Risk factors based prediction model for new-onset diabetes mellitus after first-attack acute pancreatitis

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A total of 780 patients diagnosed with AP were retrospectively enrolled in the Second Affiliated Hospital of Nanchang University from June 2016 to June 2017. A series of exclusion criteria were applied and 396 patients were finally included. With a ratio of 2:1, patients were randomly divided into two groups named training (n = 264) and validation set (n = 132). Demographic and clinical data that may be risk factors of new-onset DM after first-attack AP were collected. Univariate and multivariate analyses were used to determine potential risk factors in the training set, and a predictive nomogram was constructed. Nomogram performance was determined in the training and validation sets concerning discrimination and calibration capabilities. Finally, clinical applicability of the nomogram was assessed in the validation set by decision curve analysis. The morbidity rate of new-onset DM after first-attack AP was 8.6% (34/396) in the included patient cohort. Hyperlipemia (OR = 6.87, 95%CI = 2.33 ~ 20.26, p = 0.000), GGT ≥ 40U/L (OR = 0.07, 95%CI = 0.03 ~ 0.27, p = 0.008), serum glucose ≥ 6.1mmol/L (OR = 7.73, 95%CI = 1.89 ~ 31.64, p = 0.004), CT grade ≥ 2 or 4 points (OR = 3.16 or 4.95, 95%CI = 1.05 ~ 9.45 or 1.12 ~ 21.89, p = 0.039 or 0.035) and APACHE II grade ≥ 8 points (OR = 3.82, 95%CI = 1.19 ~ 12.27, p = 0.024) were independent risk or protective factors and were assembled for nomogram construction. Internal and external validations showed good discrimination (Area under the receiver operating characteristic curve = 0.884 and 0.770) and calibration capabilities. The decision curve analysis showed good clinical applicability. We have developed a practical nomogram to predict the risk of new-onset DM after first-attack AP based on risk factors derived from demographic and clinical data, which would contribute to the identification and management of these high-risk patients. acute pancreatitis diabetes mellitus nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hyperglycemia is a common early feature of pancreatic dysfunction such as pancreatitis, and was considered to be associated with endocrine pancreatic insufficiency due to the damage of pancreatic endocrine cells ( 1 , 2 ). Diabetes mellitus (DM) caused by excessive loss or destruction of pancreatic endocrine cells was usually called pancreatic diabetes, and had been classified as type 3c diabetes ( 3 – 5 ). Acute pancreatitis (AP) is an acute disease characterized as primary local and subsequent systemic inflammatory responses. It was reported that the incidence of newly diagnosed DM after AP (23%) was much higher compared to the general population (4–9%) ( 6 ). Therefore, the potential causal relationships and effective risk predictions for DM after AP were urgently needed to be clarified ( 7 , 8 ). A series of risk factors of DM after AP have been reported, while no widely accepted standards were established. For example, the disease severity of AP was considered correlated with the risk of new-onset DM ( 9 – 11 ), while it was not concluded in another study ( 12 ). The only few studies predicting the risk of DM after first-attack AP were not practical enough due to the incomprehensive evaluations of potential risk factors such as clinical imaging indexes, as well as the insufficient statistical and clinical validations of the constructed predictive models ( 13 , 14 ). The purpose of this study was to identify potential risk factors of new-onset DM after first-attack AP based on risk factors derived from more practical demographic and clinic data, and therefore establish an effective prediction nomogram to quantify the risks, which might contribute providing more practical management strategies for these patients. Patients and methods Patient inclusions Patients diagnosed with AP at the Second Affiliated Hospital of Nanchang University from June 2016 to June 2017 were retrospectively collected. The diagnosis of AP needs to meet at least two of the following items: 1) Consistent abdominal pain; 2) Elevated serum lipase or amylase activity (more than three times higher than the normal upper limits); 3) Abdominal imaging features of AP. Exclusion criteria were as follows: 1) Previous history of AP; 2) Previous history of DM; 3) Death in hospital or within one year after discharge; 4) Age under 18 or over 100 years old; 5) Incomplete clinical data. With a ratio of 2:1, patients were randomly divided into two groups named training (n = 264) and validation set (n = 132). The inclusion process was shown in Fig. 1 . This retrospective clinical study was approved by the institutional review board committee of the Second Affiliated Hospital of Nanchang University. Clinical data collection was conducted through electronic case collection complying with the guidelines for human follow-up data collection at the Second Affiliated Hospital of Nanchang University. Due to the complete anonymity of the retrospective study, informed consent was exempted with the approval of the Ethics Committee of the Second Affiliated Hospital of Nanchang University. Data collections Demographic and clinical data were retrospectively collected, such as gender, age, history of hyperlipemia, body mass index (BMI), white blood cells (WBC), alanine aminotransferase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), gamma-glutamyl transpeptidase (GGT) and glucose, etc. The demographic and clinical data were shown in Table 1 . Specially, the acute physiological and chronic health assessment II (APACHE II) grades ( 15 ) and CT grades ( 16 ) were calculated according to the corresponding standards. Clinical indexes were collected at the time of admission. Table 1 Demographic and clinical data of patients diagnosed of first-attack AP both in the training and validation sets. Characteristics All patients (n = 396) (%) Training set (n = 264) (%) Validation set (n = 132) (%) Gender Female 183(46.21) 116(63.39) 67(36.61) Male 213(53.79) 148(69.48) 65(30.52) Age (years old) < 30 20(5.05) 16(80) 4( 20 ) 30 ~ 50 156(39.39) 103(66.03) 53(33.97) 50 ~ 70 155(39.14) 102(65.81) 53(34.19) ≥ 70 65(16.41) 43(66.15) 22(33.85) BMI (kg/m 2 ) < 24 218(55.05) 149(68.35) 69(31.65) 24 ~ 28 111(28.03) 68(61.26) 43(38.74) ≥ 28 67(16.92) 47(70.15) 20(29.85) Bile duct stones Negative 159(40.15) 109(68.55) 50(31.45) Positive 237(59.85) 155(65.4) 82(34.6) Hyperlipemia Negative 307(77.53) 202(65.8) 105(34.2) Positive 89(22.47) 62(69.66) 27(30.34) Smoking or alcohol None 307(77.53) 208(67.75) 99(32.25) Smoking 43(10.86) 27(62.79) 16(37.21) Alcohol 28(7.07) 17(60.71) 11(39.29) Smoking and alcohol 18(4.55) 12(66.67) 6(33.33) WBC (×10 9 /L) < 9.5 138(34.85) 97(70.29) 41(29.71) ≥ 9.5 258(65.15) 167(64.73) 91(35.27) Hemoglobin (g/L) ≥ 130 138(34.85) 99(71.74) 39(28.26) < 130 258(65.15) 165(63.95) 93(36.05) Neutrophil (%) < 75 108(27.27) 74(68.52) 34(31.48) ≥ 75 288(72.73) 190(65.97) 98(34.03) Total protein (g/L) ≥ 65 129(32.58) 84(65.12) 45(34.88) < 65 267(67.42) 180(67.42) 87(32.58) Albumin (g/L) ≥ 40 82(20.71) 53(64.63) 29(35.37) < 40 314(79.29) 211(67.2) 103(32.8) ALT (U/L) < 9 23(5.81) 18(78.26) 5(21.74) 9 ~ 50 227(57.32) 149(65.64) 78(34.36) ≥ 50 146(36.87) 97(66.44) 49(33.56) AST (U/L) < 15 28(7.07) 19(67.86) 9(32.14) 15 ~ 40 205(51.77) 136(66.34) 69(33.66) ≥ 40 163(41.16) 109(66.87) 54(33.13) Total bilirubin (µmol/L) < 17.1 171(43.18) 117(68.42) 54(31.58) ≥ 17.1 225(56.82) 147(65.33) 78(34.67) Direct bilirubin (µmol/L) < 3.4 117(29.55) 81(69.23) 36(30.77) ≥ 3.4 279(70.45) 183(65.59) 96(34.41) ALP (U/L) < 125 224(56.57) 146(65.18) 78(34.82) ≥ 125 172(43.43) 118(68.6) 54(31.4) GGT (U/L) < 15 18(4.55) 14(77.78) 4(22.22) 15 ~ 40 87(21.97) 55(63.22) 32(36.78) ≧ 40 291(73.48) 195(67.01) 96(32.99) Glucose (mmol/L) < 3.9 16(4.04) 11(68.75) 5(31.25) 3.9 ~ 6.1 178(44.95) 115(64.61) 63(35.39) ≥ 6.1 202(51.01) 138(68.32) 64(31.68) Creatinine (µmol/L) < 57 124(31.31) 87(70.16) 37(29.84) 57 ~ 111 247(62.37) 163(65.99) 84(34.01) ≥ 111 25(6.31) 14(56) 11(44) Lesion location Whole pancreas 284(71.72) 181(63.73) 103(36.27) Head of pancreas 30(7.58) 21(70) 9( 30 ) Body and/or tail of pancreas 82(20.71) 62(75.61) 20(24.39) CT grades (points) 0 234(59.09) 151(64.53) 83(35.47) 2 123(31.06) 87(70.73) 36(29.27) 4 39(9.85) 26(66.67) 13(33.33) APACHE II grades (points) < 8 317(80.05) 214(67.51) 103(32.49) ≥ 8 79(19.95) 50(63.29) 29(36.71) AP, acute pancreatitis; BMI, body mass index; WBC, white blood cells; ALT, alanine aminotransferase; AST, aspartate transaminase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transpeptidase; APACHE II, acute physiological and chronic health assessment II. Patient follow-ups Patient follow-ups were accomplished by outpatient visits or telephone calls. The end point was defined as the diagnosis of DM terminated at the time of one year after discharge. The diagnosis of DM needs to meet any of the following items: 1) Fasting plasma glucose ≥ 7.0mmol/L; 2) Random blood glucose ≥ 11.1mmol/L; 3) Fasting plasma glucose < 7.0mmol/L and 2-hour postprandial blood glucose ≥ 11.1mmol/L after a 75g oral glucose tolerance test. Statistical analyses The chi-squared test or Fisher's exact test was used to compare categorical variables. The risk factors of new-onset DM after first-attack AP were obtained through univariate and multivariate logistic regression analyses in the training set. A predictive nomogram was constructed based on the independent risk factors. Internal and external validations of the nomogram were conducted in the training and validation sets. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the discrimination capability of the nomogram. AUC > 0.7 was generally considered to have good discrimination capability. The calibration curve was used to determine the calibration capability of the nomogram according to the fitness between the predicted and actual value. Finally, decision curve analysis (DCA) was used to determine the clinical applicability of the nomogram by balancing the benefits of true positive diagnostic testing with the costs of false positive diagnostic testing. SPSS version 23.0, R Studio version 1.3.1056 with rms and foreign packages were used for statistical analyses. A two-tailed p < 0.05 was considered statistically significant. All methods were performed in accordance with the relevant guidelines and regulation. Result Patient characteristics A total of 396 patients were included for analyses in this study, among which 264 patients were divided into the training set and the other 132 patients were divided into the validation set. For the overall patient cohort, 34 patients (8.6%) were diagnosed of DM at the terminal follow-up time. The detailed patient characteristics were shown in Table 1 . Risk factor analyses Univariate and multivariate logistic regression analyses showed that hyperlipemia (OR = 6.87, 95%CI = 2.33 ~ 20.26, p = 0.000), GGT ≥ 40U/L (OR = 0.07, 95%CI = 0.03 ~ 0.27, p = 0.008), serum glucose ≥ 6.1mmol/L (OR = 7.73, 95%CI = 1.89 ~ 31.64, p = 0.004), CT grade ≥ 2 or 4 points (OR = 3.16 or 4.95, 95%CI = 1.05 ~ 9.45 or 1.12 ~ 21.89, p = 0.039 or 0.035) and APACHE II grade ≥ 8 points (OR = 3.82, 95%CI = 1.19 ~ 12.27, p = 0.024) were independent risk or protective factors for new-onset DM after first-attack AP (Table 2 ). Table 2 Univariate and multivariate analyses for risk factors of new-onset DM after first-attack AP. Variables Univariate analyses Multivariate analyses OR 95%CI p OR 95%CI p Hyperlipemia Positive 6.04 2.62 ~ 13.88 0.000 6.87 2.33 ~ 20.26 0.000 Negative reference reference GGT(U/L) ≥ 40 0.09 0.02 ~ 0.76 0.001 0.07 0.03 ~ 0.27 0.008 < 40 reference reference Glucose (mmol/L) < 3.9 3.73 0.35 ~ 39.29 0.776 10.71 0.73 ~ 156.27 0.083 ≥ 6.1 7.46 2.18 ~ 25.56 0.035 7.73 1.89 ~ 31.64 0.004 3.9 ~ 6.1 reference reference CT grade (points) 2 2.52 0.91 ~ 5.78 0.617 3.16 1.05 ~ 9.45 0.039 4 4.73 1.52 ~ 14.71 0.036 4.95 1.12 ~ 21.89 0.035 0 reference reference APACHE II grade (points) ≥ 8 2.89 1.23 ~ 6.79 0.014 3.82 1.19 ~ 12.27 0.024 < 8 reference reference DM, diabetes mellitus; AP, acute pancreatitis; GGT, gamma-glutamyl transpeptidase; APACHE II, acute physiological and chronic health assessment II; OR, odd ratio; CI, confidence interval. Nomogram construction and validations A predictive nomogram was constructed based on the above five independent risk factors derived from multivariate logistic regression analyses using the R software (Fig. 2 ). The 1-year risk of new-onset DM after first-attack AP can be individually calculated by summing the total points derived from each risk factor. The AUC value was 0.884 in the training set and 0.770 in the validation set, which presented good discrimination capability (Fig. 3 ). Calibration curve analyses showed good fitness between the predicted and actual value both in the training and validation sets, which indicated that the calibration capability of the nomogram was favorable enough (Fig. 4 ). Clinical applicability of the nomogram Finally, DCA was used to evaluate the clinical applicability of the nomogram. As shown in Fig. 5 , the generated red curve indicated that the nomogram was clinically applicable for accurate risk predictions for the new-onset DM after first-attack AP at a threshold probability ranging from 10–40%. Discussion AP is one of the most important etiologies for pancreatic diabetes. Comparing to the general incidence of DM (4–9%), the incidence in AP patients was much higher, especially within one year. It was reported that the overall incidence of DM after AP could be up to 23%, and the one-year incidence was about 15% ( 6 ). In the present study, 8.6% patients with first-attack AP developed new-onset DM within one year. The lower incidence might partly be attributed to the loss of potential positive patients due to the short-term follow-ups. The lower proportion of severe AP patients in this study (19.95% of APACHE II grade ≥ 8 points and 9.85% of CT grade ≥ 4 points) (Table 1 ) might be another reason for the lower incidence of DM since the positive correlation between the morbidity rate and the disease severity of AP ( 9 – 11 ). It was generally accepted that AP could lead to destruction of pancreatic endocrine cells and therefore increase the incidence of DM due to the local and systemic immune response. This type of pancreatic diabetes had been classified as type 3c diabetes ( 3 – 5 ). As reported previously, risk factors for the development of DM after AP could be generally classified into two categories named patient susceptibility and disease features. In the present study, demographic and clinical data were comprehensively enrolled as candidate risk factors for further analyses, which might provide more valuable information. Our study showed that hyperlipemia, GGT ≥ 40U/L, serum glucose ≥ 6.1mmol/L, CT grade ≥ 2 or 4 points and APACHE II grade ≥ 8 points were independent risk or protective factors (Table 2 ). As for hyperlipidemia, a common etiology for AP, were also found to be correlated to the elevated risk of developing DM after AP ( 17 – 19 ). Das et al ( 6 ) proposed that AP may be a triggering factor for DM in patients with certain susceptibility conditions such as autoimmune disease, genetic susceptibility, certain metabolic factors such as obesity and hypertriglyceridemia, and/or changes in pancreatic structure and function. Another study also concluded that AP may trigger a reaction in gene susceptible people already at risk of developing DM ( 6 ). Combining the present results and previous studies, we speculated that hyperlipidemia might serve as a susceptibility factor for the development of DM after AP, and was considered a suitable index for the risk prediction of these patients. Previous studies reported that disease severity of AP, which characterized as the extend of pancreatic necrosis and endocrine dysfunction, was considered correlated with the risk of new-onset DM ( 6 , 8 – 11 , 21 – 23 ). DM might be induced when the amount of nonfunctional or dead pancreatic β-cells increased to the specific threshold value due to the severe necrosis of the pancreatic tissues caused by AP ( 24 ). Subsequent pancreatic tissue atrophy was also an important modality for the decreased normal pancreatic β-cells and insulin secretion due to the widespread pancreatitis, and was considered to be correlated to the development of DM ( 25 ). In addition, pancreatic endocrine dysfunction was more common in severe AP patients compared to those with mild AP ( 26 ). Specially, the morbidity rate of endocrine dysfunction was found up to 33% in patients with pancreatic necrosis ( 27 ). In this study, clinical data reflecting the disease severity of AP were enrolled for analyses, including CT grades and APACHE II grades. These two factors were more practical since they were preferable factors reflecting the local and systemic inflammatory responses and the pathophysiological status, and could be well quantified and were considered to be more objective. Results showed that higher CT grades and APACHE II grades were independent risk factors for the development of DM after AP. We concluded that disease severity of AP was crucial for the development of DM, and objective factors such as CT grades and APACHE II grades might be effective indexes for the establishment of risk prediction models. The elevated serum glucose in AP patients was used to be considered as transient hyperglycemia caused by carbohydrate metabolism disorders due to the acute stress, excessive secretion of catecholamines and subsequent pancreatic microcirculation disorders ( 28 , 29 ). However, it was found that AP patients with transient hyperglycemia exhibited higher incidence of DM, since a considerable number of these patients could not fully recover from high blood glucose, or might experience a short-term recovery ( 30 ). As previously reported, insulin resistance was more common in patients after AP, and might serve as a crucial factor in the onset of DM ( 26 ). The present study also showed that serum glucose ≥ 6.1mmol/L was also extracted as an independent risk factor for the development of DM after AP, which further supported the previous studies, and was considered an important risk prediction index. Besides, GGT < 40U/L was also extracted as an independent risk factor. As reported previously, higher serum GGT level was found to be positively associated with the elevated incidence of DM ( 31 ), while another study found that DM was associated with GGT levels within the normal concentration range ( 32 ). Currently, no definite study was found to explore the GGT level and the incidence of pancreatic diabetes specially cause by AP, which was classified as type 3c DM. Whether the above results were applicable in this specific patient cohort remained to be further investigated, and more subgroup analyses were urgently needed in the future. Nomogram is a simple graph that showing the incidence of indicated clinical events by adding the corresponding scores derived from each risk factor, and has been widely used in disease diagnosis and prognosis prediction. Currently, only a few studies were conducted to predict the risk of DM after first-attack AP ( 13 , 14 ). However, there existed several limitations which weakened the strength of the corresponding results in these studies. Firstly, the enrolled potential risk factors were not comprehensive enough, especially those quantizable and objective factors such as disease scoring systems based on laboratory and imaging indexes. Besides, the established prediction models were not fully validated, especially on statistical and clinical aspects. In the present study, more practical clinical indexes were enrolled for analyses, including two important scoring systems for the evaluation of the severity of AP. Results also confirmed that they were independent risk factors and were subsequently used for the establishment of the prediction nomogram. Besides, both internal and external validations in terms of the discrimination and calibration capabilities were conducted and showed favorable results, which confirmed the accuracy of the established nomogram on the statistical aspect. Furthermore, DCA was also conducted in this study to evaluate the clinical applicability by quantifying the net income of the prediction nomogram according to the threshold probability ( 33 ). Results showed that the decision curve of this nomogram was favorable and considered to be clinically effective for predicting the incidence of new-onset DM after first-attack AP. There also existed several limitations in the present study. Firstly, this was a relatively small sample-sized retrospective study, and the proposed risk prediction nomogram needed to be validated in a prospective study with a larger sample size. Besides, the definite latency period between AP attack and DM onset remained unclear, further studies with longer follow-ups were needed to investigate the disease features and the corresponding risk factors. Thirdly, the external validation was conducted only in a single center, which might weaken the application scope of this nomogram. External validations in multicenter patient cohorts would provide more convincing evidence for the nomogram. In conclusion, we established a practical nomogram to predict the risk of new-onset DM after first-attack AP based on independent risk factors derived from demographic and clinical data. This nomogram was further confirmed to be effective in terms of the discrimination and calibration capabilities, as well as the clinical applicability, which would contribute to the identification and management of these high-risk patients. Multicenter studies with a larger sample size and more comprehensive risk factor analyses and external validations were needed in the future. Declarations Conflict of interests The authors have no conflict of interest in this work. Fundings This study was supported by the Jiangxi Provincial Natural Science Foundation Youth Fund Project (No. 20212BAB216055) and the Research Project of Science and Technology from Healthy Committee of Jiangxi Province (No. 20204247). 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Gornik I, Vujaklija A, Lukić E, Madzarac G, Gasparović V. Hyperglycemia in sepsis is a risk factor for development of type II diabetes. J Crit Care. 2010;25:263–9. Sabanayagam C, Shankar A, Li J, Pollard C, Ducatman A. Serum gamma-glutamyl transferase level and diabetes mellitus among US adults. Eur J Epidemiol. 2009;24:369–73. Kawamoto R, Tabara Y, Kohara K, Miki T, Ohtsuka N, et al. Serum gamma-glutamyl transferase within its normal concentration range is related to the presence of impaired fasting glucose and diabetes among Japanese community-dwelling persons. Endocr Res. 2011;36:64–73. Talluri R, Shete S. Using the weighted area under the net benefit curve for decision curve analysis. BMC Med Inf Decis Mak. 2016;16:94. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4172981","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288320732,"identity":"e5d3bd79-6988-474f-9dcb-3ccebc264bae","order_by":0,"name":"Chen Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYFACHiA2sKlnY28+cODDD6K1FKQl8PEcSzw4s4doLR8OJ8hJ5Bgf5mAjQoPB8bMHHxcYHM5jY8j5cBioX55f7AABLWfyko1nGKQXszGc3XC4wILBcObsBAJabvCYSfMYWDO2MfZuODyDhyHB4DZhLea/eQyYGduYeR4c5mEjTosZM4+Bc2IbGw8DcVokz+QYAx2WZszGw2YADGQJwn7hO37G8DPPHxs5+fmPH3/48MNGnl+agBaFA6h8CfzKQUC+gbCaUTAKRsEoGOkAALKIQzJg+Te7AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5378-7092","institution":"Nanchang University Second Affiliated Hospital Department of Nephrology","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Yuan","suffix":""},{"id":288320733,"identity":"872403be-2cec-42ef-b084-027a09cc97b4","order_by":1,"name":"Jia Liu","email":"","orcid":"","institution":"Huadu Hospital Affiiated to Southern Medical University: Huadu District People's Hospital of Guangzhou","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":288320734,"identity":"2e3f70ab-e4b8-49bd-aa3c-0d927c70e683","order_by":2,"name":"Jiafu Guan","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiafu","middleName":"","lastName":"Guan","suffix":""},{"id":288320735,"identity":"8564ecbb-7c56-4361-8f57-3c292def5547","order_by":3,"name":"Binghai Zhou","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Binghai","middleName":"","lastName":"Zhou","suffix":""},{"id":288320736,"identity":"3c6baa04-e299-4893-bb57-d3cf9d183573","order_by":4,"name":"Huajun Wu","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huajun","middleName":"","lastName":"Wu","suffix":""},{"id":288320737,"identity":"b8a03a8e-ab55-45e3-920c-c5e9b155bbaa","order_by":5,"name":"Rongfa Yuan","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rongfa","middleName":"","lastName":"Yuan","suffix":""},{"id":288320738,"identity":"5b238c84-3185-4776-b1cc-4676e1ba602b","order_by":6,"name":"Xin Yu","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Yu","suffix":""},{"id":288320739,"identity":"2d8ceab0-e1c8-4072-9c57-2938b9c3d4f6","order_by":7,"name":"Shubing Zou","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shubing","middleName":"","lastName":"Zou","suffix":""},{"id":288320740,"identity":"a658ded9-f5fe-4c13-8502-5d9b4c346b6e","order_by":8,"name":"Kai Wang","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Wang","suffix":""},{"id":288320741,"identity":"884f9525-d044-4aa9-94ce-829b80145a0b","order_by":9,"name":"Zhigang Hu","email":"","orcid":"","institution":"Nanchang University Second Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-03-27 02:42:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4172981/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4172981/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54455026,"identity":"c48e97f0-f02d-484b-95e0-b94e4d0e72ae","added_by":"auto","created_at":"2024-04-10 18:50:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90994,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of patient inclusion process. DM, diabetes mellitus; AP, acute pancreatitis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4172981/v1/f6db84c51834df3888638f66.png"},{"id":54455027,"identity":"a333af69-d6e5-47c7-a27a-2dc3edd41dd0","added_by":"auto","created_at":"2024-04-10 18:50:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15047,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram to predict the 1-year risk of new-onset DM after first-attack AP. Firstly, draw a vertical line to the fractional point line on the top of the nomogram to obtain the corresponding points for each risk factor. Secondly, add the total points and draw a vertical line to the fractional point line on the bottom of the nomogram to get the predicted 1-year risk. GGT, gamma-glutamyl transpeptidase; APACHE II, acute physiological and chronic health assessment II. DM, diabetes mellitus; AP, acute pancreatitis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4172981/v1/8be0be5d3345d5b868b5adb2.png"},{"id":54455296,"identity":"ca410148-f219-4dac-b2cd-c038c6af8438","added_by":"auto","created_at":"2024-04-10 18:58:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":507252,"visible":true,"origin":"","legend":"\u003cp\u003eDiscrimination evaluations of the nomogram to predict the risk of new-onset DM after first-attack AP. A, AUC=0.8983 in training set; B, AUC=0.7789 in validation set. AUC, the area under the ROC curve; ROC, receiver operating characteristic. DM, diabetes mellitus; AP, acute pancreatitis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4172981/v1/1bd3f133d657f4d9413e056e.png"},{"id":54455029,"identity":"a074a7f6-6544-4db9-b0b2-7c396d49735c","added_by":"auto","created_at":"2024-04-10 18:50:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":312906,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration evaluation of the nomogram to predict the risk of new-onset DM after first-attack AP. A, training set; B, validation set. The horizontal axis represents the predicted probability of new-onset DM after first-attack AP, and the vertical axis represents the actual probability of new-onset DM after first-attack AP. Perfect prediction would correspond to the 45° broken line. The red and green lines indicate the observed (apparent) nomogram performance before and after bootstrapping. DM, diabetes mellitus; AP, acute pancreatitis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4172981/v1/5ac6ac384dcd80a092e84716.png"},{"id":54455028,"identity":"e56d49a3-981c-418c-8a31-799c188162a9","added_by":"auto","created_at":"2024-04-10 18:50:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25996,"visible":true,"origin":"","legend":"\u003cp\u003eDCA for the prediction nomogram in the validation set. The x-axis represented the risk threshold. The y-axis represented the net benefit. The green line assumed that all patients developed DM, and the blue line assumed that none of the patients developed DM. The generated red curve indicated that the nomogram was clinically applicable for accurate risk predictions for the new-onset DM after first-attack AP at a threshold probability ranging from 10% to 40%. DCA, decision curve analysis; DM, diabetes mellitus; AP, acute pancreatitis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4172981/v1/3330518a0e50749be306adf3.png"},{"id":56807592,"identity":"4a28bde7-c661-4a29-b72b-b94d8a70cad3","added_by":"auto","created_at":"2024-05-20 18:23:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":861249,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4172981/v1/f215caaf-1ffb-47fc-bfcd-976d40c51d0b.pdf"}],"financialInterests":"","formattedTitle":"Risk factors based prediction model for new-onset diabetes mellitus after first-attack acute pancreatitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHyperglycemia is a common early feature of pancreatic dysfunction such as pancreatitis, and was considered to be associated with endocrine pancreatic insufficiency due to the damage of pancreatic endocrine cells (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Diabetes mellitus (DM) caused by excessive loss or destruction of pancreatic endocrine cells was usually called pancreatic diabetes, and had been classified as type 3c diabetes (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Acute pancreatitis (AP) is an acute disease characterized as primary local and subsequent systemic inflammatory responses. It was reported that the incidence of newly diagnosed DM after AP (23%) was much higher compared to the general population (4\u0026ndash;9%) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, the potential causal relationships and effective risk predictions for DM after AP were urgently needed to be clarified (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA series of risk factors of DM after AP have been reported, while no widely accepted standards were established. For example, the disease severity of AP was considered correlated with the risk of new-onset DM (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), while it was not concluded in another study (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The only few studies predicting the risk of DM after first-attack AP were not practical enough due to the incomprehensive evaluations of potential risk factors such as clinical imaging indexes, as well as the insufficient statistical and clinical validations of the constructed predictive models (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The purpose of this study was to identify potential risk factors of new-onset DM after first-attack AP based on risk factors derived from more practical demographic and clinic data, and therefore establish an effective prediction nomogram to quantify the risks, which might contribute providing more practical management strategies for these patients.\u003c/p\u003e"},{"header":"Patients and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient inclusions\u003c/h2\u003e \u003cp\u003ePatients diagnosed with AP at the Second Affiliated Hospital of Nanchang University from June 2016 to June 2017 were retrospectively collected. The diagnosis of AP needs to meet at least two of the following items: 1) Consistent abdominal pain; 2) Elevated serum lipase or amylase activity (more than three times higher than the normal upper limits); 3) Abdominal imaging features of AP. Exclusion criteria were as follows: 1) Previous history of AP; 2) Previous history of DM; 3) Death in hospital or within one year after discharge; 4) Age under 18 or over 100 years old; 5) Incomplete clinical data. With a ratio of 2:1, patients were randomly divided into two groups named training (n\u0026thinsp;=\u0026thinsp;264) and validation set (n\u0026thinsp;=\u0026thinsp;132). The inclusion process was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This retrospective clinical study was approved by the institutional review board committee of the Second Affiliated Hospital of Nanchang University. Clinical data collection was conducted through electronic case collection complying with the guidelines for human follow-up data collection at the Second Affiliated Hospital of Nanchang University. Due to the complete anonymity of the retrospective study, informed consent was exempted with the approval of the Ethics Committee of the Second Affiliated Hospital of Nanchang University.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collections\u003c/h2\u003e \u003cp\u003eDemographic and clinical data were retrospectively collected, such as gender, age, history of hyperlipemia, body mass index (BMI), white blood cells (WBC), alanine aminotransferase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), gamma-glutamyl transpeptidase (GGT) and glucose, etc. The demographic and clinical data were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Specially, the acute physiological and chronic health assessment II (APACHE II) grades (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and CT grades (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) were calculated according to the corresponding standards. Clinical indexes were collected at the time of admission.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical data of patients diagnosed of first-attack AP both in the training and validation sets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;396) (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining set (n\u0026thinsp;=\u0026thinsp;264) (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation set (n\u0026thinsp;=\u0026thinsp;132) (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183(46.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116(63.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67(36.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213(53.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148(69.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65(30.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(5.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026thinsp;~\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156(39.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103(66.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(33.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026thinsp;~\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155(39.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102(65.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(34.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65(16.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(66.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(33.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e218(55.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149(68.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69(31.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026thinsp;~\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111(28.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(61.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(38.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67(16.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(70.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(29.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBile duct stones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159(40.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109(68.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(31.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e237(59.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155(65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82(34.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e307(77.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202(65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105(34.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89(22.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(69.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(30.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking or alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e307(77.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208(67.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99(32.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43(10.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(62.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(37.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28(7.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(60.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(39.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking and alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18(4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(33.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138(34.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97(70.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(29.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258(65.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167(64.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91(35.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138(34.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99(71.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(28.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258(65.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165(63.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93(36.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108(27.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74(68.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(31.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288(72.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190(65.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98(34.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129(32.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84(65.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(34.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e267(67.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180(67.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87(32.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82(20.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(64.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(35.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e314(79.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211(67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103(32.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23(5.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(78.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(21.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026thinsp;~\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e227(57.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149(65.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78(34.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146(36.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97(66.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49(33.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28(7.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(67.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(32.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026thinsp;~\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e205(51.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136(66.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69(33.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163(41.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109(66.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(33.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171(43.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117(68.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(31.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e225(56.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147(65.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78(34.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117(29.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81(69.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(30.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e279(70.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183(65.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96(34.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e224(56.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146(65.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78(34.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172(43.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118(68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(31.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18(4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(77.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(22.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026thinsp;~\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87(21.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(63.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(36.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≧\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e291(73.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195(67.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96(32.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16(4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(68.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(31.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.9\u0026thinsp;~\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e178(44.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115(64.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63(35.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202(51.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138(68.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64(31.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124(31.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87(70.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(29.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e57\u0026thinsp;~\u0026thinsp;111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247(62.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163(65.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84(34.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25(6.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesion location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole pancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e284(71.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181(63.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103(36.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead of pancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30(7.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody and/or tail of pancreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82(20.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(75.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(24.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT grades (points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234(59.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151(64.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83(35.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123(31.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87(70.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(29.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39(9.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(33.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II grades (points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e317(80.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214(67.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103(32.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79(19.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(63.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(36.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAP, acute pancreatitis; BMI, body mass index; WBC, white blood cells; ALT, alanine aminotransferase; AST, aspartate transaminase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transpeptidase; APACHE II, acute physiological and chronic health assessment II.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePatient follow-ups\u003c/h2\u003e \u003cp\u003ePatient follow-ups were accomplished by outpatient visits or telephone calls. The end point was defined as the diagnosis of DM terminated at the time of one year after discharge. The diagnosis of DM needs to meet any of the following items: 1) Fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0mmol/L; 2) Random blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1mmol/L; 3) Fasting plasma glucose\u0026thinsp;\u0026lt;\u0026thinsp;7.0mmol/L and 2-hour postprandial blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1mmol/L after a 75g oral glucose tolerance test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe chi-squared test or Fisher's exact test was used to compare categorical variables. The risk factors of new-onset DM after first-attack AP were obtained through univariate and multivariate logistic regression analyses in the training set. A predictive nomogram was constructed based on the independent risk factors. Internal and external validations of the nomogram were conducted in the training and validation sets. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the discrimination capability of the nomogram. AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 was generally considered to have good discrimination capability. The calibration curve was used to determine the calibration capability of the nomogram according to the fitness between the predicted and actual value. Finally, decision curve analysis (DCA) was used to determine the clinical applicability of the nomogram by balancing the benefits of true positive diagnostic testing with the costs of false positive diagnostic testing. SPSS version 23.0, R Studio version 1.3.1056 with rms and foreign packages were used for statistical analyses. A two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All methods were performed in accordance with the relevant guidelines and regulation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 396 patients were included for analyses in this study, among which 264 patients were divided into the training set and the other 132 patients were divided into the validation set. For the overall patient cohort, 34 patients (8.6%) were diagnosed of DM at the terminal follow-up time. The detailed patient characteristics were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRisk factor analyses\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses showed that hyperlipemia (OR\u0026thinsp;=\u0026thinsp;6.87, 95%CI\u0026thinsp;=\u0026thinsp;2.33\u0026thinsp;~\u0026thinsp;20.26, p\u0026thinsp;=\u0026thinsp;0.000), GGT\u0026thinsp;\u0026ge;\u0026thinsp;40U/L (OR\u0026thinsp;=\u0026thinsp;0.07, 95%CI\u0026thinsp;=\u0026thinsp;0.03\u0026thinsp;~\u0026thinsp;0.27, p\u0026thinsp;=\u0026thinsp;0.008), serum glucose\u0026thinsp;\u0026ge;\u0026thinsp;6.1mmol/L (OR\u0026thinsp;=\u0026thinsp;7.73, 95%CI\u0026thinsp;=\u0026thinsp;1.89\u0026thinsp;~\u0026thinsp;31.64, p\u0026thinsp;=\u0026thinsp;0.004), CT grade\u0026thinsp;\u0026ge;\u0026thinsp;2 or 4 points (OR\u0026thinsp;=\u0026thinsp;3.16 or 4.95, 95%CI\u0026thinsp;=\u0026thinsp;1.05\u0026thinsp;~\u0026thinsp;9.45 or 1.12\u0026thinsp;~\u0026thinsp;21.89, p\u0026thinsp;=\u0026thinsp;0.039 or 0.035) and APACHE II grade\u0026thinsp;\u0026ge;\u0026thinsp;8 points (OR\u0026thinsp;=\u0026thinsp;3.82, 95%CI\u0026thinsp;=\u0026thinsp;1.19\u0026thinsp;~\u0026thinsp;12.27, p\u0026thinsp;=\u0026thinsp;0.024) were independent risk or protective factors for new-onset DM after first-attack AP (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analyses for risk factors of new-onset DM after first-attack AP.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analyses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analyses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.62\u0026thinsp;~\u0026thinsp;13.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.33\u0026thinsp;~\u0026thinsp;20.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026thinsp;~\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u0026thinsp;~\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026thinsp;~\u0026thinsp;39.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73\u0026thinsp;~\u0026thinsp;156.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.18\u0026thinsp;~\u0026thinsp;25.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.89\u0026thinsp;~\u0026thinsp;31.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.9\u0026thinsp;~\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT grade (points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;~\u0026thinsp;5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.05\u0026thinsp;~\u0026thinsp;9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.52\u0026thinsp;~\u0026thinsp;14.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12\u0026thinsp;~\u0026thinsp;21.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II grade (points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;~\u0026thinsp;6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.19\u0026thinsp;~\u0026thinsp;12.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eDM, diabetes mellitus; AP, acute pancreatitis; GGT, gamma-glutamyl transpeptidase; APACHE II, acute physiological and chronic health assessment II; OR, odd ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eNomogram construction and validations\u003c/h2\u003e \u003cp\u003eA predictive nomogram was constructed based on the above five independent risk factors derived from multivariate logistic regression analyses using the R software (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The 1-year risk of new-onset DM after first-attack AP can be individually calculated by summing the total points derived from each risk factor. The AUC value was 0.884 in the training set and 0.770 in the validation set, which presented good discrimination capability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Calibration curve analyses showed good fitness between the predicted and actual value both in the training and validation sets, which indicated that the calibration capability of the nomogram was favorable enough (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical applicability of the nomogram\u003c/h2\u003e \u003cp\u003eFinally, DCA was used to evaluate the clinical applicability of the nomogram. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the generated red curve indicated that the nomogram was clinically applicable for accurate risk predictions for the new-onset DM after first-attack AP at a threshold probability ranging from 10\u0026ndash;40%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAP is one of the most important etiologies for pancreatic diabetes. Comparing to the general incidence of DM (4\u0026ndash;9%), the incidence in AP patients was much higher, especially within one year. It was reported that the overall incidence of DM after AP could be up to 23%, and the one-year incidence was about 15% (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In the present study, 8.6% patients with first-attack AP developed new-onset DM within one year. The lower incidence might partly be attributed to the loss of potential positive patients due to the short-term follow-ups. The lower proportion of severe AP patients in this study (19.95% of APACHE II grade\u0026thinsp;\u0026ge;\u0026thinsp;8 points and 9.85% of CT grade\u0026thinsp;\u0026ge;\u0026thinsp;4 points) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) might be another reason for the lower incidence of DM since the positive correlation between the morbidity rate and the disease severity of AP (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt was generally accepted that AP could lead to destruction of pancreatic endocrine cells and therefore increase the incidence of DM due to the local and systemic immune response. This type of pancreatic diabetes had been classified as type 3c diabetes (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). As reported previously, risk factors for the development of DM after AP could be generally classified into two categories named patient susceptibility and disease features. In the present study, demographic and clinical data were comprehensively enrolled as candidate risk factors for further analyses, which might provide more valuable information. Our study showed that hyperlipemia, GGT\u0026thinsp;\u0026ge;\u0026thinsp;40U/L, serum glucose\u0026thinsp;\u0026ge;\u0026thinsp;6.1mmol/L, CT grade\u0026thinsp;\u0026ge;\u0026thinsp;2 or 4 points and APACHE II grade\u0026thinsp;\u0026ge;\u0026thinsp;8 points were independent risk or protective factors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As for hyperlipidemia, a common etiology for AP, were also found to be correlated to the elevated risk of developing DM after AP (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Das et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) proposed that AP may be a triggering factor for DM in patients with certain susceptibility conditions such as autoimmune disease, genetic susceptibility, certain metabolic factors such as obesity and hypertriglyceridemia, and/or changes in pancreatic structure and function. Another study also concluded that AP may trigger a reaction in gene susceptible people already at risk of developing DM (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Combining the present results and previous studies, we speculated that hyperlipidemia might serve as a susceptibility factor for the development of DM after AP, and was considered a suitable index for the risk prediction of these patients.\u003c/p\u003e \u003cp\u003ePrevious studies reported that disease severity of AP, which characterized as the extend of pancreatic necrosis and endocrine dysfunction, was considered correlated with the risk of new-onset DM (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). DM might be induced when the amount of nonfunctional or dead pancreatic β-cells increased to the specific threshold value due to the severe necrosis of the pancreatic tissues caused by AP (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Subsequent pancreatic tissue atrophy was also an important modality for the decreased normal pancreatic β-cells and insulin secretion due to the widespread pancreatitis, and was considered to be correlated to the development of DM (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In addition, pancreatic endocrine dysfunction was more common in severe AP patients compared to those with mild AP (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Specially, the morbidity rate of endocrine dysfunction was found up to 33% in patients with pancreatic necrosis (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In this study, clinical data reflecting the disease severity of AP were enrolled for analyses, including CT grades and APACHE II grades. These two factors were more practical since they were preferable factors reflecting the local and systemic inflammatory responses and the pathophysiological status, and could be well quantified and were considered to be more objective. Results showed that higher CT grades and APACHE II grades were independent risk factors for the development of DM after AP. We concluded that disease severity of AP was crucial for the development of DM, and objective factors such as CT grades and APACHE II grades might be effective indexes for the establishment of risk prediction models.\u003c/p\u003e \u003cp\u003eThe elevated serum glucose in AP patients was used to be considered as transient hyperglycemia caused by carbohydrate metabolism disorders due to the acute stress, excessive secretion of catecholamines and subsequent pancreatic microcirculation disorders (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, it was found that AP patients with transient hyperglycemia exhibited higher incidence of DM, since a considerable number of these patients could not fully recover from high blood glucose, or might experience a short-term recovery (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). As previously reported, insulin resistance was more common in patients after AP, and might serve as a crucial factor in the onset of DM (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The present study also showed that serum glucose\u0026thinsp;\u0026ge;\u0026thinsp;6.1mmol/L was also extracted as an independent risk factor for the development of DM after AP, which further supported the previous studies, and was considered an important risk prediction index. Besides, GGT\u0026thinsp;\u0026lt;\u0026thinsp;40U/L was also extracted as an independent risk factor. As reported previously, higher serum GGT level was found to be positively associated with the elevated incidence of DM (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), while another study found that DM was associated with GGT levels within the normal concentration range (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Currently, no definite study was found to explore the GGT level and the incidence of pancreatic diabetes specially cause by AP, which was classified as type 3c DM. Whether the above results were applicable in this specific patient cohort remained to be further investigated, and more subgroup analyses were urgently needed in the future.\u003c/p\u003e \u003cp\u003eNomogram is a simple graph that showing the incidence of indicated clinical events by adding the corresponding scores derived from each risk factor, and has been widely used in disease diagnosis and prognosis prediction. Currently, only a few studies were conducted to predict the risk of DM after first-attack AP (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, there existed several limitations which weakened the strength of the corresponding results in these studies. Firstly, the enrolled potential risk factors were not comprehensive enough, especially those quantizable and objective factors such as disease scoring systems based on laboratory and imaging indexes. Besides, the established prediction models were not fully validated, especially on statistical and clinical aspects. In the present study, more practical clinical indexes were enrolled for analyses, including two important scoring systems for the evaluation of the severity of AP. Results also confirmed that they were independent risk factors and were subsequently used for the establishment of the prediction nomogram. Besides, both internal and external validations in terms of the discrimination and calibration capabilities were conducted and showed favorable results, which confirmed the accuracy of the established nomogram on the statistical aspect. Furthermore, DCA was also conducted in this study to evaluate the clinical applicability by quantifying the net income of the prediction nomogram according to the threshold probability (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Results showed that the decision curve of this nomogram was favorable and considered to be clinically effective for predicting the incidence of new-onset DM after first-attack AP.\u003c/p\u003e \u003cp\u003eThere also existed several limitations in the present study. Firstly, this was a relatively small sample-sized retrospective study, and the proposed risk prediction nomogram needed to be validated in a prospective study with a larger sample size. Besides, the definite latency period between AP attack and DM onset remained unclear, further studies with longer follow-ups were needed to investigate the disease features and the corresponding risk factors. Thirdly, the external validation was conducted only in a single center, which might weaken the application scope of this nomogram. External validations in multicenter patient cohorts would provide more convincing evidence for the nomogram.\u003c/p\u003e \u003cp\u003eIn conclusion, we established a practical nomogram to predict the risk of new-onset DM after first-attack AP based on independent risk factors derived from demographic and clinical data. This nomogram was further confirmed to be effective in terms of the discrimination and calibration capabilities, as well as the clinical applicability, which would contribute to the identification and management of these high-risk patients. Multicenter studies with a larger sample size and more comprehensive risk factor analyses and external validations were needed in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interests\u003c/h2\u003e \u003cp\u003eThe authors have no conflict of interest in this work.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFundings\u003c/h2\u003e \u003cp\u003eThis study was supported by the Jiangxi Provincial Natural Science Foundation Youth Fund Project (No. 20212BAB216055) and the Research Project of Science and Technology from Healthy Committee of Jiangxi Province (No. 20204247).\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets generated or analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNair S, Yadav D, Pitchumoni CS. 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Serum gamma-glutamyl transferase within its normal concentration range is related to the presence of impaired fasting glucose and diabetes among Japanese community-dwelling persons. Endocr Res. 2011;36:64\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalluri R, Shete S. Using the weighted area under the net benefit curve for decision curve analysis. BMC Med Inf Decis Mak. 2016;16:94.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"acute pancreatitis, diabetes mellitus, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-4172981/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4172981/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo construct a practical prediction model for the risk of new-onset diabetes mellitus (DM) in patients with first-attack acute pancreatitis (AP) based on risk factors derived from demographic and clinical data. A total of 780 patients diagnosed with AP were retrospectively enrolled in the Second Affiliated Hospital of Nanchang University from June 2016 to June 2017. A series of exclusion criteria were applied and 396 patients were finally included. With a ratio of 2:1, patients were randomly divided into two groups named training (n\u0026thinsp;=\u0026thinsp;264) and validation set (n\u0026thinsp;=\u0026thinsp;132). Demographic and clinical data that may be risk factors of new-onset DM after first-attack AP were collected. Univariate and multivariate analyses were used to determine potential risk factors in the training set, and a predictive nomogram was constructed. Nomogram performance was determined in the training and validation sets concerning discrimination and calibration capabilities. Finally, clinical applicability of the nomogram was assessed in the validation set by decision curve analysis. The morbidity rate of new-onset DM after first-attack AP was 8.6% (34/396) in the included patient cohort. Hyperlipemia (OR\u0026thinsp;=\u0026thinsp;6.87, 95%CI\u0026thinsp;=\u0026thinsp;2.33\u0026thinsp;~\u0026thinsp;20.26, p\u0026thinsp;=\u0026thinsp;0.000), GGT\u0026thinsp;\u0026ge;\u0026thinsp;40U/L (OR\u0026thinsp;=\u0026thinsp;0.07, 95%CI\u0026thinsp;=\u0026thinsp;0.03\u0026thinsp;~\u0026thinsp;0.27, p\u0026thinsp;=\u0026thinsp;0.008), serum glucose\u0026thinsp;\u0026ge;\u0026thinsp;6.1mmol/L (OR\u0026thinsp;=\u0026thinsp;7.73, 95%CI\u0026thinsp;=\u0026thinsp;1.89\u0026thinsp;~\u0026thinsp;31.64, p\u0026thinsp;=\u0026thinsp;0.004), CT grade\u0026thinsp;\u0026ge;\u0026thinsp;2 or 4 points (OR\u0026thinsp;=\u0026thinsp;3.16 or 4.95, 95%CI\u0026thinsp;=\u0026thinsp;1.05\u0026thinsp;~\u0026thinsp;9.45 or 1.12\u0026thinsp;~\u0026thinsp;21.89, p\u0026thinsp;=\u0026thinsp;0.039 or 0.035) and APACHE II grade\u0026thinsp;\u0026ge;\u0026thinsp;8 points (OR\u0026thinsp;=\u0026thinsp;3.82, 95%CI\u0026thinsp;=\u0026thinsp;1.19\u0026thinsp;~\u0026thinsp;12.27, p\u0026thinsp;=\u0026thinsp;0.024) were independent risk or protective factors and were assembled for nomogram construction. Internal and external validations showed good discrimination (Area under the receiver operating characteristic curve\u0026thinsp;=\u0026thinsp;0.884 and 0.770) and calibration capabilities. The decision curve analysis showed good clinical applicability.\u003c/p\u003e \u003cp\u003eWe have developed a practical nomogram to predict the risk of new-onset DM after first-attack AP based on risk factors derived from demographic and clinical data, which would contribute to the identification and management of these high-risk patients.\u003c/p\u003e","manuscriptTitle":"Risk factors based prediction model for new-onset diabetes mellitus after first-attack acute pancreatitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 18:50:51","doi":"10.21203/rs.3.rs-4172981/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c41de33-13da-4b7f-80c0-c6f379993d08","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-20T18:18:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 18:50:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4172981","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4172981","identity":"rs-4172981","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}