that it is difficult to carry out external verification when these models are established or after publication, limiting their robustness and validity.\u003c/p\u003e \u003cp\u003eWe therefore derived and validated a new nomogram-CH\u003csub\u003e2\u003c/sub\u003eID to improve the capability of estimating the risk of inadequate bowel preparation for these patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data source\u003c/h2\u003e \u003cp\u003eWe undertook a prospective, observational study in a tertiary hospital in China. Taking account of an inadequate bowel preparation rate of 20% based on previous studies, the necessary sample size was calculated to be at least 650 participants in the derivation dataset with 13 variables included in the analysis. Adults scheduled for colonoscopy who agreed to participate in this study were included. In addition, when the same patient underwent repeated colonoscopy within 1 month, only the first procedure was included. The bowel preparation regimen was the most widely used protocol in our hospital, that is, consuming a low-residue diet for one day and then drinking 2L polyethylene glycol (PEG) 4\u0026ndash;6 hours before the colonoscopy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Patients who did not attempt to follow the regimen were excluded.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome\u003c/h3\u003e\n\u003cp\u003eOur outcome was inadequate bowel preparation, defined by the Boston Bowel Preparation Score (BBPS) of total score less than 6 or if any colon segment less than 2[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. All of the trained and qualified clinical data collectors in this study had previously passed the BBPS Educational Program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cori.org/bbps/\u003c/span\u003e\u003cspan address=\"http://www.cori.org/bbps/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For confirmation, all of the results were reviewed by another researcher based on the endoscopic video. All authors had access to the study data and reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch3\u003ePredictor variables\u003c/h3\u003e\n\u003cp\u003eWe examined the predictor variables using a questionnaire based on the established risk factors incorporated into the previous models and also selected candidate variables according to our clinical practice. Before the patient entered the endoscopic room, a specified data collector applied the questionnaire to collect the patients\u0026rsquo; data, including basic demographics and clinical features.\u003c/p\u003e \u003cp\u003eThe 13 predictor variables presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For demographic factors, illiteracy was considered to be equated with a primary education or less, and body mass index (BMI) was the most recent value recorded before the baseline date. For clinical factors, diabetes or hypertension was deemed clinically diagnosed, regardless of whether it was under drug control. All types of invasive abdominal operations were considered to be surgeries, including mini-laparotomy tubal ligation. Constipation was based on defecation during the past week (\u0026lt;\u0026thinsp;3 bowel movements/week and at least one of the following: straining, hard stools defined as Bristol scale 1 or 2, and incomplete evacuation)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For patients with a previous history of colonoscopy, we traced whether their records of bowel preparation condition were available, and if not, they were classified as no history of colonoscopy. All indications for an abnormal tumor index, a positive stool occult blood test, and treatment for polyps or other abdominal discomfort symptoms were defined as a diagnosis, and patients with previous colorectal surgery or polypectomy were defined as surveillance. The medication factor only focused on whether they did or did not take any medications on a regular basis.\u003c/p\u003e\n\u003ch3\u003eDerivation and validation of the models\u003c/h3\u003e\n\u003cp\u003eWe allocated the data from March 1, 2020 to December 31, 2020 as the derivation dataset and the data from January 1, 2021 to April 30, 2021 as the validation cohort. Comparing the baseline characteristics, t-tests or Mann-Whitney U tests was conducted for continuous variables and Fisher\u0026rsquo;s exact test or the Chi-square test for categorical variables as appropriate. Variable conversion was carried out only when the nonlinearity value of the continuous variable was \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eLogistic regression analysis was performed to identify predictors independently associated with inadequate bowel preparation, estimating odds ratios (ORs) and 95% confidence intervals (95% CIs). The variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate regression or with clinical significance were incorporate in the multivariate regression analysis. Then, a backward elimination procedure was performed to select the significant variables which was finally presented in the nomogram.\u003c/p\u003e \u003cp\u003eThe discrimination of the new model was assessed by drawing the receiver operating characteristic curve (ROC) and computing the area under the curve (AUC) in the derivation and validation cohorts. The calibration of the predictive model was assessed by drawing calibration curves and computing the Hosmer-Lemeshow test. The internal validation was performed by enhanced bootstrapping with 1000 bootstrapping resamples. To evaluate the clinical effectiveness of the model, we drew a decision curve analysis (DCA) curve.\u003c/p\u003e \u003cp\u003eThis study abided by the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement for reporting, and the analyses were conducted using R software (version 4.0.3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.Rproject.org\u003c/span\u003e\u003cspan address=\"http://www.Rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A two-sided \u003cem\u003ep\u003c/em\u003e value\u0026lt;0.05 was defined as statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eOverall, 1,644 patients participated in our study (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). We excluded 365 patients who did not follow the 2 L PEG regimen, 37 patients who exceeded the 6-hour interval between the end of preparation and the time of colonoscopy, 15 patients who did not receive their first colonoscopy within 1 month, and 11 patients who failed the colonoscopy due to inadequate bowel preparation. Finally, 1,216 patients undergoing colonoscopy in China met our criteria. Among these, the derivation cohort aged 18\u0026ndash;87 years included 916 patients, and the validation dataset age 22\u0026ndash;83 years included 300 patients. The characteristics of the patients undergoing colonoscopy in the development and validation cohorts are listed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eAmong the people with inadequate bowel preparation, nearly half of the patients were hospitalized (50.9% in the development cohort and 52.4% in the validation cohort). Common diseases such as diabetes or hypertension were correlated with 70% of cases of inadequate bowel preparation, while constipation occurred in 90%. For the indication of colonoscopy, the most common was a general physical examination, followed by diagnosis and surveillance.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eRisk factors selection\u003c/h3\u003e\n\u003cp\u003eFor the continuous variables in the dataset, we tested their linearity initially, and both the nonlinearity values of age and BMI were 0.20 and 0.16, respectively. Therefore, we did not convert them into another form. After univariate analysis, the participants\u0026rsquo; baseline characteristics, including older age, low education, hospitalization, high BMI, diabetes, constipation, a history of inadequate bowel preparation, and physical examination for colonoscopy, were significantly associated with inadequate bowel preparation. The multivariate logistic regression analyses showed that hospitalization, diabetes, constipation, a history of inadequate bowel preparation, and an indication for colonoscopy were independent risk factors for inadequate bowel preparation (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eNomogram construction and validation\u003c/h3\u003e\n\u003cp\u003eThe predictive nomogram for adequate bowel preparation was established based on the previous five prognostic factors selected by backward analysis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). To use the nomogram, first, locate various indicators of subjects on the relevant axis. Then the fraction of the corresponding point was obtained on the top abscissa. Add the scores and find the corresponding point on the total points axis, and then find the corresponding percentage on the predicted value to represent the possibility of adequate bowel preparation. For the internal validation, the ROC curve showed that the resulting model had a fairly good discriminatory ability with an AUC of 0.76 (0.72\u0026ndash;0.80). The Hosmer\u0026ndash;Lemeshow test showed no statistical significance (P\u0026thinsp;=\u0026thinsp;0.97), the slope was 1, and the intercept was 0, indicating that the model fit well. In addition, the calibration plan was graphically displayed, and the predicted and observed data agreed well in the validation cohort (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). In the external validation cohort, the model demonstrated good discriminatory ability with an AUC of 0.77 (0.70\u0026ndash;0.84). As shown in the calibration curve (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec), nonstatistical significance (P\u0026thinsp;=\u0026thinsp;0.35) was obtained in the Hosmer\u0026ndash;Lemeshow test, the slope was 0.97, and the intercept was 0.17, which also indicated good calibration.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical use\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the DCA of the nomogram. The decision curve from the development cohort demonstrated that the capacity of the model to predict the occurrence of adequate bowel preparation was superior to a \u0026lsquo;treat-all-patients\u0026rsquo; or \u0026lsquo;treat-no-patient\u0026rsquo; management approach. We found that the threshold probability for clinical decisions was less than 80%. Finally, we derived a web-based application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nbdy.shinyapps.io/abppapp/\u003c/span\u003e\u003c/span\u003e) as an easy-to-use tool for physicians to assess the probability of adequate colon cleansing among adults.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this observational, prospective study, we found that clinical factors, including hospitalization, diabetes, constipation, a history of inadequate bowel preparation, and the indication for colonoscopy, were independent risk factors for inadequate bowel preparation. Different from previous studies targeting inpatients or outpatients only, based on these risk factors, we built an easy-to-use, brief, and individualized nomogram applicable-CH\u003csub\u003e2\u003c/sub\u003eID to all adults to identify those at risk for inadequate bowel preparation before colonoscopy.\u003c/p\u003e \u003cp\u003eDifferent from most previous models, our study included fewer predictors and confirmed in a large cohort of patients the critical role of simple clinical factors such as hospitalization, diabetes, constipation and previous colonoscopy in predicting inadequate bowel preparation. Consistent with existing models and other previous studies, we found that diabetes and constipation are independent risk factors[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the indications, Hassan et al. suggested a correlation between occult blood positivity and bowel preparation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. We detailed the indications for patients undergoing colonoscopy, considered it an independent risk factor for inadequate bowel preparation (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), and used it as a predictive factor[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. We speculate that this may be caused by the protopathy itself and the consequent importance of the colonoscopy to the patient.\u003c/p\u003e \u003cp\u003eIn our model, hospitalization status was also identified as an important influencing factor. A specific model was developed for this population in 2020, but they used a different bowel preparation method than in previous studies. Larger doses of laxative may not make it cleaner. Regarding hospitalization and a history of inadequate bowel preparation, Dik also believed these were high-risk factors[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Kumar et al. evaluated the relationship between functional status and bowel preparation and thought it might be a predictive factor[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A history of inadequate bowel preparation showed a strong correlation with current inadequate bowel preparation (OR\u0026thinsp;=\u0026thinsp;45.41, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). In clinical practice, we found that patients could not clearly describe whether there was a history of inadequate bowel preparation. Compared with the existing imaging evidence, 5% of the patients misdescribed their previous bowel preparation condition. Only the relationship between bowel preparation and the previous colonoscopy results in patients with available imaging evidence was analyzed, and the results showed that this factor was a strong independent risk factor, as mentioned above. However, the current colonoscopy examination report shows few bowel preparation images and lacks a description or score of the status of the colon cleanliness, such as the BBPS[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, we consider it necessary to standardize the endoscopy report, ensuring it includes the results for the bowel preparation conditions[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This change may help improve the model in the future.\u003c/p\u003e \u003cp\u003eCompared to the previous models, our nomogram is based on both outpatients and inpatients to improve the generalizability of the model, and the predictors are fewer and easier to obtain. In addition, we validated the model internally and externally, showing excellent discrimination and calibration. At present, there are many bowel preparation regimens, and the dosages vary from 1 L to 4 L[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Individualized bowel preparation for patients with different risks would help to improve the adequate bowel preparation rate and patient compliance. For example, for low-risk patients, using 2 L or less medication regimens can not only achieve a considerable bowel preparation quality but also achieve higher patient satisfaction[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and for high-risk patients, high-dose medications, a combination of laxative drugs or a split-dose may be used to obtain higher quality bowel preparation[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our study excluded patients with inadequate bowel preparation due to a lack of compliance. This is different from the uncleanliness caused by patients' physiological factors, and we can improve the cleanliness of these patients by improving their education[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, for the high-risk patients predicted by the model, a personalized intensive bowel preparation regime may be helpful.\u003c/p\u003e \u003cp\u003eMoreover, as in many previous studies, we also used the BBPS score as an outcome indicator. The purpose of our study was to predict colon cleanliness based on individual conditions that patients cannot change; however, some of the BBPS scores can reach a satisfactory level after endoscopic irrigation and other operations even if the Ottawa Bowel Preparation Scale (OBPS) score is evaluated as disappointing when entering the colon. This type of patient is not the focus of the prediction. In addition, in clinical practice, we found that some endoscopes have more stringent requirements for bowel preparation cleanliness. As reported in some previous studies, they believe that a BBPS\u0026thinsp;\u0026ge;\u0026thinsp;8 means adequate bowel cleaning, which is different from the current widely used cutoff value of 6[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, more research is necessary to prove the relationship between different BBPS scores and the detection rate of polyps and adenomas. The model may be improved and updated in this respect.\u003c/p\u003e \u003cp\u003eThere are limitations of the present study. First, our model lacks a detailed analysis of the influencing factors of routine medication use. Most outpatients are unable to describe the specific drugs they are taking. Although detailed information can be obtained for inpatients, the use of medications is complex, and different drugs have different effects on intestinal motility[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additional studies may be needed to determine the effects of various drugs on bowel preparation. Second, we only analyzed the significance of diabetes and hypertension. They are the most common types of chronic diseases worldwide and the patients\u0026rsquo; status is easier to obtain than for other diseases, which makes the model have better clinical practicability[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Third, in our study involving more than 1200 patients in total, we found a rate of inadequate bowel preparation as high as 24%. The reason for this might be the unified bowel preparation regime that was adopted in this study. This unsatisfactory rate is consistent with previous studies, underlining the need for new and more effective models to identify patients at high risk of inadequate bowel preparation. Finally, the source of these data is discontinuous data. We did not include all patients who underwent colonoscopy during the study period, but no participants were specifically selected during the data collection, and there were no significant differences in their baseline data such as age and sex.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study identified patient-related unchangeable factors influencing the adequacy of colon cleansing among adults undergoing colonoscopy. After validation internally and externally, the inclusion of a few easily evaluated variables in the new model can help doctors easily identify those at most risk of inadequate bowel preparation. Overall, the nomogram is feasible and could be used to make reasonable predictions. Future work could help improve the accuracy of the nomogram.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants without whom this study was impossible. The authors thank AJE for the language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003econception and design: XY and LX; analysis and interpretation of the data: HG, CL, SZ and WW; drafting of the article: XY; critical revision of the article for important intellectual content: JX and ZZ; final approval of the article: XY, HG, SZ, CL, WW, JX, ZZ and LX.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Zhejiang Provincial Health Commission had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided their written informed consent to participate in this study. The study protocol was reviewed and approved by the medical ethics committee of Ningbo first hospital (2019-R066). The study was registered with Clinical Trials.gov (NCT04290715) and adhered to the Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSharma P, Burke CA, Johnson DA, Cash BD. The importance of colonoscopy bowel preparation for the detection of colorectal lesions and colorectal cancer prevention. Endoscopy Int open. 2020;8(5):E673\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassan C, East J, Radaelli F, Spada C, Benamouzig R, Bisschops R, et al. 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World J Gastroenterol. 2015;21(13):3994\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaida M, Sinagra E, Morreale GC, Sferrazza S, Scalisi G, Schillaci D, et al. Effectiveness of very low-volume preparation for colonoscopy: A prospective, multicenter observational study. World J Gastroenterol. 2020;26(16):1950\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan X, Zhang Z, Xie J, Zhang Y, Xu L, Wang W et al. (2021) Comparison of 1L Adjuvant Auxiliary Preparations with 2L Solely Polyethylene Glycol plus Ascorbic Acid Regime for Bowel Cleaning: A Meta-analysis of Randomized, Controlled Trials. Biomed Res Int 2021: 6638858.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng CL, Kuo YL, Liu NJ, Lin CH, Tang JH, Tsui YN, et al. Impact of Bowel Preparation with Low-Volume (2-Liter) and Intermediate-Volume (3-Liter) Polyethylene Glycol on Colonoscopy Quality: A Prospective Observational Study. Digestion. 2015;92(3):156\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRestellini S, Kherad O, Menard C, Martel M, Barkun AN. Do adjuvants add to the efficacy and tolerance of bowel preparations? A meta-analysis of randomized trials. Endoscopy. 2018;50(2):159\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorton N, Garber A, Hasson H, Lopez R, Burke CA. Impact of Single- vs. Split-Dose Low-Volume Bowel Preparations on Bowel Movement Kinetics, Patient Inconvenience, and Polyp Detection: A Prospective Trial. Am J Gastroenterol. 2016;111(9):1330\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarco Antonio Alvarez-Gonzalez, Miguel \u0026Aacute;ngel Pantale\u0026oacute;n S\u0026aacute;nchez, Bel\u0026eacute;n Bernad Cabredo, Ana Garc\u0026iacute;aRodr\u0026iacute;guez, Larramona SF, Nogales O et al. 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Can J Cardiol. 2018;34(5):575\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eParticipant Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 35.6676%;\"\u003e\n \u003cp\u003eDevelopment cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 35.4985%;\"\u003e\n \u003cp\u003eValidation cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIBP\u003c/strong\u003e, N = 224\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eABP\u003c/strong\u003e, N = 692\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u003cem\u003eP Value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIBP\u003c/strong\u003e, N = 63\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eABP\u003c/strong\u003e, N = 237\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u003cem\u003eP Value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eage\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e60 (50, 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e54 (45, 62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e64 (52, 71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e52 (43, 63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e105 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e317 (45.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e24 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e97 (40.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e119 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e375 (54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e39 (61.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e140 (59.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eeducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eilliteracy or primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e92 (41.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e231 (33.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e25 (39.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e63 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ejunior high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e64 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e179 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e20 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e73 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e27 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e117 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e6 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e33 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ecollege or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e41 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e165 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e12 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e68 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ehospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e110 (49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e493 (71.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e33 (52.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e81 (34.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e114 (50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e199 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e13 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e45 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eBMI\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e23.37 (4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e22.87 (3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e23.99 (3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e23.06 (3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ecomorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e158 (70.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e626 (90.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e32 (50.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e174 (73.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e66 (29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e66 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e31 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e63 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e158 (70.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e632 (91.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e43 (68.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e224 (94.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e66 (29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e60 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e20 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e13 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ehypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e153 (68.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e530 (76.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e46 (73.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e181 (76.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e71 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e162 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e17 (27.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e56 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eASA score\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e114 (50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e484 (69.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e32 (50.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e174 (73.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e110 (49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e208 (30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e31 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e63 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003esurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ecolorectal surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e14 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e20 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e1 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e8 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eother abdominal/pelvis surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e79 (35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e217 (31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e28 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e74 (31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eno surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e131 (58.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e455 (65.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e34 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e155 (65.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003econstipation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e203 (90.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e680 (98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e57 (90.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e233 (98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e21 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e12 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e6 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e4 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eexamination time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003emorning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e45 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e149 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e12 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e47 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eafternoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e179 (79.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e543 (78.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e51 (81.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e190 (80.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ehistory of colonoscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ehistory of ABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e5 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e80 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e4 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e24 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ehistory of IBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e28 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e11 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e3 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e191 (85.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e601 (86.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e56 (88.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e212 (89.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eindication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ediagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e88 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e217 (31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e24 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e72 (30.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003ephysical examination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e106 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e410 (59.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e29 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e151 (63.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003esurveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e30 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e65 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e10 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e13 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003emedication use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e46 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e263 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e29 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e123 (51.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e178 (79.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e429 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e34 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e112 (48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eTCA use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e219 (97.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e687 (99.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e62 (98.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e236 (99.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e5 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e5 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e1 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003eOpioid use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e224 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e692 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e63 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e237 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.8186%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.0322%;\"\u003e\n \u003cp\u003e\u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.509%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.34%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003e\u003c/em\u003en (%); \u003csup\u003ea\u0026nbsp;\u003c/sup\u003eMedian (IQR);\u003csup\u003e\u0026nbsp;b\u0026nbsp;\u003c/sup\u003eMedian (SD). ABP, adequate bowel preparation; BMI, body mass index; IBP, inadequate bowel preparation; IQR, inter quartile range; SD, standard deviation; TCA, tricyclic antidepressant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Univariate and Multivariate Logistic Regression Analysis of Adequate Bowel Preparation Risk Factors Based on Preoperative Data in the Development Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 34.2342%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 30.0901%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.97, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e0.97, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.68, 1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eeducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eilliteracy or primary\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ejunior high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.68, 1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.98, 3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ecollege or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.94, 2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ehospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e1.61, 4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e1.63, 3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.91, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e0.92, 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e1.99, 4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e2.08, 5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ehypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.70, 1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003esurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eother abdominal/pelvis surgery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ecolorectal surgery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.26, 1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eno surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.63, 1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003econstipation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e7.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e3.18, 17.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e3.16, 16.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eexamination time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eafternoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003emorning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.92, 2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ehistory of colonoscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ehistory of ABP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e10.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e3.48, 29.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e9.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e3.43, 28.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ehistory of IBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.08, 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e0.09, 0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eindication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003esurveillance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ephysical examination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e1.37, 6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e1.72, 7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003ediagnosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.84, 4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e1.11, 4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003emedication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6126%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7477%;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4955%;\"\u003e\n \u003cp\u003e0.83, 2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.991%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.1261%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003e\u003c/em\u003eOR, odds ratio; CI, confidence interval; ABP, adequate bowel preparation; BMI, body mass index; IBP, inadequate bowel preparation.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bowel preparation, Colonoscopy, Prediction, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-6187293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6187293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdequate bowel preparation rate is not satisfactory at present. External verification for inadequate bowel preparation is needed.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 1,216 patients undergoing colonoscopy in a tertiary hospital in China were included. Inadequate bowel preparation was defined by the Boston Bowel Preparation Scale. 13 risk factors were included into consideration. A logistic regression model derived from the derivation cohort was used to develop the nomogram. Measures of discrimination and calibration were determined in the internal and external validation cohorts separately. Decision curve analyses (DCA) was also used for assessing the clinical efficacy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere was a total of 224 patients with inadequate bowel preparation (24.5%) in the derivation cohort. Only 5 risk factors were considered in the final model, including hospitalization, diabetes, constipation, history of colonoscopy and indication for colonoscopy. The model had high levels of discrimination (the areas under the receiver operating characteristic curves, AUC\u0026thinsp;=\u0026thinsp;0.76) and excellent calibration in both cohorts. The Hosmer\u0026ndash;Lemeshow test showed no statistical significance, and DCA performed preferable net benefit in the nomogram.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAn updated inadequate bowel preparation nomogram was well developed and validated. The new model can help doctors easily identify those at most risk of inadequate bowel preparation.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eClinical Trials.gov, NCT04290715. Registered on February 25, 2020.\u003c/p\u003e","manuscriptTitle":"Development and validation of a nomogram for estimation of inadequate bowel preparation before colonoscopy in adults: an observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 08:25:41","doi":"10.21203/rs.3.rs-6187293/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-04-17T17:07:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-20T10:55:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-18T15:23:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T14:37:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2025-03-18T14:36:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"72e9d34b-ba39-4179-833d-67c33a6d74a5","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T08:25:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 08:25:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6187293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6187293","identity":"rs-6187293","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}