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Construction and verification of a risk prediction model of psychological distress in psychiatric nurses

BMC NURSING 2025年第1期24卷 1-13页

作者： Lu, Qinghua Wang, Mengjia Zuo, Yi Tang, Yingxue Zhang, Rui Zhang, Jie Shandong Univ Shandong Mental Hlth Ctr Dept Infect Management Jinan Peoples R China Shandong Second Med Univ Sch Nursing Weifang Peoples R China Zibo Cent Hosp Zibo Peoples R China Shandong Univ Shandong Mental Hlth Ctr Dept Addict Med Jinan Peoples R China Shandong Univ Dept Geriatr Med Qilu Hosp Jinan Peoples R China

Background Psychiatric nurses are more likely to experience psychological distress due to various factors in work and life. Establishing an early warning model of psychological distress for psychiatric nurses is helpful for reducing the incidence of psychological distress. Aims To explore the influencing factors of psychological distress in psychiatric nurses and construct and verify a risk prediction nomogram model. Methods A total of 812 psychiatric nurses were selected from psychiatric hospitals in Shandong Province from August to September 2022. They were divided into a negative group (K10 < 16 points) and a positive group (K10 >= 16 points) according to whether they experienced psychological distress. The elements contributing to psychological discomfort in psychiatric nurses were investigated via multivariate logistic regression analysis. R4.2.3 software and the rms program package were used to construct a risk prediction nomogram model for psychiatric nurses' psychological distress. The prediction effect and degree of fit of the nomogram model were evaluated via receiver operating characteristic (ROC) curves, calibration curves, and the Hosmer-Lomoshow goodness-of-fit test. Results Logistic regression analysis revealed that five indices, namely, senior nurses' professional title, self-efficacy of psychological capital (PCQ-R), emotional exhaustion (EE) and personal accomplishment (PA) in job burnout (MBI), and the total Pittsburgh Sleep Quality Index (PSQI) score, were independent risk factors for the psychological distress of psychiatric nurses (P < 0.05). The area under the ROC curve (AUC) of the constructed nomogram prediction model was 0.91695% CI (95% CI: 0.891-0.941), the best cutoff value was 0.610, the sensitivity was 89.4%, and the specificity was 81.1%. The results of the calibration curve analysis revealed that the calibration curve of the column graph model for predicting the psychological distress of psychiatric nurses was close to the ideal curv

关键词： Psychological distress Psychiatric nurses risk prediction model

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Characteristics, risk factors and a risk prediction model of tocilizumab-induced hypofibrinogenemia: a retrospective real-world study of inpatients

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BMC PHARMACOLOGY & TOXICOLOGY 2025年第1期26卷 1-8页

作者： Cai, Le Wen, Xiao Qiu, Zihan Fu, An Guo, Daihong Zhu, Man Chinese Peoples Liberat Army Gen Hosp Med Supplies Ctr Dept Pharm 28 Fu Xing Rd Beijing 100853 Peoples R China Tian Jin Med Univ Sch Pharm Tianjin 300070 Peoples R China

Objective The occurrence of hypofibrinogenemia after tocilizumab treatment has attracted increasing attention, which may cause bleeding and even life-threatening. This study aims to explore the risk factors for tocilizumab-induced hypofibrinogenemia (T-HFIB) and construct a risk prediction model. Methods A total of 221 inpatients that received tocilizumab from 2015 to 2023 were retrospectively collected and divided into T-HFIB group or control group. The risk factors for T-HFIB were obtained by logistic regression equation and used to establish the nomogram. Results T-HFIB was observed in 121 of 221 patients (54.75%). Multifactorial logistic regression analysis revealed that infection (OR = 2.002, 95%CI:1.018 similar to 3.935), COVID-19 (OR = 3.752, 95%CI:1.264 similar to 11.139), CAR-T therapy (OR = 4.409, 95%CI:2.017 similar to 0.894), and concomitant glucocorticoids (OR = 5.303, 95%CI:0.227 similar to 0.894) were identified as independent risk factors for T-HFIB, while high baseline fibrinogen level (OR = 0.813, 95%CI:0.670 similar to 0.988) and concomitant antirheumatic drugs (OR = 0.451, 95%CI:0.227 similar to 0.894) were identified as protective factors. A nomogram was established, and area under the curve (AUC) of prediction model was 0.772 (95%CI:0.709 similar to 0.836). Calibration curve showed a good prediction accuracy for the occurrence of T-HFIB. Conclusion The infection, COVID-19, CAR-T therapy, and concomitant glucocorticoids were independent risk factors for T-HFIB, while high baseline fibrinogen and concomitant antirheumatic drugs were protective factors. This nomogram can help early identify the patients at potential high risk of developing T-HFIB.

关键词： Tocilizumab Hypofibrinogenemia risk factors risk prediction model

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Development and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery

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BMC ANESTHESIOLOGY 2025年第1期25卷 1-9页

作者： Yap, Edward N. Huang, Jie Chiu, Joshua Chang, Robert W. Cohn, Bradley Hwang, Judith C. F. Reed, Mary Kaiser Permanente South San Francisco Dept Anesthesia Permanente Med Grp 1200 El Camino Real South San Francisco CA 94080 USA Univ Calif San Francisco Dept Anesthesia & Perioperat Care 505 Parnassus Ave San Francisco CA 94143 USA Kaiser Permanente Div Res 2000 Broadway Oakland CA 94612 USA Permanente Med Grp Inc Dept Vasc Surg 1200 El Camino Real South San Francisco CA 94080 USA Permanente Med Grp Inc Dept Anesthesia 3600 Broadway Oakland CA 94611 USA Permanente Med Grp Inc Dept Anesthesia 975 Sereno Dr Vallejo CA 94589 USA

BackgroundClinical determination of patients at high risk of poor surgical outcomes is complex and may be supported by clinical tools to summarize the patient's own personalized electronic health record (EHR) history and vitals data through predictive risk models. Since prior models were not readily available for EHR-integration, our objective was to develop and validate a risk stratification tool, named the Assessment of Geriatric Emergency Surgery (AGES) score, predicting risk of 30-day major postoperative complications in geriatric patients under consideration for urgent and emergency surgery using pre-surgical existing electronic health record (EHR) *** 65-years and older undergoing urgent or emergency non-cardiac surgery within 21 hospitals 2017-2021 were used to develop the model (randomly split: 80% training, 20% test). The primary outcome was a 30-day composite outcome including several postoperative major complications and mortality;secondary outcomes included common individual complications included in the primary composite outcome (sepsis, progressive renal insufficiency or renal failure, and mortality). Patients' EHR-based clinical history, vital signs, labs, and demographics were included in logistic regression, LASSO, decision tree, Random Forest, and XGBoost models. Area under the receiver operating characteristics curve (AUCROC) was used to compare model ***, 66,262 patients (median [IQR] age 78 [(70.9-84.0], female 53.9%, White race 68.5%) received urgent or emergency non-cardiac surgery (25.7% orthopedic cases, 21.9% general surgery cases). AUCROC ranged from 0.655 (Decision Tree) - 0.804 (XGBoost) for the primary composite outcome. XGBoost AUCROC was 0.823, 0.781, and 0.839 in predicting outcomes of sepsis, progressive renal insufficiency or renal failure, and mortality, *** developed a model to accurately predict major postoperative complications in geriatric patients undergoing urgen

关键词： risk prediction model Geriatric surgery Emergency surgery

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A risk prediction model for venous thromboembolism in hospitalized patients with thoracic trauma: a machine learning, national multicenter retrospective study

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WORLD JOURNAL OF EMERGENCY SURGERY 2025年第1期20卷 1-9页

作者： Liu, Kaibin Qian, Di Zhang, Dongsheng Jin, Zhichao China Chest Injury Research Society CCIRS, Yi Yang, Yi Zhao, Yanfang Shanghai Jiao Tong Univ Dept Thorac Surg Sch Med Affiliated Peoples Hosp 6 Shanghai 200233 Peoples R China Naval Med Univ Fac Hlth Serv Dept Hlth Stat 800 Xiangyin Rd Shanghai 200433 Peoples R China Shijiazhuang Third Hosp Dept Cardiothorac Surg Shijiazhuang 050000 Hebei Peoples R China Shanghai Sixth Peoples Hosp Dept Thorac Surg 600 Yishan Rd Shanghai 200235 Peoples R China

BackgroundEarly treatment and prevention are the keys to reducing the mortality of VTE in patients with thoracic trauma. This study aimed to develop and validate an automatic prediction model based on machine learning for VTE risk screening in patients with thoracic *** this national multicenter retrospective study, the clinical data of chest trauma patients hospitalized in 33 hospitals in China from October 2020 to September 2021 were collected for model training and testing. The data of patients with thoracic trauma at Shanghai Sixth People's Hospital from October 2021 to September 2022 were included for further verification. The performance of the model was measured mainly by the area under the receiver operating characteristic curve (AUROC) and the mean accuracy (mAP), and the sensitivity, specificity, positive predictive value, and negative predictive value were also *** total of 3116 patients were included in the training and validation of the model. External validation was performed in 408 patients. The random forest (RF) model was selected as the final model, with an AUROC of 0879 (95% CI 0856-0902) in the test dataset. In the external validation, the AUROC was 0.83 (95% CI 0.794-0.866), the specificity was 0.756 (95% CI 0.713-0.799), the sensitivity was 0.821 (95% CI 0.692-0.923), the negative predictive value was 0.976 (95% CI 0.958-0.993), and the positive likelihood ratio was *** model can be used to quickly screen for the risk of VTE in patients with thoracic trauma. More than 90% of unnecessary VTE tests can be avoided, which can help clinicians target interventions to high-risk groups and ensure resource optimization. Although further validation and improvement are needed, this study has considerable clinical value.

关键词： Thoracic trauma Venous thromboembolism Rib fracture risk prediction model Random forest Machine learning

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Construction of a risk prediction model of diquat poisoning based on clinical indicators

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CRITICAL REVIEWS IN TOXICOLOGY 2025年第1期55卷 55-62页

作者： Qian, Weiwei Zhou, Jian Ren, Yan Bai, Yangjuan Peng, Aihua Lv, Lin Ma, Zengwen He, Chengtong Zhou, Yue Tong, Jiale Zhang, Yanzi Cao, Yu Xu, Shuyun Sichuan Univ West China Hosp Emergency Dept Chengdu 610041 Peoples R China Sichuan Univ West China Hosp Lab Emergency Med Chengdu 610041 Peoples R China Sichuan Univ Disaster Med Ctr Chengdu 610041 Peoples R China Sichuan Univ Shangjinnanfu Hosp West China Hosp Emergency Dept Chengdu Peoples R China Shenzhen Univ Hlth Sci Ctr Int Canc Ctr Dept Immunol Shenzhen Peoples R China Sichuan Univ West China Hosp Dept Lab Med Res CtrClin Lab Med Chengdu Peoples R China

This study aims to explore the clinical characteristics and prognostic factors in patients with diquat (DQ) poisoning and to develop a clinical risk assessment model to improve diagnosis and treatment strategies. Data from 60 patients with DQ poisoning, including basic characteristics, poisoning severity, and inflammatory response indicators, were collected. The plasma concentration of DQ was measured using liquid chromatography-mass spectrometry. The included patients were categorized into survival and death groups based on their 30-day outcomes. Fisher's exact test was used to identify statistically significant clinical indicators (p < .05), and logistic regression within a generalized linear model (GLM) framework was employed to analyze these indicators alongside the severity index of diquat poisoning (SIDP), followed by the construction of a prognostic model. The performance of the model was evaluated through receiver operating characteristic (ROC) analysis, and the accuracy of the model was assessed. Additionally, two independent sample Wilcoxon tests compared the clinical indicators between high-risk and low-risk groups. Fisher's exact test identified significant differences in variables such as oral drug dosage (ODD), time from poisoning to admission (TFPTA), state of consciousness (SOC), Glasgow Coma Scale (GCS), white blood cells (WBC), myoglobin (Myo), high-flow nasal cannula (HFNC), invasive mechanical ventilation (IMV), acute kidney injury (AKI), and acute lung injury (ALI) (p < .05) between the survival and death groups. The GLM-based risk assessment model demonstrated high predictive accuracy, with an area under the ROC curve (AUC) of 0.97 (SE 0.017, 95% CI 0.939-1.001), indicating potent prognostic capability. The Wilcoxon test revealed that ODD, Myo, SIDP, aspartate transferase (AST), creatine kinase (CK), hemoglobin (Hb), cardiac troponin (cTnT), and serum creatinine (Cr) levels were significantly higher in the high-risk group. The clinical risk ass

关键词： Diquat plasma concentration poisoning severity index risk prediction model drug blood concentration

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Construction and validation of a fall risk prediction model in elderly maintenance hemodialysis patients: a multicenter prospective cohort study

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RENAL FAILURE 2025年第1期47卷 2455524页

作者： Li, Lin Xu, Wenbin Fang, Yiqian Jiang, Qian Zhou, Yanfei Chen, Yan Yang, Qian Chengdu Med Coll Sch Nursing Chengdu Peoples R China Sichuan Prov Peoples Hosp Dept Neonatal Intens Care Unit Chengdu Peoples R China

PurposeTo analyze the risk factors for falls in elderly maintenance hemodialysis patients, construct a nomogram prediction model and validate the *** maintenance hemodialysis patients face a high risk of falls, and there are fewer and less effective fall-specific assessment *** total of 871 elderly hemodialysis patients from 9 hospitals in Chengdu City from October 2023 to December 2024 were selected as the study objects. Baseline characteristics and fall outcomes of patients in the fall group and non-fall group were recorded and compared through 6-month follow-up. Multivariable logistic regression analysis was employed to identify independent risk factors, and construct the nomogram prediction model and complete the internal verification of the model. 218 elderly maintenance hemodialysis patients from three other hospitals in Chengdu City were selected for a 6-month follow-up of falls from January to February 2024 to complete the external validation of the *** incidence of falls in elderly maintenance hemodialysis patients was 31.96%, and logistic regression analysis showed that age, sex, visual impairment, intradialytic hypotension, cognitive impairment and depression were independent risk factors for falls. Both internal and external validation of the model demonstrated area under the curve greater than 0.80. Furthermore, calibration plots, the Hosmer-Lemeshow test, and clinical decision curves all demonstrated that the model had good calibration and clinical *** nomogram constructed based on the above risk factors can provide scientific basis and practical tools for early clinical identification of high-risk groups of falls.

关键词： Fall hemodialysis risk prediction model risk factors

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Cost-effectiveness of a machine learning risk prediction model (LungFlag) in the selection of high-risk individuals for non-small cell lung cancer screening in Spain

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JOURNAL OF MEDICAL ECONOMICS 2025年第1期28卷 147-156页

作者： Trujillo, Juan Carlos Soriano, Joan B. Marzo, Merce Higuera, Oliver Gorospe, Luis Pajares, Virginia Olmedo, Maria Eugenia Arrabal, Natalia Flores, Andres Garcia, Jose Francisco Crespo, Maria Carcedo, David Heuser, Carolina Obradovic, Milan M. S. Olghi, Nicolo Choman, Eran N. Seijo, Luis M. Hosp Santa Creu & Sant Pau Hosp del Mar Barcelona 08025 Spain Hosp Univ la Princesa UAM Serv Neumol Vitoria Spain Primary Hlth Care Catalan Hlth Inst Canc Res Grp Barcelona Spain Hosp Univ La Paz Med Oncol Serv Madrid Spain Hosp Univ Ramon & Cajal Serv Radiodiagnost Madrid Spain Hosp Univ Ramon & Cajal Med Oncol Madrid Spain Roche Farma SA Pricing Madrid Spain Roche Farma SA Med Affairs Madrid Spain Hygeia Consulting SL HEOR Madrid Spain Hoffmann La Roche AG Global Access Evidence Basel Switzerland Hoffmann La Roche AG Med Affairs Basel Switzerland Hoffmann La Roche AG Digital Hlth Business Lead CH-4070 Basel Switzerland Medial EarlySign Hod Hasharon Israel Clin Univ Navarra Navarra Spain

ObjectiveThe LungFlag risk prediction model uses individualized clinical variables to identify individuals at high-risk of non-small cell lung cancer (NSCLC) for screening with low-dose computed tomography (LDCT). This study evaluates the cost-effectiveness of LungFlag implementation in the Spanish setting for the identification of individuals at high-risk of *** model combining a decision-tree with a Markov model was adapted to the Spanish setting to calculate health outcomes and costs over a lifetime horizon, comparing two hypothetical scenarios: screening with LungFlag versus non-screening, and screening with LungFlag versus screening the entire population meeting 2013 US Preventive Services Task Force (USPSTF) criteria. model inputs were obtained from the literature and the clinical practice of a multidisciplinary expert panel. Only direct costs (of 2023), obtained from local sources, were considered. Deterministic and probabilistic sensitivity analyses were performed to assess the robustness of our *** cohort of 3,835,128 individuals meeting 2013 USPSTF criteria would require 2,147,672 LDCTs scans. However, using LungFlag would only require 232,120 LDCTs scans. Cost-effectiveness results showed that LungFlag was dominant versus non-screening scenario, and outperformed the scenario where the entire population were screened since the observed loss of effectiveness (-224,031 life years [LYs] and -97,612 quality-adjusted life years [QALYs]) was largely offset by the significant cost savings provided (7,053 million). The resulting incremental cost-effectiveness ratio (ICER) for this strategy of screening the whole population versus using LungFlag was 72,000/QALY, showing that LungFlag is cost-effective. Various were described, such as the source of the efficacy or adherence rates, and other limitations inherent to cost-effectiveness *** LungFlag for the selection of high-risk individuals for NSCLC scree

关键词： Cost-effectiveness analysis LungFlag non-small cell lung cancer risk prediction model lung cancer screening I18 C52

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Inflammation-Driven Prognosis in Advanced Heart Failure: A Machine Learning-Based risk prediction model for One-Year Mortality

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Journal of Inflammation Research 2025年 18卷 5047-5060页

作者： Zhou, Min Du, Xiue Department of Intensive Care Unit The Affiliated Hospital of Xuzhou Medical University Jiangsu Xuzhou 221004 China Department of Intensive Care Unit Suining County People’s Hospital Jiangsu Xuzhou 221200 China

Background: To develop a machine learning (ML)-based prediction model focused on the one-year mortality risk in patients with advanced heart failure (AdHF), aiming to improve prediction accuracy by integrating inflammatory biomarkers and clinical para-meters, assist clinical decision-making, and enhance patient outcomes. Methods: A retrospective cohort study. Data were obtained from the electronic medical records system of the Affiliated Hospital of Xuzhou Medical University. AdHF patients admitted to the ICU and cardiology department from January 2015 to December 2023 were included with a one-year follow-up. 52 variables potentially affecting prognosis were incorporated. The LASSO algorithm was used for feature selection and dimensionality reduction. Data were split into training and validation sets. Seven ML algorithms were applied to build and evaluate models. The SHAP method was used for model analysis and a dynamic nomogram was created. Results: The study included 715 AdHF patients. The random forest (RF) model performed best, with an area under the curve (AUC) of 0.83 (95% confidence interval: 0.77–0.88), an accuracy of 0.72, a sensitivity of 0.74, and an F1 score of 0.73. Key predictors of one-year mortality risk included Beta blockers, ACEI/ARB/ARNI, BNP, CRP, NLR, AF, MI, NYHA class, and age. SHAP analysis revealed that elevated CRP, NLR, and age were associated with increased risk, while Beta blockers, ACEI/ARB/ARNI, and lower BNP values were associated with reduced risk. An online dynamic nomogram was developed to provide personalized risk predictions based on patient-specific conditions. Conclusion: A successful ML-based prediction model was developed to accurately predict the one-year mortality risk in AdHF patients, with inflammation-driven factors being significant. The RF model integrating clinical features and inflammatory markers showed excellent performance and could assist clinical decision-making. Future research should conduct larger, multi-cen

关键词： advanced heart failure inflammation machine learning one-year mortality risk prediction model

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Development and validation of a risk prediction model for premenopausal breast cancer in 19 cohorts

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Breast cancer research : BCR 2025年第1期27卷 67-67页

作者： Brantley, Kristen D. Jones, Michael E. Tamimi, Rulla M. Rosner, Bernard A. Kraft, Peter Nichols, Hazel B. Adami, Hans-Olov Aizpurua, Amaia de Gonzalez, Amy Berrington Blot, William J. Braaten, Tonje Chen, Yu DeHart, Jessica Clague Dossus, Laure Elias, Sjoerd Fortner, Renée T Garcia-Closas, Montserrat Gram, Inger T. Håkansson, Niclas Hankinson, Susan E. Kitahara, Cari M. Koh, Woon-Puay Linet, Martha S. MacInnis, Robert J. Masala, Giovanna Mellemkjær, Lene Milne, Roger L. Muller, David C. Park, Hannah Lui Ruddy, Kathryn J. Sandin, Sven Shu, Xiao-Ou Tin Tin, Sandar Truong, Thérèse Vachon, Celine M. Vatten, Lars J. Visvanathan, Kala Weiderpass, Elisabete Willett, Walter Wolk, Alicja Yuan, Jian-Min Zheng, Wei Sandler, Dale P. Schoemaker, Minouk J. Swerdlow, Anthony J. Eliassen, A Heather Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Department of Medical Oncology Dana-Farber Cancer Institute Boston 02115 MA United States Division of Genetics and Epidemiology Institute of Cancer Research London United Kingdom Department of Population Health Sciences Weill Cornell Medicine New York NY USA Department of Medicine Channing Division of Network Medicine Brigham and Women's Hospital Boston MA United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States Division of Cancer Epidemiology and Genetics Transdivisional Research Program National Cancer Institute National Institutes of Health Bethesda MD United States University of North Carolina Gillings School of Global Public Health Chapel Hill NC United States Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden Clinical Effectiveness Research Group Institute of Health and Society University of Oslo Oslo Norway Ministry of Health of the Basque Government Sub Directorate for Public Health and Addictions of Gipuzkoa San Sebastian Spain Biogipuzkoa Health Research Institute Epidemiology of Chronic and Communicable Diseases Group San Sebastián Spain Clinical Cancer Epidemiology Institute of Cancer Research Sutton London UK Department of Medicine Division of Epidemiology Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center Vanderbilt University Medical Center Nashville TN United States Department of Community Medicine UiT The Arctic University of Norway Tromsø Norway Department of Population Health Division of Epidemiology NYU Grossman School of Medicine New York NY USA School of Community and Global Health Claremont Graduate University Claremont CA United States Nutrition and Metabolism Branch International Agency for Research On Cancer Lyon France Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Universi

BACKGROUND: Incidence of premenopausal breast cancer (BC) has risen in recent years, though most existing BC prediction models are not generalizable to young women due to underrepresentation of this age group in model development. METHODS: Using questionnaire-based data from 19 prospective studies harmonized within the Premenopausal Breast Cancer Collaborative Group (PBCCG), representing 783,830 women, we developed a premenopausal BC risk prediction model. The data were split into training (2/3) and validation (1/3) datasets with equal distribution of cohorts in each. In the training dataset variables were chosen from known and hypothesized risk factors: age, age at menarche, age at first birth, parity, breastfeeding, height, BMI, young adulthood BMI, recent weight change, alcohol consumption, first-degree family history of BC, and personal history of benign breast disease (BBD). Hazard ratios (HR) and 95% confidence intervals (CI) were estimated by Cox proportional hazards regression using age as time scale, stratified by cohort. Given that complete information on all risk factors was not available in all cohorts, coefficients were estimated separately in groups of cohorts with the same available covariate information, adjusted to account for the correlation between missing and non-missing variables and meta-analyzed. Absolute risk of BC (in situ or invasive) within 5 years, was determined using country-, age-, and birth cohort-specific incidence rates. Discrimination (area under the curve, AUC) and calibration (Expected/Observed, E/O) were evaluated in the validation dataset. We compared our model with a literature-based model for women < 50 years (iCARE-Lit). RESULTS: Selected model risk factors were age at menarche, parity, height, current and young adulthood BMI, family history of BC, and personal BBD history. Predicted absolute 5-year risk ranged from 0% to 5.7%. The model overestimated risk on average [E/O risk = 1.18 (1.14-1.23)], with underestimation of ris

关键词： Premenopausal breast cancer risk prediction model Young-onset breast cancer

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External validation and application of risk prediction model for ventilator-associated pneumonia in ICU patients with mechanical ventilation: A prospective cohort study

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INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2025年 199卷 105910-105910页

作者： Li, Jiaying Li, Guifang Liu, Ziqing Wang, Jincong Jia, Xueping Yang, Hongxiao Wang, Yongqin Ningxia Med Univ Sch Nursing Yinchuan 750004 Ningxia Peoples R China Sun Yat Sen Univ Sun Yat Sen Mem Hosp Dept Crit Care Med Guangzhou 510120 Guangdong Peoples R China Ningxia Med Univ Dept Crit Care Med Gen Hosp Yinchuan 750004 Ningxia Peoples R China Weifang Peoples Hosp Dept Orthopaed Trauma Weifang 261000 Shandong Peoples R China Alxa Vocat & Tech Coll Coll Med Nursing Alxa League 750306 Nei Mongol Peoples R China

Background: Early identification and prevention of ventilator-associated pneumonia (VAP) in patients with mechanical ventilation (MV) through reliable prediction model undergoing a rigorous and standardized process is essential for clinical decision-making. Objective: This study aims to externally validate the VAP prediction model previously developed by a tertiary hospital in Northwestern China, using data from different time periods or hospitals, and to develop a web-based model calculator for clinical application to evaluate the model's prediction performance and generalizability. Methods: We prospectively collected MV patients data from the ICUs of two tertiary hospitals in Northwestern China for external validation of the model. Temporal and geographical validation were performed at the hospital where the model was developed and another hospital, respectively. The area under the receiver operating characteristic curve (AUC), Howsmer-Lemeshow test, calibration curve and decision curve analysis (DCA) were used to evaluate the model's discrimination, calibration and clinical applicability, respectively. A web-based model calculator was further developed and applied to MV patients in one of the hospitals to obtain the prediction probabilities of VAP risk. model performance was evaluated using a confusion matrix and diagnostic tests. Results: The temporal and geographical validation cohorts included 416 and 410 patients, and the AUCs were 0.814 and 0.800, respectively. The Hosmer-Lemeshow tests (both P > 0.05) and calibration curves showed a relatively high consistency. The DCA revealed the model threshold probabilities in the temporal (2.0 % to 50.0 %) and geographical validation (5.0 % to 70.0 %). The web-based model calculator (https://*** ***/VAPDynNomapp/) was applied to 202 patients in clinical practice. The cut-off value of the prediction probability was 0.096, with an accuracy of 0.911, a sensitivity of 0.900, a specificity of 0.912, and a

关键词： Ventilator-associated pneumonia risk prediction model Machine learning Generalizability prediction performance

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