Performance of SOAR (systolic blood pressure, oxygenation, age and respiratory rate) scoring criteria in community-acquired pneumonia: a prospective multi-centre study

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).

Predictive value of arterial blood lactate to serum albumin ratio for in-hospital mortality of patients with community-acquired pneumonia admitted to the Intensive Care Unit

OBJECTIVE

To investigate the predictive value of the arterial blood lactate to serum albumin ratio (LAR) on in-hospital mortality of patients with community-acquired pneumonia (CAP) admitted to the Intensive Care Unit (ICU).

METHODS

Clinical datasets of 1720 CAP patients admitted to ICU from MIMIC-IV database were retrospectively analyzed. Patients were randomly assigned to the training cohort (n=1204) and the validation cohort (n=516) in a ratio of 7:3. X-tile software was used to find the optimal cut-off value for LAR. The receiver operating curve (ROC) analysis was conducted to compare the performance between LAR and other indicators. Univariate and multivariate Cox regression analyses were applied to select prognostic factors associated with in-hospital mortality. Based on the observed prognostic factors, a nomogram model was created in training cohort, and the validation cohort was utilized to further validate the nomogram.

RESULTS

The optimal cut-off value for LAR in CAP patients admitted to ICU was 1.6 (the units of lactate and albumin were, respectively, 'mmol/L' and 'g/dL'). The ROC analysis showed that the discrimination abilities of LAR were superior to other indicators except Sequential Organ Failure Assessment score and Simplified acute physiology score (SAPSII), which had the same abilities. Age, mean arterial pressure, SpO2, heart rate, SAPSII score, neutrophil-to-lymphocyte ratio, and LAR were found to be independent predictors of poor overall survival in the training cohort by multivariate Cox regression analysis and were incorporated into the nomogram for in-hospital mortality as independent factors. The nomogram model, exhibiting medium discrimination, had a C-index of 0.746 (95% CI = 0.715-0.777) in the training cohort and 0.716 (95% CI = 0.667-0.765) in the validation cohort.

CONCLUSION

LAR could predict in-hospital mortality of patients with CAP admitted to ICU independently as a readily accessible biomarker. The nomogram that included LAR with other independent factors performed well in predicting in-hospital mortality.

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.

A new prediction model for assessing the clinical outcomes of ICU patients with community-acquired pneumonia: a decision tree analysis

PURPOSE

We aimed to develop a new scoring index based on decision-tree analysis to predict clinical outcomes of patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU).

METHODS

Data of 3519 ICU patients with CAP were obtained from the Medical Information Mart for Intensive Care III (MIMIC III) 2001-2012 database and analysed between 30-d survivors and non-survivors. Accuracy, sensitivity, and specificity of the new decision tree model were compared with those of CURB-65 and SOAR.

RESULTS

The newly developed classification and regression tree (CART) model identified coexisting illnesses as the most important single discriminating factor between survivors and non-survivors. The CART model area under the curve (AUC) 0.661 was superior to that of CURB-65 (0.609) and SOAR (0.589). CART sensitivity was 73.4%, and specificity 49.0%. CURB-65 and SOAR sensitivity for predicting 30-d mortality were 74.5 and 80.7%, and specificity was 42.3 and 33.9%, respectively. After smoothing, the CART model had higher sensitivity and specificity than both CURB-65 and SOAR.

CONCLUSIONS

The new CART prediction model has higher specificity and better receiver operating characteristics (ROC) curves than CURB-65 and SOAR score indices although its accuracy and sensitivity are only moderately better than the other systems. Key messages The new CART prediction model has higher specificity and better ROC curves than CURB-65 and SOAR score indices. However, accuracy and sensitivity of the new CART prediction model are only moderately better than the other systems in predicting 30-day mortality in CAP patients.

Emergency Medicine Evaluation of Community-Acquired Pneumonia: History, Examination, Imaging and Laboratory Assessment, and Risk Scores

BACKGROUND

Pneumonia is a common infection, accounting for approximately one million hospitalizations in the United States annually. This potentially life-threatening disease is commonly diagnosed based on history, physical examination, and chest radiograph.

OBJECTIVE

To investigate emergency medicine evaluation of community-acquired pneumonia including history, physical examination, imaging, and the use of risk scores in patient assessment.

DISCUSSION

Pneumonia is the number one cause of death from infectious disease. The condition is broken into several categories, the most common being community-acquired pneumonia. Diagnosis centers on history, physical examination, and chest radiograph. However, all are unreliable when used alone, and misdiagnosis occurs in up to one-third of patients. Chest radiograph has a sensitivity of 46-77%, and biomarkers including white blood cell count, procalcitonin, and C-reactive protein provide little benefit in diagnosis. Biomarkers may assist admitting teams, but require further study for use in the emergency department. Ultrasound has shown utility in correctly identifying pneumonia. Clinical gestalt demonstrates greater ability to diagnose pneumonia. Clinical scores including Pneumonia Severity Index (PSI); Confusion, blood Urea nitrogen, Respiratory rate, Blood pressure, age 65 score (CURB-65); and several others may be helpful for disposition, but should supplement, not replace, clinical judgment. Patient socioeconomic status must be considered in disposition decisions.

CONCLUSION

The diagnosis of pneumonia requires clinical gestalt using a combination of history and physical examination. Chest radiograph may be negative, particularly in patients presenting early in disease course and elderly patients. Clinical scores can supplement clinical gestalt and assist in disposition when used appropriately.

A new method to predict hospital mortality in severe community acquired pneumonia

BACKGROUND & AIMS

The aim of this study is to develop a new method that is able to accurately predict the 28day hospital mortality in patients with severe community acquired pneumonia (SCAP) at an early stage.

METHODS

We selected 37,348 SCAP patients in ICU from 173 hospitals during 2011.1-2013.12. The predictive factors for 28day hospital mortality were evaluated retrospectively. All cases underwent intensive care, blood routine, blood biochemical tests and arterial blood gas analysis. Under the Classification and Regression Tree (CART) analysis, a new clinical scoring system was developed for early prediction in SCAP patients. The receiver-operating characteristic (ROC) curve was plotted to calculate the area under the receiver operating characteristic curve (AUC).

RESULTS

A novel clinical model named CLCGH scoring system, including Serum creatinine (Cr) >259.5μmol/L, leukocyte (WBC)>17.35×10⁹/L, C-reactive protein (CRP)>189.4μg/mL, GCS≤9 and serum HCO₃^-≤17.65mmol/L, was carried out and each index was an independent factor for hospital mortality in SCAP. In validation cohort, the AUC of the new scoring system was 0.889 for prediction of hospital mortality, which was similar to SOFA score 0.877, APACHEII score 0.864, and was better than the PSI score 0.761 and CURB-65 score 0.767.

CONCLUSIONS

The new scoring system CLCGH is an efficient, accurate and objective method to predicate the early hospital mortality among SCAP patients.

Toward a Blood-Borne Biomarker of Chronic Hypoxemia: Red Cell Distribution Width and Respiratory Disease

Hypoxemia (systemic oxygen desaturation) marks the presence, risk, and progression of many diseases. Episodic or nocturnal hypoxemia can be challenging to detect and quantify. A sensitive, specific, and convenient marker of recent oxygen desaturation represents an unmet medical need. Observations of acclimatization to high altitude in humans and animals reveals several proteosomic, ventilatory, and hematological responses to low oxygen tension. Of these, increased red cell distribution width (RDW) appears to have the longest persistence. Literature review and analyses of a 2M patient database across the full disease pathome revealed that increased RDW is predictive of poor outcome for certain diseases including many if not all hypoxigenic conditions. Comprehensive review of diseases impacting the respiratory axis show many are associated with increased RDW and no apparent counterexamples. The mechanism linking RDW to outcome is unknown. Conjectural roles for iron deficiency, inflammation, and oxidative stress have not been born out experimentally. Sports-doping studies show that erythropoietin (EPO) injection can induce formation of unusually large red blood cells (RBC) in sufficient numbers to increase RDW. Because endogenous EPO responds strongly to hypoxemia, this molecule could potentially mediate a long-lived RDW response to low oxygenation. RDW may be a guidepost signaling that unexploited information is embedded in subtle RBC variation. Applying modern techniques of measurement and analysis to certain RBC characteristics may yield a more specific and sensitive marker of chronic pulmonary and circulatory diseases, sleep apnea, and opioid inhibition of breathing.

Improving outcomes in community-acquired pneumonia

PURPOSE OF REVIEW

Community-acquired pneumonia (CAP) is a pervasive disease that is encountered in outpatient and inpatient settings. CAP is the leading cause of death from an infectious disease and accounts for significant worldwide morbidity and mortality. This update reviews current advances that can be used to promote improved outcomes in CAP.

RECENT FINDINGS

Early recognition of CAP and its severe presentations, with appropriate site of care decisions, leads to reduced patient mortality. In addition to traditional prognostic tools, certain serum biomarkers can assist in defining disease severity and guide treatment and management strategies. The use of macrolides as part of combination antibiotic therapy has shown beneficial mortality effects across the CAP disease spectrum, especially for those with severe illness. When treating community-associated, methicillin-resistant Staphylococcus aureus pneumonia, use of an antitoxin antibiotic is likely to be valuable. Adjunctive therapy with corticosteroids may prevent delayed clinical resolution in selected patients with severe CAP. Recent data expand on the interaction of CAP with comorbid disease, particularly cardiovascular disease, and its impact on mortality in CAP patients.

SUMMARY

Improved diagnostic tools, optimized treatment regimens, and enhanced understanding of CAP-induced perturbations in comorbid disease states hold promise to improve patient outcomes.