Comparison of Different Scoring Systems for Prediction of Mortality and ICU Admission in Elderly CAP Population

Lv, Chunxin; Chen, Yue; Shi, Wen; Pan, Teng; Deng, Jinhai; Xu, Jiayi

doi:10.2147/cia.s335315

Cited by 19 publications

(18 citation statements)

References 53 publications

(61 reference statements)

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“…Meanwhile, DCA revealed that the predictive model had significant net benefits for most threshold probabilities, suggestive of the potential clinical benefit of this predictive model ( Figure 4B ). Additionally, to test the feasibility of the application of our model in clinic, we compared the ROC of NB-DAPA nomogram model with PSI, CURB-65 and qSOFA, which have been commonly used in clinic and shown great predictive ability in CAP ( 18 ), to better explore the predictive function of mortality among elderly patients. The AUC values of NB-DAPA nomogram model, PSI, qSOFA and CURB-65 were 0.90 (95%CI: 0.86–0.93), 0.85 (95%CI: 0.81–0.87), 0.71 (95% CI: 0.66–0.76), 0.86 (95% CI: 0.83–0.90), respectively ( Table 4 ).…”

Section: Resultsmentioning

confidence: 99%

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

Shi

et al. 2022

Front. Med.

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BackgroundThe incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. The assessment tools including CURB-65 and qSOFA have been applied in early detection of high-risk patients with CAP. However, several disadvantages exist to limit the efficiency of these tools for accurate assessment in elderly CAP. Therefore, we aimed to explore a more comprehensive tool to predict mortality in elderly CAP population by establishing a nomogram model.MethodsWe retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. The least absolute shrinkage and selection operator (LASSO) logistic regression combined with multivariate analyses were used to select independent predictive factors and established nomogram models via R software. Calibration plots, decision curve analysis (DCA) and receiver operating characteristic curve (ROC) were generated to assess predictive performance.ResultsLASSO and multiple logistic regression analyses showed the age, pulse, NLR, albumin, BUN, and D-dimer were independent risk predictors. A nomogram model (NB-DAPA model) was established for predicting mortality of CAP in elderly patients. In both training and validation set, the area under the curve (AUC) of the NB-DAPA model showed superiority than CURB-65 and qSOFA. Meanwhile, DCA revealed that the predictive model had significant net benefits for most threshold probabilities.ConclusionOur established NB-DAPA nomogram model is a simple and accurate tool for predicting in-hospital mortality of CAP, adapted for patients aged 65 years and above. The predictive performance of the NB-DAPA model was better than PSI, CURB-65 and qSOFA.

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Section: Resultsmentioning

confidence: 99%

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

Shi

et al. 2022

Front. Med.

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“…When evaluating the performance of PSI and CURB-65 scores to predict in-hospital mortality in SCAP patients aged ≥ 80 years via the receiver operating characteristics curve, the area under the curves (AUCs) were 0.61 (95% CI 0.51–0.71) for the CURB-65 and 0.52 (95% CI 0.41–0.62) for the PSI respectively [ 6 ]. Lv et al found that CURB-65 score did not exhibit superiority in predicting hospital mortality of patients aged 65-years and older with CAP [ 33 ]. Similar to previous studies, our research demonstrated that both PSI and CURB-65 did not reveal well predictive of in-hospital mortality in elderly SCAP patients with CVDs.…”

Section: Discussionmentioning

confidence: 99%

Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients with comorbid cardiovascular disease: a retrospective cohort study

Gong

Huang

et al. 2022

BMC Pulm Med

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Background Researchers have linked cardiovascular disease (CVD) with advancing age; however, how it drives disease progression in elderly severe community acquired pneumonia (SCAP) patients is still unclear. This study aims to identify leading risk predictors of in-hospital mortality in elderly SCAP patients with CVD, and construct a comprehensive nomogram for providing personalized prediction. Patients and methods The study retrospectively enrolled 2365 elderly patients identified SCAP. Among them, 413 patients were found to have CVD. The LASSO regression and multivariate logistic regression analysis were utilized to select potential predictors of in-hospital mortality in elderly SCAP patients with CVD. By incorporating these features, a nomogram was then developed and subjected to internal validations. Discrimination, calibration, and clinical use of the nomogram were assessed via C-index, calibration curve analysis, and decision plot. Results Compared with patients without CVD, elderly SCAP patients with CVD had a significant poor outcome. Further analysis of the CVD population identified 7 independent risk factors for in-hospital mortality in elderly SCAP patients, including age, the use of vasopressor, numbers of primary symptoms, body temperature, monocyte, CRP and NLR. The nomogram model incorporated these 7 predictors showed sufficient predictive accuracy, with the C-index of 0.800 (95% CI 0.758–0.842). High C-index value of 0.781 was obtained in the internal validation via bootstrapping validation. Moreover, the calibration curve indicative a good consistency of risk prediction, and the decision curve manifested that the nomogram had good overall net benefits. Conclusion An integrated nomogram was developed to facilitate the personalized prediction of in-hospital mortality in elderly SCAP patients with CVD.

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“…MEWS also helps predict the mortality of COVID-19 patients, with AUC values of 0.913 and 0.833 ( 36 , 37 ). Lv et al found that MEWS shows superiority over the quick Sequential Organ Function Assessment (qSOFA), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥ 65 (CURB-65), and NEWS scores in predicting hospital mortality, and NEWS showed superiority over the other scores in predicting ICU admission in patients with community-acquired pneumonia (CAP) ( 38 ). Klepstad et al showed that the higher NEWS in gastrointestinal surgical patients at ICU discharge was the predictive factor of ICU readmission ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

“…The most important advantage of MEWS and NEWS scores compared with other scoring systems, such as APACHE IV, SOFA, and Simplified Acute Physiology Score (SAPS), are their simplicity. They consist of basic physiological measurements in contrast to APACHE IV, SOFA, and SAPS, which, for instance, need documentation of laboratory results, making them a simpler tool with facilitated assessment procedures ( 38 , 39 ). The advantage of these simple scoring systems could be the early identification of patients who were becoming increasingly unstable.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study

et al. 2022

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BackgroundModified Early Warning Score (MEWS) and National Early Warning Score (NEWS) are widely used in predicting the mortality and intensive care unit (ICU) admission of critically ill patients. This study was conducted to evaluate and compare the prognostic value of NEWS and MEWS for predicting ICU readmission, mortality, and related outcomes in critically ill patients at the time of ICU discharge.MethodsThis multicenter, prospective, observational study was conducted over a year, from April 2019 to March 2020, in the general ICUs of two university-affiliated hospitals in Northwest Iran. MEWS and NEWS were compared based on the patients’ outcomes (including mortality, ICU readmission, time to readmission, discharge type, mechanical ventilation (MV), MV duration, and multiple organ failure after readmission) using the univariable and multivariable binary logistic regression. The receiver operating characteristic (ROC) curve was used to determine the outcome predictability of MEWS and NEWS.ResultsA total of 410 ICU patients were enrolled in this study. According to multivariable logistic regression analysis, both MEWS and NEWS were predictors of ICU readmission, time to readmission, MV status after readmission, MV duration, and multiple organ failure after readmission. The area under the ROC curve (AUC) for predicting mortality was 0.91 (95% CI = 0.88–0.94, P < 0.0001) for the NEWS and 0.88 (95% CI = 0.84–0.91, P < 0.0001) for the MEWS. There was no significant difference between the AUC of the NEWS and the MEWS for predicting mortality (P = 0.082). However, for ICU readmission (0.84 vs. 0.71), time to readmission (0.82 vs. 0.67), MV after readmission (0.83 vs. 0.72), MV duration (0.81 vs. 0.67), and multiple organ failure (0.833 vs. 0.710), the AUCs of MEWS were significantly greater (P < 0.001).ConclusionNational Early Warning Score and MEWS values of >4 demonstrated high sensitivity and specificity in identifying the risk of mortality for the patients’ discharge from ICU. However, we found that the MEWS showed superiority over the NEWS score in predicting other outcomes. Eventually, MEWS could be considered an efficient prediction score for morbidity and mortality of critically ill patients.

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Comparison of Different Scoring Systems for Prediction of Mortality and ICU Admission in Elderly CAP Population

Cited by 19 publications

References 53 publications

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

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

Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients with comorbid cardiovascular disease: a retrospective cohort study

Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study

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