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

Predictive value of early warning scores in in-hospital mortality of patients readmitted to the surgical intensive care unit after major abdominal surgery

BACKGROUND/AIMS

Early warning scores are simple scores obtained by measuring physiological parameters and have been regarded as useful tools for detecting clinical deterioration. This study aimed to evaluate the impact of early warning scores in predicting in-hospital mortality in critically ill patients readmitted to the surgical intensive care unit.

METHODS

The study was conducted at a tertiary referral teaching hospital in South Korea. A total of 161 patients who underwent major abdominal surgery were readmitted to the surgical intensive care unit during hospitalization. To clarify the predictors of mortality in patients after surgical intensive care unit readmission, clinical data, including the 3 types of early warning scores at the time of deterioration before readmission, were analyzed.

RESULTS

The incidence of readmission to the surgical intensive care unit was 6.0%, and the mean duration between the first discharge from the surgical intensive care unit and readmission was 11.2 days. Of the 161 patients, 58 (36.0%) died in hospital. In the multivariate analyses, a higher Modified Early Warning Score at readmission was independently associated with 30-day and in-hospital mortality. The receiver operating characteristic curve of Modified Early Warning Score at readmission demonstrated fair predictive power for 30-day (area under the curve = 0.709) and in-hospital (area under the curve = 0.697) mortality in patients readmitted to the surgical intensive care unit after major abdominal surgery.

CONCLUSIONS

The Modified Early Warning Score at readmission is associated with mortality in critically ill patients readmitted to the surgical intensive care unit and can be an independent predictor of both 30-day and in-hospital mortality.

The prognostic value of NT-proBNP in 28-day mortality and post-discharge survival in pneumonia: a retrospective cohort study from Taiwan

BACKGROUND

NT-proBNP, traditionally used to assess heart failure, is increasingly recognized for its prognostic value in other diseases. This study evaluates its value in pneumonia.

RESEARCH DESIGN AND METHODS

We conducted a retrospective cohort study of adult patients hospitalized for pneumonia at Wan Fang Hospital (2017-2021) to investigate whether elevated NT-proBNP levels predicted poorer outcomes. Logistic regression identified risk factors for 28-day mortality, while the Cox regression model identified predictors of post-discharge survival.

RESULTS

Among 2,805 patients (79.6 ± 13.4 years, female 45%), the 28-day mortality rate was 18.2%, and the median post-discharge follow-up time was 359 days. Moderately (increased but < 10000 pg/mL) and severely (>10000 pg/mL) elevated NT-proBNP levels had higher 28-day mortality compared to normal NT-proBNP; adjusted odds ratios: 2.24 (1.34-3.75, p = 0.002) and 3.57 (2.03-6.27, p < 0.001). Moderately and severely elevated NT-proBNP levels related to shorter survival time than normal NT-proBNP levels; adjusted hazard ratios 1.60 (1.28-2.00, p < 0.001) and 2.03 (1.56-2.63, p < 0.001). All ratios were adjusted with comorbidities, sex, age, and clinical and laboratory tests.

CONCLUSIONS

Elevated NT-proBNP levels predict higher 28-day mortality and shorter survival time in patients with pneumonia across most subpopulations. This marker holds potential as a prognostic biomarker for pneumonia, especially in high-risk patients.

Comparison of early warning scoring systems for predicting stroke occurrence among hospitalized patients: A study using smart clinical data warehouse

BACKGROUND

This study aimed to evaluate the predictive ability of two widely used early warning scoring systems, the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS), for predicting stroke occurrence in hospitalized patients.

METHODS

The study enrolled 5,474 patients admitted to the intensive care unit from the general ward using data from the Smart Clinical Data Warehouse (CDW). MEWS and NEWS were calculated based on vital signs and clinical parameters within four hours of stroke onset. Stroke occurrence was categorized as ischemic or hemorrhagic. Logistic regression and receiver operating characteristic curve analyses were performed to assess the predictive abilities of the scoring systems.

RESULTS

Of the enrolled patients, 33.9% (n = 1853) experienced stroke, comprising 783 cases of ischemic stroke and 1,070 cases of hemorrhagic stroke. Both the MEWS and the NEWS were found to significantly predict overall stroke occurrence with a cutoff value of 4 (MEWS>4; OR [95% CI]: 13.90 [11.51-16.79], p<0.001; NEWS>4; OR [95% CI]: 6.71 [5.75-7.83], p<0.001). Parameters, such as prior malignancy, atrial fibrillation, AVPU response, heart rate, respiratory rate, and oxygen saturation, are also associated with stroke occurrence. The predictive ability of MEWS and NEWS was good for overall stroke occurrence. (AUC of MEWS: 0.92, 95% CI [0.91-0.93], p<0.001; AUC of NEWS: 0.85, 95% CI [0.84-0.86], p<0.001). The predictive ability was considered fair for ischemic stroke but good for hemorrhagic stroke.

CONCLUSION

MEWS and NEWS demonstrated significant predictive abilities for overall stroke occurrence among hospitalized patients, with MEWS slightly outperforming NEWS.

Comparative Effectiveness of CURB-65 and qSOFA Scores in Predicting Pneumonia Outcomes: A Systematic Review

Pneumonia is a leading cause of hospitalization and mortality worldwide, often progressing to sepsis, making early accurate severity assessment crucial for effective clinical decision-making. This systematic review compares the CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥65 years) and qSOFA scoring systems in predicting pneumonia outcomes, including short-term mortality and ICU admission, to provide evidence-based recommendations for their clinical application. A comprehensive search was conducted across multiple databases, including PubMed, Medline, Web of Science, Google Scholar, Cochrane Library, and BMJ Journals, using specific keywords related to pneumonia and the scoring systems. Eligible studies included adult patients diagnosed with community-acquired, hospital-acquired, or healthcare-associated pneumonia (HAP), where CURB-65 or qSOFA scores were calculated within 24 hours of admission. Data extraction focused on study characteristics, patient demographics, and outcome measures, with quantitative synthesis comparing the predictive performance of the two scores. Sensitivity, specificity, and area under the ROC curve (AUC) values were assessed, and potential sources of heterogeneity and publication bias were examined. The analysis included 22 studies with a total of 25,846 participants, revealing varying predictive accuracy across different settings. CURB-65 demonstrated superior sensitivity (76.52%) and AUC (0.747) for mortality prediction, making it a more reliable tool for identifying high-risk pneumonia patients who require intensive management. Conversely, qSOFA exhibited superior specificity (86.08%) and better performance in predicting ICU admissions, with an AUC of 0.714, highlighting its utility in identifying patients who may need critical care interventions. This systematic review underscores the strengths and limitations of both CURB-65 and qSOFA in predicting pneumonia outcomes. While CURB-65 is more effective for mortality prediction, qSOFA excels in predicting ICU admissions. A combined approach leveraging both scoring systems could enhance patient assessment and management. Further research with larger, prospective studies is recommended to validate these findings and optimize the clinical use of CURB-65 and qSOFA in pneumonia management.

Metabolic syndrome is associated with mortality in elderly patients with acute respiratory distress syndrome

BACKGROUND

This study aims to evaluate the association of metabolic syndrome (MetS) with the risk of all-cause mortality in elderly patients with acute respiratory distress syndrome (ARDS).

METHODS

Elderly ARDS patients (≥ 65 years) enrolled from our hospital between January 2018 and July 2023 were divided into the MetS group or the non-MetS group. The outcomes were 28-day and 90-day all-cause mortality rates in the total population and two subgroups stratified by age (65-75 years and ≥ 75 years). Multivariate Cox regression was employed to assess the association of MetS with all-cause mortality, after controlling for potential cofounding factors.

RESULTS

A total of 946 patients were divided into the MetS group (n = 410) or the non-MetS group (n = 536). The 28-day and 90-day all-cause mortality rates were significantly higher for MetS group compared to non-MetS group in the total population and two subgroups (all P < 0.01). Multivariate Cox regression indicated that MetS was significantly associated with a higher risk of 90-day all-cause mortality in the total population (HR = 1.62, 95% CI: 1.22-2.15; P < 0.01), and subgroups of patients aged 65-75 years (HR = 1.52, 95% CI: 1.04-2.21; P = 0.03) and ≥ 75 years (HR = 1.90, 95% CI: 1.23-2.94; P < 0.01). Moreover, with each MetS criterion added from 0 to 1 to 2, 3, and 4 of 4 criteria, both 28-day and 90-day all-cause mortality rates significantly increased (both P < 0.01).

CONCLUSION

MetS was associated with higher risks of 28-day and 90-day all-cause mortality in elderly patients with ARDS.

Comparison of Four Severity Assessment Scoring Systems in Critically Ill Patients for Predicting Patient Outcomes: A Prospective Observational Study From a Single Tertiary Center in Central India

Background and aim A variety of scoring systems are employed in intensive care units (ICUs) with the objective of predicting patient morbidity and mortality. The present study aimed to compare four different severity assessment scoring systems, namely, Acute Physiology and Chronic Health Evaluation II (APACHE II), Rapid Emergency Medicine Score (REMS), Sequential Organ Failure Assessment (SOFA), and Simplified Acute Physiologic Score II (SAPS II) to predict prognosis of all patients admitted to a mixed medical ICU of a tertiary care teaching hospital in central India. Methods The prospective observational study included 1136 patients aged 18 years or more, admitted to the mixed medical ICU. All patients underwent severity assessment using the four scoring systems, namely APACHE II, SOFA, REMS, and SAPS II, after admission. Predicted mortality was calculated from each of the scores and actual patient outcomes were noted. Receiver operating curve analysis was undertaken to identify the cut-off value of individual scoring systems for predicting mortality with optimum sensitivity and specificity. Calibration and discrimination were employed to ascertain the validity of each scoring model. Bivariate and multivariable logistic regression analyses among the study participants were conducted to identify the best scoring system, after adjusting for potential confounders. Results Final analysis was done on 957 study participants (mean (±SD) age-58.4 (±12.9) years; males-62.2%). The mortality rate was 14.7%. APACHE II, SOFA, SAPS II, and REMS scores were significantly higher among the non-survivors as compared to the survivors (p<0.05). SAPS II was found to have the highest AUC of 0.981 (p<0.001). SAPS II score >58 had 93.6% sensitivity, 94.1% specificity, 73.3% PPV, 98.8% NPV, and 94.0% diagnostic accuracy in predicting mortality. This scoring system also had the best calibration. Binary logistic regression showed that all four scoring systems were significantly associated with ICU mortality. After adjusting for each other, only SAPS II remained significantly associated with ICU mortality. Conclusion Both SAPS II and APACHE II were observed to have good calibration and discriminatory power; however, SAPS II had the best prediction power suggesting that it may be a useful tool for clinicians and researchers in assessing the severity of illness and mortality risk in critically ill patients.

Ageism and Dementia-ism in Health Care: A Proposed Framework to Guide Treatment Decisions in Frail Older Persons

Global population ageing underscores the imperative of ageism and dementia-ism in the heath care setting as there is both anecdotal and published evidence that older persons are liable to receive less than optimum evidence-based treatments on account of their age and apparent frailty. This article explores the reasons for this phenomenon and limitations of current approaches of clinical decision making. We propose a framework to guide decision making to optimize care, with a paradigm shift in redefining a default toward clinical practice guideline-recommended treatments, guided by evidence-based medicine within an ethical means-end proportionality framework, and regularly reviewed advance care plans and goals of care conversations to secure the best interests of older persons.

Association between advanced lung cancer inflammation index and in-hospital mortality in ICU patients with community-acquired pneumonia: A retrospective analysis of the MIMIC-IV database

Objective

The objective of the present study was to explore the correlation between the advanced lung cancer inflammation index (ALI) and in-hospital mortality among patients diagnosed with community-acquired pneumonia (CAP).

Methods

Data from the Medical Information Mart for Intensive Care-IV database were adopted to analyze the in-hospital mortality of ICU patients with CAP. Upon admission to the ICU, fundamental data including vital signs, critical illness scores, comorbidities, and laboratory results, were collected. The in-hospital mortality of all CAP patients was documented. Multivariate logistic regression (MLR) models and restricted cubic spline (RCS) analysis together with subgroup analyses were conducted.

Results

This study includes 311 CAP individuals, involving 218 survivors as well as 93 nonsurvivors. The participants had an average age of 63.57 years, and the females accounted for approximately 45.33%. The in-hospital mortality was documented to be 29.90%. MLR analysis found that ALI was identified as an independent predictor for in-hospital mortality among patients with CAP solely in the Q1 group with ALI ≤ 39.38 (HR: 2.227, 95% CI: 1.026-4.831, P = 0.043). RCS analysis showed a nonlinear relationship between the ALI and in-hospital mortality, with a turning point at 81, and on the left side of the inflection point, a negative correlation was observed between ALI and in-hospital mortality (HR: 0.984, 95% CI: 0.975-0.994, P = 0.002). The subgroup with high blood pressure showed significant interaction with the ALI.

Conclusion

The present study demonstrated a nonlinear correlation of the ALI with in-hospital mortality among individuals with CAP. Additional confirmation of these findings requires conducting larger prospective investigations.

Utility of ED triage tools in predicting the need for intensive respiratory or vasopressor support in adult patients with COVID-19

BACKGROUND

Serum and radiological parameters used to predict prognosis in COVID patients are not feasible in the Emergency Department. Due to its damaging effect on multiple organs and lungs, scores used to assess multiorgan damage and pneumonia such as Pandemic Medical Early Warning Score (PMEWS), National Early Warning Score 2 (NEWS2), WHO score, quick Sequential Organ Failure Assessment (qSOFA), and DS-CRB 65 can be used to triage patients in the Emergency Department. They can be used to predict patients with the highest risk of seven-day mortality and need for intensive respiratory or vasopressor support (IRVS).

PURPOSE

The primary purpose was to find the score with the highest AUC in predicting IRVS and mortality at seven days. Additional objective was to find out any independent factors associated with IRVS and mortality.

METHODS

The data of adult patients who presented to the Emergency Department (ED) between April 1, 2021 and June 30, 2021 were collected. The WHO score, CRB-65, DS-CRB 65, PMEWS, NEWS2, and qSOFA score were calculated for all patients. Statistical analysis was done and an ROC curve was calculated for all the tools for mortality and need for IRVS at seven days.

FINDINGS

677 patients presented to the Emergency Department with COVID-19 during the period above. Presence of Diabetes Mellitus (p = 0.001), Hypertension (p = 0.001), and chronic kidney disease(CKD) (p = 0.04) was significantly associated with need for IRVS. Age, duration of symptoms, pulse rate, respiratory rate, room air saturation, mental status at admission, and time to IRVS need were identified as independent predictors of in-hospital mortality. The longer the time to IRVS need from ED arrival, the higher the likelihood of mortality. PMEWS (0.830) had the highest AUC, followed by NEWS2 (0.805). A PMEWS cut-off of 6.5 was 74.2% sensitive and 78.3% specific in predicting the need for IRVS. ROC analysis to predict 7-day mortality showed that PMEWS had an AUC of 0.802 (0.766-0.839). QSOFA performed poorly in predicting IRVS (AUC 0.645) and 7-day mortality (AUC 0.677).

CONCLUSION

PMEWS may be used for triaging patients presenting to the Emergency Department with COVID-19 and accurately predicts the need for IRVS and seven day mortality.

Predictive Value of Lysophosphatidylcholine for Determining the Disease Severity and Prognosis of Elderly Patients with Community-Acquired Pneumonia

Purpose

To investigate the clinical value of serum lysophosphatidylcholine (LPC) as a predictive biomarker for determining disease severity and mortality risk in hospitalized elderly patients with community-acquired pneumonia (CAP).

Methods

This prospective, single-center study enrolled 208 elderly patients, including 67 patients with severe CAP (SCAP) and 141 with non-SCAP between November 1st, 2020, and November 30th, 2021 at the Qingdao Municipal Hospital, Shandong Province, China. The demographic and clinical parameters were recorded for all the included patients. Serum LPC levels were measured on day 1 and 6 after admission using ELISA. Propensity score matching (PSM) was used to balance the baseline variables between SCAP and non-SCAP patient groups. Receiver operative characteristic (ROC) curve analysis was used to compare the predictive performances of LPC and other clinical parameters in discriminating between SCAP and non-SCAP patients and determining the 30-day mortality risk of the hospitalized CAP patients. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors associated with SCAP. Cox proportional hazard regression analysis was used to determine if serum LPC was an independent risk factor for the 30-day mortality of CAP patients.

Results

The serum LPC levels at admission were significantly higher in the non-SCAP patients than in the SCAP patients (P = 0.011). Serum LPC level <24.36 ng/mL, and PSI score were independent risk factors for the 30-day mortality in the elderly patients with CAP. The risk of 30-day mortality in the elderly CAP patients with low serum LPC levels (< 24.36ng/mL) was >5-fold higher than in the patients with high serum LPC levels (≥ 24.36ng/mL).

Conclusion

Low serum LPC levels were associated with significantly higher disease severity and 30-day mortality in the elderly patients with CAP. Therefore, serum LPC is a promising predictive biomarker for the early identification of elderly CAP patients with poor prognosis.

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

Background

Based 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.

Methods

In 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).

Results

Eight 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.

Conclusion

This 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.

Erector spinae muscle-based nomogram for predicting in-hospital mortality among older patients with severe community-acquired pneumonia

BACKGROUND

No multivariable model incorporating erector spinae muscle (ESM) has been developed to predict clinical outcomes in older patients with severe community-acquired pneumonia (SCAP). This study aimed to construct a nomogram based on ESM to predict in-hospital mortality in patients with SCAP.

METHODS

Patients aged ≥ 65 years with SCAP were enrolled in this prospective observational study. Least absolute selection and shrinkage operator and multivariable logistic regression analyses were used to identify risk factors for in-hospital mortality. A nomogram prediction model was constructed. The predictive performance was evaluated using the concordance index (C-index), calibration curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis.

RESULTS

A total of 490 patients were included, and the in-hospital mortality rate was 36.1%. The nomogram included the following independent risk factors: mean arterial pressure, peripheral capillary oxygen saturation, Glasgow Coma Scale score (GCS), lactate, lactate dehydrogenase, blood urea nitrogen levels, and ESM cross-sectional area. Incorporating ESM into the base model with other risk factors significantly improved the C-index from 0.803 (95% confidence interval [CI], 0.761-0.845) to 0.836 (95% CI, 0.798-0.873), and these improvements were confirmed by category-free NRI and IDI. The ESM-based nomogram demonstrated a high level of discrimination, good calibration, and overall net benefits for predicting in-hospital mortality compared with the combination of confusion, urea, respiratory rate, blood pressure, and age ≥ 65 years (CURB-65), Pneumonia Severity Index (PSI), Acute Physiology and Chronic Health Evaluation II (APACHEII), and Sequential Organ Failure Assessment (SOFA).

CONCLUSIONS

The proposed ESM-based nomogram for predicting in-hospital mortality among older patients with SCAP may help physicians to promptly identify patients prone to adverse outcomes.

TRIAL REGISTRATION

This study was registered at www.chictr.org.cn (registration number Chi CTR-2300070377).

Can we recognize severe community-acquired pneumonia without pneumonia severity index? Use of modified qSOFA with procalcitonin

OBJECTIVE

The aim of this study is to analyze the diagnostic value of Quick Sequential Organ Failure Assessment (qSOFA), modified qSOFA, National Early Warning Score (NEWS) and NEWS + Lactate (NEWS + L) scores in the detection of severe community-acquired pneumonia (CAP).

METHODS

This research is a retrospective cohort study. Patients admitted to the Emergency Department (ED) with the diagnosis of CAP were divided into severe and mild pneumonia regarding their Pneumonia Severity Index (PSI) scores. The accuracies of lactate, procalcitonin (PCT) values, NEWS and qSOFA scores, as well as score combinations (NEWS + L and qSOFA + PCT) in predicting patients with severe CAP were analyzed.

RESULTS

Median qSOFA value in the severe CAP group (0 [IQR 0-1]) was found to be higher than median qSOFA value (0 [IQR 0-0]) in the mild CAP group (p < 0.001). The rate of qSOFA positivity (qSOFA ≥ 2) was significantly higher in the severe CAP group (17.1%, n = 21) compared to the mild CAP group (1.3%, n = 1) (p < 0.001). Median qSOFA + PCT for the severe CAP group (2 [IQR 1-2]) was higher than the mild CAP group (1 [IQR 0-1]) (p < 0.001). Mean NEWS score for severe CAP (4.95 ± 3.09) was found to be 1.69 (95% CI 0.92-2.46) higher than mean NEWS score of the mild CAP group (3.26 ± 2.39) (p < 0.001). The severe CAP NEWS + L score (6.97 ± 3.71) was higher than the mild CAP NEWS + L score (4.94 ± 2.48) (p < 0.001). Blood lactate level was not significant in the evaluation of severe CAP (p = 0.221). PCT (AUROC = 0.685 [95% CI 0.610-0.760]; p = 0.038), NEWS score (AUROC = 0.658 95% CI [0.582-0.733]; p < 0.001), qSOFA (AUROC = 0.686) were calculated to have adequate accuracy for the detection of severe CAP. [95% CI 0.613-0.759]; p = 0.037), NEWS + L score (AUROC = 0.665 [95% CI 0.589-0.740]; p = 0.038). The qSOFA + PCT score (AUROC = 0.758 [95% 0.691-0.825]; p = 0.034) was also found to be a highly accurate predictor of severe CAP.

CONCLUSION

In this study, we found a combination of qSOFA and PCT to be the most reliable method of detecting severe CAP.

Utility of the New Early Warning Score (NEWS) in combination with the neutrophil-lymphocyte ratio for the prediction of prognosis in older patients with pneumonia

OBJECTIVE

Predictors of prognosis are necessary for use in routine clinical practice for older patients with pneumonia, given the ageing of the population. Recently, the National Early Warning Score (NEWS), a comprehensive predictor of severity that consists solely of physiological indicators, has been proposed to predict the prognosis of pneumonia. The neutrophil/lymphocyte ratio (NLR) is a simple index of inflammation that may also be predictive of pneumonia. In the present study, we aimed to determine whether NEWS or a combination of NEWS and NLR predicts mortality in older patients with pneumonia.

DESIGN

A retrospective cohort study.

SETTING

A general hospital in Japan.

PARTICIPANTS

We collected data from patients aged ≥65 years with pneumonia who were admitted between 2018 and 2020 (n=282; age=85.3 (7.9)). Data regarding vital signs, demographics and the length of hospital stay, in addition to the NEWS and NLR, were extracted from the participants' electronic medical records.

INTERVENTION

The utility of the combination of NEWS and NLR was assessed using NEWS×NLR and NEWS+NLR.

MAIN OUTCOME MEASURES

Their predictive ability for 30-day mortality as the primary outcome was assessed using receiver operating characteristic (ROC) curve analysis.

RESULTS

According to the NEWS classification, 80 (28.3%), 64 (22.7%) and 138 (48.9%) of the participants were at low, medium and high risk of mortality, respectively. The 30-day mortality for the entire cohort was 9.2% (n=26), and the mortality rate increased with the NEWS classification: low, 1.3%; medium, 7.8%; and high, 14.5%. The NLRs were 6.0 (4.2-9.8), 6.8 (4.8-10.4) and 14.6 (9.4-22.2), respectively (p<0.001). The areas under the ROC curves for 30-day mortality were 0.73 for the NEWS score, 0.84 for NEWS×NLR and 0.83 for NEWS+NLR, indicating that the combinations represent superior predictors of mortality to the NEWS alone. NEWS×NLR and NEWS+NLR tended to have better sensitivity, accuracy, positive predictive value and negative predictive value than NEWS alone (p=0.06).

CONCLUSIONS

A combination of the NEWS and NLR (NEWS×NLR or NEWS+NLR) may be superior to the NEWS alone for the prediction of 30-day mortality in older patients with pneumonia. However, further validation of these combinations for use in the prediction of prognosis is required.

Performance of Six Clinical Physiological Scoring Systems in Predicting In-Hospital Mortality in Elderly and Very Elderly Patients with Acute Upper Gastrointestinal Bleeding in Emergency Department

Background and Objectives: The aim of this study is to compare the performance of six clinical physiological-based scores, including the pre-endoscopy Rockall score, shock index (SI), age shock index (age SI), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), and Modified Early Warning Score (MEWS), in predicting in-hospital mortality in elderly and very elderly patients in the emergency department (ED) with acute upper gastrointestinal bleeding (AUGIB). Materials and Methods: Patients older than 65 years who visited the ED with a clinical diagnosis of AUGIB were enrolled prospectively from July 2016 to July 2021. The six scores were calculated and compared with in-hospital mortality. Results: A total of 336 patients were recruited, of whom 40 died. There is a significant difference between the patients in the mortality group and survival group in terms of the six scoring systems. MEWS had the highest area under the curve (AUC) value (0.82). A subgroup analysis was performed for a total of 180 very elderly patients (i.e., older than 75 years), of whom 27 died. MEWS also had the best predictive performance in this subgroup (AUC, 0.82). Conclusions: This simple, rapid, and obtainable-by-the-bed parameter could assist emergency physicians in risk stratification and decision making for this vulnerable group.

Development and validation of a survival prediction model in elder patients with community-acquired pneumonia: a MIMIC-population-based study

BACKGROUND

To develop a prediction model predicting in-hospital mortality of elder patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU).

METHODS

In this cohort study, data of 619 patients with CAP aged ≥ 65 years were obtained from the Medical Information Mart for Intensive Care III (MIMIC III) 2001-2012 database. To establish the robustness of predictor variables, the sample dataset was randomly partitioned into a training set group and a testing set group (ratio: 6.5:3.5). The predictive factors were evaluated using multivariable logistic regression, and then a prediction model was constructed. The prediction model was compared with the widely used assessments: Sequential Organ Failure Assessment (SOFA), Pneumonia Severity Index (PSI), systolic blood pressure, oxygenation, age and respiratory rate (SOAR), CURB-65 scores using positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), area under the curve (AUC) and 95% confidence interval (CI). The decision curve analysis (DCA) was used to assess the net benefit of the prediction model. Subgroup analysis based on the pathogen was developed.

RESULTS

Among 402 patients in the training set, 90 (24.63%) elderly CAP patients suffered from 30-day in-hospital mortality, with the median follow-up being 8 days. Hemoglobin/platelets ratio, age, respiratory rate, international normalized ratio, ventilation use, vasopressor use, red cell distribution width/blood urea nitrogen ratio, and Glasgow coma scales were identified as the predictive factors that affect the 30-day in-hospital mortality. The AUC values of the prediction model, the SOFA, SOAR, PSI and CURB-65 scores, were 0.751 (95% CI 0.749-0.752), 0.672 (95% CI 0.670-0.674), 0.607 (95% CI 0.605-0.609), 0.538 (95% CI 0.536-0.540), and 0.645 (95% CI 0.643-0.646), respectively. DCA result demonstrated that the prediction model could provide greater clinical net benefits to CAP patients admitted to the ICU. Concerning the pathogen, the prediction model also reported better predictive performance.

CONCLUSION

Our prediction model could predict the 30-day hospital mortality in elder patients with CAP and guide clinicians to identify the high-risk population.

Nomogram for prediction of severe community-acquired pneumonia development in diabetic patients: a multicenter study

Background

Diabetic patients with community-acquired pneumonia (CAP) have an increased risk of progressing to severe CAP. It is essential to develop predictive tools at the onset of the disease for early identification and intervention. This study aimed to develop and validate a clinical feature-based nomogram to identify diabetic patients with CAP at risk of developing severe CAP.

Method

A retrospective cohort study was conducted between January 2019 to December 2020. 1026 patients with CAP admitted in 48 hospitals in Shanghai were enrolled. All included patients were randomly divided into the training and validation samples with a ratio of 7:3. The nomogram for the prediction of severe CAP development was established based on the results of the multivariate logistic regression analysis and other predictors with clinical relevance. The nomogram was then assessed using receiver operating characteristic curves (ROC), calibration curve, and decision curve analysis (DCA).

Results

Multivariate analysis showed that chronic kidney dysfunction, malignant tumor, abnormal neutrophil count, abnormal lymphocyte count, decreased serum albumin level, and increased HbA1c level at admission was independently associated with progression to severe CAP in diabetic patients. A nomogram was established based on these above risk factors and other predictors with clinical relevance. The area under the curve (AUC) of the nomogram was 0.87 (95% CI 0.83–0.90) in the training set and 0.84 (95% CI 0.78–0.90). The calibration curve showed excellent agreement between the predicted possibility by the nomogram and the actual observation. The decision curve analysis indicated that the nomogram was applicable with a wide range of threshold probabilities due to the net benefit.

Conclusion

Our nomogram can be applied to estimate early the probabilities of severe CAP development in diabetic patients with CAP, which has good prediction accuracy and discrimination abilities. Since included biomarkers are common, our findings may be performed well in clinical practice and improve the early management of diabetic patients with CAP.

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

Background

The 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.

Methods

We 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.

Results

LASSO 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.

Conclusion

Our 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.

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

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.

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

BACKGROUND

Modified 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.

METHODS

This 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.

RESULTS

A 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).

CONCLUSION

National 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.