{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T06:06:03Z","timestamp":1789538763813,"version":"build-2803163510"},"reference-count":33,"publisher":"International Union Against Tuberculosis and Lung Disease","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJTLD Open"],"accepted":{"date-parts":[[2026,5,7]]},"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>SUMMARY<\/jats:title>\n                  <jats:sec>\n                    <jats:title>BACKGROUND<\/jats:title>\n                    <jats:p>China has a high TB burden and limited primary care. We therefore developed a risk prediction model using routine clinical and lab indicators to flag severe TB cases early.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>In a case\u2013control study of 2,012 TB patients (499 severe, 1,513 non-severe) from four hospitals (2012\u20132022), patients were split into training (n = 1,408) and validation (n = 604) sets. Univariable and multivariable logistic regression identified predictors to build a nomogram (i.e., a tool that integrates patient-specific data to predict the probability of a specific clinical event), with model performance assessed using receiver operating characteristic (ROC) curves, calibration, and decision curves.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Multivariable analysis identified seven independent predictors: advanced age, history of TB, lymphocytopenia, monocytosis, neutrophilia, hyponatremia, and hypoalbuminemia. The area under the ROC curve (AUC) for the training set was 0.834 (95% confidence interval [CI]: 0.796\u20130.872), with a sensitivity of 89.0% and specificity of 63.4%; the validation set AUC was 0.807 (95% CI: 0.746\u20130.867), with sensitivity 82.0% and specificity 71.7%. The calibration curve showed high consistency between predicted probabilities and actual observations. Decision curve analysis demonstrated clinical net benefit of the model within the threshold probability range of 5%\u201395%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>This model, based on routine indicators, effectively identifies high-risk severe TB cases, offering a practical and cost-efficient tool for early patient stratification in primary care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.5588\/ijtldopen.26.0061","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:49:16Z","timestamp":1789537756000},"page":"561-567","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a primary care\u2013accessible risk prediction model for severe pulmonary TB"],"prefix":"10.5588","volume":"3","author":[{"given":"X.","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"W.","family":"Shu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C.","family":"Bei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Y.","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Y.","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L.","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3554","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"2026091522491213000_3.9.561.1","unstructured":"World Health Organization . 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