<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Wu X</submitter><funding>First Affiliated Hospital of Xinxiang Medical University</funding><funding>Tuberculosis Research Institute of Xinxiang Medical University</funding><funding>Guangdong Provincial Natural Science Foundation</funding><funding>Shenzhen Science and Technology Program</funding><pagination>744</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12103012</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>25(1)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Refractory mycoplasma pneumoniae pneumonia (RMPP) can result in severe complications and long-term effects. Early identification of RMPP and appropriate treatments can effectively alleviate complications and restrict the progression of sequelae. There is currently a dearth of a comprehensive and efficient model for predicting and evaluating RMPP.&lt;h4>Methods&lt;/h4>The development cohort consisted of patients with mycoplasma pneumoniae pneumonia (MPP) who underwent fiberoptic bronchoscopy between January 2019 and October 2021. Multivariable logistic regression analysis was used to identify independent risk factors for RMPP, and a nomogram model was developed that included initial admission examinations and clinical characteristics. The accuracy of the model was validated usi</pubmed_abstract><journal>BMC infectious diseases</journal><pubmed_title>Predictive value of an early comprehensive assessment model for refractory mycoplasma pneumoniae pneumonia and internal validation.</pubmed_title><pmcid>PMC12103012</pmcid><funding_grant_id>QN-2022-A10</funding_grant_id><funding_grant_id>QN-2022-A05</funding_grant_id><funding_grant_id>JCYJ20210324125413036</funding_grant_id><funding_grant_id>XYJHB202105</funding_grant_id><funding_grant_id>2021A1515011109</funding_grant_id><pubmed_authors>Lu W</pubmed_authors><pubmed_authors>Ren Y</pubmed_authors><pubmed_authors>Zhang R</pubmed_authors><pubmed_authors>Fan S</pubmed_authors><pubmed_authors>Liu W</pubmed_authors><pubmed_authors>Liu X</pubmed_authors><pubmed_authors>Wang M</pubmed_authors><pubmed_authors>He S</pubmed_authors><pubmed_authors>Li S</pubmed_authors><pubmed_authors>Wang T</pubmed_authors><pubmed_authors>Zhang X</pubmed_authors><pubmed_authors>Wu X</pubmed_authors><pubmed_authors>Xu Y</pubmed_authors></additional><is_claimable>false</is_claimable><name>Predictive value of an early comprehensive assessment model for refractory mycoplasma pneumoniae pneumonia and internal validation.</name><description>&lt;h4>Background&lt;/h4>Refractory mycoplasma pneumoniae pneumonia (RMPP) can result in severe complications and long-term effects. Early identification of RMPP and appropriate treatments can effectively alleviate complications and restrict the progression of sequelae. There is currently a dearth of a comprehensive and efficient model for predicting and evaluating RMPP.&lt;h4>Methods&lt;/h4>The development cohort consisted of patients with mycoplasma pneumoniae pneumonia (MPP) who underwent fiberoptic bronchoscopy between January 2019 and October 2021. Multivariable logistic regression analysis was used to identify independent risk factors for RMPP, and a nomogram model was developed that included initial admission examinations and clinical characteristics. The accuracy of the model was validated usi</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 May</publication><modification>2025-07-26T03:06:32.117Z</modification><creation>2025-07-26T03:06:32.117Z</creation></dates><accession>S-EPMC12103012</accession><cross_references><pubmed>40413448</pubmed><doi>10.1186/s12879-025-11133-9</doi></cross_references></HashMap>