Sabiha Khan, Karuna Reddy, Momtaz Ahmed, Donald Wilson, Bibhya Sharma
{"title":"预测T2DM患者下肢多次截肢的部分比例优势模型。","authors":"Sabiha Khan, Karuna Reddy, Momtaz Ahmed, Donald Wilson, Bibhya Sharma","doi":"10.1186/s12911-025-03112-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background or introduction: </strong>Multiple Lower extremity amputation (MLEA) is an unfortunate outcome following a lower extremity amputation (LEA) in individuals with diabetes. The challenges faced by individual with MLEA are significantly higher than those who have undergone a single amputation. Therefore, developing a reliable and accurate method for determining risk factors associated with MLEA is essential for reducing the incidence of this outcome among diabetic patients.</p><p><strong>Objectives: </strong>This study aimed to explore the demographic and clinical characteristics of diabetic inpatients with foot ulcers. The goal was to develop a statistical model to determine the risk factors of MLEA among patients type 2 diabetic mellitus (T2DM).</p><p><strong>Methods: </strong>Data for statistical model development were collected from patients' folders involving 1,972 patients with T2DM who were hospitalized for acute diabetic foot ulcers (DFU) at three tertiary care hospitals in Fiji from 2016 to 2019. This cross-sectional study was conducted in accordance with the STROBE guidelines focusing on patients who experienced MLEA at the hospitals. Patients were categorized into three ordinal outcomes: no-amputation, primary amputation, and multiple amputations. A partial proportional odds model was developed to fit the ordinal outcome and determine the risk factors associated with MLEA. The proposed model was validated by comparing it to a proportional odds model and a multinomial logistics regression model.</p><p><strong>Results: </strong>The proposed partial proportional odds model (PPOM) identified several risk factors for MLEA, including age, gender, ethnicity, hypertension, anemia, leukocytosis, and thrombocytosis.</p><p><strong>Conclusions: </strong>The analytical findings reveal that the PPOM is appropriate for determining the risk factors associated with MLEA in T2DM patients in Fiji.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"287"},"PeriodicalIF":3.8000,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12323144/pdf/","citationCount":"0","resultStr":"{\"title\":\"Partial proportional odds model for predicting multiple lower extremity amputation among T2DM patients.\",\"authors\":\"Sabiha Khan, Karuna Reddy, Momtaz Ahmed, Donald Wilson, Bibhya Sharma\",\"doi\":\"10.1186/s12911-025-03112-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background or introduction: </strong>Multiple Lower extremity amputation (MLEA) is an unfortunate outcome following a lower extremity amputation (LEA) in individuals with diabetes. The challenges faced by individual with MLEA are significantly higher than those who have undergone a single amputation. Therefore, developing a reliable and accurate method for determining risk factors associated with MLEA is essential for reducing the incidence of this outcome among diabetic patients.</p><p><strong>Objectives: </strong>This study aimed to explore the demographic and clinical characteristics of diabetic inpatients with foot ulcers. The goal was to develop a statistical model to determine the risk factors of MLEA among patients type 2 diabetic mellitus (T2DM).</p><p><strong>Methods: </strong>Data for statistical model development were collected from patients' folders involving 1,972 patients with T2DM who were hospitalized for acute diabetic foot ulcers (DFU) at three tertiary care hospitals in Fiji from 2016 to 2019. This cross-sectional study was conducted in accordance with the STROBE guidelines focusing on patients who experienced MLEA at the hospitals. Patients were categorized into three ordinal outcomes: no-amputation, primary amputation, and multiple amputations. A partial proportional odds model was developed to fit the ordinal outcome and determine the risk factors associated with MLEA. The proposed model was validated by comparing it to a proportional odds model and a multinomial logistics regression model.</p><p><strong>Results: </strong>The proposed partial proportional odds model (PPOM) identified several risk factors for MLEA, including age, gender, ethnicity, hypertension, anemia, leukocytosis, and thrombocytosis.</p><p><strong>Conclusions: </strong>The analytical findings reveal that the PPOM is appropriate for determining the risk factors associated with MLEA in T2DM patients in Fiji.</p>\",\"PeriodicalId\":9340,\"journal\":{\"name\":\"BMC Medical Informatics and Decision Making\",\"volume\":\"25 1\",\"pages\":\"287\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2025-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12323144/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Informatics and Decision Making\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12911-025-03112-6\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Informatics and Decision Making","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12911-025-03112-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
Partial proportional odds model for predicting multiple lower extremity amputation among T2DM patients.
Background or introduction: Multiple Lower extremity amputation (MLEA) is an unfortunate outcome following a lower extremity amputation (LEA) in individuals with diabetes. The challenges faced by individual with MLEA are significantly higher than those who have undergone a single amputation. Therefore, developing a reliable and accurate method for determining risk factors associated with MLEA is essential for reducing the incidence of this outcome among diabetic patients.
Objectives: This study aimed to explore the demographic and clinical characteristics of diabetic inpatients with foot ulcers. The goal was to develop a statistical model to determine the risk factors of MLEA among patients type 2 diabetic mellitus (T2DM).
Methods: Data for statistical model development were collected from patients' folders involving 1,972 patients with T2DM who were hospitalized for acute diabetic foot ulcers (DFU) at three tertiary care hospitals in Fiji from 2016 to 2019. This cross-sectional study was conducted in accordance with the STROBE guidelines focusing on patients who experienced MLEA at the hospitals. Patients were categorized into three ordinal outcomes: no-amputation, primary amputation, and multiple amputations. A partial proportional odds model was developed to fit the ordinal outcome and determine the risk factors associated with MLEA. The proposed model was validated by comparing it to a proportional odds model and a multinomial logistics regression model.
Results: The proposed partial proportional odds model (PPOM) identified several risk factors for MLEA, including age, gender, ethnicity, hypertension, anemia, leukocytosis, and thrombocytosis.
Conclusions: The analytical findings reveal that the PPOM is appropriate for determining the risk factors associated with MLEA in T2DM patients in Fiji.
期刊介绍:
BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.