Development and Validation of a Demographic and Clinical Characteristic-Based Nomogram for Predicting Physical Disability in Newly Diagnosed Leprosy Patients

Development and Validation of a Demographic and Clinical Characteristic-Based Nomogram for Predicting Physical Disability in Newly Diagnosed Leprosy Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and Validation of a Demographic and Clinical Characteristic-Based Nomogram for Predicting Physical Disability in Newly Diagnosed Leprosy Patients XiaoJun Yu, Jun He, Lu Ma, Shuo Kou, Qin Yang, Shun Zha, YanFang Zhao, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6782091/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Disabilities and deformities are common complications of leprosy disease. We previously revealed the risk factors for physical disability in leprosy patients in Yunnan, China, and in this study, aimed to develop and validate a nomogram for predicting physical disability in leprosy patients in the Chinese population. Methods The data of newly diagnosed leprosy patients were extracted from the Leprosy Management Information System (LEPMIS) in China. The data from Yunnan and Guizhou provinces were divided into training and validation cohorts, respectively. A nomogram to predict the risk of disability in newly diagnosed leprosy patients was constructed and validated with bootstrap resampling. Results A total of 17237 newly diagnosed leprosy patients who were evaluated for the grade of disability were included in the study, with 11261, 1939, and 3987 patients diagnosed as having Grade 0 disability (G0D), Grade 1 disability (G1D), and Grade 2 disability (G2D), respectively. Data on sex, age, nationality, occupation type, the symptom-to-diagnosis interval,contact history, skin lesions, nerve damage, leprosy reaction, Ridley-Jopling classification, and World Health Organization (WHO) classification were entered into the nomogram. The nomogram demonstrated good discriminative ability, with an area under the receiver operating characteristic curve of 0.753 and 0.770 for G2D in the training and validation cohorts, respectively. The calibration curve of the nomogram showed favorable consistency between the predicted and actual values in both the training and validation cohorts. Conclusions A nomogram was developed and validated for predicting the risk of disability in newly diagnosed leprosy patients in China based on demographic and clinical characteristics. The nomogram exhibited excellent calibration, indicating that it may have clinical utility to assist clinicians in evaluating the probability of disability and to eliminate the disability burden for leprosy patients. Health sciences/Health care/Public health/Epidemiology Health sciences/Medical research/Epidemiology Health sciences/Neurology/Neurological disorders Health sciences/Signs and symptoms/Neurological manifestations Health sciences/Risk factors leprosy Disability risk factors nomogram newly diagnosed China Yunnan Guizhou Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Among communicable diseases, leprosy remains a leading cause of peripheral neuropathy and disability worldwide despite extensive efforts to reduce the disease burden [ 1 ]. Despite general progress in reducing the prevalence of leprosy-related physical disability, the proportion of physical disability among leprosy patients remains high in the Chinese population[ 2 – 8 ]. According to recent studies, the proportion of patients with Grade 2 disability (G2D) was 21.3% among new patients in Yunnan, China, during 1990–2019[ 2 , 3 ]. Among new patients, the proportion of patients with G2D was 35.7% and that of patients with Grade 1 disability (G1D) constituted 10.1% in Guizhou, China, during 2008–2012[ 4 ]. The proportion of patients with G2D was 20.6% among men and 17.3% among women in Sichuan, China, during 2000–2015[ 5 ]. The annual proportion of new patients with G2D was reported as 50% in 2008 and 23% in 2017 in Shandong, China[ 6 ]. Among new patients, the proportion of those with G2D was 31.0% in Shaanxi, China, during 1998–2018[ 7 ]. Among new patients, the proportion of those with G2D was 38.2–21.7% in chongqing, China, during 1949–2019[ 8 ]. In 2009, the World Health Organization (WHO) launched the Enhanced Global Strategy for Further Reducing the Disease Burden due to Leprosy for 2011–2015, under which the target was to reduce the number of newly diagnosed leprosy patients with G2D per 100 000 total population (G2DR) by at least 35% between the end of 2010 and the end of 2015[ 9 ]. In 2017, the WHO launched a more comprehensive strategy: the Global Leprosy Strategy 2016–2020 “Accelerating towards a leprosy free world”. The targets were zero disability among newly diagnosed child patients; a reduction in G2D to less than 1 per million population among newly diagnosed patients; and zero countries with legislation allowing discrimination based on leprosy[ 10 ]. In 2021, the WHO launched the “Towards Zero Leprosy. Global Leprosy (Hansen’s Disease) Strategy 2021–2030”, under which the target of “a world with zero leprosy infections and disease, zero disability, and zero leprosy-related stigma and discrimination” is possible[ 11 ]. The WHO expects that by using G2D as an indicator and focusing interventions on reducing G2D, delayed detection and treatment of leprosy patients will also be reduced as will the number of new leprosy patients in the population[ 9 ]. In our previous study in Yunnan, China, the risk factors for physical disability among new leprosy patients were identified based on demographic and clinical variables[ 3 ]. However, to our knowledge, a visualized statistical prediction model, such as a predictive nomogram tool, for individualized disability prediction in newly diagnosed leprosy patients has not yet been constructed. The objective of the current study was to establish a nomogram model for individualized physical disability prediction in newly diagnosed leprosy patients in the Chinese population based on demographic and clinical variables. Materials and Methods Study Population Leprosy patients registered in the Leprosy Management Information System (LEPMIS) in Yunnan and Guizhou provinces, southwestern China, between January 1990 and December 2019 were enrolled in this study. Leprosy patients from Yunnan Province were included in the training cohort and leprosy patients from Guizhou province were included in the validation cohort. The inclusion and the exclusion criteria were described previously[ 3 ]. Ethics Statement Ethical approval for this study was obtained from the Ethics Committee of Yunnan Center for Disease Control and Prevention (Ethical Approval - No. 2023-19), with a full waiver of informed consent. The research utilized de-identified data from the national Leprosy Management Information System (LEPMIS), which were collected for routine public health surveillance purposes and contained no personal identifiers. The waiver was justified based on the retrospective design, minimal risk to participants, and the impossibility of obtaining consent for historical data. Data Processing The same variables were retrieved both from training and validation cohorts as described previously[ 3 ]. Physical disability was graded and divided into G0D, G1D, and G2D based on the WHO classification[ 12 ]. For statistical analysis, leprosy patients with complete demographic and clinical data were included, and those with missing data were excluded. Construction, Validation and Calibration of the Nomogram SPSS 16.0 (SPSS, Inc.) was used for multinomial and ordinal logistic regression, performed in the training cohort as described previously[ 3 ]and in the validation cohort by selecting the most significant predictive factors associated with the risk of physical disability. Based on the multinomial and ordinal logistic regression results, a nomogram was constructed to predict the risk of G1D, G2D, and physical disability by R 3.6.1 ( http://www.r-project.org ). Receiver operating characteristic (ROC) curves were calculated by bootstrapping and used to evaluate the nomogram’s discriminative ability. Calibration plots were used to evaluate the nomogram’s calibration ability. Results Baseline Characteristics of the Patients A total of 17237 newly diagnosed leprosy patients were included in this study, with the training cohort comprising 10644 patients and the validation cohort comprising 6593 patients (Fig. 1 ). The baseline characteristics of the newly diagnosed leprosy patients in the training cohort were described previously[ 3 ], and those of patients in the validation cohort are summarized in Table 1 . In the validation cohort, 61.42%, 11.83%, and 26.75% of the patients were categorized as having G0D, G1D and G2D, respectively. In the validation cohort, the median age at diagnosis was 37.00 years; 74.03% of the patients were men, 55.88% were of Han ethnicity, and 47.41% were farmers. Overall, 47.15% of the patients had been diagnosed within 2 years, 50.69% were diagnosed by passive case detection, 67.08% had a history of contact with a leprosy patient, 4.60% had leprosy reactions, and 70.58% had multibacillary leprosy (Table 1 ). Regarding the Ridley-Jopling classification, 38.18% of the patients had lepromatous (LL), 26.14% had borderline-lepromatous (BL), 5.99% had borderline-borderline (BB), 11.58% had borderline-tuberculoid (BT), 16.40% had tuberculoid (TT), and 1.39% had indeterminate (I) forms of leprosy in the training and validation cohorts (Table 1 ). In the validation cohort, the median diagnosis durations were 19, 25, and 44 months for leprosy patients with G0D, G1D, and G2D, respectively. Validation Analysis The risk factors for physical disability in leprosy patients in Yunnan province were evaluated in the training cohort (Table 2 ) ( P < 0.05)[ 11 ]. In the validation cohort, multinominal and ordinal logistic regression analyses were also performed to determine the variables associated with physical disability in leprosy patients in Guizhou province (Table 2 ). These analyses were used to construct the nomogram for predicting disability in leprosy patients. Compared with those of the training cohort, similar results were achieved in the validation cohort. In addition to the above indicators, detection mode and contact history were significantly associated with disability in leprosy patients (Table 2 ). Development of Multivariate Nomogram Model Eleven variables were selected based on the results of the multinominal and ordinal logistic regression analyses, and the R model was used to establish the nomogram for predicting the probability of disability in newly diagnosed leprosy patients (Figs. 2 – 4 ). The given score of each variable is shown in Table 3. Internal and External Validation In order to evaluate the performance and discriminative ability of the prediction model, we calculated the area under the ROC curve (AUC) of the training and validation cohorts, respectively. For G1D, the AUC values for the training and validation cohorts were 0.622 [95% confidence interval (CI), 0.604–0.639] and 0.593 (95% CI, 0.570–0.616) respectively. For G2D, the AUC values for the training and validation cohorts were 0.753 (95% CI, 0.741–0.765) and 0.770 (95% CI, 0.757–0.784), respectively. For G1D + G2D, the AUC values for the training and validation cohorts were 0.699(95% CI, 0.688–0.710) and 0.716 (95% CI, 0.702–0.729), respectively. The AUC values demonstrated that the model has a good discriminative ability (Table 4 and Fig. 5 ). The calibration curve showed that the predicted results of the nomogram model were in good agreement with the actual results (Fig. 6 ). Discussion The number of newly diagnosed leprosy patients has decreased dramatically in China during recent decades; however, the proportion of disability among newly diagnosed leprosy patients is still high[ 2 – 8 ]. Despite previous studies reporting risk factors for disability in newly diagnosed leprosy patients in China and worldwide[ 3 , 13 ], a nomogram has not yet been developed. Thus, we sought to develop a visible individualized nomogram for predicting disability in leprosy patients based on the Chinese population. The training and validation cohorts were obtained from the LEPMIS of Yunnan and Guizhou provinces, southwestern China, from 1990–2019. Covering approximately 394,000 and 176,167 square kilometers with populations of 47.71 and 35.08 million in 2020, Yunnan and Guizhou provinces have the first and second highest leprosy burdens in China, respectively. The wide geographic distribution of patients and large cohort sample sizes guarantee the representativeness and generalizability of the results for Chinese leprosy patients[ 14 ]. Delayed diagnosis, nerve damage, the absence of skin lesions, WHO and Ridley-Jopling classifications, leprosy reactions, advanced age, rural occupation, Han ethnicity, and male sex were identified as risk factors for disability in leprosy patients through logistic regression analysis in the validation cohort. These findings were in high concordance with previous reports on risk factors for disability in leprosy patients in Yunnan, China[ 3 ]. In addition, detection mode and contact history were also related to the risk of physical disability in leprosy patients in the validation cohort. Notably, delayed diagnosis is the most important risk factor that has been identified in many populations, and some similar studies also support the relationship between nerve damage and severe disability[ 15 – 18 ]. This implies that we should pay more attention to the early diagnosis and neurological aspect of leprosy disease. Nomogram validation is essential to avoid overfitting of models and determine generalizability[ 14 , 19 ]. The discriminative ability was slightly increased in the external validation cohort compared with that of the training cohort. The calibration curves demonstrated agreement between the predicted and actual values in the training cohort. In this study, favorable results were also replicated well in the validation cohort. To our knowledge, this is the first nomogram developed for predicting disability inpatients with leprosy disease. In our nomogram, the diagnosis interval was the greatest contributor to the risk of G2D in leprosy patients, followed by the Ridley-Jopling classification, nerve damage, age, and skin lesions, while the WHO classification, occupation type, ethnicity, sex, and leprosy reaction showed the smallest effects on G2D risk in leprosy patients. As the risk factors were identified from demographic and clinical variables, the nomogram can easily be used by specialized medical staff, physicians and the community. This scoring system should help medical staff pay more attention to the leprosy disability burden. The strengths of our study are that the data for the nomogram were from two large, specialized Chinese agencies for leprosy prevention and control with long-term follow-up data, and both internal and external verification were performed. The study had some limitations. First, the AUC was not impressively high, especially for patients with G1D. Second, our prediction model was constructed by demographic and clinical variables. Wider geographic recruitment, more enrolled patients, and more biological markers might improve the predictive value of this model. Conclusions In conclusion, a prognostic nomogram for disability in newly diagnosed leprosy patients in the Chinese population was established and verified based on demographic and clinical variables in a large LEPMIS cohort. This nomogram might help clinicians to identify risk factors for disability in leprosy patients and provide early interventions to further reduce peripheral neuropathy and disability in these patients. Abbreviations Leprosy Management Information System (LEPMIS); Grade 0 disability (G0D); Grade 1 disability(G1D); Grade 2 disability (G2D); World Health Organization (WHO); G2D per 100 000 total population (G2DR); Centers for Disease Control and Prevention (CDC); Lepromatous (LL); Borderline-lepromatous (BL); Borderline-borderline (BB); Borderline-tuberculoid (BT); Tuberculoid (TT); Indeterminate(I); Receiver operating characteristic (ROC); Area under the ROC curve (AUC); Positive predictive value (PPV); Negative predictive value (NPV). Declarations Acknowledgments We would like to thank all participants for their participation in the LEPMIS and leprosy control and prevention. Funding This study was funded by the Health Commission of Yunnan Province (No: 2017NS098) and National Natural Science Foundation of China (81260436). Author information Guizhou Center for Disease Control and Prevention, Guizhou, China JinLan Li Yunnan Center for Disease Control and Prevention , Yunnan, China Xiaojun Yu, Jun He, Shun Zha, Shuo Kou, Tiejun Shui Beijing Tropical Medicine Research Institute, Beijing Friendship Hospital, Capital Medical University, Beijing, China Xiaohua Chen Beijing Key Laboratory for Research on Prevention and Treatment of Tropical Diseases, Capital Medical University, Beijing, China Xiaohua Chen Guiyang Center for Disease Control and Prevention, Guizhou, China Lu Ma, Qin Yang Yan’an Hospital of Kunming City, Yunnan, China Haijun Yang Qiannan Center for Disease Control, Guizhou, China Yanfang Zhao Tianjin University, Tianjin, China Xiangyu Yan Qiandongnan Prefecture Center for Disease Control, Guizhou, China Tao Li Contributions Xiaojun Yu and Jun He have equally contributed to this work. Resources: JLL, TJS, XJY, JH, LM, SK, QY, YFZ, TL, and SZ. Conceptualization: XHC, JLL and TJS. Project administration and funding acquisition: JLL and TJS. Analyzed and interpreted the data, and critically revised the article: XHC, JLL, TJS, XJY, JH, HJY and XYY. Methodology, investigation, Writing-original draft: XHC. Writing- review & editing: XJY and JH. Corresponding author Correspondence to Tiejun Shui & Xiaohua Chen & Jinlan Li. Ethics declarations Conflict of Interest The authors declare no competing interests. Ethical Approval Ethical approval for this study was obtained from the Ethics Committee of Yunnan Center for Disease Control and Prevention (Ethical Approval - No. 2023-19), with a full waiver of informed consent. The research utilized de-identified data from the national Leprosy Management Information System (LEPMIS), which were collected for routine public health surveillance purposes and contained no personal identifiers. The waiver was justified based on the retrospective design, minimal risk to participants, and the impossibility of obtaining consent for historical data. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Mathers, C. D., Ezzati, M. & Lopez, A. D. Measuring the burden of neglected tropical diseases: the global burden of disease framework. PLoS Negl. Trop. Dis. 1 , e114 (2007). Shui, T. J. et al. Towards the elimination of leprosy in Yunnan, China: A time-series analysis of surveillance data. PLoS Negl. Trop. Dis. 15 , e0009201 (2021). Chen, X., Liu, H. B., Shui, T. J. & Zha, S. Risk factors for physical disability in patients with leprosy disease in Yunnan, China: Evidence from a retrospective observational study. PLoS Negl. Trop. Dis. 15 , e0009923 (2021). Li, J. et al. 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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-6782091","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":515261486,"identity":"bfa4288b-772f-4c3a-b3d6-238fb45530e4","order_by":0,"name":"XiaoJun Yu","email":"","orcid":"","institution":"Yunnan Center for Disease Control And Prevention","correspondingAuthor":false,"prefix":"","firstName":"XiaoJun","middleName":"","lastName":"Yu","suffix":""},{"id":515261487,"identity":"7d0a6535-6e34-4729-9b25-62c7c136a6aa","order_by":1,"name":"Jun He","email":"","orcid":"","institution":"Yunnan Center for Disease Control And 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09:29:22","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187090,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6782091/v1/e463dd50898305db7c1fea5e.html"},{"id":91835088,"identity":"e1e4c179-bdb4-4cfa-b982-a82c959956ec","added_by":"auto","created_at":"2025-09-22 09:29:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72583,"visible":true,"origin":"","legend":"\u003cp\u003eStudy cohort.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6782091/v1/a9d83d6bea1f545cb5f41237.png"},{"id":91836244,"identity":"27efdbc5-9145-40c0-96e9-03558d6d7a57","added_by":"auto","created_at":"2025-09-22 09:37:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34030,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive nomogram model of Grade 1 disability (G1D) in newly diagnosed leprosy patients.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6782091/v1/574f150807cfa1162809d639.png"},{"id":91835107,"identity":"39236cf8-fe57-452d-bf07-44622dac52fe","added_by":"auto","created_at":"2025-09-22 09:29:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32999,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive nomogram model of Grade 2 disability (G2D) in newly diagnosed leprosy patients.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6782091/v1/34df2e1928dec459fd5c4bdf.png"},{"id":91835091,"identity":"273b1917-a615-46c7-9e4d-4b33bf7d9151","added_by":"auto","created_at":"2025-09-22 09:29:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33717,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive nomogram model of total physical disability (G1D+G2D) in newly diagnosed leprosy patients.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6782091/v1/b4c55160c69a5ca9725d6a1e.png"},{"id":91835090,"identity":"88f955a1-9921-4b29-9856-6e2bb5b17add","added_by":"auto","created_at":"2025-09-22 09:29:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":47703,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of the nomogram for predicting physical disability in newly diagnosed leprosy patients. ROC curves of the nomogram for predicting physical disability in newly diagnosed leprosy patients in the training cohort (A) and validation cohort (B).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6782091/v1/113baa7d5a14192ec4736da8.png"},{"id":91835098,"identity":"ec2494ed-68e5-49ed-88c5-b04f07386958","added_by":"auto","created_at":"2025-09-22 09:29:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57848,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the nomogram in the training and validation cohorts. Calibration curves of the nomogram inpatients with G1D (A, D), G2D (B, E), and total physical disability (C, F) in the training (Yunnan province) (A,B, C) and validation cohorts (Guizhou province) (D,E, F).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6782091/v1/10099ed9b3baf7b41b459cd1.png"},{"id":91840457,"identity":"45e2420e-21c8-4d7d-8d6d-e6d7f33049fe","added_by":"auto","created_at":"2025-09-22 09:54:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1121121,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6782091/v1/a4a99c1e-e16f-4f17-b88f-17cf39d8817d.pdf"},{"id":91836245,"identity":"3e56cf8d-9740-4175-ba0c-934d4c5c3362","added_by":"auto","created_at":"2025-09-22 09:37:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":58703,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6782091/v1/5b84b9a146eb28c510060bec.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Demographic and Clinical Characteristic-Based Nomogram for Predicting Physical Disability in Newly Diagnosed Leprosy Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAmong communicable diseases, leprosy remains a leading cause of peripheral neuropathy and disability worldwide despite extensive efforts to reduce the disease burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite general progress in reducing the prevalence of leprosy-related physical disability, the proportion of physical disability among leprosy patients remains high in the Chinese population[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. According to recent studies, the proportion of patients with Grade 2 disability (G2D) was 21.3% among new patients in Yunnan, China, during 1990\u0026ndash;2019[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among new patients, the proportion of patients with G2D was 35.7% and that of patients with Grade 1 disability (G1D) constituted 10.1% in Guizhou, China, during 2008\u0026ndash;2012[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The proportion of patients with G2D was 20.6% among men and 17.3% among women in Sichuan, China, during 2000\u0026ndash;2015[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The annual proportion of new patients with G2D was reported as 50% in 2008 and 23% in 2017 in Shandong, China[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among new patients, the proportion of those with G2D was 31.0% in Shaanxi, China, during 1998\u0026ndash;2018[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among new patients, the proportion of those with G2D was 38.2\u0026ndash;21.7% in chongqing, China, during 1949\u0026ndash;2019[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn 2009, the World Health Organization (WHO) launched the Enhanced Global Strategy for Further Reducing the Disease Burden due to Leprosy for 2011\u0026ndash;2015, under which the target was to reduce the number of newly diagnosed leprosy patients with G2D per 100 000 total population (G2DR) by at least 35% between the end of 2010 and the end of 2015[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In 2017, the WHO launched a more comprehensive strategy: the Global Leprosy Strategy 2016\u0026ndash;2020 \u0026ldquo;Accelerating towards a leprosy free world\u0026rdquo;. The targets were zero disability among newly diagnosed child patients; a reduction in G2D to less than 1 per million population among newly diagnosed patients; and zero countries with legislation allowing discrimination based on leprosy[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In 2021, the WHO launched the \u0026ldquo;Towards Zero Leprosy. Global Leprosy (Hansen\u0026rsquo;s Disease) Strategy 2021\u0026ndash;2030\u0026rdquo;, under which the target of \u0026ldquo;a world with zero leprosy infections and disease, zero disability, and zero leprosy-related stigma and discrimination\u0026rdquo; is possible[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The WHO expects that by using G2D as an indicator and focusing interventions on reducing G2D, delayed detection and treatment of leprosy patients will also be reduced as will the number of new leprosy patients in the population[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In our previous study in Yunnan, China, the risk factors for physical disability among new leprosy patients were identified based on demographic and clinical variables[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, to our knowledge, a visualized statistical prediction model, such as a predictive nomogram tool, for individualized disability prediction in newly diagnosed leprosy patients has not yet been constructed. The objective of the current study was to establish a nomogram model for individualized physical disability prediction in newly diagnosed leprosy patients in the Chinese population based on demographic and clinical variables.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eLeprosy patients registered in the Leprosy Management Information System (LEPMIS) in Yunnan and Guizhou provinces, southwestern China, between January 1990 and December 2019 were enrolled in this study. Leprosy patients from Yunnan Province were included in the training cohort and leprosy patients from Guizhou province were included in the validation cohort. The inclusion and the exclusion criteria were described previously[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthics Statement\u003c/h3\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Ethics Committee of Yunnan Center for Disease Control and Prevention (Ethical Approval - No. 2023-19), with a full waiver of informed consent. The research utilized de-identified data from the national Leprosy Management Information System (LEPMIS), which were collected for routine public health surveillance purposes and contained no personal identifiers. The waiver was justified based on the retrospective design, minimal risk to participants, and the impossibility of obtaining consent for historical data.\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003eData Processing\u003c/h3\u003e\n\u003cp\u003eThe same variables were retrieved both from training and validation cohorts as described previously[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Physical disability was graded and divided into G0D, G1D, and G2D based on the WHO classification[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For statistical analysis, leprosy patients with complete demographic and clinical data were included, and those with missing data were excluded.\u003c/p\u003e\n\u003ch3\u003eConstruction, Validation and Calibration of the Nomogram\u003c/h3\u003e\n\u003cp\u003eSPSS 16.0 (SPSS, Inc.) was used for multinomial and ordinal logistic regression, performed in the training cohort as described previously[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]and in the validation cohort by selecting the most significant predictive factors associated with the risk of physical disability.\u003c/p\u003e\u003cp\u003eBased on the multinomial and ordinal logistic regression results, a nomogram was constructed to predict the risk of G1D, G2D, and physical disability by R 3.6.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Receiver operating characteristic (ROC) curves were calculated by bootstrapping and used to evaluate the nomogram\u0026rsquo;s discriminative ability. Calibration plots were used to evaluate the nomogram\u0026rsquo;s calibration ability.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics of the Patients\u003c/h2\u003e\n \u003cp\u003eA total of 17237 newly diagnosed leprosy patients were included in this study, with the training cohort comprising 10644 patients and the validation cohort comprising 6593 patients (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The baseline characteristics of the newly diagnosed leprosy patients in the training cohort were described previously[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e], and those of patients in the validation cohort are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In the validation cohort, 61.42%, 11.83%, and 26.75% of the patients were categorized as having G0D, G1D and G2D, respectively.\u003c/p\u003e\n \u003cp\u003eIn the validation cohort, the median age at diagnosis was 37.00 years; 74.03% of the patients were men, 55.88% were of Han ethnicity, and 47.41% were farmers. Overall, 47.15% of the patients had been diagnosed within 2 years, 50.69% were diagnosed by passive case detection, 67.08% had a history of contact with a leprosy patient, 4.60% had leprosy reactions, and 70.58% had multibacillary leprosy (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Regarding the Ridley-Jopling classification, 38.18% of the patients had lepromatous (LL), 26.14% had borderline-lepromatous (BL), 5.99% had borderline-borderline (BB), 11.58% had borderline-tuberculoid (BT), 16.40% had tuberculoid (TT), and 1.39% had indeterminate (I) forms of leprosy in the training and validation cohorts (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In the validation cohort, the median diagnosis durations were 19, 25, and 44 months for leprosy patients with G0D, G1D, and G2D, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eValidation Analysis\u003c/h3\u003e\n\u003cp\u003eThe risk factors for physical disability in leprosy patients in Yunnan province were evaluated in the training cohort (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. In the validation cohort, multinominal and ordinal logistic regression analyses were also performed to determine the variables associated with physical disability in leprosy patients in Guizhou province (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These analyses were used to construct the nomogram for predicting disability in leprosy patients. Compared with those of the training cohort, similar results were achieved in the validation cohort. In addition to the above indicators, detection mode and contact history were significantly associated with disability in leprosy patients (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDevelopment of Multivariate Nomogram Model\u003c/h3\u003e\n\u003cp\u003eEleven variables were selected based on the results of the multinominal and ordinal logistic regression analyses, and the R model was used to establish the nomogram for predicting the probability of disability in newly diagnosed leprosy patients (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The given score of each variable is shown in Table 3.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eInternal and External Validation\u003c/h2\u003e\n \u003cp\u003eIn order to evaluate the performance and discriminative ability of the prediction model, we calculated the area under the ROC curve (AUC) of the training and validation cohorts, respectively. For G1D, the AUC values for the training and validation cohorts were 0.622 [95% confidence interval (CI), 0.604\u0026ndash;0.639] and 0.593 (95% CI, 0.570\u0026ndash;0.616) respectively. For G2D, the AUC values for the training and validation cohorts were 0.753 (95% CI, 0.741\u0026ndash;0.765) and 0.770 (95% CI, 0.757\u0026ndash;0.784), respectively. For G1D\u0026thinsp;+\u0026thinsp;G2D, the AUC values for the training and validation cohorts were 0.699(95% CI, 0.688\u0026ndash;0.710) and 0.716 (95% CI, 0.702\u0026ndash;0.729), respectively. The AUC values demonstrated that the model has a good discriminative ability (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe calibration curve showed that the predicted results of the nomogram model were in good agreement with the actual results (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe number of newly diagnosed leprosy patients has decreased dramatically in China during recent decades; however, the proportion of disability among newly diagnosed leprosy patients is still high[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite previous studies reporting risk factors for disability in newly diagnosed leprosy patients in China and worldwide[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], a nomogram has not yet been developed. Thus, we sought to develop a visible individualized nomogram for predicting disability in leprosy patients based on the Chinese population.\u003c/p\u003e\u003cp\u003eThe training and validation cohorts were obtained from the LEPMIS of Yunnan and Guizhou provinces, southwestern China, from 1990\u0026ndash;2019. Covering approximately 394,000 and 176,167 square kilometers with populations of 47.71 and 35.08\u0026nbsp;million in 2020, Yunnan and Guizhou provinces have the first and second highest leprosy burdens in China, respectively. The wide geographic distribution of patients and large cohort sample sizes guarantee the representativeness and generalizability of the results for Chinese leprosy patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Delayed diagnosis, nerve damage, the absence of skin lesions, WHO and Ridley-Jopling classifications, leprosy reactions, advanced age, rural occupation, Han ethnicity, and male sex were identified as risk factors for disability in leprosy patients through logistic regression analysis in the validation cohort. These findings were in high concordance with previous reports on risk factors for disability in leprosy patients in Yunnan, China[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, detection mode and contact history were also related to the risk of physical disability in leprosy patients in the validation cohort. Notably, delayed diagnosis is the most important risk factor that has been identified in many populations, and some similar studies also support the relationship between nerve damage and severe disability[\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This implies that we should pay more attention to the early diagnosis and neurological aspect of leprosy disease.\u003c/p\u003e\u003cp\u003eNomogram validation is essential to avoid overfitting of models and determine generalizability[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The discriminative ability was slightly increased in the external validation cohort compared with that of the training cohort. The calibration curves demonstrated agreement between the predicted and actual values in the training cohort. In this study, favorable results were also replicated well in the validation cohort.\u003c/p\u003e\u003cp\u003eTo our knowledge, this is the first nomogram developed for predicting disability inpatients with leprosy disease. In our nomogram, the diagnosis interval was the greatest contributor to the risk of G2D in leprosy patients, followed by the Ridley-Jopling classification, nerve damage, age, and skin lesions, while the WHO classification, occupation type, ethnicity, sex, and leprosy reaction showed the smallest effects on G2D risk in leprosy patients. As the risk factors were identified from demographic and clinical variables, the nomogram can easily be used by specialized medical staff, physicians and the community. This scoring system should help medical staff pay more attention to the leprosy disability burden.\u003c/p\u003e\u003cp\u003eThe strengths of our study are that the data for the nomogram were from two large, specialized Chinese agencies for leprosy prevention and control with long-term follow-up data, and both internal and external verification were performed. The study had some limitations. First, the AUC was not impressively high, especially for patients with G1D. Second, our prediction model was constructed by demographic and clinical variables. Wider geographic recruitment, more enrolled patients, and more biological markers might improve the predictive value of this model.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, a prognostic nomogram for disability in newly diagnosed leprosy patients in the Chinese population was established and verified based on demographic and clinical variables in a large LEPMIS cohort. This nomogram might help clinicians to identify risk factors for disability in leprosy patients and provide early interventions to further reduce peripheral neuropathy and disability in these patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLeprosy Management Information System (LEPMIS); Grade 0 disability (G0D); Grade 1 disability(G1D); Grade 2 disability (G2D); World Health Organization (WHO); G2D per 100 000 total population (G2DR); Centers for Disease Control and Prevention (CDC); Lepromatous (LL); Borderline-lepromatous (BL); Borderline-borderline (BB); Borderline-tuberculoid (BT); Tuberculoid (TT); Indeterminate(I); Receiver operating characteristic (ROC); Area under the ROC curve (AUC); Positive predictive value (PPV); Negative predictive value (NPV).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all participants for their participation in the LEPMIS and leprosy control and prevention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Health Commission of Yunnan Province (No: 2017NS098) and National Natural Science Foundation of China (81260436).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuizhou Center for Disease Control and Prevention, Guizhou, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJinLan Li\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYunnan Center for Disease Control and Prevention\u003c/strong\u003e\u003cstrong\u003e, Yunnan, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaojun Yu, Jun He, Shun Zha, Shuo Kou, Tiejun Shui\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBeijing Tropical Medicine Research Institute, Beijing Friendship Hospital, Capital Medical University, Beijing, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaohua Chen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBeijing Key Laboratory for Research on Prevention and Treatment of Tropical Diseases, Capital Medical University, Beijing, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaohua Chen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuiyang Center for Disease Control and Prevention, Guizhou, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLu Ma, Qin Yang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYan\u0026rsquo;an Hospital of Kunming City, Yunnan, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaijun Yang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQiannan Center for Disease Control, Guizhou, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYanfang Zhao\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTianjin University, Tianjin, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiangyu Yan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQiandongnan Prefecture Center for Disease Control, Guizhou, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTao Li\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaojun Yu\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand Jun He\u0026nbsp;have equally contributed to this work.\u003c/p\u003e\n\u003cp\u003eResources: JLL, TJS, XJY, JH, LM, SK, QY, YFZ, TL, and SZ. Conceptualization: XHC, JLL and TJS. Project administration and funding acquisition: JLL and TJS. Analyzed and interpreted the data, and critically revised the article: XHC, JLL, TJS, XJY, JH, HJY and XYY. Methodology, investigation, Writing-original draft: XHC. Writing- review \u0026amp; editing: XJY and JH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Tiejun Shui \u0026amp; Xiaohua Chen\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026amp; Jinlan Li.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Ethics Committee of Yunnan Center for Disease Control and Prevention (Ethical Approval - No. 2023-19), with a full waiver of informed consent. The research utilized de-identified data from the national Leprosy Management Information System (LEPMIS), which were collected for routine public health surveillance purposes and contained no personal identifiers. The waiver was justified based on the retrospective design, minimal risk to participants, and the impossibility of obtaining consent for historical data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublisher\u0026rsquo;s note\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMathers, C. D., Ezzati, M. \u0026amp; Lopez, A. D. Measuring the burden of neglected tropical diseases: the global burden of disease framework. \u003cem\u003ePLoS Negl. Trop. Dis.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, e114 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShui, T. J. et al. Towards the elimination of leprosy in Yunnan, China: A time-series analysis of surveillance data. \u003cem\u003ePLoS Negl. Trop. Dis.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, e0009201 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, X., Liu, H. B., Shui, T. J. \u0026amp; Zha, S. Risk factors for physical disability in patients with leprosy disease in Yunnan, China: Evidence from a retrospective observational study. \u003cem\u003ePLoS Negl. Trop. Dis.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, e0009923 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, J. et al. How to improve early case detection in low endemic areas with pockets of leprosy: a study of newly detected leprosy patients in Guizhou Province, People's Republic of China. \u003cem\u003eLepr. Rev.\u003c/em\u003e \u003cb\u003e87\u003c/b\u003e, 23\u0026ndash;31 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, Y. Y., Yu, M. W., Ning, Y. \u0026amp; Wang, H. A study on gender differences in newly detected leprosy cases in Sichuan, China, 2000\u0026ndash;2015. \u003cem\u003eInt. J. Dermatol.\u003c/em\u003e \u003cb\u003e57\u003c/b\u003e, 1492\u0026ndash;1499 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChu, T. et al. Comprehensive measures succeeded in improving early detection of leprosy cases in post-elimination era: Experience from Shandong province, China. \u003cem\u003ePLoS Negl. Trop. Dis.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, e0007891 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, Q. P., Li, G., Li, C., Lin, Z. X. \u0026amp; Chen, P. Epidemiological situation of leprosy in a province in China: a long time to diagnosis and a high rate of deformity. \u003cem\u003eBMC Public. Health\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, 1790 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, Y. et al. Epidemiological Characteristics and Factors Associated with Cure of Leprosy in Chongqing, China, from 1949 to 2019. \u003cem\u003eAm. J. Trop. Med. Hyg.\u003c/em\u003e \u003cb\u003e108\u003c/b\u003e, 165\u0026ndash;173 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrganization, W. H. \u003cem\u003eEnhanced Global Strategy for Further Reducing the Disease Burden due to Leprosy (Plan Period: 2011\u0026ndash;2015)\u003c/em\u003e (WHO Regional Office for South-East Asia, 2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrganization, W. H. \u003cem\u003eGlobal Leprosy Strategy 2016\u0026ndash;2020. Accelerating Towards a Leprosy-Free World\u003c/em\u003e (World Health Organization, 2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrganization, W. H. \u003cem\u003eTowards Zero Leprosy. Global Leprosy (Hansen\u0026rsquo;s Disease) Strategy 2021\u0026ndash;2030\u003c/em\u003e (World Health Organization, 2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrganization, W. H. \u0026amp; WHO Expert Committee on Leprosy. : Seventh Report. Technical Report Series 847. Genev a: World Health Organization (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Paula, H. L. et al. Risk Factors for Physical Disability in Patients With Leprosy: A Systematic Review and Meta-analysis. \u003cem\u003eJAMA Dermatol.\u003c/em\u003e \u003cb\u003e155\u003c/b\u003e, 1120\u0026ndash;1128 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang, W. et al. Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer. \u003cem\u003eJ. Clin. Oncol.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 861\u0026ndash;869 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaposo, M. T. et al. Grade 2 disabilities in leprosy patients from Brazil: Need for follow-up after completion of multidrug therapy. \u003cem\u003ePLoS Negl. Trop. Dis.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, e0006645 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSales, A. M. et al. Progression of leprosy disability after discharge: is multidrug therapy enough? \u003cem\u003eTrop. Med. Int. Health\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 1145\u0026ndash;1153 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMonteiro, L. D. et al. [Physical disabilities in leprosy patients after discharge from multidrug therapy in Northern Brazil]. \u003cem\u003eCad Saude Publica\u003c/em\u003e. \u003cb\u003e29\u003c/b\u003e, 909\u0026ndash;920 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSouza, L. W. F. [Leprosy reactions in discharged patients following cure by multidrug therapy]. \u003cem\u003eRev. Soc. Bras. Med. Trop.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 737\u0026ndash;739 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIasonos, A., Schrag, D., Raj, G. V. \u0026amp; Panageas, K. S. How to build and interpret a nomogram for cancer prognosis. \u003cem\u003eJ. Clin. Oncol.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 1364\u0026ndash;1370 (2008).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"leprosy, Disability, risk factors, nomogram, newly diagnosed, China, Yunnan, Guizhou","lastPublishedDoi":"10.21203/rs.3.rs-6782091/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6782091/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDisabilities and deformities are common complications of leprosy disease. We previously revealed the risk factors for physical disability in leprosy patients in Yunnan, China, and in this study, aimed to develop and validate a nomogram for predicting physical disability in leprosy patients in the Chinese population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of newly diagnosed leprosy patients were extracted from the Leprosy Management Information System (LEPMIS) in China. The data from Yunnan and Guizhou provinces were divided into training and validation cohorts, respectively. A nomogram to predict the risk of disability in newly diagnosed leprosy patients was constructed and validated with bootstrap resampling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 17237 newly diagnosed leprosy patients who were evaluated for the grade of disability were included in the study, with 11261, 1939, and 3987 patients diagnosed as having Grade 0 disability (G0D), Grade 1 disability (G1D), and Grade 2 disability (G2D), respectively. Data on sex, age, nationality, occupation type, the symptom-to-diagnosis interval,contact history, skin lesions, nerve damage, leprosy reaction, Ridley-Jopling classification, and World Health Organization (WHO) classification were entered into the nomogram. The nomogram demonstrated good discriminative ability, with an area under the receiver operating characteristic curve of 0.753 and 0.770 for G2D in the training and validation cohorts, respectively. The calibration curve of the nomogram showed favorable consistency between the predicted and actual values in both the training and validation cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA nomogram was developed and validated for predicting the risk of disability in newly diagnosed leprosy patients in China based on demographic and clinical characteristics. The nomogram exhibited excellent calibration, indicating that it may have clinical utility to assist clinicians in evaluating the probability of disability and to eliminate the disability burden for leprosy patients.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Demographic and Clinical Characteristic-Based Nomogram for Predicting Physical Disability in Newly Diagnosed Leprosy Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 09:29:15","doi":"10.21203/rs.3.rs-6782091/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-06T17:41:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267821797414182600529288678179522831780","date":"2025-09-25T16:16:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T07:10:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-11T04:35:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-11T11:55:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-11T05:51:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-30T07:20:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5eb73def-e5f8-4847-a9bc-28e2754f0ba7","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54731056,"name":"Health sciences/Health care/Public health/Epidemiology"},{"id":54731057,"name":"Health sciences/Medical research/Epidemiology"},{"id":54731058,"name":"Health sciences/Neurology/Neurological disorders"},{"id":54731059,"name":"Health sciences/Signs and symptoms/Neurological manifestations"},{"id":54731060,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-09-22T09:29:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 09:29:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6782091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6782091","identity":"rs-6782091","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}