Exploration of prognostic factors for prediction of mortality in elderly CAP population using a nomogram model

Lv, Chunxin; Li, Mengyuan; Shi, Wen; Pan, Teng; Muhith, Abdul; Peng, Weixiong; Xu, Jiayi; Deng, Jinhai

doi:10.3389/fmed.2022.976148

Cited by 5 publications

(9 citation statements)

References 43 publications

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“…Therefore, a growing number of studies emphasize the importance of NLR to improve the ability of predicting adverse outcome in CAP patients when combined with other factors. A nomogram model composed by NLR was established by Lv et al 32 to predict mortality in elderly patients with CAP, and the AUC of the model was 0.9, which was proved to be superior to CURB-65 and PSI. Collectively, NLR is a simple, easily measured, yet promising marker for predicting outcomes in patients with CAP.…”

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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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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“…The length of stay in neuro-ICU was not easily obtainable after admission, which may have hindered the model’s ability to assess quickly. A recent study suggested that a simple model with easily obtained variables would be more suitable and applicable in clinical practice ( 54 ). Therefore, in selecting future prediction model factors, it may be necessary to consider the time limit for obtaining predictors.…”

Section: Discussionmentioning

confidence: 99%

Development and application of a nomogram model for the prediction of carbapenem-resistant Klebsiella pneumoniae infection in neuro-ICU patients

Lu,

Zhang,

Shi

et al. 2024

Microbiol Spectr

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This study aimed to develop and validate a simple-to-use nomogram model to assess the risk of infection caused by carbapenem-resistant Klebsiella pneumoniae (CRKP) in neurocritically ill patients. The clinical data of 544 patients with CRKP infection admitted to a neurointensive care unit (neuro-ICU) were retrospectively analyzed. The demographic data, laboratory test results, and clinical characteristics of patients in the neuro-ICU were collected. Subsequently, multivariate regression analysis was used to construct a nomogram to predict the risk of CRKP infection in these patients. The calibration ability, clinical effectiveness, and discriminative ability of the nomogram were evaluated. The incidence of CRKP infection was estimated to be 6.43%, and a majority of bacterial isolates causing the infection were found in sputum (74.3%). Multivariate regression analysis showed that the number of antibiotics of ≥2 [odds ratio (OR): 9.08, 95% confidence interval (CI): 2.78–29.71], undergoing surgery (OR: 3.84, 95% CI: 1.09–13.54), and long neuro-ICU stay (OR: 1.08, 95% CI: 1.01–1.14) were associated with CRKP infection in neurocritically ill patients. The nomogram model demonstrated good calibration and discrimination in both the training and validation sets, with area under the curve values of 0.860 and 0.907, respectively. This study developed and validated a nomogram that combines three easily accessed variables during clinical practice to predict the risk of nosocomial CRKP infection in neuro-ICU patients. The tool demonstrated a good predictive performance and discrimination, which might serve as a useful tool to facilitate early detection and reduction of the CRKP infection risk in neurocritically ill patients. IMPORTANCE Patients in neuro-ICU are at a high risk of developing nosocomial CRKP infection owing to complex conditions, critical illness, and frequent invasive procedures. However, studies focused on constructing prediction models for assessing the risk of CRKP infection in neurocritically ill patients are lacking at present. Therefore, this study aims to establish a simple-to-use nomogram for predicting the risk of CRKP infection in patients admitted to the neuro-ICU. Three easily accessed variables were included in the model, including the number of antibiotics used, surgery, and the length of neuro-ICU stay. This nomogram might serve as a useful tool to facilitate early detection and reduction of the CRKP infection risk of neurocritically ill patients.

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“… 10 Another research conducted at Fudan University in China found that the average age of elderly patients with community-acquired pneumonia was 79.8±8.8 years. 11 …”

Section: Discussionmentioning

confidence: 99%

“…However, similar studies done in Spain and China showed the male to female ratio of 1.73 and 1.25 respectively. 10 , 11 …”

Section: Discussionmentioning

confidence: 99%

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Community-acquired Pneumonia among Elderly Patients Admitted to the Department of Medicine in a Tertiary Care Centre: A Descriptive Cross-sectional Study

Karki¹,

Khadka²,

Joti³

et al. 2023

J Nepal Med Assoc

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Introduction: Community-acquired pneumonia is an acute infection of lung tissue in an immunocompetent patient who have not recently been hospitalized or has been hospitalized for less than 48 hours and acquired it in the community. It continues to have a substantial effect on the elderly, who are impacted more often and severely than younger groups. It is the third most common hospital diagnosis in adults over the age of 65 years, and the sixth major cause of mortality in developed nations. The aim of this study was to find out the prevalence of community-acquired pneumonia among elderly patients admitted to the Department of Medicine in a tertiary care centre. Methods: A descriptive cross-sectional study was done in the Department of Medicine in a tertiary care centre where data were collected from 1 December 2021 and 1 December 2022 after obtaining ethical approval from the Institutional Review Committee (Reference number: 465/2079/80). Data on community-acquired pneumonia in the elderly was collected from the hospital records. Convenience sampling method was used. Point estimate and 95% Confidence Interval were calculated. Results: Among 385 patients, community-acquired pneumonia was seen in 14 (3.64%) (1.77-5.51, 95% Confidence Interval) with a mean age of 70.57±10.21 years. Conclusions: The prevalence of community-acquired pneumonia among elderly patients was found to be lower compared to other studies conducted in similar settings.

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Exploration of prognostic factors for prediction of mortality in elderly CAP population using a nomogram model

Cited by 5 publications

References 43 publications

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

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

Development and application of a nomogram model for the prediction of carbapenem-resistant Klebsiella pneumoniae infection in neuro-ICU patients

Community-acquired Pneumonia among Elderly Patients Admitted to the Department of Medicine in a Tertiary Care Centre: A Descriptive Cross-sectional Study

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