Age, Pulse, Urea, and Albumin Score: A Tool for Predicting the Short-Term and Long-Term Outcomes of Community-Acquired Pneumonia Patients With Diabetes

Ma, Chunxiao; Wang, Ning; Su, Quan-Wei; Yan, Yan; Wang, Si-Qiong; Ma, Chao; Liu, Xiaoli; Dong, Shao-Chen; Lü, Na; Yin, Li-Yong; Yin, Fei

doi:10.3389/fendo.2022.882977

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…A novel clinically available tool that provides an early assessment and rapid prediction of ICU admission is warranted considering that risk stratification of patients with CAP and CTD remains challenging caused by In our study cohort, the median PSI was only 80 (IQR: 63,103) points, which was slightly lower than that of previous similar studies of CAP patients with a median PSI of about 100 points [34][35][36][37][38]. This may be caused by the differences of included population.…”

Section: Discussionmentioning

confidence: 62%

An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease

Huang,

Gong,

Wei

et al. 2024

Respir Res

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Background There is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-based model for predicting the need for ICU admission among those patients. Methods This was a retrospective study on patients admitted into a University Hospital in China between November 2008 and November 2021. Patients were included if they were diagnosed with CAP and CTD during admission and hospitalization. Data related to demographics, CTD types, comorbidities, vital signs and laboratory results during the first 24 h of hospitalization were collected. The baseline variables were screened to identify potential predictors via three methods, including univariate analysis, least absolute shrinkage and selection operator (Lasso) regression and Boruta algorithm. Nine supervised machine learning algorithms were used to build prediction models. We evaluated the performances of differentiation, calibration, and clinical utility of all models to determine the optimal model. The Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) techniques were performed to interpret the optimal model. Results The included patients were randomly divided into the training set (1070 patients) and the testing set (459 patients) at a ratio of 70:30. The intersection results of three feature selection approaches yielded 16 predictors. The eXtreme gradient boosting (XGBoost) model achieved the highest area under the receiver operating characteristic curve (AUC) (0.941) and accuracy (0.913) among various models. The calibration curve and decision curve analysis (DCA) both suggested that the XGBoost model outperformed other models. The SHAP summary plots illustrated the top 6 features with the greatest importance, including higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and C-reactive protein (CRP), lower level of CD4 + T cell, lymphocyte and serum sodium, and positive serum (1,3)-β-D-glucan test (G test). Conclusion We successfully developed, evaluated and explained a machine learning-based model for predicting ICU admission in patients with CAP and CTD. The XGBoost model could be clinical referenced after external validation and improvement.

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Section: Discussionmentioning

confidence: 62%

An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease

Huang,

Gong,

Wei

et al. 2024

Respir Res

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“…These variables have been studied and used in clinic. BUN and Pulse have been applied to some assessment scores, such as CURB-65 and PSI, and demonstrated to predict the prognosis of patients with CAP 23 – 26 . Additionally, although NLR and Albumin do not belong to common assessment score systems of CAP severity such as CURB-65 and PSI, we also found out NLR and Albumin can improve the predictive ability for mortality of elderly CAP, even in age-based elderly subgroups.…”

Section: Discussionmentioning

confidence: 99%

Establishment of risk model for elderly CAP at different age stages: a single-center retrospective observational study

Lv,

Pan,

Shi

et al. 2023

Sci Rep

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Community-acquired pneumonia (CAP) is one of the main reasons of mortality and morbidity in elderly population, causing substantial clinical and economic impacts. However, clinically available score systems have been shown to demonstrate poor prediction of mortality for patients aged over 65. Especially, no existing clinical model can predict morbidity and mortality for CAP patients among different age stages. Here, we aimed to understand the impact of age variable on the establishment of assessment model and explored prognostic factors and new biomarkers in predicting mortality. We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. We used univariate and multiple logistic regression analyses to study the prognostic factors of mortality in each age-based subgroup. The prediction accuracy of the prognostic factors was determined by the Receiver Operating Characteristic curves and the area under the curves. Combination models were established using several logistic regressions to save the predicted probabilities. Four factors with independently prognostic significance were shared among all the groups, namely Albumin, BUN, NLR and Pulse, using univariate analysis and multiple logistic regression analysis. Then we built a model with these 4 variables (as ABNP model) to predict the in-hospital mortality in all three groups. The AUC value of the ABNP model were 0.888 (95% CI 0.854–0.917, p < 0.000), 0.912 (95% CI 0.880–0.938, p < 0.000) and 0.872 (95% CI 0.833–0.905, p < 0.000) in group 1, 2 and 3, respectively. We established a predictive model for mortality based on an age variable -specific study of elderly patients with CAP, with higher AUC value than PSI, CURB-65 and qSOFA in predicting mortality in different age groups (66–75/ 76–85/ over 85 years).

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“…Low serum albumin at admission was considered as an independent risk parameter for predicting mortality in CAP patients ( 26 ). In a study exploring CAP patients, Ma et al ( 27 ) found that lower levels of albumin at admission were indicative of higher short-term mortality. There are two reasons that contribute to this relationship.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a predictive model for 30-day mortality in patients with severe community-acquired pneumonia in intensive care units

Zhang,

Peng,

Zhang

et al. 2024

Front. Med.

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BackgroundBased on the high prevalence and fatality rates associated with severe community-acquired pneumonia (SCAP), this study endeavored to construct an innovative nomogram for early identification of individuals at high risk of all-cause death within a 30-day period among SCAP patients receiving intensive care units (ICU) treatment.MethodsIn this single-center, retrospective study, 718 SCAP patients were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for the development of a predictive model. A total of 97 patients eligible for inclusion were included from Chongqing General Hospital, China between January 2020 and July 2023 for external validation. Clinical data and short-term prognosis were collected. Risk factors were determined using the least absolute shrinkage and selection operator (LASSO) and multiple logistic regression analysis. The model’s performance was evaluated through area under the curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsEight risk predictors, including age, presence of malignant cancer, heart rate, mean arterial pressure, albumin, blood urea nitrogen, prothrombin time, and lactate levels were adopted in a nomogram. The nomogram exhibited high predictive accuracy, with an AUC of 0.803 (95% CI: 0.756–0.845) in the training set, 0.756 (95% CI: 0.693–0.816) in the internal validation set, 0.778 (95% CI: 0.594–0.893) in the external validation set concerning 30-day mortality. Meanwhile, the nomogram demonstrated effective calibration through well-fitted calibration curves. DCA confirmed the clinical application value of the nomogram.ConclusionThis simple and reliable nomogram can help physicians assess the short-term prognosis of patients with SCAP quickly and effectively, and could potentially be adopted widely in clinical settings after more external validations.

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Age, Pulse, Urea, and Albumin Score: A Tool for Predicting the Short-Term and Long-Term Outcomes of Community-Acquired Pneumonia Patients With Diabetes

Cited by 5 publications

References 21 publications

An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease

An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease

Establishment of risk model for elderly CAP at different age stages: a single-center retrospective observational study

Development and validation of a predictive model for 30-day mortality in patients with severe community-acquired pneumonia in intensive care units

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