Development and Validation of a Nomogram to Predict risk of Sepsis in Non-ventilator Hospital- Acquired Pneumonia

Development and Validation of a Nomogram to Predict risk of Sepsis in Non-ventilator Hospital- Acquired Pneumonia | 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 Research Article Development and Validation of a Nomogram to Predict risk of Sepsis in Non-ventilator Hospital- Acquired Pneumonia Han Zhou, Zhenchao Wu, Rui Wu, Beibei Liu, Yipeng Du, Ning Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7847940/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To identify risk factors for progression to sepsis in patients with non-ventilator hospital-acquired pneumonia (NV-HAP) and develop a practical and accurate nomogram to improve clinical outcomes. Methods: We retrospectively enrolled 408 hospitalized patients diagnosed with hospital-acquired pneumonia at Peking University Third Hospital between January 2017 and December 2021. Clinical and laboratory data were collected, and patients were randomly assigned to a training cohort and a validation cohort. Univariate and multivariate logistic regression analyses were performed in the training cohort to identify independent risk factors associated with progression to sepsis. A predictive nomogram was then constructed based on these independent predictors and validated using the validation cohort. Results: A total of 368 patients were ultimately included. Multivariate logistic regression analysis identified male sex (OR = 2.22, 95% CI: 1.09–4.51), coagulation dysfunction (OR = 2.35, 95% CI: 1.04–5.30), acute myocardial infarction (OR = 8.58, 95% CI: 2.10–35.04), chronic kidney disease (OR = 2.73, 95% CI: 1.15–6.51), underlying respiratory disease (OR = 0.31, 95% CI: 0.12–0.79), oxygenation index (OR = 0.99, 95% CI: 0.99–1.00), platelet count (OR = 0.99, 95% CI: 0.98–0.99), and total bilirubin (OR = 1.03, 95% CI: 1.01–1.06) as independent predictors for progression to sepsis in NV-HAP patients. The nomogram demonstrated good predictive performance, with C-index values of 0.73 in the training cohort and 0.64 in the validation cohort. Calibration curves indicated acceptable agreement between predicted and actual outcomes, and decision curve analysis confirmed favorable clinical utility in both cohorts. Conclusion: In patients with NV-HAP, clinicians should pay particular attention to specific independent risk factors identified in this study, such as male sex, coagulation dysfunction, acute myocardial infarction, chronic kidney disease, underlying respiratory diseases, decreased oxygenation index, thrombocytopenia, and elevated total bilirubin. The developed nomogram effectively predicts the risk of progression to sepsis, providing clinicians with a practical tool for timely intervention and potentially improving patient outcomes. Non-ventilator hospital-acquired pneumonia Sepsis Risk prediction Nomogram Prognostic model Figures Figure 1 Figure 2 Figure 3 1. Introduction Sepsis, defined as life-threatening organ dysfunction resulting from a dysregulated host response to infection, remains a significant global health challenge associated with high morbidity and mortality[1,2]. Although notable progress has been made in the early identification and management of sepsis and septic shock over the past decade, the overall incidence and mortality rates remain high due to its complex pathophysiology, nonspecific early clinical manifestations, and rapid progression[3,4]. Based on the source of infection, sepsis is commonly classified into community-acquired sepsis and hospital-acquired sepsis[5]. Studies have consistently shown that hospital-acquired sepsis is associated with longer hospital stays, higher mortality, and poorer outcomes compared to community-acquired sepsis[5,6].Pneumonia remains the most common trigger for sepsis[7]. Hospital-acquired pneumonia (HAP) is defined as pneumonia occurring 48 hours or more after admission, excluding infections already incubating at the time of hospital admission, and represents one of the most prevalent healthcare-associated infections[8]. The incidence and mortality associated with HAP are significantly influenced by patient age, underlying comorbidities, and invasive procedures, such as endotracheal intubation and mechanical ventilation. Mechanical ventilation is a high-risk factor for sepsis susceptibility in patients with HAP[9]. Clinically, HAP is generally categorized into ventilator-associated pneumonia (VAP) and non-ventilator hospital-acquired pneumonia (NV-HAP), depending on the presence or absence of invasive mechanical ventilation. Gradually emerging large-scale studies indicate that the risk of developing sepsis among NV-HAP patients may be comparable to that in VAP cases. Compared to VAP, NV-HAP has received considerably less attention in clinical research, leading to potential underestimation of its associated risk of progression to sepsis[10,11], which underscoring the need for focused investigations into the factors influencing sepsis progression in NV-HAP patients and facilitate early identification and intervention, potentially improving patient outcomes[12]. At present, several clinical scoring systems are widely employed to assess the severity and prognosis of sepsis, including systemic inflammatory response syndrome (SIRS), sequential organ failure assessment (SOFA), quick SOFA (qSOFA), and the national early warning score (NEWS). A recent study has demonstrated that these scoring systems exhibit limited efficacy in predicting the development of sepsis among pneumonia patients [13]. .This finding underscores the necessity of developing a novel early warning model specifically designed for predicting sepsis in patients with non-ventilator hospital-acquired pneumonia (NV-HAP). Originally emerging from oncology for individualized prognostic estimation, nomograms have gained increasing application across diverse medical disciplines, demonstrating utility in generating quantifiable predictions of disease progression and clinical outcomes [14]. Accordingly, this study was undertaken to (1) identify clinical predictors of sepsis progression in non-ventilator hospital-acquired pneumonia (NV-HAP) patients, and (2) develop and validate a disease-specific nomogram for this population. The resulting model provides clinicians with an intuitive bedside tool for early risk stratification, thereby enabling timely therapeutic interventions and potentially mitigating adverse clinical trajectories. 2. Methods 2.1 Study Population This retrospective study included 408 patients diagnosed with hospital-acquired pneumonia (HAP) in Peking University Third Hospital between January 2017 and December 2021. After screening theinclusion and exclusion criteria, 368 patients with non-ventilator hospital-acquired pneumonia (NV-HAP) were ultimately included and randomly allocated to a training cohort (n = 260) and a validation cohort (n = 108). An independent external validation cohort of patients (n = 68) was subsequently collected from the same institution between January and December 2022. Inclusion criteria: a) Diagnosis consistent with the Chinese Guidelines for Diagnosis and Treatment of Hospital-acquired Pneumonia and Ventilator-associated Pneumonia (2018 version). b) Absence of invasive mechanical ventilation before pneumonia onset. Exclusion criteria: a) Lower respiratory tract infection already present at admission and subsequently aggravated after hospitalization. b) New pulmonary infiltrates that could not be differentiated from progression of pre-existing conditions. c) Incomplete clinical data, insufficient for calculating SOFA scores. 2.2 Data Collection Clinical data were collected from Electronic Medical Record System, including demographic data (age, gender), vital signs [temperature, pulse rate, respiratory rate, systolic and diastolic blood pressure, Glasgow Coma Scale (GCS) score], laboratory data [White blood cell (WBC) count, platelet count, hematocrit. Biochemistry panel: Blood urea nitrogen (BUN), creatinine, glucose, total bilirubin, potassium, sodium, chloride, calcium. Inflammatory markers: C-reactive protein (CRP), procalcitonin (PCT). Coagulation tests: Activated partial thromboplastin time (APTT), D-dimer], oxygenation level [Fraction of inspired oxygen (FiO₂), PaO₂/FiO₂ ratio, oxygen therapy].) obtained within 24 hours after NV-HAP diagnosis Comorbidities: Malignancy, coronary artery disease, hypertension, heart failure, structural respiratory diseases, diabetes mellitus, hepatic diseases, chronic kidney disease (with or without dialysis), cerebrovascular diseases, immunosuppression (chemotherapy or glucocorticoid treatment), venous thromboembolism (VTE). 2.3 Outcome Definition and Grouping Sepsis was defined according to the Sepsis-3 criteria (Third International Consensus Definitions for Sepsis and Septic Shock, 2016) as suspected or confirmed infection accompanied by an acute increase in SOFA score of ≥2 points. Based on these criteria, patients were classified into sepsis and non-sepsis groups. 2.4 Statistical Analysis Statistical analysis was performed using SPSS software (version 25.0). Patients were randomly divided into training and validation cohorts. In the training cohort, clinical and laboratory parameters were compared between sepsis and non-sepsis groups. Continuous data were expressed as mean ± standard deviation ( x̄ ± s ) if normally distributed and compared using independent-sample t-tests. Non-normally distributed continuous data were expressed as median and interquartile range [M(Q 25 , Q 75 )] and compared using Mann-Whitney U tests. Categorical variables were presented as frequencies (percentages) and compared using Chi-square tests. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for sepsis in NV-HAP patients. Variables showing statistical significance in the multivariate analysis were incorporated into the nomogram. R software (version 4.1.3) and associated packages (rms, glmnet, pROC, and DecisionCurve) were used for nomogram construction, model validation, and performance evaluation. Model discrimination was evaluated using the concordance index (C-index) and receiver operating characteristic (ROC) curves. Calibration plots and decision curve analysis (DCA) were used to assess the agreement between predicted probabilities and observed outcomes, as well as the clinical utility of the nomogram in both training and validation cohorts. A two-sided P-value <0.05 was considered statistically significant. 3. Results 3.1 Clinical Data This retrospective study collected clinical data from 408 patients diagnosed with non-ventilator hospital-acquired pneumonia (NV-HAP) admitted to our single-center tertiary hospital between January 2017 and December 2021. Patients were excluded if their exact diagnosis date was unclear (n = 5) or if critical clinical data were missing (defined as the inability to calculate Sequential Organ Failure Assessment [SOFA] scores due to incomplete laboratory or clinical parameters; n = 35). Ultimately, 368 eligible patients were included in the final analysis. These patients were randomly divided into a training set (n = 260) and a validation set (n = 108) at a ratio of approximately 7:3. The detailed patient enrollment and exclusion process is illustrated in Fig. 1 . Statistical analysis showed significant differences between sepsis and non-sepsis groups in terms of age and comorbidities (Table 1 ). However, there were no statistically significant differences between the training and validation sets regarding demographic characteristics (gender, age), underlying diseases, comorbidities, vital signs, and laboratory parameters (all P > 0.05; Table 1 ). Table 1 Baseline clinical characteristics of training and validation sets Variables Training set (N = 260) Validation set (N = 108) sepsis (n = 131) non-sepsis (n = 129) P value sepsis (n = 57) non-sepsis (n = 51) P value Gender (male) 42 (32.06%) 59 (45.74%) 0.024 33 (57.89%) 36 (70.59%) 0.17 Age (years) 68.87 ± 15.32 66.65 ± 16.4 0.26 68.61 ± 15.69 68.76 ± 13.89 0.808 Respiratory failure 66 (50.38%) 14 (10.85%) < 0.001 34 (59.65%) 8 (15.69%) < 0.001 Circulatory failure 11 (8.4%) 1 (0.78%) 0.003 4 (7.02%) 0 (0%) 0.156 Renal failure 40 (30.53%) 14 (10.85%) < 0.001 14 (24.56%) 4 (7.84%) 0.02 Hepatic failure 14 (10.69%) 4 (3.1%) 0.016 2 (3.92%) 8 (14.04%) 0.139 Gastrointestinal bleeding 7 (5.34%) 2 (1.55%) 0.182 8 (14.04%) 0 (0%) 0.016 Coagulation dysfunction 49 (37.4%) 25 (19.38%) 0.001 22 (38.6%) 9 (17.65%) 0.016 Hypertension 80 (61.07%) 81 (62.79%) 0.775 32 (56.14%) 30 (58.82%) 0.016 Acute myocardial infarction 19 (14.5%) 4 (3.1%) 0.001 3 (5.26%) 1 (1.96%) 0.691 Coronary heart disease 44 (33.59%) 28 (21.71%) 0.032 14 (24.56%) 15 (29.41%) 0.57 Chronic heart failure 49 (37.4%) 37 (28.68%) 0.135 18 (31.58%) 12 (23.53%) 0.351 Arrhythmia 24 (18.32%) 25 (19.38%) 0.827 8 (15.69%) 8 (14.04%) 0.885 Diabetes mellitus 54 (41.22%) 43 (33.33%) 0.189 15 (26.32%) 16 (31.37%) 0.562 Cerebrovascular disease 35 (26.72%) 37 (28.68%) 0.723 12 (21.05%) 13 (25.49%) 0.585 Chronic kidney disease 40 (30.53%) 20 (15.5%) 0.004 15 (26.32%) 10 (19.61%) 0.409 Dialysis treatment 1 (0.76%) 2 (1.55%) 0.989 0 (0%) 1 (1.96%) 0.472 Liver disease 0 (0%) 1 (0.78%) 0.496 0 (0%) 0 (0%) 0.521 Respiratory diseases 28 (21.37%) 34 (26.36%) 0.346 16 (28.07%) 12 (23.53%) 0.591 Immunosuppression 44 (33.59%) 27 (20.93%) 0.022 24 (42.11%) 9 (17.65%) 0.006 Solid tumor 20 (15.27%) 15 (11.63%) 0.39 14 (24.56%) 7 (13.73%) 0.155 Hematologic malignancy 16 (12.21%) 8 (6.2%) 0.094 10 (17.54%) 4 (7.84%) 0.134 Hypoproteinemia 44 (33.59%) 32 (24.81%) 0.12 20 (35.09%) 6 (11.76%) 0.005 Thrombotic disease 18 (13.74%) 19 (14.73%) 0.82 9 (15.79%) 8 (15.69%) 0.988 Systolic blood pressure (mmHg) 125.99 ± 17.25 120.52 ± 17.75 0.012 118.18 ± 15.67 129.45 ± 15.75 < 0.001 Diastolic blood pressure (mmHg) 65.5 ± 11.62 71.55 ± 11.32 < 0.001 64.84 ± 10.74 71.86 ± 11.03 0.001 Peripheral oxygen saturation (%) 98 (96,99) 98 (97,99) 0.094 97 (95,98) 98 (97,98) 0.036 FiO₂ 0.29 (0.21,0.41) 0.21 (0.21,0.29) < 0.001 0.33 (0.29,0.45) 0.21 (0.21,0.29) < 0.001 Oxygenation index 320.73 ± 112.97 401.06 ± 98.3 < 0.001 317.9 (211.27,468.03) 473.7 (326.11,479.37) < 0.001 Platelet count (×10⁹/L) 116.27 ± 87.51 209.41 ± 103.87 < 0.001 122.46 ± 100.07 194.55 ± 94.57 < 0.001 Creatinine (µmol/L) 149.88 ± 134.76 119.58 ± 105.93 0.045 134.39 ± 116.85 121.71 ± 106.89 0.892 Respiratory support 30 (22.9%) 13 (10.08%) 0.005 15 (26.32%) 7 (13.73%) 0.105 Vasoactive drugs 6 (4.58%) 1 (0.78%) 0.131 3 (5.26%) 0 (0%) 0.282 GCS score 15 (15,15) 15 (15,15) 0.019 15 (15,15) 15 (15,15) 0.096 3.2 Risk Factors for Sepsis in Patients with Non-ventilator Hospital-acquired Pneumonia Hospital-acquired pneumonia (HAP) patients without ventilator support are at risk of developing sepsis, which significantly worsens their prognosis. To identify independent factors associated with progression to sepsis in these patients, we conducted univariate and multivariate logistic regression analyses in the training cohort (n = 260). Among them, 131 patients progressed to sepsis (sepsis group), while 129 patients did not (non-sepsis group). Using stepwise backward logistic regression analysis, we identified gender (OR 2.22, 95% CI: 1.09–4.51), coagulation dysfunction (OR 2.35, 95% CI: 1.04–5.30), acute myocardial infarction (OR 8.58, 95% CI: 2.10–35.04), chronic kidney disease (OR 2.73, 95% CI: 1.15–6.51), respiratory diseases (OR 0.31, 95% CI: 0.12–0.79, protective factor), oxygenation index (OR 0.99, 95% CI: 0.99–1.00), platelet count (OR 0.99, 95% CI: 0.98–0.99), and bilirubin (OR 1.03, 95% CI: 1.01–1.06) as independent influencing factors for progression to sepsis (Table 2 ). Table 2 Multivariate logistic regression analysis of factors influencing progression to sepsis in the training set Variables OR [95% CI] P-value Gender 2.22[1.09,4.51] 0.028 Coagulation dysfunction 2.35[1.04,5.3] 0.039 Acute myocardial infarction 8.58[2.1,35.04] 0.003 Chronic kidney disease 2.73[1.15,6.51] 0.023 Respiratory diseases 0.31[0.12,0.79] 0.014 Oxygenation index 0.99[0.99,1] < 0.001 Platelet count 0.99[0.98,0.99] < 0.001 Bilirubin 1.03[1.01,1.06] 0.008 3.3 Development and Validation of a Nomogram for Predicting Progression to Sepsis in Patients with Non-ventilator Hospital-acquired Pneumonia Based on the independent risk factors identified by multivariate logistic regression analysis in the training cohort, we developed a predictive nomogram (Fig. 2 ). The nomogram incorporates the following variables (listed from top to bottom): gender, coagulation dysfunction, acute myocardial infarction, chronic kidney disease, respiratory diseases, oxygenation index, platelet count, bilirubin, total points, and the probability of progression to sepsis. Each variable corresponds to a scale with specific point values. By summing the points corresponding to each patient's clinical characteristics, clinicians can estimate the individual risk of developing sepsis. The predictive model was internally and externally validated using the bootstrap resampling method (1000 iterations). The concordance index (C-index) was 0.73 in the training group and 0.64 in the validation group, suggesting satisfactory predictive performance. Calibration curves demonstrated good agreement between predicted and actual probabilities, closely approximating the diagonal ideal line within an acceptable range. Decision curve analysis (DCA) was performed to assess the clinical value and real-world applicability of the predictive model. Generally, a greater distance of the red wavy line from the intersection point of the black straight line and gray curve, moving towards the upper right corner, indicates higher clinical benefit. Our results indicated substantial clinical benefit of the predictive nomogram in the validation cohort (Fig. 3 ). The predictive performance of the nomogram was further evaluated in an independent external validation cohort (n = 68). The concordance index (C-index) in the validation group was 0.64, indicating moderate discriminatory ability. The calibration curve in the validation set showed good agreement between predicted and observed outcomes, and Decision curve analysis (DCA) in the external cohort demonstrated consistent net clinical benefit across a range of threshold probabilities. The Hosmer–Lemeshow goodness-of-fit test indicated no significant difference between observed and predicted probabilities (X² = 7.49, df = 3, p = 0.058), suggesting acceptable calibration of the nomogram in the external validation cohort (Supplementary Fig. 1–3). 4. Discussion Although global awareness of sepsis has increased significantly in recent years and diagnostic and therapeutic approaches have become more standardized, its incidence and mortality remain substantial. Ongoing and future efforts should prioritize early prevention and intervention strategies. The lower respiratory tract, especially the lungs, is the most frequent site of infection in sepsis patients, commonly presenting as community-acquired pneumonia (CAP) or hospital-acquired pneumonia (HAP). HAP encompasses both ventilator-associated pneumonia (VAP) and non-ventilator-associated hospital-acquired pneumonia (NV-HAP). Mechanical ventilation is a known risk factor for HAP, and evidence-based preventive measures—such as reducing the duration of mechanical ventilation—can markedly decrease the incidence of VAP[ 9 ]. In contrast to VAP, however, the risk of NV-HAP progressing to sepsis is often underestimated. A 2020 retrospective study involving over 110,000 cases revealed that the proportion of NV-HAP cases advancing to sepsis may exceed 36%, a rate comparable to that of VAP-associated sepsis[ 12 ]. A nomogram for predicting the risk of sepsis progression following non-ventilator-associated hospital-acquired pneumonia (NV-HAP) remains absent from the current literature. This study analyzed the clinical data, symptoms, and laboratory indicators of 368 patients with NV-HAP and ultimately selected eight independent factors—gender (OR 2.22, 95% CI: 1.09–4.51), coagulation dysfunction (OR 2.35, 95% CI: 1.04–5.30), acute myocardial infarction (OR 8.58, 95% CI: 2.10–35.04), chronic kidney disease (OR 2.73, 95% CI: 1.15–6.51), respiratory diseases (OR 0.31, 95% CI: 0.12–0.79, protective factor), oxygenation index (OR 0.99, 95% CI: 0.99–1.00), platelet count (OR 0.99, 95% CI: 0.98–0.99), and bilirubin (OR 1.03, 95% CI: 1.01–1.06)—to construct a nomogram. This study found that male gender was significantly associated with an increased risk of NV-HAP progressing to sepsis (OR 2.22, 95% CI: 1.09–4.51). Previous studies have shown that males may have a higher susceptibility to severe infections due to differences in immune response and hormonal regulation[ 15 ]. For example, testosterone has been suggested to suppress immune function, whereas estrogen may have protective effects[ 16 ]. This highlights the need for gender-specific strategies in the early identification and management of at-risk patients. Coagulation dysfunction was identified as an independent risk factor for NV-HAP progression to sepsis (OR 2.35, 95% CI: 1.04–5.30). Sepsis-associated coagulopathy is a well-documented phenomenon, characterized by microthrombosis, disseminated intravascular coagulation (DIC), and impaired organ perfusion[ 17 ]. These changes not only exacerbate organ dysfunction but also contribute to the progression of pulmonary infections to systemic sepsis. Early monitoring of coagulation parameters and timely intervention may help mitigate this risk[ 18 ]. Acute myocardial infarction (AMI) was strongly associated with sepsis progression (OR 8.58, 95% CI: 2.10–35.04). The high odds ratio suggests that AMI patients are particularly vulnerable, likely due to systemic inflammation, ischemia-reperfusion injury, and impaired cardiac output, which can exacerbate organ dysfunction and immune dysregulation. Clinicians should be vigilant in monitoring AMI patients with NV-HAP for early signs of sepsis[ 19 ]. Chronic kidney disease (CKD) was another independent risk factor (OR 2.73, 95% CI: 1.15–6.51). CKD patients often exhibit impaired immune responses, increased systemic inflammation, and reduced renal clearance of toxins, all of which contribute to an elevated risk of sepsis[ 20 ]. Furthermore, CKD is frequently associated with electrolyte imbalances and acid-base disturbances, which can further complicate the clinical course. Interestingly, respiratory diseases were identified as a protective factor against NV-HAP progression to sepsis (OR 0.31, 95% CI: 0.12–0.79). This finding may be explained by the fact that patients with pre-existing respiratory conditions are often closely monitored and receive timely interventions, such as oxygen therapy or bronchodilators, which may reduce the risk of disease progression. However, further research is needed to confirm this hypothesis. The oxygenation index (OR 0.99, 95% CI: 0.99–1.00) reflects pulmonary function and oxygen exchange capacity. Lower oxygenation index values are indicative of severe hypoxemia, which may predispose patients to respiratory failure and subsequent sepsis[ 21 ]. Regular assessment of oxygenation status is critical for early intervention[ 22 ]. Platelet count was found to be inversely associated with the risk of sepsis progression (OR 0.99, 95% CI: 0.98–0.99). Thrombocytopenia is a common finding in sepsis and is often associated with poor outcomes due to impaired coagulation and increased bleeding risk[ 23 , 24 ]. Monitoring platelet levels may provide valuable prognostic information. Bilirubin levels were positively associated with sepsis progression (OR 1.03, 95% CI: 1.01–1.06). Elevated bilirubin levels are indicative of liver dysfunction, which is frequently observed in critically ill patients[ 21 , 25 ]. Hyperbilirubinemia has been linked to increased inflammation, oxidative stress, and impaired detoxification capacity, all of which contribute to disease progression. In summary, we developed and validated a practical nomogram based on readily available clinical variables to predict the risk of sepsis in patients with non-ventilator-associated hospital-acquired pneumonia. The model demonstrated acceptable discrimination, calibration, and clinical utility in both training and validation cohorts, providing a potentially useful tool for early risk stratification in clinical settings. This study has certain limitations. First, the sample size was relatively small, and the study was a single-center retrospective analysis without considering time factors, which may limit the generalizability of the conclusions. Second, the study did not include comprehensive indicators, such as inflammatory markers and intervention measures (especially the use of antibiotics), which could influence the results. Therefore, large-scale, high-quality prospective studies are needed to validate the clinical predictive value of the nomogram. Conclusion For NV-HAP patients, clinical assessment should extend beyond general clinical data to incorporate specific potential risk factors—such as acute respiratory failure, elevated creatinine, and increased bilirubin levels—to predict sepsis progression. The implementation of predictive models may facilitate early detection and improve patient outcomes. Declarations Acknowledgements We thank all staff members in the Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, for providing clinical data collection. Special acknowledgment is given to the Youth Innovation Team led by Dr. Wu Z.-C. Author Contributions Zhou H. and Wu Z.-C. contributed to the conceptualization and study design. Zhou H. was responsible for data extraction, statistical analysis, and manuscript drafting. Wu Z.-C. contributed to figure preparation and data management. Liu B.-B., Du Y.-P. and Lu M. were involved in data collection, data interpretation, and critical revision of the manuscript. Wu R. and Shen N. contributed to the literature review, data validation, and manuscript editing. All authors read and approved the final manuscript. Funding This study was supported by Key Clinical Projects of Peking University Third Hospital (Grant No. BYSYZD2022007), Beijing Key Clinical Specialty Funding (010071) and the Clinical Cohort Construction Program of Peking University Third Hospital (BYSYDL2019007). Availability of Data and Materials The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request. Ethics Approval and Consent to Participate The study was approved by the Ethics Committee of Peking University Third Hospital (Approval No.). Written informed consent was waived due to the retrospective nature of the study. Consent for Publication Not applicable. Competing Interests The authors declare that they have no competing interests. Clinical Trial Registration Not applicable. References Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). 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J Clin Invest. 2022;132(23):e153014. Ostermeier B, Soriano-Sarabia N, Maggirwar SB. Platelet-Released Factors: Their Role in Viral Disease and Applications for Extracellular Vesicle (EV) Therapy. Int J Mol Sci. 2022;23(4):2321. Madrazo M, López-Cruz I, Piles L, Viñola S, Alberola J, Eiros JM, et al. Risk Factors and the Impact of Multidrug-Resistant Bacteria on Community-Acquired Urinary Sepsis. Microorganisms. 2023;11(5):1278. Additional Declarations No competing interests reported. Supplementary Files SupplementarynoteDefinitionandClinicalInterpretationofPredictiveVariables.docx SupplementaryTable1.docx SupplementaryFigure1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-7847940","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550424487,"identity":"dd5efb68-f526-42ae-b9f2-a85a947ed4a4","order_by":0,"name":"Han Zhou","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhou","suffix":""},{"id":550424488,"identity":"09d01ad3-e415-4595-91a9-e635b7cbf229","order_by":1,"name":"Zhenchao Wu","email":"","orcid":"","institution":"Peking University Third 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1","display":"","copyAsset":false,"role":"figure","size":139771,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient enrollment.\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7847940/v1/7d5269985f16176ad5d8b9cb.jpeg"},{"id":96968567,"identity":"9020cfba-d355-4828-891d-cb5459d9a7c3","added_by":"auto","created_at":"2025-11-28 07:01:32","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69355,"visible":true,"origin":"","legend":"\u003cp\u003eSepsis Risk Nomogram.\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7847940/v1/4bf7fb4e31b33ab1cbcddd59.jpeg"},{"id":97136731,"identity":"93ca1b97-2009-42f7-bcf3-0d31e053fa9a","added_by":"auto","created_at":"2025-12-01 09:56:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1287715,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance evaluation of the prediction model. (A) Receiver operating characteristic (ROC) curve of the nomogram in the training set, showing an area under the curve (AUC) of 0.73, indicating good discriminative ability. (B) Calibration curves in the training cohort (top) and validation cohort (bottom). The dotted line represents the apparent curve, the solid line represents the bias-corrected curve using bootstrap resamples, and the dashed line indicates the ideal reference line. (C) Decision curve analysis (DCA) in the training set (top) and validation set (bottom), illustrating the clinical net benefit of using the nomogram across a range of threshold probabilities compared with the “treat-all” and “treat-none” strategies.\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7847940/v1/5210818e871438108780c625.jpeg"},{"id":99798408,"identity":"c9b37a26-764a-4889-aedf-97d045cda7b9","added_by":"auto","created_at":"2026-01-08 13:48:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2363459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7847940/v1/1fdb3121-b4fd-4598-9b8f-a5b16583ec20.pdf"},{"id":96968566,"identity":"248c90e0-2405-46f3-9a3c-64cf4eab4dc6","added_by":"auto","created_at":"2025-11-28 07:01:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20568,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarynoteDefinitionandClinicalInterpretationofPredictiveVariables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7847940/v1/98c6427a485052ce13c1f91d.docx"},{"id":97137457,"identity":"e94659b3-595f-4e84-a002-f4fec5a127d6","added_by":"auto","created_at":"2025-12-01 09:57:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24410,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7847940/v1/68046df94068216179539e7d.docx"},{"id":97138871,"identity":"16d86569-b89b-4b27-9498-79a5a7cffce2","added_by":"auto","created_at":"2025-12-01 09:59:24","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":110312,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7847940/v1/801918903dd28a1de5c9653a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Nomogram to Predict risk of Sepsis in Non-ventilator Hospital- Acquired Pneumonia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSepsis, defined as life-threatening organ dysfunction resulting from a dysregulated host response to infection, remains a significant global health challenge associated with high morbidity and mortality[1,2]. Although notable progress has been made in the early identification and management of sepsis and septic shock over the past decade, the overall incidence and mortality rates remain high due to its complex pathophysiology, nonspecific early clinical manifestations, and rapid progression[3,4]. Based on the source of infection, sepsis is commonly classified into community-acquired sepsis and hospital-acquired sepsis[5]. Studies have consistently shown that hospital-acquired sepsis is associated with longer hospital stays, higher mortality, and poorer outcomes compared to community-acquired sepsis[5,6].Pneumonia remains the most common trigger for sepsis[7]. Hospital-acquired pneumonia (HAP) is defined as pneumonia occurring 48 hours or more after admission, excluding infections already incubating at the time of hospital admission, and represents one of the most prevalent healthcare-associated infections[8]. The incidence and mortality associated with HAP are significantly influenced by patient age, underlying comorbidities, and invasive procedures, such as endotracheal intubation and mechanical ventilation. Mechanical ventilation is a high-risk factor for sepsis susceptibility in patients with HAP[9].\u0026nbsp; Clinically, HAP is generally categorized into ventilator-associated pneumonia (VAP) and non-ventilator hospital-acquired pneumonia (NV-HAP), depending on the presence or absence of invasive mechanical ventilation. \u0026nbsp; Gradually emerging large-scale studies indicate that the risk of developing sepsis among NV-HAP patients may be comparable to that in VAP cases. Compared to VAP, NV-HAP has received considerably less attention in clinical research, leading to potential underestimation of its associated risk of progression to sepsis[10,11], which underscoring the need for focused investigations into the factors influencing sepsis progression in NV-HAP patients and facilitate early identification and intervention, potentially improving patient outcomes[12].\u003c/p\u003e\n\u003cp\u003eAt present, several clinical scoring systems are widely employed to assess the severity and prognosis of sepsis, including systemic inflammatory response syndrome (SIRS), sequential organ failure assessment (SOFA), quick SOFA (qSOFA), and the national early warning score (NEWS). A recent study has demonstrated that these scoring systems exhibit limited efficacy in predicting the development of sepsis among pneumonia patients [13]. .This finding underscores the necessity of developing a novel early warning model specifically designed for predicting sepsis in patients with non-ventilator hospital-acquired pneumonia (NV-HAP).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOriginally emerging from oncology for individualized prognostic estimation, nomograms have gained increasing application across diverse medical disciplines, demonstrating utility in generating quantifiable predictions of disease progression and clinical outcomes [14].\u003c/p\u003e\n\u003cp\u003eAccordingly, this study was undertaken to (1) identify clinical predictors of sepsis progression in non-ventilator hospital-acquired pneumonia (NV-HAP) patients, and (2) develop and validate a disease-specific nomogram for this population. The resulting model provides clinicians with an intuitive bedside tool for early risk stratification, thereby enabling timely therapeutic interventions and potentially mitigating adverse clinical trajectories.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study included 408 patients diagnosed with hospital-acquired pneumonia (HAP) in Peking University Third Hospital between January 2017 and December 2021. After screening theinclusion and exclusion criteria, 368 patients with non-ventilator hospital-acquired pneumonia (NV-HAP) were ultimately included and randomly allocated to a training cohort (n = 260) and a validation cohort (n = 108). An independent external validation cohort of patients (n = 68) was subsequently collected from the same institution between January and December 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria:\u0026nbsp;\u003c/strong\u003ea) Diagnosis consistent with the Chinese Guidelines for Diagnosis and Treatment of Hospital-acquired Pneumonia and Ventilator-associated Pneumonia (2018 version). b) Absence of invasive mechanical ventilation before pneumonia onset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria:\u003c/strong\u003e a) Lower respiratory tract infection already present at admission and subsequently aggravated after hospitalization. b) New pulmonary infiltrates that could not be differentiated from progression of pre-existing conditions. c) Incomplete clinical data, insufficient for calculating SOFA scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical data were collected from Electronic Medical Record System, including demographic data (age, gender), vital signs [temperature, pulse rate, respiratory rate, systolic and diastolic blood pressure, Glasgow Coma Scale (GCS) score], laboratory data [White blood cell (WBC) count, platelet count, hematocrit. Biochemistry panel: Blood urea nitrogen (BUN), creatinine, glucose, total bilirubin, potassium, sodium, chloride, calcium. Inflammatory markers: C-reactive protein (CRP), procalcitonin (PCT). Coagulation tests: Activated partial thromboplastin time (APTT), D-dimer], oxygenation level [Fraction of inspired oxygen (FiO₂), PaO₂/FiO₂ ratio, oxygen therapy].) obtained within 24 hours after NV-HAP diagnosis Comorbidities: Malignancy, coronary artery disease, hypertension, heart failure, structural respiratory diseases, diabetes mellitus, hepatic diseases, chronic kidney disease (with or without dialysis), cerebrovascular diseases, immunosuppression (chemotherapy or glucocorticoid treatment), venous thromboembolism (VTE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Outcome Definition and Grouping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSepsis was defined according to the Sepsis-3 criteria (Third International Consensus Definitions for Sepsis and Septic Shock, 2016) as suspected or confirmed infection accompanied by an acute increase in SOFA score of \u0026ge;2 points. Based on these criteria, patients were classified into sepsis and non-sepsis groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using SPSS software (version 25.0). Patients were randomly divided into training and validation cohorts. In the training cohort, clinical and laboratory parameters were compared between sepsis and non-sepsis groups. Continuous data were expressed as mean \u0026plusmn; standard deviation (\u003cem\u003ex̄\u003c/em\u003e \u0026plusmn; \u003cem\u003es\u003c/em\u003e) if normally distributed and compared using independent-sample t-tests. Non-normally distributed continuous data were expressed as median and interquartile range [M(Q\u003csub\u003e25\u003c/sub\u003e, Q\u003csub\u003e75\u003c/sub\u003e)] and compared using Mann-Whitney U tests. Categorical variables were presented as frequencies (percentages) and compared using Chi-square tests.\u003c/p\u003e\n\u003cp\u003eUnivariate and multivariate logistic regression analyses were conducted to identify independent risk factors for sepsis in NV-HAP patients. Variables showing statistical significance in the multivariate analysis were incorporated into the nomogram.\u003c/p\u003e\n\u003cp\u003eR software (version 4.1.3) and associated packages (rms, glmnet, pROC, and DecisionCurve) were used for nomogram construction, model validation, and performance evaluation. Model discrimination was evaluated using the concordance index (C-index) and receiver operating characteristic (ROC) curves. Calibration plots and decision curve analysis (DCA) were used to assess the agreement between predicted probabilities and observed outcomes, as well as the clinical utility of the nomogram in both training and validation cohorts. A two-sided P-value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Clinical Data\u003c/h2\u003e\u003cp\u003eThis retrospective study collected clinical data from 408 patients diagnosed with non-ventilator hospital-acquired pneumonia (NV-HAP) admitted to our single-center tertiary hospital between January 2017 and December 2021. Patients were excluded if their exact diagnosis date was unclear (n\u0026thinsp;=\u0026thinsp;5) or if critical clinical data were missing (defined as the inability to calculate Sequential Organ Failure Assessment [SOFA] scores due to incomplete laboratory or clinical parameters; n\u0026thinsp;=\u0026thinsp;35). Ultimately, 368 eligible patients were included in the final analysis. These patients were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;260) and a validation set (n\u0026thinsp;=\u0026thinsp;108) at a ratio of approximately 7:3. The detailed patient enrollment and exclusion process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eStatistical analysis showed significant differences between sepsis and non-sepsis groups in terms of age and comorbidities (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, there were no statistically significant differences between the training and validation sets regarding demographic characteristics (gender, age), underlying diseases, comorbidities, vital signs, and laboratory parameters (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline clinical characteristics of training and validation sets\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eTraining set (N\u0026thinsp;=\u0026thinsp;260)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eValidation set (N\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003esepsis\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003enon-sepsis\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;129)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003esepsis\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003enon-sepsis\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (32.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (45.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33 (57.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36 (70.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.87\u0026thinsp;\u0026plusmn;\u0026thinsp;15.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.65\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.61\u0026thinsp;\u0026plusmn;\u0026thinsp;15.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68.76\u0026thinsp;\u0026plusmn;\u0026thinsp;13.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (50.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (10.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34 (59.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (15.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCirculatory failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (7.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (30.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (10.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (24.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (7.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatic failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (10.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (3.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (14.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal bleeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (5.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (14.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoagulation dysfunction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (37.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (19.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (38.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9 (17.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 (61.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (62.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (56.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30 (58.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute myocardial infarction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (5.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (1.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (33.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (21.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (24.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (29.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic heart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (37.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (28.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (31.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (23.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArrhythmia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (18.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (19.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (15.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (14.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (41.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (33.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (26.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16 (31.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (26.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (28.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (21.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 (25.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (30.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (15.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (26.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (19.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDialysis treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (1.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (21.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (26.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (28.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (23.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmunosuppression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (33.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (20.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24 (42.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9 (17.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (15.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (11.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (24.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (13.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematologic malignancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (12.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (17.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (7.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypoproteinemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (33.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (24.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (35.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (11.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThrombotic disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (13.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (14.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (15.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (15.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125.99\u0026thinsp;\u0026plusmn;\u0026thinsp;17.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120.52\u0026thinsp;\u0026plusmn;\u0026thinsp;17.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e118.18\u0026thinsp;\u0026plusmn;\u0026thinsp;15.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e129.45\u0026thinsp;\u0026plusmn;\u0026thinsp;15.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.55\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71.86\u0026thinsp;\u0026plusmn;\u0026thinsp;11.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeripheral oxygen saturation (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (96,99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (97,99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97 (95,98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98 (97,98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFiO₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003cp\u003e(0.21,0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003cp\u003e(0.21,0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003cp\u003e(0.29,0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003cp\u003e(0.21,0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxygenation index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e320.73\u0026thinsp;\u0026plusmn;\u0026thinsp;112.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e401.06\u0026thinsp;\u0026plusmn;\u0026thinsp;98.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e317.9 (211.27,468.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e473.7 (326.11,479.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116.27\u0026thinsp;\u0026plusmn;\u0026thinsp;87.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e209.41\u0026thinsp;\u0026plusmn;\u0026thinsp;103.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122.46\u0026thinsp;\u0026plusmn;\u0026thinsp;100.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e194.55\u0026thinsp;\u0026plusmn;\u0026thinsp;94.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149.88\u0026thinsp;\u0026plusmn;\u0026thinsp;134.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119.58\u0026thinsp;\u0026plusmn;\u0026thinsp;105.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e134.39\u0026thinsp;\u0026plusmn;\u0026thinsp;116.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e121.71\u0026thinsp;\u0026plusmn;\u0026thinsp;106.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (10.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (26.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (13.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVasoactive drugs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (4.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (5.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (15,15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (15,15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (15,15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (15,15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Risk Factors for Sepsis in Patients with Non-ventilator Hospital-acquired Pneumonia\u003c/h2\u003e\u003cp\u003eHospital-acquired pneumonia (HAP) patients without ventilator support are at risk of developing sepsis, which significantly worsens their prognosis. To identify independent factors associated with progression to sepsis in these patients, we conducted univariate and multivariate logistic regression analyses in the training cohort (n\u0026thinsp;=\u0026thinsp;260). Among them, 131 patients progressed to sepsis (sepsis group), while 129 patients did not (non-sepsis group). Using stepwise backward logistic regression analysis, we identified gender (OR 2.22, 95% CI: 1.09\u0026ndash;4.51), coagulation dysfunction (OR 2.35, 95% CI: 1.04\u0026ndash;5.30), acute myocardial infarction (OR 8.58, 95% CI: 2.10\u0026ndash;35.04), chronic kidney disease (OR 2.73, 95% CI: 1.15\u0026ndash;6.51), respiratory diseases (OR 0.31, 95% CI: 0.12\u0026ndash;0.79, protective factor), oxygenation index (OR 0.99, 95% CI: 0.99\u0026ndash;1.00), platelet count (OR 0.99, 95% CI: 0.98\u0026ndash;0.99), and bilirubin (OR 1.03, 95% CI: 1.01\u0026ndash;1.06) as independent influencing factors for progression to sepsis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression analysis of factors influencing progression to sepsis in the training set\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.22[1.09,4.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoagulation dysfunction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.35[1.04,5.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute myocardial infarction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.58[2.1,35.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.73[1.15,6.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.31[0.12,0.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxygenation index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99[0.99,1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99[0.98,0.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilirubin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03[1.01,1.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3 Development and Validation of a Nomogram for Predicting Progression to Sepsis in Patients with Non-ventilator Hospital-acquired Pneumonia\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the independent risk factors identified by multivariate logistic regression analysis in the training cohort, we developed a predictive nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The nomogram incorporates the following variables (listed from top to bottom): gender, coagulation dysfunction, acute myocardial infarction, chronic kidney disease, respiratory diseases, oxygenation index, platelet count, bilirubin, total points, and the probability of progression to sepsis. Each variable corresponds to a scale with specific point values. By summing the points corresponding to each patient's clinical characteristics, clinicians can estimate the individual risk of developing sepsis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe predictive model was internally and externally validated using the bootstrap resampling method (1000 iterations). The concordance index (C-index) was 0.73 in the training group and 0.64 in the validation group, suggesting satisfactory predictive performance. Calibration curves demonstrated good agreement between predicted and actual probabilities, closely approximating the diagonal ideal line within an acceptable range.\u003c/p\u003e\u003cp\u003eDecision curve analysis (DCA) was performed to assess the clinical value and real-world applicability of the predictive model. Generally, a greater distance of the red wavy line from the intersection point of the black straight line and gray curve, moving towards the upper right corner, indicates higher clinical benefit. Our results indicated substantial clinical benefit of the predictive nomogram in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe predictive performance of the nomogram was further evaluated in an independent external validation cohort (n\u0026thinsp;=\u0026thinsp;68). The concordance index (C-index) in the validation group was 0.64, indicating moderate discriminatory ability. The calibration curve in the validation set showed good agreement between predicted and observed outcomes, and Decision curve analysis (DCA) in the external cohort demonstrated consistent net clinical benefit across a range of threshold probabilities. The Hosmer\u0026ndash;Lemeshow goodness-of-fit test indicated no significant difference between observed and predicted probabilities (X\u0026sup2; = 7.49, df\u0026thinsp;=\u0026thinsp;3, p\u0026thinsp;=\u0026thinsp;0.058), suggesting acceptable calibration of the nomogram in the external validation cohort (Supplementary Fig.\u0026nbsp;1\u0026ndash;3).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAlthough global awareness of sepsis has increased significantly in recent years and diagnostic and therapeutic approaches have become more standardized, its incidence and mortality remain substantial. Ongoing and future efforts should prioritize early prevention and intervention strategies. The lower respiratory tract, especially the lungs, is the most frequent site of infection in sepsis patients, commonly presenting as community-acquired pneumonia (CAP) or hospital-acquired pneumonia (HAP). HAP encompasses both ventilator-associated pneumonia (VAP) and non-ventilator-associated hospital-acquired pneumonia (NV-HAP). Mechanical ventilation is a known risk factor for HAP, and evidence-based preventive measures\u0026mdash;such as reducing the duration of mechanical ventilation\u0026mdash;can markedly decrease the incidence of VAP[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In contrast to VAP, however, the risk of NV-HAP progressing to sepsis is often underestimated. A 2020 retrospective study involving over 110,000 cases revealed that the proportion of NV-HAP cases advancing to sepsis may exceed 36%, a rate comparable to that of VAP-associated sepsis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A nomogram for predicting the risk of sepsis progression following non-ventilator-associated hospital-acquired pneumonia (NV-HAP) remains absent from the current literature. This study analyzed the clinical data, symptoms, and laboratory indicators of 368 patients with NV-HAP and ultimately selected eight independent factors\u0026mdash;gender (OR 2.22, 95% CI: 1.09\u0026ndash;4.51), coagulation dysfunction (OR 2.35, 95% CI: 1.04\u0026ndash;5.30), acute myocardial infarction (OR 8.58, 95% CI: 2.10\u0026ndash;35.04), chronic kidney disease (OR 2.73, 95% CI: 1.15\u0026ndash;6.51), respiratory diseases (OR 0.31, 95% CI: 0.12\u0026ndash;0.79, protective factor), oxygenation index (OR 0.99, 95% CI: 0.99\u0026ndash;1.00), platelet count (OR 0.99, 95% CI: 0.98\u0026ndash;0.99), and bilirubin (OR 1.03, 95% CI: 1.01\u0026ndash;1.06)\u0026mdash;to construct a nomogram. This study found that male gender was significantly associated with an increased risk of NV-HAP progressing to sepsis (OR 2.22, 95% CI: 1.09\u0026ndash;4.51). Previous studies have shown that males may have a higher susceptibility to severe infections due to differences in immune response and hormonal regulation[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For example, testosterone has been suggested to suppress immune function, whereas estrogen may have protective effects[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This highlights the need for gender-specific strategies in the early identification and management of at-risk patients. Coagulation dysfunction was identified as an independent risk factor for NV-HAP progression to sepsis (OR 2.35, 95% CI: 1.04\u0026ndash;5.30). Sepsis-associated coagulopathy is a well-documented phenomenon, characterized by microthrombosis, disseminated intravascular coagulation (DIC), and impaired organ perfusion[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These changes not only exacerbate organ dysfunction but also contribute to the progression of pulmonary infections to systemic sepsis. Early monitoring of coagulation parameters and timely intervention may help mitigate this risk[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Acute myocardial infarction (AMI) was strongly associated with sepsis progression (OR 8.58, 95% CI: 2.10\u0026ndash;35.04). The high odds ratio suggests that AMI patients are particularly vulnerable, likely due to systemic inflammation, ischemia-reperfusion injury, and impaired cardiac output, which can exacerbate organ dysfunction and immune dysregulation. Clinicians should be vigilant in monitoring AMI patients with NV-HAP for early signs of sepsis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Chronic kidney disease (CKD) was another independent risk factor (OR 2.73, 95% CI: 1.15\u0026ndash;6.51). CKD patients often exhibit impaired immune responses, increased systemic inflammation, and reduced renal clearance of toxins, all of which contribute to an elevated risk of sepsis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, CKD is frequently associated with electrolyte imbalances and acid-base disturbances, which can further complicate the clinical course. Interestingly, respiratory diseases were identified as a protective factor against NV-HAP progression to sepsis (OR 0.31, 95% CI: 0.12\u0026ndash;0.79). This finding may be explained by the fact that patients with pre-existing respiratory conditions are often closely monitored and receive timely interventions, such as oxygen therapy or bronchodilators, which may reduce the risk of disease progression. However, further research is needed to confirm this hypothesis. The oxygenation index (OR 0.99, 95% CI: 0.99\u0026ndash;1.00) reflects pulmonary function and oxygen exchange capacity. Lower oxygenation index values are indicative of severe hypoxemia, which may predispose patients to respiratory failure and subsequent sepsis[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Regular assessment of oxygenation status is critical for early intervention[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Platelet count was found to be inversely associated with the risk of sepsis progression (OR 0.99, 95% CI: 0.98\u0026ndash;0.99). Thrombocytopenia is a common finding in sepsis and is often associated with poor outcomes due to impaired coagulation and increased bleeding risk[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Monitoring platelet levels may provide valuable prognostic information. Bilirubin levels were positively associated with sepsis progression (OR 1.03, 95% CI: 1.01\u0026ndash;1.06). Elevated bilirubin levels are indicative of liver dysfunction, which is frequently observed in critically ill patients[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Hyperbilirubinemia has been linked to increased inflammation, oxidative stress, and impaired detoxification capacity, all of which contribute to disease progression.\u003c/p\u003e\u003cp\u003eIn summary, we developed and validated a practical nomogram based on readily available clinical variables to predict the risk of sepsis in patients with non-ventilator-associated hospital-acquired pneumonia. The model demonstrated acceptable discrimination, calibration, and clinical utility in both training and validation cohorts, providing a potentially useful tool for early risk stratification in clinical settings. This study has certain limitations. First, the sample size was relatively small, and the study was a single-center retrospective analysis without considering time factors, which may limit the generalizability of the conclusions. Second, the study did not include comprehensive indicators, such as inflammatory markers and intervention measures (especially the use of antibiotics), which could influence the results. Therefore, large-scale, high-quality prospective studies are needed to validate the clinical predictive value of the nomogram.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFor NV-HAP patients, clinical assessment should extend beyond general clinical data to incorporate specific potential risk factors\u0026mdash;such as acute respiratory failure, elevated creatinine, and increased bilirubin levels\u0026mdash;to predict sepsis progression. The implementation of predictive models may facilitate early detection and improve patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all staff members in the Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, for providing clinical data collection. Special acknowledgment is given to the Youth Innovation Team led by Dr. Wu Z.-C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhou H. and Wu Z.-C. contributed to the conceptualization and study design. Zhou H. was responsible for data extraction, statistical analysis, and manuscript drafting. Wu Z.-C. contributed to figure preparation and data management. Liu B.-B., Du Y.-P. and Lu M. were involved in data collection, data interpretation, and critical revision of the manuscript. Wu R. and Shen N. contributed to the literature review, data validation, and manuscript editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Key Clinical Projects of Peking University Third Hospital (Grant No. BYSYZD2022007), Beijing Key Clinical Specialty Funding (010071) and the Clinical Cohort Construction Program of Peking University Third Hospital (BYSYDL2019007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics Approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Peking University Third Hospital (Approval No.). Written informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSepsis. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/sepsis\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/sepsis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCecconi M, Evans L, Levy M, Rhodes A. Sepsis and septic shock. Lancet Lond Engl. 2018;392(10141):75\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeymour CW, Liu VX, Iwashyna TJ, Brunkhorst FM, Rea TD, Scherag A, et al. Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):762\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRhee C, Wang R, Zhang Z, Fram D, Kadri SS, Klompas M, et al. Epidemiology of Hospital-Onset Versus Community-Onset Sepsis in U.S. Hospitals and Association With Mortality: A Retrospective Analysis Using Electronic Clinical Data. Crit Care Med. 2019;47(9):1169\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTonai M, Shiraishi A, Karumai T, Endo A, Kobayashi H, Fushimi K, et al. Hospital-onset sepsis and community-onset sepsis in critical care units in Japan: a retrospective cohort study based on a Japanese administrative claims database. Crit Care. 2022;26:136.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKotfis K, Wittebole X, Jaschinski U, Sol\u0026eacute;-Viol\u0026aacute;n J, Kashyap R, Leone M, et al. A worldwide perspective of sepsis epidemiology and survival according to age: Observational data from the ICON audit. J Crit Care. 2019;51:122\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTorres A, Niederman MS, Chastre J, Ewig S, Fernandez-Vandellos P, Hanberger H, et al. International ERS/ESICM/ESCMID/ALAT guidelines for the management of hospital-acquired pneumonia and ventilator-associated pneumonia: Guidelines for the management of hospital-acquired pneumonia (HAP)/ventilator-associated pneumonia (VAP) of the European Respiratory Society (ERS), European Society of Intensive Care Medicine (ESICM), European Society of Clinical Microbiology and Infectious Diseases (ESCMID) and Asociaci\u0026oacute;n Latinoamericana del T\u0026oacute;rax (ALAT). Eur Respir J. 2017;50(3):1700582.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalil AC, Metersky ML, Klompas M, Muscedere J, Sweeney DA, Palmer LB, et al. Management of Adults With Hospital-acquired and Ventilator-associated Pneumonia: 2016 Clinical Practice Guidelines by the Infectious Diseases Society of America and the American Thoracic Society. Clin Infect Dis Off Publ Infect Dis Soc Am. 2016;63(5):e61\u0026ndash;111.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMicek ST, Chew B, Hampton N, Kollef MH. A Case-Control Study Assessing the Impact of Nonventilated Hospital-Acquired Pneumonia on Patient Outcomes. Chest. 2016;150(5):1008\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiBiase LM, Weber DJ, Sickbert-Bennett EE, Anderson DJ, Rutala WA. The growing importance of non-device-associated healthcare-associated infections: a relative proportion and incidence study at an academic medical center, 2008\u0026ndash;2012. Infect Control Hosp Epidemiol. 2014;35(2):200\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiuliano KK, Baker D. Sepsis in the Context of Nonventilator Hospital-Acquired Pneumonia. Am J Crit Care Off Publ Am Assoc Crit-Care Nurses. 2020;29(1):9\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWen JN, Li N, Guo CX, Shen N, He B. Performance and comparison of assessment models to predict 30-day mortality in patients with hospital-acquired pneumonia. Chin Med J (Engl). 2020;133(24):2947\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark SY, Nomogram. An analogue tool to deliver digital knowledge. J Thorac Cardiovasc Surg. 2018;155(4):1793.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalerneau LM, Bailly S, Terzi N, Ruckly S, Garrouste-Orgeas M, Oziel J, et al. Non-ventilator-associated ICU-acquired pneumonia (NV-ICU-AP) in patients with acute exacerbation of COPD: From the French OUTCOMEREA cohort. Crit Care Lond Engl. 2023;27(1):359.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDias SP, Brouwer MC, van de Beek D. Sex and Gender Differences in Bacterial Infections. Infect Immun. 2022;90(10):e0028322.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiustozzi M, Ehrlinder H, Bongiovanni D, Borovac JA, Guerreiro RA, Gąsecka A, et al. Coagulopathy and sepsis: Pathophysiology, clinical manifestations and treatment. Blood Rev. 2021;50:100864.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIba T, Helms J, Connors JM, Levy JH. The pathophysiology, diagnosis, and management of sepsis-associated disseminated intravascular coagulation. J Intensive Care. 2023;11(1):24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHechtman RK, Heath ME, Horowitz JK, McLaughlin E, Posa PJ, Blamoun J et al. Epidemiology and management of sepsis among previously healthy patients. CHEST Crit Care. 2025;100148.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorges do Nascimento IJ, Marcolino MS, Abdulazeem HM, Weerasekara I, Azzopardi-Muscat N, Gon\u0026ccedil;alves MA, et al. Impact of Big Data Analytics on People\u0026rsquo;s Health: Overview of Systematic Reviews and Recommendations for Future Studies. J Med Internet Res. 2021;23(4):e27275.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantacroce E, D\u0026rsquo;Angerio M, Ciobanu AL, Masini L, Lo Tartaro D, Coloretti I, et al. Advances and Challenges in Sepsis Management: Modern Tools and Future Directions. Cells. 2024;13(5):439.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalusko V, Wenzl FA, Vandenbriele C, Panoulas V, L\u0026uuml;scher TF, Gorog DA. Current and novel biomarkers in cardiogenic shock. Eur J Heart Fail. 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCampbell RA, Manne BK, Banerjee M, Middleton EA, Ajanel A, Schwertz H, et al. IFITM3 regulates fibrinogen endocytosis and platelet reactivity in nonviral sepsis. J Clin Invest. 2022;132(23):e153014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOstermeier B, Soriano-Sarabia N, Maggirwar SB. Platelet-Released Factors: Their Role in Viral Disease and Applications for Extracellular Vesicle (EV) Therapy. Int J Mol Sci. 2022;23(4):2321.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMadrazo M, L\u0026oacute;pez-Cruz I, Piles L, Vi\u0026ntilde;ola S, Alberola J, Eiros JM, et al. Risk Factors and the Impact of Multidrug-Resistant Bacteria on Community-Acquired Urinary Sepsis. Microorganisms. 2023;11(5):1278.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-ventilator hospital-acquired pneumonia; Sepsis; Risk prediction; Nomogram; Prognostic model","lastPublishedDoi":"10.21203/rs.3.rs-7847940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7847940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo identify risk factors for progression to sepsis in patients with non-ventilator hospital-acquired pneumonia (NV-HAP) and develop a practical and accurate nomogram to improve clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe retrospectively enrolled 408 hospitalized patients diagnosed with hospital-acquired pneumonia at Peking University Third Hospital between January 2017 and December 2021. Clinical and laboratory data were collected, and patients were randomly assigned to a training cohort and a validation cohort. Univariate and multivariate logistic regression analyses were performed in the training cohort to identify independent risk factors associated with progression to sepsis. A predictive nomogram was then constructed based on these independent predictors and validated using the validation cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 368 patients were ultimately included. Multivariate logistic regression analysis identified male sex (OR = 2.22, 95% CI: 1.09–4.51), coagulation dysfunction (OR = 2.35, 95% CI: 1.04–5.30), acute myocardial infarction (OR = 8.58, 95% CI: 2.10–35.04), chronic kidney disease (OR = 2.73, 95% CI: 1.15–6.51), underlying respiratory disease (OR = 0.31, 95% CI: 0.12–0.79), oxygenation index (OR = 0.99, 95% CI: 0.99–1.00), platelet count (OR = 0.99, 95% CI: 0.98–0.99), and total bilirubin (OR = 1.03, 95% CI: 1.01–1.06) as independent predictors for progression to sepsis in NV-HAP patients.\u003cstrong\u003e \u003c/strong\u003eThe nomogram demonstrated good predictive performance, with C-index values of 0.73 in the training cohort and 0.64 in the validation cohort. Calibration curves indicated acceptable agreement between predicted and actual outcomes, and decision curve analysis confirmed favorable clinical utility in both cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e In patients with NV-HAP, clinicians should pay particular attention to specific independent risk factors identified in this study, such as male sex, coagulation dysfunction, acute myocardial infarction, chronic kidney disease, underlying respiratory diseases, decreased oxygenation index, thrombocytopenia, and elevated total bilirubin. The developed nomogram effectively predicts the risk of progression to sepsis, providing clinicians with a practical tool for timely intervention and potentially improving patient outcomes.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Nomogram to Predict risk of Sepsis in Non-ventilator Hospital- Acquired Pneumonia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 07:01:27","doi":"10.21203/rs.3.rs-7847940/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91306024-b211-400c-a554-10904bd47d0f","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T04:54:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 07:01:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7847940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7847940","identity":"rs-7847940","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}